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Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


1

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

January 23, 2008" ,"Next Update: October 2007" ,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, " ,"2005...

2

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2007 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

3

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2008 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

4

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2003 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

5

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2009 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

6

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

Base Year)",,,," " ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

7

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2004 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

8

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2009 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid" ,"Projected Year Base","Year",,"FRCC",...

9

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

and 2007 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid" ,"Projected Year Base","Year",,"FRCC",...

10

Utility Name Retail Sales for 2010 (MWh) Projected Annual Cost  

E-Print Network (OSTI)

All POUs Utility Name Retail Sales for 2010 (MWh) Projected Annual Cost 20122013 ($) Projected Annual Cost 20132014 ($) Projected Annual Cost 20142015 ($) Legend LADWP 22,856,346 720,123 720,123 720 Attachment B Response Utility Name Retail Sales for 2010 (MWh) Projected Annual Cost 2012 2013 ($) LADWP 22

11

Property:Com sales (mwh) | Open Energy Information  

Open Energy Info (EERE)

sales (mwh) sales (mwh) Jump to: navigation, search This is a property of type Number. Sales to commercial consumers Pages using the property "Com sales (mwh)" Showing 25 pages using this property. (previous 25) (next 25) 4 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - April 2008 + 14,949 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - August 2008 + 26,367 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - December 2008 + 15,395 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2008 + 16,880 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2009 + 16,286 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2008 + 17,519 +

12

Property:Oth sales (mwh) | Open Energy Information  

Open Energy Info (EERE)

other consumers other consumers Pages using the property "Oth sales (mwh)" Showing 25 pages using this property. (previous 25) (next 25) C Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - April 2008 + 1,113 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - December 2008 + 1,202 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - February 2008 + 536 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - February 2009 + 2,187 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - January 2008 + 707 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - January 2009 + 1,537 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - June 2008 + 697 + Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - March 2008 + 880 +

13

Property:Ind sales (mwh) | Open Energy Information  

Open Energy Info (EERE)

industrial consumers industrial consumers Pages using the property "Ind sales (mwh)" Showing 25 pages using this property. (previous 25) (next 25) 4 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - April 2008 + 18,637 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - August 2008 + 19,022 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - December 2008 + 14,148 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2008 + 18,516 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2009 + 14,517 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2008 + 17,398 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2009 + 14,930 +

14

Property:Tot sales (mwh) | Open Energy Information  

Open Energy Info (EERE)

all consumers all consumers Pages using the property "Tot sales (mwh)" Showing 25 pages using this property. (previous 25) (next 25) 4 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - April 2008 + 69,154 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - August 2008 + 104,175 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - December 2008 + 78,855 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2008 + 93,756 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2009 + 87,806 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2008 + 87,721 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2009 + 88,236 +

15

Property:Res sales (mwh) | Open Energy Information  

Open Energy Info (EERE)

residential consumers residential consumers Pages using the property "Res sales (mwh)" Showing 25 pages using this property. (previous 25) (next 25) 4 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - April 2008 + 35,568 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - August 2008 + 58,786 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - December 2008 + 49,312 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2008 + 58,360 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - February 2009 + 57,003 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2008 + 52,804 + 4-County Electric Power Assn (Mississippi) EIA Revenue and Sales - January 2009 + 56,047 +

16

winter_peak_2005.xls  

U.S. Energy Information Administration (EIA) Indexed Site

2b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year)...

17

Total Cost Per MwH for all common large scale power generation sources |  

Open Energy Info (EERE)

Total Cost Per MwH for all common large scale power generation sources Total Cost Per MwH for all common large scale power generation sources Home > Groups > DOE Wind Vision Community In the US DOEnergy, are there calcuations for real cost of energy considering the negative, socialized costs of all commercial large scale power generation soruces ? I am talking about the cost of mountain top removal for coal mined that way, the trip to the power plant, the sludge pond or ash heap, the cost of the gas out of the stack, toxificaiton of the lakes and streams, plant decommision costs. For nuclear yiou are talking about managing the waste in perpetuity. The plant decomission costs and so on. What I am tring to get at is the 'real cost' per MWh or KWh for the various sources ? I suspect that the costs commonly quoted for fossil fuels and nucelar are

18

Peak Oil  

Science Journals Connector (OSTI)

Wissenschaftliche Voraussagen deuten auf Peak Oil, das Maximum globaler Erdlfrderung, in unserer ... der demokratischen Systeme fhren. Psychoanalytische Betrachtung darf Peak Oil fr die Zivilisation als e...

Dr. Manuel Haus; Dr. med. Christoph Biermann

2013-03-01T23:59:59.000Z

19

Property:Building/SPPurchasedEngyNrmlYrMwhYrElctrtyTotal | Open Energy  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:Building/SPPurchasedEngyNrmlYrMwhYrElctrtyTotal Jump to: navigation, search This is a property of type String. Electricity, total Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrElctrtyTotal" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 1400.0 + Sweden Building 05K0002 + 686.9 + Sweden Building 05K0003 + 321.8 + Sweden Building 05K0004 + 1689.9 + Sweden Building 05K0005 + 122.6 + Sweden Building 05K0006 + 843.1 + Sweden Building 05K0007 + 1487.0 + Sweden Building 05K0008 + 315.0 + Sweden Building 05K0009 + 1963.0 + Sweden Building 05K0010 + 66.52 + Sweden Building 05K0011 + 391.0 + Sweden Building 05K0012 + 809.65 +

20

monthly_peak_2003.xls  

U.S. Energy Information Administration (EIA) Indexed Site

O Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Property:Building/SPPurchasedEngyNrmlYrMwhYrDigesterLandfillGas | Open  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrDigesterLandfillGas SPPurchasedEngyNrmlYrMwhYrDigesterLandfillGas Jump to: navigation, search This is a property of type String. Digester / landfill gas Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrDigesterLandfillGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

22

Property:Building/SPPurchasedEngyForPeriodMwhYrWoodChips | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrWoodChips SPPurchasedEngyForPeriodMwhYrWoodChips Jump to: navigation, search This is a property of type String. Wood chips Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrWoodChips" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

23

Property:Building/SPPurchasedEngyNrmlYrMwhYrDstrtHeating | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrDstrtHeating SPPurchasedEngyNrmlYrMwhYrDstrtHeating Jump to: navigation, search This is a property of type String. District heating Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrDstrtHeating" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 2193.0 + Sweden Building 05K0002 + 521.2 + Sweden Building 05K0003 + 498.4 + Sweden Building 05K0004 + 1869.0 + Sweden Building 05K0005 + 646.0 + Sweden Building 05K0006 + 1843.0 + Sweden Building 05K0007 + 1542.0 + Sweden Building 05K0008 + 898.0 + Sweden Building 05K0009 + 2313.0 + Sweden Building 05K0010 + 65.0 + Sweden Building 05K0011 + 1032.0 + Sweden Building 05K0012 + 1256.0 + Sweden Building 05K0013 + 1817.6002445 + Sweden Building 05K0014 + 162.0 + Sweden Building 05K0015 + 158.0 +

24

Property:Building/SPPurchasedEngyNrmlYrMwhYrLogs | Open Energy Information  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrLogs SPPurchasedEngyNrmlYrMwhYrLogs Jump to: navigation, search This is a property of type String. Logs Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrLogs" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

25

Property:Building/SPPurchasedEngyNrmlYrMwhYrNaturalGas | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrNaturalGas SPPurchasedEngyNrmlYrMwhYrNaturalGas Jump to: navigation, search This is a property of type String. Natural gas Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrNaturalGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

26

Property:Building/SPPurchasedEngyForPeriodMwhYrLogs | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrLogs SPPurchasedEngyForPeriodMwhYrLogs Jump to: navigation, search This is a property of type String. Logs Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrLogs" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

27

Property:Building/SPPurchasedEngyNrmlYrMwhYrWoodChips | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrWoodChips SPPurchasedEngyNrmlYrMwhYrWoodChips Jump to: navigation, search This is a property of type String. Wood chips Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrWoodChips" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

28

Property:Building/SPPurchasedEngyNrmlYrMwhYrOther | Open Energy Information  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrOther SPPurchasedEngyNrmlYrMwhYrOther Jump to: navigation, search This is a property of type String. Other Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrOther" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

29

Property:Building/SPPurchasedEngyForPeriodMwhYrDstrtColg | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrDstrtColg SPPurchasedEngyForPeriodMwhYrDstrtColg Jump to: navigation, search This is a property of type String. District cooling Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrDstrtColg" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 762.0 + Sweden Building 05K0002 + 322.0 + Sweden Building 05K0003 + 51.9 + Sweden Building 05K0004 + 908.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 345.0 + Sweden Building 05K0007 + 450.0 + Sweden Building 05K0008 + 123.0 + Sweden Building 05K0009 + 600.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 78.0 + Sweden Building 05K0012 + 340.0 + Sweden Building 05K0013 + 420.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

30

Property:Building/SPPurchasedEngyForPeriodMwhYrPellets | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrPellets SPPurchasedEngyForPeriodMwhYrPellets Jump to: navigation, search This is a property of type String. Pellets Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrPellets" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

31

Property:Building/SPPurchasedEngyForPeriodMwhYrOil-FiredBoiler | Open  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrOil-FiredBoiler SPPurchasedEngyForPeriodMwhYrOil-FiredBoiler Jump to: navigation, search This is a property of type String. Oil-fired boiler Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrOil-FiredBoiler" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

32

Property:Building/SPPurchasedEngyForPeriodMwhYrOther | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrOther SPPurchasedEngyForPeriodMwhYrOther Jump to: navigation, search This is a property of type String. Other Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrOther" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

33

Property:Building/SPPurchasedEngyNrmlYrMwhYrTotal | Open Energy Information  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrTotal SPPurchasedEngyNrmlYrMwhYrTotal Jump to: navigation, search This is a property of type String. Total Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrTotal" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 4355.0 + Sweden Building 05K0002 + 1530.1 + Sweden Building 05K0003 + 872.1 + Sweden Building 05K0004 + 4466.9 + Sweden Building 05K0005 + 768.6 + Sweden Building 05K0006 + 3031.1 + Sweden Building 05K0007 + 3479.0 + Sweden Building 05K0008 + 1336.0 + Sweden Building 05K0009 + 4876.0 + Sweden Building 05K0010 + 131.52 + Sweden Building 05K0011 + 1501.0 + Sweden Building 05K0012 + 2405.65 + Sweden Building 05K0013 + 3436.6002445 + Sweden Building 05K0014 + 389.66 + Sweden Building 05K0015 + 270.0 +

34

Property:Building/SPPurchasedEngyNrmlYrMwhYrPellets | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrPellets SPPurchasedEngyNrmlYrMwhYrPellets Jump to: navigation, search This is a property of type String. Pellets Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrPellets" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

35

Property:Building/SPPurchasedEngyForPeriodMwhYrTotal | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrTotal SPPurchasedEngyForPeriodMwhYrTotal Jump to: navigation, search This is a property of type String. Total Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrTotal" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 4228.0 + Sweden Building 05K0002 + 1501.1 + Sweden Building 05K0003 + 847.1 + Sweden Building 05K0004 + 4360.9 + Sweden Building 05K0005 + 727.6 + Sweden Building 05K0006 + 2915.1 + Sweden Building 05K0007 + 3385.0 + Sweden Building 05K0008 + 1282.0 + Sweden Building 05K0009 + 4739.0 + Sweden Building 05K0010 + 127.52 + Sweden Building 05K0011 + 1436.0 + Sweden Building 05K0012 + 2334.65 + Sweden Building 05K0013 + 3323.0 + Sweden Building 05K0014 + 381.66 + Sweden Building 05K0015 + 257.0 +

36

Property:Building/SPPurchasedEngyForPeriodMwhYrElctrtyTotal | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrElctrtyTotal SPPurchasedEngyForPeriodMwhYrElctrtyTotal Jump to: navigation, search This is a property of type String. Electricity, total Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrElctrtyTotal" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 1399.0 + Sweden Building 05K0002 + 686.9 + Sweden Building 05K0003 + 321.8 + Sweden Building 05K0004 + 1689.9 + Sweden Building 05K0005 + 122.6 + Sweden Building 05K0006 + 843.1 + Sweden Building 05K0007 + 1487.0 + Sweden Building 05K0008 + 315.0 + Sweden Building 05K0009 + 1963.0 + Sweden Building 05K0010 + 66.52 + Sweden Building 05K0011 + 391.0 + Sweden Building 05K0012 + 809.65 + Sweden Building 05K0013 + 1199.0 + Sweden Building 05K0014 + 227.66 +

37

Property:Building/SPPurchasedEngyNrmlYrMwhYrOil-FiredBoiler | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrOil-FiredBoiler SPPurchasedEngyNrmlYrMwhYrOil-FiredBoiler Jump to: navigation, search This is a property of type String. Oil-fired boiler Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrOil-FiredBoiler" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

38

Property:Building/SPPurchasedEngyNrmlYrMwhYrTownGas | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrTownGas SPPurchasedEngyNrmlYrMwhYrTownGas Jump to: navigation, search This is a property of type String. Town gas Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrTownGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

39

Property:Building/SPPurchasedEngyForPeriodMwhYrDstrtHeating | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrDstrtHeating SPPurchasedEngyForPeriodMwhYrDstrtHeating Jump to: navigation, search This is a property of type String. District heating Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrDstrtHeating" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 2067.0 + Sweden Building 05K0002 + 492.2 + Sweden Building 05K0003 + 473.4 + Sweden Building 05K0004 + 1763.0 + Sweden Building 05K0005 + 605.0 + Sweden Building 05K0006 + 1727.0 + Sweden Building 05K0007 + 1448.0 + Sweden Building 05K0008 + 844.0 + Sweden Building 05K0009 + 2176.0 + Sweden Building 05K0010 + 61.0 + Sweden Building 05K0011 + 967.0 + Sweden Building 05K0012 + 1185.0 + Sweden Building 05K0013 + 1704.0 + Sweden Building 05K0014 + 154.0 + Sweden Building 05K0015 + 145.0 +

40

Property:Building/SPPurchasedEngyForPeriodMwhYrDigesterLandfillGas | Open  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrDigesterLandfillGas SPPurchasedEngyForPeriodMwhYrDigesterLandfillGas Jump to: navigation, search This is a property of type String. Digester / landfill gas Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrDigesterLandfillGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
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41

Property:Building/SPPurchasedEngyNrmlYrMwhYrDstrtColg | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyNrmlYrMwhYrDstrtColg SPPurchasedEngyNrmlYrMwhYrDstrtColg Jump to: navigation, search This is a property of type String. District cooling Pages using the property "Building/SPPurchasedEngyNrmlYrMwhYrDstrtColg" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 762.0 + Sweden Building 05K0002 + 322.0 + Sweden Building 05K0003 + 51.9 + Sweden Building 05K0004 + 908.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 345.0 + Sweden Building 05K0007 + 450.0 + Sweden Building 05K0008 + 123.0 + Sweden Building 05K0009 + 600.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 78.0 + Sweden Building 05K0012 + 340.0 + Sweden Building 05K0013 + 420.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

42

Property:Building/SPPurchasedEngyForPeriodMwhYrTownGas | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrTownGas SPPurchasedEngyForPeriodMwhYrTownGas Jump to: navigation, search This is a property of type String. Town gas Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrTownGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

43

Property:Building/SPPurchasedEngyForPeriodMwhYrNaturalGas | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyForPeriodMwhYrNaturalGas SPPurchasedEngyForPeriodMwhYrNaturalGas Jump to: navigation, search This is a property of type String. Natural gas Pages using the property "Building/SPPurchasedEngyForPeriodMwhYrNaturalGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

44

Peak Oil  

Science Journals Connector (OSTI)

At the start of the new millennium, the expression Peak Oil was unknown. Nevertheless, a discussion about when the worlds rate of oil production would reach its maximum had already ... . King Hubbert presented...

Kjell Aleklett

2012-01-01T23:59:59.000Z

45

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

Indiana Rt Peak",41246,41247,41247,31.5,31.5,31.5,-1.5,1600,2,3 Indiana Rt Peak",41246,41247,41247,31.5,31.5,31.5,-1.5,1600,2,3 "Indiana Rt Peak",41247,41248,41248,34,33.5,33.75,2.25,1600,2,3 "Indiana Rt Peak",41248,41249,41249,37.25,37,37.13,3.38,8000,10,9 "Indiana Rt Peak",41249,41250,41250,34.25,33.25,33.67,-3.46,2400,3,6 "Indiana Rt Peak",41250,41253,41253,38.25,37,37.5,3.83,12800,16,13 "Indiana Rt Peak",41253,41254,41254,37.75,37.5,37.63,0.13,1600,2,4 "Indiana Rt Peak",41254,41255,41255,34,34,34,-3.63,2400,3,4 "Indiana Rt Peak",41255,41256,41256,32.25,32,32.19,-1.81,3200,4,6 "Indiana Rt Peak",41256,41257,41257,31,31,31,-1.19,1600,2,3 "Indiana Rt Peak",41257,41260,41260,33,32,32.5,1.5,1600,2,4 "Indiana Rt Peak",41260,41261,41261,33.9,33.5,33.66,1.16,3200,4,7

46

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

1246,41247,41247,27.5,27.5,27.5,0.17,800,1,2 1246,41247,41247,27.5,27.5,27.5,0.17,800,1,2 "Entergy Peak",41247,41248,41248,28.5,28.5,28.5,1,800,1,2 "Entergy Peak",41248,41249,41249,30,30,30,1.5,800,1,2 "Entergy Peak",41250,41253,41253,30,29,29.5,-0.5,1600,2,3 "Entergy Peak",41253,41254,41254,30,29.75,29.88,0.38,1600,2,2 "Entergy Peak",41254,41255,41255,29.75,29.75,29.75,-0.13,800,1,2 "Entergy Peak",41269,41270,41270,32,32,32,2.25,1600,2,2 "Entergy Peak",41355,41358,41358,38.5,38.5,38.5,6.5,800,1,2 "Entergy Peak",41367,41368,41368,35,35,35,-3.5,800,1,2 "Entergy Peak",41425,41428,41428,37,37,37,2,800,1,2 "Entergy Peak",41436,41437,41437,42,42,42,5,800,1,2 "Entergy Peak",41446,41449,41449,41,41,41,-1,800,1,2

47

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

40182,40183,40183,60.5,60.5,60.5,7.5,800,1,2 40182,40183,40183,60.5,60.5,60.5,7.5,800,1,2 "Entergy Peak",40183,40184,40184,62.25,62.25,62.25,1.75,800,1,2 "Entergy Peak",40189,40190,40190,63.5,60.75,62.42,0.17,2400,3,3 "Entergy Peak",40190,40191,40191,46,45,45.5,-16.92,1600,2,2 "Entergy Peak",40196,40197,40197,40,40,40,-5.5,800,1,2 "Entergy Peak",40197,40198,40198,40,40,40,0,800,1,2 "Entergy Peak",40198,40199,40199,38,38,38,-2,800,1,2 "Entergy Peak",40199,40200,40200,38,38,38,0,800,1,2 "Entergy Peak",40204,40205,40205,47,47,47,9,800,1,2 "Entergy Peak",40205,40206,40206,45,45,45,-2,800,1,2 "Entergy Peak",40206,40207,40207,48,48,48,3,800,1,2 "Entergy Peak",40210,40211,40211,43,43,43,-5,800,1,2

48

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

Da LMP Peak",41246,41247,41247,48,45.75,47.16,-7.85,40000,48,21 Da LMP Peak",41246,41247,41247,48,45.75,47.16,-7.85,40000,48,21 "Nepool MH Da LMP Peak",41247,41248,41248,58.5,55,57.81,10.65,26400,32,21 "Nepool MH Da LMP Peak",41248,41249,41249,79.75,75,76.49,18.68,32800,39,18 "Nepool MH Da LMP Peak",41249,41250,41250,65,50.5,51.47,-25.02,35200,42,23 "Nepool MH Da LMP Peak",41250,41253,41253,47,45.5,46.48,-4.99,12800,16,14 "Nepool MH Da LMP Peak",41253,41254,41254,50,46,47.3,0.82,38400,44,22 "Nepool MH Da LMP Peak",41254,41255,41255,70,57,59.54,12.24,39200,49,19 "Nepool MH Da LMP Peak",41255,41256,41256,50,48.25,48.97,-10.57,53600,59,29 "Nepool MH Da LMP Peak",41256,41257,41257,39.25,38.5,38.98,-9.99,11200,14,10 "Nepool MH Da LMP Peak",41257,41260,41260,45,45,45,6.02,3200,4,6

49

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

39815,39818,39818,42,39,41,4.5,2400,3,4 39815,39818,39818,42,39,41,4.5,2400,3,4 "Entergy Peak",39818,39819,39819,44.5,44.5,44.5,3.5,800,1,2 "Entergy Peak",39819,39820,39820,44.5,44,44.25,-0.25,1600,2,4 "Entergy Peak",39820,39821,39821,46,45,45.5,1.25,2400,3,6 "Entergy Peak",39821,39822,39822,45,45,45,-0.5,800,1,2 "Entergy Peak",39822,39825,39825,45,40,42.5,-2.5,1600,2,3 "Entergy Peak",39825,39826,39826,48,48,48,5.5,1600,2,3 "Entergy Peak",39827,39828,39828,55,53,54,6,1600,2,4 "Entergy Peak",39828,39829,39829,56,53,54.33,0.33,2400,3,5 "Entergy Peak",39832,39833,39833,42.5,42.5,42.5,-11.83,800,1,2 "Entergy Peak",39833,39834,39834,43,42,42.5,0,1600,2,4 "Entergy Peak",39836,39839,39839,40,38,39,-3.5,1600,2,3

50

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

546,40547,40547,37,37,37,0,800,1,2 546,40547,40547,37,37,37,0,800,1,2 "Entergy Peak",40547,40548,40548,36,36,36,-1,800,1,2 "Entergy Peak",40548,40549,40549,33.75,33.75,33.75,-2.25,1600,2,2 "Entergy Peak",40550,40553,40553,42,42,42,8.25,800,1,2 "Entergy Peak",40555,40556,40556,52.75,49,50.88,8.88,1600,2,3 "Entergy Peak",40562,40563,40563,38.5,38,38.1,-12.78,4000,5,4 "Entergy Peak",40563,40564,40564,39,39,39,0.9,800,1,2 "Entergy Peak",40567,40568,40568,39,39,39,0,800,1,2 "Entergy Peak",40568,40569,40569,38,38,38,-1,800,1,2 "Entergy Peak",40571,40574,40574,36,36,36,-2,800,1,2 "Entergy Peak",40574,40575,40575,39.5,39.5,39.5,3.5,800,1,2 "Entergy Peak",40575,40576,40576,37,36.5,36.75,-2.75,1600,2,2

51

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

PJM-West Real Time Peak",41276,41277,41277,44,41.75,42.64,-6.4,60000,72,34 PJM-West Real Time Peak",41276,41277,41277,44,41.75,42.64,-6.4,60000,72,34 "PJM-West Real Time Peak",41277,41278,41278,37,36,36.53,-6.11,19200,23,23 "PJM-West Real Time Peak",41278,41281,41281,36.5,36,36.17,-0.36,41600,48,32 "PJM-West Real Time Peak",41281,41282,41282,33.05,32.5,32.61,-3.56,20800,26,18 "PJM-West Real Time Peak",41282,41283,41283,33.75,32.5,32.91,0.3,37600,43,30 "PJM-West Real Time Peak",41283,41284,41284,31,30.25,30.64,-2.27,26400,31,26 "PJM-West Real Time Peak",41284,41285,41285,29.9,29.25,29.66,-0.98,38400,26,23 "PJM-West Real Time Peak",41285,41288,41288,32.5,31.5,32.14,2.48,40000,50,28 "PJM-West Real Time Peak",41288,41289,41289,37.5,34.5,36.5,4.36,64800,74,35

52

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

SP-15 Gen DA LMP Peak",39904,39905,39905,30.85,30,30.44,"na",69200,129,16 SP-15 Gen DA LMP Peak",39904,39905,39905,30.85,30,30.44,"na",69200,129,16 "SP-15 Gen DA LMP Peak",39905,39906,39907,28.7,27.5,28.03,-2.41,119200,103,17 "SP-15 Gen DA LMP Peak",39906,39909,39909,31.5,30.25,30.5,2.47,43200,89,17 "SP-15 Gen DA LMP Peak",39909,39910,39910,33.3,32.45,32.83,2.33,40800,80,20 "SP-15 Gen DA LMP Peak",39910,39911,39912,29,28,28.69,-4.14,116000,117,22 "SP-15 Gen DA LMP Peak",39911,39913,39914,27.25,26.55,26.88,-1.81,96800,110,21 "SP-15 Gen DA LMP Peak",39912,39916,39916,28.5,27.5,28.01,1.13,58000,119,19 "SP-15 Gen DA LMP Peak",39916,39917,39917,26.65,25,26.27,-1.74,26400,51,17 "SP-15 Gen DA LMP Peak",39917,39918,39918,28.25,27.7,27.97,1.7,55600,101,20

53

SPACE TECHNOLOGY Actual Estimate  

E-Print Network (OSTI)

SPACE TECHNOLOGY TECH-1 Actual Estimate Budget Authority (in $ millions) FY 2011 FY 2012 FY 2013 FY.7 247.0 Exploration Technology Development 144.6 189.9 202.0 215.5 215.7 214.5 216.5 Notional SPACE TECHNOLOGY OVERVIEW .............................. TECH- 2 SBIR AND STTR

54

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

1246,41247,41247,28.5,26.5,27.76,-0.16,63200,141,25 1246,41247,41247,28.5,26.5,27.76,-0.16,63200,141,25 "Mid Columbia Peak",41247,41248,41248,28.5,27,27.86,0.1,79200,187,26 "Mid Columbia Peak",41248,41249,41249,28,23.5,27.02,-0.84,76000,170,25 "Mid Columbia Peak",41249,41250,41251,23.25,21.25,22.44,-4.58,159200,191,23 "Mid Columbia Peak",41250,41253,41253,25.25,21.25,23.45,1.01,74800,176,25 "Mid Columbia Peak",41253,41254,41254,23.75,20.75,22.51,-0.94,92800,209,26 "Mid Columbia Peak",41254,41255,41255,24.5,23,23.84,1.33,100800,222,27 "Mid Columbia Peak",41255,41256,41256,28,25.5,26.88,3.04,80800,182,26 "Mid Columbia Peak",41256,41257,41258,27.75,26.5,27.13,0.25,152000,171,25 "Mid Columbia Peak",41257,41260,41260,25.75,23.25,24.43,-2.7,76000,180,25

55

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

182,40183,40183,89,82.75,86.08,20.49,214400,242,55 182,40183,40183,89,82.75,86.08,20.49,214400,242,55 "PJM Wh Real Time Peak",40183,40184,40184,80.65,74.5,77.16,-8.92,270400,295,56 "PJM Wh Real Time Peak",40184,40185,40185,80.5,77.5,78.92,1.76,93600,111,47 "PJM Wh Real Time Peak",40185,40186,40186,86,78.25,80.64,1.72,278400,316,62 "PJM Wh Real Time Peak",40186,40189,40189,82.75,72,80.64,0,81600,98,36 "PJM Wh Real Time Peak",40189,40190,40190,73,65.75,67.86,-12.78,178400,205,50 "PJM Wh Real Time Peak",40190,40191,40191,55.25,53,53.89,-13.97,162400,180,50 "PJM Wh Real Time Peak",40191,40192,40192,49.75,48,48.84,-5.05,97600,109,45 "PJM Wh Real Time Peak",40192,40193,40193,46.25,43.5,44.65,-4.19,99200,117,46 "PJM Wh Real Time Peak",40193,40196,40196,46,44.95,45.38,0.73,59200,71,35

56

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

546,40547,40547,51,47.5,48.71,-0.32,96800,116,39 546,40547,40547,51,47.5,48.71,-0.32,96800,116,39 "PJM Wh Real Time Peak",40547,40548,40548,49.25,47.45,48.14,-0.57,64000,67,40 "PJM Wh Real Time Peak",40548,40549,40549,53.5,51.5,52.27,4.13,55200,66,37 "PJM Wh Real Time Peak",40549,40550,40550,60.5,57,58.43,6.16,80000,93,39 "PJM Wh Real Time Peak",40550,40553,40553,63.5,57,60.43,2,105600,124,41 "PJM Wh Real Time Peak",40553,40554,40554,69.5,64.25,66.98,6.55,128800,145,44 "PJM Wh Real Time Peak",40554,40555,40555,72.25,62,67.54,0.56,158400,194,51 "PJM Wh Real Time Peak",40555,40556,40556,84,75,80.13,12.59,92800,116,46 "PJM Wh Real Time Peak",40556,40557,40557,89.5,80.5,84.09,3.96,108800,133,42 "PJM Wh Real Time Peak",40557,40560,40560,57.55,55,56.11,-27.98,88800,105,40

57

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

40182,40183,40183,52.5,51.5,51.85,0.9,67600,116,25 40182,40183,40183,52.5,51.5,51.85,0.9,67600,116,25 "SP-15 Gen DA LMP Peak",40183,40184,40184,51.75,50.5,51.01,-0.84,61600,115,25 "SP-15 Gen DA LMP Peak",40184,40185,40185,53,50.5,51.39,0.38,59600,115,24 "SP-15 Gen DA LMP Peak",40185,40186,40187,58.5,55,56.79,5.4,394400,381,29 "SP-15 Gen DA LMP Peak",40186,40189,40189,51.25,50.75,51,-5.79,59200,116,26 "SP-15 Gen DA LMP Peak",40189,40190,40190,50.25,49,49.8,-1.2,53600,102,25 "SP-15 Gen DA LMP Peak",40190,40191,40192,51.5,50.75,51.12,1.32,59200,61,19 "SP-15 Gen DA LMP Peak",40191,40193,40194,49,48.25,48.35,-2.77,77600,71,20 "SP-15 Gen DA LMP Peak",40192,40196,40196,50.5,50,50.3,1.95,38800,71,18 "SP-15 Gen DA LMP Peak",40193,40197,40197,51.35,50,50.93,0.63,66800,84,19

58

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

355,38356,38356,41,39,40.13,6.73,12000,14,13 355,38356,38356,41,39,40.13,6.73,12000,14,13 "PJM Wh Real Time Peak",38356,38357,38357,41,40,40.57,0.44,13600,15,15 "PJM Wh Real Time Peak",38357,38358,38358,44,42,43.23,2.66,30400,35,16 "PJM Wh Real Time Peak",38358,38359,38359,46.25,44,45.07,1.84,17600,22,12 "PJM Wh Real Time Peak",38359,38362,38362,39.5,38.75,39.17,-5.9,9600,12,11 "PJM Wh Real Time Peak",38362,38363,38363,45,41.5,43.31,4.14,26400,32,17 "PJM Wh Real Time Peak",38363,38364,38364,44,41.25,41.8,-1.51,16000,19,15 "PJM Wh Real Time Peak",38364,38365,38365,39.5,38.5,39.1,-2.7,10400,13,13 "PJM Wh Real Time Peak",38365,38366,38366,51.5,47,48.26,9.16,57600,58,17 "PJM Wh Real Time Peak",38366,38369,38369,65,63,63.48,15.22,23200,21,14

59

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

1246,41247,41247,44.25,43.5,43.87,2.68,16400,29,14 1246,41247,41247,44.25,43.5,43.87,2.68,16400,29,14 "SP-15 Gen DA LMP Peak",41247,41248,41248,43,42,42.36,-1.51,36800,59,23 "SP-15 Gen DA LMP Peak",41248,41249,41249,40.25,39.75,40,-2.36,17200,24,11 "SP-15 Gen DA LMP Peak",41249,41250,41251,37,36.5,36.56,-3.44,31200,28,13 "SP-15 Gen DA LMP Peak",41250,41253,41253,41.25,40,40.84,4.28,12000,26,16 "SP-15 Gen DA LMP Peak",41253,41254,41254,39.5,38.5,39.08,-1.76,12400,26,15 "SP-15 Gen DA LMP Peak",41254,41255,41255,39.45,39,39.11,0.03,15600,26,13 "SP-15 Gen DA LMP Peak",41255,41256,41256,43.75,42,43.02,3.91,16000,32,20 "SP-15 Gen DA LMP Peak",41256,41257,41258,43,40.5,42.17,-0.85,38400,32,18 "SP-15 Gen DA LMP Peak",41257,41260,41260,42,41.5,41.62,-0.55,6400,10,11

60

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

720,38721,38721,69,68,68.6,1.54,74400,63,23 720,38721,38721,69,68,68.6,1.54,74400,63,23 "PJM Wh Real Time Peak",38721,38722,38722,74.25,69,70.77,2.17,68000,68,33 "PJM Wh Real Time Peak",38722,38723,38723,77.75,73.5,76.91,6.14,61600,70,35 "PJM Wh Real Time Peak",38723,38726,38726,74,69,70.06,-6.85,55200,57,22 "PJM Wh Real Time Peak",38726,38727,38727,63,61.75,62.52,-7.54,60800,72,29 "PJM Wh Real Time Peak",38727,38728,38728,55,51,53.51,-9.01,68800,55,30 "PJM Wh Real Time Peak",38728,38729,38729,50.5,49,49.37,-4.14,56000,55,25 "PJM Wh Real Time Peak",38729,38730,38730,50.6,49.5,50.17,0.8,54400,55,25 "PJM Wh Real Time Peak",38730,38733,38733,63.5,59,60.85,10.68,36800,37,23 "PJM Wh Real Time Peak",38733,38734,38734,65,64,64.63,3.78,12000,10,13

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

815,39818,39818,58.5,55.25,56.28,5.13,40000,45,27 815,39818,39818,58.5,55.25,56.28,5.13,40000,45,27 "PJM Wh Real Time Peak",39818,39819,39819,60.25,57.75,58.92,2.64,109600,119,41 "PJM Wh Real Time Peak",39819,39820,39820,58,55,56.66,-2.26,49600,60,29 "PJM Wh Real Time Peak",39820,39821,39821,55.55,55,55.21,-1.45,48000,56,34 "PJM Wh Real Time Peak",39821,39822,39822,63,60.75,61.9,6.69,38400,46,28 "PJM Wh Real Time Peak",39822,39825,39825,69,66,67.63,5.73,62400,74,37 "PJM Wh Real Time Peak",39825,39826,39826,66.5,61,64.03,-3.6,91200,107,40 "PJM Wh Real Time Peak",39826,39827,39827,85.5,80,82.91,18.88,103200,124,50 "PJM Wh Real Time Peak",39827,39828,39828,100,88,93.22,10.31,110400,135,51 "PJM Wh Real Time Peak",39828,39829,39829,110,93,98.58,5.36,77600,93,37

62

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

449,39450,39450,131,114,125.81,37.67,95200,116,49 449,39450,39450,131,114,125.81,37.67,95200,116,49 "PJM Wh Real Time Peak",39450,39451,39451,106,99,102.43,-23.38,78400,96,39 "PJM Wh Real Time Peak",39451,39454,39454,54,52.5,53.44,-48.99,65600,74,34 "PJM Wh Real Time Peak",39454,39455,39455,45,41,42.69,-10.75,87200,98,48 "PJM Wh Real Time Peak",39455,39456,39456,47.5,45,46.31,3.62,47200,57,36 "PJM Wh Real Time Peak",39456,39457,39457,59.5,54.25,57.53,11.22,35200,44,34 "PJM Wh Real Time Peak",39457,39458,39458,51,46.25,48.3,-9.23,72800,88,51 "PJM Wh Real Time Peak",39458,39461,39461,76.5,70,74.88,26.58,103200,121,42 "PJM Wh Real Time Peak",39461,39462,39462,80,75.5,77.94,3.06,109600,127,40 "PJM Wh Real Time Peak",39462,39463,39463,72,68,70.47,-7.47,78400,95,35

63

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

911,40912,40912,56,52,53.84,-11.87,161600,191,55 911,40912,40912,56,52,53.84,-11.87,161600,191,55 "PJM Wh Real Time Peak",40912,40913,40913,39,38,38.7,-15.14,45600,54,30 "PJM Wh Real Time Peak",40913,40914,40914,33.25,33,33.05,-5.65,42400,53,33 "PJM Wh Real Time Peak",40914,40917,40917,37.25,36.5,36.8,3.75,43200,51,34 "PJM Wh Real Time Peak",40917,40918,40918,36,35.25,35.53,-1.27,48000,57,31 "PJM Wh Real Time Peak",40918,40919,40919,35,34.2,34.6,-0.93,32000,40,28 "PJM Wh Real Time Peak",40919,40920,40920,35.5,35,35.14,0.54,43200,48,27 "PJM Wh Real Time Peak",40920,40921,40921,40.75,38.6,39.44,4.3,108000,111,39 "PJM Wh Real Time Peak",40921,40924,40924,43.5,41.6,42.69,3.25,61600,74,39 "PJM Wh Real Time Peak",40924,40925,40925,35.25,34.5,34.68,-8.01,36000,44,23

64

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

8,50.33,2.26,87200,193,30 8,50.33,2.26,87200,193,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.5,48.4,-1.93,70400,154,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45,46.48,-1.92,62000,146,28 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49,51.48,5,90400,108,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44.5,45.53,-5.95,38800,94,28

65

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

7,49.6,0.49,22400,56,24 7,49.6,0.49,22400,56,24 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",54,56.09,6.49,29200,73,27 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57.5,60.07,3.98,28400,71,26 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",50,55.19,-4.88,32800,41,20 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.5,56.14,0.95,20800,52,22

66

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

62,66.21,-0.74,44400,109,30 62,66.21,-0.74,44400,109,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",60,64.12,-2.09,45200,113,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",59,60.9,-3.22,99200,123,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62,63.2,2.3,50400,114,31 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",61.75,62.98,-0.22,48800,122,31

67

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

22.6,23.25,-1.53,6400,14,16 22.6,23.25,-1.53,6400,14,16 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18.25,18.97,-4.28,6400,8,9 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18,19.32,0.35,5600,14,10 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",17,17.24,-2.08,7200,12,10 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18,18.61,1.38,7200,17,17

68

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

150,150,,400,1,2 150,150,,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",180,180,30,2400,3,4 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",310,310,130,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",350,350,40,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",165,165,-185,800,1,2

69

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

32.5,33.04,-3.33,15200,19,19 32.5,33.04,-3.33,15200,19,19 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",37,37.32,4.28,7600,19,18 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",35,35.46,-1.86,9600,24,22 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",37,38.66,3.2,14800,36,27 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.75,40.34,1.69,9200,23,22

70

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

62.5,65.15,3.64,62800,150,34 62.5,65.15,3.64,62800,150,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",54.25,61.54,-3.61,153600,172,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",60.5,62.02,0.48,81200,188,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",61.75,62.73,0.71,69600,168,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62.75,63.47,0.74,74400,170,34

71

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

1,45.5,-0.2,22800,57,25 1,45.5,-0.2,22800,57,25 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43.5,45.44,-0.06,96000,198,32 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.25,43.27,-2.17,89600,210,33 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39,42.7,-0.57,118400,261,35 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.5,43.86,1.16,169600,196,33

72

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

46,48.6,-4.22,46000,115,33 46,48.6,-4.22,46000,115,33 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.5,49.21,0.61,51600,120,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.75,46.71,-2.5,123200,150,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.5,49.35,2.64,63600,151,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.3,49.44,0.09,65600,163,34

73

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Definitions Definitions Definitions Since 2006, EIA has reported two measures of aggregate capacity, one based on demonstrated peak working gas storage, the other on working gas design capacity. Demonstrated Peak Working Gas Capacity: This measure sums the highest storage inventory level of working gas observed in each facility over the 5-year range from May 2005 to April 2010, as reported by the operator on the Form EIA-191M, "Monthly Underground Gas Storage Report." This data-driven estimate reflects actual operator experience. However, the timing for peaks for different fields need not coincide. Also, actual available maximum capacity for any storage facility may exceed its reported maximum storage level over the last 5 years, and is virtually certain to do so in the case of newly commissioned or expanded facilities. Therefore, this measure provides a conservative indicator of capacity that may understate the amount that can actually be stored.

74

Desert Peak EGS Project  

Energy.gov (U.S. Department of Energy (DOE))

Desert Peak EGS Project presentation at the April 2013 peer review meeting held in Denver, Colorado.

75

Economics of Peak Oil  

Science Journals Connector (OSTI)

Abstract Peak oil refers to the future decline in world production of crude oil and the accompanying potentially calamitous effects. The peak oil literature typically rejects economic analysis. This article argues that economic analysis is indeed appropriate for analyzing oil scarcity because standard economic models can replicate the observed peaks in oil production. Moreover, the emphasis on peak oil is misplaced as peaking is not a good indicator of scarcity, peak oil techniques are overly simplistic, the catastrophes predicted by the peak oil literature are unlikely, and the literature does not contribute to correcting identified market failures. Efficiency of oil markets could be improved by instead focusing on remedying market failures such as excessive private discount rates, environmental externalities, market power, insufficient innovation incentives, incomplete futures markets, and insecure property rights.

S.P. Holland

2013-01-01T23:59:59.000Z

76

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATIONPHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

U.S. Energy Information Administration (EIA) Indexed Site

TRANSPORTATIONPHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

77

Peak power ratio generator  

DOE Patents (OSTI)

A peak power ratio generator is described for measuring, in combination with a conventional power meter, the peak power level of extremely narrow pulses in the gigahertz radio frequency bands. The present invention in a preferred embodiment utilizes a tunnel diode and a back diode combination in a detector circuit as the only high speed elements. The high speed tunnel diode provides a bistable signal and serves as a memory device of the input pulses for the remaining, slower components. A hybrid digital and analog loop maintains the peak power level of a reference channel at a known amount. Thus, by measuring the average power levels of the reference signal and the source signal, the peak power level of the source signal can be determined.

Moyer, Robert D. (Albuquerque, NM)

1985-01-01T23:59:59.000Z

78

Desert Peak EGS Project  

Energy.gov (U.S. Department of Energy (DOE))

Geothermal Technologies Program 2010 Peer Review Desert Peak EGS Project, for the Engineered Geothermal Systems Demonstration Projects and Innovative Exploration Technologies. Objective to stimulate permeability in tight well 27-15 and improve connection to rest of the field; improve overall productivity or injectivity. Successful stimulation yields more production and enables more power generation.

79

,"Table 3a. January Monthly Peak Hour Demand, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

Organization (MRO)." ," * The MRO, SERC, and SPP regional boundaries were altered as utilities changed reliability organizations. The historical data series " ,"have not been...

80

,"Table 3a. January Monthly Peak Hour Demand, Actual and Projected...  

U.S. Energy Information Administration (EIA) Indexed Site

which oversees electric reliability. * NERC Regional names may be found on the EIA web page for electric reliability. " ," * Regional name and function has changed from...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Peak Oil, Peak Energy Mother Nature Bats Last  

E-Print Network (OSTI)

Peak Oil, Peak Energy Mother Nature Bats Last Martin Sereno 1 Feb 2011 (orig. talk: Nov 2004) #12;Oil is the Lifeblood of Industrial Civilization · 80 million barrels/day, 1000 barrels/sec, 1 cubicPods to the roads themselves) · we're not "addicted to oil" -- that's like saying a person has an "addiction

Sereno, Martin

82

Peak oil: diverging discursive pipelines.  

E-Print Network (OSTI)

??Peak oil is the claimed moment in time when global oil production reaches its maximum rate and henceforth forever declines. It is highly controversial as (more)

Doctor, Jeff

2012-01-01T23:59:59.000Z

83

summer_peak_2004.xls  

Annual Energy Outlook 2012 (EIA)

(Megawatts and 2004 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN...

84

winter_peak_2003.xls  

Gasoline and Diesel Fuel Update (EIA)

and 2003 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN...

85

summer_peak_2003.xls  

U.S. Energy Information Administration (EIA) Indexed Site

(Megawatts and 2003 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN...

86

winter_peak_2004.xls  

U.S. Energy Information Administration (EIA) Indexed Site

and 2004 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN...

87

A Sensitivity Study of Building Performance Using 30-Year Actual Weather  

NLE Websites -- All DOE Office Websites (Extended Search)

Sensitivity Study of Building Performance Using 30-Year Actual Weather Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Title A Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Publication Type Conference Paper Year of Publication 2013 Authors Hong, Tianzhen, Wen-Kuei Chang, and Hung-Wen Lin Date Published 05/2013 Keywords Actual meteorological year, Building simulation, Energy use, Peak electricity demand, Typical meteorological year, Weather data Abstract Traditional energy performance calculated using building simulation with the typical meteorological year (TMY) weather data represents the energy performance in a typical year but not necessarily the average or typical energy performance of a building in long term. Furthermore, the simulated results do not provide the range of variations due to the change of weather, which is important in building energy management and risk assessment of energy efficiency investment. This study analyzes the weather impact on peak electric demand and energy use by building simulation using 30-year actual meteorological year (AMY) weather data for three types of office buildings at two design efficiency levels across all 17 climate zones. The simulated results from the AMY are compared to those from TMY3 to determine and analyze the differences. It was found that yearly weather variation has significant impact on building performance especially peak electric demand. Energy savings of building technologies should be evaluated using simulations with multi-decade actual weather data to fully consider investment risk and the long term performance.

88

Economic vulnerability to Peak Oil  

Science Journals Connector (OSTI)

Abstract Peak Oil, which refers to the maximum possible global oil production rate, is increasingly gaining attention in both science and policy discourses. However, little is known about how this phenomenon will impact economies, despite its apparent imminence and potential dangers. In this paper, we construct a vulnerability map of the U.S. economy, combining two approaches for analyzing economic systems, i.e. inputoutput analysis and social network analysis (applied to economic data). Our approach reveals the relative importance of individual economic sectors, and how vulnerable they are to oil price shocks. As such, our dual-analysis helps identify which sectors, due to their strategic position, could put the entire U.S. economy at risk from Peak Oil. For the U.S., such sectors would include Iron Mills, Fertilizer Production and Transport by Air. Our findings thus provide early warnings to downstream companies about potential trouble in their supply chain, and inform policy action for Peak Oil. Although our analysis is embedded in a Peak Oil narrative, it is just as valid and useful in the context of developing a climate roadmap toward a low carbon economy.

Christian Kerschner; Christina Prell; Kuishuang Feng; Klaus Hubacek

2013-01-01T23:59:59.000Z

89

Definition: Variable Peak Pricing | Open Energy Information  

Open Energy Info (EERE)

Variable Peak Pricing Variable Peak Pricing Jump to: navigation, search Dictionary.png Variable Peak Pricing Variable Peak Pricing (VPP) is a hybrid of time-of-use and real-time pricing where the different periods for pricing are defined in advance (e.g., on-peak=6 hours for summer weekday afternoon; off-peak= all other hours in the summer months), but the price established for the on-peak period varies by utility and market conditions.[1] Related Terms real-time pricing References ↑ https://www.smartgrid.gov/category/technology/variable_peak_pricing [[C LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ategory: Smart Grid Definitionssmart grid,off-peak,on-peak,smart grid, |Template:BASEPAGENAME]]smart grid,off-peak,on-peak,smart grid, Retrieved from "http://en.openei.org/w/index.php?title=Definition:Variable_Peak_Pricing&oldid=50262

90

Silver Peak Innovative Exploration Project  

Energy.gov (U.S. Department of Energy (DOE))

DOE Geothermal Peer Review 2010 - Presentation. Project objectives: Reduce the high level of risk during the early stages of geothermal project development by conducting a multi-faceted and innovative exploration and drilling program at Silver Peak. Determine the combination of techniques that are most useful and cost-effective in identifying the geothermal resource through a detailed, post-project evaluation of the exploration and drilling program.

91

Price hub","Trade date","Delivery start date","Delivery  

U.S. Energy Information Administration (EIA) Indexed Site

MWh","Low price MWh","Wtd avg price MWh","Change","Daily volume MWh","Number of trades","Number of counterparties" "ERCOT North 345KV Peak","applicationvnd.ms-excel","applicat...

92

Table 13. Coal Production, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Coal Production, Projected vs. Actual" Coal Production, Projected vs. Actual" "Projected" " (million short tons)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",999,1021,1041,1051,1056,1066,1073,1081,1087,1098,1107,1122,1121,1128,1143,1173,1201,1223 "AEO 1995",,1006,1010,1011,1016,1017,1021,1027,1033,1040,1051,1066,1076,1083,1090,1108,1122,1137 "AEO 1996",,,1037,1044,1041,1045,1061,1070,1086,1100,1112,1121,1135,1156,1161,1167,1173,1184,1190 "AEO 1997",,,,1028,1052,1072,1088,1105,1110,1115,1123,1133,1146,1171,1182,1190,1193,1201,1209 "AEO 1998",,,,,1088,1122,1127.746338,1144.767212,1175.662598,1176.493652,1182.742065,1191.246948,1206.99585,1229.007202,1238.69043,1248.505981,1260.836914,1265.159424,1284.229736

93

Table 22. Energy Intensity, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Energy Intensity, Projected vs. Actual" Energy Intensity, Projected vs. Actual" "Projected" " (quadrillion Btu / real GDP in billion 2005 chained dollars)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",11.24893441,11.08565002,10.98332766,10.82852279,10.67400621,10.54170176,10.39583203,10.27184573,10.14478673,10.02575883,9.910410202,9.810812106,9.69894802,9.599821783,9.486985399,9.394733753,9.303329725,9.221322623 "AEO 1995",,10.86137373,10.75116461,10.60467959,10.42268977,10.28668187,10.14461664,10.01081222,9.883759026,9.759022105,9.627404949,9.513643295,9.400418762,9.311729546,9.226142899,9.147374752,9.071102491,8.99599906 "AEO 1996",,,10.71047701,10.59846153,10.43655044,10.27812088,10.12746866,9.9694713,9.824165152,9.714832565,9.621874334,9.532324916,9.428169355,9.32931308,9.232716414,9.170931044,9.086870061,9.019963901,8.945602337

94

Economic effects of peak oil  

Science Journals Connector (OSTI)

Assuming that global oil production peaked, this paper uses scenario analysis to show the economic effects of a possible supply shortage and corresponding rise in oil prices in the next decade on different sectors in Germany and other major economies such as the US, Japan, China, the OPEC or Russia. Due to the price-inelasticity of oil demand the supply shortage leads to a sharp increase in oil prices in the second scenario, with high effects on GDP comparable to the magnitude of the global financial crises in 2008/09. Oil exporting countries benefit from high oil prices, whereas oil importing countries are negatively affected. Generally, the effects in the third scenario are significantly smaller than in the second, showing that energy efficiency measures and the switch to renewable energy sources decreases the countries' dependence on oil imports and hence reduces their vulnerability to oil price shocks on the world market.

Christian Lutz; Ulrike Lehr; Kirsten S. Wiebe

2012-01-01T23:59:59.000Z

95

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATION PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

U.S. Energy Information Administration (EIA) Indexed Site

UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

96

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATION PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

U.S. Energy Information Administration (EIA) Indexed Site

UTILITY FOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

97

Peak load management: Potential options  

SciTech Connect

This report reviews options that may be alternatives to transmission construction (ATT) applicable both generally and at specific locations in the service area of the Bonneville Power Administration (BPA). Some of these options have potential as specific alternatives to the Shelton-Fairmount 230-kV Reinforcement Project, which is the focus of this study. A listing of 31 peak load management (PLM) options is included. Estimated costs and normalized hourly load shapes, corresponding to the respective base load and controlled load cases, are considered for 15 of the above options. A summary page is presented for each of these options, grouped with respect to its applicability in the residential, commercial, industrial, and agricultural sectors. The report contains comments on PLM measures for which load shape management characteristics are not yet available. These comments address the potential relevance of the options and the possible difficulty that may be encountered in characterizing their value should be of interest in this investigation. The report also identifies options that could improve the efficiency of the three customer utility distribution systems supplied by the Shelton-Fairmount Reinforcement Project. Potential cogeneration options in the Olympic Peninsula are also discussed. These discussions focus on the options that appear to be most promising on the Olympic Peninsula. Finally, a short list of options is recommended for investigation in the next phase of this study. 9 refs., 24 tabs.

Englin, J.E.; De Steese, J.G.; Schultz, R.W.; Kellogg, M.A.

1989-10-01T23:59:59.000Z

98

Table 14. Coal Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Coal Production, Projected vs. Actual Coal Production, Projected vs. Actual (million short tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 914 939 963 995 1031 1080 AEO 1983 900 926 947 974 1010 1045 1191 AEO 1984 899 921 948 974 1010 1057 1221 AEO 1985 886 909 930 940 958 985 1015 1041 1072 1094 1116 AEO 1986 890 920 954 962 983 1017 1044 1073 1097 1126 1142 1156 1176 1191 1217 AEO 1987 917 914 932 962 978 996 1020 1043 1068 1149 AEO 1989* 941 946 977 990 1018 1039 1058 1082 1084 1107 1130 1152 1171 AEO 1990 973 987 1085 1178 1379 AEO 1991 1035 1002 1016 1031 1043 1054 1065 1079 1096 1111 1133 1142 1160 1193 1234 1272 1309 1349 1386 1433 AEO 1992 1004 1040 1019 1034 1052 1064 1074 1087 1102 1133 1144 1156 1173 1201 1229 1272 1312 1355 1397 AEO 1993 1039 1043 1054 1065 1076 1086 1094 1102 1125 1136 1148 1161 1178 1204 1237 1269 1302 1327 AEO 1994 999 1021

99

Peak Oil Food Network | Open Energy Information  

Open Energy Info (EERE)

Network Network Jump to: navigation, search Name Peak Oil Food Network Place Crested Butte, Colorado Zip 81224 Website http://www.PeakOilFoodNetwork. References Peak Oil Food Network[1] LinkedIn Connections This article is a stub. You can help OpenEI by expanding it. The Peak Oil Food Network is a networking organization located in Crested Butte, Colorado, and is open to the general public that seeks to promote the creation of solutions to the challenge of food production impacted by the peak phase of global oil production. Private citizens are encouraged to join and contribute by adding comments, writing blog posts or adding to discussions about food and oil related topics. Peak Oil Food Network can be followed on Twitter at: http://www.Twitter.com/PeakOilFoodNtwk Peak Oil Food Network on Twitter

100

AUTOMATED CRITICAL PEAK PRICING FIELD TESTS  

E-Print Network (OSTI)

AUTOMATED CRITICAL PEAK PRICING FIELD TESTS: 2006 PROGRAM DESCRIPTION AND RESULTS APPENDICES.................................................................................... 5 B.2. DR Automation Server User Guide

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Definition: On-Peak | Open Energy Information  

Open Energy Info (EERE)

Definition Definition Edit with form History Facebook icon Twitter icon » Definition: On-Peak Jump to: navigation, search Dictionary.png On-Peak Those hours or other periods defined by NAESB business practices, contract, agreements, or guides as periods of higher electrical demand.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Also Known As peak load Related Terms demand, peak demand References ↑ Glossary of Terms Used in Reliability Standards Temp Like Like You like this.Sign Up to see what your friends like. late:ISGANAttributionsmart grid,smart grid, Retrieved from "http://en.openei.org/w/index.php?title=Definition:On-Peak&oldid=502536"

102

Table 23. Energy Intensity, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Energy Intensity, Projected vs. Actual Energy Intensity, Projected vs. Actual (quadrillion Btu / $Billion Nominal GDP) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 20.1 18.5 16.9 15.5 14.4 13.2 AEO 1983 19.9 18.7 17.4 16.2 15.1 14.0 9.5 AEO 1984 20.1 19.0 17.7 16.5 15.5 14.5 10.2 AEO 1985 20.0 19.1 18.0 16.9 15.9 14.7 13.7 12.7 11.8 11.0 10.3 AEO 1986 18.3 17.8 16.8 16.1 15.2 14.3 13.4 12.6 11.7 10.9 10.2 9.5 8.9 8.3 7.8 AEO 1987 17.6 17.0 16.3 15.4 14.5 13.7 12.9 12.1 11.4 8.2 AEO 1989* 16.9 16.2 15.2 14.2 13.3 12.5 11.7 10.9 10.2 9.6 9.0 8.5 8.0 AEO 1990 16.1 15.4 11.7 8.6 6.4 AEO 1991 15.5 14.9 14.2 13.6 13.0 12.5 11.9 11.3 10.8 10.3 9.7 9.2 8.7 8.3 7.9 7.4 7.0 6.7 6.3 6.0 AEO 1992 15.0 14.5 13.9 13.3 12.7 12.1 11.6 11.0 10.5 10.0 9.5 9.0 8.6 8.1 7.7 7.3 6.9 6.6 6.2 AEO 1993 14.7 13.9 13.4 12.8 12.3 11.8 11.2 10.7 10.2 9.6 9.2 8.7 8.3 7.8 7.4 7.1 6.7 6.4

103

Mt Peak Utility | Open Energy Information  

Open Energy Info (EERE)

Peak Utility Peak Utility Jump to: navigation, search Name Mt Peak Utility Facility Mt Peak Utility Sector Wind energy Facility Type Small Scale Wind Facility Status In Service Owner Mnt Peak Utility Energy Purchaser Mnt Peak Utility Location Midlothian TX Coordinates 32.42144978°, -97.02427357° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":32.42144978,"lon":-97.02427357,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

104

Peak Treatment Systems | Open Energy Information  

Open Energy Info (EERE)

Agreement Partnership Year 1998 Link to project description http:www.nrel.govnewspress199804licns.html Peak Treatment Systems is a company located in Golden, CO....

105

Measured Peak Equipment Loads in Laboratories  

E-Print Network (OSTI)

of measured equipment load data for laboratories, designersmeasured peak equipment load data from 39 laboratory spacesmeasured equipment load data from various laboratory spaces

Mathew, Paul A.

2008-01-01T23:59:59.000Z

106

Monthly Generation System Peak (pbl/generation)  

NLE Websites -- All DOE Office Websites (Extended Search)

Generation > Generation Hydro Power Wind Power Monthly GSP BPA White Book Dry Year Tools Firstgov Monthly Generation System Peak (GSP) This site is no longer maintained. Page last...

107

On the reliability of peak-flux distributions, with an application to solar flares  

E-Print Network (OSTI)

Narrow-band radio spikes have been recorded during a solar flare with unprecedented resolution. This unique example allows to study the effect of low resolution in previously published peak-flux distributions of radio spikes. We give a general, analytical expression for how an actual peak-flux distribution is changed in shape if the peaks are determined with low temporal and/or frequency resolution. It turns out that, generally, low resolution tends to cause an exponential behavior at large flux values if the actual distribution is of power-law shape. The distribution may be severely altered if the burst-duration depends on the peak-flux. The derived expression is applicable also to peak-flux distributions derived at other wavelengths (e.g. soft and hard X-rays, EUV). We show that for the analyzed spike-event the resolution was sufficient for a reliable peak flux distribution. It can be fitted by generalized power-laws or by an exponential.

H. Isliker; A. O. Benz

2001-06-08T23:59:59.000Z

108

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network (OSTI)

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles and Steve Simmons GRAC, Reciprocating Engines Next steps 2 #12;Definitions Baseload Energy: power generated (or conserved) across a period of time to serve system demands for electricity Peaking Capacity: capability of power generating

109

Storm Peak Lab Cloud Property Validation  

NLE Websites -- All DOE Office Websites (Extended Search)

Storm Peak Lab Cloud Storm Peak Lab Cloud Property Validation Experiment (STORMVEX) Operated by the Atmospheric Radiation Measurement (ARM) Climate Research Facility for the U.S. Department of Energy, the second ARM Mobile Facility (AMF2) begins its inaugural deployment November 2010 in Steamboat Springs, Colorado, for the Storm Peak Lab Cloud Property Validation Experiment, or STORMVEX. For six months, the comprehensive suite of AMF2 instruments will obtain measurements of cloud and aerosol properties at various sites below the heavily instrumented Storm Peak Lab, located on Mount Werner at an elevation of 3220 meters. The correlative data sets that will be created from AMF2 and Storm Peak Lab will equate to between 200 and 300 in situ aircraft flight hours in liquid, mixed phase, and precipitating

110

Definition: Peak Demand | Open Energy Information  

Open Energy Info (EERE)

Peak Demand Peak Demand Jump to: navigation, search Dictionary.png Peak Demand The highest hourly integrated Net Energy For Load within a Balancing Authority Area occurring within a given period (e.g., day, month, season, or year)., The highest instantaneous demand within the Balancing Authority Area.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Related Terms Balancing Authority Area, energy, demand, balancing authority, smart grid References ↑ Glossary of Terms Used in Reliability Standards An inli LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ne Glossary Definition Retrieved from

111

LNG production for peak shaving operations  

SciTech Connect

LNG production facilities are being developed as an alternative or in addition to underground storage throughout the US to provide gas supply during peak gas demand periods. These facilities typically involved a small liquefaction unit with a large LNG storage tank and gas sendout facilities capable of responding to peak loads during the winter. Black and Veatch is active in the development of LNG peak shaving projects for clients using a patented mixed refrigerant technology for efficient production of LNG at a low installed cost. The mixed refrigerant technology has been applied in a range of project sizes both with gas turbine and electric motor driven compression systems. This paper will cover peak shaving concepts as well as specific designs and projects which have been completed to meet this market need.

Price, B.C.

1999-07-01T23:59:59.000Z

112

Peak Oil Futures: Same Crisis, Different Responses  

Science Journals Connector (OSTI)

Peak oil theory predicts that global oil production will soon start a terminal decline. ... resource and technology will be available to replace oil as the backbone resource of industrial society. ... understand ...

Jrg Friedrichs

2012-01-01T23:59:59.000Z

113

A perspective on the CMB acoustic peak  

E-Print Network (OSTI)

CMB angular spectrum measurements suggest a flat universe. This paper clarifies the relation between geometry and the spherical harmonic index of the first acoustic peak ($\\ell_{peak}$). Numerical and analytic calculations show that $\\ell_{peak}$ is approximately a function of $\\Omega_K/\\Omega_M$ where $\\Omega_K$ and $\\Omega_M$ are the curvature ($\\Omega_K > 0$ implies an open geometry) and mass density today in units of critical density. Assuming $\\Omega_K/\\Omega_M \\ll 1$, one obtains a simple formula for $\\ell_{peak}$, the derivation of which gives another perspective on the widely-recognized $\\Omega_M$-$\\Omega_\\Lambda$ degeneracy in flat models. This formula for near-flat cosmogonies together with current angular spectrum data yields familiar parameter constraints.

T. A. Marriage

2002-03-11T23:59:59.000Z

114

Flow shop scheduling with peak power consumption constraints  

E-Print Network (OSTI)

Mar 29, 2012 ... Flow shop scheduling with peak power consumption constraints ... Keywords: scheduling, flow shop, energy, peak power consumption, integer...

K. Fang

2012-03-29T23:59:59.000Z

115

Shale Gas Production: Potential versus Actual GHG Emissions  

E-Print Network (OSTI)

Shale Gas Production: Potential versus Actual GHG Emissions Francis O'Sullivan and Sergey Paltsev://globalchange.mit.edu/ Printed on recycled paper #12;1 Shale Gas Production: Potential versus Actual GHG Emissions Francis O'Sullivan* and Sergey Paltsev* Abstract Estimates of greenhouse gas (GHG) emissions from shale gas production and use

116

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Methodology Methodology Methodology Demonstrated Peak Working Gas Capacity Estimates: Estimates are based on aggregation of the noncoincident peak levels of working gas inventories at individual storage fields as reported monthly over a 60-month period ending in April 2010 on Form EIA-191M, "Monthly Natural Gas Underground Storage Report." The months of measurement for the peak storage volumes by facilities may differ; i.e., the months do not necessarily coincide. As such, the noncoincident peak for any region is at least as big as any monthly volume in the historical record. Data from Form EIA-191M, "Monthly Natural Gas Underground Storage Report," are collected from storage operators on a field-level basis. Operators can report field-level data either on a per reservoir basis or on an aggregated reservoir basis. It is possible that if all operators reported on a per reservoir basis that the demonstrated peak working gas capacity would be larger. Additionally, these data reflect inventory levels as of the last day of the report month, and a facility may have reached a higher inventory on a different day of the report month, which would not be recorded on Form EIA-191M.

117

Silver Peak Geothermal Project | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Geothermal Project Silver Peak Geothermal Project Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Development Project: Silver Peak Geothermal Project Project Location Information Coordinates 37.755°, -117.63472222222° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.755,"lon":-117.63472222222,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

118

Pilot Peak Geothermal Project | Open Energy Information  

Open Energy Info (EERE)

Pilot Peak Geothermal Project Pilot Peak Geothermal Project Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Development Project: Pilot Peak Geothermal Project Project Location Information Coordinates 38.342266666667°, -118.10361111111° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.342266666667,"lon":-118.10361111111,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

119

Silver Peak Geothermal Area | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Geothermal Area Silver Peak Geothermal Area Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Geothermal Resource Area: Silver Peak Geothermal Area Contents 1 Area Overview 2 History and Infrastructure 3 Regulatory and Environmental Issues 4 Exploration History 5 Well Field Description 6 Geology of the Area 7 Geofluid Geochemistry 8 NEPA-Related Analyses (5) 9 Exploration Activities (26) 10 References Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"TERRAIN","zoom":6,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"500px","height":"300px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.746167220142,"lon":-117.60267734528,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

120

Desert Peak Geothermal Area | Open Energy Information  

Open Energy Info (EERE)

Desert Peak Geothermal Area Desert Peak Geothermal Area Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Geothermal Resource Area: Desert Peak Geothermal Area Contents 1 Area Overview 2 History and Infrastructure 3 Regulatory and Environmental Issues 4 Exploration History 5 Well Field Description 6 Geology of the Area 7 Geofluid Geochemistry 8 NEPA-Related Analyses (3) 9 Exploration Activities (8) 10 References Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"TERRAIN","zoom":6,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"500px","height":"300px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.75,"lon":-118.95,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Vad r Peak Oil och existerar det?; What is Peak Oil and does it exist?.  

E-Print Network (OSTI)

?? The purpose of this study is the reports of Peak Oil in Swedish newspapers. In otherwords, how do the news portray or describe the (more)

Wlimaa, Peter

2013-01-01T23:59:59.000Z

122

Silver Peak Geothermal Area | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Geothermal Area Silver Peak Geothermal Area (Redirected from Silver Peak Area) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Geothermal Resource Area: Silver Peak Geothermal Area Contents 1 Area Overview 2 History and Infrastructure 3 Regulatory and Environmental Issues 4 Exploration History 5 Well Field Description 6 Geology of the Area 7 Geofluid Geochemistry 8 NEPA-Related Analyses (5) 9 Exploration Activities (26) 10 References Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"TERRAIN","zoom":6,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"500px","height":"300px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.746167220142,"lon":-117.60267734528,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

123

Desert Peak Geothermal Area | Open Energy Information  

Open Energy Info (EERE)

Desert Peak Geothermal Area Desert Peak Geothermal Area (Redirected from Desert Peak Area) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Geothermal Resource Area: Desert Peak Geothermal Area Contents 1 Area Overview 2 History and Infrastructure 3 Regulatory and Environmental Issues 4 Exploration History 5 Well Field Description 6 Geology of the Area 7 Geofluid Geochemistry 8 NEPA-Related Analyses (3) 9 Exploration Activities (8) 10 References Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"TERRAIN","zoom":6,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"500px","height":"300px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.75,"lon":-118.95,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

124

GeoPeak Energy | Open Energy Information  

Open Energy Info (EERE)

GeoPeak Energy GeoPeak Energy Jump to: navigation, search Logo: GeoPeak Energy Name GeoPeak Energy Address 285 Davidson Avenue Place Somerset, New Jersey Zip 08873 Sector Solar Product Residential and Commercial PV Solar Installations Number of employees 11-50 Company Type For Profit Phone number 732-377-3700 Website http://www.geopeakenergy.com Coordinates 40.5326723°, -74.5284554° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.5326723,"lon":-74.5284554,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

125

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network (OSTI)

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles GRAC 2/27/14 #12;Today Vernon, WA PSE Klamath Generation Peakers June 2002 (2) 54 MW P&W FT8 Twin- pac 95 MW Klamath, OR IPP; winter-only PPA w/ PSE Dave Gates Generating Station Jan 2011 (3) P&W SWIFTPAC 150 MW Anaconda, MT North

126

Scott McPeak Research Statement  

E-Print Network (OSTI)

Scott McPeak Research Statement My main research interest is in tools and techniques to improve software quality. In this statement I describe my past involvement in several research projects whose goal and server proxy I co-wrote with Dan Bonachea.) Our group's efforts on CCured have made it more than a mere

California at Berkeley, University of

127

AUTOMATED CRITICAL PEAK PRICING FIELD TESTS  

E-Print Network (OSTI)

AUTOMATED CRITICAL PEAK PRICING FIELD TESTS: 2006 PROGRAM DESCRIPTION AND RESULTS) for development of the DR Automation Server System This project could not have been completed without extensive: Greg Watson and Mark Lott · C&C Building Automation: Mark Johnson and John Fiegel · Chabot Space

128

MODELING THE GLOBAL PEAKS AND COOLING SY  

E-Print Network (OSTI)

of assessed building energy consumption and indoor air temperature peaks. At last, the coupling of the urban energy consumption. Building uses are an important part of the global energy use thus a good conception until the year 2100 highlight a regular increase building energy consumption and indoor At last

Boyer, Edmond

129

Estimation of Regional Actual Evapotranspiration in the Panama Canal Watershed  

Science Journals Connector (OSTI)

The upper Ro Chagres basin is a part of the Panama Canal Watershed. The least known water balance...SEBAL...). We use an image from March 27, 2000, for estimation of the distribution of the regional actual evapo...

Jan M.H. Hendrickx; Wim G.M. Bastiaanssen; Edwin J.M. Noordman

2005-01-01T23:59:59.000Z

130

Peak Oil Awareness Network | Open Energy Information  

Open Energy Info (EERE)

Awareness Network Awareness Network Jump to: navigation, search Name Peak Oil Awareness Network Place Crested Butte, Colorado Zip 81224 Website http://www.PeakOilAwarenessNet Coordinates 38.8697146°, -106.9878231° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.8697146,"lon":-106.9878231,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

131

Definition: Critical Peak Pricing | Open Energy Information  

Open Energy Info (EERE)

Pricing Pricing Jump to: navigation, search Dictionary.png Critical Peak Pricing When utilities observe or anticipate high wholesale market prices or power system emergency conditions, they may call critical events during a specified time period (e.g., 3 p.m.-6 p.m. on a hot summer weekday), the price for electricity during these time periods is substantially raised. Two variants of this type of rate design exist: one where the time and duration of the price increase are predetermined when events are called and another where the time and duration of the price increase may vary based on the electric grid's need to have loads reduced;[1] Related Terms electricity generation References ↑ https://www.smartgrid.gov/category/technology/critical_peak_pricing Ret LikeLike UnlikeLike

132

Definition: Critical Peak Rebates | Open Energy Information  

Open Energy Info (EERE)

Rebates Rebates Jump to: navigation, search Dictionary.png Critical Peak Rebates When utilities observe or anticipate high wholesale market prices or power system emergency conditions, they may call critical events during pre-specified time periods (e.g., 3 p.m.-6 p.m. summer weekday afternoons), the price for electricity during these time periods remains the same but the customer is refunded at a single, predetermined value for any reduction in consumption relative to what the utility deemed the customer was expected to consume.[1] Related Terms electricity generation References ↑ https://www.smartgrid.gov/category/technology/critical_peak_rebates [[C LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ategory: Smart Grid Definitions|Template:BASEPAGENAME]]

133

Central peaking of magnetized gas discharges  

SciTech Connect

Partially ionized gas discharges used in industry are often driven by radiofrequency (rf) power applied at the periphery of a cylinder. It is found that the plasma density n is usually flat or peaked on axis even if the skin depth of the rf field is thin compared with the chamber radius a. Previous attempts at explaining this did not account for the finite length of the discharge and the boundary conditions at the endplates. A simple 1D model is used to focus on the basic mechanism: the short-circuit effect. It is found that a strong electric field (E-field) scaled to electron temperature T{sub e}, drives the ions inward. The resulting density profile is peaked on axis and has a shape independent of pressure or discharge radius. This universal profile is not affected by a dc magnetic field (B-field) as long as the ion Larmor radius is larger than a.

Chen, Francis F. [Electrical Engineering Department, University of California, Los Angeles, California 90095 (United States)] [Electrical Engineering Department, University of California, Los Angeles, California 90095 (United States); Curreli, Davide [Department of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana Champaign, Urbana, Illinois 61801 (United States)] [Department of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana Champaign, Urbana, Illinois 61801 (United States)

2013-05-15T23:59:59.000Z

134

Eyesight and the solar Wien peak  

Science Journals Connector (OSTI)

It is sometimes said that humans see best at yellowgreen wavelengths because they have evolved under a Sun whose blackbody spectrum has a Wien peak in the green part of the spectrum. However as a function of frequency the solar blackbody spectrum peaks in the infrared. Why did human vision not evolve toward a peak sensitivity in this range if the eye is an efficient quantum detector of photons? The puzzle is resolved if we assume that natural selection acted in such a way as to maximize the amount of energy that can be detected by the retina across a range of wavelengths (whose upper and lower limits are fixed by biological constraints). It is then found that our eyes are indeed perfectly adapted to life under a class G2 star. Extending this reasoning allows educated guesses to be made about the kind of eyesight that might have evolved in extrasolar planetary systems such as that of the red dwarf Gliese 876.

James M. Overduin

2003-01-01T23:59:59.000Z

135

Mean and peak wind loads on heliostats  

SciTech Connect

Mean and peak wind loads on flat rectangular or circular heliostats were measured on models in a boundary layer wind tunnel which included an atmospheric surface layer simulation. Horizontal and vertical forces, moments about horizontal axes at the ground level and at the centerline of the heliostat, and the moment about the vertical axis through the heliostat center were measured. Results showed that loads are higher than predicted from results obtained in a uniform, low-turbulence flow due to the presence of turbulence. Reduced wind loads were demonstrated for heliostats within a field of heliostats and upper bound curves were developed to provide preliminary design coefficients.

Peterka, J.A.; Tan, Z.; Cermak, J.E.; Bienkiewicz, B.

1989-05-01T23:59:59.000Z

136

Self-actualization as it relates to aerobic physical fitness  

E-Print Network (OSTI)

higher than the aerobic and archery group on the TC, Ex, and C scales. The archery group was significantly higher than the preaerobic and aerobic groups on the Fr and S scales. Females from the preaerobic group were significantly lower than archery... Inventory Sav Self-actualization values measures how well a person holds and lives by values of se 1f- ac tualizing people Ex Existentiality measures ability to flexibly apply self-actualizing values to one's own life Fr Feeling reactivity measures...

Russell, Kathryn Terese Vecchio

2012-06-07T23:59:59.000Z

137

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of  

NLE Websites -- All DOE Office Websites (Extended Search)

Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year ActualWeather Data Title A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year ActualWeather Data Publication Type Journal Year of Publication 2013 Authors Hong, Tianzhen, Wen-Kuei Chang, and Hung-Wen Lin Keywords Actual meteorological year, Building simulation, Energy use, Peak electricity demand, Typical meteorological year, Weather data Abstract Buildings consume more than one third of the world's total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

138

Rank Name Peak Date Peak Location Bomb Peak Gradient Min Depth (Hr-Dy-Mn-Yr) (Lat, Lon) (Bergeron) (hPa/1000km) (hPa)  

E-Print Network (OSTI)

Rank Name Peak Date Peak Location Bomb Peak Gradient Min Depth (Hr-Dy-Mn-Yr) (Lat, Lon) (Bergeron, and northwest europe (Cambride Univ. Pr.). 1 #12;Figure S1(a): Evolution of 'Daria' (the top ranked storm arrow is approximately 50 m s-1). 2 #12;Figure S1(b): As for Figure S1(a) but for the storm ranked

Caballero, Rodrigo

139

experiment actually sees," Smith says. "When we were  

E-Print Network (OSTI)

experiment actually sees," Smith says. "When we were finished, we got much more ­ a method in science depend on atoms and molecules moving," Smith says. "We want to create movies of molecules science development," Smith says.--Morgan McCorkle A theoretical technique developed at ORNL is bringing

Pennycook, Steve

140

COORDINATING ADVICE AND ACTUAL TREATMENT Thomas A. Russ  

E-Print Network (OSTI)

. Unfortunately, this information is not always immediately available. For example, the exact fluid infused via an intravenous line can only be determined after someone checks the infusion bottle to determine how much fluid differ in timing and exact amount from what is actually done. For example, an infusion order might call

Russ, Thomas A.

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141

Peak Sun Silicon Corp | Open Energy Information  

Open Energy Info (EERE)

Corp Corp Jump to: navigation, search Name Peak Sun Silicon Corp Place Carlsbad, California Zip 92008 Product US-based manufacturer of granular electronic-grade polysilicon for the PV industry. Coordinates 31.60396°, -100.641609° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":31.60396,"lon":-100.641609,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

142

Oil hills, ridges, peaks, cliffs and ravines  

Science Journals Connector (OSTI)

In an earlier paper Tanner and Berry (1985) considered the decay of a disturbance to an otherwise uniform thin oil film. This was followed analytically using the Navier-Stokes equation, and optically by interferometry. Solutions were obtained in the form of a series of three-dimensional hills and of two-dimensional ridges, decaying with time in a self-similar manner. The present work extends this in several ways. By better control of the applied disturbance, more of the original series are produced and illustrated. The original hill series is extended to a doubly-infinite one, providing the possibility, as with the ridges, of different time decay rates for each azimuthal structure. Negative j values, giving either vertical growth or static vertical heights, are considered and in a few cases produced experimentally. Finally nonlinear peaks, cliffs and ravines having self-similar scaling properties are studied. In all cases, good agreement between theory and experiment is obtained.

L H Tanner

1986-01-01T23:59:59.000Z

143

Gamow peak approximation near strong resonances  

E-Print Network (OSTI)

We discuss the most effective energy range for charged particle induced reactions in a plasma environment at a given plasma temperature. The correspondence between the plasma temperature and the most effective energy should be modified from the one given by the Gamow peak energy, in the presence of a significant incident-energy dependence in the astrophysical S-factor as in the case of resonant reactions. The suggested modification of the effective energy range is important not only in thermonuclear reactions at high temperature in the stellar environment, e.g., in advanced burning stages of massive stars and in explosive stellar environment, as it has been already claimed, but also in the application of the nuclear reactions driven by ultra-intense laser pulse irradiations.

Kimura, Sachie

2013-01-01T23:59:59.000Z

144

BroadPeak: a novel algorithm for identifying broad peaks in dif-fuse ChIP-seq datasets  

E-Print Network (OSTI)

1 BroadPeak: a novel algorithm for identifying broad peaks in dif- fuse ChIP-seq datasets JianrongIP-seq datasets. We show that BroadPeak is a linear time algorithm that requires only two parame- ters, and we validate its performance on real and simulated histone modification ChIP-seq datasets. BroadPeak calls

Jordan, King

145

LBNL-6280E A Fresh Look at Weather Impact on Peak Electricity Demand and  

NLE Websites -- All DOE Office Websites (Extended Search)

280E 280E A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30- Year Actual Weather Data Tianzhen Hong 1 , Wen-kuei Chang 2 , Hung-Wen Lin 2 1 Environmental Energy Technologies Division 2 Green Energy and Environment Laboratories, Industrial Technology Research Institute, Taiwan, ROC May 2013 This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, the U.S.-China Clean Energy Research Center for Building Energy Efficiency, of the U.S. Department of Energy under Contract No. DE-AC02-

146

Peak Population: Timing and Influences of Peak Energy on the World and the United States  

E-Print Network (OSTI)

Peak energy is the notion that the worlds total production of usable energy will reach a maximum value and then begin an inexorable decline. Ninety-two percent of the worlds energy is currently derived from the non-renewable sources (oil, coal...

Warner, Kevin 1987-

2012-11-28T23:59:59.000Z

147

Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Final July 01, 2010 Final July 01, 2010 1 Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance On Real Property 1. The following is the FY 2010 implementation procedures for the field offices/sites to determine and report deferred maintenance on real property as required by the Statement of Federal Financial Accounting Standards (SFFAS) No. 6, Accounting for Property, Plant, and Equipment (PP&E) and DOE Order 430.1B, Real Property Asset Management (RPAM). a. This document is intended to assist field offices/sites in consistently and accurately applying the appropriate methods to determine and report deferred maintenance estimates and reporting of annual required and actual maintenance costs. b. This reporting satisfies the Department's obligation to recognize and record deferred

148

Table 5. Domestic Crude Oil Production, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Domestic Crude Oil Production, Projected vs. Actual Domestic Crude Oil Production, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 2508 2373 2256 2161 2088 2022 1953 1891 1851 1825 1799 1781 1767 1759 1778 1789 1807 1862 AEO 1995 2402 2307 2205 2095 2037 1967 1953 1924 1916 1905 1894 1883 1887 1887 1920 1945 1967 AEO 1996 2387 2310 2248 2172 2113 2062 2011 1978 1953 1938 1916 1920 1927 1949 1971 1986 2000 AEO 1997 2362 2307 2245 2197 2143 2091 2055 2033 2015 2004 1997 1989 1982 1975 1967 1949 AEO 1998 2340 2332 2291 2252 2220 2192 2169 2145 2125 2104 2087 2068 2050 2033 2016 AEO 1999 2340 2309 2296 2265 2207 2171 2141 2122 2114 2092 2074 2057 2040 2025 AEO 2000 2193 2181 2122 2063 2016 1980 1957 1939 1920 1904 1894 1889 1889

149

Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Draft July 9, 2009 Draft July 9, 2009 1 Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance On Real Property 1. The following is the FY 2009 implementation procedures for the field offices/sites to determine and report deferred maintenance on real property as required by the Statement of Federal Financial Accounting Standards (SFFAS) No. 6, Accounting for Property, Plant, and Equipment (PP&E) and DOE Order 430.1B, Real Property Asset Management (RPAM). a. This document is intended to assist field offices/sites in consistently and accurately applying the appropriate methods to determine and report deferred maintenance estimates and reporting of annual required and actual maintenance costs. b. This reporting satisfies the Department's obligation to recognize and record deferred

150

Table 12. Total Coal Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Coal Consumption, Projected vs. Actual" Coal Consumption, Projected vs. Actual" "Projected" " (million short tons)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",920,928,933,938,943,948,953,958,962,967,978,990,987,992,1006,1035,1061,1079 "AEO 1995",,935,940,941,947,948,951,954,958,963,971,984,992,996,1002,1013,1025,1039 "AEO 1996",,,937,942,954,962,983,990,1004,1017,1027,1033,1046,1067,1070,1071,1074,1082,1087 "AEO 1997",,,,948,970,987,1003,1017,1020,1025,1034,1041,1054,1075,1086,1092,1092,1099,1104 "AEO 1998",,,,,1009,1051,1043.875977,1058.292725,1086.598145,1084.446655,1089.787109,1096.931763,1111.523926,1129.833862,1142.338257,1148.019409,1159.695312,1162.210815,1180.029785

151

Table 4. Total Petroleum Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Petroleum Consumption, Projected vs. Actual Petroleum Consumption, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 6450 6566 6643 6723 6811 6880 6957 7059 7125 7205 7296 7377 7446 7523 7596 7665 7712 7775 AEO 1995 6398 6544 6555 6676 6745 6822 6888 6964 7048 7147 7245 7337 7406 7472 7537 7581 7621 AEO 1996 6490 6526 6607 6709 6782 6855 6942 7008 7085 7176 7260 7329 7384 7450 7501 7545 7581 AEO 1997 6636 6694 6826 6953 7074 7183 7267 7369 7461 7548 7643 7731 7793 7833 7884 7924 AEO 1998 6895 6906 7066 7161 7278 7400 7488 7597 7719 7859 7959 8074 8190 8286 8361 AEO 1999 6884 7007 7269 7383 7472 7539 7620 7725 7841 7949 8069 8174 8283 8351 AEO 2000 7056 7141 7266 7363 7452 7578 7694 7815 7926 8028 8113 8217 8288

152

Table 6. Petroleum Net Imports, Projected vs. Actual Projected  

U.S. Energy Information Administration (EIA) Indexed Site

Petroleum Net Imports, Projected vs. Actual Petroleum Net Imports, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 2935 3201 3362 3504 3657 3738 3880 3993 4099 4212 4303 4398 4475 4541 4584 4639 4668 4672 AEO 1995 2953 3157 3281 3489 3610 3741 3818 3920 4000 4103 4208 4303 4362 4420 4442 4460 4460 AEO 1996 3011 3106 3219 3398 3519 3679 3807 3891 3979 4070 4165 4212 4260 4289 4303 4322 4325 AEO 1997 3099 3245 3497 3665 3825 3975 4084 4190 4285 4380 4464 4552 4617 4654 4709 4760 AEO 1998 3303 3391 3654 3713 3876 4053 4137 4298 4415 4556 4639 4750 4910 4992 5087 AEO 1999 3380 3442 3888 4022 4153 4238 4336 4441 4545 4652 4780 4888 4999 5073 AEO 2000 3599 3847 4036 4187 4320 4465 4579 4690 4780 4882 4968 5055 5113

153

Tropical Africa: Calculated Actual Aboveground Live Biomass in Open and  

NLE Websites -- All DOE Office Websites (Extended Search)

Calculated Actual Aboveground Live Biomass in Open and Calculated Actual Aboveground Live Biomass in Open and Closed Forests (1980) image Brown, S., and G. Gaston. 1996. Tropical Africa: Land Use, Biomass, and Carbon Estimates For 1980. ORNL/CDIAC-92, NDP-055. Carbon Dioxide Information Analysis Center, U.S. Department of Energy, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A. More Maps Land Use Maximum Potential Biomass Density Area of Closed Forests (By Country) Mean Biomass of Closed Forests (By Country) Area of Open Forests (By Country) Mean Biomass of Open Forests (By County) Percent Forest Cover (By Country) Total Forest Biomass (By Country) Population Density - 1990 (By Administrative Unit) Population Density - 1980 (By Administrative Unit) Population Density - 1970 (By Administrative Unit) Population Density - 1960 (By Administrative Unit)

154

Table 7b. Natural Gas Wellhead Prices, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

b. Natural Gas Wellhead Prices, Projected vs. Actual" b. Natural Gas Wellhead Prices, Projected vs. Actual" "Projected Price in Nominal Dollars" " (nominal dollars per thousand cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",1.983258692,2.124739238,2.26534793,2.409252566,2.585728477,2.727400662,2.854942053,2.980927152,3.13861755,3.345819536,3.591100993,3.849544702,4.184279801,4.510016556,4.915074503,5.29147351,5.56022351,5.960471854 "AEO 1995",,1.891706924,1.998384058,1.952818035,2.064227053,2.152302174,2.400016103,2.569033816,2.897681159,3.160088567,3.556344605,3.869033816,4.267391304,4.561932367,4.848599034,5.157246377,5.413405797,5.660917874 "AEO 1996",,,1.630674532,1.740334763,1.862956911,1.9915856,2.10351261,2.194934146,2.287655669,2.378991658,2.476043002,2.589847464,2.717610782,2.836870306,2.967124845,3.117719429,3.294003735,3.485657428,3.728419409

155

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data  

E-Print Network (OSTI)

changes of HVAC source EUI between AMY and TMY3. (a) largeof total building source EUI. (a) large office, 90.1-2004a) changes in HVAC source EUI; (b) changes in total source

Hong, Tianzhen

2014-01-01T23:59:59.000Z

156

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data  

E-Print Network (OSTI)

53: Total energy use in buildings evaluation and analysisTY. A design day for building load and energy estimation.Building and Environment, 1999; 34(4): 469-477. [5] Hong TZ,

Hong, Tianzhen

2014-01-01T23:59:59.000Z

157

books & arts prayed-for group actually did worse  

E-Print Network (OSTI)

all on the afterlife); the problem of evil, or why bad things happen to good people (why can't God and Innovation at CERN, in Geneva. Fittingly for an exhibition at CERN, several particle physicists feature among particle, made at Brookhaven National Laboratory in November 1974 -- on the right is the unmissable peak

Loss, Daniel

158

SunPeak Solar LLC | Open Energy Information  

Open Energy Info (EERE)

SunPeak Solar LLC Jump to: navigation, search Name: SunPeak Solar LLC Place: Palm Desert, California Zip: 92260 Product: US project developer and asset manager, focussing on PV...

159

A Multimethod analysis of the Phenomenon of Peak-Oil.  

E-Print Network (OSTI)

??El concepto de Peak-Oil (el cnit del petrleo) es complejo y a menudo malentendido. Despus de aclarar que el Peak-Oil es tanto un problema de (more)

Kerschner, Christian

2012-01-01T23:59:59.000Z

160

THE COMPACT STEEP SPECTRUM AND GHZ PEAKED SPECTRUM RADIO SOURCES  

E-Print Network (OSTI)

THE COMPACT STEEP SPECTRUM AND GHZ PEAKED SPECTRUM RADIO SOURCES Christopher P. O'Dea Space@stsci.edu ABSTRACT I review the radio to X­ray properties of GHz Peaked Spectrum (GPS) and Compact Steep Spectrum The GHz Peaked Spectrum (GPS) and Compact Steep Spectrum (CSS) radio sources make up significant fractions

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
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161

Promoting Employment Across Kansas (PEAK) (Kansas) | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Promoting Employment Across Kansas (PEAK) (Kansas) Promoting Employment Across Kansas (PEAK) (Kansas) Promoting Employment Across Kansas (PEAK) (Kansas) < Back Eligibility Agricultural Commercial Construction Developer Fuel Distributor Industrial Utility Savings Category Alternative Fuel Vehicles Hydrogen & Fuel Cells Buying & Making Electricity Water Home Weatherization Solar Wind Program Info State Kansas Program Type Corporate Tax Incentive Provider Commerce Promoting Employment Across Kansas (PEAK) allows for the retention of employee payroll withholding taxes for qualified companies or third parties performing services on behalf of such companies. This program offers qualified companies the ability to retain 95 percent of their payroll withholding tax for up to five to seven years. PEAK is available to new

162

Table 10. Natural Gas Net Imports, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Natural Gas Net Imports, Projected vs. Actual" Natural Gas Net Imports, Projected vs. Actual" "Projected" " (trillion cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",2.02,2.4,2.66,2.74,2.81,2.85,2.89,2.93,2.95,2.97,3,3.16,3.31,3.5,3.57,3.63,3.74,3.85 "AEO 1995",,2.46,2.54,2.8,2.87,2.87,2.89,2.9,2.9,2.92,2.95,2.97,3,3.03,3.19,3.35,3.51,3.6 "AEO 1996",,,2.56,2.75,2.85,2.88,2.93,2.98,3.02,3.06,3.07,3.09,3.12,3.17,3.23,3.29,3.37,3.46,3.56 "AEO 1997",,,,2.82,2.96,3.16,3.43,3.46,3.5,3.53,3.58,3.64,3.69,3.74,3.78,3.83,3.87,3.92,3.97 "AEO 1998",,,,,2.95,3.19,3.531808376,3.842532873,3.869043112,3.894513845,3.935930967,3.976293564,4.021911621,4.062207222,4.107616425,4.164502144,4.221304417,4.277039051,4.339964867

163

Table 12. Total Coal Consumption, Projected vs. Actual Projected  

U.S. Energy Information Administration (EIA) Indexed Site

Total Coal Consumption, Projected vs. Actual Total Coal Consumption, Projected vs. Actual Projected (million short tons) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 920 928 933 938 943 948 953 958 962 967 978 990 987 992 1006 1035 1061 1079 AEO 1995 935 940 941 947 948 951 954 958 963 971 984 992 996 1002 1013 1025 1039 AEO 1996 937 942 954 962 983 990 1004 1017 1027 1033 1046 1067 1070 1071 1074 1082 1087 AEO 1997 948 970 987 1003 1017 1020 1025 1034 1041 1054 1075 1086 1092 1092 1099 1104 AEO 1998 1009 1051 1044 1058 1087 1084 1090 1097 1112 1130 1142 1148 1160 1162 1180 AEO 1999 1040 1075 1092 1109 1113 1118 1120 1120 1133 1139 1150 1155 1156 1173 AEO 2000 1053 1086 1103 1124 1142 1164 1175 1184 1189 1194 1199 1195 1200 AEO 2001 1078 1112 1135 1153 1165 1183 1191 1220 1228 1228 1235 1240

164

Table 22. Total Carbon Dioxide Emissions, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Total Carbon Dioxide Emissions, Projected vs. Actual Total Carbon Dioxide Emissions, Projected vs. Actual (million metric tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 AEO 1983 AEO 1984 AEO 1985 AEO 1986 AEO 1987 AEO 1989* AEO 1990 AEO 1991 AEO 1992 AEO 1993 5009 5053 5130 5207 5269 5335 5401 5449 5504 5562 5621 5672 5724 5771 5819 5867 5918 5969 AEO 1994 5060 5130 5185 5240 5287 5335 5379 5438 5482 5529 5599 5658 5694 5738 5797 5874 5925 AEO 1995 5137 5174 5188 5262 5309 5361 5394 5441.3 5489.0 5551.3 5621.0 5679.7 5727.3 5775.0 5841.0 5888.7 AEO 1996 5182 5224 5295 5355 5417 5464 5525 5589 5660 5735 5812 5879 5925 5981 6030 AEO 1997 5295 5381 5491 5586 5658 5715 5781 5863 5934 6009 6106 6184 6236 6268 AEO 1998 5474 5621 5711 5784 5893 5957 6026 6098 6192 6292 6379 6465 6542 AEO 1999 5522 5689 5810 5913 5976 6036 6084 6152 6244 6325 6418 6493 AEO 2000

165

Table 16. Total Electricity Sales, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Electricity Sales, Projected vs. Actual Electricity Sales, Projected vs. Actual (billion kilowatt-hours) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 2364 2454 2534 2626 2708 2811 AEO 1983 2318 2395 2476 2565 2650 2739 3153 AEO 1984 2321 2376 2461 2551 2637 2738 3182 AEO 1985 2317 2360 2427 2491 2570 2651 2730 2808 2879 2949 3026 AEO 1986 2363 2416 2479 2533 2608 2706 2798 2883 2966 3048 3116 3185 3255 3324 3397 AEO 1987 2460 2494 2555 2622 2683 2748 2823 2902 2977 3363 AEO 1989* 2556 2619 2689 2760 2835 2917 2994 3072 3156 3236 3313 3394 3473 AEO 1990 2612 2689 3083 3488.0 3870.0 AEO 1991 2700 2762 2806 2855 2904 2959 3022 3088 3151 3214 3282 3355 3427 3496 3563 3632 3704 3776 3846 3916 AEO 1992 2746 2845 2858 2913 2975 3030 3087 3146 3209 3276 3345 3415 3483 3552 3625 3699 3774 3847 3921 AEO 1993 2803 2840 2893 2946 2998 3052 3104 3157 3214 3271 3327

166

Table 5. Domestic Crude Oil Production, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Domestic Crude Oil Production, Projected vs. Actual" Domestic Crude Oil Production, Projected vs. Actual" "Projected" " (million barrels)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",2507.55,2372.5,2255.7,2160.8,2087.8,2022.1,1952.75,1890.7,1850.55,1825,1799.45,1781.2,1766.6,1759.3,1777.55,1788.5,1806.75,1861.5 "AEO 1995",,2401.7,2306.8,2204.6,2095.1,2036.7,1967.35,1952.75,1923.55,1916.25,1905.3,1894.35,1883.4,1887.05,1887.05,1919.9,1945.45,1967.35 "AEO 1996",,,2387.1,2310.45,2248.4,2171.75,2113.35,2062.25,2011.15,1978.3,1952.75,1938.15,1916.25,1919.9,1927.2,1949.1,1971,1985.6,2000.2 "AEO 1997",,,,2361.55,2306.8,2244.75,2197.3,2142.55,2091.45,2054.95,2033.05,2014.8,2003.85,1996.55,1989.25,1981.95,1974.65,1967.35,1949.1

167

Table 16. Total Energy Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Energy Consumption, Projected vs. Actual" Total Energy Consumption, Projected vs. Actual" "Projected" " (quadrillion Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",88.02,89.53,90.72,91.73,92.71,93.61,94.56,95.73,96.69,97.69,98.89,100,100.79,101.7,102.7,103.6,104.3,105.23 "AEO 1995",,89.21,89.98,90.57,91.91,92.98,93.84,94.61,95.3,96.19,97.18,98.38,99.37,100.3,101.2,102.1,102.9,103.88 "AEO 1996",,,90.6,91.26,92.54,93.46,94.27,95.07,95.94,96.92,97.98,99.2,100.38,101.4,102.1,103.1,103.8,104.69,105.5 "AEO 1997",,,,92.64,93.58,95.13,96.59,97.85,98.79,99.9,101.2,102.4,103.4,104.7,105.8,106.6,107.2,107.9,108.6 "AEO 1998",,,,,94.68,96.71,98.61027527,99.81855774,101.254303,102.3907928,103.3935776,104.453476,105.8160553,107.2683716,108.5873566,109.8798981,111.0723877,112.166893,113.0926208

168

Table 7a. Natural Gas Wellhead Prices, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

a. Natural Gas Wellhead Prices, Projected vs. Actual" a. Natural Gas Wellhead Prices, Projected vs. Actual" "Projected Price in Constant Dollars" " (constant dollars per thousand cubic feet in ""dollar year"" specific to each AEO)" ,"AEO Dollar Year",1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",1992,1.9399,2.029,2.1099,2.1899,2.29,2.35,2.39,2.42,2.47,2.55,2.65,2.75,2.89,3.01,3.17,3.3,3.35,3.47 "AEO 1995",1993,,1.85,1.899,1.81,1.87,1.8999,2.06,2.14,2.34,2.47,2.69,2.83,3.02,3.12,3.21,3.3,3.35,3.39 "AEO 1996",1994,,,1.597672343,1.665446997,1.74129355,1.815978527,1.866241336,1.892736554,1.913619637,1.928664207,1.943216205,1.964540124,1.988652706,2.003382921,2.024799585,2.056392431,2.099974155,2.14731431,2.218094587

169

Table 14a. Average Electricity Prices, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

a. Average Electricity Prices, Projected vs. Actual a. Average Electricity Prices, Projected vs. Actual Projected Price in Constant Dollars (constant dollars, cents per kilowatt-hour in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1995 1993 6.80 6.80 6.70 6.70 6.70 6.70 6.70 6.80 6.80 6.90 6.90 6.90 7.00 7.00 7.10 7.10 7.20 AEO 1996 1994 7.09 6.99 6.94 6.93 6.96 6.96 6.96 6.97 6.98 6.97 6.98 6.95 6.95 6.94 6.96 6.95 6.91 AEO 1997 1995 6.94 6.89 6.90 6.91 6.86 6.84 6.78 6.73 6.66 6.60 6.58 6.54 6.49 6.48 6.45 6.36

170

Table 4. Total Petroleum Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Petroleum Consumption, Projected vs. Actual" Total Petroleum Consumption, Projected vs. Actual" "Projected" " (million barrels)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",6449.55,6566.35,6643,6723.3,6810.9,6880.25,6956.9,7059.1,7124.8,7205.1,7296.35,7376.65,7446,7522.65,7595.65,7665,7712.45,7774.5 "AEO 1995",,6398.45,6544.45,6555.4,6675.85,6745.2,6821.85,6887.55,6964.2,7048.15,7146.7,7245.25,7336.5,7405.85,7471.55,7537.25,7581.05,7621.2 "AEO 1996",,,6489.7,6526.2,6606.5,6708.7,6781.7,6854.7,6942.3,7008,7084.65,7175.9,7259.85,7329.2,7383.95,7449.65,7500.75,7544.55,7581.05 "AEO 1997",,,,6635.7,6694.1,6825.5,6953.25,7073.7,7183.2,7267.15,7369.35,7460.6,7548.2,7643.1,7730.7,7792.75,7832.9,7884,7924.15

171

Table 9. Natural Gas Production, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Natural Gas Production, Projected vs. Actual" Natural Gas Production, Projected vs. Actual" "Projected" " (trillion cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",17.71,17.68,17.84,18.12,18.25,18.43,18.58,18.93,19.28,19.51,19.8,19.92,20.13,20.18,20.38,20.35,20.16,20.19 "AEO 1995",,18.28,17.98,17.92,18.21,18.63,18.92,19.08,19.2,19.36,19.52,19.75,19.94,20.17,20.28,20.6,20.59,20.88 "AEO 1996",,,18.9,19.15,19.52,19.59,19.59,19.65,19.73,19.97,20.36,20.82,21.25,21.37,21.68,22.11,22.47,22.83,23.36 "AEO 1997",,,,19.1,19.7,20.17,20.32,20.54,20.77,21.26,21.9,22.31,22.66,22.93,23.38,23.68,23.99,24.25,24.65 "AEO 1998",,,,,18.85,19.06,20.34936142,20.27427673,20.60257721,20.94442177,21.44076347,21.80969238,22.25416183,22.65365219,23.176651,23.74545097,24.22989273,24.70069313,24.96691322

172

Table 7a. Natural Gas Wellhead Prices, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

a. Natural Gas Wellhead Prices, Projected vs. Actual a. Natural Gas Wellhead Prices, Projected vs. Actual Projected Price in Constant Dollars (constant dollars per thousand cubic feet in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1992 1.94 2.03 2.11 2.19 2.29 2.35 2.39 2.42 2.47 2.55 2.65 2.75 2.89 3.01 3.17 3.30 3.35 3.47 AEO 1995 1993 1.85 1.90 1.81 1.87 1.90 2.06 2.14 2.34 2.47 2.69 2.83 3.02 3.12 3.21 3.30 3.35 3.39 AEO 1996 1994 1.60 1.67 1.74 1.82 1.87 1.89 1.91 1.93 1.94 1.96 1.99 2.00 2.02 2.06 2.10 2.15 2.22

173

CORRELATION BETWEEN PEAK ENERGY AND PEAK LUMINOSITY IN SHORT GAMMA-RAY BURSTS  

SciTech Connect

A correlation between the peak luminosity and the peak energy has been found by Yonetoku et al. as L{sub p} {proportional_to}E{sup 2.0}{sub p,i} for 11 pre-Swift long gamma-ray bursts (GRBs). In this study, for a greatly expanded sample of 148 long GRBs in the Swift era, we find that the correlation still exists, but most likely with a slightly different power-law index, i.e., L{sub p} {proportional_to} E{sup 1.7}{sub p,i}. In addition, we have collected 17 short GRBs with necessary data. We find that the correlation of L{sub p} {proportional_to} E{sup 1.7}{sub p,i} also exists for this sample of short events. It is argued that the radiation mechanism of both long and short GRBs should be similar, i.e., of quasi-thermal origin caused by the photosphere, with the dissipation occurring very near the central engine. Some key parameters of the process are constrained. Our results suggest that the radiation processes of both long and short bursts may be dominated by thermal emission, rather than by the single synchrotron radiation. This might put strong physical constraints on the theoretical models.

Zhang, Z. B.; Chen, D. Y. [Department of Physics, College of Sciences, Guizhou University, Guiyang 550025 (China); Huang, Y. F., E-mail: sci.zbzhang@gzu.edu.cn, E-mail: hyf@nju.edu.cn [Department of Astronomy, Nanjing University, Nanjing 210093 (China)

2012-08-10T23:59:59.000Z

174

Pose estimation of an uncooperative spacecraft from actual space imagery  

Science Journals Connector (OSTI)

This paper addresses the preliminary design of a spaceborne monocular vision-based navigation system for on-orbit-servicing and formation-flying applications. The aim is to estimate the pose of a passive space resident object using its known three-dimensional model and single low-resolution two-dimensional images collected on-board the active spacecraft. In contrast to previous work, no supportive means are available on the target satellite (e.g., light emitting diodes) and no a-priori knowledge of the relative position and attitude is available (i.e., lost-in-space scenario). Three fundamental mechanisms - perceptual organisation, true perspective projection, and random sample consensus - are exploited to overcome the limitations of monocular passive optical navigation in space. The preliminary design is conducted and validated making use of actual images collected in the frame of the PRISMA mission at about 700 km altitude and 10 m inter-spacecraft separation.

Simone D'Amico; Mathias Benn; John L. Jørgensen

2014-01-01T23:59:59.000Z

175

On peaked solitary waves of Camassa-Holm equation  

E-Print Network (OSTI)

Unlike the Boussinesq, KdV and BBM equations, the celebrated Casamma-Holm (CH) equation can model both phenomena of soliton interaction and wave breaking. Especially, it has peaked solitary waves in case of omega=0. Besides, in case of omega > 0, its solitary wave "becomes $C^\\infty$ and there is no derivative discontinuity at its peak", as mentioned by Camassa and Holm in 1993 (PRL). However, it is found in this article that the CH equation has peaked solitary waves even in case of omega > 0. Especially, all of these peaked solitary waves have an unusual property: their phase speeds have nothing to do with the height of peakons or anti-peakons. Therefore, in contrast to the traditional view-points, the peaked solitary waves are a common property of the CH equation: in fact, all mainstream models of shallow water waves admit such kind of peaked solitary waves

Liao, Shijun

2012-01-01T23:59:59.000Z

176

Mercury Vapor At Desert Peak Area (Varekamp & Buseck, 1983) ...  

Open Energy Info (EERE)

Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Mercury Vapor At Desert Peak Area (Varekamp & Buseck, 1983) Exploration Activity Details...

177

Peak Oil: Knowledge, Attitudes, and Programming Activities in Public Health.  

E-Print Network (OSTI)

?? Peak Oil, or the world reaching the maximum rate of petroleum extraction, poses risks such as depletion of energy resources, amplification of existing threats (more)

Tuckerman, Samantha Lynn

2012-01-01T23:59:59.000Z

178

Peak Oil, Energiesicherheit und die Grenzen des Marktes  

Science Journals Connector (OSTI)

Der lpreis wird von zahlreichen Faktoren beeinflusst. Die OPEC spielt bei der Preisbildung derzeit nur eine geringe Rolle. Ein Peak Oil wird die lpreise stark beeinflussen und zahlreiche...

Dr. Nikolaus Supersberger

2009-04-01T23:59:59.000Z

179

Residential implementation of critical-peak pricing of electricity  

E-Print Network (OSTI)

to time-of-day electricity pricing: first empirical results.S. The trouble with electricity markets: understandingresidential peak-load electricity rate structures. Journal

Herter, Karen

2006-01-01T23:59:59.000Z

180

Gas Flux Sampling At Desert Peak Area (Lechler And Coolbaugh...  

Open Energy Info (EERE)

2007) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Gas Flux Sampling At Desert Peak Area (Lechler And Coolbaugh, 2007) Exploration Activity...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Peak Oil: Testing Hubberts Curve via Theoretical Modeling  

Science Journals Connector (OSTI)

A theoretical model of conventional oil production has been developed. The model does ... method is correct, and does not use oil production data as an input. The theoretical ... agreement with actual production ...

S. H. Mohr; G. M. Evans

2008-03-01T23:59:59.000Z

182

Table 18. Total Delivered Commercial Energy Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Commercial Energy Consumption, Projected vs. Actual Total Delivered Commercial Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 6.8 6.9 6.9 7.0 7.1 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.4 7.5 7.5 7.5 7.5 7.6 AEO 1995 6.9 6.9 7.0 7.0 7.0 7.1 7.1 7.1 7.1 7.1 7.2 7.2 7.2 7.2 7.3 7.3 7.3 AEO 1996 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.5 7.6 7.6 7.7 7.7 7.8 7.9 8.0 8.0 8.1 AEO 1997 7.4 7.4 7.4 7.5 7.5 7.6 7.7 7.7 7.8 7.8 7.9 7.9 8.0 8.1 8.1 8.2 AEO 1998 7.5 7.6 7.7 7.8 7.9 8.0 8.0 8.1 8.2 8.3 8.4 8.4 8.5 8.6 8.7 AEO 1999 7.4 7.8 7.9 8.0 8.1 8.2 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 AEO 2000 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.5 8.7 8.7 8.8 AEO 2001 7.8 8.1 8.3 8.6 8.7 8.9 9.0 9.2 9.3 9.5 9.6 9.7 AEO 2002 8.2 8.4 8.7 8.9 9.0 9.2 9.4 9.6 9.7 9.9 10.1

183

Table 21. Total Transportation Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Transportation Energy Consumption, Projected vs. Actual Transportation Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 18.6 18.2 17.7 17.3 17.0 16.9 AEO 1983 19.8 20.1 20.4 20.4 20.5 20.5 20.7 AEO 1984 19.2 19.0 19.0 19.0 19.1 19.2 20.1 AEO 1985 20.0 19.8 20.0 20.0 20.0 20.1 20.3 AEO 1986 20.5 20.8 20.8 20.6 20.7 20.3 21.0 AEO 1987 21.3 21.5 21.6 21.7 21.8 22.0 22.0 22.0 21.9 22.3 AEO 1989* 21.8 22.2 22.4 22.4 22.5 22.5 22.5 22.5 22.6 22.7 22.8 23.0 23.2 AEO 1990 22.0 22.4 23.2 24.3 25.5 AEO 1991 22.1 21.6 21.9 22.1 22.3 22.5 22.8 23.1 23.4 23.8 24.1 24.5 24.8 25.1 25.4 25.7 26.0 26.3 26.6 26.9 AEO 1992 21.7 22.0 22.5 22.9 23.2 23.4 23.6 23.9 24.1 24.4 24.8 25.1 25.4 25.7 26.0 26.3 26.6 26.9 27.1 AEO 1993 22.5 22.8 23.4 23.9 24.3 24.7 25.1 25.4 25.7 26.1 26.5 26.8 27.2 27.6 27.9 28.1 28.4 28.7 AEO 1994 23.6

184

Table 10. Natural Gas Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Production, Projected vs. Actual Production, Projected vs. Actual (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 14.74 14.26 14.33 14.89 15.39 15.88 AEO 1983 16.48 16.27 16.20 16.31 16.27 16.29 14.89 AEO 1984 17.48 17.10 17.44 17.58 17.52 17.32 16.39 AEO 1985 16.95 17.08 17.11 17.29 17.40 17.33 17.32 17.27 17.05 16.80 16.50 AEO 1986 16.30 16.27 17.15 16.68 16.90 16.97 16.87 16.93 16.86 16.62 16.40 16.33 16.57 16.23 16.12 AEO 1987 16.21 16.09 16.38 16.32 16.30 16.30 16.44 16.62 16.81 17.39 AEO 1989* 16.71 16.71 16.94 17.01 16.83 17.09 17.35 17.54 17.67 17.98 18.20 18.25 18.49 AEO 1990 16.91 17.25 18.84 20.58 20.24 AEO 1991 17.40 17.48 18.11 18.22 18.15 18.22 18.39 18.82 19.03 19.28 19.62 19.89 20.13 20.07 19.95 19.82 19.64 19.50 19.30 19.08 AEO 1992 17.43 17.69 17.95 18.00 18.29 18.27 18.51 18.75 18.97

185

Table 17. Total Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption, Projected vs. Actual Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 79.1 79.6 79.9 80.8 82.1 83.3 AEO 1983 78.0 79.5 81.0 82.4 83.9 84.6 89.0 AEO 1984 78.5 79.4 81.2 83.1 85.1 86.4 93.0 AEO 1985 77.6 78.5 79.8 81.2 82.7 83.3 84.2 85.0 85.7 86.3 87.2 AEO 1986 77.0 78.8 79.8 80.7 81.5 82.9 83.8 84.6 85.3 86.0 86.6 87.4 88.3 89.4 90.2 AEO 1987 78.9 80.0 82.0 82.8 83.9 85.1 86.2 87.1 87.9 92.5 AEO 1989* 82.2 83.8 84.5 85.4 86.2 87.1 87.8 88.7 89.5 90.4 91.4 92.4 93.5 AEO 1990 84.2 85.4 91.9 97.4 102.8 AEO 1991 84.4 85.0 86.0 87.0 87.9 89.1 90.4 91.8 93.1 94.3 95.6 97.1 98.4 99.4 100.3 101.4 102.5 103.6 104.7 105.8 AEO 1992 84.7 87.0 88.0 89.2 90.5 91.4 92.4 93.4 94.5 95.6 96.9 98.0 99.0 100.0 101.2 102.2 103.2 104.3 105.2 AEO 1993 87.0 88.3 89.8 91.4 92.7 94.0 95.3 96.3 97.5 98.6

186

Table 3. Gross Domestic Product, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Gross Domestic Product, Projected vs. Actual Gross Domestic Product, Projected vs. Actual (cumulative average percent growth in projected real GDP from first year shown for each AEO) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 4.3% 3.8% 3.6% 3.3% 3.2% 3.2% AEO 1983 3.3% 3.3% 3.4% 3.3% 3.2% 3.1% 2.7% AEO 1984 2.7% 2.4% 2.9% 3.1% 3.1% 3.1% 2.7% AEO 1985 2.3% 2.2% 2.7% 2.8% 2.9% 3.0% 3.0% 3.0% 2.9% 2.8% 2.8% AEO 1986 2.6% 2.5% 2.7% 2.5% 2.5% 2.6% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% AEO 1987 2.7% 2.3% 2.4% 2.5% 2.5% 2.6% 2.6% 2.5% 2.4% 2.3% AEO 1989* 4.0% 3.4% 3.1% 3.0% 2.9% 2.8% 2.7% 2.7% 2.7% 2.6% 2.6% 2.6% 2.6% AEO 1990 2.9% 2.3% 2.5% 2.5% 2.4% AEO 1991 0.8% 1.0% 1.7% 1.8% 1.8% 1.9% 2.0% 2.1% 2.1% 2.1% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% AEO 1992 -0.1% 1.6% 2.0% 2.2% 2.3% 2.2% 2.2% 2.2% 2.2% 2.3% 2.3% 2.3% 2.3% 2.2%

187

Table 20. Total Industrial Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Industrial Energy Consumption, Projected vs. Actual Industrial Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 24.0 24.1 24.4 24.9 25.5 26.1 AEO 1983 23.2 23.6 23.9 24.4 24.9 25.0 25.4 AEO 1984 24.1 24.5 25.4 25.5 27.1 27.4 28.7 AEO 1985 23.2 23.6 23.9 24.4 24.8 24.8 24.4 AEO 1986 22.2 22.8 23.1 23.4 23.4 23.6 22.8 AEO 1987 22.4 22.8 23.7 24.0 24.3 24.6 24.6 24.7 24.9 22.6 AEO 1989* 23.6 24.0 24.1 24.3 24.5 24.3 24.3 24.5 24.6 24.8 24.9 24.4 24.1 AEO 1990 25.0 25.4 27.1 27.3 28.6 AEO 1991 24.6 24.5 24.8 24.8 25.0 25.3 25.7 26.2 26.5 26.1 25.9 26.2 26.4 26.6 26.7 27.0 27.2 27.4 27.7 28.0 AEO 1992 24.6 25.3 25.4 25.6 26.1 26.3 26.5 26.5 26.0 25.6 25.8 26.0 26.1 26.2 26.4 26.7 26.9 27.2 27.3 AEO 1993 25.5 25.9 26.2 26.8 27.1 27.5 27.8 27.4 27.1 27.4 27.6 27.8 28.0 28.2 28.4 28.7 28.9 29.1 AEO 1994 25.4 25.9

188

Table 8. Natural Gas Wellhead Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Wellhead Prices, Projected vs. Actual Natural Gas Wellhead Prices, Projected vs. Actual (current dollars per thousand cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 4.32 5.47 6.67 7.51 8.04 8.57 AEO 1983 2.93 3.11 3.46 3.93 4.56 5.26 12.74 AEO 1984 2.77 2.90 3.21 3.63 4.13 4.79 9.33 AEO 1985 2.60 2.61 2.66 2.71 2.94 3.35 3.85 4.46 5.10 5.83 6.67 AEO 1986 1.73 1.96 2.29 2.54 2.81 3.15 3.73 4.34 5.06 5.90 6.79 7.70 8.62 9.68 10.80 AEO 1987 1.83 1.95 2.11 2.28 2.49 2.72 3.08 3.51 4.07 7.54 AEO 1989* 1.62 1.70 1.91 2.13 2.58 3.04 3.48 3.93 4.76 5.23 5.80 6.43 6.98 AEO 1990 1.78 1.88 2.93 5.36 9.2 AEO 1991 1.77 1.90 2.11 2.30 2.42 2.51 2.60 2.74 2.91 3.29 3.75 4.31 5.07 5.77 6.45 7.29 8.09 8.94 9.62 10.27 AEO 1992 1.69 1.85 2.03 2.15 2.35 2.51 2.74 3.01 3.40 3.81 4.24 4.74 5.25 5.78 6.37 6.89 7.50 8.15 9.05 AEO 1993 1.85 1.94 2.09 2.30

189

Table 16. Total Energy Consumption, Projected vs. Actual Projected  

U.S. Energy Information Administration (EIA) Indexed Site

Total Energy Consumption, Projected vs. Actual Total Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 88.0 89.5 90.7 91.7 92.7 93.6 94.6 95.7 96.7 97.7 98.9 100.0 100.8 101.7 102.7 103.6 104.3 105.2 AEO 1995 89.2 90.0 90.6 91.9 93.0 93.8 94.6 95.3 96.2 97.2 98.4 99.4 100.3 101.2 102.1 102.9 103.9 AEO 1996 90.6 91.3 92.5 93.5 94.3 95.1 95.9 96.9 98.0 99.2 100.4 101.4 102.1 103.1 103.8 104.7 105.5 AEO 1997 92.6 93.6 95.1 96.6 97.9 98.8 99.9 101.2 102.4 103.4 104.7 105.8 106.6 107.2 107.9 108.6 AEO 1998 94.7 96.7 98.6 99.8 101.3 102.4 103.4 104.5 105.8 107.3 108.6 109.9 111.1 112.2 113.1 AEO 1999 94.6 97.0 99.2 100.9 102.0 102.8 103.6 104.7 106.0 107.2 108.5 109.7 110.8 111.8

190

Table 9. Natural Gas Production, Projected vs. Actual Projected  

U.S. Energy Information Administration (EIA) Indexed Site

Natural Gas Production, Projected vs. Actual Natural Gas Production, Projected vs. Actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 17.71 17.68 17.84 18.12 18.25 18.43 18.58 18.93 19.28 19.51 19.80 19.92 20.13 20.18 20.38 20.35 20.16 20.19 AEO 1995 18.28 17.98 17.92 18.21 18.63 18.92 19.08 19.20 19.36 19.52 19.75 19.94 20.17 20.28 20.60 20.59 20.88 AEO 1996 18.90 19.15 19.52 19.59 19.59 19.65 19.73 19.97 20.36 20.82 21.25 21.37 21.68 22.11 22.47 22.83 23.36 AEO 1997 19.10 19.70 20.17 20.32 20.54 20.77 21.26 21.90 22.31 22.66 22.93 23.38 23.68 23.99 24.25 24.65 AEO 1998 18.85 19.06 20.35 20.27 20.60 20.94 21.44 21.81 22.25 22.65 23.18 23.75 24.23 24.70 24.97 AEO 1999 18.80 19.13 19.28 19.82 20.23 20.77 21.05 21.57 21.98 22.47 22.85 23.26 23.77 24.15

191

Table 19. Total Delivered Industrial Energy Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Industrial Energy Consumption, Projected vs. Actual Total Delivered Industrial Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 25.4 25.9 26.3 26.7 27.0 27.1 26.8 26.6 26.9 27.2 27.7 28.1 28.3 28.7 29.1 29.4 29.7 30.0 AEO 1995 26.2 26.3 26.5 27.0 27.3 26.9 26.6 26.8 27.1 27.5 27.9 28.2 28.4 28.7 29.0 29.3 29.6 AEO 1996 26.5 26.6 27.3 27.5 26.9 26.5 26.7 26.9 27.2 27.6 27.9 28.2 28.3 28.5 28.7 28.9 29.2 AEO 1997 26.2 26.5 26.9 26.7 26.6 26.8 27.1 27.4 27.8 28.0 28.4 28.7 28.9 29.0 29.2 29.4 AEO 1998 27.2 27.5 27.2 26.9 27.1 27.5 27.7 27.9 28.3 28.7 29.0 29.3 29.7 29.9 30.1 AEO 1999 26.7 26.4 26.4 26.8 27.1 27.3 27.5 27.9 28.3 28.6 28.9 29.2 29.5 29.7 AEO 2000 25.8 25.5 25.7 26.0 26.5 26.9 27.4 27.8 28.1 28.3 28.5 28.8 29.0

192

Table 18. Total Residential Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Residential Energy Consumption, Projected vs. Actual Residential Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 10.1 10.1 10.1 10.1 10.2 10.2 AEO 1983 9.8 9.9 10.0 10.1 10.2 10.1 10.0 AEO 1984 9.9 9.9 10.0 10.2 10.3 10.3 10.5 AEO 1985 9.8 10.0 10.1 10.3 10.6 10.6 10.9 AEO 1986 9.6 9.8 10.0 10.3 10.4 10.8 10.9 AEO 1987 9.9 10.2 10.3 10.3 10.4 10.5 10.5 10.5 10.5 10.6 AEO 1989* 10.3 10.5 10.4 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 AEO 1990 10.4 10.7 10.8 11.0 11.3 AEO 1991 10.2 10.7 10.7 10.8 10.8 10.8 10.9 10.9 10.9 11.0 11.0 11.0 11.1 11.2 11.2 11.3 11.4 11.4 11.5 11.6 AEO 1992 10.6 11.1 11.1 11.1 11.1 11.1 11.2 11.2 11.3 11.3 11.4 11.5 11.5 11.6 11.7 11.8 11.8 11.9 12.0 AEO 1993 10.7 10.9 11.0 11.0 11.0 11.1 11.1 11.1 11.1 11.2 11.2 11.2 11.2 11.3 11.3 11.4 11.4 11.5 AEO 1994 10.3 10.4 10.4 10.4

193

Table 6. Domestic Crude Oil Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Domestic Crude Oil Production, Projected vs. Actual Domestic Crude Oil Production, Projected vs. Actual (million barrels per day) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 8.79 8.85 8.84 8.80 8.66 8.21 AEO 1983 8.67 8.71 8.66 8.72 8.80 8.63 8.11 AEO 1984 8.86 8.70 8.59 8.45 8.28 8.25 7.19 AEO 1985 8.92 8.96 9.01 8.78 8.38 8.05 7.64 7.27 6.89 6.68 6.53 AEO 1986 8.80 8.63 8.30 7.90 7.43 6.95 6.60 6.36 6.20 5.99 5.80 5.66 5.54 5.45 5.43 AEO 1987 8.31 8.18 8.00 7.63 7.34 7.09 6.86 6.64 6.54 6.03 AEO 1989* 8.18 7.97 7.64 7.25 6.87 6.59 6.37 6.17 6.05 6.00 5.94 5.90 5.89 AEO 1990 7.67 7.37 6.40 5.86 5.35 AEO 1991 7.23 6.98 7.10 7.11 7.01 6.79 6.48 6.22 5.92 5.64 5.36 5.11 4.90 4.73 4.62 4.59 4.58 4.53 4.46 4.42 AEO 1992 7.37 7.17 6.99 6.89 6.68 6.45 6.28 6.16 6.06 5.91 5.79 5.71 5.66 5.64 5.62 5.63 5.62 5.55 5.52 AEO 1993 7.20 6.94 6.79 6.52 6.22 6.00 5.84 5.72

194

Table 17. Total Delivered Residential Energy Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Residential Energy Consumption, Projected vs. Actual Total Delivered Residential Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 10.3 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.5 10.5 10.5 10.5 10.5 10.6 10.6 AEO 1995 11.0 10.8 10.8 10.8 10.8 10.8 10.8 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.8 10.8 10.9 AEO 1996 10.4 10.7 10.7 10.7 10.8 10.8 10.9 10.9 11.0 11.2 11.2 11.3 11.4 11.5 11.6 11.7 11.8 AEO 1997 11.1 10.9 11.1 11.1 11.2 11.2 11.2 11.3 11.4 11.5 11.5 11.6 11.7 11.8 11.9 12.0 AEO 1998 10.7 11.1 11.2 11.4 11.5 11.5 11.6 11.7 11.8 11.9 11.9 12.1 12.1 12.2 12.3 AEO 1999 10.5 11.1 11.3 11.3 11.4 11.5 11.5 11.6 11.6 11.7 11.8 11.9 12.0 12.1 AEO 2000 10.7 10.9 11.0 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 12.0

195

Table 2. Real Gross Domestic Product, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Real Gross Domestic Product, Projected vs. Actual Real Gross Domestic Product, Projected vs. Actual Projected Real GDP Growth Trend (cumulative average percent growth in projected real GDP from first year shown for each AEO) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 3.1% 3.2% 2.9% 2.8% 2.7% 2.7% 2.6% 2.6% 2.6% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% AEO 1995 3.7% 2.8% 2.5% 2.7% 2.7% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% AEO 1996 2.6% 2.2% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% 2.2% 2.2% 1.6% AEO 1997 2.1% 1.9% 2.0% 2.2% 2.3% 2.3% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.1% 2.1% 1.5% AEO 1998 3.4% 2.9% 2.6% 2.5% 2.4% 2.4% 2.3% 2.3% 2.3% 2.3% 2.3% 2.3% 2.3% 2.2% 1.8% AEO 1999 3.4% 2.5% 2.5% 2.4% 2.4% 2.4% 2.3% 2.4% 2.4% 2.4% 2.4% 2.4% 2.4% 1.8% AEO 2000 3.8% 2.9% 2.7% 2.6% 2.6% 2.6% 2.6% 2.6% 2.5% 2.5%

196

Table 7. Petroleum Net Imports, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Net Imports, Projected vs. Actual Petroleum Net Imports, Projected vs. Actual (million barrels per day) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 7.58 7.45 7.12 6.82 6.66 7.09 AEO 1983 5.15 5.44 5.73 5.79 5.72 5.95 6.96 AEO 1984 4.85 5.11 5.53 5.95 6.31 6.59 8.65 AEO 1985 4.17 4.38 4.73 4.93 5.36 5.72 6.23 6.66 7.14 7.39 7.74 AEO 1986 5.15 5.38 5.46 5.92 6.46 7.09 7.50 7.78 7.96 8.20 8.47 8.74 9.04 9.57 9.76 AEO 1987 5.81 6.04 6.81 7.28 7.82 8.34 8.71 8.94 8.98 10.01 AEO 1989* 6.28 6.84 7.49 7.96 8.53 8.83 9.04 9.28 9.60 9.64 9.75 10.02 10.20 AEO 1990 7.20 7.61 9.13 9.95 11.02 AEO 1991 7.28 7.25 7.34 7.48 7.72 8.10 8.57 9.09 9.61 10.07 10.51 11.00 11.44 11.72 11.86 12.11 12.30 12.49 12.71 12.91 AEO 1992 6.86 7.42 7.88 8.16 8.55 8.80 9.06 9.32 9.50 9.80 10.17 10.35 10.56 10.61 10.85 11.00 11.15 11.29 11.50 AEO 1993 7.25 8.01 8.49 9.06

197

Table 7b. Natural Gas Wellhead Prices, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

b. Natural Gas Wellhead Prices, Projected vs. Actual b. Natural Gas Wellhead Prices, Projected vs. Actual Projected Price in Nominal Dollars (nominal dollars per thousand cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1.98 2.12 2.27 2.41 2.59 2.73 2.85 2.98 3.14 3.35 3.59 3.85 4.18 4.51 4.92 5.29 5.56 5.96 AEO 1995 1.89 2.00 1.95 2.06 2.15 2.40 2.57 2.90 3.16 3.56 3.87 4.27 4.56 4.85 5.16 5.41 5.66 AEO 1996 1.63 1.74 1.86 1.99 2.10 2.19 2.29 2.38 2.48 2.59 2.72 2.84 2.97 3.12 3.29 3.49 3.73 AEO 1997 2.03 1.82 1.90 1.99 2.06 2.13 2.21 2.32 2.43 2.54 2.65 2.77 2.88 3.00 3.11 3.24 AEO 1998 2.30 2.20 2.26 2.31 2.38 2.44 2.52 2.60 2.69 2.79 2.93 3.06 3.20 3.35 3.48 AEO 1999 1.98 2.15 2.20 2.32 2.43 2.53 2.63 2.76 2.90 3.02 3.12 3.23 3.35 3.47

198

Table 20. Total Delivered Transportation Energy Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Transportation Energy Consumption, Projected vs. Actual Total Delivered Transportation Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 23.6 24.1 24.5 24.7 25.1 25.4 25.7 26.2 26.5 26.9 27.2 27.6 27.9 28.3 28.6 28.9 29.2 29.5 AEO 1995 23.3 24.0 24.2 24.7 25.1 25.5 25.9 26.2 26.5 26.9 27.3 27.7 28.0 28.3 28.5 28.7 28.9 AEO 1996 23.9 24.1 24.5 24.8 25.3 25.7 26.0 26.4 26.7 27.1 27.5 27.8 28.1 28.4 28.6 28.9 29.1 AEO 1997 24.7 25.3 25.9 26.4 27.0 27.5 28.0 28.5 28.9 29.4 29.8 30.3 30.6 30.9 31.1 31.3 AEO 1998 25.3 25.9 26.7 27.1 27.7 28.3 28.8 29.4 30.0 30.6 31.2 31.7 32.3 32.8 33.1 AEO 1999 25.4 26.0 27.0 27.6 28.2 28.8 29.4 30.0 30.6 31.2 31.7 32.2 32.8 33.1 AEO 2000 26.2 26.8 27.4 28.0 28.5 29.1 29.7 30.3 30.9 31.4 31.9 32.5 32.9

199

Table 22. Energy Intensity, Projected vs. Actual Projected  

U.S. Energy Information Administration (EIA) Indexed Site

Energy Intensity, Projected vs. Actual Energy Intensity, Projected vs. Actual Projected (quadrillion Btu / real GDP in billion 2005 chained dollars) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 11.2 11.1 11.0 10.8 10.7 10.5 10.4 10.3 10.1 10.0 9.9 9.8 9.7 9.6 9.5 9.4 9.3 9.2 AEO 1995 10.9 10.8 10.6 10.4 10.3 10.1 10.0 9.9 9.8 9.6 9.5 9.4 9.3 9.2 9.1 9.1 9.0 AEO 1996 10.7 10.6 10.4 10.3 10.1 10.0 9.8 9.7 9.6 9.5 9.4 9.3 9.2 9.2 9.1 9.0 8.9 AEO 1997 10.3 10.3 10.2 10.1 9.9 9.8 9.7 9.6 9.5 9.4 9.3 9.2 9.2 9.1 9.0 8.9 AEO 1998 10.1 10.1 10.1 10.0 9.9 9.8 9.7 9.6 9.5 9.5 9.4 9.3 9.2 9.1 9.0 AEO 1999 9.6 9.7 9.7 9.7 9.6 9.4 9.3 9.1 9.0 8.9 8.8 8.7 8.6 8.5 AEO 2000 9.4 9.4 9.3 9.2 9.1 9.0 8.9 8.8 8.7 8.7 8.6 8.5 8.4 AEO 2001 8.7 8.6 8.5 8.4 8.3 8.1 8.0 7.9 7.8 7.6 7.5 7.4

200

Table 15. Average Electricity Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Average Electricity Prices, Projected vs. Actual Average Electricity Prices, Projected vs. Actual (nominal cents per kilowatt-hour) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 6.38 6.96 7.63 8.23 8.83 9.49 AEO 1983 6.85 7.28 7.74 8.22 8.68 9.18 13.12 AEO 1984 6.67 7.05 7.48 7.89 8.25 8.65 11.53 AEO 1985 6.62 6.94 7.32 7.63 7.89 8.15 8.46 8.85 9.20 9.61 10.04 AEO 1986 6.67 6.88 7.05 7.18 7.35 7.52 7.65 7.87 8.31 8.83 9.41 10.01 10.61 11.33 12.02 AEO 1987 6.63 6.65 6.92 7.12 7.38 7.62 7.94 8.36 8.86 11.99 AEO 1989* 6.50 6.75 7.14 7.48 7.82 8.11 8.50 8.91 9.39 9.91 10.49 11.05 11.61 AEO 1990 6.49 6.72 8.40 10.99 14.5 AEO 1991 6.94 7.31 7.59 7.82 8.18 8.38 8.54 8.73 8.99 9.38 9.83 10.29 10.83 11.36 11.94 12.58 13.21 13.88 14.58 15.21 AEO 1992 6.97 7.16 7.32 7.56 7.78 8.04 8.29 8.57 8.93 9.38 9.82 10.26 10.73 11.25 11.83 12.37 12.96 13.58 14.23 AEO 1993

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201

Table 11. Natural Gas Net Imports, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Net Imports, Projected vs. Actual Natural Gas Net Imports, Projected vs. Actual (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 1.19 1.19 1.19 1.19 1.19 1.19 AEO 1983 1.08 1.16 1.23 1.23 1.23 1.23 1.23 AEO 1984 0.99 1.05 1.16 1.27 1.43 1.57 2.11 AEO 1985 0.94 1.00 1.19 1.45 1.58 1.86 1.94 2.06 2.17 2.32 2.44 AEO 1986 0.74 0.88 0.62 1.03 1.05 1.27 1.39 1.47 1.66 1.79 1.96 2.17 2.38 2.42 2.43 AEO 1987 0.84 0.89 1.07 1.16 1.26 1.36 1.46 1.65 1.75 2.50 AEO 1989* 1.15 1.32 1.44 1.52 1.61 1.70 1.79 1.87 1.98 2.06 2.15 2.23 2.31 AEO 1990 1.26 1.43 2.07 2.68 2.95 AEO 1991 1.36 1.53 1.70 1.82 2.11 2.30 2.33 2.36 2.42 2.49 2.56 2.70 2.75 2.83 2.90 2.95 3.02 3.09 3.17 3.19 AEO 1992 1.48 1.62 1.88 2.08 2.25 2.41 2.56 2.68 2.70 2.72 2.76 2.84 2.92 3.05 3.10 3.20 3.25 3.30 3.30 AEO 1993 1.79 2.08 2.35 2.49 2.61 2.74 2.89 2.95 3.00 3.05 3.10

202

Table 8. Total Natural Gas Consumption, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

Total Natural Gas Consumption, Projected vs. Actual Total Natural Gas Consumption, Projected vs. Actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 19.87 20.21 20.64 20.99 21.20 21.42 21.60 21.99 22.37 22.63 22.95 23.22 23.58 23.82 24.09 24.13 24.02 24.14 AEO 1995 20.82 20.66 20.85 21.21 21.65 21.95 22.12 22.25 22.43 22.62 22.87 23.08 23.36 23.61 24.08 24.23 24.59 AEO 1996 21.32 21.64 22.11 22.21 22.26 22.34 22.46 22.74 23.14 23.63 24.08 24.25 24.63 25.11 25.56 26.00 26.63 AEO 1997 22.15 22.75 23.24 23.64 23.86 24.13 24.65 25.34 25.82 26.22 26.52 27.00 27.35 27.70 28.01 28.47 AEO 1998 21.84 23.03 23.84 24.08 24.44 24.81 25.33 25.72 26.22 26.65 27.22 27.84 28.35 28.84 29.17 AEO 1999 21.35 22.36 22.54 23.18 23.65 24.17 24.57 25.19 25.77 26.41 26.92 27.42 28.02 28.50

203

Peak Oil and REMI PI+: State Fiscal Implications  

E-Print Network (OSTI)

, nation, and states) · Shale oil not included ­ Shale oil reserve estimates 2.0 Trillion bbls in USPeak Oil and REMI PI+: State Fiscal Implications Jim Peach Arrowhead Center Prosper Project is peak oil? · Why peak oil (and gas) matters ­ (In energy and non-energy states) ­ National Real GDP

Johnson, Eric E.

204

Energy solutions for CO2 emission peak and subsequent decline  

E-Print Network (OSTI)

Energy solutions for CO2 emission peak and subsequent decline Edited by Leif Sønderberg Petersen and Hans Larsen Risø-R-1712(EN) September 2009 Proceedings Risø International Energy Conference 2009 #12;Editors: Leif Sønderberg Petersen and Hans Larsen Title: Energy solutions for CO2 emission peak

205

On Transforming Spectral Peaks in Voice Conversion Elizabeth Godoy 1  

E-Print Network (OSTI)

On Transforming Spectral Peaks in Voice Conversion Elizabeth Godoy 1 , Olivier Rosec1 , Thierry.chonavel@telecom-bretagne.eu Abstract This paper explores the benefits of transforming spectral peaks in voice conversion. First, in examining classic GMM- based transformation with cepstral coefficients, we show that the lack of transformed

Paris-Sud XI, Université de

206

Emcore/SunPeak Solar Power Plant | Open Energy Information  

Open Energy Info (EERE)

Emcore/SunPeak Solar Power Plant Emcore/SunPeak Solar Power Plant < Emcore Jump to: navigation, search Name Emcore/SunPeak Solar Power Plant Facility Emcore/SunPeak Sector Solar Facility Type Concentrating Photovoltaic Developer SunPeak Solar Location Albuquerque, New Mexico Coordinates 35.0844909°, -106.6511367° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.0844909,"lon":-106.6511367,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

207

Geographies of peak oil: The other carbon problem  

Science Journals Connector (OSTI)

This extended editorial introduction to a themed issue of Geoforum on geographies of peak oil has three objectives. First, it provides a concise account of the peak oil claim, identifying the key protagonists in the debate, and outlining different stances with regard to the timing, shape and composition (conventional vs. non-conventional hydrocarbons) of the peak. Second, after briefly characterising the limited engagement with peak oil by human geographers, it offers a provisional set of claims about what a geographical analysis of peak oil might yield. Finally, it introduces each of the papers and, in doing so, makes the case for a fuller and more sustained engagement by geography with this other carbon problem.

Gavin Bridge

2010-01-01T23:59:59.000Z

208

E-Print Network 3.0 - actual results satellitenexperiment Sample...  

NLE Websites -- All DOE Office Websites (Extended Search)

The actual case here corresponds to the minor windows (U0.5) case in Table 6. Table A1: Load and energy... .96) 6343.77 (3316.14) 933.65 (901.44) Major windows (Actual) Diff. - -...

209

Table 19. Total Commercial Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Commercial Energy Consumption, Projected vs. Actual Commercial Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 6.6 6.7 6.8 6.8 6.8 6.9 AEO 1983 6.4 6.6 6.8 6.9 7.0 7.1 7.2 AEO 1984 6.2 6.4 6.5 6.7 6.8 6.9 7.3 AEO 1985 5.9 6.1 6.2 6.3 6.4 6.5 6.7 AEO 1986 6.2 6.3 6.4 6.4 6.5 7.1 7.4 AEO 1987 6.1 6.1 6.3 6.4 6.6 6.7 6.8 6.9 6.9 7.3 AEO 1989* 6.6 6.7 6.9 7.0 7.0 7.1 7.2 7.3 7.3 7.4 7.5 7.6 7.7 AEO 1990 6.6 6.8 7.1 7.4 7.8 AEO 1991 6.7 6.9 7.0 7.1 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.6 8.7 AEO 1992 6.8 7.1 7.2 7.3 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 AEO 1993 7.2 7.3 7.4 7.4 7.5 7.6 7.7 7.7 7.8 7.9 7.9 8.0 8.0 8.1 8.1 8.1 8.2 8.2 AEO 1994 6.8 6.9 6.9 7.0 7.1 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.4 7.5 7.5 7.5 7.5 AEO 1995 6.94 6.9 7.0 7.0 7.0 7.1 7.1 7.1 7.1 7.1 7.2 7.2 7.2 7.2 7.3 7.3 AEO 1996 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.5 7.6 7.6 7.7 7.7 7.8 7.9 8.0

210

A model of peak production in oil fields  

Science Journals Connector (OSTI)

We developed a model for oil production on the basis of simple physical considerations. The model provides a basic understanding of Hubberts empirical observation that the production rate for an oil-producing region reaches its maximum when approximately half the recoverable oil has been produced. According to the model the oil production rate at a large field must peak before drilling peaks. We use the model to investigate the effects of several drilling strategies on oil production. Despite the models simplicity predictions for the timing and magnitude of peak production match data on oil production from major oil fields throughout the world.

Daniel M. Abrams; Richard J. Wiener

2010-01-01T23:59:59.000Z

211

Track B - Critical Guidance for Peak Performance Homes | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Track B - Critical Guidance for Peak Performance Homes Track B - Critical Guidance for Peak Performance Homes Track B - Critical Guidance for Peak Performance Homes Presentations from Track B, Critical Guidance for Peak Performance Homes of the U.S. Department of Energy Building America program's 2012 Residential Energy Efficiency Stakeholder Meeting are provided below as Adobe Acrobat PDFs. These presentations for this track covered the following topics: Ventilation Strategies in High Performance Homes; Combustion Safety in Tight Houses; Implementation Program Case Studies; Field Testing from Start to Finish; and Humidity Control and Analysis. why_we_ventilate.pdf formaldehyde_new_homes.pdf whole_bldg_ventilation.pdf combustion_safety_codes.pdf combustion_diagnostics.pdf test_protocols_results.pdf utility_incentive_programs.pdf

212

EA-1921: Silver Peak Area Geothermal Exploration Project Environmental  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

921: Silver Peak Area Geothermal Exploration Project 921: Silver Peak Area Geothermal Exploration Project Environmental Assessment, Esmeralda County, Nevada EA-1921: Silver Peak Area Geothermal Exploration Project Environmental Assessment, Esmeralda County, Nevada SUMMARY The Bureau of Land Management (BLM)(lead agency) and DOE are jointly preparing this EA, which evaluates the potential environmental impacts of a project proposed by Rockwood Lithium Inc (Rockwood), formerly doing business as Chemetall Foote Corporation. Rockwood has submitted to the BLM, Tonopah Field Office, an Operations Plan for the construction, operation, and maintenance of the Silver Peak Area Geothermal Exploration Project within Esmeralda County, Nevada. The purpose of the project is to determine subsurface temperatures, confirm the existence of geothermal resources, and

213

Multispectral Imaging At Silver Peak Area (Laney, 2005) | Open Energy  

Open Energy Info (EERE)

Laney, 2005) Laney, 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Multispectral Imaging At Silver Peak Area (Laney, 2005) Exploration Activity Details Location Silver Peak Area Exploration Technique Multispectral Imaging Activity Date Usefulness not indicated DOE-funding Unknown Notes Geology and Geophysics of Geothermal Systems, Gregory Nash, 2005. A third objective was testing ASTER multispectral data for small-scale mapping of the geology of the northern Silver Peak Range, Nevada near the Fish Lake Valley geothermal field. References Patrick Laney (2005) Federal Geothermal Research Program Update - Fiscal Year 2004 Retrieved from "http://en.openei.org/w/index.php?title=Multispectral_Imaging_At_Silver_Peak_Area_(Laney,_2005)&oldid=511017"

214

EA-1921: Silver Peak Area Geothermal Exploration Project Environmental  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

921: Silver Peak Area Geothermal Exploration Project 921: Silver Peak Area Geothermal Exploration Project Environmental Assessment, Esmeralda County, Nevada EA-1921: Silver Peak Area Geothermal Exploration Project Environmental Assessment, Esmeralda County, Nevada SUMMARY The Bureau of Land Management (BLM)(lead agency) and DOE are jointly preparing this EA, which evaluates the potential environmental impacts of a project proposed by Rockwood Lithium Inc (Rockwood), formerly doing business as Chemetall Foote Corporation. Rockwood has submitted to the BLM, Tonopah Field Office, an Operations Plan for the construction, operation, and maintenance of the Silver Peak Area Geothermal Exploration Project within Esmeralda County, Nevada. The purpose of the project is to determine subsurface temperatures, confirm the existence of geothermal resources, and

215

Resistivity Tomography At Silver Peak Area (DOE GTP) | Open Energy  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Facebook icon Twitter icon » Resistivity Tomography At Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Single-Well and Cross-Well Resistivity At Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak Area Exploration Technique Single-Well and Cross-Well Resistivity Activity Date Usefulness not indicated DOE-funding Unknown References (1 January 2011) GTP ARRA Spreadsheet Retrieved from "http://en.openei.org/w/index.php?title=Resistivity_Tomography_At_Silver_Peak_Area_(DOE_GTP)&oldid=689883" Categories:

216

Application of Thermal Storage, Peak Shaving and Cogeneration for Hospitals  

E-Print Network (OSTI)

Energy costs of hospitals can be managed by employing various strategies to control peak electrical demand (KW) while at the same time providing additional security of operation in the event that an equipment failure or a disruption of power from...

McClure, J. D.; Estes, J. M.; Estes, M. C.

1987-01-01T23:59:59.000Z

217

Off peak cooling using an ice storage system  

E-Print Network (OSTI)

The electric utilities in the United States have entered a period of slow growth due to a combination of increased capital costs and a staggering rise in the costs for fuel. In addition to this, the rise in peak power ...

Quinlan, Edward Michael

1980-01-01T23:59:59.000Z

218

Potential Peak Load Reductions From Residential Energy Efficient Upgrades  

E-Print Network (OSTI)

of the distribution network can be improved; and added environmental pollution can be minimized. Energy efficiency improvements, especially through residential programs, are increasingly being used to mitigate this rise in peak demand. This paper examines...

Meisegeier, D.; Howes, M.; King, D.; Hall, J.

2002-01-01T23:59:59.000Z

219

Robust powder auto-indexing using many peaks  

Science Journals Connector (OSTI)

A new algorithm, CONOGRAPH, carries out exhaustive powder auto-indexing in a short time, even if the q values of many peaks are used for robust powder auto-indexing. Some results from CONOGRAPH are presented.

Oishi-Tomiyasu, R.

2014-03-11T23:59:59.000Z

220

Optimization of Demand Response Through Peak Shaving , D. Craigie  

E-Print Network (OSTI)

Optimization of Demand Response Through Peak Shaving G. Zakeri , D. Craigie , A. Philpott , M. Todd for the demand response of such a consumer. We will establish a monotonicity result that indicates fuel supply

Todd, Michael J.

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221

The peak of oil productionTimings and market recognition  

Science Journals Connector (OSTI)

Energy is essential for present societies. In particular, transportation systems depend on petroleum-based fuels. That world oil production is set to pass a peak is now a reasonably accepted concept, although its date is far from consensual. In this work, we analyze the true expectations of the oil market participants about the future availability of this fundamental energy source. We study the evolution through time of the curves of crude oil futures prices, and we conclude that the market participants, among them the crude oil producers, already expect a near-term peak of oil production. This agrees with many technical predictions for the date of peak production, including our own, that point to peak dates around the end of the present decade. If this scenario is confirmed, it can cause serious social and economical problems because societies will have little time to perform the necessary adjustments.

Pedro de Almeida; Pedro D. Silva

2009-01-01T23:59:59.000Z

222

Peak Oil and the Arctic National Wildlife Refuge  

Science Journals Connector (OSTI)

When Peak Oil is reached, oil production is slated to decline. If the ... worlds economic engine is still running on oil, there is potential for instability in the global economy as oil becomes scarcer and more ...

Peter Van Tuyn

2014-01-01T23:59:59.000Z

223

High Energy Density Science with High Peak Power Light Sources  

Science Journals Connector (OSTI)

High energy density (HED) science is a growing sub-field of plasma and condensed matter physics. I will examine how recent technological developments in high peak power, petawatt-class...

Ditmire, Todd

224

Structural Analysis of the Desert Peak-Brady Geothermal Fields,  

Open Energy Info (EERE)

Structural Analysis of the Desert Peak-Brady Geothermal Fields, Structural Analysis of the Desert Peak-Brady Geothermal Fields, Northwestern Nevada: Implications for Understanding Linkages Between Northeast-Trending Structures and Geothermal Reservoirs in the Humboldt Structural Zone Jump to: navigation, search OpenEI Reference LibraryAdd to library Conference Paper: Structural Analysis of the Desert Peak-Brady Geothermal Fields, Northwestern Nevada: Implications for Understanding Linkages Between Northeast-Trending Structures and Geothermal Reservoirs in the Humboldt Structural Zone Abstract Detailed geologic mapping, delineation of Tertiary strata, analysis of faults and folds, and a new gravity survey have elucidated the structural controls on the Desert Peak and Brady geothermal fields in the Hot Springs Mountains of northwestern Nevada. The fields lie within the Humboldt

225

Twin Peaks Motel Space Heating Low Temperature Geothermal Facility | Open  

Open Energy Info (EERE)

Peaks Motel Space Heating Low Temperature Geothermal Facility Peaks Motel Space Heating Low Temperature Geothermal Facility Jump to: navigation, search Name Twin Peaks Motel Space Heating Low Temperature Geothermal Facility Facility Twin Peaks Motel Sector Geothermal energy Type Space Heating Location Ouray, Colorado Coordinates 38.0227716°, -107.6714487° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[]}

226

Silver Peak, Nevada: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Peak, Nevada: Energy Resources Peak, Nevada: Energy Resources (Redirected from Silver Peak, NV) Jump to: navigation, search Name Silver Peak, Nevada Equivalent URI DBpedia GeoNames ID 5512346 Coordinates 37.7549309°, -117.6348148° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.7549309,"lon":-117.6348148,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

227

Daily load profile and monthly power peaks evaluation of the urban substation of the capital of Jordan Amman  

Science Journals Connector (OSTI)

The hourly recorded power of an urban substation of the National Electric Power Company (NEPCO) in the capital of Jordan Amman is used to calculate the diversity and conversion factors of the substation. These factors are used to estimate the daily load power profile and the monthly peak power of the substation. The results show that the conversion factors are almost independent of the number of feeders in the substation, while the diversity factors vary in substations that have six feeders or less. The results show a good correlation between the estimated and actual recorded data of the daily load profile with less than 5% percentage error.

Nabeel I.A. Tawalbeh

2012-01-01T23:59:59.000Z

228

Jiminy Peak Ski Resort Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Jiminy Peak Ski Resort Wind Farm Jiminy Peak Ski Resort Wind Farm Jump to: navigation, search Name Jiminy Peak Ski Resort Wind Farm Facility Jiminy Peak Ski Resort Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Jiminy Peak Mountain Resort Developer Sustainable Energy Developments Energy Purchaser Jiminy Peak Mountain Resort Location Hancock MA Coordinates 42.5554°, -73.2898° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.5554,"lon":-73.2898,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

229

Peak oil: The four stages of a new idea  

Science Journals Connector (OSTI)

The present paper reviews the reactions and the path of acceptance of the theory known as peak oil. The theory was proposed for the first time by M.K. Hubbert in the 1950s as a way to describe the production pattern of crude oil. According to Hubbert, the production curve is bell shaped and approximately symmetric. Hubbert's theory was verified with good approximation for the case of oil production in the United States that peaked in 1971, and is now being applied to the worldwide oil production. It is generally believed that the global peak of oil production (peak oil) will take place during the first decade of the 21st century, and some analysts believe that it has already occurred in 2005 or 2006. The theory and its consequences have unpleasant social and economical implications. The present paper is not aimed at assessing the peak date but offers a discussion on the factors that affect the acceptance and the diffusion of the concept of peak oil with experts and with the general public. The discussion is based on a subdivision of four stages of acceptance, loosely patterned after a sentence by Thomas Huxley.

Ugo Bardi

2009-01-01T23:59:59.000Z

230

Achieving sustainable urban transport mobility in post peak oil era  

Science Journals Connector (OSTI)

Peak oil is the term used to describe the point at which global oil production will peak and thereafter start to decline. Recognising that transport uses a significant portion of global energy, the shortage of fossil fuel in post peak oil era will pose a global challenge in the transport sector. The paper presents an assessment of international research to illustrate the possible time frame of peak oil. It investigates the key implications of the oil shortage that threaten to render the urban transport system of Australia ineffective. Synthesis of documented research evidence suggests three major implications in the urban transport sector: (1) a reduction of mobility for individuals, (2) an increase of transport disadvantage, and (3) a disruption of urban freight movement. In addition, the paper explores strategies to cope with the devastating effects of the shortage of the fossil fuel in the post peak oil era. A number of strategies to achieve sustainable mobility in the future urban transport system are presented. These strategies are summarised into three main themes: (1) a mode shift to alternate transport modes, (2) an integration of land use and transport planning, and (3) a global technical effort for alternate fuels and vehicles. It is expected that a concerted global effort in this regard can have a far-reaching effect in achieving sustainability in urban transport mobility.

Md Aftabuzzaman; Ehsan Mazloumi

2011-01-01T23:59:59.000Z

231

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma Reducing Peak Demand to Defer Power Plant Construction in Oklahoma Located in the heart of "Tornado Alley," Oklahoma Gas & Electric Company's (OG&E) electric grid faces significant challenges from severe weather, hot summers, and about 2% annual load growth. To better control costs and manage electric reliability under these conditions, OG&E is pursuing demand response strategies made possible by implementation of smart grid technologies, tools, and techniques from 2010-2012. The objective is to engage customers in lowering peak demand using smart technologies in homes and businesses and to achieve greater efficiencies on the distribution system. The immediate goal: To defer two 165 MW power plants currently planned for

232

Silver Peak, Nevada: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Peak, Nevada: Energy Resources Peak, Nevada: Energy Resources Jump to: navigation, search Name Silver Peak, Nevada Equivalent URI DBpedia GeoNames ID 5512346 Coordinates 37.7549309°, -117.6348148° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.7549309,"lon":-117.6348148,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

233

Price Server System for Automated Critical Peak Pricing  

NLE Websites -- All DOE Office Websites (Extended Search)

Price Server System for Automated Critical Peak Pricing Price Server System for Automated Critical Peak Pricing Speaker(s): David S. Watson Date: June 3, 2005 - 12:00pm Location: 90-3148 Overview of current California Energy Commission (CEC)/Demand Response Research Center (DRRC) Auto-CPP project: This summer, some select commercial CPP customers of PG&E will have the option of joining the Automated Critical Peak Pricing pilot. The pilot will have the same tariffs as standard CPP programs, but will include an added feature: automated shedding of electric loads. Through use of the Price Server System, day-ahead CPP event signals initiated by PG&E will ultimately cause electric loads to be automatically curtailed on commercial customer sites. These optional predetermined shed strategies will occur without

234

Cuttings Analysis At Desert Peak Area (Laney, 2005) | Open Energy  

Open Energy Info (EERE)

Desert Peak Area (Laney, 2005) Desert Peak Area (Laney, 2005) Exploration Activity Details Location Desert Peak Area Exploration Technique Cuttings Analysis Activity Date Usefulness not indicated DOE-funding Unknown Notes Remote Sensing for Exploration and Mapping of Geothermal Resources, Wendy Calvin, 2005. Task 1: Detailed analysis of hyperspectral imagery obtained in summer of 2003 over Brady's Hot Springs region was completed and validated (Figure 1). This analysis provided a local map of both sinter and tufa deposits surrounding the Ormat plant, identified fault extensions not previously recognized from field mapping and has helped constrain where to put additional wells that were drilled at the site. Task 2: Initial analysis of Landsat and ASTER data for Buffalo Valley and Pyramid Lake was

235

The Peak/Dip Picture of the Cosmic Web  

E-Print Network (OSTI)

The initial shear field plays a central role in the formation of large-scale structures, and in shaping the geometry, morphology, and topology of the cosmic web. We discuss a recent theoretical framework for the shear tensor, termed the `peak/dip picture', which accounts for the fact that halos/voids may form from local extrema of the density field - rather than from random spatial positions; the standard Doroshkevich's formalism is generalized, to include correlations between the density Hessian and shear field at special points in space around which halos/voids may form. We then present the `peak/dip excursion-set-based' algorithm, along with its most recent applications - merging peaks theory with the standard excursion set approach.

Rossi, Graziano

2014-01-01T23:59:59.000Z

236

Desert Peak II Geothermal Facility | Open Energy Information  

Open Energy Info (EERE)

II Geothermal Facility II Geothermal Facility Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Desert Peak II Geothermal Facility General Information Name Desert Peak II Geothermal Facility Facility Desert Peak II Sector Geothermal energy Location Information Location Churchill, Nevada Coordinates 39.753854931241°, -118.95378112793° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.753854931241,"lon":-118.95378112793,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

237

E-Print Network 3.0 - actuales relacionadas con Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

for: actuales relacionadas con Page: << < 1 2 3 4 5 > >> 1 Departamento de Fsica (EPS) Universidad Carlos III de Madrid Summary: fsica relacionada con la implosin de los...

238

E-Print Network 3.0 - actuales clasificaciones del Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

Collection: Mathematics 30 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

239

E-Print Network 3.0 - actuales del sector Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

Collection: Engineering 60 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

240

Scenario Analysis of Peak Demand Savings for Commercial Buildings with  

NLE Websites -- All DOE Office Websites (Extended Search)

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Title Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Publication Type Conference Paper LBNL Report Number LBNL-3636e Year of Publication 2010 Authors Yin, Rongxin, Sila Kiliccote, Mary Ann Piette, and Kristen Parrish Conference Name 2010 ACEEE Summer Study on Energy Efficiency in Buildings Conference Location Pacific Grove, CA Keywords demand response and distributed energy resources center, demand response research center, demand shifting (pre-cooling), DRQAT Abstract This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30% using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Silver Peak Innovative Exploration Project (Ram Power Inc.)  

SciTech Connect

Data generated from the Silver Peak Innovative Exploration Project, in Esmeralda County, Nevada, encompasses a deep-circulation (amagmatic) meteoric-geothermal system circulating beneath basin-fill sediments locally blanketed with travertine in western Clayton Valley (lithium-rich brines from which have been mined for several decades). Spring- and shallow-borehole thermal-water geochemistry and geothermometry suggest that a Silver Peak geothermal reservoir is very likely to attain the temperature range 260- 300oF (~125-150oC), and may reach 300-340oF (~150-170oC) or higher (GeothermEx, Inc., 2006). Results of detailed geologic mapping, structural analysis, and conceptual modeling of the prospect (1) support the GeothermEx (op. cit.) assertion that the Silver Peak prospect has good potential for geothermal-power production; and (2) provide a theoretical geologic framework for further exploration and development of the resource. The Silver Peak prospect is situated in the transtensional (regional shearing coupled with extension) Walker Lane structural belt, and squarely within the late Miocene to Pliocene (11 Ma to ~5 Ma) Silver Peak-Lone Mountain metamorphic core complex (SPCC), a feature that accommodated initial displacement transfer between major right-lateral strike- slip fault zones on opposite sides of the Walker Lane. The SPCC consists essentially of a ductiley-deformed lower plate, or core, of Proterozoic metamorphic tectonites and tectonized Mesozoic granitoids separated by a regionally extensive, low-angle detachment fault from an upper plate of severely stretched and fractured structural slices of brittle, Proterozoic to Miocene-age lithologies. From a geothermal perspective, the detachment fault itself and some of the upper-plate structural sheets could function as important, if secondary, subhorizontal thermal-fluid aquifers in a Silver Peak hydrothermal system.

Miller, Clay

2010-01-01T23:59:59.000Z

242

Injection Solvent Effect on Peak Height in Ion Exchange HPLC  

Science Journals Connector (OSTI)

......2. To further evaluate the effect of the injection volume only...injection volume were varied. Effect of weak injection solvent There...same eluent ion strength. The effect of eluent ion strength. Figure...nitrate in the mobile phase. 418 ship of the peak height of phenylacetate......

Hyunjoo Kim Lee; Norman E. Hoffman

1992-10-01T23:59:59.000Z

243

SCHOOL OF HISTORY & PHILOSOPHY Peak Carbon. Climate change and energy  

E-Print Network (OSTI)

SCHOOL OF HISTORY & PHILOSOPHY Peak Carbon. Climate change and energy policy ARTS2241 S2, 2010 #12 to be overcome before Australia can make deep cuts in greenhouse emissions, particularly from energy generation AIMS · Create awareness of the `bigger picture' that connects concerns over climate change and energy

Green, Donna

244

Scalable Scheduling of Building Control Systems for Peak Demand Reduction  

E-Print Network (OSTI)

Behl, Rahul Mangharam and George J. Pappas Department of Electrical and Systems Engineering University operation of sub- systems such as heating, ventilating, air conditioning and refrigeration (HVAC&R) systems is fundamental for their efficient behavior, especially in elec- trical systems and the electric grid [1]. Peak

Pappas, George J.

245

Providing Regulation Services and Managing Data Center Peak Power Budgets  

E-Print Network (OSTI)

-based peak shaving. However, none of these publications consider the feasibility of using the energy storage AND RELATED WORK Substantial integration of electric vehicles and renewable energy sources into the electric utility companies use to ensure stability. It includes multiple mechanisms, such as demand-response (DR

Simunic, Tajana

246

Why Military and Intelligence Agencies Are Peeking at Peak Oil  

Science Journals Connector (OSTI)

In the spring of 2003 I received a telephone call that was, to me, astonishing. A lady introduced herself and told me that she worked for MUST. She and a colleague wanted to come to Uppsala to discuss Peak Oil wi...

Kjell Aleklett

2012-01-01T23:59:59.000Z

247

Green Scheduling: Scheduling of Control Systems for Peak Power Reduction  

E-Print Network (OSTI)

approach to fine-grained coordination of energy demand by scheduling energy consuming control systems of the system variables only, control system execution (i.e. when energy is supplied to the system-Scheduling; Energy Systems; Peak Power Reduction; Load Balancing; I. INTRODUCTION During a major sporting event

Pappas, George J.

248

Categorical Exclusion for Pinnacle Peak Substation PCB contaminated Electrical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Categorical Exclusion for Pinnacle Peak Substation PCB contaminated Electrical Equipment Removal Project located north of Phoenix, Maricopa County, Arizona RECORD OF CATEGORICAL EXCLUSION DETERMINATION A. Proposed Action: Western proposes drain and dispose of PCB contaminated oil from two bushings, and decontaminate one· bushing and rack, break apart PCB contaminated concrete and excavate PCB contaminated soil at Pinnacle Peak Substation. Western will be use existing access roads and vehicles such as cranes, backhoes, dozers, bucket trucks, crew trucks and pickup trucks to bring personnel and equipment to the work area. This work is necessary to maintain the safety and reliability of the bulk electrical system. The project is located in Maricopa County, Arizona. The attached map shows the

249

ARM - Field Campaign - Colorado: The Storm Peak Lab Cloud Property  

NLE Websites -- All DOE Office Websites (Extended Search)

govCampaignsColorado: The Storm Peak Lab Cloud Property Validation govCampaignsColorado: The Storm Peak Lab Cloud Property Validation Experiment (STORMVEX) Campaign Links STORMVEX Website Related Campaigns Colorado: CFH/CMH Deployment to StormVEx 2011.02.01, Mace, AMF Colorado: SP2 Deployment at StormVEx 2010.11.15, Sedlacek, AMF Colorado : Cavity Attenuated Phase Shift 2010.11.15, Massoli, AMF Colorado: Infrared Thermometer (IRT) 2010.11.15, Mace, AMF Colorado: StormVEX Aerosol Size Distribution 2010.11.15, Hallar, AMF Colorado: Direct Measurements of Snowfall 2010.11.15, McCubbin, AMF Colorado: Thunderhead Radiative Flux Analysis Campaign 2010.11.15, Long, AMF Colorado: Ice Nuclei and Cloud Condensation Nuclei Characterization 2010.11.15, Cziczo, AMF Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA.

250

Saving Power at Peak Hours (LBNL Science at the Theater)  

ScienceCinema (OSTI)

California needs new, responsive, demand-side energy technologies to ensure that periods of tight electricity supply on the grid don't turn into power outages. Led by Berkeley Lab's Mary Ann Piette, the California Energy Commission (through its Public Interest Energy Research Program) has established a Demand Response Research Center that addresses two motivations for adopting demand responsiveness: reducing average electricity prices and preventing future electricity crises. The research seeks to understand factors that influence "what works" in Demand Response. Piette's team is investigating the two types of demand response, load response and price response, that may influence and reduce the use of peak electric power through automated controls, peak pricing, advanced communications, and other strategies.

Piette, Mary Ann

2011-04-28T23:59:59.000Z

251

Wanxiang Silicon Peak Electronics Co Ltd | Open Energy Information  

Open Energy Info (EERE)

Wanxiang Silicon Peak Electronics Co Ltd Wanxiang Silicon Peak Electronics Co Ltd Jump to: navigation, search Name Wanxiang Silicon-Peak Electronics Co Ltd Place Kaihua, Zhejiang Province, China Zip 324300 Sector Solar Product Maker of monocrystalline silicon ingots and wafers and subsidiary of the Wanxiang Group which includes solar cell and module maker Wanxiang Solar. Coordinates 29.140209°, 118.405113° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.140209,"lon":118.405113,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

252

Deconvolution of mixed gamma emitters using peak parameters  

SciTech Connect

When evaluating samples containing mixtures of nuclides using gamma spectroscopy the situation sometimes arises where the nuclides present have photon emissions that cannot be resolved by the detector. An example of this is mixtures of {sup 241}Am and plutonium that have L x-ray emissions with slightly different energies which cannot be resolved using a high-purity germanium detector. It is possible to deconvolute the americium L x-rays from those plutonium based on the {sup 241}Am 59.54 keV photon. However, this requires accurate knowledge of the relative emission yields. Also, it often results in high uncertainties in the plutonium activity estimate due to the americium yields being approximately an order of magnitude greater than those for plutonium. In this work, an alternative method of determining the relative fraction of plutonium in mixtures of {sup 241}Am and {sup 239}Pu based on L x-ray peak location and shape parameters is investigated. The sensitivity and accuracy of the peak parameter method is compared to that for conventional peak decovolution.

Gadd, Milan S [Los Alamos National Laboratory; Garcia, Francisco [Los Alamos National Laboratory; Magadalena, Vigil M [Los Alamos National Laboratory

2011-01-14T23:59:59.000Z

253

K2 Energy Solutions formerly Peak Energy Solutions | Open Energy  

Open Energy Info (EERE)

Energy Solutions formerly Peak Energy Solutions Energy Solutions formerly Peak Energy Solutions Jump to: navigation, search Name K2 Energy Solutions (formerly Peak Energy Solutions) Place Henderson, Nevada Zip 89074 Product Nevada-based designer and fabricator of Lithium Iron Phosphate (LFP) batteries for such applications as EVs, power tools and larger-scale storage. Coordinates 38.83461°, -82.140509° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.83461,"lon":-82.140509,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

254

Soil Sampling At Silver Peak Area (Henkle, Et Al., 2005) | Open...  

Open Energy Info (EERE)

Silver Peak Area (Henkle, Et Al., 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Soil Sampling At Silver Peak Area (Henkle, Et Al., 2005)...

255

XAFS Study of Phase-Change Recording Material Using Actual Media  

Science Journals Connector (OSTI)

The influence of the interface layer to the local structure for atomic arrangement of a GeBiTe phase-change material was investigated by using XAFS on the actual rewritable HD DVD...

Nakai, Tsukasa; Yoshiki, Masahiko; Satoh, Yasuhiro

256

E-Print Network 3.0 - actual del ultrasonido Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

Summary: : evolucin histrica y situacin actual. 8 l) Evaluacin de la capacidad de carga del Parque para los... Proyectos A lo largo del ao 2010 han estado vigentes 85...

257

E-Print Network 3.0 - anciano consideraciones actuales Sample...  

NLE Websites -- All DOE Office Websites (Extended Search)

mitigacin de los efectos del cambio climtico y con... polticas De proseguir las emisiones de GEI a una tasa igual o superior a la actual, el calentamiento Source: Binette,...

258

E-Print Network 3.0 - actual terrestrial rabies Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 56 innovati nNREL Advances a Unique Crystalline Silicon Solar Cell Summary: actually begins at another of the U.S. Department of Energy (DOE)...

259

E-Print Network 3.0 - actual del huemul Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 88 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

260

E-Print Network 3.0 - actual del franciscanismo Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 75 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

E-Print Network 3.0 - actual del control Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 30 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

262

E-Print Network 3.0 - actual del tabaquismo Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 91 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

263

E-Print Network 3.0 - actual del no-acceso Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 73 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

264

E-Print Network 3.0 - actual del rabdomiosarcoma Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 74 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

265

E-Print Network 3.0 - actual del estreptococo Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 80 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

266

Transverse Polarization for Energy Calibration at the Z peak  

E-Print Network (OSTI)

In this paper we deal with aspects of transverse polarization for the purpose of energy calibration of proposed circular colliders like the FCC-ee and the CEPC. The main issues of such a measurement will be discussed. The possibility of using this method to accurately determine the energy at the WW threshold as well as the Z peak will be addressed. The use of wigglers for reducing long polarization times will be discussed and a possible strategy will be presented for minimising the energy uncertainty error in these large machines.

Koratzinos, M

2015-01-01T23:59:59.000Z

267

Peak oil supply or oil not for sale?  

Science Journals Connector (OSTI)

Abstract The restrictions imposed by climate change are inevitable and will be exerted either via precautionary mitigation of (mainly energy-related) CO2 emissions or via irreversible impacts on ecosystems and on human habitats. Either way, oil markets are bound to incur drastic shrinking. Concern over peak oil supply will crumble when the irrevocable peak oil demand is created. Replacing oil in the world's energy economies requires redirected market forces, notably in the form of steadily increasing oil end-use prices. Yet, thus far, crude oil prices have obeyed the market fundamentals of expanding-contracting demand and oligopolistic supply. A hockey stick supply curve supports high sales prices, providing large rents to submarginal sources. Cutting oil demand and maintaining high prices implies reducing the supply hockey stick's length by curtailing some oil producers. In such a scenario, the alliances, goals, and tactics of oil geopolitics are set to change. We identify a distribution over friendly and hostile oil suppliers, with others drifting in between the two sides. Conflicts and warfare are less aimed at conquering oil fields for exploitation than at paralyzing production capabilities of opponents or of unreliable transient sources. Covert warfare and instigation of internal conflicts are likely tactics to exhaust hostile opponents.

Aviel Verbruggen; Thijs Van de Graaf

2013-01-01T23:59:59.000Z

268

Design and development of a 6 MW peak, 24 kW average power S-band klystron  

SciTech Connect

A 6 MW peak, 24 kW average power S-band Klystron is under development at CEERI, Pilani under an MoU between BARC and CEERI. The design of the klystron has been completed. The electron gun has been designed using TRAK and MAGIC codes. RF cavities have been designed using HFSS and CST Microwave Studio while the complete beam wave interaction simulation has been done using MAGIC code. The thermal design of collector and RF window has been done using ANSYS code. A Gun Collector Test Module (GCTM) was developed before making actual klystron to validate gun perveance and thermal design of collector. A high voltage solid state pulsed modulator has been installed for performance valuation of the tube. The paper will cover the design aspects of the tube and experimental test results of GCTM and klystron. (author)

Joshi, L.M.; Meena, Rakesh; Nangru, Subhash; Kant, Deepender; Pal, Debashis; Lamba, O.S.; Jindal, Vishnu; Jangid, Sushil Kumar, E-mail: joslm@rediffmail.com [Central Electronics Engineering Research Institute, Council of Scientific and Industrial Research, Pilani (India); Chakravarthy, D.P.; Dixit, Kavita [Bhabha Atomic Research Centre, Mumbai (India)

2011-07-01T23:59:59.000Z

269

Measurement of a Peak in the Cosmic Microwave Background Power  

Science Journals Connector (OSTI)

We describe a measurement of the angular power spectrum of anisotropies in the cosmic microwave background (CMB) at scales of 03 to 5 from the North American test flight of the Boomerang experiment. Boomerang is a balloon-borne telescope with a bolometric receiver designed to map CMB anisotropies on a long-duration balloon flight. During a 6 hr test flight of a prototype system in 1997, we mapped more than 200 deg2 at high Galactic latitudes in two bands centered at 90 and 150 GHz with a resolution of 26' and 165 FWHM, respectively. Analysis of the maps gives a power spectrum with a peak at angular scales of 1 with an amplitude 70 ?KCMB.

P. D. Mauskopf; P. A. R. Ade; P. de Bernardis; J. J. Bock; J. Borrill; A. Boscaleri; B. P. Crill; G. DeGasperis; G. De Troia; P. Farese; P. G. Ferreira; K. Ganga; M. Giacometti; S. Hanany; V. V. Hristov; A. Iacoangeli; A. H. Jaffe; A. E. Lange; A. T. Lee; S. Masi; A. Melchiorri; F. Melchiorri; L. Miglio; T. Montroy; C. B. Netterfield; E. Pascale; F. Piacentini; P. L. Richards; G. Romeo; J. E. Ruhl; E. Scannapieco; F. Scaramuzzi; R. Stompor; N. Vittorio

2000-01-01T23:59:59.000Z

270

Southern California Edison 32MWh Wind Integration Project  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

, Southern California Edison , Southern California Edison Tehachapi Wind Energy Storage (TSP) Project Loïc Gaillac, Naum Pinsky Southern California Edison November 3, 2010 Funded in part by the Energy Storage Systems Program of the U.S. Department Of Energy through National Energy Technology Laboratory 2 © Copyright 2010, Southern California Edison Outline * Policy Challenges - The challenge/opportunity * Testing a Solution: Tehachapi Storage Project Overview - Description of the project & objectives - Operational uses - Conceptual layout 3 © Copyright 2010, Southern California Edison CA 2020: Energy Policy Initiatives Highlighting potential areas for storage applications: * High penetration of Solar and Wind generation - Executive order requiring 33% of generated electricity to come from

271

Insights from Smart Meters: The Potential for Peak-Hour Savings from Behavior-Based Programs  

SciTech Connect

The rollout of smart meters in the last several years has opened up new forms of previously unavailable energy data. Many utilities are now able in real-time to capture granular, household level interval usage data at very high-frequency levels for a large proportion of their residential and small commercial customer population. This can be linked to other time and locationspecific information, providing vast, constantly growing streams of rich data (sometimes referred to by the recently popular buzz word, big data). Within the energy industry there is increasing interest in tapping into the opportunities that these data can provide. What can we do with all of these data? The richness and granularity of these data enable many types of creative and cutting-edge analytics. Technically sophisticated and rigorous statistical techniques can be used to pull interesting insights out of this highfrequency, human-focused data. We at LBNL are calling this behavior analytics. This kind of analytics has the potential to provide tremendous value to a wide range of energy programs. For example, highly disaggregated and heterogeneous information about actual energy use would allow energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; would enable evaluation, measurement and verification (EM&V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and would provide better insights in to the energy and peak hour savings associated with specifics types of EE and DR programs (e.g., behavior-based (BB) programs). In this series, Insights from Smart Meters, we will present concrete, illustrative examples of the type of value that insights from behavior analytics of these data can provide (as well as pointing out its limitations). We will supply several types of key findings, including: Novel results, which answer questions the industry previously was unable to answer; Proof-of-concept analytics tools that can be adapted and used by others; and Guidelines and protocols that summarize analytical best practices. This report focuses on one example of the kind of value that analysis of this data can provide: insights into whether behavior-based (BB) efficiency programs have the potential to provide peak-hour energy savings.

Todd, Annika; Perry, Michael; Smith, Brian; Sullivan, Michael; Cappers, Peter; Goldman, Charles

2014-03-25T23:59:59.000Z

272

Gasoline direct injection: Actual trends and future strategies for injection and combustion systems  

SciTech Connect

Recent developments have raised increased interest on the concept of gasoline direct injection as the most promising future strategy for fuel economy improvement of SI engines. The general requirements for mixture preparation and combustion systems in a GDI engine are presented in view of known and actual systems regarding fuel economy and emission potential. The characteristics of the actually favored injection systems are discussed and guidelines for the development of appropriate combustion systems are derived. The differences between such mixture preparation strategies as air distributed fuel and fuel wall impingement are discussed, leading to the alternative approach to the problem of mixture preparation with the fully air distributing concept of direct mixture injection.

Fraidl, G.K.; Piock, W.F.; Wirth, M.

1996-09-01T23:59:59.000Z

273

Silver Peak Innovative Exploration Project Geothermal Project | Open Energy  

Open Energy Info (EERE)

Innovative Exploration Project Geothermal Project Innovative Exploration Project Geothermal Project Jump to: navigation, search Last modified on July 22, 2011. Project Title Silver Peak Innovative Exploration Project Project Type / Topic 1 Recovery Act: Geothermal Technologies Program Project Type / Topic 2 Validation of Innovative Exploration Technologies Project Description The scope of this three phase project includes tasks to validate a variety of innovative exploration and drilling technologies which aim to accurately characterize the geothermal site and thereby reduce project risk. Phase 1 exploration will consist of two parts: 1) surface and near surface investigations and 2) subsurface geophysical surveys and modeling. The first part of Phase 1 includes: a hyperspectral imaging survey (to map thermal anomalies and geothermal indicator minerals), shallow temperature probe measurements, and drilling of temperature gradient wells to depths of 1000 feet. In the second part of Phase 1, 2D & 3D geophysical modeling and inversion of gravity, magnetic, and magnetotelluric datasets will be used to image the subsurface. This effort will result in the creation of a 3D model composed of structural, geological, and resistivity components. The 3D model will then be combined with the temperature data to create an integrated model that will be used to prioritize drill target locations.

274

Logistic curves, extraction costs and effective peak oil  

Science Journals Connector (OSTI)

Debates about the possibility of a near-term maximum in world oil production have become increasingly prominent over the past decade, with the focus often being on the quantification of geologically available and technologically recoverable amounts of oil in the ground. Economically, the important parameter is not a physical limit to resources in the ground, but whether market price signals and costs of extraction will indicate the efficiency of extracting conventional or nonconventional resources as opposed to making substitutions over time for other fuels and technologies. We present a hybrid approach to the peak-oil question with two models in which the use of logistic curves for cumulative production are supplemented with data on projected extraction costs and historical rates of capacity increase. While not denying the presence of large quantities of oil in the ground, even with foresight, rates of production of new nonconventional resources are unlikely to be sufficient to make up for declines in availability of conventional oil. Furthermore we show how the logistic-curve approach helps to naturally explain high oil prices even when there are significant quantities of low-cost oil yet to be extracted.

Robert J. Brecha

2012-01-01T23:59:59.000Z

275

A COGNITIVE-SYSTEMIC RECONSTRUCTION OF MASLOW'S THEORY OF SELF-ACTUALIZATION  

E-Print Network (OSTI)

A COGNITIVE-SYSTEMIC RECONSTRUCTION OF MASLOW'S THEORY OF SELF-ACTUALIZATION by Francis Heylighen1-order, cognitive-sys- temic framework. A hierarchy of basic needs is derived from the ur- gency of perturbations: material, cognitive and subjective. Material and/or cognitive incompetence during child- hood create

Toint, Philippe

276

SAMPLE GENERAL TERMS WHEN PURCHASING SERVICES* ACTUAL TERMS REQUIRED WILL BE DETERMINED BY CONTRACTS &  

E-Print Network (OSTI)

1 SAMPLE GENERAL TERMS WHEN PURCHASING SERVICES* ACTUAL TERMS REQUIRED WILL BE DETERMINED Contracts and Procurement (x4532) if you have questions regarding purchasing services. 1. Independent Status in an independent capacity and not as officers or employees or agents of the State of California. While Contractor

de Lijser, Peter

277

Design and evaluation of seasonal storage hydrogen peak electricity supply system  

E-Print Network (OSTI)

The seasonal storage hydrogen peak electricity supply system (SSHPESS) is a gigawatt-year hydrogen storage system which stores excess electricity produced as hydrogen during off-peak periods and consumes the stored hydrogen ...

Oloyede, Isaiah Olanrewaju

2011-01-01T23:59:59.000Z

278

Signal Peak-Tracker based on the Teager-Kaiser Energy (TKE) Operator  

E-Print Network (OSTI)

Described is a modification of the TKE operator from its usual `energy form'. The resulting `peak-tracker' (or peak-detector) is especially useful in studies that involve the frequency domain.

Randall D. Peters

2010-10-25T23:59:59.000Z

279

The evolution and present status of the study on peak oil in China  

Science Journals Connector (OSTI)

Peak oil theory is a theory concerning long-term oil reserves and the rate of oil production. Peak oil refers to the maximum rate of the production of oil or gas in any area under consideration. ... from three as...

Xiongqi Pang; Lin Zhao; Lianyong Feng; Qingyang Meng; Xu Tang

2009-06-01T23:59:59.000Z

280

Two kinds of peaked solitary waves of the KdV, BBM and Boussinesq equations  

Science Journals Connector (OSTI)

It is well-known that the celebrated Camassa-Holm equation has the peaked solitary waves, which have ... solutions of peaked solitary waves of the KdV equation, the BBM equation and the Boussinesq equation are gi...

ShiJun Liao

2012-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

THE ROLE OF BUILDING TECHNOLOGIES IN REDUCING AND CONTROLLING PEAK ELECTRICITY DEMAND  

E-Print Network (OSTI)

LBNL-49947 THE ROLE OF BUILDING TECHNOLOGIES IN REDUCING AND CONTROLLING PEAK ELECTRICITY DEMAND? ..................................... 8 What are the seasonal aspects of electric peak demand?............................ 9 What because of the California electricity crisis (Borenstein 2001). Uncertainties surrounding the reliability

282

MonteCarlo and Analytical Methods for Forced Outage Rate Calculations of Peaking Units  

E-Print Network (OSTI)

(unavailability) of such units. This thesis examines the representation of peaking units using a four-state model and performs the analytical calculations and Monte Carlo simulations to examine whether such a model does indeed represent the peaking units...

Rondla, Preethi 1988-

2012-10-26T23:59:59.000Z

283

The suppression of fluorescence peaks in energy-dispersive X-ray diffraction  

Science Journals Connector (OSTI)

It is shown experimentally that diffraction peaks which are normally obscured by fluorescence peaks in energy-dispersive X-ray diffraction can be revealed by tuning of the X-ray tube excitation voltage in order to suppress the fluorescence peaks.

Hansford, G.M.

2014-09-30T23:59:59.000Z

284

Polyribosomes in Rat Tissues: IV. On the Abnormal Dimer Peak in Hepatomas  

Science Journals Connector (OSTI)

...previously (11) that the dimer peak which is present in both the...between the monomer and dimer peaks. Also only slight changes are...height of the monomer and dimer peaks when the Novikoff hepatoma was...in an equal volume of mineral oil 12 hr before removal of the...

Thomas E. Webb and Van R. Potter

1966-05-01T23:59:59.000Z

285

Result Demonstration Report Pigweed Control in Grain Sorghum Using Peak. 1996 to 1999  

E-Print Network (OSTI)

74 78 Peak + Methylated Oil 0.75 oz + 1 pt 78 88 93 1) WAT = Weeks after treatment application. #12Result Demonstration Report Pigweed Control in Grain Sorghum Using Peak. 1996 to 1999 Brent Bean Summary Studies were conducted from 1996 to 1999 to evaluate pigweed control in grain sorghum using Peak

Mukhtar, Saqib

286

"Table 21. Total Energy Related Carbon Dioxide Emissions, Projected vs. Actual"  

U.S. Energy Information Administration (EIA) Indexed Site

Total Energy Related Carbon Dioxide Emissions, Projected vs. Actual" Total Energy Related Carbon Dioxide Emissions, Projected vs. Actual" "Projected" " (million metric tons)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",5060,5129.666667,5184.666667,5239.666667,5287.333333,5335,5379,5437.666667,5481.666667,5529.333333,5599,5657.666667,5694.333333,5738.333333,5797,5874,5925.333333,5984 "AEO 1995",,5137,5173.666667,5188.333333,5261.666667,5309.333333,5360.666667,5393.666667,5441.333333,5489,5551.333333,5621,5679.666667,5727.333333,5775,5841,5888.666667,5943.666667 "AEO 1996",,,5181.817301,5223.645142,5294.776326,5354.687297,5416.802205,5463.67395,5525.288005,5588.52771,5660.226888,5734.87972,5812.398031,5879.320068,5924.814575,5981.291626,6029.640422,6086.804077,6142.120972

287

Actual and Estimated Energy Savings Comparison for Deep Energy Retrofits in the Pacific Northwest  

SciTech Connect

Seven homes from the Pacific Northwest were selected to evaluate the differences between estimated and actual energy savings achieved from deep energy retrofits. The energy savings resulting from these retrofits were estimated, using energy modeling software, to save at least 30% on a whole-house basis. The modeled pre-retrofit energy use was trued against monthly utility bills. After the retrofits were completed, each of the homes was extensively monitored, with the exception of one home which was monitored pre-retrofit. This work is being conducted by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy Building Technologies Program as part of the Building America Program. This work found many discrepancies between actual and estimated energy savings and identified the potential causes for the discrepancies. The differences between actual energy use and modeled energy use also suggest improvements to improve model accuracy. The difference between monthly whole-house actual and estimated energy savings ranged from 75% more energy saved than predicted by the model to 16% less energy saved for all the monitored homes. Similarly, the annual energy savings difference was between 36% and -14%, which was estimated based on existing monitored savings because an entire year of data is not available. Thus, on average, for all six monitored homes the actual energy use is consistently less than estimates, indicating home owners are saving more energy than estimated. The average estimated savings for the eight month monitoring period is 43%, compared to an estimated savings average of 31%. Though this average difference is only 12%, the range of inaccuracies found for specific end-uses is far greater and are the values used to directly estimate energy savings from specific retrofits. Specifically, the monthly post-retrofit energy use differences for specific end-uses (i.e., heating, cooling, hot water, appliances, etc.) ranged from 131% under-predicted to 77% over-predicted by the model with respect to monitored energy use. Many of the discrepancies were associated with occupant behavior which influences energy use, dramatically in some cases, actual versus modeled weather differences, modeling input limitations, and complex homes that are difficult to model. The discrepancy between actual and estimated energy use indicates a need for better modeling tools and assumptions. Despite the best efforts of researchers, the estimated energy savings are too inaccurate to determine reliable paybacks for retrofit projects. While the monitored data allows researchers to understand why these differences exist, it is not cost effective to monitor each home with the level of detail presented here. Therefore an appropriate balance between modeling and monitoring must be determined for more widespread application in retrofit programs and the home performance industry. Recommendations to address these deficiencies include: (1) improved tuning process for pre-retrofit energy use, which currently utilized broad-based monthly utility bills; (2) developing simple occupant-based energy models that better address the many different occupant types and their impact on energy use; (3) incorporating actual weather inputs to increase accuracy of the tuning process, which uses utility bills from specific time period; and (4) developing simple, cost-effective monitoring solutions for improved model tuning.

Blanchard, Jeremy; Widder, Sarah H.; Giever, Elisabeth L.; Baechler, Michael C.

2012-10-01T23:59:59.000Z

288

The Multiple Peril Crop Insurance Actual Production History (APH) Insurance Plan  

E-Print Network (OSTI)

Economics, Professor and Extension Economist? Management, The Texas A&M System; and Extension Agricultural Economist, Kansas State University Agricultural Experiment Station and Cooperative Extension Service. The U.S. Dept. of Agriculture?s (USDA) Risk..., levels of coverage, price elections, applicable premium rates and subsidy amounts. The special provisions list program calendar dates and contain general and special statements that may further define, limit or modify coverage. MPCI?s Actual...

Stokes, Kenneth; Barnaby, G. A. Art; Waller, Mark L.; Outlaw, Joe

2008-10-07T23:59:59.000Z

289

Characterization, Leaching, and Filtration Testing for Tributyl Phosphate (TBP, Group 7) Actual Waste Sample Composites  

SciTech Connect

.A testing program evaluating actual tank waste was developed in response to Task 4 from the M-12 External Flowsheet Review Team (EFRT) issue response plan. The bulk water-insoluble solid wastes that are anticipated to be delivered to the Waste Treatment and Immobilization Plant (WTP) were identified according to type such that the actual waste testing could be targeted to the relevant categories. Eight broad waste groupings were defined. Samples available from the 222S archive were identified and obtained for testing. The actual waste-testing program included homogenizing the samples by group, characterizing the solids and aqueous phases, and performing parametric leaching tests. The tributyl phosphate sludge (TBP, Group 7) is the subject of this report. The Group 7 waste was anticipated to be high in phosphorus as well as aluminum in the form of gibbsite. Both are believed to exist in sufficient quantities in the Group 7 waste to address leaching behavior. Thus, the focus of the Group 7 testing was on the removal of both P and Al. The waste-type definition, archived sample conditions, homogenization activities, characterization (physical, chemical, radioisotope, and crystal habit), and caustic leaching behavior as functions of time, temperature, and hydroxide concentration are discussed in this report. Testing was conducted according to TP-RPP-WTP-467.

Edwards, Matthew K.; Billing, Justin M.; Blanchard, David L.; Buck, Edgar C.; Casella, Amanda J.; Casella, Andrew M.; Crum, J. V.; Daniel, Richard C.; Draper, Kathryn E.; Fiskum, Sandra K.; Jagoda, Lynette K.; Jenson, Evan D.; Kozelisky, Anne E.; MacFarlan, Paul J.; Peterson, Reid A.; Shimskey, Rick W.; Snow, Lanee A.; Swoboda, Robert G.

2009-03-09T23:59:59.000Z

290

Treatability studies of actual listed waste sludges from the Oak Ridge Reservation (ORR)  

SciTech Connect

Oak Ridge National Laboratory (ORNL) and Savannah River Technology Center (SRTC) are investigating vitrification for various low-level and mixed wastes on the Oak Ridge Reservation (ORR). Treatability studies have included surrogate waste formulations at the laboratory-, pilot-, and field-scales and actual waste testing at the laboratory- and pilot-scales. The initial waste to be processing through SRTC`s Transportable Vitrification System (TVS) is the K-1407-B and K-1407-C (B/C) Pond sludge waste which is a RCRA F-listed waste. The B/C ponds at the ORR K-25 site were used as holding and settling ponds for various waste water treatment streams. Laboratory-, pilot-, and field- scale ``proof-of-principle`` demonstrations are providing needed operating parameters for the planned field-scale demonstration with actual B/C Pond sludge waste at ORR. This report discusses the applied systems approach to optimize glass compositions for this particular waste stream through laboratory-, pilot-, and field-scale studies with surrogate and actual B/C waste. These glass compositions will maximize glass durability and waste loading while optimizing melt properties which affect melter operation, such as melt viscosity and melter refractory corrosion. Maximum waste loadings minimize storage volume of the final waste form translating into considerable cost savings.

Jantzen, C.M.; Peeler, D.K. [Westinghouse Savannah River Co., Aiken, SC (United States); Gilliam, T.M.; Bleier, A.; Spence, R.D. [Oak Ridge National Lab., TN (United States)

1996-05-06T23:59:59.000Z

291

Laboratory stabilization/solidification of surrogate and actual mixed-waste sludge in glass and grout  

SciTech Connect

Grouting and vitrification are currently the most likely stabilization/solidification technologies for mixed wastes. Grouting has been used to stabilize and solidify hazardous and low-level waste for decades. Vitrification has long been developed as a high-level-waste alternative and has been under development recently as an alternative treatment technology for low-level mixed waste. Laboratory testing has been performed to develop grout and vitrification formulas for mixed-waste sludges currently stored in underground tanks at Oak Ridge National Laboratory (ORNL) and to compare these waste forms. Envelopes, or operating windows, for both grout and soda-lime-silica glass formulations for a surrogate sludge were developed. One formulation within each envelope was selected for testing the sensitivity of performance to variations ({+-}10 wt%) in the waste form composition and variations in the surrogate sludge composition over the range previously characterized in the sludges. In addition, one sludge sample of an actual mixed-waste tank was obtained, a surrogate was developed for this sludge sample, and grout and glass samples were prepared and tested in the laboratory using both surrogate and the actual sludge. The sensitivity testing of a surrogate tank sludge in selected glass and grout formulations is discussed in this paper, along with the hot-cell testing of an actual tank sludge sample.

Spence, R.D.; Gilliam, T.M.; Mattus, C.H.; Mattus, A.J.

1998-03-03T23:59:59.000Z

292

Determination of the bias in LOFT fuel peak cladding temperature data from the blowdown phase of large-break LOCA experiments  

SciTech Connect

Data from the Loss-of-Fluid Test (LOFT) Program help quantify the margin of safety inherent in pressurized water reactors during postulated loss-of-coolant accidents (LOCAs). As early as 1979, questions arose concerning the accuracy of LOFT fuel rod cladding temperature data during several large-break LOCA experiments. This report analyzes how well externally-mounted fuel rod cladding thermocouples in LOFT accurately reflected actual cladding surface temperature during large-break LOCA experiments. In particular, the validity of the apparent core-wide fuel rod cladding quench exhibited during blowdown in LOFT Experiments L2-2 and L2-3 is studied. Also addressed is the question of whether the externally-mounted thermocouples might have influenced cladding temperature. The analysis makes use of data and information from several sources, including later, similar LOFT Experiments in which fuel centerline temperature measurements were made, experiments in other facilities, and results from a detailed FRAP-T6 model of the LOFT fuel rod. The analysis shows that there can be a significant difference (referred to as bias) between the surface-mounted thermocouple reading and the actual cladding temperature, and that the magnitude of this bias depends on the rate of heat transfer between the fuel rod cladding and coolant. The results of the analysis demonstrate clearly that a core-wide cladding quench did occur in Experiments L2-2 and L2-3. Further, it is shown that, in terms of peak cladding temperature recording during LOFT large-break LOCA experiments, the mean bias is 11.4 {plus_minus} 16.2K (20.5 {plus_minus} 29.2{degrees} F). The best-estimate value of peak cladding temperature for LOFT LP-02-6 is 1,104.8 K. The best-estimate peak cladding temperature for LOFT LP-LB-1 is 1284.0 K.

Berta, V.T.; Hanson, R.G.; Johnsen, G.W.; Schultz, R.R. [EG and G Idaho, Inc., Idaho Falls, ID (United States)

1993-05-01T23:59:59.000Z

293

ACTUAL WASTE TESTING OF GYCOLATE IMPACTS ON THE SRS TANK FARM  

SciTech Connect

Glycolic acid is being studied as a replacement for formic acid in the Defense Waste Processing Facility (DWPF) feed preparation process. After implementation, the recycle stream from DWPF back to the high-level waste Tank Farm will contain soluble sodium glycolate. Most of the potential impacts of glycolate in the Tank Farm were addressed via a literature review and simulant testing, but several outstanding issues remained. This report documents the actual-waste tests to determine the impacts of glycolate on storage and evaporation of Savannah River Site high-level waste. The objectives of this study are to address the following: ? Determine the extent to which sludge constituents (Pu, U, Fe, etc.) dissolve (the solubility of sludge constituents) in the glycolate-containing 2H-evaporator feed. ? Determine the impact of glycolate on the sorption of fissile (Pu, U, etc.) components onto sodium aluminosilicate solids. The first objective was accomplished through actual-waste testing using Tank 43H and 38H supernatant and Tank 51H sludge at Tank Farm storage conditions. The second objective was accomplished by contacting actual 2H-evaporator scale with the products from the testing for the first objective. There is no anticipated impact of up to 10 g/L of glycolate in DWPF recycle to the Tank Farm on tank waste component solubilities as investigated in this test. Most components were not influenced by glycolate during solubility tests, including major components such as aluminum, sodium, and most salt anions. There was potentially a slight increase in soluble iron with added glycolate, but the soluble iron concentration remained so low (on the order of 10 mg/L) as to not impact the iron to fissile ratio in sludge. Uranium and plutonium appear to have been supersaturated in 2H-evaporator feed solution mixture used for this testing. As a result, there was a reduction of soluble uranium and plutonium as a function of time. The change in soluble uranium concentration was independent of added glycolate concentration. The change in soluble plutonium content was dependent on the added glycolate concentration, with higher levels of glycolate (5 g/L and 10 g/L) appearing to suppress the plutonium solubility. The inclusion of glycolate did not change the dissolution of or sorption onto actual-waste 2H-evaporator pot scale to an extent that will impact Tank Farm storage and concentration. The effects that were noted involved dissolution of components from evaporator scale and precipitation of components onto evaporator scale that were independent of the level of added glycolate.

Martino, C.

2014-05-28T23:59:59.000Z

294

Impact of Smart Grid Technologies on Peak Load to 2050 | Open Energy  

Open Energy Info (EERE)

Impact of Smart Grid Technologies on Peak Load to 2050 Impact of Smart Grid Technologies on Peak Load to 2050 Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Impact of Smart Grid Technologies on Peak Load to 2050 Focus Area: Crosscutting Topics: Deployment Data Website: www.iea.org/papers/2011/smart_grid_peak_load.pdf Equivalent URI: cleanenergysolutions.org/content/impact-smart-grid-technologies-peak-l Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Demonstration & Implementation Regulations: Cost Recovery/Allocation This working paper analyses the evolution of peak load demand to 2050 in four key regions: Organisation for Economic Co-operation and Development

295

Pressure Temperature Log At Silver Peak Area (DOE GTP) | Open Energy  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Facebook icon Twitter icon » Pressure Temperature Log At Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Pressure Temperature Log At Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak Area Exploration Technique Pressure Temperature Log Activity Date Usefulness not indicated DOE-funding Unknown References (1 January 2011) GTP ARRA Spreadsheet Retrieved from "http://en.openei.org/w/index.php?title=Pressure_Temperature_Log_At_Silver_Peak_Area_(DOE_GTP)&oldid=511053" Categories: Exploration Activities

296

E-Print Network 3.0 - annihilation coincidence peak Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

peak is seen at 3375 keV with 6000... . the annihilation spectra from the polyethylene and gold tar- ... Source: Golovchenko, Jene A. - Department of Physics, Harvard...

297

RESCHEDULED: Webinar on Material Handling Fuel Cells for Building Electric Peak Shaving Applications  

Energy.gov (U.S. Department of Energy (DOE))

The Fuel Cell Technologies Office will present a live webinar entitled "Material Handling Fuel Cells for Building Electric Peak Shaving Applications".

298

The origin of brucite in hydrothermally altered limestone near Devil Peak, Nevada.  

E-Print Network (OSTI)

??Open-space brucite was identified in veins crosscutting hydrothermally altered limestone near the Devil Peak rhyolite plug in southern Nevada. The brucite occurs with serpentine, calcite, (more)

Knupp, Rhonda L.

1999-01-01T23:59:59.000Z

299

E-Print Network 3.0 - artificial extra peaks Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

A L . 2004 American Meteorological Society Summary: with theory, extratropical stochastic wind forces a decadal spectral peak in the tropical and eastern boundary... forcing, with...

300

E-Print Network 3.0 - adduct peak elimination Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

in 1,2-eliminations observed for HF loss... peak could be the CF3 + adduct of acrolein ... Source: Morton, Thomas Hellman - Department of Chemistry, University of...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control  

E-Print Network (OSTI)

combination of the total energy consumption and the peakalso reduces the total energy consumption of the occupancyTotal and Peak Energy Consumption Minimization of Building

Maasoumy, Mehdi; Sangiovanni-Vincentelli, Alberto

2012-01-01T23:59:59.000Z

302

Konsekvenser av Peak Oil i relation till fysisk planering - En fallstudie av Vxj kommun.  

E-Print Network (OSTI)

??Arbetets syfte r att uppmrksamma den problematik som r kopplad till Peak Oil, samt genom att exemplifiera med Vxj kommun, underska p vilket stt fysisk (more)

Edholm, Hedvig

2012-01-01T23:59:59.000Z

303

Food production after peak oil| Oregon's Willamette river basin as a bioregional case study.  

E-Print Network (OSTI)

?? Agriculture will experience radical new challenges in the next forty years. Peak oil, which is likely to occur before 2020, will result in potentially (more)

Hruska, Tracy

2010-01-01T23:59:59.000Z

304

2-M Probe At Desert Peak Area (Sladek, Et Al., 2007) | Open Energy...  

Open Energy Info (EERE)

Sladek, Et Al., 2007) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: 2-M Probe At Desert Peak Area (Sladek, Et Al., 2007) Exploration Activity...

305

Table 3b. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

b. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual b. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual Projected Price in Nominal Dollars (nominal dollars per barrel) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 17.06 17.21 18.24 19.43 20.64 22.12 23.76 25.52 27.51 29.67 31.86 34.00 36.05 38.36 40.78 43.29 45.88 48.37 AEO 1995 15.24 17.27 18.23 19.26 20.39 21.59 22.97 24.33 25.79 27.27 28.82 30.38 32.14 33.89 35.85 37.97 40.28 AEO 1996 17.16 17.74 18.59 19.72 20.97 22.34 23.81 25.26 26.72 28.22 29.87 31.51 33.13 34.82 36.61 38.48 40.48

306

Table 11a. Coal Prices to Electric Generating Plants, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

a. Coal Prices to Electric Generating Plants, Projected vs. Actual a. Coal Prices to Electric Generating Plants, Projected vs. Actual Projected Price in Constant Dollars (constant dollars per million Btu in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1992 1.47 1.48 1.53 1.57 1.58 1.57 1.61 1.63 1.68 1.69 1.70 1.72 1.70 1.76 1.79 1.81 1.88 1.92 AEO 1995 1993 1.39 1.39 1.38 1.40 1.40 1.39 1.39 1.42 1.41 1.43 1.44 1.45 1.46 1.46 1.46 1.47 1.50 AEO 1996 1994 1.32 1.29 1.28 1.27 1.26 1.26 1.25 1.27 1.27 1.27 1.28 1.27 1.28 1.27 1.28 1.26 1.28

307

Table 11b. Coal Prices to Electric Generating Plants, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

b. Coal Prices to Electric Generating Plants, Projected vs. Actual" b. Coal Prices to Electric Generating Plants, Projected vs. Actual" "Projected Price in Nominal Dollars" " (nominal dollars per million Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",1.502753725,1.549729719,1.64272351,1.727259934,1.784039735,1.822135762,1.923203642,2.00781457,2.134768212,2.217425497,2.303725166,2.407715232,2.46134106,2.637086093,2.775389073,2.902293046,3.120364238,3.298013245 "AEO 1995",,1.4212343,1.462640338,1.488780998,1.545300242,1.585877053,1.619428341,1.668671498,1.7584219,1.803937198,1.890547504,1.968695652,2.048913043,2.134750403,2.205281804,2.281690821,2.375434783,2.504830918 "AEO 1996",,,1.346101641,1.350594221,1.369020126,1.391737646,1.421340737,1.458772082,1.496497523,1.561369914,1.619940033,1.674758358,1.749420803,1.800709877,1.871110564,1.924495246,2.006850327,2.048938234,2.156821499

308

"Table 20. Total Delivered Transportation Energy Consumption, Projected vs. Actual"  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Transportation Energy Consumption, Projected vs. Actual" Total Delivered Transportation Energy Consumption, Projected vs. Actual" "Projected" " (quadrillion Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",23.62,24.08,24.45,24.72,25.06,25.38,25.74,26.16,26.49,26.85,27.23,27.55,27.91,28.26,28.61,28.92,29.18,29.5 "AEO 1995",,23.26,24.01,24.18,24.69,25.11,25.5,25.86,26.15,26.5,26.88,27.28,27.66,27.99,28.25,28.51,28.72,28.94 "AEO 1996",,,23.89674759,24.08507919,24.47502899,24.84881783,25.25887871,25.65527534,26.040205,26.38586426,26.72540092,27.0748024,27.47158241,27.80837631,28.11616135,28.3992157,28.62907982,28.85912895,29.09081459 "AEO 1997",,,,24.68686867,25.34906006,25.87225533,26.437994,27.03513145,27.52499771,27.96490097,28.45482063,28.92999458,29.38239861,29.84147453,30.26097488,30.59760475,30.85550499,31.10873222,31.31938744

309

"Table 19. Total Delivered Industrial Energy Consumption, Projected vs. Actual"  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Industrial Energy Consumption, Projected vs. Actual" Total Delivered Industrial Energy Consumption, Projected vs. Actual" "Projected" " (quadrillion Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",25.43,25.904,26.303,26.659,26.974,27.062,26.755,26.598,26.908,27.228,27.668,28.068,28.348,28.668,29.068,29.398,29.688,30.008 "AEO 1995",,26.164,26.293,26.499,27.044,27.252,26.855,26.578,26.798,27.098,27.458,27.878,28.158,28.448,28.728,29.038,29.298,29.608 "AEO 1996",,,26.54702756,26.62236823,27.31312376,27.47668697,26.90313339,26.47577946,26.67685979,26.928811,27.23795407,27.58448499,27.91057103,28.15050595,28.30145734,28.518,28.73702901,28.93001263,29.15872662 "AEO 1997",,,,26.21291769,26.45981795,26.88483478,26.67847443,26.55107968,26.78246968,27.07367604,27.44749539,27.75711339,28.02446072,28.39156621,28.69999783,28.87316602,29.01207631,29.19475644,29.37683575

310

File:Theoretical vs Actual Data Lesson Plan .pdf | Open Energy Information  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search File Edit History Facebook icon Twitter icon » File:Theoretical vs Actual Data Lesson Plan .pdf Jump to: navigation, search File File history File usage Metadata File:Theoretical vs Actual Data Lesson Plan .pdf Size of this preview: 463 × 599 pixels. Other resolution: 464 × 600 pixels. Go to page 1 2 Go! next page → next page → Full resolution ‎(1,275 × 1,650 pixels, file size: 257 KB, MIME type: application/pdf, 2 pages) File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 09:33, 3 January 2014 Thumbnail for version as of 09:33, 3 January 2014 1,275 × 1,650, 2 pages (257 KB) Foteri (Talk | contribs) Category:Wind for Schools Portal CurriculaCategory:Wind for Schools High School Curricula

311

Table 3a. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual  

U.S. Energy Information Administration (EIA) Indexed Site

a. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual a. Imported Refiner Acquisition Cost of Crude Oil, Projected vs. Actual Projected Price in Constant Dollars (constant dollars per barrel in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1992 16.69 16.43 16.99 17.66 18.28 19.06 19.89 20.72 21.65 22.61 23.51 24.29 24.90 25.60 26.30 27.00 27.64 28.16 AEO 1995 1993 14.90 16.41 16.90 17.45 18.00 18.53 19.13 19.65 20.16 20.63 21.08 21.50 21.98 22.44 22.94 23.50 24.12 AEO 1996 1994 16.81 16.98 17.37 17.98 18.61 19.27 19.92 20.47 20.97 21.41 21.86 22.25 22.61 22.97 23.34 23.70 24.08

312

Laboratory Demonstration of the Pretreatment Process with Caustic and Oxidative Leaching Using Actual Hanford Tank Waste  

SciTech Connect

This report describes the bench-scale pretreatment processing of actual tank waste materials through the entire baseline WTP pretreatment flowsheet in an effort to demonstrate the efficacy of the defined leaching processes on actual Hanford tank waste sludge and the potential impacts on downstream pretreatment processing. The test material was a combination of reduction oxidation (REDOX) tank waste composited materials containing aluminum primarily in the form of boehmite and dissolved S saltcake containing Cr(III)-rich entrained solids. The pretreatment processing steps tested included caustic leaching for Al removal solids crossflow filtration through the cell unit filter (CUF) stepwise solids washing using decreasing concentrations of sodium hydroxide with filtration through the CUF oxidative leaching using sodium permanganate for removing Cr solids filtration with the CUF follow-on solids washing and filtration through the CUF ion exchange processing for Cs removal evaporation processing of waste stream recycle for volume reduction combination of the evaporated product with dissolved saltcake. The effectiveness of each process step was evaluated by following the mass balance of key components (such as Al, B, Cd, Cr, Pu, Ni, Mn, and Fe), demonstrating component (Al, Cr, Cs) removal, demonstrating filterability by evaluating filter flux rates under various processing conditions (transmembrane pressure, crossflow velocities, wt% undissolved solids, and PSD) and filter fouling, and identifying potential issues for WTP. The filterability was reported separately (Shimskey et al. 2008) and is not repeated herein.

Fiskum, Sandra K.; Billing, Justin M.; Buck, Edgar C.; Daniel, Richard C.; Draper, Kathryn E.; Edwards, Matthew K.; Jenson, Evan D.; Kozelisky, Anne E.; MacFarlan, Paul J.; Peterson, Reid A.; Shimskey, Rick W.; Snow, Lanee A.

2009-01-01T23:59:59.000Z

313

PERFORMANCE TESTING OF THE NEXT-GENERATION CSSX SOLVENT WITH ACTUAL SRS TANK WASTE  

SciTech Connect

Efforts are underway to qualify the Next-Generation Solvent for the Caustic Side Solvent Extraction (CSSX) process. Researchers at multiple national laboratories have been involved in this effort. As part of the effort to qualify the solvent extraction system at the Savannah River Site (SRS), SRNL performed a number of tests at various scales. First, SRNL completed a series of batch equilibrium, or Extraction-Scrub-Strip (ESS), tests. These tests used {approx}30 mL of Next-Generation Solvent and either actual SRS tank waste, or waste simulant solutions. The results from these cesium mass transfer tests were used to predict solvent behavior under a number of conditions. At a larger scale, SRNL assembled 12 stages of 2-cm (diameter) centrifugal contactors. This rack of contactors is structurally similar to one tested in 2001 during the demonstration of the baseline CSSX process. Assembly and mechanical testing found no issues. SRNL performed a nonradiological test using 35 L of cesium-spiked caustic waste simulant and 39 L of actual tank waste. Test results are discussed; particularly those related to the effectiveness of extraction.

Pierce, R.; Peters, T.; Crowder, M.; Fink, S.

2011-11-01T23:59:59.000Z

314

"Table 18. Total Delivered Commercial Energy Consumption, Projected vs. Actual"  

U.S. Energy Information Administration (EIA) Indexed Site

Total Delivered Commercial Energy Consumption, Projected vs. Actual" Total Delivered Commercial Energy Consumption, Projected vs. Actual" "Projected" " (quadrillion Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",6.82,6.87,6.94,7,7.06,7.13,7.16,7.22,7.27,7.32,7.36,7.38,7.41,7.45,7.47,7.5,7.51,7.55 "AEO 1995",,6.94,6.9,6.95,6.99,7.02,7.05,7.08,7.09,7.11,7.13,7.15,7.17,7.19,7.22,7.26,7.3,7.34 "AEO 1996",,,7.059859276,7.17492485,7.228339195,7.28186655,7.336973667,7.387932777,7.442782879,7.501244545,7.561584473,7.623688221,7.684037209,7.749266148,7.815915108,7.884147644,7.950204372,8.016282082,8.085801125 "AEO 1997",,,,7.401538849,7.353548527,7.420701504,7.48336792,7.540113449,7.603093624,7.663851738,7.723834991,7.783358574,7.838726044,7.89124918,7.947964668,8.008976936,8.067288399,8.130317688,8.197405815

315

Plasmonic Nature of the Terahertz Conductivity Peak in Single-Wall Carbon Nanotubes  

E-Print Network (OSTI)

Plasmonic Nature of the Terahertz Conductivity Peak in Single-Wall Carbon Nanotubes Qi Zhang, Erik resonance is expected to occur in metallic and doped semiconducting carbon nanotubes in the terahertz conductivity peak commonly observed for carbon nanotube ensembles remains controversial. Here we present

Kono, Junichiro

316

Distributed Battery Control to Improve Peak Power Shaving Efficiency in Data Centers  

E-Print Network (OSTI)

Rack PDU BackupMain Bus-type power network Utility Diesel Generator ATS PDU Server Rack Server RackDistributed Battery Control to Improve Peak Power Shaving Efficiency in Data Centers Baris Aksanli, Eddie Pettis and Tajana S. Rosing UCSD, Google Stored energy in batteries can be used to cap peak power

Simunic, Tajana

317

20 th International Sacramento Peak Summer Workshop Advanced Solar Polarimetry -Theory, Observation, and Instrumentation  

E-Print Network (OSTI)

in the Quiet Sun Alexei A. Pevtsov National Solar Observatory/Sacramento Peak, PO Box 62, Sunspot, New Mexico20 th International Sacramento Peak Summer Workshop Advanced Solar Polarimetry - Theory in the solar activity on all spatial scales. It is believed that the strong magnetic #12;eld (active regions

Pevtsov, Alexei A.

318

An Approximate Method to Assess the Peaking Capability of the NW Hydroelectric System  

E-Print Network (OSTI)

DRAFT 1 An Approximate Method to Assess the Peaking Capability of the NW Hydroelectric System September 26, 2005 The best way to assess the hydroelectric system's peaking capability is to simulate its. This model simulates the operation of the major hydroelectric projects over a one-week (168 hour) period

319

InSAR At Desert Peak Area (Laney, 2005) | Open Energy Information  

Open Energy Info (EERE)

InSAR At Desert Peak Area (Laney, 2005) InSAR At Desert Peak Area (Laney, 2005) Exploration Activity Details Location Desert Peak Area Exploration Technique InSAR Activity Date Usefulness not indicated DOE-funding Unknown Notes InSAR Ground Displacement Analysis, Gary Oppliger and Mark Coolbaugh. This project supports increased utilization of geothermal resources in the Western United States by developing basic measurements and interpretations that will assist reservoir management and expansion at Bradys, Desert Peak and the Desert Peak EGS study area (80 km NE of Reno, Nevada) and will serve as a technology template for other geothermal fields. Raw format European Space Agency (ESA) ERS 1/2 satellite synthetic Aperture Radar (SAR) radar scenes acquired from 1992 through 2002 are being processed to

320

On the portents of peak oil (and other indicators of resource scarcity)  

Science Journals Connector (OSTI)

Economists have studied various indicators of resource scarcity but largely ignored the phenomenon of peaking due to its connection to non-economic (physical) theories of resource exhaustion. I consider peaking from the economic point of view, where economic forces determine the shape of the equilibrium extraction path. Within that framework, I ask whether the timing of peak production reveals anything useful about scarcity. I find peaking to be an ambiguous indicator. If someone announced the peak would arrive earlier than expected, and you believed them, you would not know whether the news was good or bad. However, I also show that the traditional economic indicators of resource scarcity (price, cost, and rent) fare no better, and argue that previous studies have misconstrued the connection between changes in underlying scarcity and movements in these traditional indicators.

James L. Smith

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Predicted and actual productions of horizontal wells in heavy-oil fields  

Science Journals Connector (OSTI)

This paper discusses the comparison of predicted and actual cumulative and daily oil production. The predicted results were obtained from the use of Joshi's equation, wherein, the effects of anisotropy and eccentricity were included. The cumulative production obtained from the use of equations developed by Borisov, Giger, Renard and Dupuy resulted in errors in excess of 100%, thus, they were not considered applicable for predicting cumulative and daily flows of heavy oils in horizontal wells. The wells considered in this analysis varied from 537 to 1201 metres with corresponding well bores of 0.089 to. 0.110 m. Using Joshi's equation, the predicted cumulative oil-production was within a 20% difference for up to 12 months of production for long wells and up to 24 months for short wells. Short wells were defined as those being under 1000 m.

Peter Catania

2000-01-01T23:59:59.000Z

322

Submitted to ApJ Letters, June 29, 2005 Are Presolar Silicon Carbide Grains from Novae Actually from Supernovae?  

E-Print Network (OSTI)

Submitted to ApJ Letters, June 29, 2005 Are Presolar Silicon Carbide Grains from Novae Actually stellar nucleosynthesis and mixing. The best-studied presolar phase, silicon carbide (SiC), exhibits

Nittler, Larry R.

323

Energy Conservation and Comfort of Heat Pump Desiccant Air Conditioning System in Actual Living Space in Summer  

E-Print Network (OSTI)

Energy Conservation and Comfort of Heat Pump Desiccant Air Conditioning System in Actual Living and total heat exchanger in terms of both energy conservation and thermal comfort in summer. 1. COP

Miyashita, Yasushi

324

Peak Oil  

Science Journals Connector (OSTI)

Between 2000 and 2010, world oil prices advanced from approximately $25 per barrel to more than $100 per barrel. The price appreciation of oil over the decade was around ten times the rate of inflation.

Robert Rapier

2012-01-01T23:59:59.000Z

325

First versus subsequent return-stroke current and field peaks in negative cloud-to-ground lightning discharges  

E-Print Network (OSTI)

First versus subsequent return-stroke current and field peaks in negative cloud-to-ground lightning examine relative magnitudes of electric field peaks of first and subsequent return strokes in negative, the electric field peak of the first stroke is appreciably, 1.7 to 2.4 times, larger than the field peak

Florida, University of

326

Decarbonization and the time-delay between peak CO2 emissions and concentrations  

E-Print Network (OSTI)

Carbon-dioxide (CO2) is the main contributor to anthropogenic global warming, and the timing of its peak concentration in the atmosphere is likely to govern the timing of maximum radiative forcing. While dynamics of atmospheric CO2 is governed by multiple time-constants, we idealize this by a single time-constant to consider some of the factors describing the time-delay between peaks in CO2 emissions and concentrations. This time-delay can be understood as the time required to bring CO2 emissions down from its peak to a small value, and is governed by the rate of decarbonizaton of economic activity. This decarbonization rate affects how rapidly emissions decline after having achieved their peak, and a rapid decline in emissions is essential for limiting peak radiative forcing. Long-term mitigation goals for CO2 should therefore consider not only the timing of peak emissions, but also the rate of decarbonization. We discuss implications for mitigation of the fact that the emissions peak corresponds to small bu...

Seshadri, Ashwin K

2015-01-01T23:59:59.000Z

327

Development of oil formation theories and their importance for peak oil  

Science Journals Connector (OSTI)

This paper reviews the historical development of both biogenic and non-biogenic petroleum formation. It also examines the recent claim that the so-called abiotic oil formation theory undermines the concept of peak oil, i.e. the notion that world oil production is destined to reach a maximum that will be followed by an irreversible decline. We show that peak oil is first and foremost a matter of production flows. Consequently, the mechanism of oil formation does not strongly affect depletion. We would need to revise the theory beyond peak oil only for the extreme and unlikely hypothesis of abiotic petroleum formation.

Mikael Hk; Ugo Bardi; Lianyong Feng; Xiongqi Pang

2010-01-01T23:59:59.000Z

328

Higher-order pair-conversion peaks in heavy-ion collisions  

Science Journals Connector (OSTI)

We analyze quantum electrodynamic pair creation from vibrating nuclear quasimolecules which may occur in collisions of heavy ions. We find that higher-order processes, which can be phenomenologically relevant for sufficiently long lived systems, can result in coincident narrow peaks even for subcritical systems. The Z dependence of the energy of the peaks can be much softer than that predicted for positrons from sparking of the vacuum. Our results may be relevant to peaks which have been observed at the Gesellschaft fr Schwerionenforschung (GSI).

Denis Carrier and Lawrence M. Krauss

1988-09-01T23:59:59.000Z

329

BENCH-SCALE STEAM REFORMING OF ACTUAL TANK 48H WASTE  

SciTech Connect

Fluidized Bed Steam Reforming (FBSR) has been demonstrated to be a viable technology to remove >99% of the organics from Tank 48H simulant, to remove >99% of the nitrate/nitrite from Tank 48H simulant, and to form a solid product that is primarily carbonate based. The technology was demonstrated in October of 2006 in the Engineering Scale Test Demonstration Fluidized Bed Steam Reformer1 (ESTD FBSR) at the Hazen Research Inc. (HRI) facility in Golden, CO. The purpose of the Bench-scale Steam Reformer (BSR) testing was to demonstrate that the same reactions occur and the same product is formed when steam reforming actual radioactive Tank 48H waste. The approach used in the current study was to test the BSR with the same Tank 48H simulant and same Erwin coal as was used at the ESTD FBSR under the same operating conditions. This comparison would allow verification that the same chemical reactions occur in both the BSR and ESTD FBSR. Then, actual radioactive Tank 48H material would be steam reformed in the BSR to verify that the actual tank 48H sample reacts the same way chemically as the simulant Tank 48H material. The conclusions from the BSR study and comparison to the ESTD FBSR are the following: (1) A Bench-scale Steam Reforming (BSR) unit was successfully designed and built that: (a) Emulated the chemistry of the ESTD FBSR Denitration Mineralization Reformer (DMR) and Carbon Reduction Reformer (CRR) known collectively as the dual reformer flowsheet. (b) Measured and controlled the off-gas stream. (c) Processed real (radioactive) Tank 48H waste. (d) Met the standards and specifications for radiological testing in the Savannah River National Laboratory (SRNL) Shielded Cells Facility (SCF). (2) Three runs with radioactive Tank 48H material were performed. (3) The Tetraphenylborate (TPB) was destroyed to > 99% for all radioactive Bench-scale tests. (4) The feed nitrate/nitrite was destroyed to >99% for all radioactive BSR tests the same as the ESTD FBSR. (5) The radioactive Tank 48H DMR product was primarily made up of soluble carbonates. The three most abundant species were thermonatrite, [Na{sub 2}CO{sub 3} {center_dot} H{sub 2}O], sodium carbonate, [Na{sub 2}CO{sub 3}], and trona, [Na{sub 3}H(CO{sub 3}){sub 2} {center_dot} 2H{sub 2}O] the same as the ESTD FBSR. (6) Insoluble solids analyzed by X-Ray Diffraction (XRD) did not detect insoluble carbonate species. However, they still may be present at levels below 2 wt%, the sensitivity of the XRD methodology. Insoluble solids XRD characterization indicated that various Fe/Ni/Cr/Mn phases are present. These crystalline phases are associated with the insoluble sludge components of Tank 48H slurry and impurities in the Erwin coal ash. The percent insoluble solids, which mainly consist of un-burnt coal and coal ash, in the products were 4 to 11 wt% for the radioactive runs. (7) The Fe{sup +2}/Fe{sub total} REDOX measurements ranged from 0.58 to 1 for the three radioactive Bench-scale tests. REDOX measurements > 0.5 showed a reducing atmosphere was maintained in the DMR indicating that pyrolysis was occurring. (8) Greater than 90% of the radioactivity was captured in the product for all three runs. (9) The collective results from the FBSR simulant tests and the BSR simulant tests indicate that the same chemistry occurs in the two reactors. (10) The collective results from the BSR simulant runs and the BSR radioactive waste runs indicates that the same chemistry occurs in the simulant as in the real waste. The FBSR technology has been proven to destroy the organics and nitrates in the Tank 48H waste and form the anticipated solid carbonate phases as expected.

Burket, P; Gene Daniel, G; Charles Nash, C; Carol Jantzen, C; Michael Williams, M

2008-09-25T23:59:59.000Z

330

Table 12. Coal Prices to Electric Generating Plants, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Coal Prices to Electric Generating Plants, Projected vs. Actual Coal Prices to Electric Generating Plants, Projected vs. Actual (nominal dollars per million Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 2.03 2.17 2.33 2.52 2.73 2.99 AEO 1983 1.99 2.10 2.24 2.39 2.57 2.76 4.29 AEO 1984 1.90 2.01 2.13 2.28 2.44 2.61 3.79 AEO 1985 1.68 1.76 1.86 1.95 2.05 2.19 2.32 2.49 2.66 2.83 3.03 AEO 1986 1.61 1.68 1.75 1.83 1.93 2.05 2.19 2.35 2.54 2.73 2.92 3.10 3.31 3.49 3.68 AEO 1987 1.52 1.55 1.65 1.75 1.84 1.96 2.11 2.27 2.44 3.55 AEO 1989* 1.50 1.51 1.68 1.77 1.88 2.00 2.13 2.26 2.40 2.55 2.70 2.86 3.00 AEO 1990 1.46 1.53 2.07 2.76 3.7 AEO 1991 1.51 1.58 1.66 1.77 1.88 1.96 2.06 2.16 2.28 2.41 2.57 2.70 2.85 3.04 3.26 3.46 3.65 3.87 4.08 4.33 AEO 1992 1.54 1.61 1.66 1.75 1.85 1.97 2.03 2.14 2.26 2.44 2.55 2.69 2.83 3.00 3.20 3.40 3.58 3.78 4.01 AEO 1993 1.92 1.54 1.61 1.70

331

Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts  

SciTech Connect

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles. Using data collected from Volts enrolled in The EV Project, this paper compares the real-world UF of two groups of Chevrolet Volts to estimated UF's based on J2841. The actual observed fleet utility factors (FUF) for the MY2011/2012 and MY2013 Volt groups studied were observed to be 72% and 74%, respectively. Using the EPA CD ranges, the method prescribed by J2841 estimates a FUF of 65% and 68% for the MY2011/2012 and MY2013 Volt groups, respectively. Volt drivers achieved higher percentages of distance traveled in EV mode for two reasons. First, they had fewer long-distance travel days than drivers in the national travel survey referenced by J2841. Second, they charged more frequently than the J2841 assumption of once per day - drivers of Volts in this study averaged over 1.4 charging events per day. Although actual CD range varied widely as driving conditions varied, the average CD ranges for the two Volt groups studied matched the EPA CD range estimates, so CD range variation did not affect FUF results.

John Smart; Thomas Bradley; Stephen Schey

2014-04-01T23:59:59.000Z

332

EA-1863: Vegetation Management on the Glen Canyon-Pinnacle Peak  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

63: Vegetation Management on the Glen Canyon-Pinnacle Peak 63: Vegetation Management on the Glen Canyon-Pinnacle Peak Transmission Lines Spanning the Coconino National Forest, Coconino County, Arizona EA-1863: Vegetation Management on the Glen Canyon-Pinnacle Peak Transmission Lines Spanning the Coconino National Forest, Coconino County, Arizona Summary DOE's Western Area Power Administration is preparing this EA to evaluate the environmental impacts of updating the vegetation management and right-of-way maintenance program for Western's Glen Canyon to Pinnacle Peak 345-kV transmission lines, which cross the Coconino National Forest, Coconino County, Arizona. For more information on this EA, contact: Ms. Linette King at: lking@wapa.gov. Public Comment Opportunities No public comment opportunities available at this time.

333

EA-1863: Vegetation Management on the Glen Canyon-Pinnacle Peak  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

3: Vegetation Management on the Glen Canyon-Pinnacle Peak 3: Vegetation Management on the Glen Canyon-Pinnacle Peak Transmission Lines Spanning the Coconino National Forest, Coconino County, Arizona EA-1863: Vegetation Management on the Glen Canyon-Pinnacle Peak Transmission Lines Spanning the Coconino National Forest, Coconino County, Arizona Summary DOE's Western Area Power Administration is preparing this EA to evaluate the environmental impacts of updating the vegetation management and right-of-way maintenance program for Western's Glen Canyon to Pinnacle Peak 345-kV transmission lines, which cross the Coconino National Forest, Coconino County, Arizona. For more information on this EA, contact: Ms. Linette King at: lking@wapa.gov. Public Comment Opportunities No public comment opportunities available at this time.

334

New Methods In Exploration At The Socorro Peak Kgra- A Gred Iii Project |  

Open Energy Info (EERE)

Methods In Exploration At The Socorro Peak Kgra- A Gred Iii Project Methods In Exploration At The Socorro Peak Kgra- A Gred Iii Project Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Conference Paper: New Methods In Exploration At The Socorro Peak Kgra- A Gred Iii Project Details Activities (6) Areas (1) Regions (0) Abstract: New Mexico Institute of Mining and Technology is investigating a Known Geothermal Resource Area in Socorro NM in attempts at locating a low temperature (65-100 °C) geothermal reservoir for direct-use heating on campus. The KGRA is positioned near the Socorro Peak mountain block, a Basin and Range normal-fault terrain superimposed by an Oligocene caldera margin. Preexisting evidence of this geothermal resource includes heat gradients upwards of 490mW/m2 from thermal-gradient wells, tepid spring

335

Peak Power Reduction Strategies for the Lighting Systems in Government Buildings  

E-Print Network (OSTI)

presents an approach developed to reduce the peak power demand in the lighting. The approach included optimum use of daylight, time of day control and delamping. The implementation of this approach for eight government buildings with occupancy of between 7...

Al-Nakib, D.; Al-Mulla, A. A.; Maheshwari, G. P.

2010-01-01T23:59:59.000Z

336

Univariate time-series forecasting of monthly peak demand of electricity in northern India  

Science Journals Connector (OSTI)

This study forecasts the monthly peak demand of electricity in the northern region of India using univariate time-series techniques namely Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) and Holt-Winters Multiplicative Exponential Smoothing (ES) for seasonally unadjusted monthly data spanning from April 2000 to February 2007. In-sample forecasting reveals that the MSARIMA model outperforms the ES model in terms of lower root mean square error, mean absolute error and mean absolute percent error criteria. It has been found that ARIMA (2, 0, 0) (0, 1, 1)12 is the best fitted model to explain the monthly peak demand of electricity, which has been used to forecast the monthly peak demand of electricity in northern India, 15 months ahead from February 2007. This will help Northern Regional Load Dispatch Centre to make necessary arrangements a priori to meet the future peak demand.

Sajal Ghosh

2008-01-01T23:59:59.000Z

337

Redesigning experimental equipment for determining peak pressure in a simulated tank car transfer line  

E-Print Network (OSTI)

When liquids are transported from storage tanks to tank cars, improper order of valve openings can cause pressure surges in the transfer line. To model this phenomenon and predict the peak pressures in such a transfer line, ...

Diaz, Richard A

2007-01-01T23:59:59.000Z

338

On The Portents of Peak Oil (And Other Indicators of Resource Scarcity)  

E-Print Network (OSTI)

Although economists have studied various indicators of resource scarcity (e.g., unit cost, resource rent, and market price), the phenomenon of peaking has largely been ignored due to its connection to non-economic theories ...

Smith, James L.

339

Demand response: a strategy to address residential air-conditioning peak load in Australia  

Science Journals Connector (OSTI)

Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year. Residential air-conditioning is widely believed to be the prime cause of...

Robert Smith; Ke Meng; Zhaoyang Dong

2013-12-01T23:59:59.000Z

340

Discovery and geology of the Desert Peak geothermal field: a case history. Bulletin 97  

SciTech Connect

A case history of the exploration, development (through 1980), and geology of the Desert Peak geothermal field is presented. Sections on geochemistry, geophysics, and temperature-gradient drilling are included.

Benoit, W.R.; Hiner, J.E.; Forest, R.T.

1982-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

E-Print Network 3.0 - ag peaks disappear Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

The Journal of Physical Chemistry C is published by the American Chemical Summary: to the formation of oxide species for Pt and Ag. However, after several cycles, this peak...

342

Response of Professional Societies and Conservation Organizations to Peak Oil and Economic Growth  

Science Journals Connector (OSTI)

Peaking of the worlds oil supply is resulting in economic, social, ... way to live and is utterly dependent on oil. Addressing current environmental problems is already a ... up their efforts to address global i...

David L. Trauger; Rhonda D. Jackson

2014-01-01T23:59:59.000Z

343

The Formation of ASPO and the Growing Influence of the Peak Oil Community  

Science Journals Connector (OSTI)

The first question to be asked is why nobodly noticed the peak oil issue before? Well, in fact, people ... students Al-Jarri and Al-Fattah who plotted oil and gas production of every country using ... past decade...

Charles A. S. Hall; Carlos A. Ramrez-Pascualli

2013-01-01T23:59:59.000Z

344

Changes in measured lightning return stroke peak current after the 1994 National Lightning Detection Network upgrade  

E-Print Network (OSTI)

Since a comprehensive upgrade of the US National Lightning Detection Network (NLDN) in 1994, the mean peak current of detected cloud-to-ground (CG) lightning flashes has decreased, the number of detected flashes has increased, and the percentage...

Wacker, Robert Scott

2012-06-07T23:59:59.000Z

345

Using Compressed Air Efficiency Projects to Reduce Peak Industrial Electric Demands: Lessons Learned  

E-Print Network (OSTI)

"To help customers respond to the wildly fluctuating energy markets in California, Pacific Gas & Electric (PG&E) initiated an emergency electric demand reduction program in October 2000 to cut electric use during peak periods. One component...

Skelton, J.

346

Phase-Change Frame Walls (PCFWs) for Peak Demand Reduction, Load Shifting, Energy Conservation and Comfort  

E-Print Network (OSTI)

) for lowering peak heat transfer rates across walls of residential and small commercial buildings. A PCFW is a typical wall in which phase change materials (PCMs) have been incorporated via macroencapsulation to enhance the energy storage capabilities...

Medina, M.; Stewart, R.

347

Webinar February 17: Material Handling Fuel Cells for Building Electric Peak Shaving Applications  

Energy.gov (U.S. Department of Energy (DOE))

The Fuel Cell Technologies Office will present a live webinar entitled "Material Handling Fuel Cells for Building Electric Peak Shaving Applications" on Tuesday, February 17, from 12 to 1 p.m. Eastern Standard Time.

348

College of Engineering Fall 2010 PEAK Local Situational Awareness (LSA) System for Department  

E-Print Network (OSTI)

PENNSTATE College of Engineering Fall 2010 PEAK Local Situational Awareness (LSA) System and create a working prototype that has the ability to retrieve text, audio, still photos and videos

Demirel, Melik C.

349

Predicted Versus Actual Savings for a Low-Rise Multifamily Retrofit in Boulder, Colorado  

SciTech Connect

To determine the most cost-effective methods of improving buildings, accurate analysis and prediction of the energy use of existing buildings is essential. However, multiple studies confirm that analysis methods tend to over-predict energy use in poorly insulated, leaky homes and thus, the savings associated with improving those homes. In NREL's report titled 'Assessing and Improving the Accuracy of Energy Analysis of Residential Buildings,' researchers propose a method for improving the accuracy of residential energy analysis methods. A key step in this process involves the comparisons of predicted versus metered energy use and savings. In support of this research need, CARB evaluated the retrofit of a multifamily building in Boulder, CO. The updated property is a 37 unit, 2 story apartment complex built in 1950, which underwent renovations in early 2009 to bring it into compliance with Boulder, CO's SmartRegs ordinance. Goals of the study were to: 1) evaluate predicted versus actual savings due to the improvements, 2) identify areas where the modeling assumptions may need to be changed, and 3) determine common changes made by renters that would negatively impact energy savings. In this study, CARB seeks to improve the accuracy of modeling software while assessing retrofit measures to specifically determine which are most effective for large multifamily complexes in the cold climate region. Other issues that were investigated include the effects of improving building efficiency on tenant comfort, the impact on tenant turnover rates, and the potential market barriers for this type of community scale project.

Arena, L.; Williamson, J.

2013-11-01T23:59:59.000Z

350

Temperature evolution of the spectral peak in high-temperature superconductors  

Science Journals Connector (OSTI)

Recent photoemission data in the high-temperature cuprate superconductor Bi2212 have been interpreted in terms of a sharp spectral peak with a temperature-independent lifetime, whose weight strongly decreases upon heating. By a detailed analysis of the data, we are able to extract the temperature dependence of the electron self-energy, and demonstrate that this interpretation is misleading. Rather, the spectral peak loses its integrity above Tc due to a large reduction in the electron lifetime.

M. R. Norman; A. Kaminski; J. Mesot; J. C. Campuzano

2001-03-22T23:59:59.000Z

351

Determination of a peak benzene exposure to consumers at typical self-service gasoline stations  

E-Print Network (OSTI)

DETERMINATION OF A PEAK BENZENE EXPOSURE TO CONSUMERS AT TYPICAL SELF-SERVICE GASOLINE STATIONS A Thesis by TED CARAPEZZA Submitted to the Graduate College of Texas A8M University in Partial fulfillment of the requirement for the degree... of MASTER OF SCIENCE December 1977 Major Subject: Industrial Hygiene DETERMINATION OF A PEAK BENZENE EXPOSURE TO CONSUMERS AT TYPICAL SELF-SERVICE GASOLINE STATIONS A Thesis by TED CARAPEZZA Approved as to style and content by: (. (iL, &? Chairman...

Carapezza, Ted

2012-06-07T23:59:59.000Z

352

The Fermi blazars' divide based on the diagnostic of the SEDs peak frequencies  

E-Print Network (OSTI)

We have studied the quasi-simultaneous Spectral Energy Distributions (SED) of 48 LBAS blazars, detected within the three months of the LAT Bright AGN Sample (LBAS) data taking period, combining Fermi and Swift data with radio NIR-Optical and hard-X/gamma-ray data. Using these quasi-simultaneous SEDs, sampling both the low and the high energy peak of the blazars broad band emission, we were able to apply a diagnostic tool based on the estimate of the peak frequencies of the synchrotron (S) and Inverse Compton (IC) components. Our analysis shows a Fermi blazars' divide based on the peak frequencies of the SED. The robust result is that the Synchrotron Self Compton (SSC) region divides in two the plane were we plot the peak frequency of the synchrotron SED vs the typical Lorentz factor of the electrons most contributing to the synchrotron emission and to the inverse Compton process. Objects within or below this region, radiating likely via the SSC process, are high-frequency-peaked BL Lac object (HBL), or low/in...

Tramacere, A; Giommi, P; Mazziotta, N; Monte, C

2010-01-01T23:59:59.000Z

353

Views on peak oil and its relation to climate change policy  

Science Journals Connector (OSTI)

Definitions of fossil fuel reserves and resources and assessed stock data are reviewed and clarified. Semantics explain a large stake of conflict between advocate and critical voices on peak oil. From a holistic sourcessinks perspective, limited carrying capacity of atmospheric sinks, not absolute scarcity in oil resources, will impose tight constraints on oil use. Eventually observed peaks in oil production in nearby years will result from politically imposed limits on carbon emissions, and not be caused by physical lack of oil resources. Peak-oil belief induces passive climate policy attitudes when suggesting carbon dioxide emissions will peak naturally linked to dwindling oil supplies. Active policies for reducing emissions and use of fossil fuels will also encompass higher energy end-use prices. Revenues obtained from higher levies on oil use can support financing energy efficiency and renewable energy options. But when oil producers charge the higher prices they can pump new oil for many decades, postponing peak oil to occur while extending carbon lock-in.

Aviel Verbruggen; Mohamed Al Marchohi

2010-01-01T23:59:59.000Z

354

A physical model for active galactic nuclei with double-peaked broad emission lines  

E-Print Network (OSTI)

The double-peaked broad emission lines are usually thought to be linked to accretion disks, however, the local viscous heating in the line-emitting disk portion is usually insufficient for the observed double-peaked broad-line luminosity in most sources. Our calculations show that only a small fraction (line-emitting disk portion, because the solid angle of the outer disk portion subtended to the inner region of the RIAF is too small. We propose that only those AGNs with sufficient matter above the disk (slowly moving jets or outflows) can scatter enough photons radiated from the inner disk region to the outer line-emitting disk portion. Our model predicts a power-law r-dependent line emissivity with an index ~2.5, which is consistent with \\beta~2-3 required by the model fittings for double-peaked line profiles. Using a sample of radio-loud double-peaked line emitters, we show that the outer disk regions can be efficiently illuminated by the photons scattered from the electron-positron jets with \\gamma_jline is present in strong radio quasars with relativistic jets. For radio-quiet counterparts, slow outflows with Thomson scattering depth ~0.2 can scatter sufficient photons to the line-emitting regions. This model can therefore solve the energy budget problem for double-peaked line emitters.

Xinwu Cao; Ting-Gui Wang

2006-07-19T23:59:59.000Z

355

Peak Oil Netherlands Foundation (PONL) was founded in May 2005 by a group of citizens who are concerned about the effects of a premature peak in oil and other fossil fuels production. The main aims of  

E-Print Network (OSTI)

#12;Peak Oil Netherlands Foundation (PONL) was founded in May 2005 by a group of citizens who are concerned about the effects of a premature peak in oil and other fossil fuels production. The main aims of this report, the other people in the Peak Oil Netherlands Foundation for their work, peakoil.com & the oildrum

Keeling, Stephen L.

356

Micro-Earthquake At Desert Peak Geothermal Area (2011) | Open Energy  

Open Energy Info (EERE)

Desert Peak Geothermal Area Desert Peak Geothermal Area (2011) Exploration Activity Details Location Desert Peak Geothermal Area Exploration Technique Micro-Earthquake Activity Date 2011 Usefulness not indicated DOE-funding Unknown Exploration Basis Determine seismicity before and after reservoir stimulation for EGS Notes The overall goal is to gather high resolution seismicity data before, during and after stimulation activities at the EGS projects. This will include both surface and borehole deployments (as necessary in available boreholes) to provide high quality seismic data for improved processing and interpretation methodologies. This will allow the development and testing of seismic methods for understanding the performance of the EGS systems, as well as aid in developing induced seismicity mitigation techniques that can

357

Have we run out of oil yet? Oil Peaking analysis from an optimist's perspective  

NLE Websites -- All DOE Office Websites (Extended Search)

4 4 (2006) 515-531 Have we run out of oil yet? Oil peaking analysis from an optimist's perspective $ David L. Greene à , Janet L. Hopson, Jia Li Oak Ridge National Laboratory, National Transportation Research Center, University of Tennessee, 2360 Cherahala Boulevard, Knoxville, TN 37932, USA Available online 27 December 2005 Abstract This study addresses several questions concerning the peaking of conventional oil production from an optimist's perspective. Is the oil peak imminent? What is the range of uncertainty? What are the key determining factors? Will a transition to unconventional oil undermine or strengthen OPEC's influence over world oil markets? These issues are explored using a model combining alternative world energy scenarios with an accounting of resource depletion and a market-based simulation of transition to unconventional oil resources. No political or

358

PEAK FLUX DISTRIBUTIONS OF SOLAR RADIO TYPE-I BURSTS FROM HIGHLY RESOLVED SPECTRAL OBSERVATIONS  

SciTech Connect

Solar radio type-I bursts were observed on 2011 January 26 by high resolution observations with the radio telescope AMATERAS in order to derive their peak flux distributions. We have developed a two-dimensional auto burst detection algorithm that can distinguish each type-I burst element from complex noise storm spectra that include numerous instances of radio frequency interference (RFI). This algorithm removes RFI from the observed radio spectra by applying a moving median filter along the frequency axis. Burst and continuum components are distinguished by a two-dimensional maximum and minimum search of the radio dynamic spectra. The analysis result shows that each type-I burst element has one peak flux without double counts or missed counts. The peak flux distribution of type-I bursts derived using this algorithm follows a power law with a spectral index between 4 and 5.

Iwai, K. [Nobeyama Solar Radio Observatory, National Astronomical Observatory of Japan, Nobeyama, Nagano 384-1305 (Japan); Masuda, S.; Miyoshi, Y. [Solar-Terrestrial Environment Laboratory, Nagoya University, Nagoya, Aichi 464-8601 (Japan); Tsuchiya, F.; Morioka, A.; Misawa, H., E-mail: kazumasa.iwai@nao.ac.jp [Planetary Plasma and Atmospheric Research Center, Tohoku University, Sendai, Miyagi 980-8578 (Japan)

2013-05-01T23:59:59.000Z

359

Initial increase, ''peaking effect'', in the internal friction of copper following pulsed neutron and electron irradiation  

SciTech Connect

Under certain experimental conditions the internal friction in metals can first increase and following prolonged irradiation decrease. Many models have been proposed to account for this ''peaking effect''; however, in many of the cases, no effort is made to distinguish between the influence of interstitials and/or vacancies. To determine the nature of the point defect responsible for the peaking effect in high purity copper, we have performed a series of pulsed irradiations using neutrons and electrons. In all of the experiments an initial very rapid rise in the internal friction and Young's modulus was observed. These data show that a fast diffusing defect is responsible for the peaking effect: i.e. the interstitial.

Simpson, H.M.; Parkin, D.M.; Goldstone, J.A.; Hemsky, J.W.

1985-01-01T23:59:59.000Z

360

A DOUBLE-PEAKED OUTBURST OF A 0535+26 OBSERVED WITH INTEGRAL, RXTE, AND SUZAKU  

SciTech Connect

The Be/X-ray binary A 0535+26 showed a normal (type I) outburst in 2009 August. It is the fourth in a series of normal outbursts associated with the periastron, but is unusual because it presented a double-peaked light curve. The two peaks reached a flux of {approx}450 mCrab in the 15-50 keV range. We present results of the timing and spectral analysis of INTEGRAL, RXTE, and Suzaku observations of the outburst. The energy-dependent pulse profiles and their evolution during the outburst are studied. No significant differences with respect to other normal outbursts are observed. The centroid energy of the fundamental cyclotron line shows no significant variation during the outburst. A spectral hardening with increasing luminosity is observed. We conclude that the source is accreting in the sub-critical regime. We discuss possible explanations for the double-peaked outburst.

Caballero, I. [Laboratoire AIM, CEA/IRFU, CNRS/INSU, Universite Paris Diderot, CEA DSM/IRFU/SAp, F-91191 Gif-sur-Yvette (France); Pottschmidt, K.; Marcu, D. M. [Center for Space Science and Technology, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 (United States); Barragan, L.; Wilms, J.; Kreykenbohm, I. [Dr. Karl Remeis-Sternwarte and ECAP, FAU Erlangen-Nuremberg, Sternwartstr. 7, D-96049 Bamberg (Germany); Ferrigno, C. [ISDC Data Centre for Astrophysics, University of Geneva, Chemin d'Ecogia 16, CH-1290 Versoix (Switzerland); Klochkov, D.; Suchy, S.; Santangelo, A.; Staubert, R. [Institut fuer Astronomie und Astrophysik, Sand 1, D-72076 Tuebingen (Germany); Zurita Heras, J. A. [Francois Arago Centre, APC (UMR 7164 Universite Paris Diderot, CNRS/IN2P3, CEA/DSM, Observatoire de Paris), 13 rue Watt, F-75205 Paris Cedex 13 (France); Kretschmar, P. [European Space Astronomy Centre (ESA/ESAC), Science Operations Department, Villanueva de la Canada, E-28080 Madrid (Spain); Fuerst, F. [Space Radiation Lab, California Institute of Technology, MC 290-17 Cahill, 1200 E. California Blvd., Pasadena, CA 91125 (United States); Rothschild, R. [Center for Astrophysics and Space Science, UCSD, La Jolla, CA 92093 (United States); Finger, M. H. [National Space Science and Technology Center, 320 Sparkman Drive NW, Huntsville, AL 35805 (United States); Camero-Arranz, A. [Institut de Ciencies de l'Espai (IEEC-CSIC), Campus UAB, Fac. de Ciencies, Torre C5, parell, 2a planta, E-08193 Barcelona (Spain); Makishima, K. [Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033 (Japan); Enoto, T. [Cosmic Radiation Laboratory, RIKEN, 2-1, Hirosawa, Wako City, Saitama 351-0198 (Japan); Iwakiri, W., E-mail: isabel.caballero@cea.fr [Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura, Saitama 338-8570 (Japan); and others

2013-02-20T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Estimating coal production peak and trends of coal imports in China  

SciTech Connect

More than 20 countries in the world have already reached a maximum capacity in their coal production (peak coal production) such as Japan, the United Kingdom and Germany. China, home to the third largest coal reserves in the world, is the world's largest coal producer and consumer, making it part of the Big Six. At present, however, China's coal production has not yet reached its peak. In this article, logistic curves and Gaussian curves are used to predict China's coal peak and the results show that it will be between the late 2020s and the early 2030s. Based on the predictions of coal production and consumption, China's net coal import could be estimated for coming years. This article also analyzes the impact of China's net coal import on the international coal market, especially the Asian market, and on China's economic development and energy security. 16 refs., 5 figs., 6 tabs.

Bo-qiang Lin; Jiang-hua Liu [Xiamen University, Xiamen (China). China Center for Energy Economics Research (CCEER)

2010-01-15T23:59:59.000Z

362

MEPSA: a flexible peak search algorithm designed for uniformly spaced time series  

E-Print Network (OSTI)

We present a novel algorithm aimed at identifying peaks within a uniformly sampled time series affected by uncorrelated Gaussian noise. The algorithm, called "MEPSA" (multiple excess peak search algorithm), essentially scans the time series at different timescales by comparing a given peak candidate with a variable number of adjacent bins. While this has originally been conceived for the analysis of gamma-ray burst light (GRB) curves, its usage can be readily extended to other astrophysical transient phenomena, whose activity is recorded through different surveys. We tested and validated it through simulated featureless profiles as well as simulated GRB time profiles. We showcase the algorithm's potential by comparing with the popular algorithm by Li and Fenimore, that is frequently adopted in the literature. Thanks to its high flexibility, the mask of excess patterns used by MEPSA can be tailored and optimised to the kind of data to be analysed without modifying the code. The C code is made publicly availabl...

Guidorzi, C

2015-01-01T23:59:59.000Z

363

Core Analysis At Desert Peak Area (Laney, 2005) | Open Energy Information  

Open Energy Info (EERE)

Core Analysis At Desert Peak Area (Laney, 2005) Core Analysis At Desert Peak Area (Laney, 2005) Exploration Activity Details Location Desert Peak Area Exploration Technique Core Analysis Activity Date Usefulness not indicated DOE-funding Unknown Notes Remote Sensing for Exploration and Mapping of Geothermal Resources, Wendy Calvin, 2005. Task 1: Detailed analysis of hyperspectral imagery obtained in summer of 2003 over Brady's Hot Springs region was completed and validated (Figure 1). This analysis provided a local map of both sinter and tufa deposits surrounding the Ormat plant, identified fault extensions not previously recognized from field mapping and has helped constrain where to put additional wells that were drilled at the site. Task 2: Initial analysis of Landsat and ASTER data for Buffalo Valley and Pyramid Lake was

364

The Building Energy Report Card is used to compare the actual annual energy consumption of buildings to a  

E-Print Network (OSTI)

The Building Energy Report Card is used to compare the actual annual energy consumption Thermal Unit (Btu). For convenience, this annual energy consumption is expressed as thousands of Btus (i of buildings to a State of Minnesota "target." This target represents the amount of energy that would

Ciocan-Fontanine, Ionut

365

General Project Sequence The following are typical steps on many projects. Actual required steps may vary from project to project  

E-Print Network (OSTI)

General Project Sequence The following are typical steps on many projects. Actual required steps may vary from project to project depending upon the scope, complexity, and specific features. Time periods indicated will vary depending on the nature of the project and needs of the user group

Mather, Patrick T.

366

An experimental and computational leakage investigation of labyrinth seals with rub grooves of actual size and shape  

E-Print Network (OSTI)

to that of a modified convex wall geometry. The test facility is a 33 times enlargement of the actual seal. The pressure drop leakage rate and flow visualization digital images for the standard geometry seal were measured at various Reynolds numbers...

Ambrosia, Matthew Stanley

2001-01-01T23:59:59.000Z

367

R-Process Freezeout, Nuclear Deformation, and the Rare-Earth Element Peak  

E-Print Network (OSTI)

We use network calculations of r-process nucleosynthesis to explore the origin of the peak in the solar r-process abundance distribution near nuclear mass number A = 160. The peak is due to a subtle interplay of nuclear deformation and beta decay, and forms not in the steady phase of the r-process, but only just prior to freezeout, as the free neutrons rapidly disappear. Its existence should therefore help constrain the conditions under which the r-process occurs and freezes out.

R. Surman; J. Engel; J. R. Bennett; B. S. Meyer

1997-01-03T23:59:59.000Z

368

Automated method for the systematic interpretation of resonance peaks in spectrum data  

DOE Patents (OSTI)

A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.

Damiano, Brian (Knoxville, TN); Wood, Richard T. (Knoxville, TN)

1997-01-01T23:59:59.000Z

369

PowerHerd: Dynamic Satisfaction of Peak Power Constraints in Interconnection Networks  

E-Print Network (OSTI)

. Cooling costs can be pro- hibitive (consider the high power budgets of industry-standard six- foot serverPowerHerd: Dynamic Satisfaction of Peak Power Constraints in Interconnection Networks Li Shang Li,peh,jha}@ee.princeton.edu Niraj K. Jha ABSTRACT Power consumption is a critical issue in interconnection network design, driven

Shang, Li

370

The last breath of the young gigahertz-peaked spectrum radio source PKS 1518+047  

Science Journals Connector (OSTI)

......observed on 2008 March 5 (project code BD129) with the VLBA...carried out on 2001 March 28 (project code BS085). The data reduction...Peak, Mauna Kea and North Liberty had erratic system temperatures...obtained on 1998 August 22 (project code AS637). The data reduction......

M. Orienti; M. Murgia; D. Dallacasa

2010-03-01T23:59:59.000Z

371

Origin of the zero-bias conductance peaks observed ubiquitously in high-T-c superconductors  

E-Print Network (OSTI)

principal axes orientations. They can Five rise to a zero-bias conductance peak (ZBCP) in quasiparticle tunneling along any axis as shown in our model calculation. When the counterelectrode is a low-T-c SC, its gap is shown to appear as a dip at the center...

Hu, Chia-Ren.

1998-01-01T23:59:59.000Z

372

Peaking profiles for achieving long-term temperature targets with more likelihood at lower costs  

Science Journals Connector (OSTI)

...parties take full advantage of the flexible...12) and the energy model...included the solar forcings according...last 22 years (solar) and 100...MAC curves of energy- and industry-related...data from the Energy Modeling...indicate that a disadvantage of the peaking...

Michel G. J. den Elzen; Detlef P. van Vuuren

2007-01-01T23:59:59.000Z

373

A Model for Predicting Daily Peak Visitation and Implications for Recreation Management and Water Quality: Evidence  

E-Print Network (OSTI)

A Model for Predicting Daily Peak Visitation and Implications for Recreation Management and Water carrying capacity. Keywords Visitation model Á Recreation management Á Water quality Á River visitation Á Clark, Fort Collins, Colorado 80523, USA 123 Environmental Management DOI 10.1007/s00267-008-9079-5 #12

374

(2013) 128 Data Center Demand Response: Avoiding the Coincident Peak via  

E-Print Network (OSTI)

(2013) 1­28 Data Center Demand Response: Avoiding the Coincident Peak via Workload Shifting.chen@hp.com Abstract Demand response is a crucial aspect of the future smart grid. It has the potential to provide centers' participation in demand response is becoming increasingly important given their high

Wierman, Adam

375

OG&E Uses Time-Based Rate Program to Reduce Peak Demand  

Office of Environmental Management (EM)

46.0kWh 6 Critical Peak Event 46.0kWh 46.0kWh 7 (included in the above) Demand Response to Time-Based Rates The figure below shows 24-hour load profiles for the average...

376

Smart Operations of Air-Conditioning and Lighting Systems in Government Buildings for Peak Power Reduction  

E-Print Network (OSTI)

During the summer 2007 smart operation strategies for air-conditioning (A/C) and lighting systems were developed and tested in a number of governmental buildings in Kuwait as one of the solutions to reduce the national peak demand for electrical...

Al-Hadban, Y.; Maheshwari, G. P.; Al-Nakib, D.; Al-Mulla, A.; Alasseri, R.

377

Lightning morphology and impulse charge moment change of high peak current negative strokes  

E-Print Network (OSTI)

with two unusual flash types that both initially develop as positive (normal) intracloud lightning currents, and how is this variability connected to the in- cloud structure of lightning flashes? [3Lightning morphology and impulse charge moment change of high peak current negative strokes Gaopeng

Cummer, Steven A.

378

Neutron scattering evidence of a boson peak in protein hydration water Alessandro Paciaroni,1  

E-Print Network (OSTI)

Neutron scattering evidence of a boson peak in protein hydration water Alessandro Paciaroni,1 Anna Viterbo, Italy Received 24 February 1999 Measurement of the low temperature neutron excess of scattering, has been detected by neutron scattering and Raman spectros- copy in a large variety of glassy systems

Tuscia, Università Degli Studi Della

379

Duct Leakage Impacts on Airtightness, Infiltration, and Peak Electrical Demand in Florida Homes  

E-Print Network (OSTI)

return leak from the attic can increase cooling electrical demand by 100%. Duct repairs in a typical. electrically heated Florida home reduce winter peak demand by about 1.6 kW per house at about one-sixth the cost of building new electrical generation...

Cummings, J. B.; Tooley, J. J.; Moyer, N.

1990-01-01T23:59:59.000Z

380

First Tracer Test After Circulation in Desert Peak 27-15  

DOE Data Explorer (OSTI)

Following the successful stimulation of Desert Peak target EGS well 27-15, a circulation test was initiated by injecting a conservative tracer (1,5-nds) in combination with a reactive tracer (7-amino-1,3-naphthalene disulfonate). The closest production well 74-21 was monitored over the subsequent several months.

Peter Rose

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Contribution of Peaks of Virus Load to Simian Immunodeficiency Virus Pathogenesis  

Science Journals Connector (OSTI)

...late peak in virus load. This indicates...the statistical power of this argument...measures the virus load exactly at its...underestimated in virus load data. We studied whether...higher statistical power than model 2...based on the same data (26). However...relation between virus load in plasma and survival...

Roland R. Regoes; Silvija I. Staprans; Mark B. Feinberg; Sebastian Bonhoeffer

2002-03-01T23:59:59.000Z

382

Stimulation at Desert Peak -modeling with the coupled THM code FEHM  

SciTech Connect

Numerical modeling of the 2011 shear stimulation at the Desert Peak well 27-15. This submission contains the FEHM executable code for a 64-bit PC Windows-7 machine, and the input and output files for the results presented in the included paper from ARMA-213 meeting.

sharad kelkar

2013-04-30T23:59:59.000Z

383

Inserting Test Points to Control Peak Power During Scan Testing Ranganathan Sankaralingam and Nur A. Touba  

E-Print Network (OSTI)

simply reducing the average power dissipation per clock cycle. Proceedings of the 17th IEEE International. The average power dissipation during scan testing can be controlled by reducing the scan frequency. However, the peak power during scan testing cannot be controlled by reducing clock frequency and hence is more

Touba, Nur A.

384

Future world oil production: Growth, plateau, or peak?1 Larry Hughes and Jacinda Rudolph  

E-Print Network (OSTI)

Energy Systems 2010 #12;Future world oil production: Growth, plateau, or peak? Larry Hughes2 and Jacinda governments to reduce their energy intensity (6), the growth in oil production resumed in the mid-1980s World Energy Outlook, production is projected to increase to 103.8 million barrels of oil a day by 2030

Hughes, Larry

385

Author's personal copy Synergistic roles of off-peak electrolysis and thermochemical  

E-Print Network (OSTI)

, Ontario, Canada L8S 4K1 a r t i c l e i n f o Article history: Received 10 June 2008 Received in revised, but electrolysis can take advantage of low electricity prices during off-peak hours, as well as intermittent and de million tonnes per year by 2023. In Alberta alone, oil sands development is requiring huge quantities

Naterer, Greg F.

386

Demonstration of Smart Building Controls to Manage Building Peak Loads: Innovative Non-Wires Technologies  

SciTech Connect

As a part of the non-wires solutions effort, BPA in partnership with Pacific Northwest National Laboratory (PNNL) is exploring the use of two distributed energy resources (DER) technologies in the City of Richland. In addition to demonstrating the usefulness of the two DER technologies in providing peak demand relief, evaluation of remote direct load control (DLC) is also one of the primary objectives of this demonstration. The concept of DLC, which is used to change the energy use profile during peak hours of the day, is not new. Many utilities have had success in reducing demand at peak times to avoid building new generation. It is not the need for increased generation that is driving the use of direct load control in the Northwest, but the desire to avoid building additional transmission capacity. The peak times at issue total between 50 and 100 hours a year. A transmission solution to the problem would cost tens of millions of dollars . And since a ?non wires? solution is just as effective and yet costs much less, the capital dollars for construction can be used elsewhere on the grid where building new transmission is the only alternative. If by using DLC, the electricity use can be curtailed, shifted to lower use time periods or supplemented through local generation, the existing system can be made more reliable and cost effective.

Katipamula, Srinivas; Hatley, Darrel D.

2004-12-22T23:59:59.000Z

387

Driving Smart Growth: Electric Vehicle Adoption and OffPeak Electricity Rates  

E-Print Network (OSTI)

Driving Smart Growth: Electric Vehicle Adoption and OffPeak Electricity Rates Peter Driving Smart Growth: Electric Vehicle Adoption Page 2 Executive Summary Reducing our dependence to electric vehicles (EVs)1 is core to reducing reliance on fossil fuels and driving smart growth

Holsinger, Kent

388

First Tracer Test After Circulation in Desert Peak 27-15  

SciTech Connect

Following the successful stimulation of Desert Peak target EGS well 27-15, a circulation test was initiated by injecting a conservative tracer (1,5-nds) in combination with a reactive tracer (7-amino-1,3-naphthalene disulfonate). The closest production well 74-21 was monitored over the subsequent several months.

Rose, Peter

2013-11-16T23:59:59.000Z

389

Generation of high peak power pulse using 2 stage erbium-doped fiber amplifier  

E-Print Network (OSTI)

-doped fiber. For the second stage, two 1480nm pump lasers were used to pump erbium-doped fiber in both forward and backward propagating direction. The signal laser was modulated to produce pulses with high repetition rate high peak power. The first stage...

Lee, Kyung-Woo

2012-06-07T23:59:59.000Z

390

Origin of the first sharp diffraction peak in the structure factor of covalent glasses  

Science Journals Connector (OSTI)

A model is proposed for the first sharp diffraction peak (FSDP) in glasses. The FSDP is a chemical-order prepeak due to interstitial volume around cation-centered structural units. Calculated FSDP positions of some covalent glasses agree well with experiment, and the anomalous temperature and pressure dependences of the FSDP can be understood in terms of density effects.

S. R. Elliott

1991-08-05T23:59:59.000Z

391

TSNo s02-peak103534-O Effect of Sulfate on Lead Desorption from Goethite.  

E-Print Network (OSTI)

TSNo s02-peak103534-O Title Effect of Sulfate on Lead Desorption from Goethite. abstract metals such as lead. It has been shown that lead adsorption is enhanced on goethite in the presence for this increased adsorption is the formation of a ternary complex on the goethite surface. While mechanistic

Sparks, Donald L.

392

Peak discharge of a Pleistocene lava-dam outburst flood in Grand Canyon, Arizona, USA  

E-Print Network (OSTI)

produced the largest known flood on the Colorado River in Grand Canyon. The Hyaloclastite Dam was up to 366 Canyon; Colorado river; Pleistocene floods; Lava dams; Hydraulic modeling; Paleoflood indicators; DamPeak discharge of a Pleistocene lava-dam outburst flood in Grand Canyon, Arizona, USA Cassandra R

393

How avian nest site selection responds to predation risk: testing an `adaptive peak hypothesis'  

E-Print Network (OSTI)

How avian nest site selection responds to predation risk: testing an `adaptive peak hypothesis., Arcata, CA 95521, USA Summary 1. Nest predation limits avian fitness, so birds should favour nest sites that minimize predation risk. Nevertheless, preferred nest microhabitat features are often uncorrelated

394

Imminence of peak in US coal production and overestimation of reserves  

E-Print Network (OSTI)

. The estimated energy ultimate recoverable reserves (URR) from the logistic model is 2750 quadrillion BTU (2900, coal reserves, coal production forecast, peak coal, USA energy, non- linear fitting #12;3 1 reported coal reserves of any nation, containing approximately 28% of the world

Khare, Sanjay V.

395

On the Nonlinear Transfer of Energy in the Peak of a Gravity-Wave Spectrum. II  

Science Journals Connector (OSTI)

...Nonlinear Transfer of Energy in the Peak of a Gravity-Wave Spectrum. II M. J...nonlinear transfer of energy within a continuous spectrum of water waves. The spectrum is assumed...narrow, that is, the wave energy is initially concentrated...

1976-01-01T23:59:59.000Z

396

Appropriate Loads for Peak-Power During Resisted Sprinting on a Non-Motorized Treadmill  

E-Print Network (OSTI)

-motorized treadmill (Force 3.0, Woodway, Waukesha, WI, USA). Similar to session 2, this session was preceded by a dynamic warm-up involving calisthenics, submaximal walking, and submaximal jogging on the treadmill. Chia and Lim (2008) determined that peak power...

Andre, Matthew J.; Fry, Andrew C.; Lane, Michael T.

2013-10-08T23:59:59.000Z

397

Dear Speaker -  

Energy Savers (EERE)

Industrial Electricity Prices 2008 2012 USA 68MWh 66MWh Germany 130MWh 148MWh Japan 115MWh 194MWh France 104MWh 116MWh Source: OECD Electricity Statistics 2013...

398

MASS-ANGULAR-MOMENTUM RELATIONS IMPLIED BY MODELS OF TWIN PEAK QUASI-PERIODIC OSCILLATIONS  

SciTech Connect

Twin peak quasi-periodic oscillations (QPOs) appear in the X-ray power-density spectra of several accreting low-mass neutron star (NS) binaries. Observations of the peculiar Z-source Circinus X-1 display unusually low QPO frequencies. Using these observations, we have previously considered the relativistic precession (RP) twin peak QPO model to estimate the mass of the central NS in Circinus X-1. We have shown that such an estimate results in a specific mass-angular-momentum (M - j) relation rather than a single preferred combination of M and j. Here we confront our previous results with another binary, the atoll source 4U 1636-53 that displays the twin peak QPOs at very high frequencies, and extend the consideration to various twin peak QPO models. In analogy to the RP model, we find that these imply their own specific M - j relations. We explore these relations for both sources and note differences in the {chi}{sup 2} behavior that represent a dichotomy between high- and low-frequency sources. Based on the RP model, we demonstrate that this dichotomy is related to a strong variability of the model predictive power across the frequency plane. This variability naturally comes from the radial dependence of characteristic frequencies of orbital motion. As a consequence, the restrictions on the models resulting from observations of low-frequency sources are weaker than those in the case of high-frequency sources. Finally we also discuss the need for a correction to the RP model and consider the removing of M - j degeneracies, based on the twin peak QPO-independent angular momentum estimates.

Toeroek, Gabriel; Bakala, Pavel; Sramkova, Eva; Stuchlik, Zdenek; Urbanec, Martin; Goluchova, Katerina, E-mail: pavel.bakala@fpf.slu.cz, E-mail: martin.urbanec@fpf.slu.cz, E-mail: zdenek.stuchlik@fpf.slu.cz, E-mail: terek@volny.cz, E-mail: sram_eva@centrum.cz [Institute of Physics, Faculty of Philosophy and Science, Silesian University in Opava, Bezrucovo nam. 13, CZ-746 01 Opava (Czech Republic)

2012-12-01T23:59:59.000Z

399

Scenarios for a South African CSP Peaking System in the Short Term  

Science Journals Connector (OSTI)

Abstract The South African Integrated Resource Plan is a policy document, which by law allocates the energy resources that will be built to meet the future electricity needs of South Africa. The current Integrated Resource Plan indicates the electricity generation types that will be built from 2010 to 2030. It states that most of the future peak load will be met by Open Cycle Gas Turbines which operate using diesel and represents an allocation of 4,930M W. Further, the Integrated Resource Plan does not identify CSP as a potential peaking solution and allocates 1,200M W of capacity to CSP. This represents less than 2% of total capacity in 2030. This paper investigates the feasibility of utilizing CSP Plants as peaking plants in the short to medium term based on a proposition that under certain scenarios, a fleet of unsubsidized CSP peaking plants could drop the LCOE of the current Integrated Resource Plan. This is done by modeling a contemporary CSP tower system with Thermal Energy Storage. The Gemasolar CSP plant is used as the reference plant in order to obtain operating parameters. Our analysis suggests that at current fuels costs, diesel powered Open Cycle Gas Turbines produce electricity in excess of 5.08 ZAR/kWh (?0.63 US$/kWh), significantly above current CSP energy generating costs. This is the context that informed the undertaking of this study, to influence policy and provide technical evidence that CSP can guarantee and deliver energy at competitive costs in the short term. Two alternate scenarios show a lower LCOE for providing peak power. The most promising is a combined distributed CSP system wit h diesel powered Open Cycle Gas Turbine system as backup. The LCOE for this system is 2.78 ZAR (?0.34 $/kWh) or a drop of 45% when no fuel price inflation is considered. This system also increases security of supply due to a lower dependence on fuel prices.

C. Silinga; P. Gauch

2014-01-01T23:59:59.000Z

400

Actual Crimes Reported For: Offense Type (includes attempts) 2010 2011 2012 2010 2011 2012 2010 2011 2012  

E-Print Network (OSTI)

0 0 0 0 0 Referral 0 0 0 0 0 0 0 0 0 Drug Law Violations Arrest 0 3 4 0 1 0 0 4 4 Referral 0 0 0 0 0 0 0 0 0 Liquor Law Violations Arrest 0 0 0 0 0 0 0 0 0 Referral 0 0 0 0 0 0 0 0 0 OSU-Tulsa Campus Crime Statistics Act. Number of Arrests/Referrals for Select Offenses #12;Actual Crimes Reported For

Veiga, Pedro Manuel Barbosa

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Microsoft Word - BUGS_The Next Smart Grid Peak Resource Final 4_19.docx  

NLE Websites -- All DOE Office Websites (Extended Search)

April 15, 2010 April 15, 2010 DOE/NETL-2010/1406 Backup Generators (BUGS): The Next Smart Grid Peak Resource Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 ii DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or

402

Multiobjective demand side management solutions for utilities with peak demand deficit  

Science Journals Connector (OSTI)

Abstract Demand side management (DSM) is a growing concept around the world, particularly in urban India, recently due to presence of time of day (TOD) tariffs for the large commercial and industrial customers. Residential customers are not exposed to TOD tariff structure so far in India. This encourages commercial and industrial customers to schedule their flexible loads as per TOD tariff to extract maximum benefit of it and helps utilities to reduce their peak load demand and reshape the load curve. For efficient DSM implementation, this paper presents a multiobjective DSM solutions based on integer genetic algorithm to benefit both utilities and consumers. The proposed algorithm provides new directions on effective implementation of DSM techniques for Indian utilities. Simulations were carried out on Indian practical distribution system with large commercial and industrial loads. The simulation results of the proposed algorithm shows that the presented DSM technique comprehends reasonable savings to both utility and consumers simultaneously, while reducing the system peak.

Nandkishor Kinhekar; Narayana Prasad Padhy; Hari Om Gupta

2014-01-01T23:59:59.000Z

403

Non-Debye excess heat capacity and boson peak of binary lithium borate glasses  

Science Journals Connector (OSTI)

The non-Debye excess heat capacities of binary lithium borate glasses with different Li2O compositions of x=8, 14 and 22 (mol%) are investigated to understand origin of the boson peak. The low-temperature heat capacities are measured between 2 and 50K by a relaxation calorimeter. The experimental non-Debye heat capacities with x=14 is successfully reproduced using the excess vibrational density of states measured by inelastic neutron scattering. This finding indicates that the non-Debye heat capacities of lithium borate glasses originate from the excess vibrational density of states measureable by inelastic neutron scattering. Moreover, it is demonstrated that all of the excess heat capacity spectra lie on a single master curve by the scaling using boson peak temperature and intensity.

Yu Matsuda; Hitoshi Kawaji; Tooru Atake; Yasuhisa Yamamura; Shuma Yasuzuka; Kazuya Saito; Seiji Kojima

2011-01-01T23:59:59.000Z

404

Periodic transmission peaks in non-periodic disordered one-dimensional photonic structures  

E-Print Network (OSTI)

A better understanding of the optical properties of a device structure characterized by a random arrangement of materials with different dielectric properties at a length scale comparable to the wavelength of light is crucial for the realization of new optical and optoelectronic devices. Here we have studied the light transmission of disordered photonic structures made with two and three different materials, characterized by the same optical thickness. In their transmission spectra a formation of peaks, with a transmission of up to 75%, is evident. The spectral position of such peaks is very regular, which is a result of the constraint that all layers have the same optical thickness. This gives rise to a manifold of applications such as new types of bandpass filters and resonators for distributed feedback lasers.

Kriegel, Ilka

2015-01-01T23:59:59.000Z

405

Constraints on dark energy from baryon acoustic peak and galaxy cluster gas mass measurements  

E-Print Network (OSTI)

We use baryon acoustic peak measurements by Eisenstein et al. (2005) and Percival et al. (2007a) and galaxy cluster gas mass fraction measurements of Allen et al. (2008) to constrain parameters of three different dark energy models. For time-independent dark energy, the Percival et al. (2007a) constraints, which make use of the WMAP measurement of the apparent acoustic horizon angle, most effectively constrain a cosmological parameter close to spatial curvature and favor a close to spatially flat model. In a spatially-flat model the Percival et al. (2007a) data less effectively constrain time-varying dark energy. The joint baryon acoustic peak and galaxy cluster gas mass constraints are consistent with but tighter than those derived from other data. A time-independent cosmological constant in a spatially-flat model provides a good fit to the joint data, but slowly-evolving dark energy can not yet be ruled out.

Lado Samushia; Bharat Ratra

2008-06-17T23:59:59.000Z

406

Can an inhomogeneous metric be detected with the baryonic acoustic oscillation peak?  

E-Print Network (OSTI)

The scalar averaging approach to cosmology interprets dark energy as the growth of average, void-dominated, negative spatial curvature during the virialisation epoch, leaving the metric a priori unspecified, while models with a Friedmann-Lemaitre-Robertson-Walker (FLRW) metric assume comoving spatial rigidity of metrical properties. The former predicts that voids are hyperbolic and that superclusters occupy positively curved space, and that a best-fit metric should be close to the void case modelled as a constant-curvature metric on a given time slice. Thus, comoving separations near superclusters should be compressed in comparison to the homogeneous case. We demonstrate this by measuring the two-point auto-correlation function on comoving scales in order to detect shifts in the baryonic acoustic oscillation (BAO) peak location for Large Red Galaxy (LRG) pairs of the Sloan Digital Sky Survey Data Release 7. In tangential directions, subsets of pairs overlapping with superclusters or voids show the BAO peak. T...

Roukema, Boudewijn F; Ostrowski, Jan J; France, Martin J

2014-01-01T23:59:59.000Z

407

Gaussian Approximation of Peak Values in the Integrated Sachs-Wolfe Effect  

E-Print Network (OSTI)

The accelerating expansion of the universe at recent epochs is encoded in the cosmic microwave background: a few percent of the total temperature fluctuations are generated by evolving gravitational potentials which trace the large-scale structures in the universe. This signature of dark energy, the Integrated Sachs-Wolfe Effect, has been detected by averaging temperatures in the WMAP sky maps corresponding to the directions of superstructures in the Sloan Digital Sky Survey data release 6. We model the maximum average peak signal expected in the standard $\\Lambda$CDM cosmological model, using Gaussian random realizations of the microwave sky, including correlations between different physical contributions to the temperature fluctuations and between different redshift ranges of the evolving gravitational potentials. We find good agreement with the mean temperature peak amplitude from previous theoretical estimates based on large-scale structure simulations, but with larger statistical uncertainties. We apply ...

Aiola, Simone; Wang, Bingjie

2014-01-01T23:59:59.000Z

408

Radial Structure of Shell Modulations Near Peak Compression of Spherical Implosions  

SciTech Connect

The structure of shell modulations is measured at peak compression of directly driven spherical implosions using absorption of titanium-doped layers placed at various distances of 1, 5, 7, and 9 mm from the inner surface of 20-mm-thick plastic CH shells filled with 18 atm of D3He gas. The modulations are measured using the ratios of monochromatic core images taken inside and outside of the titanium 1s-2p absorption spectral region. Peak-compression, time-integrated areal-density modulations are higher at the inner shell surface, which is unstable during the deceleration phase of an implosion with a modulation level of 59{+-}14%, The perturbations are lower in the central part of the shell, having a modulation level of 18{+-}5%. The outer surface of the shell, which is unstable during the acceleration phase of an implosion, has a modulation level of 52{+-}20%.

Smalyuk, V.A.; Dumanis, S.B.; Marshall, F.J.; Delettrez, J.A.; Meyerhofer, D.D.; Regan, S.P.; Sangster, T.C.; Yaakobi, B.; Koch, J.A.

2003-03-11T23:59:59.000Z

409

Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large  

NLE Websites -- All DOE Office Websites (Extended Search)

Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Retail Building Title Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Retail Building Publication Type Report LBNL Report Number LBNL-59293 Year of Publication 2006 Authors Hotchi, Toshifumi, Alfred T. Hodgson, and William J. Fisk Keywords market sectors, technologies Abstract Mock Critical Peak Pricing (CPP) events were implemented in a Target retail store in the San Francisco Bay Area by shutting down some of the building's packaged rooftop air-handling units (RTUs). Measurements were made to determine how this load shedding strategy would affect the outdoor air ventilation rate and the concentrations of volatile organic compounds (VOCs) in the sales area. Ventilation rates prior to and during load shedding were measured by tracer gas decay on two days. Samples for individual VOCs, including formaldehyde and acetaldehyde, were collected from several RTUs in the morning prior to load shedding and in the late afternoon. Shutting down a portion (three of 11 and five of 12, or 27 and 42%) of the RTUs serving the sales area resulted in about a 30% reduction in ventilation, producing values of 0.50-0.65 air changes per hour. VOCs with the highest concentrations (>10 μg/m3) in the sales area included formaldehyde, 2-butoxyethanol, toluene and decamethylcyclopentasiloxane. Substantial differences in concentrations were observed among RTUs. Concentrations of most VOCs increased during a single mock CPP event, and the median increase was somewhat higher than the fractional decrease in the ventilation rate. There are few guidelines for evaluating indoor VOC concentrations. For formaldehyde, maximum concentrations measured in the store during the event were below guidelines intended to protect the general public from acute health risks.

410

Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large  

NLE Websites -- All DOE Office Websites (Extended Search)

Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Retail Building Title Indoor Air Quality Impacts of a Peak Load Shedding Strategy for a Large Retail Building Publication Type Report Year of Publication 2006 Authors Hotchi, Toshifumi, Alfred T. Hodgson, and William J. Fisk Publisher Lawrence Berkeley National Laboratory Abstract Mock Critical Peak Pricing (CPP) events were implemented in a Target retail store in the San Francisco Bay Area by shutting down some of the building's packaged rooftop air-handling units (RTUs). Measurements were made to determine how this load shedding strategy would affect the outdoor air ventilation rate and the concentrations of volatile organic compounds (VOCs) in the sales area. Ventilation rates prior to and during load shedding were measured by tracer gas decay on two days. Samples for individual VOCs, including formaldehyde and acetaldehyde, were collected from several RTUs in the morning prior to load shedding and in the late afternoon. Shutting down a portion (three of 11 and five of 12, or 27 and 42%) of the RTUs serving the sales area resulted in about a 30% reduction in ventilation, producing values of 0.50-0.65 air changes per hour. VOCs with the highest concentrations (>10 μg/m3) in the sales area included formaldehyde, 2-butoxyethanol, toluene and decamethylcyclopentasiloxane. Substantial differences in concentrations were observed among RTUs. Concentrations of most VOCs increased during a single mock CPP event, and the median increase was somewhat higher than the fractional decrease in the ventilation rate. There are few guidelines for evaluating indoor VOC concentrations. For formaldehyde, maximum concentrations measured in the store during the event were below guidelines intended to protect the general public from acute health risks

411

Sequence Stratigraphy and Detrital Zircon Geochronology of the Swan Peak Quartzite, Southeastern Idaho  

E-Print Network (OSTI)

environments (Webb, 1958; Ketner, 1968). Recent biostratigraphic data (Sweet, 2000) indicates that some of these quartzite units are Cincinnatian (451 ? 443.7 Ma; Gradstein et al., 2004) deposited during an extensive continental flooding of the Paleozoic... equivalents (e.g. Swan Peak Quartzite, Kinnikinic Quartzite, Mt. Wilson Formation, etc.) were deposited during the Cincinnatian (443.7 ? 451 Ma; Sweet, 2000; Gradstein et al., 2004). 6 FIG. 2.?Conodont biostratigraphy constrains the Ordovician...

Wulf, Tracy David

2012-02-14T23:59:59.000Z

412

Appendix A. Individual Evaluations of 30 Peak Discharges from 28 Extraordinary Floods in the United States  

E-Print Network (OSTI)

States #12;#12;Appendix A: Seco Creek 55 Location: This flood site is located at 29.4750 N and 99.3000 W,000 ft3 /s, as published in Crippen and Bue (1977). The rating is poor. Drainage area: 142 mi2 . Data by several gaging-station records in the area that show a major peak discharge occurring on or about May 31

413

The host galaxies of Compact Steep Spectrum and Gigahertz-Peaked Spectrum radio sources  

E-Print Network (OSTI)

I will review some of the developments in studies of the host galaxy properties of Compact Steep Spectrum (CSS) and GigaHertz-Peaked Spectrum (GPS) radio sources. In contrast to previous reviews structured around observational technique, I will discuss the host galaxy properties in terms of morphology, stellar content and warm gas properties and discuss how compact, young radio-loud AGN are key objects for understanding galaxy evolution.

J. Holt

2008-12-15T23:59:59.000Z

414

GEOSCIENCE INFORMATION SERVICES: Peak Performances - Proceedings of the 45th Meeting of the Geoscience Information Society  

E-Print Network (OSTI)

GEOSCIENCE INFORMATION SOCIETY GEOSCIENCE INFORMATION SERVICES: Peak Performances Proceedings ? Volume 41? 2010 Proceedings of the 45th Meeting of the Geoscience Information Society October 31-November 3, 2010 Denver, Colorado... of the papers provided in this proceedings volume were given at the 2010 Annual Joint Meeting of the Geoscience Information Society and the Geological Society of America (GSA) held in Denver, Colorado October 31-November 3, 2010. The papers are arranged...

GeoScience Information Society

2010-01-01T23:59:59.000Z

415

Testing the gamma-ray burst variability/peak luminosity correlation on a Swift homogeneous sample  

E-Print Network (OSTI)

We test the gamma-ray burst correlation between temporal variability and peak luminosity of the $\\gamma$-ray profile on a homogeneous sample of 36 Swift/BAT GRBs with firm redshift determination. This is the first time that this correlation can be tested on a homogeneous data sample. The correlation is confirmed, as long as the 6 GRBs with low luminosity (tested on low-luminosity GRBs. Our results show that these GRBs are definite outliers.

D. Rizzuto; C. Guidorzi; P. Romano; S. Covino; S. Campana; M. Capalbi; G. Chincarini; G. Cusumano; D. Fugazza; V. Mangano; A. Moretti; M. Perri; G. Tagliaferri

2007-04-19T23:59:59.000Z

416

Impact of Reflective Roofing on Cooling Electrical Use and Peak Demand in a Florida Retail Mall  

E-Print Network (OSTI)

on Energy Efficiency in Buildings, American Council for an Energy Efficient Economy, Washington D.C., Vol. 9, p. 1, August, 1992. Akbari, H., Bretz, S., Kurn, D.M. and Hanford, J., ?Peak Power and Cooling Energy Savings of High Albedo Roofs,? Energy... positive pressure dehumidified air ventilation in hot humid climates, quiet exhaust fan ventilation in cool climates, solar water heaters, heat pump water heaters, high efficiency right sized heating/cooling equipment, and gas fired combo space...

Parker, D. S.; Sonne, J. K.; Sherwin, J. R.

2002-01-01T23:59:59.000Z

417

Can an inhomogeneous metric be detected with the baryonic acoustic oscillation peak?  

E-Print Network (OSTI)

The scalar averaging approach to cosmology interprets dark energy as the growth of average, void-dominated, negative spatial curvature during the virialisation epoch, leaving the metric a priori unspecified, while models with a Friedmann-Lemaitre-Robertson-Walker (FLRW) metric assume comoving spatial rigidity of metrical properties. The former predicts that voids are hyperbolic and that superclusters occupy positively curved space, and that a best-fit metric should be close to the void case modelled as a constant-curvature metric on a given time slice. Thus, comoving separations near superclusters should be compressed in comparison to the homogeneous case. We demonstrate this by measuring the two-point auto-correlation function on comoving scales in order to detect shifts in the baryonic acoustic oscillation (BAO) peak location for Large Red Galaxy (LRG) pairs of the Sloan Digital Sky Survey Data Release 7. In tangential directions, subsets of pairs overlapping with superclusters or voids show the BAO peak. The tangential BAO peak location for overlap with Nadathur & Hotchkiss superclusters is 4.3\\pm1.6 Mpc/h less than that for LRG pairs unselected for supercluster overlap, and 6.6\\pm2.8 Mpc/h less than that of the complementary pairs. Liivamagi et al. superclusters give corresponding differences of 3.7\\pm2.9 Mpc/h and 6.3\\pm2.6 Mpc/h, respectively. We have found moderately significant evidence (Kolmogorov-Smirnov tests suggest very significant evidence) that the BAO peak location for supercluster-overlapping pairs is compressed by about 6% compared to that of the complementary sample, providing a potential challenge to FLRW models and a benchmark for predictions from backreaction models.

Boudewijn F. Roukema; Thomas Buchert; Jan J. Ostrowski; Martin J. France

2014-10-07T23:59:59.000Z

418

Ulysses solar wind plasma observations from peak southerly latitude through perihelion and beyond  

Science Journals Connector (OSTI)

We present Ulysses solar wind plasma data from the peak southerly latitude of ?80.2 through +64.9 latitude on June 7 1995. Ulysses encountered fast wind throughout this time except for a 43 equatorial band. Mass flux was nearly constant with latitude while speed (density) had positive (negative) poleward gradients. Momentum flux was highest at high latitudes suggesting a latitudinal asymmetry in the heliopause cross section. Solar wind energy flux density was also highest at high latitudes.

J. L. Phillips; S. J. Bame; W. C. Feldman; J. T. Gosling; D. J. McComas; B. E. Goldstein; M. Neugebauer; C. M. Hammond

1996-01-01T23:59:59.000Z

419

Backup Generators (BUGS): The Next Smart Grid Peak Resource? | Open Energy  

Open Energy Info (EERE)

Backup Generators (BUGS): The Next Smart Grid Peak Resource? Backup Generators (BUGS): The Next Smart Grid Peak Resource? Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Backup Generators (BUGS): The Next Smart Grid Peak Resource? Focus Area: Crosscutting Topics: Potentials & Scenarios Website: www.netl.doe.gov/smartgrid/referenceshelf/articles/10-18-2010_BUGS%20a Equivalent URI: cleanenergysolutions.org/content/backup-generators-bugs-next-smart-gri Language: English Policies: "Deployment Programs,Financial Incentives,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Demonstration & Implementation Regulations: "Resource Integration Planning,Energy Standards" is not in the list of possible values (Agriculture Efficiency Requirements, Appliance & Equipment Standards and Required Labeling, Audit Requirements, Building Certification, Building Codes, Cost Recovery/Allocation, Emissions Mitigation Scheme, Emissions Standards, Enabling Legislation, Energy Standards, Feebates, Feed-in Tariffs, Fuel Efficiency Standards, Incandescent Phase-Out, Mandates/Targets, Net Metering & Interconnection, Resource Integration Planning, Safety Standards, Upgrade Requirements, Utility/Electricity Service Costs) for this property.

420

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

SciTech Connect

This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30percent using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

Yin, Rongxin; Kiliccote, Sila; Piette, Mary Ann; Parrish, Kristen

2010-05-14T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Peak oil analyzed with a logistic function and idealized Hubbert curve  

Science Journals Connector (OSTI)

A logistic function is used to characterize peak and ultimate production of global crude oil and petroleum-derived liquid fuels. Annual oil production data were incrementally summed to construct a logistic curve in its initial phase. Using a curve-fitting approach, a population-growth logistic function was applied to complete the cumulative production curve. The simulated curve was then deconstructed into a set of annual oil production data producing an idealized Hubbert curve. An idealized Hubbert curve (IHC) is defined as having properties of production data resulting from a constant growth-rate under fixed resource limits. An IHC represents a potential production curve constructed from cumulative production data and provides a new perspective for estimating peak production periods and remaining resources. The IHC model data show that idealized peak oil production occurred in 2009 at 83.2Mb/d (30.4Gb/y). IHC simulations of truncated historical oil production data produced similar results and indicate that this methodology can be useful as a prediction tool.

Brian Gallagher

2011-01-01T23:59:59.000Z

422

Peak Oil profiles through the lens of a general equilibrium assessment  

Science Journals Connector (OSTI)

This paper disentangles the interactions between oil production profiles, the dynamics of oil prices and growth trends. We do so through a general equilibrium model in which Peak Oil endogenously emerges from the interplay between the geological, technical, macroeconomic and geopolitical determinants of supply and demand under non-perfect expectations. We analyze the macroeconomic effects of oil production profiles and demonstrate that Peak Oil dates that differ only slightly may lead to very different time profiles of oil prices, exportation flows and economic activity. We investigate Middle-East's trade-off between different pricing trajectories in function of two alternative objectives (maximisation of oil revenues or households welfare) and assess its impact on OECD growth trajectories. A sensitivity analysis highlights the respective roles of the amount of resources, inertia on the deployment of non conventional oil and short-term oil price dynamics on Peak Oil dates and long-term oil prices. It also examines the effects of these assumptions on OECD growth and Middle-East strategic tradeoffs.

Henri Waisman; Julie Rozenberg; Olivier Sassi; Jean-Charles Hourcade

2012-01-01T23:59:59.000Z

423

Virtual reality simulation game approach to investigate transport adaptive capacity for peak oil planning  

Science Journals Connector (OSTI)

The peak and decline of world oil production is an emerging issue for transportation and urban planners. Peak oil from an energy perspective means that there will be progressively less fuel. Our work treats changes in oil supply as a risk to transport activity systems. A virtual reality survey method, based on the sim game concept, has been developed to audit the participants normal weekly travel activity, and to explore participants travel adaptive capacity. The travel adaptive capacity assessment (TACA) Sim survey uses avatars, Google Map, 2D scenes, interactive screens and feedback scores. Travel adaptive capacity is proposed as a measure of long-range resilience of activity systems to fuel supply decline. Mode adaptive potential is proposed as an indicator of the future demand growth for less energy intensive travel. Both adaptation indicators can be used for peak oil vulnerability assessment. A case study was conducted involving 90 participants in Christchurch New Zealand. All of the participants were students, general staff or academics at the University of Canterbury. The adaptive capacity was assessed by both simulated extreme fuel price shock and by asking, do you have an alternative mode? without price pressure. The travel adaptive capacity in number of kilometers was 75% under a 5-fold fuel price increase. The mode adaptive potential was 33% cycling, 21% walking and 22% bus. Academics had adaptive capacity of only 15% of trips by canceling or carrying out their activity from home compared to 1018% for students.

Montira Watcharasukarn; Shannon Page; Susan Krumdieck

2012-01-01T23:59:59.000Z

424

Local Implications of Globally Restricted Mobility: A study of Queenstowns vulnerability to peak oil and climate change.  

E-Print Network (OSTI)

??This thesis employs a case study approach to investigate local implications of globally restricted mobility by examining Queenstowns vulnerability to peak oil and climate change. (more)

Walsh, Tim

2011-01-01T23:59:59.000Z

425

Experimental evaluation of actual delivered dose using mega-voltage cone-beam CT and direct point dose measurement  

SciTech Connect

Radiation therapy in patients is planned by using computed tomography (CT) images acquired before start of the treatment course. Here, tumor shrinkage or weight loss or both, which are common during the treatment course for patients with head-and-neck (H and N) cancer, causes unexpected differences from the plan, as well as dose uncertainty with the daily positional error of patients. For accurate clinical evaluation, it is essential to identify these anatomical changes and daily positional errors, as well as consequent dosimetric changes. To evaluate the actual delivered dose, the authors proposed direct dose measurement and dose calculation with mega-voltage cone-beam CT (MVCBCT). The purpose of the present study was to experimentally evaluate dose calculation by MVCBCT. Furthermore, actual delivered dose was evaluated directly with accurate phantom setup. Because MVCBCT has CT-number variation, even when the analyzed object has a uniform density, a specific and simple CT-number correction method was developed and applied for the H and N site of a RANDO phantom. Dose distributions were calculated with the corrected MVCBCT images of a cylindrical polymethyl methacrylate phantom. Treatment processes from planning to beam delivery were performed for the H and N site of the RANDO phantom. The image-guided radiation therapy procedure was utilized for the phantom setup to improve measurement reliability. The calculated dose in the RANDO phantom was compared to the measured dose obtained by metal-oxide-semiconductor field-effect transistor detectors. In the polymethyl methacrylate phantom, the calculated and measured doses agreed within about +3%. In the RANDO phantom, the dose difference was less than +5%. The calculated dose based on simulation-CT agreed with the measured dose within3%, even in the region with a high dose gradient. The actual delivered dose was successfully determined by dose calculation with MVCBCT, and the point dose measurement with the image-guided radiation therapy procedure.

Matsubara, Kana, E-mail: matsubara-kana@hs.tmu.ac.jp [Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa-ku Tokyo (Japan); Kohno, Ryosuke [National Cancer Center Hospital East, Chiba (Japan); National Cancer Center Research Institute, Chiba (Japan); Nishioka, Shie; Shibuya, Toshiyuki; Ariji, Takaki; Akimoto, Tetsuo [National Cancer Center Hospital East, Chiba (Japan); Saitoh, Hidetoshi [Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa-ku Tokyo (Japan)

2013-07-01T23:59:59.000Z

426

Microsoft Word - July Heat 7-19 v3  

Gasoline and Diesel Fuel Update (EIA)

forecast to peak today, with the highest forecast to peak today, with the highest temperatures nearing triple digits across the Northeast and Mid-Atlantic. Electricity prices: Day-ahead prices for certain peak hours reached above $200/MWh in New England and PJM West, above $300/MWh in New York City, and hit $350/MWh in Long Island. Usual prices for these areas are $30-$60/MWh. The on-peak daily price at PJM West continued to decline from its Wednesday peak as the heat moved out of the Midwest. Electricity demand: System demand in New York is expected to surpass its all-time system peak while PJM and New England are forecast to be 96% and 99% of their respective all-time peaks.

427

Automated Critical Peak Pricing Field Tests: 2006 Program Description and Results APPENDICES  

NLE Websites -- All DOE Office Websites (Extended Search)

Automated Critical Peak Pricing Field Tests: 2006 Program Description and Results APPENDICES Mary Ann Piette David Watson Naoya Motegi Sila Kiliccote Lawrence Berkeley National Laboratory MS90R3111 1 Cyclotron Road Berkeley, California 94720 August 30, 2007 This work described in this report was coordinated by the Demand Response Research Center and funded by the California Energy Commission, Public Interest Energy Research Program, under Work for Others Contract No. 150-99-003, Am #1 and by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. LBNL Report Number 62218 2 Table of Contents List of Tables ......................................................................................................................................3

428

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems  

SciTech Connect

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification. (authors)

Souza, Rose Mary G.P.; Moreira, Joao M.L. [Centro Tecnologico da Marinha em Sao Paulo - CTMSP, avenida Prof. Lineu Prestes, 2468 - Butanta, Sao Paulo (Brazil)

2006-07-01T23:59:59.000Z

429

An insoluble residue study of the Comanche Peak and Edwards limestones of Kimble County, Texas  

E-Print Network (OSTI)

through 5 of the Uuager sectioa. XXIII. Units 18 through 22 of the Segovie sectioa. . . XXIV. Units 15 through 18 of the Begovie section. . . XXV. Uaits 13 through 16 of the Segovia sectioa. . . . . . . . . 66 68 XXVI. Units 8 through 13... this horisou wU, 1 be celle4 the Waterfall horisoc. Ths Waterfall horisoc was cot observe4 at the Rcsgsr sectioc because oaly Chs lover part of the R4wsrds liosstoca was erposod la that ssctioa. Ths Watec'fall horisocc is 115 feet above Cha Cocsaccchs peak...

Jurik, Paul Peter

2012-06-07T23:59:59.000Z

430

Microfacies of the Commache Peak Limestone (Lower Cretaceous), north-central Texas  

E-Print Network (OSTI)

LIIGESTONE W4LNUT FORMATION ARENACEOUS GROUP RED RIVER GROUP Q GLEN ROSE OR ALTERNAI'ING SECS I-0 TRINTY OR BASAL SANDS GLEN RDSE FORMATION I-? 7 NAVIS PEAK IL & Gl D ALLIED FORMATIONS Fig. 1. History of stratigraphic nomenclature for the Texas... Cretaceous. Down-to-basin fault- ing w1thin the Balcones system, which began during Glen Rose (pre- Fredericksburg) deposition, tended only to cause very slight thick- ening 1n the downdip direct1on within the Lower Cretaceous rocks on the shelf (Hayward...

Gruebel, Marilyn May

2012-06-07T23:59:59.000Z

431

The Glass-like Structure of Globular Proteins and the Boson Peak  

E-Print Network (OSTI)

Vibrational spectra of proteins and topologically disordered solids display a common anomaly at low frequencies, known as Boson peak. We show that such feature in globular proteins can be deciphered in terms of an energy landscape picture, as it is for glassy systems. Exploiting the tools of Euclidean random matrix theory, we clarify the physical origin of such anomaly in terms of a mechanical instability of the system. As a natural explanation, we argue that such instability is relevant for proteins in order for their molecular functions to be optimally rooted in their structures.

Stefano Ciliberti; Paolo De Los Rios; Francesco Piazza

2005-11-23T23:59:59.000Z

432

Blue Emission Peak of GeO{sub 2} Particles Grown Using Thermal Evaporation  

SciTech Connect

In this paper we report a simple thermal evaporation technique (horizontal tube furnace) to grow large quantities of GeO{sub 2} particles with diameters ranging from tens of nanometer to 500 nm on n-type (100) Si substrate free of catalyst. The particles were grown at temperature about 1000 degree sign C for 2 hrs and characterized by scanning electron microscopy (SEM), X-Ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX) and photoluminescence (PL) spectroscopy. The photoluminescence spectrum reveals several emission peaks around 400 nm at room temperature. Raman measurement also measured at room temperature for this GeO{sub 2} particles.

Sulieman, Kamal Mahir [School of Physics, Universiti Sains Malaysia (USM), 11800 Minden, Penang (Malaysia); Physics Department, Alzaiem Alazhary University, 1432-Khartoum (Sudan); Jumidali, M. M. [School of Physics, Universiti Sains Malaysia (USM), 11800 Minden, Penang (Malaysia); Faculty of Applied Science, Universiti Teknologi MARA, 13500 Penang (Malaysia); Hashim, M. R. [School of Physics, Universiti Sains Malaysia (USM), 11800 Minden, Penang (Malaysia)

2010-07-07T23:59:59.000Z

433

Abraham H. Maslow's "peak experiences": an analytical paradigm for studying some poems of Theodore Roethke  

E-Print Network (OSTI)

that wi I I be seen in other poems containing peak-experiences. Came to lakes; came to dead water Ponds with moss and leaves floating, Planks sunk in the sand. The poet is not interested in the scene yet. "Came to" sounds like a dry journal entry... of the frog being kissed so that he becomes a prince. The deadness and beigeness of early morning in Section I also show the light slowly coming on the scene Came to lakes; came to dead water Ponds with moss and leaves floating, Planks sunk in the sand...

Taylor, Mary Katherine

1981-01-01T23:59:59.000Z

434

Ulysses solar wind plasma observations from peak southerly latitude through perihelion and beyond  

SciTech Connect

We present Ulysses solar wind plasma data from the peak southerly latitude of {minus}80.2{degrees} through +64.9{degrees} latitude on June 7, 1995. Ulysses encountered fast wind throughout this time except for a 43{degrees} band centered on the solar equator. Median mass flux was nearly constant with latitude, while speed and density had positive and negative poleward gradients, respectively. Solar wind momentum flux was highest at high latitudes, suggesting a latitudinal asymmetry in the heliopause cross section. Solar wind energy flux density was also highest at high latitudes.

Phillips, J.L.; Bame, S.J.; Feldman, W.C.; Gosling, J.T.; McComas, D.J. [Los Alamos National Lab., NM (United States); Goldstein, B.E.; Neugebauer, M. [Jet Propulsion Lab., Pasadena, CA (United States); Hammond, C.M. [SRI International, Menlo Park, CA (United States)

1995-09-01T23:59:59.000Z

435

An Energy and Peak Loads Analysis of the TYC/TRC Building Final Report  

E-Print Network (OSTI)

with the ASHRAE standards and (iii) modifying the building to comply with the California standards. These options not only reduce the peak loads but also reduce the total energy use. The energy consumption of the TYC/TRC Building was compared with the energy... consumption of the building modified to comply with the ASHRAE and California standards. A net reduction of 38% and 44% was obtained using the ASHRAE and California standards, respectively. The California standards are more stringent and are a better choice...

Katipamula, S.; O'Neal, D. L.

1987-01-01T23:59:59.000Z

436

High-speed Light Peak optical link for high energy applications  

Science Journals Connector (OSTI)

Abstract Optical links provide high speed data transmission with low mass fibers favorable for applications in high energy experiments. We report investigation of a compact Light Peak optical engine designed for data transmission at 4.8Gbps. The module is assembled with bare die VCSEL, PIN diodes and a control IC aligned within a prism receptacle for light coupling to fiber ferrule. Radiation damage in the receptacle was examined with 60Co gamma ray. Radiation induced single event effects in the optical engine were studied with protons, neutrons and X-ray tests.

F.X. Chang; F. Chiang; B. Deng; J. Hou; S. Hou; C. Liu; T. Liu; P.K. Teng; C.H. Wang; T. Xu; J. Ye

2014-01-01T23:59:59.000Z

437

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data  

E-Print Network (OSTI)

An accurate and computationally efficient algorithm for ground peak identification in large. In the current study, an accurate and computationally efficient algorithm was devel- oped for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation

Mountrakis, Giorgos

438

OFF-SHORE WIND AND GRID-CONNECTED PV: HIGH PENETRATION PEAK SHAVING FOR NEW YORK CITY  

E-Print Network (OSTI)

OFF-SHORE WIND AND GRID-CONNECTED PV: HIGH PENETRATION PEAK SHAVING FOR NEW YORK CITY Richard Perez-shore wind and PV generation using the city of New York as a test case. While wind generation is not known the source of the energy that can meet the demand. While the peak-time availability of wind generation

Perez, Richard R.

439

Peripheral peaking and shrinkage phenomenon in the s channel based on the statistical bootstrap model with spin  

Science Journals Connector (OSTI)

We observe that the spectrum of the statistical bootstrap model with spin can indeed give rise to peripheral peaks in two-body scattering. This further confirms the conclusion of an earlier work by Kogitz et al. We also present a new refined solution to the bootstrap model, which provides the shrinkage phenomenon for peripheral peaks.

Charles B. Chiu

1976-04-01T23:59:59.000Z

440

Transmission planning for Indian power grid: a mixed integer programming approachp  

E-Print Network (OSTI)

) time-block (peak, intermediate, base) l index for transmission line voltage level (400, 220 and 132 kV transmission line, Rs/km LCAP power carrying capacity of an inter-state tie line for a particular voltage class, MW LF transmission loss factor per unit power transfer per km line length, MWh/MWh- km LGTH length

Dragoti-?ela, Eranda

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441

The role of non conventional oil in the attenuation of peak oil  

Science Journals Connector (OSTI)

In this paper, the possible substitution of conventional with non conventional oil is studied using system dynamics models. The model proposed in this paper is based on geological, economic and technological aspects, and it fits approximately the behaviour observed by Hubbert. A first validation of the model has been made with the USA oil production data. These USA data show that there is a good coincidence between our model and the reality. This model has been expanded in order to include the substitution of the conventional oil with the non conventional one for the World. Two models with different ways to treat the contribution of non conventional oil have been developed and tested: a base model (business as usual), which extrapolates the last two decades growth of this type of oil into the future, and a model that explores how much non conventional oil would be needed in order to avoid a peak and decrease in the global non renewable fuel production. The results show that, even under some hypotheses that we consider optimistic, the attenuation of the peak oil decline requires more than 10% of sustained growth of non conventional oil production over at least the next two decades.

Carlos de Castro; Luis Javier Miguel; Margarita Mediavilla

2009-01-01T23:59:59.000Z

442

Efficiency peaks in the transient electroluminescence of multilayer organic light-emitting devices  

SciTech Connect

It is shown that when multilayer organic light-emitting devices (OLEDs) containing hole (h{sup +}) and electron (e{sup -}) transporting layers (HTLs and ETLs, respectively) are biased with microsecond to millisecond voltage pulses higher than a threshold value V{sub th}, the electroluminescence (EL) intensity increases dramatically to a peak value which then relaxes to the lower dc value; the relaxation time decreases strongly with increasing pulse amplitude. Since the current waveforms are essentially rectangular, the transient EL is proportional to the external quantum efficiency {eta}. The value of V{sub th} coincides with the bias for maximum dc efficiency typically observed when {eta} is monitored vs V. This relation and the apparent absence of the transient peak in single-layer OLEDs suggest that it is due either to internal field redistribution processes in the ETL and HTL or to space charges, e.g., trapped polarons which accumulate at the HTL/ETL interface, and quench the emitting singlet excitons. It is concluded that highly efficient OLED operation may be achieved at high brightness by pulsed bias at an optimized duty cycle. (c) 2000 American Institute of Physics.

Savvate'ev, V. [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States)] [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States); Friedl, J. [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States)] [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States); Zou, L. [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States)] [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States); Oldham, W. J. [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)] [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States); Shinar, J. [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States)] [Ames Laboratory - USDOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011-3020 (United States)

2000-04-17T23:59:59.000Z

443

Development of an Efficient Maintenance Scheme for Peak Efficiency of Boilers  

E-Print Network (OSTI)

AbstractPresently the world has enormous advancement in science and technology the topic considered here is just a drop out of an ocean of knowledge. Higher product quality, better reliability, better availability of plants, optimization of cost and efficient working of boilers is the chief concern now a days. Generally the production can be increased by the efficient use of boilers and hence there is a lot of scope to minimize the boiler operation cost. A boiler maintenance improvement program must include two aspects: (1) action to bring the boiler to peak efficiency and (2) action to maintain the efficiency at the maximum level. Good maintenance and efficiency start with having a working knowledge of the components associated with the boiler, keeping records, etc., and end with cleaning heat transfer surfaces, adjusting the air-to-fuel ratio, etc. A well-planned maintenance program avoids unnecessary down time or costly repairs. It also promotes safety and aids boiler code and local inspectors. An inspection schedule listing the procedures should be established. Thus in this paper an attempt is made to develop an efficient maintenance scheme by which boilers can be used with peak efficiency.

Amit Kumar Jain; Anupam Singhal

444

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns  

Science Journals Connector (OSTI)

Abstract In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS?MRI database which is the magnetic resonance imaging (MRI) database and VIA/IELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.

Subrahmanyam Murala; Q.M. Jonathan Wu

2014-01-01T23:59:59.000Z

445

Making appropriate comparisons of estimated and actual costs of reducing SO{sub 2} emissions under Title IV  

SciTech Connect

A current sentiment within some parts of the environmental policy community is that market-based regulatory approaches such as emissions trading have proven so effective that actual costs will be only a small fraction of what ex ante cost estimation procedures would project. With this line of reasoning, some have dismissed available cost estimates for major proposed new regulations, such as the new PM and ozone NAAQS, as not meaningful for policy decisions. The most commonly used evidence in support of this position is the experience with SO{sub 2} reductions under Title IV of the 1990 Clean Air Act Amendments. In Title IV, a market for emissions allowances has been used to achieve reductions in sulfur dioxides (SO{sub 2}) to ameliorate acid rain. It is commonly asserted today that the cost of achieving the SO{sub 2} emissions reductions has been only one-tenth or less of what Title IV was originally expected to cost. This paper demonstrates that, to the contrary, actual costs for SO{sub 2} reductions remain roughly in line with original estimates associated with Title IV. Erroneous conclusions about Title IV`s costs are due to inappropriate comparisons of a variety of different measures that appear to be comparable only because they are all stated in dollars per ton. Program cost estimates include the total costs of a fully-implemented regulatory program. The very low costs of Title IV that are commonly cited today are neither directly reflective of a fully implemented Title IV, (which is still many years away) nor reflective of all the costs already incurred. Further, a careful review of history finds that the initial cost estimates that many cite were never associated with Title IV. Technically speaking, people are comparing the estimated control costs for the most-costly power plant associated with earlier acid rain regulatory proposals with prices from a market that do not directly reflect total costs.

Smith, A.E. [DFI/Aeronomics Inc., Washington, DC (United States)

1998-12-31T23:59:59.000Z

446

Secondary phi meson peak as an indicator of a QCD phase transition in ultrarelativistic heavy-ion collisions  

Science Journals Connector (OSTI)

In a previous paper, we have shown that a double phi peak structure appears in the dilepton invariant mass spectrum if a first order QCD phase transition occurs in ultrarelativistic heavy-ion collisions. Furthermore, the transition temperature can be determined from the transverse momentum distribution of the low mass phi peak. In this work, we extend the study to the case that a smooth crossover occurs in the quark-gluon plasma to the hadronic matter transition. We find that the double phi peak structure still exists in the dilepton spectrum and thus remains a viable signal for the formation of the quark-gluon plasma in ultrarelativistic heavy-ion collisions.

M. Asakawa and C. M. Ko

1994-12-01T23:59:59.000Z

447

Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results  

NLE Websites -- All DOE Office Websites (Extended Search)

i Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results Mary Ann Piette David Watson Naoya Motegi Sila Kiliccote Lawrence Berkeley National Laboratory MS90R3111 1 Cyclotron Road Berkeley, California 94720 June 19, 2007 LBNL Report Number 62218 ii Acknowledgements The work described in this report was funded by the Emerging Technologies Program at Pacific Gas and Electric Company. Additional funding was provided by the Demand Response Research Center which is funded by the California Energy Commission (Energy Commission), Public Interest Energy Research (PIER) Program, under Work for Others Contract No.500-03-026, Am #1 and by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors are grateful for the extensive

448

Microsoft Word - Rockwood (CFC) Silver Peak Area EA (Proof Copy) V2.docx  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

the Interior the Interior Bureau of Land Management Environmental Assessment # DOI-BLM-NV-B020-2012-0214-EA DOE/EA-1921 DATE: October 2012 Silver Peak Area Geothermal Exploration Project ENVIRONMENTAL ASSESSMENT Geothermal Lease: NVN-87008 Tonopah Field Office P.O. Box 911 1553 S. Main Street Tonopah, NV 89049 Phone: 775-482-7800 Fax: 775-482-7810 BLM Mission Statement It is the mission of the Bureau of Land Management to sustain the health, diversity, and productivity of the public lands for the use and enjoyment of present and future generations. TABLE OF CONTENTS 1. Introduction ........................................................................................................................1 1.1 Location and Summary of Proposed Action...................................................................... 1

449

DOE/SC-ARM-10-021 STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment  

NLE Websites -- All DOE Office Websites (Extended Search)

1 1 STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment Science and Operations Plan J Mace Principal Investigator S Matrosov B Orr M Shupe R Coulter P Lawson A Sedlacek G Hallar L Avallone I McCubbin C Long R Marchand September 2010 DISCLAIMER This report was prepared as an account of work sponsored by the U.S. Government. Neither the United States nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service

450

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

NLE Websites -- All DOE Office Websites (Extended Search)

36E 36E Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California R. Yin, S. Kiliccote, M.A. Piette, K. Parrish Environmental Energy Technologies Division May 2010 Presented at the 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, August 15-20, 2010, and published in the Proceedings DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information,

451

Using computational modeling to compare X-ray tube Practical Peak Voltage for Dental Radiology  

Science Journals Connector (OSTI)

Abstract The Practical Peak Voltage-PPV has been adopted to measure the voltage applied to an X-ray tube. The PPV was recommended by the IEC document and accepted and published in the TRS no. 457 code of practice. The PPV is defined and applied to all forms of waves and is related to the spectral distribution of X-rays and to the properties of the image. The calibration of X-rays tubes was performed using the MCNPX Monte Carlo code. An X-ray tube for Dental Radiology (operated from a single phase power supply) and an X-ray tube used as a reference (supplied from a constant potential power supply) were used in simulations across the energy range of interest of 40kV to 100kV. Results obtained indicated a linear relationship between the tubes involved.

Deisemar Holanda Cassiano; Samanda Cristine Arruda Correa; Edmilson Monteiro de Souza; Ademir Xaxier da Silva; Jos Guilherme Pereira Peixoto; Ricardo Tadeu Lopes

2014-01-01T23:59:59.000Z

452

Method and device for remotely monitoring an area using a low peak power optical pump  

DOE Patents (OSTI)

A method and device for remotely monitoring an area using a low peak power optical pump comprising one or more pumping sources, one or more lasers; and an optical response analyzer. Each pumping source creates a pumping energy. The lasers each comprise a high reflectivity mirror, a laser media, an output coupler, and an output lens. Each laser media is made of a material that emits a lasing power when exposed to pumping energy. Each laser media is optically connected to and positioned between a corresponding high reflectivity mirror and output coupler along a pumping axis. Each output coupler is optically connected to a corresponding output lens along the pumping axis. The high reflectivity mirror of each laser is optically connected to an optical pumping source from the one or more optical pumping sources via an optical connection comprising one or more first optical fibers.

Woodruff, Steven D.; Mcintyre, Dustin L.; Jain, Jinesh C.

2014-07-22T23:59:59.000Z

453

Observation, modeling, and temperature dependence of doubly peaked electric fields in irradiated silicon pixel sensors  

E-Print Network (OSTI)

We show that doubly peaked electric fields are necessary to describe grazing-angle charge collection measurements of irradiated silicon pixel sensors. A model of irradiated silicon based upon two defect levels with opposite charge states and the trapping of charge carriers can be tuned to produce a good description of the measured charge collection profiles in the fluence range from 0.5x10^{14} Neq/cm^2 to 5.9x10^{14} Neq/cm^2. The model correctly predicts the variation in the profiles as the temperature is changed from -10C to -25C. The measured charge collection profiles are inconsistent with the linearly-varying electric fields predicted by the usual description based upon a uniform effective doping density. This observation calls into question the practice of using effective doping densities to characterize irradiated silicon.

M. Swartz; V. Chiochia; Y. Allkofer; D. Bortoletto; L. Cremaldi; S. Cucciarelli; A. Dorokhov; C. Hoermann; D. Kim; M. Konecki; D. Kotlinski; K. Prokofiev; C. Regenfus; T. Rohe; D. A. Sanders; S. Son; T. Speer

2006-01-05T23:59:59.000Z

454

Origin of the narrow, single peak in the fission-fragment mass distribution for 258Fm  

SciTech Connect

We discuss the origin of the narrowness of the single peak at mass-symmetric division in the fragment mass-yield curve for spontaneous fission of {sup 258}Fm. For this purpose, we employ the macroscopic-microscopic model and calculate a potential-energy curve at the mass-symmetric compact scission configuration, as a function of the fragment mass number, which is obtained from the single-particle wave-function densities. In the calculations, we minimize total energies by varying the deformations of the two fragments, with constraints on the mass quadrupole moment, and by keeping the neck radius zero. The energies thus become functions of mass asymmetry. Using the obtained potential, we solve the one-dimensional Schroedinger equation with a microscopic coordinate-dependent inertial mass to calculate the fragment mass-yield curve. The calculated mass yield, expressed in terms of the microscopic mass density, is consistent with the extremely narrow experimental mass distribution.

Moller, Peter [Los Alamos National Laboratory; Ickhikawa, Takatoshi [RIKEN; Iwamoto, Akira [JAEA

2008-01-01T23:59:59.000Z

455

Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction  

SciTech Connect

The widespread adoption of demand response enabled appliances and thermostats can result in significant reduction to peak electrical demand and provide potential grid stabilization benefits. GE has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information from the utility grid, often in the form of price. However due to a number of factors, including the number of demand response enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors can be modeled and simulated in open-source software, GridLAB-D, including evaluation of appliance controls, improvement to current algorithms, and development of aggregate control methodologies. This report is the first in a series of three reports describing the potential of GE's demand response enabled appliances to provide benefits to the utility grid. The first report will describe the modeling methodology used to represent the GE appliances in the GridLAB-D simulation environment and the estimated potential for peak demand reduction at various deployment levels. The second and third reports will explore the potential of aggregated group actions to positively impact grid stability, including frequency and voltage regulation and spinning reserves, and the impacts on distribution feeder voltage regulation, including mitigation of fluctuations caused by high penetration of photovoltaic distributed generation and the effects on volt-var control schemes.

Fuller, Jason C.; Vyakaranam, Bharat GNVSR; Prakash Kumar, Nirupama; Leistritz, Sean M.; Parker, Graham B.

2012-04-29T23:59:59.000Z

456

ON THERMALIZATION IN GAMMA-RAY BURST JETS AND THE PEAK ENERGIES OF PHOTOSPHERIC SPECTRA  

SciTech Connect

The low-energy spectral slopes of the prompt emission of most gamma-ray bursts (GRBs) are difficult to reconcile with radiatively efficient optically thin emission models irrespective of the radiation mechanism. An alternative is to ascribe the radiation around the spectral peak to a thermalization process occurring well inside the Thomson photosphere. This quasi-thermal spectrum can evolve into the observed non-thermal shape by additional energy release at moderate to small Thomson optical depths, which can readily give rise to the hard spectral tail. The position of the spectral peak is determined by the temperature and Lorentz factor of the flow in the thermalization zone, where the total number of photons carried by the jet is established. To reach thermalization, dissipation alone is not sufficient and photon generation requires an efficient emission/absorption process in addition to scattering. We perform a systematic study of all relevant photon production mechanisms searching for possible conditions in which thermalization can take place. We find that a significant fraction of the available energy should be dissipated at intermediate radii, {approx}10{sup 10} to a few Multiplication-Sign 10{sup 11} cm, and the flow there should be relatively slow: the bulk Lorentz factor could not exceed a few tens for all but the most luminous bursts with the highest E {sub pk} values. The least restrictive constraint for successful thermalization, {Gamma} {approx}< 20, is obtained if synchrotron emission acts as the photon source. This requires, however, a non-thermal acceleration deep below the Thomson photosphere transferring a significant fraction of the flow energy to relativistic electrons with Lorentz factors between 10 and 100. Other processes require bulk flow Lorentz factors of order of a few for typical bursts. We examine the implications of these results to different GRB photospheric emission models.

Vurm, Indrek; Piran, Tsvi [Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904 (Israel)] [Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Lyubarsky, Yuri, E-mail: indrek.vurm@gmail.com [Physics Department, Ben-Gurion University, P.O. Box 653, Beer-Sheva 84105 (Israel)] [Physics Department, Ben-Gurion University, P.O. Box 653, Beer-Sheva 84105 (Israel)

2013-02-20T23:59:59.000Z

457

Effect of high strain rates on peak stress in a Zr-based bulk metallic glass  

SciTech Connect

The mechanical behavior of Zr{sub 41.25}Ti{sub 13.75}Cu{sub 12.5}Ni{sub 10}Be{sub 22.5} (LM-1) has been extensively characterized under quasistatic loading conditions; however, its mechanical behavior under dynamic loading conditions is currently not well understood. A Split-Hopkinson pressure bar (SHPB) and a single-stage gas gun are employed to characterize the mechanical behavior of LM-1 in the strain-rate regime of 10{sup 2}-10{sup 5}/s. The SHPB experiments are conducted with a tapered insert design to mitigate the effects of stress concentrations and preferential failure at the specimen-insert interface. The higher strain-rate plate-impact compression-and-shear experiments are conducted by impacting a thick tungsten carbide (WC) flyer plate with a sandwich sample comprising a thin bulk metallic glass specimen between two thicker WC target plates. Specimens employed in the SHPB experiments failed in the gage-section at a peak stress of approximately 1.8 GPa. Specimens in the high strain-rate plate-impact experiments exhibited a flow stress in shear of approximately 0.9 GPa, regardless of the shear strain-rate. The flow stress under the plate-impact conditions was converted to an equivalent flow stress under uniaxial compression by assuming a von Mises-like material behavior and accounting for the plane strain conditions. The results of these experiments, when compared to the previous work conducted at quasistatic loading rates, indicate that the peak stress of LM-1 is essentially strain rate independent over the strain-rate range up to 10{sup 5}/s.

Sunny, George; Yuan Fuping; Prakash, Vikas [Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, Ohio 44106-7222 (United States); Lewandowski, John [Department of Materials Science and Engineering, Case Western Reserve University, Cleveland, Ohio 44106-7222 (United States)

2008-11-01T23:59:59.000Z

458

Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building  

SciTech Connect

The objective of this study was to demonstrate the potential for reducing peak-period electrical demand in moderate-weight commercial buildings by modifying the control of the HVAC system. An 80,000 ft{sup 2} office building with a medium-weight building structure and high window-to-wall ratio was used for a case study in which zone temperature set-points were adjusted prior to and during occupancy. HVAC performance data and zone temperatures were recorded using the building control system. Additional operative temperature sensors for selected zones and power meters for the chillers and the AHU fans were installed for the study. An energy performance baseline was constructed from data collected during normal operation. Two strategies for demand shifting using the building thermal mass were then programmed in the control system and implemented progressively over a period of one month. It was found that a simple demand limiting strategy performed well in this building. This strategy involved maintaining zone temperatures at the lower end of the comfort region during the occupied period up until 2 pm. Starting at 2 pm, the zone temperatures were allowed to float to the high end of the comfort region. With this strategy, the chiller power was reduced by 80-100% (1-2.3 W/ft{sup 2}) during normal peak hours from 2-5 pm, without causing any thermal comfort complaints. The effects on the demand from 2-5 pm of the inclusion of pre-cooling prior to occupancy are unclear.

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2006-08-01T23:59:59.000Z

459

Peak demand reduction from pre-cooling with zone temperature reset in an office building  

SciTech Connect

The objective of this study was to demonstrate the potential for reducing peak-period electrical demand in moderate-weight commercial buildings by modifying the control of the HVAC system. An 80,000 ft{sup 2} office building with a medium-weight building structure and high window-to-wall ratio was used for a case study in which zone temperature set-points were adjusted prior to and during occupancy. HVAC performance data and zone temperatures were recorded using the building control system. Additional operative temperature sensors for selected zones and power meters for the chillers and the AHU fans were installed for the study. An energy performance baseline was constructed from data collected during normal operation. Two strategies for demand shifting using the building thermal mass were then programmed in the control system and implemented progressively over a period of one month. It was found that a simple demand limiting strategy performed well in this building. This strategy involved maintaining zone temperatures at the lower end of the comfort region during the occupied period up until 2 pm. Starting at 2 pm, the zone temperatures were allowed to float to the high end of the comfort region. With this strategy, the chiller power was reduced by 80-100% (1-2.3 W/ft{sup 2}) during normal peak hours from 2-5 pm, without causing any thermal comfort complaints. The effects on the demand from 2-5 pm of the inclusion of pre-cooling prior to occupancy are unclear.

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2004-08-01T23:59:59.000Z

460

Why E-government Usage Lags Behind: Explaining the Gap Between Potential and Actual Usage of Electronic Public Services in the Netherlands  

Science Journals Connector (OSTI)

Most of the EU-15 countries illustrate a gap between potential usage and actual usage of electronic public services. Using a model ... the case of current Dutch electronic governmental service usage. Motivational...

Alexander van Deursen; Jan van Dijk; Wolfgang Ebbers

2006-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

High-Performance with Solar Electric Reduced Peak Demand: Premier Homes Rancho Cordoba, CA- Building America Top Innovation  

Energy.gov (U.S. Department of Energy (DOE))

This Building America Innovations profile describes Building America solar home research that has demonstrated the ability to reduce peak demand by 75%. Numerous field studies have monitored power production and system effectiveness.

462

Energy, power, and office buildings : design and analysis of an off-peak cooling system using structural mass storage  

E-Print Network (OSTI)

As the electric utilities face ever increasing peak power production requirements, (mostly from the commercial sector) scheduled "time-of-day" pricing schemes have become imperative. At present, most conservation strategies ...

Mathis, Rory Christopher

1982-01-01T23:59:59.000Z

463

Project title: Stimulation at Desert Peak and Bradys reservoirs: modeling with the coupled THM code FEHM  

Energy.gov (U.S. Department of Energy (DOE))

Project title: Stimulation at Desert Peak and Bradys reservoirs: modeling with the coupled THM code FEHM presentation at the April 2013 peer review meeting held in Denver, Colorado.

464

Methods, systems and apparatus for approximation of peak summed fundamental and third harmonic voltages in a multi-phase machine  

DOE Patents (OSTI)

Methods, system and apparatus are provided for quickly approximating a peak summed magnitude (A) of a phase voltage (Vph) waveform in a multi-phase system that implements third harmonic injection.

Ransom, Ray M. (Big Bear City, CA); Gallegos-Lopez, Gabriel (Torrance, CA); Kinoshita, Michael H. (Redondo Beach, CA)

2012-07-31T23:59:59.000Z

465

Investigation of active faulting at the Emigrant Peak fault in Nevada using shallow seismic reflection and ground penetrating radar  

E-Print Network (OSTI)

The objective of this study was to assess fault displacement, off-fault deformation, and alluvial fan stratigraphy at the Emigrant Peak fault zone (EPFZ) in Fish Lake Valley, Nevada utilizing shallow seismic reflection (SSR) and ground penetrating...

Christie, Michael Wayne

2007-12-18T23:59:59.000Z

466

Secondary Phi-Meson Peak as an Indicator of a Qcd Phase-Transition in Ultrarelativistic Heavy-Ion Collisions  

E-Print Network (OSTI)

PHYSICAL REVIEW C VOLUME 50, NUMBER 6 DECEMBER 1994 Secondary phi meson peak as an indicator of a +CD phase transition in ultrarelativistic heavy-ion collisions M. Asakawa* Nuclear Science Division, MS 70A $80-7, Laurrence Berkeley Laboratory... invariant mass spectrum if a first order /CD phase transition occurs in ultrarelativistic heavy- ion collisions. Furthermore, the transition temperature can be determined from the transverse momentum distribution of the low mass phi peak. In this work, we...

Asakawa, M.; Ko, Che Ming.

1994-01-01T23:59:59.000Z

467

Industrial-Load-Shaping: The Practice of and Prospects for Utility/Industry Cooperation to Manage Peak Electricity Demand  

E-Print Network (OSTI)

INDUSTRIAL-LOAD-SHAPI1IG: TIlE PRACTICE OF AND PROSPECTS FOR UTILITY/INDUSTRY COOPERATION TO MAUGE PEAK ELECTRICITY DEMAND Donald J. BuIes and David E. Rubin Consultants, Pacific Gas and Electric Company San Francisco, California Michael F.... Maniates Energy and Resources Group, University of California Berkeley, California ABSTRACT Load-management programs designed to reduce demand for electricity during peak periods are becoming increasingly important to electric utilities. For a gf...

Bules, D. J.; Rubin, D. E.; Maniates, M. F.

468

Development of a Dispatchable PV Peak Shainv System. PV: Bonus Program - Phase 1 Report. Volume 1  

SciTech Connect

This report summarizes the work performed by Delmarva Power and Light and its subcontractors in Phase 1 of the US Department of Energy's PV:BONUS Program. The purpose of the program is to develop products and systems for buildings which utilize photovoltaic (N) technology. Beginning with a cooperative research effort with the University of Delaware's Center for Energy and Environmental Policy Research Delmarva Power developed and demonstrated the concept of Dispatchable PV Peak Shaving. This concept and the system which resulted horn the development work are unique from other grid-connected PV systems because it combines a PV, battery energy storage, power conversion and control technologies into an integrated package. Phase 1 began in July 1993 with the installation of a test and demonstration system at Delmarva's Northern Division General Office building near Newark, Delaware. Following initial testing throughout the summer and fall of 1993, significant modifications were made under an amendment to the DOE contract. Work on Phase 1 concluded in the early spring of 1995. Significant progress towards the goal of commercializing the system was made during Phase 1, and is summarized. Based on progress in Phase 1, a proposal to continue the work in Phase 2 was submitted to the US DOE in May 1995. A contract amendment and providing funds for the Phase 2 work is expected in July 1995.

None

1995-10-01T23:59:59.000Z

469

MELT WIRE SENSORS AVAILABLE TO DETERMINE PEAK TEMPERATURES IN ATR IRRADIATION TESTING  

SciTech Connect

In April 2007, the Department of Energy (DOE) designated the Advanced Test Reactor (ATR) a National Scientific User Facility (NSUF) to advance US leadership in nuclear science and technology. By attracting new users from universities, laboratories, and industry, the ATR will support basic and applied nuclear research and development and help address the nation's energy security needs. In support of this new program, the Idaho National Laboratory (INL) has developed in-house capabilities to fabricate, test, and qualify new and enhanced temperature sensors for irradiation testing. Although most efforts emphasize sensors capable of providing real-time data, selected tasks have been completed to enhance sensors provided in irradiation locations where instrumentation leads cannot be included, such as drop-in capsule and Hydraulic Shuttle Irradiation System (HSIS) or 'rabbit' locations. To meet the need for these locations, the INL has developed melt wire temperature sensors for use in ATR irradiation testing. Differential scanning calorimetry and environmental testing of prototypical sensors was used to develop a library of 28 melt wire materials, capable of detecting peak irradiation temperatures ranging from 85 to 1500C. This paper will discuss the development work and present test results.

K. L. Davis; D. Knudson; J. Daw; J. Palmer; J. L. Rempe

2012-07-01T23:59:59.000Z

470

Design optimization of the electrically peaking hybrid (ELPH) vehicle. Research report  

SciTech Connect

Electrically Peaking Hybrid (ELPH) is a parallel hybrid electric vehicle propulsion concept that was invented at Texas A and M University, by the advanced vehicle systems research group. Over the past six years, design methodologies, component development, and system optimization work has been going on for this invention. This project was a first attempt in integrating the above developments into an optimized design of an ELPH passenger car. Design specifications were chosen for a full size passenger car, performing as well as any conventional car, over the EPA-FTP-75 combined city/highway drive cycles. The results of this design project were two propulsion systems. Both were appropriate for commercial production, from the points of view of cost, availability of the technologies, and components. One utilized regenerative braking and the other did not. Substantial fuel savings and emissions reductions resulted from simulating these designs on the FTP-75 drive cycle. For example, the authors` ELPH full size car, with regenerative braking, was capable of delivering over 50 miles per gallon in city driving, with corresponding reductions in its emissions. This project established the viability of the authors` ELPH concept and their design methodologies, in computer simulations. More work remains to be done on investigating more advanced power plants, such as fuel cells, and more advanced components, such as switched reluctance motor drives, for the authors` designs. Furthermore, the authors` design optimization can be carried out to more detailed levels, for prototyping and production.

Ehsani, M.; Gao, Y.; Butler, K.

1998-10-01T23:59:59.000Z

471

Detection of point sources on two-dimensional images based on peaks  

E-Print Network (OSTI)

This article considers the detection of point sources in two dimensional astronomical images. The detection scheme we propose is based on peak statistics. We discuss the example of the detection of far galaxies in Cosmic Microwave Background experiments throughout the paper, although the method we present is totally general and can be used in many other fields of data analysis. We assume sources with a Gaussian profile --that is a fair approximation of the profile of a point source convolved with the detector beam in microwave experiments-- on a background modeled by a homogeneous and isotropic Gaussian random field characterized by a scale-free power spectrum. Point sources are enhanced with respect to the background by means of linear filters. After filtering, we identify local maxima and apply our detection scheme, a Neyman-Pearson detector that defines our region of acceptance based on the a priori pdf of the sources and the ratio of number densities. We study the different performances of some linear fil...

Lopez-Caniego, M; Sanz, J L; Barreiro, R B

2005-01-01T23:59:59.000Z

472

Detection of point sources on two-dimensional images based on peaks  

E-Print Network (OSTI)

This article considers the detection of point sources in two dimensional astronomical images. The detection scheme we propose is based on peak statistics. We discuss the example of the detection of far galaxies in Cosmic Microwave Background experiments throughout the paper, although the method we present is totally general and can be used in many other fields of data analysis. We assume sources with a Gaussian profile --that is a fair approximation of the profile of a point source convolved with the detector beam in microwave experiments-- on a background modeled by a homogeneous and isotropic Gaussian random field characterized by a scale-free power spectrum. Point sources are enhanced with respect to the background by means of linear filters. After filtering, we identify local maxima and apply our detection scheme, a Neyman-Pearson detector that defines our region of acceptance based on the a priori pdf of the sources and the ratio of number densities. We study the different performances of some linear filters that have been used in this context in the literature: the Mexican Hat wavelet, the matched filter and the scale-adaptive filter. We consider as well an extension to two dimensions of the biparametric scale adaptive filter (BSAF). The BSAF depends on two parameters which are determined by maximizing the number density of real detections while fixing the number density of spurious detections. For our detection criterion the BSAF outperforms the other filters in the interesting case of white noise.

M. Lopez-Caniego; D. Herranz; J. L. Sanz; R. B. Barreiro

2005-03-07T23:59:59.000Z

473

A comparative study on conventional and advanced exergetic analyses of geothermal district heating systems based on actual operational data  

Science Journals Connector (OSTI)

This paper comparatively evaluates exergy destructions of a geothermal district heating system (GDHS) using both conventional and advanced exergetic analysis methods to identify the potential for improvement and the interactions among the components. As a real case study, the Afyon GDHS in Afyonkarahisar, Turkey, is considered based on actual operational data. For the first time, advanced exergetic analysis is applied to the GDHSs, in which the exergy destruction rate within each component is split into unavoidable/avoidable and endogenous/exogenous parts. The results indicate that the interconnections among all the components are not very strong. Thus, one should focus on how to reduce the internal inefficiency (destruction) rates of the components. The highest priority for improvement in the advanced exergetic analysis is in the re-injection pump (PM-IX), while it is the heat exchanger (HEX-III) in the conventional analysis. In addition, there is a substantial influence on the overall system as the total avoidable exergy destruction rate of the heat exchanger (HEX-V) has the highest value. On the overall system basis, the value for the conventional exergetic efficiency is determined to be 29.29% while that for the modified exergetic efficiency is calculated to be 34.46% through improving the overall components.

Arif Hepbasli; Ali Keeba?

2013-01-01T23:59:59.000Z

474

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

6893,36894,36894,83,83,83,800,1,2 6893,36894,36894,83,83,83,800,1,2 "Entergy",36894,36895,36895,93,93,93,800,1,2 "Entergy",36895,36896,36896,83,78.5,80.83,7200,9,4 "Entergy",36896,36899,36899,78,67,74.25,3200,4,5 "Entergy",36899,36900,36900,57,54,55.5,1600,2,4 "Entergy",36900,36901,36901,53,53,53,1600,1,2 "Entergy",36902,36903,36903,67.5,65,66.5,4000,5,3 "Entergy",36903,36906,36906,52.5,48,50.25,1600,2,3 "Entergy",36907,36908,36908,52,45,48.86,8800,11,4 "Entergy",36908,36909,36909,56,51,51.95,16800,21,6 "Entergy",36909,36910,36910,50,48.5,49.33,24000,30,7 "Entergy",36910,36913,36913,56.5,54,55.25,11200,13,7 "Entergy",36913,36914,36914,63,57,58.38,6400,8,3 "Entergy",36914,36915,36915,61.5,42,55.75,15200,19,9

475

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

449,39450,39450,180,158,161.65,26400,33,22 449,39450,39450,180,158,161.65,26400,33,22 "NEPOOL MH DA LMP",39450,39451,39451,123,108,114.27,36800,46,28 "NEPOOL MH DA LMP",39451,39454,39454,77,75.5,76.31,21600,26,17 "NEPOOL MH DA LMP",39454,39455,39455,68.25,66,67.1,41600,51,26 "NEPOOL MH DA LMP",39455,39456,39456,69.5,68,68.71,21600,27,18 "NEPOOL MH DA LMP",39456,39457,39457,81,74,75.75,30400,35,17 "NEPOOL MH DA LMP",39457,39458,39458,75,69.75,71.18,24800,31,19 "NEPOOL MH DA LMP",39458,39461,39461,80.5,77,79.38,17600,19,17 "NEPOOL MH DA LMP",39461,39462,39462,102,95,98.76,52000,64,24 "NEPOOL MH DA LMP",39462,39463,39463,90.5,87.5,88.59,34400,43,25 "NEPOOL MH DA LMP",39463,39464,39464,85,83.5,84.21,20800,26,14

476

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

815,39818,39818,43,42.5,42.75,5.17,1600,2,4 815,39818,39818,43,42.5,42.75,5.17,1600,2,4 "ERCOT-South",39818,39819,39819,40,39.5,39.88,-2.87,3200,4,3," " "ERCOT-South",39819,39820,39820,39,38,38.73,-1.15,8800,9,9 "ERCOT-South",39820,39821,39821,41.5,39,39.82,1.09,8800,11,9 "ERCOT-South",39821,39822,39822,38.75,37.5,38.03,-1.79,6400,8,10 "ERCOT-South",39822,39825,39825,43.5,43.5,43.5,5.47,800,1,2 "ERCOT-South",39825,39826,39826,55,50.5,52.95,9.45,8800,11,12,,," " "ERCOT-South",39826,39827,39827,45.5,43.5,44.44,-8.51,14400,18,18 "ERCOT-South",39827,39828,39828,45,44.25,44.68,0.24,12000,14,12 "ERCOT-South",39828,39829,39829,44,42.75,43.18,-1.5,8000,10,10 "ERCOT-South",39833,39834,39834,33,32.5,32.75,-10.43,9600,12,8

477

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

54.5,53.4,53.98,5.44,3200,4,7 54.5,53.4,53.98,5.44,3200,4,7 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49,47.25,48.27,-5.71,8000,10,12 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56,53.5,54.75,6.48,4800,6,10 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",97,87,89.96,35.21,20800,18,16 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56.25,51,53.71,-36.25,16800,19,15 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.75,46,46.33,-7.38,17600,22,17

478

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

Nepool MH Da Lmp ",39815,39818,39818,65.55,65,65.44,-5.89,12000,15,9 Nepool MH Da Lmp ",39815,39818,39818,65.55,65,65.44,-5.89,12000,15,9 "Nepool MH Da Lmp",39818,39819,39819,67,65,66.22,0.78,39200,46,22 "Nepool MH Da Lmp ",39819,39820,39820,65,63.25,63.83,-2.39,20000,24,18 "Nepool MH Da Lmp ",39820,39821,39821,67.5,65.75,66.47,2.64,28000,33,16 "Nepool MH Da Lmp ",39821,39822,39822,78.5,76,77.31,10.84,21600,27,16 "Nepool MH Da Lmp ",39822,39825,39825,100,90,94.19,16.88,28800,35,19 "Nepool MH Da Lmp ",39825,39826,39826,81,72.75,74.76,-19.43,36000,44,24 "Nepool MH Da Lmp ",39826,39827,39827,101,98,99.83,25.07,16000,20,18 "Nepool MH Da Lmp",39827,39828,39828,130,117,120.32,20.49,40000,50,27 "Nepool MH Da Lmp ",39828,39829,39829,120,106,109.76,-10.56,72800,91,35

479

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

SP 15",39449,39450,39450,74.6,69.25,73.56,97200,234,36 SP 15",39449,39450,39450,74.6,69.25,73.56,97200,234,36 "SP 15",39450,39451,39452,70,63,68.49,291200,275,37 "SP 15",39451,39454,39454,75,68,69.2,140000,326,39 "SP 15",39454,39455,39455,73.25,69,71.52,144800,329,37 "SP 15",39455,39456,39456,72.25,70.25,71.32,198000,425,35 "SP 15",39456,39457,39457,73.75,70.75,72.79,157600,351,37 "SP 15",39457,39458,39459,70.25,67.25,68.46,226400,268,33 "SP 15",39458,39461,39461,75,73.25,73.77,184000,366,38 "SP 15",39461,39462,39462,78.25,75,75.77,110800,235,34 "SP 15",39462,39463,39464,88,77.5,79.42,323200,351,36 "SP 15",39463,39465,39466,79,74.25,77.52,259200,302,36 "SP 15",39464,39468,39468,84.45,77,82.35,126400,287,36

480

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

54.55,54.05,54.37,1.9,8800,20,11 54.55,54.05,54.37,1.9,8800,20,11 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",53.25,52.75,53.09,-1.28,35200,64,16 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52,51.25,51.51,-1.58,13600,28,17 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56.5,53.25,54.08,2.57,65600,71,17 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51.15,50.8,51.01,-3.07,27600,53,19 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",50.75,50,50.18,-0.83,23200,39,11

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

084,39085,39085,62,55,55.98,10400,13,10 084,39085,39085,62,55,55.98,10400,13,10 "NEPOOL MH DA LMP",39085,39086,39086,54.75,52.75,53.53,30400,38,20 "NEPOOL MH DA LMP",39086,39087,39087,56,55,55.35,24800,31,19 "NEPOOL MH DA LMP",39087,39090,39090,58,56.5,57.08,8000,10,12 "NEPOOL MH DA LMP",39090,39091,39091,58.75,57.25,57.86,34400,41,19 "NEPOOL MH DA LMP",39091,39092,39092,60.5,59,59.8,20800,25,19 "NEPOOL MH DA LMP",39092,39093,39093,65,63.5,64.04,13600,16,15 "NEPOOL MH DA LMP",39093,39094,39094,61.25,59.75,60.82,15200,19,14 "NEPOOL MH DA LMP",39094,39097,39097,62,59,60.95,16800,21,16 "NEPOOL MH DA LMP",39097,39098,39098,69.25,67,68.25,22400,28,15 "NEPOOL MH DA LMP",39098,39099,39099,89,84.5,86.33,34400,43,26

482

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

53.5,48,50.93,,13600,17,11 53.5,48,50.93,,13600,17,11 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57.5,52.75,55,4.07,31200,39,15 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51.5,49.5,50.38,-4.62,3200,4,4 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52,49.5,51.25,0.87,19200,24,12 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.75,45.25,45.8,-5.45,21600,27,14 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43,39.5,41.3,-4.5,10400,13,8

483

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

U.S. Energy Information Administration (EIA) Indexed Site

38.75,37.25,37.95,-2.02,13600,17,14 38.75,37.25,37.95,-2.02,13600,17,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43.5,40,42.39,4.44,10000,25,20 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.5,37.75,38.26,-4.13,9200,23,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",40.25,37.25,38.46,0.2,7600,19,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",41,38,38.93,0.47,9200,23,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",38.25,36.5,37.29,-1.64,13600,17,17

484

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

623,37624,37624,37.45,33.75,35.69,28800,36,19 623,37624,37624,37.45,33.75,35.69,28800,36,19 "PJM West",37624,37627,37627,48,47,47.58,28800,32,20 "PJM West",37627,37628,37628,50.5,48,49.53,33600,42,19 "PJM West",37628,37629,37629,47,44.25,45.39,35200,44,20 "PJM West",37629,37630,37630,39,37,37.73,27200,33,19 "PJM West",37630,37631,37631,43.5,41.75,42.44,25600,27,17 "PJM West",37631,37634,37634,64,56.5,58.31,20800,26,19 "PJM West",37634,37635,37635,56,54.8,55.52,19200,24,19 "PJM West",37635,37636,37636,56.5,54.9,55.51,28000,33,19 "PJM West",37636,37637,37637,53,50.25,51.89,32000,40,22 "PJM West",37637,37638,37638,54,52,52.63,30400,38,23 "PJM West",37638,37641,37641,48.25,47,47.48,26400,33,17

485

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

258,37259,37259,33.75,32.5,33.23,10400,13,12 258,37259,37259,33.75,32.5,33.23,10400,13,12 "NEPOOL",37259,37260,37260,36.25,35,35.98,24800,31,18 "NEPOOL",37260,37263,37263,34,33.25,33.66,8800,11,12 "NEPOOL",37263,37264,37264,34,33.5,33.67,10400,13,11 "NEPOOL",37264,37265,37265,32.6,31,32.04,9600,11,13 "NEPOOL",37265,37266,37266,29.5,28.7,29.1,10400,13,11 "NEPOOL",37266,37267,37267,29.25,28.25,28.75,12000,15,12 "NEPOOL",37267,37270,37270,31,30,30.24,16800,17,13 "NEPOOL",37270,37271,37271,30.5,29.75,30.09,30400,36,15 "NEPOOL",37271,37272,37272,29.5,28.65,28.98,23200,28,15 "NEPOOL",37272,37273,37273,30.4,29.8,30.02,32800,39,16 "NEPOOL",37273,37274,37274,30,29.1,29.37,11200,14,15 "NEPOOL",37274,37277,37277,30,29.25,29.72,6400,8,9

486

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

U.S. Energy Information Administration (EIA) Indexed Site

8721,38722,38722,57.5,57.5,57.5,-22.5,800,1,2 8721,38722,38722,57.5,57.5,57.5,-22.5,800,1,2 "ERCOT-South",38748,38749,38749,57,57,57,-0.5,800,1,2 "ERCOT-South",38751,38754,38754,59,59,59,2,1600,2,3 "ERCOT-South",38786,38789,38789,48,48,48,-11,800,1,2 "ERCOT-South",38803,38804,38804,52.5,50.5,51.06,3.06,6400,8,7 "ERCOT-South",38804,38805,38805,54.75,54.75,54.75,3.69,3200,2,3 "ERCOT-South",38805,38806,38806,55.25,53.5,54.21,-0.54,4800,6,5 "ERCOT-South",38806,38807,38807,58,58,58,3.79,800,1,2,,,,," " "ERCOT-South",38810,38811,38811,60,60,60,2,800,1,2 "ERCOT-South",38811,38812,38812,64,64,64,4,800,1,2 "ERCOT-South",38812,38813,38813,63,62.5,62.63,-1.37,3200,4,6 "ERCOT-South",38813,38814,38814,62,62,62,-0.63,800,1,2