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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

Texas Nuclear Profile - Comanche Peak  

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

Comanche Peak" "Unit","Summer capacity (mw)","Net generation (thousand mwh)","Summer capacity factor (percent)","Type","Commercial operation date","License expiration date"...

2

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 +

3

Reference Designs of 50 MW / 250 MWh Energy Storage Systems  

Science Conference Proceedings (OSTI)

Energy storage solutions for Renewable Integration and Transmission and Distribution (T&D) Grid Support often require systems of 10's of MWs in scale, and energy durations of longer than 4 hours. The goals of this study were to develop cost, performance and conceptual design information for several current and emerging alternative bulk storage systems in the scale of 50 MW / 250 MWh.

2011-12-28T23:59:59.000Z

4

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 +

5

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 +

6

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 +

7

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 +

8

Total Cost Per MwH for all common large scale power generation...  

Open Energy Info (EERE)

per MWh or KWh for the various sources ? I suspect that the costs commonly quoted for fossil fuels and nucelar are artificially low and that these fake costs are used to 'sell'...

9

Peak Power at Peak Efficiency  

Peak Power At Peak Efficiency. 21. st. Industry Growth Forum. October 2008. PJ Piper (857) 350?3100. ... At <$10/bbl oil, QM Power’s electric ...

10

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

11

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 +

12

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 +

13

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 +

14

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 +

15

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 +

16

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 +

17

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 +

18

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 +

19

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 +

20

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 +

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/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 +

22

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 +

23

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 +

24

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 +

25

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 +

26

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 +

27

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 +

28

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 +

29

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 +

30

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 +

31

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 +

32

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 +

33

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 +

34

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 +

35

"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

36

"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

37

"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

38

"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

39

"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

40

"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

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.


41

"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

42

"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

43

"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

44

"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

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

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

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

1246,41247,41247,28.5,26.5,27.76,-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

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 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

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

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

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

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

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 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

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

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

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 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

53

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

The installed battery has an energy capacity of 4 MWh and acapacity appears to be decreasing with increasing battery

DeForest, Nicholas

2013-01-01T23:59:59.000Z

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

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

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

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

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

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

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 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

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

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

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

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

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

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

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

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

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

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

63

A Case Study on the Demonstration of Storage for Simultaneous Voltage Smoothing and Peak Shifting  

Science Conference Proceedings (OSTI)

Public Service of New Mexico (PNM) installed an energy storage system for simultaneous voltage smoothing and peak shifting associated with a 500 kW photovoltaic system. The energy storage system is comprised of two elements: a 0.5 MW smoothing battery utilizing UltraBatteries and a 0.25 MW/0.99MWh peak shifting battery using advanced lead acid batteries. The two battery systems, combined with a single 0.75 MW power conditioning system, are co-located with a separately installed 500 kW solar ...

2012-10-30T23:59:59.000Z

64

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.

65

Electric Power Annual  

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

Demand-Side Management Program Incremental Effects by Program Category, 2002 through 2011 Energy Efficiency Load Management Total Year Energy Savings (Thousand MWh) Actual Peak...

66

Electric Power Annual  

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

1. Demand-Side Management Program Annual Effects by Program Category, 2002 through 2011 Energy Efficiency Load Management Total Year Energy Savings (Thousand MWh) Actual Peak Load...

67

Oil Peak or Panic?  

SciTech Connect

In this balanced consideration of the peak-oil controversy, Gorelick comes down on the side of the optimists.

Greene, David L [ORNL

2010-01-01T23:59:59.000Z

68

Measured Peak Equipment Loads in Laboratories  

SciTech Connect

This technical bulletin documents measured peak equipment load data from 39 laboratory spaces in nine buildings across five institutions. The purpose of these measurements was to obtain data on the actual peak loads in laboratories, which can be used to rightsize the design of HVAC systems in new laboratories. While any given laboratory may have unique loads and other design considerations, these results may be used as a 'sanity check' for design assumptions.

Mathew, Paul A.

2007-09-12T23:59:59.000Z

69

Peaks Over Threshold Plot  

Science Conference Proceedings (OSTI)

... CAPTURE POT.OUT PEAKS OVER THRESHOLD PLOT Y17 R END OF CAPTURE . SKIP 0 READ DPST2F.DAT ITER NPOINTS THRESH R2 XR . ...

2010-12-06T23:59:59.000Z

70

Reference design of 100 MW-h lithium/iron sulfide battery system for utility load leveling  

SciTech Connect

The first year in a two-year cooperative effort between Argonne National Laboratory and Rockwell International to develop a conceptual design of a lithium alloy/iron sulfide battery for utility load leveling is presented. A conceptual design was developed for a 100 MW-h battery system based upon a parallel-series arrangement of 2.5 kW-h capacity cells. The sales price of such a battery system was estimated to be very high, $80.25/kW-h, exclusive of the cost of the individual cells, the dc-to-ac converters, site preparation, or land acquisition costs. Consequently, the second year's efforts were directed towards developing modified designs with significantly lower potential costs.

Zivi, S.M.; Kacinskas, H.; Pollack, I.; Chilenskas, A.A.; Barney, D.L.; Grieve, W.; McFarland, B.L.; Sudar, S.; Goldstein, E.; Adler, E.

1980-03-01T23:59:59.000Z

71

"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"

72

PEAK READING VOLTMETER  

DOE Patents (OSTI)

An improvement in peak reading voltmeters is described, which provides for storing an electrical charge representative of the magnitude of a transient voltage pulse and thereafter measuring the stored charge, drawing oniy negligible energy from the storage element. The incoming voltage is rectified and stored in a condenser. The voltage of the capacitor is applied across a piezoelectric crystal between two parallel plates. Amy change in the voltage of the capacitor is reflected in a change in the dielectric constant of the crystal and the capacitance between a second pair of plates affixed to the crystal is altered. The latter capacitor forms part of the frequency determlning circuit of an oscillator and means is provided for indicating the frequency deviation which is a measure of the peak voltage applied to the voltmeter.

Dyer, A.L.

1958-07-29T23:59:59.000Z

73

How People Actually Use Thermostats  

Science Conference Proceedings (OSTI)

Residential thermostats have been a key element in controlling heating and cooling systems for over sixty years. However, today's modern programmable thermostats (PTs) are complicated and difficult for users to understand, leading to errors in operation and wasted energy. Four separate tests of usability were conducted in preparation for a larger study. These tests included personal interviews, an on-line survey, photographing actual thermostat settings, and measurements of ability to accomplish four tasks related to effective use of a PT. The interviews revealed that many occupants used the PT as an on-off switch and most demonstrated little knowledge of how to operate it. The on-line survey found that 89% of the respondents rarely or never used the PT to set a weekday or weekend program. The photographic survey (in low income homes) found that only 30% of the PTs were actually programmed. In the usability test, we found that we could quantify the difference in usability of two PTs as measured in time to accomplish tasks. Users accomplished the tasks in consistently shorter times with the touchscreen unit than with buttons. None of these studies are representative of the entire population of users but, together, they illustrate the importance of improving user interfaces in PTs.

Meier, Alan; Aragon, Cecilia; Hurwitz, Becky; Mujumdar, Dhawal; Peffer, Therese; Perry, Daniel; Pritoni, Marco

2010-08-15T23:59:59.000Z

74

The Year of Peak Production  

U.S. Energy Information Administration (EIA)

When world conventional oil production will peak is, of course, the bottom-line question. It has already peaked in the United States, in 1970.

75

Peak Power Reduction Strategies for the Lighting Systems in Government Buildings  

E-Print Network (OSTI)

Lighting systems are the second major contributor to the peak power demand and energy consumption in buildings after A/C systems. They account for nearly 20% of the peak power demand and 15% of the annual energy consumption. Thus energy efficient lighting systems and their smart operation can be very effective in reducing the national peak power and energy consumption, particularly for a country like Kuwait where power demand grew from 6750 MW in 2001 to 9075 MW in 2007 (MEW, 2002- 2008). This paper 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 7:30 and 2:30 and peak power demand of 29.3 MW achieved a reduction of 2 MW in the peak power demand (around 7%). More importantly this 7% in peak load reduction and 10,628 MWh reduction in the annual energy consumption was achieved without any added cost. Also, the paper includes recommendations for retrofitting cost effective energy efficient lighting systems and implementation of more effective control.

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

2010-01-01T23:59:59.000Z

76

Predicted vs. Actual Energy Savings of Retrofitted House  

E-Print Network (OSTI)

This paper reports the results of actual energy savings and the predicted energy savings of retrofitted one-story house located in Dhahran, Saudi Arabia. The process started with modeling the house prior to retrofitting and after retrofitting. The monthly metered energy consumption is acquired from the electric company archives for seven years prior to retrofitting and recording the actual monthly energy consumption of the post retrofitting. The house model is established on DOE 2.1. Actual monthly energy consumption is used to calibrate and fine-tuning the model until the gap between actual and predicted consumption was narrowed. Then the Energy Conservation Measures (ECMs) are entered into the modeled house according to the changes in thermo-physical properties of the envelope and the changes in schedules and number of users. In order to account for those differences, electrical consumption attributed to A/C in summer was isolated and compared. The study followed the International Performance Measurement & Verification Protocol (IPMVP) in assessing the impact of energy conservation measures on actual, metered, building energy consumption. The study aimed to show the predicted savings by the simulated building model and the actual utility bills' analysis in air conditioning consumption and peak at monthly load due to building envelope.

Al-Mofeez, I.

2010-01-01T23:59:59.000Z

77

Conceptual design of electrical balance of plant for advanced battery energy storage facility. Annual report, March 1979. [20-MW, 100 MWh  

SciTech Connect

Large-scale efforts are in progress to develop advanced batteries for utility energy storage systems. Realization of the full benefits available from those systems requires development, not only of the batteries themselves, but also the ac/dc power converter, the bulk power interconnecting equipment, and the peripheral electric balance of plant equipment that integrate the battery/converter into a properly controlled and protected energy system. This study addresses these overall system aspects; although tailored to a 20-MW, 100-MWh lithium/sulfide battery system, the technology and concepts are applicable to any battery energy storage system. 42 figures, 14 tables. (RWR)

1980-01-01T23:59:59.000Z

78

Peak production in an oil depletion model with triangular field profiles  

E-Print Network (OSTI)

Peak production in an oil depletion model with triangular field profiles Dudley Stark School;1 Introduction M. King Hubbert [5] used curve fitting to predict that the peak of oil produc- tion in the U.S.A. would occur between 1965 and 1970. Oil production in the U.S.A. actually peaked in 1970 and has been

Stark, Dudley

79

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

80

Peak Oil, Peak Energy Mother Nature Bats Last  

E-Print Network (OSTI)

/Predicted (2006) Discovery, Production FSU (former Soviet Union) history Soviet Union collapse 80's oil pricePeak 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 cubic

Sereno, Martin

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: 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

82

Peaks in Raindrop Size Distributions  

Science Conference Proceedings (OSTI)

The multipeak behavior of raindrop size distributions has been studied. Peaks have been found for distinct drop diameters: 0.7, 1.0, 1.9, and possibly 3.2 mm. The probability is about 65% that at least one of these peaks exists in an observed ...

M. Steiner; A. Waldvogel

1987-10-01T23:59:59.000Z

83

Before Getting There: Potential and Actual Collaboration  

Science Conference Proceedings (OSTI)

In this paper we introduce the concepts of Actual and Potential Collaboration Spaces. The former applies to the space where collaborative activities are performed, while the second relates to the initial space where opportunities for collaboration are ... Keywords: Doc2U, PIÑAS, casual and informal interactions, potential and actual collaboration spaces, potential collaboration awareness

Alberto L. Morán; Jesús Favela; Ana María Martínez Enríquez; Dominique Decouchant

2002-09-01T23:59:59.000Z

84

Black Peak and Enchantments - CECM  

E-Print Network (OSTI)

Black Peak, North Cascades. A nice two day outing. We hiked on the Maple Pass trail, from Hwy. 20, to Heather Pass, and then on a path to Lewis lake, where ...

85

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.

86

Off peak ice storage generation  

DOE Green Energy (OSTI)

Due to the high costs associated with peak demand charges imposed by most electrical companies today, various means of shifting the peak HVAC load have been identified by the industry. This paper discusses the results of a study based upon a building site located in the high desert of the southwestern United States that evaluated ice storage as a mechanism of operating cost reductions. The discussion addresses both the seasonal and the annual cost and energy impacts of an ice storage system when used in place of an air-to-air heat pump system.

Davis, R.E.; Cerbo, F.J.

1985-01-01T23:59:59.000Z

87

Peaks, Plans and (Persnickety) Prices  

Reports and Publications (EIA)

This presentation provides information about EIA's estimates of working gas peak storage capacity, and the development of the natural gas storage industry. Natural gas shale and the need for high deliverability storage are identified as key drivers in natural gas storage capacity development. The presentation also provides estimates of planned storage facilities through 2012.

Information Center

2010-10-28T23:59:59.000Z

88

METHOD OF PEAK CURRENT MEASUREMENT  

DOE Patents (OSTI)

The measurement and recording of peak electrical currents are described, and a method for utilizing the magnetic field of the current to erase a portion of an alternating constant frequency and amplitude signal from a magnetic mediums such as a magnetic tapes is presented. A portion of the flux from the current carrying conductor is concentrated into a magnetic path of defined area on the tape. After the current has been recorded, the tape is played back. The amplitude of the signal from the portion of the tape immediately adjacent the defined flux area and the amplitude of the signal from the portion of the tape within the area are compared with the amplitude of the signal from an unerased portion of the tape to determine the percentage of signal erasure, and thereby obtain the peak value of currents flowing in the conductor.

Baker, G.E.

1959-01-20T23:59:59.000Z

89

Evaluation of concurrent peak responses  

SciTech Connect

This report deals with the problem of combining two or more concurrent responses which are induced by dynamic loads acting on nuclear power plant structures. Specifically, the acceptability of using the square root of the sum of the squares (SRSS) value of peak values as the combined response is investigated. Emphasis is placed on the establishment of a simplified criterion that is convenient and relatively easy to use by design engineers.

Wang, P.C.; Curreri, J.; Reich, M.

1983-01-01T23:59:59.000Z

90

Peak shaving through resource buffering  

E-Print Network (OSTI)

Abstract. We introduce and solve a new problem inspired by energy pricing schemes in which a client is billed for peak usage. At each timeslot the system meets an energy demand through a combination of a new request, an unreliable amount of free source energy (e.g. solar or wind power), and previously received energy. The added piece of infrastructure is the battery, which can store surplus energy for future use. More generally, the demands could represent required amounts of energy, water, or any other tenable resource which can be obtained in advance and held until needed. In a feasible solution, each demand must be supplied on time, through a combination of newly requested energy, energy withdrawn from the battery, and free source. The goal is to minimize the maximum request. In the online version of this problem, the algorithm must determine each request without knowledge of future demands or free source availability, with the goal of maximizing the amount by which the peak is reduced. We give efficient optimal algorithms for the offline problem, with and without a bounded battery. We also show how to find the optimal offline battery size, given the requirement that the final battery level equals the initial battery level. Finally, we give efficient Hn-competitive algorithms assuming the peak effective demand is revealed in advance, and provide matching lower bounds. 1

Amotz Bar-noy; Matthew P. Johnson; Ou Liu

2007-01-01T23:59:59.000Z

91

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

92

SnowPeak Energy | Open Energy Information  

Open Energy Info (EERE)

it. SnowPeak Energy is a company located in Reno, Nevada . References "SnowPeak Energy" Retrieved from "http:en.openei.orgwindex.php?titleSnowPeakEnergy&oldid35121...

93

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

94

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

95

Improving Industrial Refrigeration System Efficiency - Actual Applications  

E-Print Network (OSTI)

This paper discusses actual design and modifications for increased system efficiency and includes reduced chilled liquid flow during part load operation, reduced condensing and increased evaporator temperatures for reduced system head, thermosiphon cycle cooling during winter operation, compressor intercooling, direct refrigeration vs. brine cooling, insulation of cold piping to reduce heat gain, multiple screw compressors for improved part load operation, evaporative condensers for reduced system head and pumping energy, and using high efficiency motors.

White, T. L.

1980-01-01T23:59:59.000Z

96

Peak Electricity Impacts of Residential Water Use  

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

Peak Electricity Impacts of Residential Water Use Title Peak Electricity Impacts of Residential Water Use Publication Type Report LBNL Report Number LBNL-5736E Year of Publication...

97

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

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

Contacts Media Contacts A Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Title A Sensitivity Study of Building Performance Using 30-Year Actual...

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 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

100

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

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

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400.................................................................................. 9 Sources of Forecast Error....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2

102

"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"

103

"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"

104

Peak Underground Working Natural Gas Storage Capacity  

U.S. Energy Information Administration (EIA)

Peak Working Natural Gas Capacity. Data and Analysis from the Energy Information Administration (U.S. Dept. of Energy)

105

Determination of Hydrogen Peak Temperatures and Trapping ...  

Science Conference Proceedings (OSTI)

Presentation Title, Determination of Hydrogen Peak Temperatures and Trapping Energies of Various Lattice Defects In Iron Using Thermal Desorption ...

106

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

107

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"

108

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":""}]}

109

Optimization of Demand Response Through Peak Shaving  

E-Print Network (OSTI)

Jul 5, 2013 ... Optimization of Demand Response Through Peak Shaving. G. Zakeri(g.zakeri *** at*** auckland.ac.nz) D. Craigie(David.Craigie ***at*** ...

110

A distributed approach to taming peak demand  

Science Conference Proceedings (OSTI)

A significant portion of all energy capacity is wasted in over-provisioning to meet peak demand. The current state-of-the-art in reducing peak demand requires central authorities to limit device usage directly, and are generally reactive. We apply techniques ...

Michael Sabolish; Ahmed Amer; Thomas M. Kroeger

2012-06-01T23:59:59.000Z

111

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

112

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

113

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

114

The Boson peak in supercooled water  

E-Print Network (OSTI)

We perform extensive molecular dynamics simulations of the TIP4P/2005 model of water to investigate the origin of the Boson peak reported in experiments on supercooled water in nanoconfined pores, and in hydration water around proteins. We find that the onset of the Boson peak in supercooled bulk water coincides with the crossover to a predominantly low-density-like liquid below the Widom line $T_W$. The frequency and onset temperature of the Boson peak in our simulations of bulk water agree well with the results from experiments on nanoconfined water. Our results suggest that the Boson peak in water is not an exclusive effect of confinement. We further find that, similar to other glass-forming liquids, the vibrational modes corresponding to the Boson peak are spatially extended and are related to transverse phonons found in the parent crystal, here ice Ih.

Pradeep Kumar; K. Thor Wikfeldt; Daniel Schlesinger; Lars G. M. Pettersson; H. E. Stanley

2013-05-19T23:59:59.000Z

115

Density Forecasting for Long-Term Peak Electricity Demand  

E-Print Network (OSTI)

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007–2008.

Rob J. Hyndman; Shu Fan

2009-01-01T23:59:59.000Z

116

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

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

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)"...

117

Automated Demand Response for Critical Peak Pricing  

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

Automated Demand Response for Critical Peak Pricing Speaker(s): Naoya Motegi Date: June 9, 2005 - 12:00pm Location: Bldg. 90 California utilities have been exploring the use of...

118

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Note: 1) 'Demonstrated Peak Working Gas Capacity' is the sum of the highest storage inventory level of working gas observed in each facility over the prior 5-year period as...

119

Peak Load Shifting by Thermal Energy Storage  

Science Conference Proceedings (OSTI)

This technical update from the Electric Power Research Institute (EPRI) reviews the technology of storing energy in hot water and explores the potential for implementing this form of thermal energy storagethrough means of smart electric water heatersas a way to shift peak load on the electric grid. The report presents conceptual background, discusses strategies for peak load shifting and demand response, documents a series of laboratory tests conducted on a representative model of smart water heater, and...

2011-12-14T23:59:59.000Z

120

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

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

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

Dispatch for Macrogrid Peak- Demand Mitigation NicholasDispatch for Macrogrid Peak-Demand Mitigation Nicholasdetermine whether the peak demand on the substation feeder

DeForest, Nicholas

2013-01-01T23:59:59.000Z

122

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.

123

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":""}]}

124

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":""}]}

125

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":""}]}

126

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":""}]}

127

Multiple Attractor in Newton -Leipnik System, Peak to Peak dynamics and Chaos Control  

E-Print Network (OSTI)

The chaotic properties of Newton-Leipnik system are discussed from the view point of strange attractors. Previously, two strange attractors of this system were illustrated which occured from two different initial conditions under the same parameter condition. It is found that above system also exhibits multiple attractors under different parameter values but same initial condition and we have shown the existence of three other strange attractors with varying dimensionality under different parametric conditions. The properties of these attractors are then analyzed on the basis of Lyapunov exponents, power spectra, recurrence analysis and peak-to-peak dynamics. The peak-to-peak dynamics relies on the low dimensionality of the chaotic attractor and allows to approximately model the system. Peak-to-peak plot along with return-time plot are then effectively used to solve the optimal control problem of the system which reverts the system to a periodic situation.

Biswambhar Rakshit; Papri Saha; A. Roy Chowdhury

2005-01-07T23:59:59.000Z

128

Vad är 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)

Wälimaa, Peter

2013-01-01T23:59:59.000Z

129

Table 13. Coal Production, Projected vs. Actual Projected  

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

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...

130

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

Gasoline and Diesel Fuel Update (EIA)

b. Average Electricity Prices, Projected vs. Actual Projected Price in Nominal Dollars (nominal dollars, cents per kilowatt-hour) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002...

131

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

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

b. Average Electricity Prices, Projected vs. Actual" "Projected Price in Nominal Dollars" " (nominal dollars, cents per kilowatt-hour)" ,1993,1994,1995,1996,1997,1998,1999,2000,200...

132

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

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

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...

133

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":""}]}

134

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":""}]}

135

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":""}]}

136

Wave Modeling—Missing the Peaks  

Science Conference Proceedings (OSTI)

The paper analyzes the capability of the present wave models of properly reproducing the conditions during and at the peak of severe and extreme storms. After providing evidence that this is often not the case, the reasons for it are explored. ...

Luigi Cavaleri

2009-11-01T23:59:59.000Z

137

The Inevitable Peaking of World Oil Production  

E-Print Network (OSTI)

The era of plentiful, low-cost petroleum is approaching an end. ? Without massive mitigation the problem will be pervasive and long lasting. Oil peaking represents a liquid fuels problem, not an “energy crisis”. ? Governments will have to take the initiative on a timely basis. ? In every crisis, there are always opportunities for those that act decisively.

Robert L. Hirsch

2005-01-01T23:59:59.000Z

138

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]]

139

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":""}]}

140

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

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.


141

August 2010On 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 of resource exhaustion (the Hubbert Curve). I take a somewhat different view, one that interprets peaking as a reflection of fundamental economic determinants of an intertemporal equilibrium. From that perspective, it is reasonable to ask whether the occurrence and timing of the peak reveals anything useful regarding the state of resource exhaustion. Accordingly, I examine peaking as an indicator of resource scarcity and compare its performance to the traditional economic indicators. I find the phenomenon of peaking to be an ambiguous indicator, at best. If someone announced that the peak would arrive earlier than expected, and you believed them, you would not know whether the news was good or bad. Unfortunately, the traditional economic indicators fare no better. Their movements are driven partially by long-term trends unrelated to changes in scarcity, and partially but inconsistently driven by actual changes in scarcity. Thus, the traditional indicators provide a signal that is garbled and unreliable.

James L. Smith; Dr. James; L. Smith

2010-01-01T23:59:59.000Z

142

Peak power tracking for a solar buck charger  

E-Print Network (OSTI)

This thesis discusses the design, implementation, and testing of a buck converter with peak power tracking. The peak power tracker uses a perturb and observe algorithm to actively track the solar panel's peak power point ...

Cohen, Jeremy Michael, M. Eng. Massachusetts Institute of Technology

2010-01-01T23:59:59.000Z

143

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

Open Energy Info (EERE)

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

144

The Year of Peak Production - Energy Information Administration  

U.S. Energy Information Administration (EIA)

When world conventional oil production will peak is, of course, the bottom-line question. It has already peaked in the United States, in 1970.

145

Residential implementation of critical-peak pricing of electricity  

E-Print Network (OSTI)

L.R. Modeling alternative residential peak-load electricitydemand response to residential critical peak pricing (CPP)analysis of California residential customer response to

Herter, Karen

2006-01-01T23:59:59.000Z

146

Definition: Circuit Peak Load Management | Open Energy Information  

Open Energy Info (EERE)

Circuit Peak Load Management Jump to: navigation, search Dictionary.png Circuit Peak Load Management An application utilizing sensors, information processors, communications, and...

147

Estimates of Peak Underground Working Gas Storage Capacity in the ...  

U.S. Energy Information Administration (EIA)

Estimates of Peak Underground Working Gas Storage Capacity in the United States, 2009 Update The aggregate peak capacity for U.S. underground natural gas storage is ...

148

Magnetotellurics At Silver Peak Area (DOE GTP) | Open Energy...  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak Area Exploration Technique Magnetotellurics Activity Date Usefulness not indicated DOE-funding Unknown...

149

Multispectral Imaging At Silver Peak Area (DOE GTP) | Open Energy...  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Multispectral Imaging At Silver Peak Area (DOE GTP) Exploration...

150

Development Wells At Silver Peak Area (DOE GTP) | Open Energy...  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Development Wells At Silver Peak Area (DOE GTP) Exploration Activity...

151

Ground Magnetics At Silver Peak Area (DOE GTP) | Open Energy...  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Ground Magnetics At Silver Peak Area (DOE GTP) Exploration Activity...

152

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

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

Dispatch for Macrogrid Peak-Demand Mitigation Title Microgrid Dispatch for Macrogrid Peak-Demand Mitigation Publication Type Conference Proceedings Refereed Designation Refereed...

153

Shale gas production: potential versus actual greenhouse gas emissions*  

E-Print Network (OSTI)

Shale gas production: potential versus actual greenhouse gas emissions* Francis O Environ. Res. Lett. 7 (2012) 044030 (6pp) doi:10.1088/1748-9326/7/4/044030 Shale gas production: potential gas (GHG) emissions from shale gas production and use are controversial. Here we assess the level

154

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":""}]}

155

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.

156

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 world’s total production of usable energy will reach a maximum value and then begin an inexorable decline. Ninety-two percent of the world’s energy is currently derived from the non-renewable sources (oil, coal, natural gas and nuclear). As each of these non-renewable sources individually peaks in production, we can see total energy production peak. The human population is tightly correlated with global energy production, as agriculture and material possessions are energy intensive. It follows that peak energy should have a significant effect on world population. Using a set of mathematical models, including M King Hubbert’s oil peak mathematics, we prepared three models. The first approached the peak energy and population problem from the point of view of a “black-box” homogeneous world. The second model divides the world into ten major regions to study the global heterogeneity of the peak energy and population question. Both of these models include various scenarios for how the world population will develop based on available energy and per capita consumption of that energy. The third model examines energy and climate change within the forty-eight contiguous American states in order to identify some of the “best” and some of the “worst” states in which to live in the year 2050. The black box model indicates that peak energy will occur in 2026 at a maximum production of 104.1 billion barrels of oil equivalent (BBOE). Total energy production in 2011 was 92.78 BBOE. Three scenarios of different energy consumption rates suggest a peak world population occurring between 2026 and 2036, at 7.6-8.3 billion. The regional model indicates that even as each region protects its own energy resources, most of the world will reach peak energy by 2030, and world populations peak between 7.5 and 9 billion. A certain robustness in our conclusion is warranted as similar numbers were obtained via two separate approaches. The third model used several different parameters in order to ascertain that, in general, states that are projected to slow towards flat-line population growth and to become milder due to climate change such as Rhode Island, New York and Ohio are far more suitable with regard to an energy limited world than states that are projected to grow in population as well as become less mild due to climate change such as Texas, Arizona and Nevada. Each of these models in its own way foreshadows necessary changes that the world will experience as the 21st century progresses. The economies of the world have been, and continue to be, built on energy. When energy production is unable to continue growing it must follow that economies will be unable to grow. As the world approaches and passes peak energy, the standard of living in the less developed areas of the world cannot improve without sacrifices being made in the developed world.

Warner, Kevin 1987-

2012-12-01T23:59:59.000Z

157

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

158

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

159

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

160

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

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.


161

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)

162

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

163

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

164

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

165

Conditional model of peak and minimum loads and the load duration curve for electricity  

SciTech Connect

This report presents a model that extends the traditional model of electricity demand to account for intra-period load variation, the kind of variation that is important for evaluating marginal-cost-reflecting price structures. The time-of-day rate is one such price structure. The traditional model of electricity demand explains inter-period demand variation. It says nothing about load variation. The report explains how a model that integrates with previous studies of electricity demand might be formulated. It specifies two concrete models within this framework and estimates them for a number of different utility companies. The model's within-sample-period performance in predicting peak loads is presented for one version of the model extension along with estimations for other variations. In addition a number of plots of actual load distributions, a summation of load variation information, against the actual load distributions, are presented and used to evaluate the performance of specific models.

Trimble, J.L.; Stallings, D.E.; Thomas, B.

1980-05-01T23:59:59.000Z

166

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-

167

Steam Trap Testing and Evaluation: An Actual Plant Case Study  

E-Print Network (OSTI)

With rising steam costs and a high failure rate on the Joliet Plants standard steam trap, a testing and evaluation program was begun to find a steam trap that would work at Olin-Joliet. The basis was to conduct the test on the actual process equipment and that a minimum life be achieved. This paper deals with the history of the steam system/condensate systems, the setting up of the testing procedure, which traps were and were not tested and the results of the testing program to date.

Feldman, A. L.

1981-01-01T23:59:59.000Z

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176

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

177

Direct quantum communication without actual transmission of the message qubits  

E-Print Network (OSTI)

Recently an orthogonal state based protocol of direct quantum communication without actual transmission of particles is proposed by Salih \\emph{et al.}{[}Phys. Rev. Lett. \\textbf{110} (2013) 170502{]} using chained quantum Zeno effect. As the no-transmission of particle claim is criticized by Vaidman {[}arXiv:1304.6689 (2013){]}, the condition (claim) of Salih \\emph{et al.} is weaken here to the extent that transmission of particles is allowed, but transmission of the message qubits (the qubits on which the secret information is encoded) is not allowed. Remaining within this weaker condition it is shown that there exists a large class of quantum states, that can be used to implement an orthogonal state based protocol of secure direct quantum communication using entanglement swapping, where actual transmission of the message qubits is not required. The security of the protocol originates from monogamy of entanglement. As the protocol can be implemented without using conjugate coding its security is independent of non-commutativity.

Chitra Shukla; Anirban Pathak

2013-07-23T23:59:59.000Z

178

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

179

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

180

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...

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

Automated Critical Peak Pricing Field Tests: Program Description and Results  

E-Print Network (OSTI)

Usage: The total effective energy charge for usage duringUsage: The total effective energy charge for usage duringtotal effective TOU energy rates through offsetting summer on-peak and part-peak rate credits for usage

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Xu, Peng

2006-01-01T23:59:59.000Z

182

A Multimethod analysis of the Phenomenon of Peak-Oil.  

E-Print Network (OSTI)

??El concepto de Peak-Oil (el cénit del petróleo) es complejo y a menudo malentendido. Después de aclarar que el Peak-Oil es tanto un problema de… (more)

Kerschner, Christian

2012-01-01T23:59:59.000Z

183

Modeling-Computer Simulations At Desert Peak Area (Wisian & Blackwell...  

Open Energy Info (EERE)

navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Modeling-Computer Simulations At Desert Peak Area (Wisian & Blackwell, 2004) Exploration Activity...

184

--No Title--  

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

Forecasted Spot Energy Prices in MWH - Updated 10212013 Week Beginning Four Corners Four Corners Rockies Rockies Southwest Southwest On Peak Off Peak On Peak Off Peak On Peak...

185

--No Title--  

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

Forecasted Spot Energy Prices in MWH - Updated 1222013 Week Beginning Four Corners Four Corners Rockies Rockies Southwest Southwest On Peak Off Peak On Peak Off Peak On Peak...

186

--No Title--  

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

Forecasted Spot Energy Prices in MWH - Updated 9302013 Week Beginning Four Corners Four Corners Rockies Rockies Southwest Southwest On Peak Off Peak On Peak Off Peak On Peak...

187

Scaling distributed energy storage for grid peak reduction  

Science Conference Proceedings (OSTI)

Reducing peak demand is an important part of ongoing smart grid research efforts. To reduce peak demand, utilities are introducing variable rate electricity prices. Recent efforts have shown how variable rate pricing can incentivize consumers to use ... Keywords: battery, electricity, energy, grid, peak shaving

Aditya Mishra, David Irwin, Prashant Shenoy, Ting Zhu

2013-01-01T23:59:59.000Z

188

SNAP fuel temperature peaking with cusps in coolant channel  

SciTech Connect

Reactor Fuel Elements--temperature peaking in SNAP due to surrounding rods; systems for nuclear auxiliary power (SNAP)--reactor fuel temperataure peaking due to surrounding fuel rods; temeprature--calculations of peaking of, in SNAP fuel due to sourround fuel rods.

Treuenfels, E. W.

1963-07-19T23:59:59.000Z

189

Distributed Battery Control for Peak Power Shaving in Datacenters  

E-Print Network (OSTI)

Distributed Battery Control for Peak Power Shaving in Datacenters Baris Aksanli and Tajana Rosing to shave peak power demands. Our novel distributed battery control design has no performance impact, reduces the peak power needs, and accurately estimates and maximizes the battery lifetime. We demonstrate

Simunic, Tajana

190

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

191

Nonlinear excitations in DNA: Aperiodic models vs actual genome sequences  

E-Print Network (OSTI)

We study the effects of the sequence on the propagation of nonlinear excitations in simple models of DNA in which we incorporate actual DNA sequences obtained from human genome data. We show that kink propagation requires forces over a certain threshold, a phenomenon already found for aperiodic sequences [F. Dom\\'\\i nguez-Adame {\\em et al.}, Phys. Rev. E {\\bf 52}, 2183 (1995)]. For forces below threshold, the final stop positions are highly dependent on the specific sequence. The results of our model are consistent with the stick-slip dynamics of the unzipping process observed in experiments. We also show that the effective potential, a collective coordinate formalism introduced by Salerno and Kivshar [Phys. Lett. A {\\bf 193}, 263 (1994)] is a useful tool to identify key regions in DNA that control the dynamical behavior of large segments. Additionally, our results lead to further insights in the phenomenology observed in aperiodic systems.

Sara Cuenda; Angel Sanchez

2004-07-02T23:59:59.000Z

192

Back-Up/ Peak Shaving Fuel Cell System  

SciTech Connect

This Final Report covers the work executed by Plug Power from 8/11/03 – 10/31/07 statement of work for Topic 2: advancing the state of the art of fuel cell technology with the development of a new generation of commercially viable, stationary, Back-up/Peak-Shaving fuel cell systems, the GenCore II. The Program cost was $7.2 M with the Department of Energy share being $3.6M and Plug Power’s share being $3.6 M. The Program started in August of 2003 and was scheduled to end in January of 2006. The actual program end date was October of 2007. A no cost extension was grated. The Department of Energy barriers addressed as part of this program are: Technical Barriers for Distributed Generation Systems: o Durability o Power Electronics o Start up time Technical Barriers for Fuel Cell Components: o Stack Material and Manufacturing Cost o Durability o Thermal and water management Background The next generation GenCore backup fuel cell system to be designed, developed and tested by Plug Power under the program is the first, mass-manufacturable design implementation of Plug Power’s GenCore architected platform targeted for battery and small generator replacement applications in the telecommunications, broadband and UPS markets. The next generation GenCore will be a standalone, H2 in-DC-out system. In designing the next generation GenCore specifically for the telecommunications market, Plug Power is teaming with BellSouth Telecommunications, Inc., a leading industry end user. The final next generation GenCore system is expected to represent a market-entry, mass-manufacturable and economically viable design. The technology will incorporate: • A cost-reduced, polymer electrolyte membrane (PEM) fuel cell stack tailored to hydrogen fuel use • An advanced electrical energy storage system • A modular, scalable power conditioning system tailored to market requirements • A scaled-down, cost-reduced balance of plant (BOP) • Network Equipment Building Standards (NEBS), UL and CE certifications.

Staudt, Rhonda L.

2008-05-28T23:59:59.000Z

193

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

Science Conference Proceedings (OSTI)

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

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 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

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

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

202

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

203

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

204

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%

205

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

206

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

207

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

208

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

209

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

210

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

211

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

212

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

213

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

214

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

215

Peak power identification on power bumps during test application  

Science Conference Proceedings (OSTI)

Peak power during test can seriously impact circuit performance as well as the power safety for both CUT and tester. In this paper, we propose a method of layout-aware weighted switching activity identification flow that evaluates peak current/power ... Keywords: CMOS device, peak power identification, power bumps, test application, layout-aware weighted switching activity identification flow, dynamic power model, parasitic capacitance, resistance network, power bus, power delivery path, IR-drop, commercial power sign-off analysis tool

Wei Zhao; M. Tehranipoor

2011-07-01T23:59:59.000Z

216

Sustained Peak Low Cycle Fatigue: The Role of Coatings  

Science Conference Proceedings (OSTI)

The growth process continued by a combined process of oxidation and creep. ... of a model developed for crack growth during sustained peak low cycle fatigue.

217

Peak-shape functions for Neutron Time of Flight  

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

(Draft)-dec. 2003 Introduction A reorganization of the subroutines calculating the peak shape function and derivatives for time of flight neutron powder diffraction has been...

218

Flexible Coal: Evolution from Baseload to Peaking Plant (Brochure...  

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

the transformation of power systems Flexible Coal Evolution from Baseload to Peaking Plant The experience cited in this paper is from a generating station with multiple units...

219

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

220

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

Open Energy Info (EERE)

Page Edit History Facebook icon Twitter icon Gas Flux Sampling At Desert Peak Area (Lechler And Coolbaugh, 2007) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home...

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.


221

Flow Shop Scheduling with Peak Power Consumption Constraints  

E-Print Network (OSTI)

enterprises; for example, many energy providers use time-of-use (TOU) tariffs ( e.g. Babu and Ashok. 2008). Peak power consumption has also received some ...

222

Residential implementation of critical-peak pricing of electricity  

E-Print Network (OSTI)

residential peak-load electricity rate structures. Journalefficiency efforts. Keywords: electricity rates, residentialmust suffer higher electricity rates to pay for the bill

Herter, Karen

2006-01-01T23:59:59.000Z

223

Residential implementation of critical-peak pricing of electricity  

E-Print Network (OSTI)

Modeling alternative residential peak-load electricity rateKeywords: electricity rates, residential electricity, demandrates be targeted to the largest residential users of electricity,

Herter, Karen

2006-01-01T23:59:59.000Z

224

Poster: Thermal Energy Storage for Electricity Peak-demand Mitigation...  

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

Poster: Thermal Energy Storage for Electricity Peak-demand Mitigation: A Solution in Developing and Developed World Alike Title Poster: Thermal Energy Storage for Electricity...

225

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 Located in the heart of "Tornado Alley," Oklahoma Gas & Electric Company's (OG&E) electric grid faces significant...

226

Methods and apparatus for reducing peak wind turbine loads ...  

A method for reducing peak loads of wind turbines in a changing wind environment includes measuring or estimating an instantaneous wind speed and direction at the ...

227

Structural Analysis of the Desert Peak-Brady Geothermal Fields...  

Open Energy Info (EERE)

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...

228

Evaluation of Peak Heat Release Rates in Electrical Cabinet Fires  

Science Conference Proceedings (OSTI)

The purpose of this report is to reanalyze the peak heat release rates (HRRs) from fires occurring in electrical cabinets of nuclear power plants.

2012-02-23T23:59:59.000Z

229

Flow shop scheduling with peak power consumption constraints  

E-Print Network (OSTI)

Mar 29, 2012 ... In particular, we consider a flow shop scheduling problem with a restriction on peak power consumption, in addition to the traditional ...

230

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 of… (more)

Tuckerman, Samantha Lynn

2012-01-01T23:59:59.000Z

231

Geothermometry At Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Silver Peak Area (DOE GTP) Exploration Activity Details Location...

232

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

N ATIONAL L ABORATORY Microgrid Dispatch for Macrogrid Peak-equal opportunity employer. Microgrid Dispatch for Macrogridutility customers, microgrid solutions – the installation of

DeForest, Nicholas

2013-01-01T23:59:59.000Z

233

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

234

ENSO Impacts on Peak Wind Gusts in the United States  

Science Conference Proceedings (OSTI)

Changes in the peak wind gust magnitude in association with the warm and cold phases of the El Niño–Southern Oscillation (ENSO) are identified over the contiguous United States. All calculations of the peak wind gust are differences in the ...

Jesse Enloe; James J. O'Brien; Shawn R. Smith

2004-04-01T23:59:59.000Z

235

Green Scheduling: Scheduling of Control Systems for Peak Power Reduction  

E-Print Network (OSTI)

approaches for load shifting and model predictive control have been proposed, we present an alternative approach to reduce the peak power for a set of control systems. The proposed model is intuitive, scalableGreen Scheduling: Scheduling of Control Systems for Peak Power Reduction Truong Nghiem, Madhur Behl

Pappas, George J.

236

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

237

Diesel Rig Mechanical Peaking System Based on Flywheel Storage Technolgy  

Science Conference Proceedings (OSTI)

Flywheel energy storage technology is an emerging energy storage technology, there is a great development in recent years promising energy storage technology, with a large energy storage, high power, no pollution, use of broad, simple maintenance, enabling ... Keywords: Flywheel energy storage technology, mechanical peaking, diesel rig, peak motor

Shuguang Liu, Jia Wang

2012-07-01T23:59:59.000Z

238

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":""}]}

239

The University of Texas at Austin Jan-03 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-03 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1/93) #12;The University of Texas at Austin Feb-03 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1/93) #12;The University of Texas at Austin Mar-03 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1

Johns, Russell Taylor

240

Peaking World Oil Production: Impacts, Mitigation and Risk Management  

E-Print Network (OSTI)

The peaking of world oil production presents the U.S. and the world with an unprecedented risk management problem. As peaking is approached, liquid fuel prices and price volatility will increase dramatically, and, without timely mitigation, the economic, social, and political costs will be unprecedented. Viable mitigation options exist on both the supply and demand sides, but to have substantial impact, they must be initiated more than a decade in advance of peaking. In 2003, the world consumed nearly 80 million barrels per day (MM bpd) of oil. U.S. consumption was almost 20 MM bpd,

Robert L. Hirsch; Roger H. Bezdek; Robert M. Wendling

2005-01-01T23:59:59.000Z

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.


241

Cooling commercial buildings with off-peak power  

Science Conference Proceedings (OSTI)

Large commercial buildings use more electricity for cooling than for heating, and can account for 40% of summer peak demand. A cool storage technique in which compressors chill or freeze water during off-peak periods and the water is circulated during peak hours is in use in 100 commercial buildings. Reports indicate that these systems are economical, although little information is available, but engineers are hesitant to incorporate them because of possible damage from leaks or rust and other uncertainties. The Electric Power Research Institute is evaluating the performance of several systems to answer some of the operating and maintenance questions raised by engineers. 3 references, 3 figures. (DCK)

Lihach, N.; Rabl, V.

1983-10-01T23:59:59.000Z

242

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

243

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:

244

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

245

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

246

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"

247

Airport quotas and peak hour pricing : theory and practice  

E-Print Network (OSTI)

This report examines the leading theoretical studies not only of airport peak-hour pricing but also of the congestion costs associated with airport delays and presents a consistent formulation of both. The report also ...

Odoni, Amedeo R.

1976-01-01T23:59:59.000Z

248

Residential Response to Critical Peak Pricing of Electricity  

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

Residential Response to Critical Peak Pricing of Electricity Speaker(s): Karen Herter Date: June 30, 2005 - 12:00pm Location: Bldg. 90 A recent California study collected detailed...

249

Automated Critical Peak Pricing Field Tests: Program Description and Results  

E-Print Network (OSTI)

E-2: Baseline Peak and Maximum Demand Savings at Each Auto-45 Table 4-8: Maximum Demand saving by Site and Non-and the non-coincident maximum demand savings. If all twelve

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Xu, Peng

2006-01-01T23:59:59.000Z

250

Transient Peak Currents in Permanent Magnet Synchronous Motors  

E-Print Network (OSTI)

Transient Peak Currents in Permanent Magnet Synchronous Motors for Symmetrical Short Circuits Terms-- Permanent magnet synchronous motor, short circuit, protection measure, transient behavior I 33095 Paderborn, Germany Abstract--To enable constant-power areas with permanent magnet synchronous

Noé, Reinhold

251

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

252

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

253

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":[]}

254

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":""}]}

255

The Annual Peak in the SST Anomaly Spectrum  

Science Conference Proceedings (OSTI)

The manner in which monthly mean sea surface temperature anomalies (SSTAs) show enhanced variance at the annual period in the extratropics (an annual peak in the variance spectrum) is illustrated by observations and model simulations. A mechanism,...

Jens Möller; Dietmar Dommenget; Vladimir A. Semenov

2008-06-01T23:59:59.000Z

256

Potential of solar cooling systems for peak demand reduction  

DOE Green Energy (OSTI)

We investigated the technical feasibility of solar cooling for peak demand reduction using a building energy simulation program (DOE2.1D). The system studied was an absorption cooling system with a thermal coefficient of performance of 0.8 driven by a solar collector system with an efficiency of 50% with no thermal storage. The analysis for three different climates showed that, on the day with peak cooling load, about 17% of the peak load could be met satisfactorily with the solar-assisted cooling system without any thermal storage. A performance availability analysis indicated that the solar cooling system should be designed for lower amounts of available solar resources that coincide with the hours during which peak demand reduction is required. The analysis indicated that in dry climates, direct-normal concentrating collectors work well for solar cooling; however, in humid climates, collectors that absorb diffuse radiation work better.

Pesaran, A.A. [National Renewable Energy Lab., Golden, CO (United States); Neymark, J. [Neymark (Joel), Golden, CO (United States)

1994-11-01T23:59:59.000Z

257

Fossil fuel-fired peak heating for geothermal greenhouses  

SciTech Connect

This report examines the capital and operating costs for fossil fuel-fired peak heating systems in geothermally (direct use) heated greenhouses. Issues covered include equipment capital costs, fuel requirements, maintenance and operating costs, system control and integration into conventional hot water greenhouse heating systems. Annual costs per square foot of greenhouse floor area are developed for three climates: Helena, MT; Klamath Falls, OR and San Bernardino, CA, for both boiler and individual unit heater peaking systems. In most applications, peaking systems sized for 60% of the peak load are able to satisfy over 95% of the annual heating requirements and cost less than $0.15 per square foot per year to operate. The propane-fired boiler system has the least cost of operation in all but Helena, MT climate.

Rafferty, K.

1996-12-01T23:59:59.000Z

258

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":""}]}

259

Observation of low magnetic field density peaks in helicon plasma  

SciTech Connect

Single density peak has been commonly observed in low magnetic field (<100 G) helicon discharges. In this paper, we report the observations of multiple density peaks in low magnetic field (<100 G) helicon discharges produced in the linear helicon plasma device [Barada et al., Rev. Sci. Instrum. 83, 063501 (2012)]. Experiments are carried out using argon gas with m = +1 right helical antenna operating at 13.56 MHz by varying the magnetic field from 0 G to 100 G. The plasma density varies with varying the magnetic field at constant input power and gas pressure and reaches to its peak value at a magnetic field value of {approx}25 G. Another peak of smaller magnitude in density has been observed near 50 G. Measurement of amplitude and phase of the axial component of the wave using magnetic probes for two magnetic field values corresponding to the observed density peaks indicated the existence of radial modes. Measured parallel wave number together with the estimated perpendicular wave number suggests oblique mode propagation of helicon waves along the resonance cone boundary for these magnetic field values. Further, the observations of larger floating potential fluctuations measured with Langmuir probes at those magnetic field values indicate that near resonance cone boundary; these electrostatic fluctuations take energy from helicon wave and dump power to the plasma causing density peaks.

Barada, Kshitish K.; Chattopadhyay, P. K.; Ghosh, J.; Kumar, Sunil; Saxena, Y. C. [Institute for Plasma Research, Bhat, Gandhinagar 382428 (India)

2013-04-15T23:59:59.000Z

260

Peaking of world oil production: Impacts, mitigation, & risk management  

SciTech Connect

The peaking of world oil production presents the U.S. and the world with an unprecedented risk management problem. As peaking is approached, liquid fuel prices and price volatility will increase dramatically, and, without timely mitigation, the economic, social, and political costs will be unprecedented. Viable mitigation options exist on both the supply and demand sides, but to have substantial impact, they must be initiated more than a decade in advance of peaking.... The purpose of this analysis was to identify the critical issues surrounding the occurrence and mitigation of world oil production peaking. We simplified many of the complexities in an effort to provide a transparent analysis. Nevertheless, our study is neither simple nor brief. We recognize that when oil prices escalate dramatically, there will be demand and economic impacts that will alter our simplified assumptions. Consideration of those feedbacks will be a daunting task but one that should be undertaken. Our aim in this study is to-- • Summarize the difficulties of oil production forecasting; • Identify the fundamentals that show why world oil production peaking is such a unique challenge; • Show why mitigation will take a decade or more of intense effort; • Examine the potential economic effects of oil peaking; • Describe what might be accomplished under three example mitigation scenarios. • Stimulate serious discussion of the problem, suggest more definitive studies, and engender interest in timely action to mitigate its impacts.

Hirsch, R.L. (SAIC); Bezdek, Roger (MISI); Wendling, Robert (MISI)

2005-02-01T23:59:59.000Z

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.


261

Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results  

E-Print Network (OSTI)

together  during  this  peak  demand period to use power 21 Peak Demand Baseline study.  Their average peak demand reduction was 14% of the 

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila

2007-01-01T23:59:59.000Z

262

Storing hydroelectricity to meet peak-hour demand  

Science Conference Proceedings (OSTI)

This paper reports on pumped storage plants which have become an effective way for some utility companies that derive power from hydroelectric facilities to economically store baseload energy during off-peak hours for use during peak hourly demands. According to the Electric Power Research Institute (EPRI) in Palo Alto, Calif., 36 of these plants provide approximately 20 gigawatts, or about 3 percent of U.S. generating capacity. During peak-demand periods, utilities are often stretched beyond their capacity to provide power and must therefore purchase it from neighboring utilities. Building new baseload power plants, typically nuclear or coal-fired facilities that run 24 hours per day seven days a week, is expensive, about $1500 per kilowatt, according to Robert Schainker, program manager for energy storage at the EPRI. Schainker the that building peaking plants at $400 per kilowatt, which run a few hours a day on gas or oil fuel, is less costly than building baseload plants. Operating them, however, is more expensive because peaking plants are less efficient that baseload plants.

Valenti, M.

1992-04-01T23:59:59.000Z

263

Stress Actually Makes You Stronger ... At Least Some of the Time  

Office of Science (SC) Website

Stress Actually Makes You Stronger ... At Least Some of the Time News Featured Articles 2013 2012 2011 2010 2009 2008 2007 2006 2005 Science Headlines Presentations & Testimony...

264

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

E-Print Network (OSTI)

The Actual Production History insurance plan protects against crop losses from a number of causes. All aspects of this insurance are described, including reporting requirements for the producer.

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

2008-10-07T23:59:59.000Z

265

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

266

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":""}]}

267

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

268

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

269

Peak polarity overturn for charged particles in laser ablation process  

Science Conference Proceedings (OSTI)

The charged particles emitted during laser ablation off a brass target are detected using a metal probe in air. A special phenomenon is found in the recorded signals: following a giant electromagnetic peak observed immediately after the emission of the pulsed laser, a minor peak occurs whose polarity merely depends on the distance between the probe and the laser focal spot on the target. Under the condition of our experiment, the overturn point is 1.47 mm, i.e., the minor peak remains negative when the probe distance is less than 1.47 mm; it becomes positive while the probe is set at a distance beyond 1.47 mm. A hypothesis is proposed to explain the overturn that takes the flight behavior of the charged particles both in plasma and propagating shock wave into consideration.

Zhang, P.; Ji, Y. J.; Lai, X. M.; Bian, B. M.; Li, Z. H. [Department of Information Physics and Engineering, Nanjing University of Science and Technology, Nanjing 210094 (China)

2006-07-01T23:59:59.000Z

270

Potential Peak Load Reductions From Residential Energy Efficient Upgrades  

E-Print Network (OSTI)

The demand for electricity is continuing to grow at a substantial rate. Utilities are interested in managing this growth's peak demand for a number of reasons including: costly construction of new generation capacity can be deferred; the reliability 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 the potential peak load reductions from residential energy efficiency upgrades in hot and humid climates. First, a baseline scenario is established. Then, the demand and consumption impacts of individual upgrade measures are assessed. Several of these upgrades are then combined into a package to assess the synergistic demand and energy impacts. A sensitivity analysis is then performed to assess the impacts of housing characteristics on estimated demand and energy savings. Finally, the demand, energy, and environmental impacts are estimated at the community level.

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

2002-01-01T23:59:59.000Z

271

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":""}]}

272

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.

273

An Innovative Approach Towards National Peak Load Management  

E-Print Network (OSTI)

An innovative approach was developed and implemented in eight governmental buildings to reduce their load during the peak demand hours in summer of 2007. The innovative approach implemented in these buildings included pre-closing treatment (PCT) between 13:00 and 14:00 h and time-of-day control (TDC) after 14:00 h for air conditioning (A/C) and lighting systems. PCT realized an overall reduction of 3.43 MW, a saving of 11.7% of the buildings peak power demand; while TDC realized a total savings of 8.67 MW at 15:00 h, a saving of 30.7% of the buildings peak power demand at that hour. The temperature build up inside the buildings due to PCT and TDC was within the acceptable range, which validated the technical viability of these measures. The implementation of the innovative approach in the eight governmental buildings with a total measured peak demand of 29.3 MW achieved a reduction of 8.89 MW. This power is now available to other users leading to financial savings of $13.5 million for the nation towards the cost of constructing new power plants and distribution network equipment. More importantly, this reduction in peak power demand of well over 30% involved zero or limited expenditure. A nationwide implementation of this innovative approach in all the governmental and institutional buildings is likely to reduce the national peak power demand by 154 MW which amounts to a capital savings of $232 million towards the cost of new power generation equipment and distribution network.

Al-Mulla, A.; Maheshwari, G. P.; Al-Nakib, D.; ElSherbini, A.; Alghimlas, F.; Al-Taqi, H.; Al-Hadban, Y.

2008-10-01T23:59:59.000Z

274

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

275

Scalable Scheduling of Building Control Systems for Peak Demand Reduction  

E-Print Network (OSTI)

is model predictive control (MPC) ([6], [7]). In [6] the authors inves- tigated MPC for thermal energyScalable Scheduling of Building Control Systems for Peak Demand Reduction Truong X. Nghiem, Madhur operation of sub- systems such as heating, ventilating, air conditioning and refrigeration (HVAC&R) systems

Pappas, George J.

276

Performance of a voltage peak detection-based flickermeter  

Science Conference Proceedings (OSTI)

Voltage fluctuations and rapid voltage changes lead to lamps flickering and disturbance of visual perception may occur consequently. For evaluation of the flicker severity level by means of voltage measurement there was developed an instrument called ... Keywords: Matlab Simulink, flickermeter, interharmonics, performance analysis, voltage fluctuation, voltage peak detection

Jiri Drapela

2009-12-01T23:59:59.000Z

277

Peak Dose Assessment for Proposed DOE-PPPO Authorized Limits  

Science Conference Proceedings (OSTI)

The Oak Ridge Institute for Science and Education (ORISE), a U.S. Department of Energy (DOE) prime contractor, was contracted by the DOE Portsmouth/Paducah Project Office (DOE-PPPO) to conduct a peak dose assessment in support of the Authorized Limits Request for Solid Waste Disposal at Landfill C-746-U at the Paducah Gaseous Diffusion Plant (DOE-PPPO 2011a). The peak doses were calculated based on the DOE-PPPO Proposed Single Radionuclides Soil Guidelines and the DOE-PPPO Proposed Authorized Limits (AL) Volumetric Concentrations available in DOE-PPPO 2011a. This work is provided as an appendix to the Dose Modeling Evaluations and Technical Support Document for the Authorized Limits Request for the C-746-U Landfill at the Paducah Gaseous Diffusion Plant, Paducah, Kentucky (ORISE 2012). The receptors evaluated in ORISE 2012 were selected by the DOE-PPPO for the additional peak dose evaluations. These receptors included a Landfill Worker, Trespasser, Resident Farmer (onsite), Resident Gardener, Recreational User, Outdoor Worker and an Offsite Resident Farmer. The RESRAD (Version 6.5) and RESRAD-OFFSITE (Version 2.5) computer codes were used for the peak dose assessments. Deterministic peak dose assessments were performed for all the receptors and a probabilistic dose assessment was performed only for the Offsite Resident Farmer at the request of the DOE-PPPO. In a deterministic analysis, a single input value results in a single output value. In other words, a deterministic analysis uses single parameter values for every variable in the code. By contrast, a probabilistic approach assigns parameter ranges to certain variables, and the code randomly selects the values for each variable from the parameter range each time it calculates the dose (NRC 2006). The receptor scenarios, computer codes and parameter input files were previously used in ORISE 2012. A few modifications were made to the parameter input files as appropriate for this effort. Some of these changes included increasing the time horizon beyond 1,050 years (yr), and using the radionuclide concentrations provided by the DOE-PPPO as inputs into the codes. The deterministic peak doses were evaluated within time horizons of 70 yr (for the Landfill Worker and Trespasser), 1,050 yr, 10,000 yr and 100,000 yr (for the Resident Farmer [onsite], Resident Gardener, Recreational User, Outdoor Worker and Offsite Resident Farmer) at the request of the DOE-PPPO. The time horizons of 10,000 yr and 100,000 yr were used at the request of the DOE-PPPO for informational purposes only. The probabilistic peak of the mean dose assessment was performed for the Offsite Resident Farmer using Technetium-99 (Tc-99) and a time horizon of 1,050 yr. The results of the deterministic analyses indicate that among all receptors and time horizons evaluated, the highest projected dose, 2,700 mrem/yr, occurred for the Resident Farmer (onsite) at 12,773 yr. The exposure pathways contributing to the peak dose are ingestion of plants, external gamma, and ingestion of milk, meat and soil. However, this receptor is considered an implausible receptor. The only receptors considered plausible are the Landfill Worker, Recreational User, Outdoor Worker and the Offsite Resident Farmer. The maximum projected dose among the plausible receptors is 220 mrem/yr for the Outdoor Worker and it occurs at 19,045 yr. The exposure pathways contributing to the dose for this receptor are external gamma and soil ingestion. The results of the probabilistic peak of the mean dose analysis for the Offsite Resident Farmer indicate that the average (arithmetic mean) of the peak of the mean doses for this receptor is 0.98 mrem/yr and it occurs at 1,050 yr. This dose corresponds to Tc-99 within the time horizon of 1,050 yr.

DELIS MALDONADO

2012-06-01T23:59:59.000Z

278

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.

279

The SECIS instrument on the Lomnicky Peak Observatory  

E-Print Network (OSTI)

Heating mechanisms of the solar corona will be investigated at the high-altitude solar observatory Lomnicky Peak of the Astronomical Institute of SAS (Slovakia) using its mid-size Lyot coronagraph and post-focal instrument SECIS provided by Astronomical Institute of the University of Wroclaw (Poland). The data will be studied with respect to the energy transport and release responsible for heating the solar corona to temperatures of mega-Kelvins. In particular investigations will be focused on detection of possible high-frequency MHD waves in the solar corona. The scientific background of the project, technical details of the SECIS system modified specially for the Lomnicky Peak coronagraph, and inspection of the test data are described in the paper.

Ambroz, J; Rudawy, P; Rybak, J; Phillips, K J H

2010-01-01T23:59:59.000Z

280

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

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

Firing Excess Refinery Butane in Peaking Gas Turbines  

E-Print Network (OSTI)

New environmentally-driven regulations for motor gasoline volatility will significantly alter refinery light ends supply/demand balancing. This, in turn, will impact refinery economics. This paper presumes that one outcome will be excess refinery normal butane production, which will reduce refinery normal butane value and price. Explored is an opportunity for a new use for excess refinery normal butane- as a fuel for utility peaking gas turbines which currently fire kerosene and #2 oil. Our paper identifies the fundamental driving forces which are changing refinery butane economics, examines how these forces influence refinery production, and evaluates the potential for using normal butanes as peaking utility gas turbine fuel, especially on the US East Coast.

Pavone, A.; Schreiber, H.; Zwillenberg, M.

1989-09-01T23:59:59.000Z

282

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 the electric utility occurs. Some electric utilities offer their customers demand (KW) reduction rate incentives. Many hospitals have additional emergency back-up needs for electrical energy. Demand is relatively constant in many hospitals due to high internal loads. These factors coupled with the present competitive alternate fuel market and present opportunities for hospitals to significantly reduce operating costs and provide additional stand-by or back-up electric sources. This paper employs a hospital case study to define and illustrate three energy planning strategies applicable to hospitals. These strategies are peak shaving, thermal storage, cogeneration and/or paralleling with the electric utility.

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

1987-01-01T23:59:59.000Z

283

Analyses of Magnetic-Field Peak-Exposure Summary Measures  

Science Conference Proceedings (OSTI)

Past emphasis on exposure characterization and analyses for magnetic fields has been on measures of central tendency, such as long-term time-weighted average (TWA) exposure. Past emphasis on exposure characterization and analyses for magnetic fields has been on measures of central tendency such as long-term time-weighted average (TWA) exposure. This report examines peak exposure measures such as the maximum and 99th percentile of measurements during a day. EPRI sponsored this study to enhance industry kn...

2003-11-19T23:59:59.000Z

284

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":""}]}

285

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

286

Application of Building Precooling to Reduce Peak Cooling Requirements  

E-Print Network (OSTI)

A building cooling control strategy was developed and tested for a 1.4 million square foot (130,000 square meter) office building located in Hoffman Estates, IL. The goal of the control strategy was to utilize building thermal mass to limit the peak cooling load for continued building operation in the event of the loss of one of the four central chiller units. The algorithm was first developed and evaluated through simulation and then evaluated through tests on two identical buildings. The east building utilized the existing building control strategy while the west building used the precooling strategy developed for this project. Consistent with simulation predictions, the precooling control strategy successfully limited the peak load to 75 % of the cooling capacity for the west building, while the east building operated at 100 % of capacity. Precooling of the building mass provided an economical alternative to the purchase of an additional chiller unit. The estimated cost of installing an additional chiller was approximately $500,000. Computer models developed for this project also showed that precooling based upon cooling cost minimization could result in savings of approximately $25,000 per month during the peak cooling season. The building model was validated with experimental results and could be used in the development of a cost minimization strategy.

Kevin R. Keeney; James E. Braun, Ph.D.

1997-01-01T23:59:59.000Z

287

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":""}]}

288

Review of current Southern California edison load management programs and proposal for a new market-driven, mass-market, demand-response program  

E-Print Network (OSTI)

Once the spot market energy prices reaches $100/MWh, thehour until spot market energy prices reach $250/MWh. At thatand actual spot market energy prices. If, for example, the

Weller, G.H.

2002-01-01T23:59:59.000Z

289

Assessing Climate Information Use in Agribusiness. Part I: Actual and Potential Use and Impediments to Usage  

Science Conference Proceedings (OSTI)

A project for the development of methodology to enable agribusiness decision makers to utilize more effectively climate information involved investigation of three agribusiness firms, as well as measurement of their actual and potential use. The ...

Stanley A. Changnon; Steven T. Sonka; Steven Hofing

1988-08-01T23:59:59.000Z

290

Trends of Calculated and Simulated Actual Evaporation in the Yangtze River Basin  

Science Conference Proceedings (OSTI)

Actual evaporation in the Yangtze River basin is calculated by the complementary relationship approach—that is, the advection–aridity (AA) model with parameter validation from 1961 to 2007—and simulated by the general circulation model (GCM) ...

Yanjun Wang; Bo Liu; Buda Su; Jianqing Zhai; Marco Gemmer

2011-08-01T23:59:59.000Z

291

Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia  

Science Conference Proceedings (OSTI)

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined ...

Yongqiang Zhang; Francis H. S. Chiew; Lu Zhang; Hongxia Li

2009-08-01T23:59:59.000Z

292

Estimating Actual Evapotranspiration from Satellite and Meteorological Data in Central Bolivia  

Science Conference Proceedings (OSTI)

Spatial estimates of actual evapotranspiration are useful for calculating the water balance of river basins, quantifying hydrological services provided by ecosystems, and assessing the hydrological impacts of land-use practices. To provide this ...

Christian Seiler; Arnold F. Moene

2011-05-01T23:59:59.000Z

293

Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building  

E-Print Network (OSTI)

Peak Demand Reduction from Pre-Cooling with Zone TemperatureUniversity of California. Peak Demand Reduction from Pre-shifted in time and the peak demand is reduced. The building

Xu, Peng

2010-01-01T23:59:59.000Z

294

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

Scenario Analysis of Peak Demand Savings for CommercialScenario Analysis of Peak Demand Savings for CommercialThe whole-building peak demand of a commercial building with

Yin, Rongxin

2010-01-01T23:59:59.000Z

295

Peak demand reduction from pre-cooling with zone temperature reset in an office building  

E-Print Network (OSTI)

an Energy-Efficient Economy. Peak Demand Reduction from Pre-No. DE-AC03-76SF00098. Peak Demand Reduction from Pre-shifted in time and the peak demand is reduced. The building

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2004-01-01T23:59:59.000Z

296

Testing an Ice Storage System for Peak Load Reduction  

Science Conference Proceedings (OSTI)

Ice storage systems allow for the offset of peak building cooling power by allowing the building operator to choose a convenient window for making ice and then using that ice, rather than a traditional cooling system, to provide space cooling. For the past several years, the Electric Power Research Institute (EPRI) has tested the Ice Bear 30, a 30 ton-hour system designed to operate independently of the unitary system. This report describes the testing and its results, based on work performed at a field ...

2011-04-21T23:59:59.000Z

297

Home cogeneration system can augment peak power requirements  

SciTech Connect

The use of internal combustion engines to supplement peak power generation to homeowners is suggested. As in a car heater, internal combustion engines would recover heat from the radiators to heat the house. The IC, inlet and outlet lines, thermostat, muffler (''critical''), induction generator, and reverse power delay are schematicized. Synchronous generators are not recommended. Disadvantages include the potential pollution, high capital cost, and the resistance of homeowners ''acquainted with the problems of owning a car.'' A simple method to determine the economics of home cogeneration is given. Special consideration is paid to the induction generator, and the engine starter.

Krishnan, K.R.

1983-06-01T23:59:59.000Z

298

Peak load control energy saving and cycling system  

SciTech Connect

A control system for limiting peak load demand and/or saving electrical energy by cycling the individual loads within an electrical distribution system is described. Electrical power usage in a distribution system is continuously monitored and compared to a pre-set limit. Loads can be added and cycled according to a limit set by the operator. Loads can also be dropped in response to a signal proportional to the electrical power usage in a distribution system within limits defined by the operator.

Burch, J.

1976-10-19T23:59:59.000Z

299

Mercury Vapor At Silver Peak Area (Henkle, Et Al., 2005) | Open...  

Open Energy Info (EERE)

Mercury Vapor At Silver Peak Area (Henkle, Et Al., 2005) Exploration Activity Details Location Silver Peak Area Exploration Technique Mercury Vapor Activity Date Usefulness useful...

300

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

Open Energy Info (EERE)

Water Sampling At Silver Peak Area (Henkle, Et Al., 2005) Exploration Activity Details Location Silver Peak Area Exploration Technique Water Sampling Activity Date Usefulness...

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

Flow Test At Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Flow Test At Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak Area...

302

Density Log at Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Density Log at Silver Peak Area (DOE GTP) Exploration Activity Details...

303

Rock Density At Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

Density At Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Rock Density At Silver Peak Area (DOE GTP) Exploration...

304

2-M Probe At Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak Area Exploration Technique 2-M Probe Activity Date Usefulness not indicated DOE-funding Unknown...

305

Gamma Log At Silver Peak Area (DOE GTP) | Open Energy Information  

Open Energy Info (EERE)

Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Gamma Log At Silver Peak Area (DOE GTP) Exploration Activity Details...

306

Anisotropic Sliding Dynamics, Peak Effect, and Metastability in Stripe Systems  

E-Print Network (OSTI)

A variety of soft and hard condensed matter systems are known to form stripe patterns. Here we use numerical simulations to analyze how such stripe states depin and slide when interacting with a random substrate and with driving in different directions with respect to the orientation of the stripes. Depending on the strength and density of the substrate disorder, we find that there can be pronounced anisotropy in the transport produced by different dynamical flow phases. We also find a disorder-induced "peak effect" similar to that observed for superconducting vortex systems, which is marked by a transition from elastic depinning to a state where the stripe structure fragments or partially disorders at depinning. Under the sudden application of a driving force, we observe pronounced metastability effects similar to those found near the order-disorder transition associated with the peak effect regime for three-dimensional superconducting vortices. The characteristic transient time required for the system to reach a steady state diverges in the region where the flow changes from elastic to disordered. We also find that anisotropy of the flow persists in the presence of thermal disorder when thermally-induced particle hopping along the stripes dominates. The thermal effects can wash out the effects of the quenched disorder, leading to a thermally-induced stripe state. We map out the dynamical phase diagram for this system, and discuss how our results could be explored in electron liquid crystal systems, type-1.5 superconductors, and pattern-forming colloidal assemblies.

C. J. Olson Reichhardt; C. Reichhardt; A. R. Bishop

2010-11-03T23:59:59.000Z

307

Estimation of Lightning Stroke Peak Current as a Function of Peak Electric Field and the Normalized Amplitude of Signal Strength: Corrections and Improvements  

Science Conference Proceedings (OSTI)

The authors have made connections and improvements to published equations relating the peak current and the peak electric field intensity for return strokes of cloud-to-ground lightning. The original published equations were derived from ...

Y. P. Liaw; D. R. Cook; D. L. Sisterson

1996-06-01T23:59:59.000Z

308

Comparison of actual and predicted energy savings in Minnesota gas-heated single-family homes  

Science Conference Proceedings (OSTI)

Data available from a recent evaluation of a home energy audit program in Minnesota are sufficient to allow analysis of the actual energy savings achieved in audited homes and of the relationship between actual and predicted savings. The program, operated by Northern States Power in much of the southern half of the state, is part of Minnesota's version of the federal Residential Conservation Service. NSP conducted almost 12 thousand RCS audits between April 1981 (when the progam began) and the end of 1982. The data analyzed here, available for 346 homes that obtained an NSP energy audit, include monthly natural gas bills from October 1980 through April 1983; heating degree day data matched to the gas bills; energy audit reports; and information on household demographics, structure characteristics, and recent conservation actions from mail and telephone surveys. The actual reduction in weather-adjusted natural gas use between years 1 and 3 averaged 19 MBtu across these homes (11% of preprogram consumption); the median value of the saving was 16 MBtu/year. The variation in actual saving is quite large: gas consumption increased in almost 20% of the homes, while gas consumption decreased by more than 50 MBtu/year in more than 10% of the homes. These households reported an average expenditure of almost $1600 for the retrofit measures installed in their homes; the variation in retrofit cost, while large, was not as great as the variation in actual natural gas savings.

Hirst, E.; Goeltz, R.

1984-03-01T23:59:59.000Z

309

10-MW GTO converter for battery peaking service  

SciTech Connect

A bidirectional 18-pulse voltage source converter utilizing gate turn-off thyristors (GTO's) is described. The converter, which is rated 10 MVA, was placed in service in early 1988 to connect an energy storage battery to a utility grid. The converter is rated and controlled to operate in all four quadrants (discharge, charge, leading vars, or lagging vars) at the full 10-MVA rating. It is capable of independent rapid control of real and reactive power with a transient response of 16 ms to changes in commanded value of real or reactive power. Thus it is usable as a reactive power controller (static var control), voltage control, frequency control, power system stabilizer, or as a real power peaking station. For use as a reactive power controller only, no battery would be needed. The design, construction, control, and application of the converter are described, and performance data taken at factory power test and at the installation are given.

Walker, L.H. (Drive Development Engineering, Drive Systems, General Electric Co., Salem, VA (US))

1990-01-01T23:59:59.000Z

310

Implications of "peak oil" for atmospheric CO2 and climate  

E-Print Network (OSTI)

Peaking of global oil production may have a large effect on future atmospheric CO2 amount and climate change, depending upon choices made for subsequent energy sources. We suggest that, if estimates of oil and gas reserves by the Energy Information Administration are realistic, it is feasible to keep atmospheric CO2 from exceeding approximately 450 ppm, provided that future exploitation of the huge reservoirs of coal and unconventional fossil fuels incorporates carbon capture and sequestration. Existing coal-fired power plants, without sequestration, must be phased out before mid-century to achieve this limit on atmospheric CO2. We also suggest that it is important to "stretch" oil reserves via energy efficiency, thus avoiding the need to extract liquid fuels from coal or unconventional fossil fuels. We argue that a rising price on carbon emissions is probably needed to keep CO2 beneath the 450 ppm ceiling.

Kharecha, P A

2007-01-01T23:59:59.000Z

311

The University of Texas at Austin Jan-11 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-11 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1.7% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-11 PART I CRIMES BURGLARY/93) #12;The University of Texas at Austin Jan-11 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List Other

Johns, Russell Taylor

312

The University of Texas at Austin Jan-00 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-00 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1.3% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-00 PART I CRIMES BURGLARY of Texas at Austin Jan-00 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List Other Target Areas) #/Month

Johns, Russell Taylor

313

The University of Texas at Austin Jan-06 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-06 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1 Total % Rcvd. 1.0% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-06 PART I/93) #12;The University of Texas at Austin Jan-06 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List Other

Johns, Russell Taylor

314

The University of Texas at Austin Jan-09 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-09 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1. 8.0% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-09 PART I CRIMES/93) #12;The University of Texas at Austin Jan-09 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List Other

Johns, Russell Taylor

315

The University of Texas at Austin Jan-08 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-08 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1 Total % Rcvd. 2.2% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-08 PART I(Rev. 1/93) #12;The University of Texas at Austin Jan-08 PART I CRIMES BURGLARY & THEFT TARGET SECTION

Johns, Russell Taylor

316

The University of Texas at Austin Jan-04 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-04 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1.1% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan-04 PART I CRIMES BURGLARY University of Texas at Austin Jan-04 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List Other Target Areas

Johns, Russell Taylor

317

The University of Texas at Austin Jan-01 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-01 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1/93) #12;The University of Texas at Austin Jan-01 PART I CRIMES BURGLARY & THEFT TARGET SECTION Maintenance. 1/93)Co #12;The University of Texas at Austin Jan-01 PART I CRIMES BURGLARY & THEFT TARGET SECTION

Johns, Russell Taylor

318

The University of Texas at Austin Jan-05 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-05 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1/93) #12;The University of Texas at Austin Jan-05 PART I CRIMES BURGLARY & THEFT TARGET SECTION Maintenance - Page 2(Rev. 1/93) #12;The University of Texas at Austin Jan-05 PART I CRIMES BURGLARY & THEFT TARGET

Johns, Russell Taylor

319

The University of Texas at Austin Jan-10 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-10 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1 of Texas at Austin Jan-10 PART I CRIMES BURGLARY & THEFT TARGET SECTION Maintenance Shops Offices 6 OF REPORT DP Form #31 - Page 2(Rev. 1/93) #12;The University of Texas at Austin Jan-10 PART I CRIMES

Johns, Russell Taylor

320

The University of Texas at Austin Jan-02 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan-02 PART I CRIMES Reported Unfounded Actual Cleared % Clrd. 1 Theft Total $280 $280 Total % Rcvd 0.4% DP Form #31 - Page 1(Rev. 1/93) #12;The University of Texas/93)Co #12;The University of Texas at Austin Jan-02 PART I CRIMES BURGLARY & THEFT TARGET SECTION (List

Johns, Russell Taylor

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

The University of Texas at Austin Jan07 PART I CRIMES Reported Unfounded Actual Cleared % Clrd.  

E-Print Network (OSTI)

The University of Texas at Austin Jan07 PART I CRIMES Reported Unfounded Actual Cleared.1% DP Form #31 Page 1(Rev. 1/93) #12;The University of Texas at Austin Jan07 PART I CRIMES BURGLARY Form #31 Page 2(Rev. 1/93) #12;The University of Texas at Austin Jan07 PART I CRIMES BURGLARY

Johns, Russell Taylor

322

Modeling of Optimal Oil Production and Comparing with Actual and Contractual Oil Production: Iran Case  

E-Print Network (OSTI)

Modeling of Optimal Oil Production and Comparing with Actual and Contractual Oil Production: Iran, Davis Introduction · The Iran Oil Project, initiated in 2007, aims to find the inefficiencies and their possible sources in Iranian oil and gas policies. Background Information Assumptions · Perfect Competition

California at Davis, University of

323

Satellite-Based Actual Evapotranspiration over Drying Semiarid Terrain in West Africa  

Science Conference Proceedings (OSTI)

A simple satellite-based algorithm for estimating actual evaporation based on Makkink’s equation is applied to a seasonal cycle in 2002 at three test sites in Ghana, West Africa: at a location in the humid tropical southern region and two in the ...

D. Schüttemeyer; Ch Schillings; A. F. Moene; H. A. R. de Bruin

2007-01-01T23:59:59.000Z

324

Roof shading and wall glazing techniques for reducing peak building heating and cooling loads. Final report  

SciTech Connect

The roof shading device proved to be effective in reducing peak building cooling loads under both actual testing conditions and in selected computer simulations. The magnitude of cooling load reductions varied from case to case depending on individual circumstances. Key variables that had significant impacts on its thermal performance were the number of months of use annually, the thermal characteristics of the roof construction, hours of building use, and internal gains. Key variables that had significant impacts upon economic performance were the costs of fuel energy for heating and cooling, and heating and cooling equipment efficiency. In general, the more sensitive the building is to climate, the more effective the shading device will be. In the example case, the annual fuel savings ($.05 psf) were 6 to 10% of the estimated installation costs ($.50 to .75 psf). The Trombe wall installation at Roxborough High School proved to be effective in collecting and delivering significant amounts of solar heat energy. It was also effective in conserving heat energy by replacing obsolete windows which leaked large amounts of heat from the building. Cost values were computed for both solar energy contributions and for heat loss reductions by window replacement. Together they amount to an estimated three hundred and ninety dollars ($390.00) per year in equivalent electric fuel costs. When these savings are compared with installation cost figures it is apparent that the Trombe wall installation as designed and installed presents a potentially cost-effective method of saving fuel costs. The study results indicate that improved Trombe wall efficiency can be achieved by making design and construction changes to reduce or eliminate outside air leakage into the system and provide automatic fan control.

Ueland, M.

1981-08-01T23:59:59.000Z

325

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.

326

Dick Cheney, Peak Oil and the Final Count Down  

E-Print Network (OSTI)

In the April 2004 issue of the magazine the Middle East I found a statement that Vice-President Dick Cheney had made in a speech at the London Institute of Petroleum Autumn lunch in 1999 when he was Chairman of Halliburton. A key passage from his speech was: “That means by 2010 we will need on the order of an additional fifty million barrels a day.” It suggested that he was fully aware of the issue of peak oil. A full text of the talk had been available on the website of the Institute of Petroleum, but has now been removed (wwww.petroleum.co.uk/speeches.htm). Nevertheless, further research did bring to light a printed version, dated 24.08.00, as follows: Dick Cheney: “From the standpoint of the oil industry obviously- and I'll talk a little later on about gas- for over a hundred years we as an industry have had to deal with the pesky problem that once you find oil and pump it out of the ground you've got to turn around and find more or go out of business. Producing oil is obviously a self-depleting activity. Every year you've got to find and develop reserves equal to your output just to stand still, just to stay even. This is as true for companies as well in the broader

Kjell Aleklett

2004-01-01T23:59:59.000Z

327

Residential implementation of critical-peak pricing ofelectricity  

SciTech Connect

This paper investigates how critical-peak pricing (CPP)affects households with different usage and income levels, with the goalof informing policy makers who are considering the implementation of CPPtariffs in the residential sector. Using a subset of data from theCalifornia Statewide Pricing Pilot of 2003-2004, average load changeduring summer events, annual percent bill change, and post-experimentsatisfaction ratings are calculated across six customer segments,categorized by historical usage and income levels. Findings show thathigh-use customers respond significantly more in kW reduction than dolow-use customers, while low-use customers save significantly more inpercentage reduction of annual electricity bills than do high-usecustomers results that challenge the strategy of targeting only high-usecustomers for CPP tariffs. Across income levels, average load and billchanges were statistically indistinguishable, as were satisfaction ratesresults that are compatible with a strategy of full-scale implementationof CPP rates in the residential sector. Finally, the high-use customersearning less than $50,000 annually were the most likely of the groups tosee bill increases about 5 percent saw bill increases of 10 percent ormore suggesting that any residential CPP implementation might considertargeting this customer group for increased energy efficiencyefforts.

Herter, Karen

2006-06-29T23:59:59.000Z

328

Fracture Permeability Evolution in Desert Peak Quartz Monzonite  

SciTech Connect

Fracture flow experiments are being conducted on quartz monzonite core from the Desert Peak East EGS site, Churchill County, Nevada. The flow experiments are conducted at temperatures of 167-169 C and 5.5 MPa confining pressure through artificial fractures. Two injection fluids, a saline solution and a silica-bearing solution, have been used to date. Flow rates are typically 0.02 mL/min, but other rates have been used. The fracture surfaces are characterized with a contact profilometer. The profilometry data demonstrate that it is possible to fabricate statistically similar fracture surfaces and enable us to map aperture variations, which we use in numerical simulations. Effluent samples are collected for chemical analysis. The fluid pressure gradient is measured across the specimen and effective hydraulic apertures are calculated. The experiments show a reduction in permeability over time for both injection fluids, but a more rapid loss of permeability was observed for the silica-bearing solution. The calculated hydraulic aperture is observed to decrease by 17% for the saline solution and 75% for the silica-bearing fluid, respectively. Electrical resistivity measurements, which are sensitive to the ionic content of the pore fluid, provide additional evidence of fluid-rock interactions.

Carlson, S R; Roberts, J J; Detwiler, R L; Viani, B E; Roberts, S K

2005-05-10T23:59:59.000Z

329

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

330

Reference Designs of 50 MW / 250 MWh Energy Storage Systems  

Science Conference Proceedings (OSTI)

Electric utilities are interested energy storage solutions for renewable integration and transmission and distribution (TD) grid support that require systems of 10's of MWs in scale and energy durations of longer than 4 hours. Compressed air energy storage and pumped hydro systems are currently the lowest capital cost (/ kW-h) bulk storage options for energy durations longer than 10 hour; however, these storage facilities have geological and siting restrictions and require long permitting and deployment ...

2010-12-16T23:59:59.000Z

331

Southern California Edison 32MWh Wind Integration Project  

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

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...

332

Nuclear Hydrogen for Peak Electricity Production and Spinning Reserve  

Science Conference Proceedings (OSTI)

Nuclear energy can be used to produce hydrogen. The key strategic question is this: ''What are the early markets for nuclear hydrogen?'' The answer determines (1) whether there are incentives to implement nuclear hydrogen technology today or whether the development of such a technology could be delayed by decades until a hydrogen economy has evolved, (2) the industrial partners required to develop such a technology, and (3) the technological requirements for the hydrogen production system (rate of production, steady-state or variable production, hydrogen purity, etc.). Understanding ''early'' markets for any new product is difficult because the customer may not even recognize that the product could exist. This study is an initial examination of how nuclear hydrogen could be used in two interconnected early markets: the production of electricity for peak and intermediate electrical loads and spinning reserve for the electrical grid. The study is intended to provide an initial description that can then be used to consult with potential customers (utilities, the Electric Power Research Institute, etc.) to better determine the potential real-world viability of this early market for nuclear hydrogen and provide the starting point for a more definitive assessment of the concept. If this set of applications is economically viable, it offers several unique advantages: (1) the market is approximately equivalent in size to the existing nuclear electric enterprise in the United States, (2) the entire market is within the utility industry and does not require development of an external market for hydrogen or a significant hydrogen infrastructure beyond the utility site, (3) the technology and scale match those of nuclear hydrogen production, (4) the market exists today, and (5) the market is sufficient in size to justify development of nuclear hydrogen production techniques independent of the development of any other market for hydrogen. These characteristics make it an ideal early market for nuclear hydrogen.

Forsberg, C.W.

2005-01-20T23:59:59.000Z

333

"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

334

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

Science Conference Proceedings (OSTI)

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

335

Peak CO2? China's Emissions Trajectories to 2050  

SciTech Connect

As a result of soaring energy demand from a staggering pace of economic growth and the related growth of energy-intensive industry, China overtook the United States to become the world's largest contributor to CO{sub 2} emissions in 2007. At the same time, China has taken serious actions to reduce its energy and carbon intensity by setting both short-term energy intensity reduction goal for 2006 to 2010 as well as long-term carbon intensity reduction goal for 2020. This study focuses on a China Energy Outlook through 2050 that assesses the role of energy efficiency policies in transitioning China to a lower emission trajectory and meeting its intensity reduction goals. In the past years, LBNL has established and significantly enhanced the China End-Use Energy Model based on the diffusion of end-use technologies and other physical drivers of energy demand. This model presents an important new approach for helping understand China's complex and dynamic drivers of energy consumption and implications of energy efficiency policies through scenario analysis. A baseline ('Continued Improvement Scenario') and an alternative energy efficiency scenario ('Accelerated Improvement Scenario') have been developed to assess the impact of actions already taken by the Chinese government as well as planned and potential actions, and to evaluate the potential for China to control energy demand growth and mitigate emissions. It is a common belief that China's CO{sub 2} emissions will continue to grow throughout this century and will dominate global emissions. The findings from this research suggest that this will not likely be the case because of saturation effects in appliances, residential and commercial floor area, roadways, railways, fertilizer use, and urbanization will peak around 2030 with slowing population growth. The baseline and alternative scenarios also demonstrate that the 2020 goals can be met and underscore the significant role that policy-driven energy efficiency improvements will play in carbon mitigation along with a decarbonized power supply through greater renewable and non-fossil fuel generation.

Zhou, Nan; Fridley, David G.; McNeil, Michael; Zheng, Nina; Ke, Jing; Levine, Mark

2011-05-01T23:59:59.000Z

336

Peak Tracking by Simultaneous Inversion: Toward a One-Step Acoustic Tomography Analysis  

Science Conference Proceedings (OSTI)

A number of geophysical observing techniques, including ocean acoustic tomography, obtain sequences of records of which the observed relative maxima (“peaks”) are used to infer properties of the system via inversions. Traditionally, these peaks ...

Uwe Send

1996-10-01T23:59:59.000Z

337

Production of Hydrogen at the Forecourt Using Off-Peak Electricity: June 2005 (Milestone Report)  

DOE Green Energy (OSTI)

This milestone report provides information about the production of hydrogen at the forecourt using off-peak electricity as well as the Hydrogen Off-Peak Electricity (HOPE) model.

Levene, J. I.

2007-02-01T23:59:59.000Z

338

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

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

The VPP rates during the five-hour peak period vary daily depending on the cost of electricity. The VPP also includes a critical peak price (CPP) component that is...

339

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

340

Microsoft Word - BUGS_The Next Smart Grid Peak Resource Final...  

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

April 15, 2010 DOENETL-20101406 Backup Generators (BUGS): The Next Smart Grid Peak Resource Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 ii DISCLAIMER This...

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
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341

Preparing for the Peak: Energy Security and Atlantic Canada 1 Larry Hughes  

E-Print Network (OSTI)

region that will be particularly vulnerable to peak oil, since almost all of the region's oil is imported is destined for markets outside the region. This paper examines some of the potential impacts of peak oil the reliance on refined petroleum products for space heating and transportation. When peak oil production

Hughes, Larry

342

Available online at www.sciencedirect.com Future world oil production: growth, plateau, or peak?  

E-Print Network (OSTI)

Available online at www.sciencedirect.com Future world oil production: growth, plateau, or peak considers how long world oil production can continue to grow or if it will eventually plateau or peak and then decline. The paper concludes with the observation that whether peak oil has already occurred

Ito, Garrett

343

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

344

Implications of ``peak oil'' for atmospheric CO2 and climate Pushker A. Kharecha1  

E-Print Network (OSTI)

Implications of ``peak oil'' for atmospheric CO2 and climate Pushker A. Kharecha1 and James E environments. If conventional oil production peaks within the next few decades, it may have a large effect., and J. E. Hansen (2008), Implications of ``peak oil'' for atmospheric CO2 and climate, Global Biogeochem

345

The Actual Impact of the International Tribunal for former Yugoslavia on the Reconciliation Process in Bosnia-Herzegovina.  

E-Print Network (OSTI)

??This thesis explores the actual impact of the International Criminal Tribunal for the former Yugoslavia (ICTY) on the reconciliation process in Bosnia-Herzegovina and analyses possible… (more)

Johansen, Kristine

2011-01-01T23:59:59.000Z

346

How many people actually see the price signal? Quantifying market failures in the end use of energy  

E-Print Network (OSTI)

investment, behaviour, energy price, consumers Abstract “suggest that raising energy prices—such as in the form ofconsumers actually “see” energy prices and are therefore

Meier, Alan; Eide, Anita

2007-01-01T23:59:59.000Z

347

Actual versus predicted impacts of three ethanol plants on aquatic and terrestrial resources  

DOE Green Energy (OSTI)

To help reduce US dependence on imported petroleum, Congress passed the Energy Security Act of 1980 (public Law 96-294). This legislation authorized the US Department of Energy (DOE) to promote expansion of the fuel alcohol industry through, among other measures, its Alcohol Fuels Loan Guarantee Program. Under this program, selected proposals for the conversion of plant biomass into fuel-grade ethanol would be granted loan guarantees. of 57 applications submitted for loan guarantees to build and operate ethanol fuel projects under this program, 11 were considered by DOE to have the greatest potential for satisfying DOE`s requirements and goals. In accordance with the National Environmental Policy Act (NEPA), DOE evaluated the potential impacts of proceeding with the Loan Guarantee Program in a programmatic environmental assessment (DOE 1981) that resulted in a finding of no significant impact (FANCY) (47 Federal Register 34, p. 7483). The following year, DOE conducted site-specific environmental assessments (EAs) for 10 of the proposed projects. These F-As predicted no significant environmental impacts from these projects. Eventually, three ethanol fuel projects received loan guarantees and were actually built: the Tennol Energy Company (Tennol; DOE 1982a) facility near Jasper in southeastern Tennessee; the Agrifuels Refining Corporation (Agrifuels; DOE 1985) facility near New Liberia in southern Louisiana; and the New Energy Company of Indiana (NECI; DOE 1982b) facility in South Bend, Indiana. As part of a larger retrospective examination of a wide range of environmental effects of ethanol fuel plants, we compared the actual effects of the three completed plants on aquatic and terrestrial resources with the effects predicted in the NEPA EAs several years earlier. A secondary purpose was to determine: Why were there differences, if any, between actual effects and predictions? How can assessments be improved and impacts reduced?

Eddlemon, G.K.; Webb, J.W.; Hunsaker, D.B. Jr.; Miller, R.L.

1993-03-15T23:59:59.000Z

348

Method and apparatus for distinguishing actual sparse events from sparse event false alarms  

DOE Patents (OSTI)

Remote sensing method and apparatus wherein sparse optical events are distinguished from false events. "Ghost" images of actual optical phenomena are generated using an optical beam splitter and optics configured to direct split beams to a single sensor or segmented sensor. True optical signals are distinguished from false signals or noise based on whether the ghost image is presence or absent. The invention obviates the need for dual sensor systems to effect a false target detection capability, thus significantly reducing system complexity and cost.

Spalding, Richard E. (Albuquerque, NM); Grotbeck, Carter L. (Albuquerque, NM)

2000-01-01T23:59:59.000Z

349

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

350

"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

351

"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

352

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

353

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

354

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

355

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

356

Filtration and Leach Testing for REDOX Sludge and S-Saltcake Actual Waste Sample Composites  

Science Conference Proceedings (OSTI)

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 test program was subdivided into logical increments. 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. Under test plan TP-RPP-WTP-467, eight broad waste groupings were defined. Samples available from the 222S archive were identified and obtained for testing. Under this test plan, a waste-testing program was implemented that included: • Homogenizing the archive samples by group as defined in the test plan • Characterizing the homogenized sample groups • Performing parametric leaching testing on each group for compounds of interest • Performing bench-top filtration/leaching tests in the hot cell for each group to simulate filtration and leaching activities if they occurred in the UFP2 vessel of the WTP Pretreatment Facility. This report focuses on filtration/leaching tests performed on two of the eight waste composite samples and follow-on parametric tests to support aluminum leaching results from those tests.

Shimskey, Rick W.; Billing, Justin M.; Buck, Edgar C.; Daniel, Richard C.; Draper, Kathryn E.; Edwards, Matthew K.; Geeting, John GH; Hallen, Richard T.; Jenson, Evan D.; Kozelisky, Anne E.; MacFarlan, Paul J.; Peterson, Reid A.; Snow, Lanee A.; Swoboda, Robert G.

2009-02-20T23:59:59.000Z

357

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

Science Conference Proceedings (OSTI)

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

358

Comparison of Projections to Actual Performance in the DOE-EPRI Wind Turbine Verification Program  

DOE Green Energy (OSTI)

As part of the US Department of Energy/Electric Power Research Institute (DOE-EPRI) Wind Turbine Verification Program (TVP), Global Energy Concepts (GEC) worked with participating utilities to develop a set of performance projections for their projects based on historical site atmospheric conditions, turbine performance data, operation and maintenance (O and M) strategies, and assumptions about various energy losses. After a preliminary operation period at each project, GEC compared the actual performance to projections and evaluated the accuracy of the data and assumptions that formed the performance projections. This paper presents a comparison of 1999 power output, turbine availability, and other performance characteristics to the projections for TVP projects in Texas, Vermont, Iowa, Nebraska, Wisconsin, and Alaska. Factors that were overestimated or underestimated are quantified. Actual wind speeds are compared to projections based on long-term historical measurements. Turbine power curve measurements are compared with data provided by the manufacturers, and loss assumptions are evaluated for accuracy. Overall, the projects performed well, particularly new commercial turbines in the first few years of operation. However, some sites experienced below average wind resources and greater than expected losses. The TVP project owners successfully developed and constructed wind power plants that are now in full commercial operation, serving a total of approximately 12,000 households.

Rhoads, H.; VandenBosche, J.; McCoy, T.; Compton, A. (Global Energy Concepts, LLC); Smith, B. (National Renewable Energy Laboratory)

2000-09-11T23:59:59.000Z

359

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

Science Conference Proceedings (OSTI)

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

360

"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

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

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

362

Offset-free rail-to-rail derandomizing peak detect-and-hold circuit  

Science Conference Proceedings (OSTI)

A peak detect-and-hold circuit eliminates errors introduced by conventional amplifiers, such as common-mode rejection and input voltage offset. The circuit includes an amplifier, three switches, a transistor, and a capacitor. During a detect-and-hold phase, a hold voltage at a non-inverting in put terminal of the amplifier tracks an input voltage signal and when a peak is reached, the transistor is switched off, thereby storing a peak voltage in the capacitor. During a readout phase, the circuit functions as a unity gain buffer, in which the voltage stored in the capacitor is provided as an output voltage. The circuit is able to sense signals rail-to-rail and can readily be modified to sense positive, negative, or peak-to-peak voltages. Derandomization may be achieved by using a plurality of peak detect-and-hold circuits electrically connected in parallel.

DeGeronimo, Gianluigi (Nesconset, NY); O'Connor, Paul (Bellport, NY); Kandasamy, Anand (Coram, NY)

2003-01-01T23:59:59.000Z

363

Influence of Air Conditioner Operation on Electricity Use and Peak Demand  

E-Print Network (OSTI)

Electricity demand due to occupant controlled room air conditioners in a large mater-metered apartment building is analyzed. Hourly data on the electric demand of the building and of individual air conditioners are used in analyses of annual and time-of-day peaks. Effects of occupant schedules and behavior are examined. We conclude that room air conditioners cause a sharp annual peak demand because occupants have strongly varying thresholds with respect to toleration of high indoor temperatures. However, time-or-day peaking is smoothed by air conditioning in this building due to significant off-peak operation of air conditioners by some occupants. If occupants were billed directly for electricity, off-peak use would probably diminish making the peaks more pronounced and exacerbating the utility company's load management problems. Future studies of this type in individually metered apartment buildings are recommended.

McGarity, A. E.; Feuermann, D.; Kempton, W.; Norford, L. K.

1987-01-01T23:59:59.000Z

364

The role of building technologies in reducing and controlling peak electricity demand  

E-Print Network (OSTI)

power in real time (costs per kWh at time of system peak canto large increases in marginal costs per kWh, because of the

Koomey, Jonathan; Brown, Richard E.

2002-01-01T23:59:59.000Z

365

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

366

What Goes Up Must Come Down? An Economic Analysis of Peak Oil ?  

E-Print Network (OSTI)

While world oil production and reserves have been increasing since 1859, ‘peak oil ’ analysts claim that production is on the cusp of a period of sustained decline. I first subject the peak oil model to a number of robustness tests. The peak oil assumption of a linear relationship between the ratio of production to cumulative production and cumulative production is rejected using data from different regions, time periods, and commodities. The peak oil model predicts a single peak in discoveries, followed by peak in proved reserves, then a peak in production. Yet, world discoveries have had four distinct peaks since 1950, and world proved reserves have continued to rise. Second, I derive an economic model of oil supply in which scarcity occurs both in total reserves and in the quality of those reserves. Depletion raises drilling costs and reduces the size of undiscovered pools. Technological change driven by learning-by-doing offsets Ricardian depletion effects. The peak oil predictions are shown to be a special case of the more general model.

John R. Boyce; I Thank Scott Taylor; Linda Nøstbakken For Allowing

2009-01-01T23:59:59.000Z

367

Indoor Air Quality Impacts of a Peak Load Shedding Strategy for...  

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

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...

368

A Fresh Look at Weather Impact on Peak Electricity Demand and...  

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

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...

369

Thermal Gradient Holes At Silver Peak Area (DOE GTP) | Open Energy...  

Open Energy Info (EERE)

Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Thermal Gradient Holes At Silver Peak Area (DOE GTP) Exploration Activity Details Location...

370

Thermal And-Or Near Infrared At Silver Peak Area (DOE GTP) |...  

Open Energy Info (EERE)

Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Thermal And-Or Near Infrared At Silver Peak Area (DOE GTP) Exploration Activity Details...

371

Natural gas consumption has two peaks each year - Today in Energy ...  

U.S. Energy Information Administration (EIA)

Consumption of natural gas is seasonal, with consumption patterns among end-use sectors highly driven by weather. Total natural gas consumption peaks during the ...

372

The role of building technologies in reducing and controlling peak electricity demand  

E-Print Network (OSTI)

AND CONTROLLING PEAK ELECTRICITY DEMAND Jonathan Koomey* andData to Improve Electricity Demand Forecasts–Final Report.further research. Electricity demand varies constantly. At

Koomey, Jonathan; Brown, Richard E.

2002-01-01T23:59:59.000Z

373

A new approach for modeling the peak utility impacts from a proposed CUAC standard  

E-Print Network (OSTI)

an October-peaking load profile, rather than a more credibleof the space cooling load profiles for the months ofcommercial space cooling load profile for ECAR. This figure

LaCommare, Kristina Hamachi; Gumerman, Etan; Marnay, Chris; Chan, Peter; Coughlin, Katie

2004-01-01T23:59:59.000Z

374

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

375

Investigation of Peak Load Reduction Strategies in Residential Buildings in Cooling Dominated Climates.  

E-Print Network (OSTI)

??This investigation of peak load reduction strategies in residential buildings contributes to the global international efforts in reducing energy consumption and is related directly to… (more)

Atallah, Fady

2013-01-01T23:59:59.000Z

376

Characterization, Leaching, and Filtrations Testing of Ferrocyanide Tank sludge (Group 8) Actual Waste Composite  

SciTech Connect

This is the final report in a series of eight reports defining characterization, leach, and filtration testing of a wide variety of Hanford tank waste sludges. The information generated from this series is intended to supplement the Waste Treatment and Immobilization Plant (WTP) project understanding of actual waste behaviors associated with tank waste sludge processing through the pretreatment portion of the WTP. The work described in this report presents information on a high-iron waste form, specifically the ferrocyanide tank waste sludge. Iron hydroxide has been shown to pose technical challenges during filtration processing; the ferrocyanide tank waste sludge represented a good source of the high-iron matrix to test the filtration processing.

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

2009-02-28T23:59:59.000Z

377

Building a Model Patient Room to Test Design Innovations With Actual Patients  

E-Print Network (OSTI)

comfortable hospital environment SUMMARY Designing and constructing a new hospital is a complex and costly undertaking that involves experts from many disciplines both inside and outside the health care arena. But despite expending funds and time, hospital leaders often discover significant flaws once a hospital opens that can undermine the quality of patient care and staff effectiveness and efficiency. From 2010 to 2012, a team at the Princeton HealthCare System worked to devise an optimal design for inpatient rooms at a new hospital: the University Medical Center of Princeton at Plainsboro. The project entailed building a “functional model patient room.” This was a unique and innovative method to allow the team to test design innovations with actual patients, according to project director Susan Lorenz, DrNP, RN, vice president of patient care services and chief nursing officer for the Princeton HealthCare System. The project helped support the emerging field of evidence-based hospital design.

A Princeton; More Efficient; Key Results

2013-01-01T23:59:59.000Z

378

Exposure of Ceramics and Ceramic Matrix Composites in Simulated and Actual Combustor Environments  

DOE Green Energy (OSTI)

A high-temperature, high-pressure, tube furnace has been used to evaluate the long term stability of different monolithic ceramic and ceramic matrix composite materials in a simulated combustor environment. All of the tests have been run at 150 psia, 1204 degrees C, and 15% steam in incremental 500 h runs. The major advantage of this system is the high sample throughput; >20 samples can be exposed in each tube at the same time under similar exposure conditions. Microstructural evaluations of the samples were conducted after each 500 h exposure to characterize the extent of surface damage, to calculate surface recession rates, and to determine degradation mechanisms for the different materials. The validity of this exposure rig for simulating real combustor environments was established by comparing materials exposed in the test rig and combustor liner materials exposed for similar times in an actual gas turbine combustor under commercial operating conditions.

Brentnall, W.D.; Ferber, M.K.; Keiser, j.R.; Miriyala, N.; More, K.L.; Price, J.R.; Tortorelli, P.F.; Walker, L.R.

1999-06-07T23:59:59.000Z

379

Actual Scale MOX Powder Mixing Test for MOX Fuel Fabrication Plant in Japan  

Science Conference Proceedings (OSTI)

Japan Nuclear Fuel Ltd. (hereafter, JNFL) promotes a program of constructing a MOX fuel fabrication plant (hereafter, J-MOX) to fabricate MOX fuels to be loaded in domestic light water reactors. Since Japanese fiscal year (hereafter, JFY) 1999, JNFL, to establish the technology for a smooth start-up and the stable operation of J-MOX, has executed an evaluation test for technology to be adopted at J-MOX. JNFL, based on a consideration that J-MOX fuel fabrication comes commercial scale production, decided an introduction of MIMAS technology into J-MOX main process, from powder mixing through pellet sintering, well recognized as mostly important to achieve good quality product of MOX fuel, since it achieves good results in both fuel production and actual reactor irradiation in Europe, but there is one difference that JNFL is going to use Japanese typical plutonium and uranium mixed oxide powder converted with the micro-wave heating direct de-nitration technology (hereafter, MH-MOX) but normal PuO{sub 2} of European MOX fuel fabricators. Therefore, in order to evaluate the suitability of the MH-MOX powder for the MIMAS process, JNFL manufactured small scale test equipment, and implemented a powder mixing evaluation test up until JFY 2003. As a result, the suitability of the MH-MOX powder for the MIMAS process was positively evaluated and confirmed It was followed by a five-years test named an 'actual test' from JFY 2003 to JFY 2007, which aims at demonstrating good operation and maintenance of process equipment as well as obtaining good quality of MOX fuel pellets. (authors)

Osaka, Shuichi; Kurita, Ichiro; Deguchi, Morimoto [Japan Nuclear Fuel Ltd., 4-108, Aza okitsuke, oaza obuchi rokkasyo-mura, kamikita-gun, Aomori 039-3212 (Japan); Ito, Masanori [Japan Atomic Energy Agency, 4-33 Muramatu, Tokai-mura, Ibaraki 319-1194 (Japan); Goto, Masakazu [Nuclear Fuel Industries, Ltd., 14-10, Mita 3-chome, Minato-ku, Tokyo 108-0073 (Japan)

2007-07-01T23:59:59.000Z

380

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

E-Print Network (OSTI)

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

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

Project Number: MQP-SJB-1A03 Solar Panel Peak Power Tracking System  

E-Print Network (OSTI)

Project Number: MQP-SJB-1A03 Solar Panel Peak Power Tracking System A Major Qualifying Project of a Maximum Peak Power Tracking (MPPT) controller for a solar photovoltaic battery charging system is proposed ................................................................................................. 8 2.1.1 Solar Power Fundamentals

Brown III, Donald R.

382

Power consumption scheduling for peak load reduction in smart grid homes  

Science Conference Proceedings (OSTI)

This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes, aiming at reducing the peak load in individual homes as well as in the system-wide power transmission network. Following the task model consist ... Keywords: execution time, home controller, peak load reduction, power schedule, smart grid

Junghoon Lee; Gyung-Leen Park; Sang-Wook Kim; Hye-Jin Kim; Chang Oan Sung

2011-03-01T23:59:59.000Z

383

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

E-Print Network (OSTI)

Distributed 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.8% 99% 91.5% 84% Battery Configuration StudyBattery Configuration Study Goal: Improve the overall

Simunic, Tajana

384

A Technique to Reduce Peak Current and Average Power Dissipation in Scan Designs by Limited Capture  

Science Conference Proceedings (OSTI)

In this paper, a technique that can efficiently reduce peak and average switching activity during test application is proposed. The proposed method does not require any specific clock tree construction, special scan cells, or scan chain reordering. Test ... Keywords: ATPG, peak current reduction, average power dissipation, scan designs, clock tree construction, special scan cells, scan chain reordering

Seongmoon Wang; Wenlong Wei

2007-01-01T23:59:59.000Z

385

Weak lensing mass map and peak statistics in CFHT/Stripe82 survey  

E-Print Network (OSTI)

We present the weak lensing mass map of the 173 tiles Canada-France-Hawaii Telescope Stripe82 Survey (CS82) with the effective area ~124 square degrees and study the peak statistics, including peak abundance, correlation functions and tangential-shear profile of peaks with the mass map. We find that (1) peak abundance detected in CS82 are consistent with predictions from a Lambda-CDM cosmological model, once noise effects are properly included; (2) correlation function of peaks with different signal-to-noise ratio (SNR) can be well fitted with power laws. Combining with the SDSS-III/Constant Mass (CMASS) galaxies, the cross-correlation between CMASS galaxies and high SNR peaks can be well-fitted with a power law; (3) the tangential shear profiles of the peaks increase with SNR. We concentrate on fitting spherical models to the tangential profiles with both singular isothermal sphere (SIS) and Navarro Frenk & White (NFW) models. For the high SNR peaks, the SIS model is rejected at ~3-sigma. Comparing the D...

Shan, HuanYuan; Comparat, Johan; Jullo, Eric; Charbonnier, Aldee; Erben, Thomas; Makler, Martin; Moraes, Bruno; Van Waerbeke, Ludovic; Courbin, Frederic; Meylan, George; Tao, Charling; Taylor, James E

2013-01-01T23:59:59.000Z

386

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

387

BENCH-SCALE STEAM REFORMING OF ACTUAL TANK 48H WASTE  

Science Conference Proceedings (OSTI)

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

388

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

389

Actual Dose Variation of Parotid Glands and Spinal Cord for Nasopharyngeal Cancer Patients During Radiotherapy  

Science Conference Proceedings (OSTI)

Purpose: For intensity-modulated radiotherapy of nasopharyngeal cancer, accurate dose delivery is crucial to the success of treatment. This study aimed to evaluate the significance of daily image-guided patient setup corrections and to quantify the parotid gland volume and dose variations for nasopharyngeal cancer patients using helical tomotherapy megavoltage computed tomography (CT). Methods and Materials: Five nasopharyngeal cancer patients who underwent helical tomotherapy were selected retrospectively. Each patient had received 70 Gy in 35 fractions. Daily megavoltage CT scans were registered with the planning CT images to correct the patient setup errors. Contours of the spinal cord and parotid glands were drawn on the megavoltage CT images at fixed treatment intervals. The actual doses delivered to the critical structures were calculated using the helical tomotherapy Planned Adaptive application. Results: The maximal dose to the spinal cord showed a significant increase and greater variation without daily setup corrections. The significant decrease in the parotid gland volume led to a greater median dose in the later phase of treatment. The average parotid gland volume had decreased from 20.5 to 13.2 cm{sup 3} by the end of treatment. On average, the median dose to the parotid glands was 83 cGy and 145 cGy for the first and the last treatment fractions, respectively. Conclusions: Daily image-guided setup corrections can eliminate significant dose variations to critical structures. Constant monitoring of patient anatomic changes and selective replanning should be used during radiotherapy to avoid critical structure complications.

Han Chunhui [Division of Radiation Oncology, City of Hope National Medical Center, Duarte, CA (United States)], E-mail: chan@coh.org; Chen Yijen; Liu An; Schultheiss, Timothy E.; Wong, Jeffrey Y.C. [Division of Radiation Oncology, City of Hope National Medical Center, Duarte, CA (United States)

2008-03-15T23:59:59.000Z

390

Modeling of Boehmite Leaching from Actual Hanford High-Level Waste Samples  

SciTech Connect

The Department of Energy plans to vitrify approximately 60,000 metric tons of high level waste sludge from underground storage tanks at the Hanford Nuclear Reservation. To reduce the volume of high level waste requiring treatment, a goal has been set to remove about 90 percent of the aluminum, which comprises nearly 70 percent of the sludge. Aluminum in the form of gibbsite and sodium aluminate can be easily dissolved by washing the waste stream with caustic, but boehmite, which comprises nearly half of the total aluminum, is more resistant to caustic dissolution and requires higher treatment temperatures and hydroxide concentrations. In this work, the dissolution kinetics of aluminum species during caustic leaching of actual Hanford high level waste samples is examined. The experimental results are used to develop a shrinking core model that provides a basis for prediction of dissolution dynamics from known process temperature and hydroxide concentration. This model is further developed to include the effects of particle size polydispersity, which is found to strongly influence the rate of dissolution.

Peterson, Reid A.; Lumetta, Gregg J.; Rapko, Brian M.; Poloski, Adam P.

2007-06-27T23:59:59.000Z

391

Actinide partitioning from actual Idaho chemical processing plant acidic tank waste using centrifugal contactors  

Science Conference Proceedings (OSTI)

The TRUEX process is being evaluated at the Idaho Chemical Processing Plant (ICPP) for the separation of the actinides from acidic radioactive wastes stored at the ICPP. These efforts have culminated in a recent demonstration of the TRUEX process with actual tank waste. This demonstration was performed using 24 stages of 2-cm diameter centrifugal contactors installed in a shielded hot cell at the ICPP Remote Analytical Laboratory. An overall removal efficiency of 99.97% was obtained for the actinides. As a result, the activity of the actinides was reduced from 457 nCi/g in the feed to 0.12 nCi/g in the aqueous raffinate, which is well below the U.S. NRC Class A LLW requirement of 10 nCi/g for non-TRU waste. Iron was partially extracted by the TRUEX solvent, resulting in 23% of the Fe exiting in the strip product. Mercury was also extracted by the TRUEX solvent (76%) and stripped from the solvent in the 0.25 M Na{sub 2}CO{sub 3} wash section.

Law, J.D.; Brewer, K.N.; Todd, T.A.

1997-10-01T23:59:59.000Z

392

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

393

Actual versus design performance of solar systems in the National Solar Data Network  

Science Conference Proceedings (OSTI)

This report relates field measured performance to the designer predicted performance. The field measured data was collected by the National Solar Data Network (NSDN) over a period of six years. Data from 25 solar systems was selected from a data pool of some 170 solar systems. The scope of the project extends beyond merely presenting comparisons of data. There is an attempt to provide answers which will move the solar industry forward. As a result of some industry and research workshops, several concerns arose which can be partially allayed by careful study of the NSDN data. These are: What types of failures occurred and why. How good was the design versus actual performance. Why was predicted performance not achieved in the field. Which components should be integrated with a system type for good performance. Since the designs span several years and since design philosophies are quite variable, the measured results were also compared to f-Chart 5.1 results. This comparison is a type of normalization in that all systems are modeled with the same process. An added benefit of this normalization is a further validation of the f-Chart model on a fairly large scale. The systems were modeled using equipment design parameters, measured loads, and f-Chart weather data from nearby cities.

Logee, T.L.; Kendall, P.W.

1984-09-01T23:59:59.000Z

394

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

E-Print Network (OSTI)

This thesis presents the results obtained from generation of high repetition rate, high power output pulse using an erbium-doped fiber amplifier (EDFA). Two stage amplification was employed. The first stage setup used 980nm pump laser to pump erbium-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 produced pulse peak power of 2.52W. The overall output peak power, which is produced by the first and second stage, is 16W.

Lee, Kyung-Woo

2000-01-01T23:59:59.000Z

395

Permanent Peak Load Shift Product Deployment for Smart Grid Integration and Operation  

Science Conference Proceedings (OSTI)

This project tested and evaluated an innovative energy storage technology that provides permanent peak load shifting using electro-thermal energy storage in combination with commercial unitary rooftop air conditioning systems. Four Ice Bear 30 units were deployed at a Staples facility to store an estimated 32 kWh each of energy in 10 off-peak hours and reduce an estimated 5 kW of site energy demand for an on-peak six-hour period. The Ice Bear units are monitored and controlled with a smart grid ...

2012-11-14T23:59:59.000Z

396

COLL-C 103: Critical Approaches to the Arts & Sciences, Fall 2012 TOPIC: Pleasure, Pain, and Peak Oil  

E-Print Network (OSTI)

. Pressing environmental issues such as peak oil and climate change may well bring a radical reevaluation to peak oil and related challenges mean turning our backs on progress? Do peak oil prophets paint. Peak Oil: A Crash Course Week 1. T: Introduction to course themes R: "The End of Cheap Energy

Indiana University

397

Filtration and Leach Testing for PUREX Cladding Sludge and REDOX Cladding Sludge 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 (Barnes and Voke 2006). The test program was subdivided into logical increments. The bulk water-insoluble solid wastes that are anticipated to be delivered to the Hanford Waste Treatment and Immobilization Plant (WTP) were identified according to type such that the actual waste testing could be targeted to the relevant categories. Under test plan TP RPP WTP 467 (Fiskum et al. 2007), eight broad waste groupings were defined. Samples available from the 222S archive were identified and obtained for testing. Under this test plan, a waste testing program was implemented that included: • Homogenizing the archive samples by group as defined in the test plan. • Characterizing the homogenized sample groups. • Performing parametric leaching testing on each group for compounds of interest. • Performing bench-top filtration/leaching tests in the hot cell for each group to simulate filtration and leaching activities if they occurred in the UFP2 vessel of the WTP Pretreatment Facility. This report focuses on a filtration/leaching test performed using two of the eight waste composite samples. The sample groups examined in this report were the plutonium-uranium extraction (PUREX) cladding waste sludge (Group 3, or CWP) and reduction-oxidation (REDOX) cladding waste sludge (Group 4, or CWR). Both the Group 3 and 4 waste composites were anticipated to be high in gibbsite, thus requiring caustic leaching. WTP RPT 167 (Snow et al. 2008) describes the homogenization, characterization, and parametric leaching activities before benchtop filtration/leaching testing of these two waste groups. Characterization and initial parametric data in that report were used to plan a single filtration/leaching test using a blend of both wastes. The test focused on filtration testing of the waste and caustic leaching for aluminum, in the form of gibbsite, and its impact on filtration. The initial sample was diluted with a liquid simulant to simulate the receiving concentration of retrieved tank waste into the UFP2 vessel (< 10 wt% undissolved solids). Filtration testing was performed on the dilute waste sample and dewatered to a higher solids concentration. Filtration testing was then performed on the concentrated slurry. Afterwards, the slurry was caustic leached to remove aluminum present in the undissolved solid present in the waste. The leach was planned to simulate leaching conditions in the UFP2 vessel. During the leach, slurry supernate samples were collected to measure the dissolution rate of aluminum in the waste. After the slurry cooled down from the elevated leach temperature, the leach liquor was dewatered from the solids. The remaining slurry was rinsed and dewatered with caustic solutions to remove a majority of the dissolved aluminum from the leached slurry. The concentration of sodium hydroxide in the rinse solutions was high enough to maintain the solubility of the aluminum in the dewatered rinse solutions after dilution of the slurry supernate. Filtration tests were performed on the final slurry to compare to filtration performance before and after caustic leaching.

Shimskey, Rick W.; Billing, Justin M.; Buck, Edgar C.; Casella, Amanda J.; Crum, Jarrod V.; Daniel, Richard C.; Draper, Kathryn E.; Edwards, Matthew K.; Hallen, Richard T.; Kozelisky, Anne E.; MacFarlan, Paul J.; Peterson, Reid A.; Swoboda, Robert G.

2009-03-02T23:59:59.000Z

398

ACTUAL-WASTE TESTING OF ULTRAVIOLET LIGHT TO AUGMENT THE ENHANCED CHEMICAL CLEANING OF SRS SLUDGE  

SciTech Connect

In support of Savannah River Site (SRS) tank closure efforts, the Savannah River National Laboratory (SRNL) conducted Real Waste Testing (RWT) to evaluate Enhanced Chemical Cleaning (ECC), an alternative to the baseline 8 wt% oxalic acid (OA) chemical cleaning technology for tank sludge heel removal. ECC utilizes a more dilute OA solution (2 wt%) and an oxalate destruction technology using ozonolysis with or without the application of ultraviolet (UV) light. SRNL conducted tests of the ECC process using actual SRS waste material from Tanks 5F and 12H. The previous phase of testing involved testing of all phases of the ECC process (sludge dissolution, OA decomposition, product evaporation, and deposition tank storage) but did not involve the use of UV light in OA decomposition. The new phase of testing documented in this report focused on the use of UV light to assist OA decomposition, but involved only the OA decomposition and deposition tank portions of the process. Compared with the previous testing at analogous conditions without UV light, OA decomposition with the use of UV light generally reduced time required to reach the target of <100 mg/L oxalate. This effect was the most pronounced during the initial part of the decomposition batches, when pH was <4. For the later stages of each OA decomposition batch, the increase in OA decomposition rate with use of the UV light appeared to be minimal. Testing of the deposition tank storage of the ECC product resulted in analogous soluble concentrations regardless of the use or non-use of UV light in the ECC reactor.

Martino, C.; King, W.; Ketusky, E.

2012-07-10T23:59:59.000Z

399

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

400

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.

Note: This page contains sample records for the topic "mwh actual peak" from the National Library of EnergyBeta (NLEBeta).
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401

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.

402

Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building  

E-Print Network (OSTI)

Use of Building Thermal Mass to Offset Cooling Loads. ASHRAEThe Role of Thermal Mass on the Cooling Load of Buildings.to reduce peak cooling loads with thermal mass control.

Xu, Peng

2010-01-01T23:59:59.000Z

403

Peak demand reduction from pre-cooling with zone temperature reset in an office building  

E-Print Network (OSTI)

Use of Building Thermal Mass to Offset Cooling Loads. ASHRAEThe Role of Thermal Mass on the Cooling Load of Buildings.to reduce peak cooling loads with thermal mass control.

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2004-01-01T23:59:59.000Z

404

Peak ground velocity evaluation by artificial neural network for west america region  

Science Conference Proceedings (OSTI)

With the Peak Ground Velocity 283 records in three dimensions, the velocity attenuation relationship with distance was discussed by neural network in this paper. The earthquake magnitude, epicenter distance, site intensity and site condition were considered ...

Ben-yu Liu; Liao-yuan Ye; Mei-ling Xiao; Sheng Miao

2006-10-01T23:59:59.000Z

405

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

DOE Green Energy (OSTI)

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

406

Climatic-related Evaluations of the Summer Peak-Hours' Electric Load in Israel  

Science Conference Proceedings (OSTI)

The interrelationship between the Summer peak electric load in Israel and pertinent meteorological parameters, including the commonly used outdoor biometeorological comfort index, is evaluated conceptually and statistically. Linear regression ...

M. Segal; H. Shafir; M. Mandel; P. Alpert; Y. Balmor

1992-12-01T23:59:59.000Z

407

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

408

Photonic bandgap fibers for transmitting high peak-power pulses in the near infrared  

E-Print Network (OSTI)

Hollow-core photonic bandgap fibers (PBG) offer the opportunity to suppress highly the optical absorption and nonlinearities of their constituent materials, which makes them viable candidates for transmitting high-peak ...

Ruff, Zachary

2010-01-01T23:59:59.000Z

409

Polymer-composite fibers for transmitting high peak power pulses at 1.55 microns  

E-Print Network (OSTI)

Hollow-core photonic bandgap fibers (PBG) offer the opportunity to suppress highly the optical absorption and nonlinearities of their constituent materials, which makes them viable candidates for transmitting high-peak ...

Ruff, Zachary

410

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.

411

Exploring Cloud-to-Ground Lightning Earth Highpoint Attachment Geography by Peak Current  

Science Conference Proceedings (OSTI)

This study applied remotely sensed cloud-to-ground (CG) lightning strike location data, a digital elevation model (DEM), and a geographic information system (GIS) to characterize negative polarity peak current CG lightning Earth attachment ...

Brandon J. Vogt

2011-02-01T23:59:59.000Z

412

BATSE Observations of Gamma-Ray Burst Spectra. II. Peak Energy Evolution in Bright, Long Bursts -  

E-Print Network (OSTI)

We investigate spectral evolution in 37 bright, long gamma-ray bursts observed with the BATSE Spectroscopy Detectors. High resolution spectra are characterized by the energy of the peak of \

L. A. Ford; D. L. Band; J. L. Matteson; M. S. Briggs; G. N. Pendleton; R. D. Preece; W. S. Paciesas; B. J. Teegarden; D. M. Palmer; B. E. Schaefer; T. L. Cline; G. J. Fishman; C. Kouveliotou; C. A. Meegan; R. B. Wilson; J. P. Lestrade

1994-07-27T23:59:59.000Z

413

The Complexity of Manipulative Attacks in Nearly Single-Peaked Electorates  

E-Print Network (OSTI)

Many electoral bribery, control, and manipulation problems (which we will refer to in general as "manipulative actions" problems) are NP-hard in the general case. It has recently been noted that many of these problems fall into polynomial time if the electorate is single-peaked (i.e., is polarized along some axis/issue). However, real-world electorates are not truly single-peaked. There are usually some mavericks, and so real-world electorates tend to merely be nearly single-peaked. This paper studies the complexity of manipulative-action algorithms for elections over nearly single-peaked electorates, for various notions of nearness and various election systems. We provide instances where even one maverick jumps the manipulative-action complexity up to $\

Faliszewski, Piotr; Hemaspaandra, Lane A

2011-01-01T23:59:59.000Z

414

Monitoring System Used to Identify, Track and Allocate Peak Demand Costs  

E-Print Network (OSTI)

In 1994, Thomson Consumer Electronics (RCA) purchased a UtiliTRACK® Monitoring System for a plant in Indianapolis, Indiana primarily to allow utility costs to be billed to individual departments within Thomson as well as to outside organizations leasing space on the site. The most common way to distribute monthly electric costs within a facility when consumption by area or department is available through submetering or other means, is to apply the average cost per KWH from the utility bill to the individual consumption figures. Thomson initially used the data from the UtiliTRACK System in this way. As the plant engineer worked with system data on a daily basis and began to develop a much better understanding of the plant's electrical profile, it was clear that the percentage contribution by department or area to the plant's peak demand was not the same as that assigned based solely upon consumption. With a monthly peak exceeding 8 MW and peak demand charges accounting for more than 60% of the monthly electric bill, he realized that to be accurate and fair, costs must be allocated based both on consumption and peak demand. He asked UtiliTRACK to develop a method for tracking and allocating peak demand costs. The resulting software continuously tracks the total plant demand (the sum of 3 utility meters) and records the contribution of each monitored point at the time the peak occurs. The resulting reports and graphs not only enable the owner to accurately allocate peak demand costs but also provide a means for tracking and managing peaks on a continuous basis.

Holmes, W. A.

1998-04-01T23:59:59.000Z

415

Peaks and Valleys: Experimental asset markets with non-monotonic fundamentals  

E-Print Network (OSTI)

We report the results of an experiment designed to measure how well asset market prices track fundamentals when the latter experience peaks and troughs. We observe greater price efficiency in markets in which fundamentals rise to a peak and then decline, than in markets in which fundamentals decline to a trough and undergo a subsequent increase. The findings demonstrate that the characteristics of the time path of the fundamental value can influence the degree of market efficiency. I.

Charles N. Noussair; Owen Powell

2008-01-01T23:59:59.000Z

416

STEAM REFORMING TECHNOLOGY DEMONSTRATION FOR THE DESTRUCTION OF ORGANICS ON ACTUAL DOE SAVANNAH RIVER SITE TANK 48H WASTE 9138  

SciTech Connect

This paper describes the design of the Bench-scale Steam Reformer (BSR); a processing unit for demonstrating steam reforming technology on actual radioactive waste [1]. It describes the operating conditions of the unit used for processing a sample of Savannah River Site (SRS) Tank 48H waste. Finally, it compares the results from processing the actual waste in the BSR to processing simulant waste in the BSR to processing simulant waste in a large pilot scale unit, the Fluidized Bed Steam Reformer (FBSR), operated at Hazen Research Inc. in Golden, CO. The purpose of this work was to prove that the actual waste reacted in the same manner as the simulant waste in order to validate the work performed in the pilot scale unit which could only use simulant waste.

Burket, P

2009-02-24T23:59:59.000Z

417

DEMONSTRATION OF THE GLYCOLIC-FORMIC FLOWSHEET IN THE SRNL SHIELDED CELLS USING ACTUAL WASTE  

SciTech Connect

Glycolic acid was effective at dissolving many metals, including iron, during processing with simulants. Criticality constraints take credit for the insolubility of iron during processing to prevent criticality of fissile materials. Testing with actual waste was needed to determine the extent of iron and fissile isotope dissolution during Chemical Process Cell (CPC) processing. The Alternate Reductant Project was initiated by the Savannah River Remediation (SRR) Company to explore options for the replacement of the nitric-formic flowsheet used for the CPC at the Defense Waste Processing Facility (DWPF). The goals of the Alternate Reductant Project are to reduce CPC cycle time, increase mass throughput of the facility, and reduce operational hazards. In order to achieve these goals, several different reductants were considered during initial evaluations conducted by Savannah River National Laboratory (SRNL). After review of the reductants by SRR, SRNL, and Energy Solutions (ES) Vitreous State Laboratory (VSL), two flowsheets were further developed in parallel. The two flowsheet options included a nitric-formic-glycolic flowsheet, and a nitric-formic-sugar flowsheet. As of July 2011, SRNL and ES/VSL have completed the initial flowsheet development work for the nitric-formic-glycolic flowsheet and nitric-formic-sugar flowsheet, respectively. On July 12th and July 13th, SRR conducted a Systems Engineering Evaluation (SEE) to down select the alternate reductant flowsheet. The SEE team selected the Formic-Glycolic Flowsheet for further development. Two risks were identified in SEE for expedited research. The first risk is related to iron and plutonium solubility during the CPC process with respect to criticality. Currently, DWPF credits iron as a poison for the fissile components of the sludge. Due to the high iron solubility observed during the flowsheet demonstrations with simulants, it was necessary to determine if the plutonium in the radioactive sludge slurry demonstrated the same behavior. The second risk is related to potential downstream impacts of glycolate on Tank Farm processes. The downstream impacts will be evaluated by a separate research team. Waste Solidification Engineering (WSE) has requested a radioactive demonstration of the Glycolic-Formic Flowsheet with radioactive sludge slurry be completed in the Shielded Cells Facility of the SRNL. The Shielded Cells demonstration only included a Sludge Receipt and Adjustment Tank (SRAT) cycle, and not a Slurry Mix Evaporator (SME) cycle or the co-processing of salt products. Sludge Batch 5 (SB5) slurry was used for the demonstration since it was readily available, had been previously characterized, and was generally representative of sludges being processing in DWPF. This sample was never used in the planned Shielded Cells Run 7 (SC-7).

Lambert, D.; Pareizs, J.; Click, D.

2011-11-07T23:59:59.000Z

418

Evaluation of Travis Peak gas reservoirs, west margin of the East Texas Basin  

E-Print Network (OSTI)

Gas production from low-permeability (tight) gas sandstones is increasingly important in the USA as conventional gas reservoirs are being depleted, and its importance will increase worldwide in future decades. Travis Peak tight sandstones have produced gas since the 1940s. In this study, well log, 2D seismic, core, and production data were used to evaluate the geologic setting and reservoir characteristics of the Travis Peak formation. The primary objective was to assess the potential for basinward extension of Travis Peak gas production along the west margin of the East Texas Basin. Along the west margin of the East Texas Basin, southeast-trending Travis Peak sandstones belts were deposited by the Ancestral Red River fluvial-deltaic system. The sandstones are fine-grained, moderately well sorted, subangular to subrounded, quartz arenites and subarkoses; reservoir quality decreases with depth, primarily due to diagenetic quartz overgrowths. Evaluation of drilling mud densities suggests that strata deeper than 12,500 ft may be overpressured. Assessment of the geothermal gradient (1.6 °F/100 ft) indicates that overpressure may be relict, resulting from hydrocarbon generation by Smackover and Bossier formation potential source rocks. In the study area, Travis Peak cumulative gas production was 1.43 trillion cubic feet from January 1, 1961, through December 31, 2005. Mean daily gas production from 923 wells was 925,000 cubic ft/well/day, during the best year of production. The number of Travis Peak gas wells in “high-cost” (tight sandstone) fields increased from 18 in the decade 1966-75 to 333 in the decade 1996-2005, when high-cost fields accounted for 33.2% of the Travis Peak gas production. However, 2005 gas production from high cost fields accounted for 63.2% of the Travis Peak total production, indicating that production from high-cost gas wells has increased markedly. Along the west margin of the East Texas Basin, hydrocarbon occurs in structural, stratigraphic, and combination traps associated with salt deformation. Downdip extension of Travis Peak production will depend on the (1) burial history and diagenesis, (2) reservoir sedimentary facies, and (3) structural setting. Potential Travis Peak hydrocarbon plays include: updip pinch-outs of sandstones; sandstone pinch-outs at margins of salt-withdrawal basins; domal traps above salt structures; and deepwater sands.

Li, Yamin

2007-05-01T23:59:59.000Z

419

The influence of indoor temperature on the difference between actual and theoretical energy consumption for space heating  

Science Conference Proceedings (OSTI)

The Energy Advice procedure (EAP) is developed to evaluate the energetic performance of "existing" dwellings to generate a useful advice for the occupants of the dwelling to invest in rational energy measures. The EAP is based on a theoretical calculation ... Keywords: actual energy consumption, consumer behaviour, indoor temperature, space heating, theoretical energy consumption

Amaryllis Audenaert; Katleen Briffaerts; Dries De Boeck

2011-11-01T23:59:59.000Z

420

Association for the Study of Peak Oil & Gas – South Africa www.aspo.org.za Peak Oil and South Africa: Impacts and Mitigation  

E-Print Network (OSTI)

Senior Lecturer, School of Economics, University of Cape Town. This paper draws in places on two previous papers (see Wakeford, 2006a and 2006b). I would like to thank Rodger Duffet, Michael de Wit, Lisa Kane and Jacqui Wakeford for providing helpful comments on earlier drafts. Any remaining errors or omissions are my sole responsibility. Please note that this paper is a work-in-progress; this version supersedes all previous versions of the paper. ASPO-SA: Guiding South Africa through Peak Oil to a Sustainable FutureContents

Jeremy Wakeford

2007-01-01T23:59:59.000Z

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.


421

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

422

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

423

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

424

EVIDENCE FOR POLAR X-RAY JETS AS SOURCES OF MICROSTREAM PEAKS IN THE SOLAR WIND  

Science Conference Proceedings (OSTI)

It is proposed that the interplanetary manifestations of X-ray jets observed in solar polar coronal holes during periods of low solar activity are the peaks of the so-called microstreams observed in the fast polar solar wind. These microstreams exhibit velocity fluctuations of {+-}35 km s{sup -1}, higher kinetic temperatures, slightly higher proton fluxes, and slightly higher abundances of the low-first-ionization-potential element iron relative to oxygen ions than the average polar wind. Those properties can all be explained if the fast microstreams result from the magnetic reconnection of bright-point loops, which leads to X-ray jets which, in turn, result in solar polar plumes. Because most of the microstream peaks are bounded by discontinuities of solar origin, jets are favored over plumes for the majority of the microstream peaks.

Neugebauer, Marcia, E-mail: mneugeb@lpl.arizona.edu [Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ 85721 (United States)

2012-05-01T23:59:59.000Z

425

New Peak Moisture Design Data in the 1997 ASHRAE Handbook of Fundamentals  

E-Print Network (OSTI)

Chapter 26 of the 1997 edition of the Handbook of Fundamentals published by ASHRAE (American Society of Heating, Refrigerating and Air Conditioning Engineers) contains climatic design data that has been completely revised, recalculated and expanded. Designers of air conditioning systems for hot and humid climates will be pleased to note that, for the first time, the chapter contains values for peak moisture conditions. This is in sharp contrast to older editions, which contained only the average moisture during periods of peak dry bulb temperatures. The new data show that using earlier, temperature-based data for humidity design underestimates the true peak moisture loads by 30 to 50% depending on the humidity control level in the space. This paper explains the new data elements and suggests some of its potential implications for engineers designing air conditioning systems for hot and humid climates.

Harriman, L.

1998-01-01T23:59:59.000Z

426

Peak pressures from hydrogen deflagrations in the PFP thermal stabilization glovebox  

DOE Green Energy (OSTI)

This document describes the calculations of the peak pressures due to hydrogen deflagrations in the glovebox used for thermal stabilization (glovebox HC-21A) in PFP. Two calculations were performed. The first considered the burning of hydrogen released from a 7 inch Pu can in the Inert Atmosphere Confinement (IAC) section of the glovebox. The peak pressure increase was 12400 Pa (1.8 psi). The second calculation considered burning of the hydrogen from 25 g of plutonium hydride in the airlock leading to the main portion of the glovebox. Since the glovebox door exposes most of the airlock when open, the deflagration was assumed to pressurize the entire glovebox. The peak pressure increase was 3860 Pa (0.56 psi).

Van Keuren, J.C.

1998-08-11T23:59:59.000Z

427

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

428

Coproduction of peaking fuels in IGCC power plants: a process-screening study. Final report  

SciTech Connect

This study evaluated and compared various options for processing a portion of the medium BTU gas (MBG) produced in a coal gasification combined cycle (GCC) power plant to produce a fuel which might be suitable for peaking or intermediate load use. Two alternate objectives were investigated in separate phases of the study. The first phase examined options for processing and storing a fuel which could be withdrawn and used in absorbing daily load swings in power generation demand. The second phase investigated options for meeting the seasonal peaks in gas demand of a joint gas/electric utility by converting a portion of the MBG to substitute natural gas (SNG) during the months of peak gas demand. For each phase, process designs and cost estimates were completed for several cases, based on both Texaco and BGC-Lurgi Slagging Gasification Technology. For the purposes of this screening study, it was assumed that the peaking fuel production facilities are incremental to the base GCC plant. The costs to produce and store the peaking fuel, excluding the cost of the MBG feed, were calculated by the revenue requirement method. Various sensitivities were evaluated on case assumptions, including a sensitivity to MBG feed value. For daily peaking use, the co-production of methanol and electricity by the ''once-through'' scheme (as studied in EPRI Report AP-2212) proved the most attractive option. Other options which produced gaseous fuels (hydrogen or SNG) for on-site storage were at least 30% more costly. Storage of SNG in an existing natural gas pipeline system was at least 10% higher, excluding pipeline charges. For seasonal SNG production there was little difference between the options studied, within the accuracy of the estimates. 13 refs., 72 tabs.

Shenoy, T.A.; Solomon, J.; O'Brien, V.J.

1986-07-01T23:59:59.000Z

429

Sequence Stratigraphy and Detrital Zircon Geochronology of the Swan Peak Quartzite, Southeastern Idaho  

E-Print Network (OSTI)

The supermature Middle-Late Ordovician Swan Peak quartz arenite was deposited on the western Laurentia passive margin and is very fine to fine grained, well-rounded, well-sorted, and silica-cemented. Laurentia was positioned over the equator during the Middle-Late Ordovician, suggesting that basement rock along the Transcontinental Arch was intensely eroded in a humid climate to produce this and other coeval quartz arenites. To determine provenance for the Swan Peak Quartzite, zircon grains were analyzed using LA-ICP-MS and the results were constrained within a sequence stratigraphic framework. Depositional environments of the Swan Peak Quartzite record an offshore-to-onshore transition with five facies (A-E). Facies A only occurs at the base of the Bear Lake section and may record an incised valley or localized embayment. It is the deepest water facies in the succession containing shale and quartz arenite interbeds. Facies B through E are interpreted as lower, middle, upper shoreface/foreshore depositional environments, respectively, based on primary sedimentary structures and bioturbation. Detrital zircon age spectra of the Swan Peak Quartzite have four distinct populations: the two main populations are at 1.8 - 2.0 Ga (Paleoproterozoic) and between 2.5 - 3.0 Ga (Archean), with a smaller, but persistent, population at 2.0 - 2.1 Ga, and a very minor 0.8 - 1.2 Ga (Mesoproterozoic) population occurring mainly in the tops of the measured sections. The base of each section has a larger Archean peak whereas the top of each section is predominantly Paleoproterozoic grains. Zircon data have overlap and similarity values ranging between 0.531 - 0.771 and 0.506 - 0.881, respectively, which indicates zircon age spectra of the Swan Peak Quartzite is similar to other Cordilleran Ordovician quartzites and that recycling of heterogeneous underlying sedimentary rocks was minimal. The Wyoming Craton (2.5 - 2.8 Ga) and the Trans-Hudson Orogen (1.8 - 2.0 Ga) provinces near the paleoequator likely provided the majority of zircons in the Swan Peak Quartzite. The source for the 2.0 - 2.1 Ga grains is currently unknown and the 0.8 - 1.2 Ga grains are interpreted to reflect Mesoproterozoic Laurentian tectonism. Sediment input varied in response to sea level fluctuations. Longshore transport was likely an important process in redistributing grains along the coastline during later deposition of the Swan Peak Quartzite.

Wulf, Tracy David

2011-12-01T23:59:59.000Z

430

Electrical Energy Conservation and Peak Demand Reduction Potential for Buildings in Texas: Preliminary Results  

E-Print Network (OSTI)

This paper presents preliminary results of a study of electrical energy conservation and peak demand reduction potential for the building sector in Texas. Starting from 1980 building stocks and energy use characteristics, technical conservation potentials were calculated relative to frozen energy efficiency stock growth over the 1980-2000 period. The application of conservation supply methodology to Texas utilities is outlined, and then the energy use and peak demand savings, and their associated costs, are calculated using a prototypical building technique. Representative results are presented, for residential and commercial building types, as conservation supply curves for several end use categories; complete results of the study are presented in Ref. 1.

Hunn, B. D.; Baughman, M. L.; Silver, S. C.; Rosenfeld, A. H.; Akbari, H.

1985-01-01T23:59:59.000Z

431

Sapphire - A High Peak Brightness X-Ray Source as a Possible Option for a Next Generation UK Light Source  

E-Print Network (OSTI)

Sapphire - A High Peak Brightness X-Ray Source as a Possible Option for a Next Generation UK Light Source

Walker, R P; Christou, C; Han, J H; Kay, J

2008-01-01T23:59:59.000Z

432

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

433

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

DOE Patents (OSTI)

A method is described 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 model. 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. 1 fig.

Damiano, B.; Wood, R.T.

1997-04-22T23:59:59.000Z

434

The effect of morphological smoothening by reconstruction on the extraction of peaks and pits from digital elevation models  

Science Conference Proceedings (OSTI)

In this paper, the effect of morphological smoothening by reconstruction on the extraction of peaks and pits from digital elevation models (DEMs) is studied. First, a mathematical morphological based algorithm to extract peaks and pits from DEMs is developed. ... Keywords: DEM smoothening, Digital elevation models, Kernel, Morphological smoothening by reconstruction, Peaks and pits

Dinesh Sathyamoorthy

2007-09-01T23:59:59.000Z

435

Detection of point sources on two-dimensional images based on peaks  

Science Conference Proceedings (OSTI)

This paper 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 ... Keywords: analytical methods, data analysis methods, image processing techniques

M. López-Caniego; D. Herranz; J. L. Sanz; R. B. Barreiro

2005-01-01T23:59:59.000Z

436

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

437

Rotationally-induced asymmetry in the double-peak lightcurves of the bright EGRET pulsars?  

E-Print Network (OSTI)

Pulsed emission from the bright EGRET pulsars - Vela, Crab, and Geminga - extends up to 10 GeV. The generic gamma lightcurve features two peaks separated by 0.4 to 0.5 in phase. According to Thompson (2001) the lightcurve becomes asymmetrical above 5 GeV in such a way that the trailing peak dominates over the leading peak. We attempt to interpret this asymmetry within a single-polar-cap scenario. We investigate the role of rotational effects on the magnetic one-photon absorption rate in inducing such asymmetry. Our Monte Carlo simulations of pulsar gamma-ray beams reveal that in the case of oblique rotators with rotation periods of a few millisecond the rotational effects lead to the asymmetry of the requested magnitude. However, the rotators relevant for the bright EGRET pulsars must not have their inclination angles too large in order to keep the two peaks at a separation of 0.4 in phase. With such a condition imposed on the model rotators the resulting effects are rather minute and can hardly be reconciled with the magnitude of the observed asymmetry.

J. Dyks; B. Rudak

2001-06-05T23:59:59.000Z

438

Reducing the Peak Power through Real-Time Scheduling Techniques in Cyber-Physical Energy Systems  

E-Print Network (OSTI)

], large networks of electric cars [4], and automated energy supply and distribution for town and city of electric loads in cyber-physical energy systems. The aim of the proposed approach is to achieve predictability of the activation of electric loads to guarantee an upper bound on the peak electric power

Lipari, Giuseppe

439

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

E-Print Network (OSTI)

" and "Unconventional." Conventional oil is typically the highest quality, lightest oil, which flows from underground reservoirs with comparative ease, and it is the least expensive to produce. Unconventional oils are heavy the problem will be pervasive and long lasting. Oil peaking repre- sents a liquid fuels prob- lem

Hughes, Larry

440

The effect of multiple entrances on the elevator round trip time under up-peak traffic  

Science Conference Proceedings (OSTI)

The design of vertical transportation systems relies on the calculation of the interval as an indicator of the quality of service. This in turn involves the accurate calculation of the round trip time of a single elevator. The calculation of the round ... Keywords: Basement, Elevator, Interval, Lift, Round trip time, Up peak traffic

Lutfi Al-Sharif

2010-08-01T23:59:59.000Z

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441

Ice Thermal Storage Systems for Nuclear Power Plant Supplemental Cooling and Peak Power Shifting  

Science Conference Proceedings (OSTI)

Availability of cooling water has been one of the major issues for the nuclear power plant site selection. Cooling water issues have frequently disrupted the normal operation at some nuclear power plants during heat waves and long draught. One potential solution is to use ice thermal storage (ITS) systems that reduce cooling water requirements and boost the plant’s thermal efficiency in hot hours. ITS uses cheap off-peak electricity to make ice and uses the ice for supplemental cooling during peak demand time. ITS also provides a way to shift a large amount of electricity from off peak time to peak time. For once-through cooling plants near a limited water body, adding ITS can bring significant economic benefits and avoid forced derating and shutdown during extremely hot weather. For the new plants using dry cooling towers, adding the ITS systems can effectively reduce the efficiency loss during hot weather so that new plants could be considered in regions lack of cooling water. This paper will review light water reactor cooling issues and present the feasibility study results.

Haihua Zhao; Hongbin Zhang; Phil Sharpe; Blaise Hamanaka; Wei Yan; WoonSeong Jeong

2013-03-01T23:59:59.000Z

442

ROBUST SPEECH RECOGNITION USING FEATURES BASED ON ZERO CROSSINGS WITH PEAK AMPLITUDES  

E-Print Network (OSTI)

of white Gaussian noise added at different SNRs. 3.1. Analysis frame lengths Three different methods K.Paliwal@me.gu.edu.au ABSTRACT This paper presents an extensive study of zero crossings with peak only on a small- vocabulary isolated-word database. In the study described in this paper, we

443

The Impact of Residential Air Conditioner Charging and Sizing on Peak Electrical Demand  

E-Print Network (OSTI)

Electric utilities have had a number of air conditioner rebate and maintenance programs for many years. The purpose of these programs was to improve the efficiency of the stock of air conditioning equipment and provide better demand-side management. This paper examines the effect of refrigerant charging (proper servicing of the equipment), system sizing, and efficiency on the steady-state, coincident peak utility demand of a residential central air conditioning system. The study is based on the results of laboratory tests of a three-ton, capillary tube expansion, split-system air conditioner, system capacity and efficiency data available from manufacturer's literature, and assumptions about relative sizing of the equipment to cooling load on a residence. A qualitative discussion is provided concerning the possible impacts of transient operation and total energy use on utility program decisions. The analysis indicates that proper sizing of the unit is the largest factor affecting energy demand of the three factors (sizing, charging, and efficiency) studied in this paper. For typical oversizing of units to cooling loads in houses, both overcharging and undercharging showed significant negative impact on peak demand. The impacts of SEER changes in utility peak demand were found to be virtually independent of oversizing. For properly sized units, there was a small peak benefit to higher efficiency air conditioners.

Neal, L.; O'Neal, D. L.

1992-05-01T23:59:59.000Z

444

Peak Power Bi-directional Transfer From High Speed Flywheel to Electrical Regulated Bus Voltage System  

E-Print Network (OSTI)

of a suitable EV power supply. Industry experts have concluded that practical EVs must have energy storage's batteries can be extended considerably by supplying peak energy requirements from a secondary source to an external power supply, the braking energy must be stored `on board'. Advanced lead-acid batteries provide

Szabados, Barna

445

Galactic Chemical Evolution of the Iron Peak Elements in the Lowest Metallicity Regimes  

Science Conference Proceedings (OSTI)

We use the nucleosynthetic yields of Chieffi & Limongi (2004) in conjunction with a Salpeter initial mass function (IMF) to determine the evolution of iron peak element abundances ( Z ?=?21–28) as a function of metallicity. Since we will focus on the extremely metal poor region below [ Fe / H ]?=??1.5 we will consider input from core collapse supernovae (SNe) only

Jennifer Sobeck; Carla Frohlich; Jim Truran; Yeunjin Kim

2010-01-01T23:59:59.000Z

446

Data center demand response: Avoiding the coincident peak via workload shifting and local generation  

Science Conference Proceedings (OSTI)

Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming ... Keywords: Data center, Demand response, Online algorithm, Prediction error, Renewable penetration, Workload management

Zhenhua Liu, Adam Wierman, Yuan Chen, Benjamin Razon, Niangjun Chen

2013-10-01T23:59:59.000Z

447

A Lagged Warm Event–Like Response to Peaks in Solar Forcing in the Pacific Region  

Science Conference Proceedings (OSTI)

The forced response coincident with peaks in the 11-yr decadal solar oscillation (DSO) has been shown to resemble a cold event or La Niña–like pattern during December–February (DJF) in the Pacific region in observations and two global coupled ...

Gerald A. Meehl; Julie M. Arblaster

2009-07-01T23:59:59.000Z

448

A computational intelligence scheme for the prediction of the daily peak load  

Science Conference Proceedings (OSTI)

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important ... Keywords: Computational intelligence, Daily peak load, Mid-term load forecasting, Self-organizing map, Support vector machine

Jawad Nagi; Keem Siah Yap; Farrukh Nagi; Sieh Kiong Tiong; Syed Khaleel Ahmed

2011-12-01T23:59:59.000Z

449

Wind Plant Capacity Credit Variations: A Comparison of Results Using Multiyear Actual and Simulated Wind-Speed Data  

DOE Green Energy (OSTI)

Although it is widely recognized that variations in annual wind energy capture can be significant, it is not clear how significant this effect is on accurately calculating the capacity credit of a wind plant. An important question is raised concerning whether one year of wind data is representative of long-term patterns. This paper calculates the range of capacity credit measures based on 13 years of actual wind-speed data. The results are compared to those obtained with synthetic data sets that are based on one year of data. Although the use of synthetic data sets is a considerable improvement over single-estimate techniques, this paper finds that the actual inter- annual variation in capacity credit is still understated by the synthetic data technique.

Milligan, Michael

1997-06-01T23:59:59.000Z

450

Lighting/HVAC interactions and their effects on annual and peak HVAC requirements in commercial buildings  

SciTech Connect

Lighting measures is one effective strategy for reducing energy use in commercial buildings. Reductions in lighting energy have secondary effects on cooling/heating energy consumption and peak HVAC requirements; in general, they increase the heating and decrease cooling requirements of a building. Net change in a building`s annual and peak energy requirements, however, is difficult to quantify and depends on building characteristics, operating conditions, climate. This paper characterizes impacts of lighting/HVAC interactions on annual and peak heating/cooling requirements of prototypical US commercial buildings through computer simulations using DOE-2.1E building energy analysis program. Ten building types of two vintages and nine climates are chosen to represent the US commercial building stock. For each combination, a prototypical building is simulated with two lighting power densities, and resultant changes in heating and cooling loads are recorded. Simple concepts of Lighting Coincidence Factors are used to describe the observed interactions between lighting and HVAC requirements. (Coincidence Factor (CF) is ratio of changes in HVAC loads to those in lighting loads, where load is either annual or peak load). The paper presents tables of lighting CF for major building types and climates. These parameters can be used for regional or national cost/benefit analyses of lighting- related policies and utility DSM programs. Using Annual CFs and typical efficiencies for heating and cooling systems, net changes in space conditioning energy use from a lighting measure can be calculated. Similarly, Demand CFs can be used to estimate the changes in HVAC sizing, which can then be converted to changes in capital outlay using standard-design curves; or they can be used to estimate coincident peak reductions for the analysis of the utility`s avoided costs. Results from use of these tables are meaningful only when they involve a significantly large number of buildings.

Sezgen, A.O.; Huang, Y.J.

1994-08-01T23:59:59.000Z

451

Statistical Analysis and Dynamic Visualization of Travis Peak Production in the Eastern Texas Basin  

E-Print Network (OSTI)

Gas production has increased exponentially over the last 30 years, which is in response to the increasing demand for natural gas. This trend is speculated to continue to increase as legislation continues to be passed requiring power plants to reduce nitrogen oxide emissions. This recently happened in Colorado according to the Washington Post, giving more consideration to using natural gas. As natural gas becomes more popular there is a need to understand the production patterns and observable trends, integrating data from various sources. This research will attempt to do just that for wells producing from the Travis Peak formation. Using data from HPDI L.L.C., (www.hpdi.com) a visual representation was created for the areal distribution of peak gas rates and cumulative gas production. This allowed us to categorize wells by their production performance and we found that areas with relatively high peak gas rates also had high cumulative gas production. An analysis of these wells was done by completion year, and we found that wellhead prices of natural gas strongly influenced the annual number of new wells. We also found that the distribution of the annual number of new wells affected the average annual initial production rate and the peak gas rate of new wells. Wells located in areas of poor production performance were analyzed and it was apparent that newer wells performed relatively better than older ones and well stimulation is a major requirement for better gas production. Wells located in areas of good production performance were also analyzed and we found that the distribution of newer wells to older ones influenced the relative performance of individual wells. Overall, there was no observable trend between production variables in Travis Peak. No trend in production variable was found to be exclusively associated with good performing wells or poor performing wells.

Ayanbule, Babafemi O.

2010-08-01T23:59:59.000Z

452

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

453

Energy-Dependent $\\gamma$-Ray Burst Peak Durations and Blast-Wave Deceleration  

E-Print Network (OSTI)

Temporal analyses of the prompt gamma-ray and X-ray light curves of gamma-ray bursts reveal a tendency for the burst pulse time scales to increase with decreasing energy. For an ensemble of BATSE bursts, Fenimore et al. (1995) show that the energy dependence of burst peak durations can be represented by dependence has led to the suggestion that this effect is due to radiative processes, most notably synchrotron cooling of the non-thermal particles which produce the radiation. Here we show that a similar power-law dependence occurs, under certain assumptions, in the context of the blast-wave model and is a consequence of the deceleration of the blast-wave. This effect will obtain whether or not synchrotron cooling is important, but different degrees of cooling will cause variations in the energy dependence of the peak durations.

Chiang, J

1998-01-01T23:59:59.000Z

454

Narrow linewidth picosecond pulsed laser with mega-watt peak power at UV wavelength  

Science Conference Proceedings (OSTI)

We demonstrate a master oscillator power amplifier (MOPA) burst mode laser system to generate 66 ps/402.5 MHz pulses with mega-watt peak power at 355 nm. The seed laser is based on a direct electro-optic modulation of a fiber laser output. A very high extinction ratio (45 dB) has been achieved by using an adaptive bias control. The multi-stage Nd:YAG amplifier system allows a uniformly temporal shaping of macropulses with tunable pulse duration. The light output form the amplifier is converted to 355 nm and over 1 MW UV peak power is obtained when the laser is operating in a 5- s/10-Hz macropulse mode. The laser output has a transform limited spectrum bandwidth with a very narrow linewidth of individual laser mode. The immediate application of the laser system is the laser assisted hydrogen ion beam stripping for the Spallation Neutron Source (SNS).

Liu, Yun [ORNL; Huang, Chunning [ORNL; Deibele, Craig Edmond [ORNL

2013-01-01T23:59:59.000Z

455

STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment Science and Operations Plan  

Science Conference Proceedings (OSTI)

During the Storm Peak Lab Cloud Property Validation Experiment (STORMVEX), a substantial correlative data set of remote sensing observations and direct in situ measurements from fixed and airborne platforms will be created in a winter season, mountainous environment. This will be accomplished by combining mountaintop observations at Storm Peak Laboratory and the airborne National Science Foundation-supported Colorado Airborne Multi-Phase Cloud Study campaign with collocated measurements from the second ARM Mobile Facility (AMF2). We describe in this document the operational plans and motivating science for this experiment, which includes deployment of AMF2 to Steamboat Springs, Colorado. The intensive STORMVEX field phase will begin nominally on 1 November 2010 and extend to approximately early April 2011.

Mace, J; Matrosov, S; Shupe, M; Lawson, P; Hallar, G; McCubbin, I; Marchand, R; Orr, B; Coulter, R; Sedlacek, A; Avallone, L; Long, C

2010-09-29T23:59:59.000Z

456

Potential For Energy, Peak Demand, and Water Savings in California Tomato Processing Facilities  

E-Print Network (OSTI)

Tomato processing is a major component of California's food industry. Tomato processing is extremely energy intensive, with the processing season coinciding with the local electrical utility peak period. Significant savings are possible in the electrical energy, peak demand, natural gas consumption, and water consumption of facilities. The electrical and natural gas energy usage and efficiency measures will be presented for a sample of California tomato plants. A typical end-use distribution of electrical energy in these plants will be shown. Results from potential electrical efficiency, demand response, and natural gas efficiency measures that have applications in tomato processing facilities will be presented. Additionally, water conservation measures and the associated savings will be presented. It is shown that an estimated electrical energy savings of 12.5%, electrical demand reduction of 17.2%, natural gas savings of 6.0%, and a fresh water usage reduction of 15.6% are achievable on a facility-wide basis.

Trueblood, A. J.; Wu, Y. Y.; Ganji, A. R.

2013-01-01T23:59:59.000Z

457

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.

458

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

459

Condensate Filter/Demineralizer Qualification and Testing in Precoat Application at Comanche Peak Steam Electric Station  

Science Conference Proceedings (OSTI)

Texas Utility's Comanche Peak Steam Electric Station (CPSES) condensate filter/demineralizer (CFD) system is currently one of, if not the best performing CFD systems in the world, based on throughput and steam generator iron deposition. Minimum precoating has the potential to reduce solid waste generation by 44 percent. Using current radwaste disposal costs, operating with minimum precoat offers the potential for CPSES to decrease operational and maintenance costs by up to 69 percent in case of a primary...

2000-08-29T23:59:59.000Z

460

Peak Load Management of Thermal Loads Using Advanced Thermal Energy Storage Technologies  

Science Conference Proceedings (OSTI)

Almost 50% of electric energy delivered to residences is converted into some sort of thermal energy—hot water, air conditioning, and refrigeration. Storing energy in thermal form is cheaper especially when the medium used to store the energy is an end-use medium for example, hot water. This technical update evaluates two different technologies for storing energy—in cold water and in hot water.GreenPeak technology, a storage condensing unit (SCU) from IE Technologies, uses an ...

2013-12-20T23:59:59.000Z

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461

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.

462

A Field Study on Residential Air Conditioning Peak Loads During Summer in College Station, Texas  

E-Print Network (OSTI)

Severe capacity problems are experienced by electric utilities during hot summer afternoons. Several studies have found that, in large part, electric peak loads can be attributed to residential airconditioning use. This air-conditioning peak depends primarily on two factors: (i) the manner in which the homeowner operates his air-conditioner during the hot summer afternoons, and (ii) the amount by which the air-conditioner has been over-designed. Whole-house and air-conditioner electricity use data at 15 minute time intervals have been gathered and analyzed for 8 residences during the summer of 1991, six of which had passed the College Station Good Cents tests. Indoor air temperatures were measured by a mechanical chart recorder, while a weather station located on the main campus of Texas A&M university provided the necessary climatic data, especially ambient temperature, relative humidity and solar radiation. The data were analysed to determine the extent to which air-conditioning over-sizing and homeowner intervention contributes to peak electricity use for newer houses in College Station, Texas.

Reddy, T. A.; Vaidya, S.; Griffith, L.; Bhattacharyya, S.; Claridge, D. E.

1992-01-01T23:59:59.000Z

463

GRB110721A: AN EXTREME PEAK ENERGY AND SIGNATURES OF THE PHOTOSPHERE  

SciTech Connect

GRB110721A was observed by the Fermi Gamma-ray Space Telescope using its two instruments, the Large Area Telescope (LAT) and the Gamma-ray Burst Monitor (GBM). The burst consisted of one major emission episode which lasted for {approx}24.5 s (in the GBM) and had a peak flux of (5.7 {+-} 0.2) Multiplication-Sign 10{sup -5} erg s{sup -1} cm{sup -2}. The time-resolved emission spectrum is best modeled with a combination of a Band function and a blackbody spectrum. The peak energy of the Band component was initially 15 {+-} 2 MeV, which is the highest value ever detected in a GRB. This measurement was made possible by combining GBM/BGO data with LAT Low Energy events to achieve continuous 10-100 MeV coverage. The peak energy later decreased as a power law in time with an index of -1.89 {+-} 0.10. The temperature of the blackbody component also decreased, starting from {approx}80 keV, and the decay showed a significant break after {approx}2 s. The spectrum provides strong constraints on the standard synchrotron model, indicating that alternative mechanisms may give rise to the emission at these energies.

Axelsson, M. [Department of Physics, Royal Institute of Technology (KTH), AlbaNova, SE-106 91 Stockholm (Sweden); Baldini, L.; Bellazzini, R.; Bregeon, J. [Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa (Italy); Barbiellini, G. [Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste (Italy); Baring, M. G. [Department of Physics and Astronomy, Rice University, MS-108, P.O. Box 1892, Houston, TX 77251 (United States); Brigida, M. [Dipartimento di Fisica 'M. Merlin' dell'Universita e del Politecnico di Bari, I-70126 Bari (Italy); Bruel, P. [Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Palaiseau (France); Buehler, R.; Cameron, R. A.; Chiang, J.; Claus, R. [W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305 (United States); Caliandro, G. A. [Institut de Ciencies de l'Espai (IEEE-CSIC), Campus UAB, E-08193 Barcelona (Spain); Caraveo, P. A. [INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica, I-20133 Milano (Italy); Cecchi, C.; D'Ammando, F. [Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia (Italy); Chaves, R. C. G. [Laboratoire AIM, CEA-IRFU/CNRS/Universite Paris Diderot, Service d'Astrophysique, CEA Saclay, F-91191 Gif sur Yvette (France); Chekhtman, A. [Center for Earth Observing and Space Research, College of Science, George Mason University, Fairfax, VA 22030 (United States); Conrad, J. [Oskar Klein Centre for Cosmoparticle Physics, AlbaNova, SE-106 91 Stockholm (Sweden); Cutini, S., E-mail: magnusa@astro.su.se, E-mail: moretti@particle.kth.se, E-mail: felix@particle.kth.se, E-mail: josefin.larsson@astro.su.se, E-mail: james.m.burgess@nasa.gov [Agenzia Spaziale Italiana (ASI) Science Data Center, I-00044 Frascati (Roma) (Italy); and others

2012-10-01T23:59:59.000Z

464

The Influence of Air-Conditioning Efficiency in the Peak Load Demand for Kuwait  

E-Print Network (OSTI)

A model co-relating the peak load demand of a utility with the allowable power rating (PR) of air-conditioning (AC) systems has been developed in this paper through a well defined methodology. The model is capable to predict the extent of allowable increase in the capital cost of the AC system for an improvement in PR from its base case as well. Furthermore, effectiveness of better PR of AC system for peak load management has been analyzed for Kuwait as a case study. It is found that up to 5,752 MW in reduction in peak load demand and savings of KD 2,301 million in capital expenditures are possible for the years between 2001 and 2025 if the PR of AC systems are improved to 1.2 kW/RT from its present level of 2.0 kW/RT. Also, it is estimated that extent of increase in capital cost of AC system by 106 % is justified for reducing the expenditure for new power plants. The paper will be useful for the energy planner and policy makers in the countries of Arabian Peninsula with huge demand for air-conditioning.

Ali, A. A.; Maheshwari, G. P.

2007-01-01T23:59:59.000Z

465

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

466

A control system for improved battery utilization in a PV-powered peak-shaving system  

SciTech Connect

Photovoltaic (PV) power systems offer the prospect of allowing a utility company to meet part of the daily peak system load using a renewable resource. Unfortunately, some utilities have peak system- load periods that do not match the peak production hours of a PV system. Adding a battery energy storage system to a grid-connected PV power system will allow dispatching the stored solar energy to the grid at the desired times. Batteries, however, pose system limitations in terms of energy efficiency, maintenance, and cycle life. A new control system has been developed, based on available PV equipment and a data acquisition system, that seeks to minimize the limitations imposed by the battery system while maximizing the use of PV energy. Maintenance requirements for the flooded batteries are reduced, cycle life is maximized, and the battery is operated over an efficient range of states of charge. This paper presents design details and initial performance results on one of the first installed control systems of this type.

Palomino, E [Salt River Project, Phoenix, AZ (United States); Stevens, J. [Sandia National Labs., Albuquerque, NM (United States); Wiles, J. [New Mexico State Univ., Las Cruces, NM (United States). Southwest Technology Development Inst.

1996-08-01T23:59:59.000Z

467

A peak power tracker for small wind turbines in battery charging applications  

Science Conference Proceedings (OSTI)

This paper describes the design, implementation and testing of a prototype version of a peak power tracking system for small wind turbines in battery charging applications. The causes for the poor performance of small wind turbines in battery charging applications are explained and previously proposed configurations to increase the power output of the wind turbines are discussed. Through computer modeling of the steady-state operation the potential performance gain of the proposed system in comparison with existing systems is calculated. It is shown that one configuration consisting of reactive compensation by capacitors and a DC/DC converter is able to optimally load the wind turbine and thus obtain maximum energy capture over the whole range of wind speeds. A proof of concept of the peak power tracking system is provided by building and testing a prototype version. The peak power tracking system is tested in combination with a typical small wind turbine generator on a dynamometer. Steady-state operating curves confirming the performance improvement predicted by calculations are presented.

De Broe, A.M.; Drouilhet, S.; Gevorgian, V.

1999-12-01T23:59:59.000Z

468

Demonstration of a SREX flowsheet for the partitioning of strontium and lead from actual ICPP sodium-bearing waste  

SciTech Connect

Laboratory experimentation has indicated that the SREX process is effective for partitioning {sup 90}Sr and Pb from acidic radioactive waste solutions located at the Idaho Chemical Processing Plant. Previous countercurrent flowsheet testing of the SREX process with simulated waste resulted in 99.98% removal of Sr and 99.9% removal of Pb. Based on the results of these studies, a demonstration of the SREX flowsheet was performed. The demonstration consisted of (1) countercurrent flowsheet testing of the SREX process using simulated sodium-bearing waste spiked with {sup 85}Sr and (2) countercurrent flowsheet testing of the SREX process using actual waste from tank WM-183. All testing was performed using 24 stages of 2-cm diameter centrifugal contactors which are installed in the Remote Analytical Laboratory hot cell. The flowsheet tested consisted of an extraction section (0. 15 M 4`,4`(5)-di-(tert-butyldicyclohexo)-18-crown-6 and 1.5 M TBP in Isopar-L{reg_sign}), a 2.0 MHNO{sub 3} scrub section to remove extracted K from the SREX solvent, a 0.05 M HNO{sub 3} strip section for the removal of Sr from the SREX solvent, a 0.1 M ammonium citrate strip section for the removal of Pb from the SREX solvent, and a 3.0 M HNO{sub 3} equilibration section. The behavior of {sup 90}Sr, Pb, Na, K, Hg, H{sup +}, the actinides, and numerous other non-radioactive elements was evaluated. The described flowsheet successfully extracted and selectively stripped Sr and Ph from the SBW simulant and the actual tank waste. For the testing with actual tank waste (WM - 183), removal efficiencies of 99.995 % and >94% were obtained for {sup 90}Sr and Pb, respectively.

Law, J.D.; Wood, D.J.; Olson, L.G.; Todd, T.A.

1997-08-01T23:59:59.000Z