National Library of Energy BETA

Sample records for number peak kilowatts

  1. Table 10.9 Photovoltaic Cell and Module Shipments by Sector and End Use, 1989-2010 (Peak Kilowatts )

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

    Photovoltaic Cell and Module Shipments by Sector and End Use, 1989-2010 (Peak Kilowatts 1 ) Year By Sector By End Use Total Residential Commercial 3 Industrial 4 Electric Power 5 Other 6 Grid-Connected 2 Off-Grid 2 Centralized 7 Distributed 8 Domestic 9 Non-Domestic 10 Total Shipments of Photovoltaic Cells and Modules 11<//td> 1989 1,439 6,057 [R] 3,993 785 551 [12] 1,251 [12] 2,620 8,954 12,825 1990 1,701 8,062 [R] 2,817 826 432 [12] 469 [12] 3,097 10,271 13,837 1991 3,624 5,715 [R] 3,947

  2. FEL Achieves 10 Kilowatts | Jefferson Lab

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    FEL Achieves 10 Kilowatts Newport News, Va. - The Free-Electron Laser (FEL), supported by the Office of Naval Research and located at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility, achieved 10 kilowatts of infrared laser light in late July, making it the most powerful tunable laser in the world. The recently upgraded laser's new capabilities will enhance defense and manufacturing technologies, and support advanced studies of chemistry, physics, biology, and more.

  3. Vehicle Technologies Office Merit Review 2015: 88 Kilowatt Automotive

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Inverter with New 900 Volt Silicon Carbide MOSFET Technology | Department of Energy 88 Kilowatt Automotive Inverter with New 900 Volt Silicon Carbide MOSFET Technology Vehicle Technologies Office Merit Review 2015: 88 Kilowatt Automotive Inverter with New 900 Volt Silicon Carbide MOSFET Technology Presentation given by Cree at 2015 DOE Hydrogen and Fuel Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting about 88 kilowatt automotive inverter with new

  4. Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT

    Office of Scientific and Technical Information (OSTI)

    Phase 1 Preliminary Design Documentation (Technical Report) | SciTech Connect SciTech Connect Search Results Technical Report: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation Citation Details In-Document Search Title: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation The intent of the integral experiment request IER 299 (called KiloPower by NASA) is to assemble and

  5. Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT

    Office of Scientific and Technical Information (OSTI)

    Phase 1 Preliminary Design Documentation (Technical Report) | SciTech Connect Technical Report: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation Citation Details In-Document Search Title: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation × You are accessing a document from the Department of Energy's (DOE) SciTech Connect. This site is a product of DOE's Office of

  6. Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Phase 1 Preliminary Design Documentation (Technical Report) | SciTech Connect Technical Report: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation Citation Details In-Document Search Title: Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration. CEDT Phase 1 Preliminary Design Documentation The intent of the integral experiment request IER 299 (called KiloPower by NASA) is to assemble and evaluate the operational

  7. Number

    Office of Legacy Management (LM)

    ' , /v-i 2 -i 3 -A, This dow'at consists ~f--~-_,_~~~p.~,::, Number -------of.-&--copies, 1 Series.,-a-,-. ! 1 THE UNIVERSITY OF ROCHESTER 1; r-.' L INTRAMURALCORRESPONDENCE i"ks' 3 2.. September 25, 1947 Memo.tor Dr. A. H, Dovdy . From: Dr. H. E, Stokinger Be: Trip Report - Mayvood Chemical Works A trip vas made Nednesday, August 24th vith Messrs. Robert W ilson and George Sprague to the Mayvood Chemical F!orks, Mayvood, New Jersey one of 2 plants in the U.S.A. engaged in the

  8. NREL Finds Up to 6-cent per Kilowatt-Hour Extra Value with Concentrated

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Solar Power - News Releases | NREL Finds Up to 6-cent per Kilowatt-Hour Extra Value with Concentrated Solar Power The greater the penetration of renewables in California, the greater the value of CSP with thermal storage capacity June 9, 2014 Concentrating Solar Power (CSP) projects would add additional value of 5 or 6 cents per kilowatt hour to utility-scale solar energy in California where 33 percent renewables will be mandated in six years, a new report by the Energy Department's National

  9. Aluminum-blade development for the Mod-0A 200-kilowatt wind turbine

    SciTech Connect (OSTI)

    Linscott, B.S.; Shaltens, R.K.; Eggers, A.G.

    1981-12-01

    This report documents the operating experience with two aluminum blades used on the DOE/NASA Mod-0A 200-kilowatt wind turbine located at Clayton, New Mexico. Each Mod-0A aluminum blade is 59.9 feet long and weighs 2360 pounds. The aluminum Mod-0A blade design requirements, the selected design, fabrication procedures, and the blade analyses are discussed. A detailed chronology is presented on the operating experience of the Mod-0A aluminum blades used at Clayton, New Mexico. Blade structural damage was experienced. Inspection and damage assessment were required. Structural modifications that were incorporated to the blades successfully extended the useful operating life of the blades. The aluminum blades completed the planned 2 years of operation of the Clayton wind turbine. The blades were removed from service in August 1980 to allow testing of advanced technology wood composite blades.

  10. Bandwidth Historical Peak Days

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Bandwidth Historical Peak Days Bandwidth Historical Peak Days These plots show yearly peak days from 2000 to the present. BE CAREFUL because the graphs are autoscaling - check the scales on each axis before you compare graphs. Note that the graph for current year shows the data for the year-to-date peak. Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer Rate vs. Size Transfer

  11. Overview of Multi-Kilowatt Free-Piston Stirling Power Conversion Research at GRC

    SciTech Connect (OSTI)

    Geng, Steven M.; Mason, Lee S.; Dyson, Rodger W.; Penswick, L. Barry

    2008-01-21

    As a step towards development of Stirling power conversion for potential use in Fission Surface Power (FSP) systems, a pair of commercially available 1 kW class free-piston Stirling convertors and a pair of commercially available pressure wave generators (which will be plumbed together to create a high power Stirling linear alternator test rig) have been procured for in-house testing at Glenn Research Center. Delivery of both the Stirling convertors and the linear alternator test rig is expected by October, 2007. The 1 kW class free-piston Stirling convertors will be tested at GRC to map and verify performance. The convertors will later be modified to operate with a NaK liquid metal pumped loop for thermal energy input. The high power linear alternator test rig will be used to map and verify high power Stirling linear alternator performance and to develop power management and distribution (PMAD) methods and techniques. This paper provides an overview of the multi-kilowatt free-piston Stirling power conversion work being performed at GRC.

  12. Peak power ratio generator

    DOE Patents [OSTI]

    Moyer, Robert D. (Albuquerque, NM)

    1985-01-01

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

  13. monthly_peak_2005.xls

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

    3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid FRCC MRO NPCC RFC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak

  14. PEAK READING VOLTMETER

    DOE Patents [OSTI]

    Dyer, A.L.

    1958-07-29

    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.

  15. Desert Peak EGS Project

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Desert Peak EGS Project DOE Award: DE-FC6-02ID14406 Ethan Chabora GeothermEx, a Schlumberger Company Ezra Zemach Ormat Nevada Inc. Project Officer: Bill Vandermeer Total Project Funding: $7.6M April 22nd, 2013 This presentation does not contain any proprietary confidential, or otherwise restricted information. Insert photo of your choice 2 | US DOE Geothermal Program eere.energy.gov - Timeline * Project start date: September 2002 * Project end date: Q3 2013 * Percentage complete: 90% - Budget *

  16. monthly_peak_2004.xls

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

    Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 1996 through 2004 and Projected 2005 through 2006 (Megawatts and 2004 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid ECAR FRCC MAAC MAIN MAPP/MRO NPCC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour

  17. monthly_peak_2006.xls

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

    6 Released: February 7, 2008 Next Update: October 2008 Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Corporation Region 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid FRCC MRO NPCC RFC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak

  18. Aggregate Transfers Historical Yearly Peak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Transfers Historical Yearly Peak Aggregate Transfers Historical Yearly Peak These plots show the yearly peak days from 2000 to the present. BE CAREFUL because the graphs are autoscaling - check the scales on each axis before you compare graphs. Note that the graph for current year shows the data for the year-to-date peak. Daily Aggregate Bandwidth Daily Aggregate Bandwidth Daily Aggregate Bandwidth Daily Aggregate Bandwidth Daily Aggregate Bandwidth Daily Aggregate Bandwidth Daily Aggregate

  19. Concurrent Transfers Historical Yearly Peak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Transfers Historical Yearly Peak Concurrent Transfers Historical Yearly Peak These plots show the yearly peak days from 2000 to present. BE CAREFUL because the graphs are autoscaling - check the scales on each axis before you compare graphs. Note that the graph for current year shows the data for the year-to-date peak. Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily Storage

  20. Transfer Activity Historical Yearly Peak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Activity Historical Yearly Peak Transfer Activity Historical Yearly Peak The plots below show the yearly peak days from 2000 to the present. BE CAREFUL because the graphs are autoscaling - check the scales on each axis before you compare graphs. Note that the graph for the current year shows the data for the year-to-date peak. Transfers Started/In Progress Transfers Started/In Progress Transfers Started/In Progress Transfers Started/In Progress Transfers Started/In Progress Transfers Started/In

  1. Concurrent Transfers Historical Yearly Peak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    the graph for current year shows the data for the year-to-date peak. Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily Storage Concurrency Daily...

  2. winter_peak_2006.xls

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

    b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base ...

  3. monthly_peak_2003.xls

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

    O Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 1996 through 2003 and Projected 2004 through 2005 (Megawatts and 2003 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid ECAR FRCC MAAC MAIN MAPP/MR NPCC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW)

  4. winter_peak_2005.xls

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

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

  5. winter_peak_2004.xls

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

    b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 1990 through 2004 and Projected 2005 through 2009 (Megawatts and...

  6. Desert Peak Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Desert Peak Geothermal Area (Redirected from Desert Peak Area) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Desert Peak Geothermal Area Contents 1 Area Overview 2...

  7. Peak finding using biorthogonal wavelets

    SciTech Connect (OSTI)

    Tan, C.Y.

    2000-02-01

    The authors show in this paper how they can find the peaks in the input data if the underlying signal is a sum of Lorentzians. In order to project the data into a space of Lorentzian like functions, they show explicitly the construction of scaling functions which look like Lorentzians. From this construction, they can calculate the biorthogonal filter coefficients for both the analysis and synthesis functions. They then compare their biorthogonal wavelets to the FBI (Federal Bureau of Investigations) wavelets when used for peak finding in noisy data. They will show that in this instance, their filters perform much better than the FBI wavelets.

  8. summer_peak_2005.xls

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

    a . Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year FRCC MRO (U.S.) NPCC (U.S.) RFC SERC SPP ERCOT WECC (U.S.) 2005 758,876 46,396 39,918 58,960 190,200 190,705 41,727 60,210 130,760 Projected Contiguous U.S. FRCC MRO (U.S.) NPCC (U.S.) RFC SERC SPP

  9. summer_peak_2006.xls

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

    a . Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year FRCC MRO (U.S.) NPCC (U.S.) RFC SERC SPP ERCOT WECC (U.S.) 2006 789,475 45,751 42,194 63,241 191,920 199,052 42,882 62,339 142,096 Projected Contiguous U.S. FRCC MRO (U.S.) NPCC (U.S.) RFC SERC

  10. winter_peak_2003.xls

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

    ) Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 2b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 1990 through 2003 and Projected 2004 through 2008 (Megawatts and 2003 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP (U.S. NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990/1991 484,231 67,097

  11. Silver Peak Innovative Exploration Project

    Broader source: Energy.gov [DOE]

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

  12. 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" 1,"1,209","9,677",91.4,"PWR","application/vnd.ms-excel","application/vnd.ms-excel"

  13. Desert Peak EGS Project | Department of Energy

    Office of Environmental Management (EM)

    Desert Peak EGS Project Desert Peak EGS Project Desert Peak EGS Project presentation at the April 2013 peer review meeting held in Denver, Colorado. PDF icon desert_peak_egs_peer2013.pdf More Documents & Publications Desert Peak EGS Project Bradys EGS Project Creation of an Engineered Geothermal System through Hydraulic and Thermal Stimulation

  14. Table 8.11b Electric Net Summer Capacity: Electric Power Sector, 1949-2011 (Subset of Table 8.11a; Kilowatts)

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

    b Electric Net Summer Capacity: Electric Power Sector, 1949-2011 (Subset of Table 8.11a; Kilowatts) Year Fossil Fuels Nuclear Electric Power Hydro- electric Pumped Storage Renewable Energy Other 9 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Conventional Hydroelectric Power 5 Biomass Geo- thermal Solar/PV 8 Wind Total Wood 6 Waste 7 1949 NA NA NA NA 44,887,000 0 [5] 18,500,000 13,000 [10] NA NA NA 18,513,000 NA 63,400,000 1950 NA NA NA NA 49,987,000 0 [5] 19,200,000 13,000 [10] NA

  15. Table 8.11c Electric Net Summer Capacity: Electric Power Sector by Plant Type, 1989-2011 (Breakout of Table 8.11b; Kilowatts)

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

    c Electric Net Summer Capacity: Electric Power Sector by Plant Type, 1989-2011 (Breakout of Table 8.11b; Kilowatts) Year Fossil Fuels Nuclear Electric Power Hydro- electric Pumped Storage Renewable Energy Other 8 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Conventional Hydroelectric Power Biomass Geo- thermal Solar/PV 7 Wind Total Wood 5 Waste 6 Electricity-Only Plants 9<//td> 1989 296,541,828 77,966,348 119,304,288 364,000 494,176,464 98,160,610 18,094,424 73,579,794

  16. Table 8.11d Electric Net Summer Capacity: Commercial and Industrial Sectors, 1989-2011 (Subset of Table 8.11a; Kilowatts)

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

    d Electric Net Summer Capacity: Commercial and Industrial Sectors, 1989-2011 (Subset of Table 8.11a; Kilowatts) Year Fossil Fuels Nuclear Electric Power Hydro- electric Pumped Storage Renewable Energy Other 8 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Conventional Hydroelectric Power Biomass Geo- thermal Solar/PV 7 Wind Total Wood 5 Waste 6 Commercial Sector 9<//td> 1989 258,193 191,487 578,797 – 1,028,477 [–] – 17,942 13,144 166,392 [–] – – 197,478 – 1,225,955 1990

  17. Peak Treatment Systems | Open Energy Information

    Open Energy Info (EERE)

    Treatment Systems Jump to: navigation, search Name: Peak Treatment Systems Place: Golden, CO Website: www.peaktreatmentsystems.com References: Peak Treatment Systems1 Information...

  18. Peak Underground Working Natural Gas Storage Capacity

    Gasoline and Diesel Fuel Update (EIA)

    Previous Articles Previous Articles Estimates of Peak Underground Working Gas Storage Capacity in the United States, 2009 Update (Released, 8312009) Estimates of Peak Underground...

  19. summer_peak_1990_2004.xls

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

    c . Historical Noncoincident Summer Peak Load, Actual by North American Electric Reliability Council Region, 1990 through 2004 (Megawatts) Summer Noncoincident Peak Contiguous U.S....

  20. winter_peak_1990_2004.xls

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

    d . Historical Noncoincident Winter Peak Load, Actual by North American Electric Reliability Council Region, 1990 through 2004 (Megawatts) Winter Noncoincident Peak Load Contiguous...

  1. peak_load_2010.xls

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

    2. Noncoincident Peak Load, by North American Electric Reliability Corporation Assessment Area, 1990-2010 Actual, 2011-2015 Projected (Megawatts) Interconnection NERC Regional Assesment Area 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 FRCC 27,266 28,818 30,601 32,823 32,904 34,524 35,444 35,375 38,730 37,493 37,194 39,062 40,696 40,475 42,383 46,396 45,751 46,676 44,836 NPCC 44,116 46,594 43,658 46,706 47,581 47,705 45,094 49,269 49,566 52,855

  2. summer_peak_2003.xls

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

    3 and Projected 2004 through 2008 (Megawatts and 2003 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990 546,331 79,258 27,266 42,613 40,740 24,994 44,116 94,677 52,541 42,737 97,389 1991 551,418 81,224 28,818 45,937 41,598 25,498 46,594 95,968 51,885 41,870 92,026 1992 548,707 78,550 30,601 43,658 38,819 22,638 43,658 97,635 51,324 42,619

  3. summer_peak_2004.xls

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

    4 and Projected 2005 through 2009 (Megawatts and 2004 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP/MRO (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990 546,331 79,258 27,266 42,613 40,740 24,994 44,116 94,677 52,541 42,737 97,389 1991 551,418 81,224 28,818 45,937 41,598 25,498 46,594 95,968 51,885 41,870 92,026 1992 548,707 78,550 30,601 43,658 38,819 22,638 43,658 97,635 51,324

  4. Table 8.11a Electric Net Summer Capacity: Total (All Sectors), 1949-2011 (Sum of Tables 8.11b and 8.11d; Kilowatts)

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

    a Electric Net Summer Capacity: Total (All Sectors), 1949-2011 (Sum of Tables 8.11b and 8.11d; Kilowatts) Year Fossil Fuels Nuclear Electric Power Hydro- electric Pumped Storage Renewable Energy Other 9 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Conventional Hydroelectric Power 5 Biomass Geo- thermal Solar/PV 8 Wind Total Wood 6 Waste 7 1949 NA NA NA NA 44,887,000 0 [5] 18,500,000 13,000 [10] NA NA NA 18,513,000 NA 63,400,000 1950 NA NA NA NA 49,987,000 0 [5] 19,200,000 13,000

  5. SnowPeak Energy | Open Energy Information

    Open Energy Info (EERE)

    SnowPeak Energy Place: Reno, Nevada Zip: 89502 Product: Nevada-based concentrator PV module maker. References: SnowPeak Energy1 This article is a stub. You can help OpenEI by...

  6. Note: Proton irradiation at kilowatt-power and neutron production from a free-surface liquid-lithium target

    SciTech Connect (OSTI)

    Halfon, S.; Feinberg, G. [Soreq NRC, Yavne 81800 (Israel); Racah Institute of Physics, Hebrew University, Jerusalem 91904 (Israel); Arenshtam, A.; Kijel, D.; Weissman, L.; Aviv, O.; Berkovits, D.; Dudovitch, O.; Eisen, Y.; Eliyahu, I.; Haquin, G.; Hazenshprung, N.; Kreisel, A.; Mardor, I.; Shimel, G.; Shor, A.; Silverman, I.; Yungrais, Z. [Soreq NRC, Yavne 81800 (Israel); Paul, M., E-mail: paul@vms.huji.ac.il; Tessler, M. [Racah Institute of Physics, Hebrew University, Jerusalem 91904 (Israel)

    2014-05-15

    The free-surface Liquid-Lithium Target, recently developed at Soreq Applied Research Accelerator Facility (SARAF), was successfully used with a 1.9 MeV, 1.2 mA (2.3 kW) continuous-wave proton beam. Neutrons (?2 10{sup 10} n/s having a peak energy of ?27 keV) from the {sup 7}Li(p,n){sup 7}Be reaction were detected with a fission-chamber detector and by gold activation targets positioned in the forward direction. The setup is being used for nuclear astrophysics experiments to study neutron-induced reactions at stellar energies and to demonstrate the feasibility of accelerator-based boron neutron capture therapy.

  7. Request Number:

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3023307 Name: Madeleine Brown Organization: nJa Address: --- -------- -------- -- Country: Phone Number: United States Fax Number: n/a E-mail: --- -------- --------_._------ --- Reasonably Describe Records Description: Please send me a copy of the emails and records relating to the decision to allow the underage son of Bill Gates to tour Hanford in June 2010. Please also send the emails and records that justify the Department of Energy to prevent other minors from visiting B Reactor. Optional

  8. Request Number:

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    1074438 Name: Gayle Cooper Organization: nla Address: _ Country: United States Phone Number: Fax Number: nla E-mail: . ~===--------- Reasonably Describe Records Description: Information pertaining to the Department of Energy's cost estimate for reinstating pension benefit service years to the Enterprise Company (ENCO) employees who are active plan participants in the Hanford Site Pension Plan. This cost estimate was an outcome of the DOE's Worker Town Hall Meetings held on September 17-18, 2009.

  9. Desert Peak EGS Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

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

  10. Passive radio frequency peak power multiplier

    DOE Patents [OSTI]

    Farkas, Zoltan D.; Wilson, Perry B.

    1977-01-01

    Peak power multiplication of a radio frequency source by simultaneous charging of two high-Q resonant microwave cavities by applying the source output through a directional coupler to the cavities and then reversing the phase of the source power to the coupler, thereby permitting the power in the cavities to simultaneously discharge through the coupler to the load in combination with power from the source to apply a peak power to the load that is a multiplication of the source peak power.

  11. Monthly Generation System Peak (pbl/generation)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

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

  12. Peak Underground Working Natural Gas Storage Capacity

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Capacity Peak Underground Working Natural Gas Storage Capacity Released: September 3, 2010 for data as of April 2010 Next Release: August 2011 References Methodology Definitions...

  13. monthly_peak_byarea_2010.xls

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

    B.1. FRCC Monthly Peak Hour Demand, by North American Electric Reliability Corporation Assesment Area, 1996-2010 Actual, 2011-2012 Projected (Megawatts) FRCC Year January February ...

  14. LNG production for peak shaving operations

    SciTech Connect (OSTI)

    Price, B.C.

    1999-07-01

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

  15. QER- Comment of Cloud Peak Energy Inc

    Broader source: Energy.gov [DOE]

    Dear Ms Pickett Please find attached comments from Cloud Peak Energy as input to the Department of Energy’s Quadrennial Energy Review. If possible I would appreciate a confirmation that this email has been received Thank you.

  16. Peak Underground Working Natural Gas Storage Capacity

    Gasoline and Diesel Fuel Update (EIA)

    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...

  17. The PEAK experience in South Carolina

    SciTech Connect (OSTI)

    1998-11-01

    The PEAK Institute was developed to provide a linkage for formal (schoolteachers) and nonformal educators (extension agents) with agricultural scientists of Clemson University`s South Carolina Agricultural Experiment Station System. The goal of the Institute was to enable teams of educators and researchers to develop and provide PEAK science and math learning experiences related to relevant agricultural and environmental issues of local communities for both classroom and 4-H Club experiences. The Peak Institute was conducted through a twenty day residential Institute held in June for middle school and high school teachers who were teamed with an Extension agent from their community. These educators participated in hands-on, minds-on sessions conducted by agricultural researchers and Clemson University Cooperative Extension specialists. Participants were given the opportunity to see frontier science being conducted by scientists from a variety of agricultural laboratories.

  18. Beyond Kilowatts: Utility Business Innovation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sharing Smart Grid Experiences Through Performance Feedback Joe Miller, Smart Grid Implementation Strategy Team September 15, 2011 Prepared by: National Energy Technology Laboratory This presentation 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,

  19. Beyond Kilowatts: Utility Business Innovation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sharing Smart Grid Experiences Through Performance Feedback Joe Miller, Smart Grid Implementation Strategy Team September 15, 2011 Prepared by: National Energy Technology...

  20. Peak to Peak Charter Wins Colorado Science Bowl - News Releases | NREL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Peak to Peak Charter Wins Colorado Science Bowl Lafayette School Heads to Washington D.C. to Challenge for National Title February 13, 2010 Students from Peak to Peak Charter School won the Colorado High School Science Bowl today. They will go on to the 20th National Science Bowl in Washington D.C. on April 29 - May 4, where they will compete for the national title against more than 450 students from 68 high schools. The U.S. Department of Energy began the Science Bowl tradition in 1991 as a way

  1. Ionoacoustic characterization of the proton Bragg peak with submillimeter accuracy

    SciTech Connect (OSTI)

    Assmann, W. Reinhardt, S.; Lehrack, S.; Edlich, A.; Thirolf, P. G.; Parodi, K.; Kellnberger, S.; Omar, M.; Ntziachristos, V.; Moser, M.; Dollinger, G.

    2015-02-15

    Purpose: Range verification in ion beam therapy relies to date on nuclear imaging techniques which require complex and costly detector systems. A different approach is the detection of thermoacoustic signals that are generated due to localized energy loss of ion beams in tissue (ionoacoustics). Aim of this work was to study experimentally the achievable position resolution of ionoacoustics under idealized conditions using high frequency ultrasonic transducers and a specifically selected probing beam. Methods: A water phantom was irradiated by a pulsed 20 MeV proton beam with varying pulse intensity and length. The acoustic signal of single proton pulses was measured by different PZT-based ultrasound detectors (3.5 and 10 MHz central frequencies). The proton dose distribution in water was calculated by Geant4 and used as input for simulation of the generated acoustic wave by the matlab toolbox k-WAVE. Results: In measurements from this study, a clear signal of the Bragg peak was observed for an energy deposition as low as 10{sup 12} eV. The signal amplitude showed a linear increase with particle number per pulse and thus, dose. Bragg peak position measurements were reproducible within 30 ?m and agreed with Geant4 simulations to better than 100 ?m. The ionoacoustic signal pattern allowed for a detailed analysis of the Bragg peak and could be well reproduced by k-WAVE simulations. Conclusions: The authors have studied the ionoacoustic signal of the Bragg peak in experiments using a 20 MeV proton beam with its correspondingly localized energy deposition, demonstrating submillimeter position resolution and providing a deep insight in the correlation between the acoustic signal and Bragg peak shape. These results, together with earlier experiments and new simulations (including the results in this study) at higher energies, suggest ionoacoustics as a technique for range verification in particle therapy at locations, where the tumor can be localized by ultrasound imaging. This acoustic range verification approach could offer the possibility of combining anatomical ultrasound and Bragg peak imaging, but further studies are required for translation of these findings to clinical application.

  2. Twin Peaks Motel Space Heating Low Temperature Geothermal Facility...

    Open Energy Info (EERE)

    Peaks Motel Space Heating Low Temperature Geothermal Facility Jump to: navigation, search Name Twin Peaks Motel Space Heating Low Temperature Geothermal Facility Facility Twin...

  3. Emcore/SunPeak Solar Power Plant | Open Energy Information

    Open Energy Info (EERE)

    Solar Power Plant Facility EmcoreSunPeak Sector Solar Facility Type Concentrating Photovoltaic Developer SunPeak Solar Location Albuquerque, New Mexico Coordinates 35.0844909,...

  4. 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...

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

    Open Energy Info (EERE)

    navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Pressure Temperature Log At Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak...

  6. Magnetotellurics 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: Magnetotellurics At Silver Peak Area (DOE GTP) Exploration Activity...

  7. Geothermometry 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: Geothermometry At Silver Peak Area (DOE GTP) Exploration Activity...

  8. Cuttings Analysis 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: Cuttings Analysis At Silver Peak Area (DOE GTP) Exploration Activity...

  9. 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...

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

    Open Energy Info (EERE)

    Desert Peak Area (Laney, 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Core Analysis At Desert Peak Area (Laney, 2005) Exploration...

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

    Open Energy Info (EERE)

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

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

    Open Energy Info (EERE)

    Desert Peak Area (Laney, 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Cuttings Analysis At Desert Peak Area (Laney, 2005) Exploration...

  13. ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern...

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

    f. Historical Noncoincident Winter Peak Load, Actual by North American Electric Reliability Corporation Region, 2005 through 2010 " ,"(Megawatts)" ,"Winter Noncoincident Peak...

  14. monthly_peak_bymonth_2010.xls

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

    A.1. January Monthly Peak Hour Demand, by North American Electric Reliability Corporation Assesment Area, 1996-2010 Actual, 2011-2012 Projected (Megawatts) January NERC Regional Assesment Area 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011E 2012E FRCC 39,860 37,127 27,122 38,581 37,521 40,258 39,675 45,033 35,545 41,247 34,464 38,352 41,705 44,945 53,093 46,839 47,613 NPCC 41,680 41,208 40,009 44,199 45,227 43,553 42,039 45,987 66,215 47,041 43,661 45,002 46,803

  15. Masked Areas in Shear Peak Statistics: A Forward Modeling Approach (Journal

    Office of Scientific and Technical Information (OSTI)

    Article) | SciTech Connect Masked Areas in Shear Peak Statistics: A Forward Modeling Approach Citation Details In-Document Search Title: Masked Areas in Shear Peak Statistics: A Forward Modeling Approach Authors: Bard, D. ; /KIPAC, Menlo Park ; Kratochvil, J.M. ; /KwaZulu Natal U. ; Dawson, W. ; /LLNL, Livermore Publication Date: 2016-02-18 OSTI Identifier: 1238567 Report Number(s): SLAC-PUB-16483 arXiv:1410.5446 DOE Contract Number: AC02-76SF00515 Resource Type: Journal Article Resource

  16. Method of multi-dimensional moment analysis for the characterization of signal peaks

    DOE Patents [OSTI]

    Pfeifer, Kent B; Yelton, William G; Kerr, Dayle R; Bouchier, Francis A

    2012-10-23

    A method of multi-dimensional moment analysis for the characterization of signal peaks can be used to optimize the operation of an analytical system. With a two-dimensional Peclet analysis, the quality and signal fidelity of peaks in a two-dimensional experimental space can be analyzed and scored. This method is particularly useful in determining optimum operational parameters for an analytical system which requires the automated analysis of large numbers of analyte data peaks. For example, the method can be used to optimize analytical systems including an ion mobility spectrometer that uses a temperature stepped desorption technique for the detection of explosive mixtures.

  17. Insights from Smart Meters: The Potential for Peak Hour Savings...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    The Potential for Peak Hour Savings from Behavior-Based Programs Insights from Smart Meters: The Potential for Peak Hour Savings from Behavior-Based Programs This report focuses on ...

  18. 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...

  19. ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern...

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

    e. Historical Noncoincident Summer Peak Load, Actual by North American Electric Reliability Corporation Region, 2005 through 2009 " ,"(Megawatts)" ,,,,," " ,"Summer Noncoincident...

  20. Mask effects on cosmological studies with weak-lensing peak statistics

    SciTech Connect (OSTI)

    Liu, Xiangkun; Pan, Chuzhong; Fan, Zuhui; Wang, Qiao

    2014-03-20

    With numerical simulations, we analyze in detail how the bad data removal, i.e., the mask effect, can influence the peak statistics of the weak-lensing convergence field reconstructed from the shear measurement of background galaxies. It is found that high peak fractions are systematically enhanced because of the presence of masks; the larger the masked area is, the higher the enhancement is. In the case where the total masked area is about 13% of the survey area, the fraction of peaks with signal-to-noise ratio ? ? 3 is ?11% of the total number of peaks, compared with ?7% of the mask-free case in our considered cosmological model. This can have significant effects on cosmological studies with weak-lensing convergence peak statistics, inducing a large bias in the parameter constraints if the effects are not taken into account properly. Even for a survey area of 9 deg{sup 2}, the bias in (? {sub m}, ?{sub 8}) is already intolerably large and close to 3?. It is noted that most of the affected peaks are close to the masked regions. Therefore, excluding peaks in those regions in the peak statistics can reduce the bias effect but at the expense of losing usable survey areas. Further investigations find that the enhancement of the number of high peaks around the masked regions can be largely attributed to the smaller number of galaxies usable in the weak-lensing convergence reconstruction, leading to higher noise than that of the areas away from the masks. We thus develop a model in which we exclude only those very large masks with radius larger than 3' but keep all the other masked regions in peak counting statistics. For the remaining part, we treat the areas close to and away from the masked regions separately with different noise levels. It is shown that this two-noise-level model can account for the mask effect on peak statistics very well, and the bias in cosmological parameters is significantly reduced if this model is applied in the parameter fitting.

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

    SciTech Connect (OSTI)

    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)

    2012-08-10

    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.

  2. NOISY WEAK-LENSING CONVERGENCE PEAK STATISTICS NEAR CLUSTERS OF GALAXIES AND BEYOND

    SciTech Connect (OSTI)

    Fan Zuhui; Shan Huanyuan; Liu Jiayi

    2010-08-20

    Taking into account noise from intrinsic ellipticities of source galaxies, in this paper, we study the peak statistics in weak-lensing convergence maps around clusters of galaxies and beyond. We emphasize how the noise peak statistics is affected by the density distribution of nearby clusters, and also how cluster-peak signals are changed by the existence of noise. These are the important aspects to be thoroughly understood in weak-lensing analyses for individual clusters as well as in cosmological applications of weak-lensing cluster statistics. We adopt Gaussian smoothing with the smoothing scale {theta} {sub G} = 0.5arcmin in our analyses. It is found that the noise peak distribution near a cluster of galaxies sensitively depends on the density profile of the cluster. For a cored isothermal cluster with the core radius R{sub c} , the inner region with R {<=} R{sub c} appears noisy containing on average {approx}2.4 peaks with {nu} {>=} 5 for R{sub c} = 1.7arcmin and the true peak height of the cluster {nu} = 5.6, where {nu} denotes the convergence signal-to-noise ratio. For a Navarro-Frenk-White (NFW) cluster of the same mass and the same central {nu}, the average number of peaks with {nu} {>=} 5 within R {<=} R{sub c} is {approx}1.6. Thus a high peak corresponding to the main cluster can be identified more cleanly in the NFW case. In the outer region with R{sub c} < R {<=} 5R{sub c} , the number of high noise peaks is considerably enhanced in comparison with that of the pure noise case without the nearby cluster. For {nu} {>=} 4, depending on the treatment of the mass-sheet degeneracy in weak-lensing analyses, the enhancement factor f is in the range of {approx}5 to {approx}55 for both clusters as their outer density profiles are similar. The properties of the main-cluster-peak identified in convergence maps are also significantly affected by the presence of noise. Scatters as well as a systematic shift for the peak height are present. The height distribution is peaked at {nu} {approx} 6.6, rather than at {nu} = 5.6, corresponding to a shift of {Delta}{nu} {approx} 1, for the isothermal cluster. For the NFW cluster, {Delta}{nu} {approx} 0.8. The existence of noise also causes a location offset for the weak-lensing identified main-cluster-peak with respect to the true center of the cluster. The offset distribution is very broad and extends to R {approx} R{sub c} for the isothermal case. For the NFW cluster, it is relatively narrow and peaked at R {approx} 0.2R{sub c} . We also analyze NFW clusters of different concentrations. It is found that the more centrally concentrated the mass distribution of a cluster is, the less its weak-lensing signal is affected by noise. Incorporating these important effects and the mass function of NFW dark matter halos, we further present a model calculating the statistical abundances of total convergence peaks, true and false ones, over a large field beyond individual clusters. The results are in good agreement with those from numerical simulations. The model then allows us to probe cosmologies with the convergence peaks directly without the need of expensive follow-up observations to differentiate true and false peaks.

  3. Number | Open Energy Information

    Open Energy Info (EERE)

    Property:NumOfPlants Property:NumProdWells Property:NumRepWells Property:Number of Color Cameras Property:Number of Devices Deployed Property:Number of Plants included in...

  4. EA-2023: Crossman Peak Communications Facility; Mohave County, Arizona |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy EA-2023: Crossman Peak Communications Facility; Mohave County, Arizona EA-2023: Crossman Peak Communications Facility; Mohave County, Arizona Summary Western Area Power Administration is preparing an EA that assesses the potential environmental impacts of a proposed new microwave communication facility to be located adjacent to a privately-owned one near Crossman Peak, east of Lake Havasu City in Mohave County, Arizona. The proposal would consist of a microwave

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

    Energy Savers [EERE]

    Energy 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;

  6. Desert Peak East EGS Project; 2010 Geothermal Technology Program...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    East EGS Project; 2010 Geothermal Technology Program Peer Review Report Desert Peak East EGS Project; 2010 Geothermal Technology Program Peer Review Report DOE 2010 Geothermal...

  7. Desert Peak II Geothermal Facility | Open Energy Information

    Open Energy Info (EERE)

    Facility Desert Peak II Sector Geothermal energy Location Information Location Churchill, Nevada Coordinates 39.753854931241, -118.95378112793 Loading map......

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

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

    b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2007 and Projected 2008 through 2012 " ,"(Megawatts and 2007 ...

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

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

    b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2006 and Projected 2007 through 2011 " ,"(Megawatts and 2006 ...

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

    Open Energy Info (EERE)

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

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

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The Storm Peak Lab Cloud Property Validation Experiment (STORMVEX) Campaign Links STORMVEX Website ARM Data Discovery Browse Data Related Campaigns Colorado: CFHCMH Deployment to...

  12. Jiminy Peak Ski Resort Wind Farm | Open Energy Information

    Open Energy Info (EERE)

    Energy Developments Energy Purchaser Jiminy Peak Mountain Resort Location Hancock MA Coordinates 42.5554, -73.2898 Show Map Loading map... "minzoom":false,"mappingservi...

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

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

    2011" ,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2009 and Projected 2010 through 2014 "...

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

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

    a. Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2007 and Projected 2008 through 2012 " ,"(Megawatts and 2007...

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

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

    2010" ,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2008 and Projected 2009 through 2013 "...

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

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

    4 and Projected 2005 through 2009 " ,"(Megawatts and 2004 Base Year)",,,," " ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power...

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

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

    3 and Projected 2004 through 2008 " ,"(Megawatts and 2003 Base Year)",,,," " ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power...

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

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

    a. Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2006 and Projected 2007 through 2011 " ,"(Megawatts and 2006...

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

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

    2007" ,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, " ,"2005 and Projected 2006 through 2010 "...

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

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

    2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2009 and Projected 2010 through 2014 " ,"(Megawatts and 2009...

  1. Multispecies density peaking in gyrokinetic turbulence simulations of low collisionality Alcator C-Mod plasmas

    SciTech Connect (OSTI)

    Mikkelsen, D. R. Bitter, M.; Delgado-Aparicio, L.; Hill, K. W.; Greenwald, M.; Howard, N. T.; Hughes, J. W.; Rice, J. E.; Reinke, M. L.; Podpaly, Y.; Ma, Y.; Candy, J.; Waltz, R. E.

    2015-06-15

    Peaked density profiles in low-collisionality AUG and JET H-mode plasmas are probably caused by a turbulently driven particle pinch, and Alcator C-Mod experiments confirmed that collisionality is a critical parameter. Density peaking in reactors could produce a number of important effects, some beneficial, such as enhanced fusion power and transport of fuel ions from the edge to the core, while others are undesirable, such as lower beta limits, reduced radiation from the plasma edge, and consequently higher divertor heat loads. Fundamental understanding of the pinch will enable planning to optimize these impacts. We show that density peaking is predicted by nonlinear gyrokinetic turbulence simulations based on measured profile data from low collisionality H-mode plasma in Alcator C-Mod. Multiple ion species are included to determine whether hydrogenic density peaking has an isotope dependence or is influenced by typical levels of low-Z impurities, and whether impurity density peaking depends on the species. We find that the deuterium density profile is slightly more peaked than that of hydrogen, and that experimentally relevant levels of boron have no appreciable effect on hydrogenic density peaking. The ratio of density at r/a?=?0.44 to that at r/a?=?0.74 is 1.2 for the majority D and minority H ions (and for electrons), and increases with impurity Z: 1.1 for helium, 1.15 for boron, 1.3 for neon, 1.4 for argon, and 1.5 for molybdenum. The ion temperature profile is varied to match better the predicted heat flux with the experimental transport analysis, but the resulting factor of two change in heat transport has only a weak effect on the predicted density peaking.

  2. NSR Key Number Retrieval

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    NSR Key Number Retrieval Pease enter key in the box Submit

  3. SunPeak Solar LLC | Open Energy Information

    Open Energy Info (EERE)

    search Name: SunPeak Solar LLC Place: Palm Desert, California Zip: 92260 Product: US project developer and asset manager, focussing on PV projects in the south-west....

  4. Fossil fuel-fired peak heating for geothermal greenhouses

    SciTech Connect (OSTI)

    Rafferty, K.

    1996-12-01

    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.

  5. Silver Peak, Nevada: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    This article is a stub. You can help OpenEI by expanding it. Silver Peak is a city in Esmeralda County, Nevada. References USGS GNIS Retrieved from "http:en.openei.orgw...

  6. How Technology Keeps Beating Peak Oil Predictions | GE Global...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    will reach their maximum production." - Van H. Manning, U.S. Bureau of Mines 1956 - "U.S. oil production will likely peak between 1965 and 1970 and decline steadily thereafter." -...

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

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

    4 and Projected 2005 through 2009 " ,"(Megawatts and 2004 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western...

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

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

    3 and Projected 2004 through 2008 " ,"(Megawatts and 2003 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western...

  9. Progress in Understanding Iron Peak Elements in Young Supernova Remnants

    Office of Scientific and Technical Information (OSTI)

    (Conference) | SciTech Connect Progress in Understanding Iron Peak Elements in Young Supernova Remnants Citation Details In-Document Search Title: Progress in Understanding Iron Peak Elements in Young Supernova Remnants Authors: Eriksen, Kristoffer A. [1] ; Hughes, Jack [2] ; Fontes, Christopher J. [1] ; Colgan, James P. [1] ; Hungerford, Aimee L. [1] ; Fryer, Christopher L. [1] ; Zhang, Honglin [1] ; Badenes, Carles [3] ; Slane, Patrick [4] + Show Author Affiliations Los Alamos National

  10. February most likely month for flu season to peak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    February most likely month for flu season to peak February most likely month for flu season to peak The Los Alamos team's model is an ongoing research project that forecasts the current flu season probabilistically, similar to best-practice forecasts of weather, presidential elections, and sporting events. December 20, 2015 The Los Alamos team's model is an ongoing research project that forecasts the current flu season probabilistically, similar to best-practice forecasts of weather,

  11. TruePeak Process Laser Analyzer | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    TruePeak Process Laser Analyzer TruePeak Process Laser Analyzer In-Situ Sensors Provide Real-Time Measurements Enabling Better Control and Process Optimization Current chemical process controls use few in-situ sensors, relying instead on analytic techniques that require sample conditioning and transport, and significant turnaround time. With few exceptions, these techniques lack speed of measurement, accuracy of measurement, sensitivity of measurement, and economical measurement. In-situ sensors

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

    SciTech Connect (OSTI)

    Hirsch, R.L.; Bezdek, Roger; Wendling, Robert

    2005-02-01

    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.

  13. Big Numbers | Jefferson Lab

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Big Numbers May 16, 2011 This article has some numbers in it. In principle, numbers are just language, like English or Japanese. Nevertheless, it is true that not everyone is comfortable or facile with numbers and may be turned off by too many of them. To those people, I apologize that this article pays less attention to maximizing the readership than some I do. But sometimes it's just appropriate to indulge one's self, so here goes. When we discuss the performance of some piece of equipment, we

  14. Table 8.12b Electric Noncoincident Peak Load and Capacity Margin...

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

    b Electric Noncoincident Peak Load and Capacity Margin: Winter Peak Period, 1986-2011 ... Year Noncoincident Peak Load 1 by North American Electric Reliability Corporation (NERC) 2 ...

  15. Wavelet Approach for Operational Gamma Spectral Peak Detection - Preliminary Assessment

    SciTech Connect (OSTI)

    ,

    2012-02-01

    Gamma spectroscopy for radionuclide identifications typically involves locating spectral peaks and matching the spectral peaks with known nuclides in the knowledge base or database. Wavelet analysis, due to its ability for fitting localized features, offers the potential for automatic detection of spectral peaks. Past studies of wavelet technologies for gamma spectra analysis essentially focused on direct fitting of raw gamma spectra. Although most of those studies demonstrated the potentials of peak detection using wavelets, they often failed to produce new benefits to operational adaptations for radiological surveys. This work presents a different approach with the operational objective being to detect only the nuclides that do not exist in the environment (anomalous nuclides). With this operational objective, the raw-count spectrum collected by a detector is first converted to a count-rate spectrum and is then followed by background subtraction prior to wavelet analysis. The experimental results suggest that this preprocess is independent of detector type and background radiation, and is capable of improving the peak detection rates using wavelets. This process broadens the doors for a practical adaptation of wavelet technologies for gamma spectral surveying devices.

  16. Silver Peak Innovative Exploration Project (Ram Power Inc.)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Miller, Clay

    2010-01-01

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

  17. Silver Peak Innovative Exploration Project (Ram Power Inc.)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Miller, Clay

    2010-01-01

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

  18. Silver Peak Innovative Exploration Project (Ram Power Inc.)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Miller, Clay

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

  19. Report number codes

    SciTech Connect (OSTI)

    Nelson, R.N.

    1985-05-01

    This publication lists all report number codes processed by the Office of Scientific and Technical Information. The report codes are substantially based on the American National Standards Institute, Standard Technical Report Number (STRN)-Format and Creation Z39.23-1983. The Standard Technical Report Number (STRN) provides one of the primary methods of identifying a specific technical report. The STRN consists of two parts: The report code and the sequential number. The report code identifies the issuing organization, a specific program, or a type of document. The sequential number, which is assigned in sequence by each report issuing entity, is not included in this publication. Part I of this compilation is alphabetized by report codes followed by issuing installations. Part II lists the issuing organization followed by the assigned report code(s). In both Parts I and II, the names of issuing organizations appear for the most part in the form used at the time the reports were issued. However, for some of the more prolific installations which have had name changes, all entries have been merged under the current name.

  20. Peak Dose Assessment for Proposed DOE-PPPO Authorized Limits

    SciTech Connect (OSTI)

    Maldonado, Delis

    2012-06-01

    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.

  1. Fact #738: July 30, 2012 Number of New Light Vehicle Dealerships Decreasing

    Broader source: Energy.gov [DOE]

    The number of franchised new light vehicle dealerships peaked in 1949 with more than 49,000 dealers. By 2012, the number is less than half of that – 17,540 dealers.

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

    ScienceCinema (OSTI)

    Piette, Mary Ann

    2011-04-28

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

  3. Material Handling Fuel Cells for Building Electric Peak Shaving Applications

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Material Handling Fuel Cells for Building Electric Peak Shaving Applications U.S. Department of Energy Fuel Cell Technologies Office August 11, 2015 Presenter: Michael Penev of NREL DOE Host: Pete Devlin 2 Question and Answer * Please type your question into the question box hydrogenandfuelcells.energy.gov 3 Acknowledgments Fuel Cell Technologies Office, DOE EERE For providing funding for this project and for supporting sustainable hydrogen technology development through analysis, demonstration,

  4. EA-2023: Crossman Peak Communications Facility; Mohave County, Arizona

    Broader source: Energy.gov [DOE]

    Western Area Power Administration is preparing an EA that assesses the potential environmental impacts of a proposed new microwave communication facility to be located adjacent to a privately-owned one near Crossman Peak, east of Lake Havasu City in Mohave County, Arizona. The proposal would consist of a microwave communication facility, an access road, and an approximately 8-mile electrical service distribution line across private land and land administered by the Bureau of Land Management.

  5. Desert Peak East EGS Project; 2010 Geothermal Technology Program Peer

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Review Report | Department of Energy East EGS Project; 2010 Geothermal Technology Program Peer Review Report Desert Peak East EGS Project; 2010 Geothermal Technology Program Peer Review Report DOE 2010 Geothermal Technologies Program Peer Review PDF icon egs_008_zemach.pdf More Documents & Publications Feasibility of EGS Development at Bradys Hot Springs, Nevada Concept Testing and Development at the Raft River Geothermal Field, Idaho Creation of an Enhanced Geothermal System through

  6. Reducing Peak Demand to Defer Power Plant Construction in Oklahoma

    Energy Savers [EERE]

    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

  7. Document Details Document Number

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Document Details Document Number Date of Document Document Title/Description [Links below to each document] D195066340 Not listed. N/A REVISIONS IN STRATIGRAPHIC NOMENCLATURE OF COLUMBIA RIVER BASALT GROUP D196000240 Not listed. N/A EPA DENIAL OF LINER LEACHATE COLLECTION SYSTEM REQUIREMENTS D196005916 Not listed. N/A LATE CENOZOIC STRATIGRAPHY AND TECTONIC EVOLUTION WITHIN SUBSIDING BASIN SOUTH CENTRAL WASHINGTON D196025993 RHO-BWI-ST-14 N/A SUPRABASALT SEDIMENTS OF COLD CREEK SYNCLINE AREA

  8. Deconvolution of mixed gamma emitters using peak parameters

    SciTech Connect (OSTI)

    Gadd, Milan S; Garcia, Francisco; Magadalena, Vigil M

    2011-01-14

    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.

  9. Fossil fuel-fired peak heating for geothermal greenhouses

    SciTech Connect (OSTI)

    Rafferty, K.

    1997-01-01

    Greenhouses are a major application of low-temperature geothermal resources. In virtually all operating systems, the geothermal fluid is used in a hot water heating system to meet 100% of both the peak and annual heating requirements of the structure. This strategy is a result of the relatively low costs associated with the development of most US geothermal direct-use resources and past tax credit programs which penalized systems using any conventional fuel sources. Increasingly, greenhouse operations will encounter limitations in available geothermal resource flow due either to production or disposal considerations. As a result, it will be necessary to operate additions at reduced water temperatures reflective of the effluent from the existing operations. Water temperature has a strong influence on heating system design. Greenhouse operators tend to have unequivocal preferences regarding heating system equipment. Many growers, particularly cut flower and bedding plant operators, prefer the {open_quotes}bare tube{close_quotes} type heating system. This system places small diameter plastic tubes under the benches or adjacent to the plants. Hot water is circulated through the tubes providing heat to the plants and the air in the greenhouse. Advantages include the ability to provide the heat directly to the plants, low cost, simple installation and the lack of a requirement for fans to circulate air. The major disadvantage of the system is poor performance at low (<140{degrees}F) water temperatures, particularly in cold climates. Under these conditions, the quantity of tubing required to meet the peak heating load is substantial. In fact, under some conditions, it is simply impractical to install sufficient tubing in the greenhouse to meet the peak heating load.

  10. Methods and apparatus for reducing peak wind turbine loads

    DOE Patents [OSTI]

    Moroz, Emilian Mieczyslaw

    2007-02-13

    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 wind turbine and determining a yaw error of the wind turbine relative to the measured instantaneous wind direction. The method further includes comparing the yaw error to a yaw error trigger that has different values at different wind speeds and shutting down the wind turbine when the yaw error exceeds the yaw error trigger corresponding to the measured or estimated instantaneous wind speed.

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

    Open Energy Info (EERE)

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

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

    Open Energy Info (EERE)

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

  13. 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...

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

    Open Energy Info (EERE)

    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...

  15. 2-M Probe At Silver Peak Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

    GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: 2-M Probe At Silver Peak Area (DOE GTP) Exploration Activity Details Location Silver Peak...

  16. 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...

  17. Neutron 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: Neutron Log At Silver Peak Area (DOE GTP) Exploration Activity Details...

  18. Slim Holes 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: Slim Holes At Silver Peak Area (DOE GTP) Exploration Activity Details...

  19. Core Analysis 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: Core Analysis At Silver Peak Area (DOE GTP) Exploration Activity...

  20. AlphaSpectrum ASPECT analysis code for background correction & peak integration

    Energy Science and Technology Software Center (OSTI)

    2005-04-13

    The ASPECT code provides a means for rapid analysis of energy-resolved spectra obtained by multi-channel pulse-height analysis (MCA) during (or after) counting of alpha-emissions from a filter air sample (or other suitably prepared sample) utilizing a solid-state detector, or other detector having sufficient energy resolution indiviual radioisotope peaks indentified in a spectrum are fitted using a peak shape algorithm by non-linear least-square fitting procedures that minimize Chi-square differences between the data and a fitted peakmore » function. The code accomplishes the identification of all significant peaks present in the spectrum with automatic recalibration to the 7.68 Po-214 alpha peak from the Radon-222 decay chain, the subtraction of all radon progeny interference overlaps with lower energy peaks in the energy range of Pu-238, Am-241, Pu-239, and U-234/Th-232, and the integration of the counts in any peak identified for these transuranic radionuclides. The output is therefore in the form of isotope specific net transuranic CPM, DPM or concentration, available in near real-time during air sampling. In this "copyright" version, the assumption is made that the alpha spectra to be analyzed have been stored by unique name in sequential form: "FileName(i)", where "FileName" can be any name and i is the index number of the file saved (e.g., i = 1,2, ..., n). this format is one automatically generated by the alpha Environmental Continuous Air Monitor (ECAM), developed by Los Alamos National Laboratory, and manufactured by Canberra Industries, a Laboratory Industrial Partner for this technology. It is assumed in this version of the code that the alpha spectrum data are stored in a 256 channel spectrum, although a larger num ber of channels could be easily accommodated by small code changes. The ECAM data output format is RADNET compliant (an inidustry standard developed at Los Alamos), and include, in addition to a 256-channel alpha spectrum, data on the count time of the spectrum, sample volume represented, the total volume of air sampled by the filter, and other relevant data on the sample. Dummy variable assignments could be made in the code for all variables except for the alpha spectrum if the count rate, concentration, date stamp, and other outputs were not desired, but this option in not automatically available. The code could be implemented in an embedded form and thereby operate independently of user inputs. However, in the present version, the code is designed to operate off-line, accessing stored spectrum data (and other relevant sampling data) from stored files. In this form the user can select the characteristics of peak identification, the sigma-multiplier for the Critical Level determination, and whether or not the data are smoothed before analysis. This version is a development version, from which the user could prepare an embedded version not requiring operator intervention. In any case, the core program of peak identification, fitting, and interference correction is the same.« less

  1. AlphaSpectrum ASPECT analysis code for background correction & peak integration

    Energy Science and Technology Software Center (OSTI)

    2005-04-13

    The ASPECT code provides a means for rapid analysis of energy-resolved spectra obtained by multi-channel pulse-height analysis (MCA) during (or after) counting of alpha-emissions from a filter air sample (or other suitably prepared sample) utilizing a solid-state detector, or other detector having sufficient energy resolution indiviual radioisotope peaks indentified in a spectrum are fitted using a peak shape algorithm by non-linear least-square fitting procedures that minimize Chi-square differences between the data and a fitted peakmorefunction. The code accomplishes the identification of all significant peaks present in the spectrum with automatic recalibration to the 7.68 Po-214 alpha peak from the Radon-222 decay chain, the subtraction of all radon progeny interference overlaps with lower energy peaks in the energy range of Pu-238, Am-241, Pu-239, and U-234/Th-232, and the integration of the counts in any peak identified for these transuranic radionuclides. The output is therefore in the form of isotope specific net transuranic CPM, DPM or concentration, available in near real-time during air sampling. In this "copyright" version, the assumption is made that the alpha spectra to be analyzed have been stored by unique name in sequential form: "FileName(i)", where "FileName" can be any name and i is the index number of the file saved (e.g., i = 1,2, ..., n). this format is one automatically generated by the alpha Environmental Continuous Air Monitor (ECAM), developed by Los Alamos National Laboratory, and manufactured by Canberra Industries, a Laboratory Industrial Partner for this technology. It is assumed in this version of the code that the alpha spectrum data are stored in a 256 channel spectrum, although a larger num ber of channels could be easily accommodated by small code changes. The ECAM data output format is RADNET compliant (an inidustry standard developed at Los Alamos), and include, in addition to a 256-channel alpha spectrum, data on the count time of the spectrum, sample volume represented, the total volume of air sampled by the filter, and other relevant data on the sample. Dummy variable assignments could be made in the code for all variables except for the alpha spectrum if the count rate, concentration, date stamp, and other outputs were not desired, but this option in not automatically available. The code could be implemented in an embedded form and thereby operate independently of user inputs. However, in the present version, the code is designed to operate off-line, accessing stored spectrum data (and other relevant sampling data) from stored files. In this form the user can select the characteristics of peak identification, the sigma-multiplier for the Critical Level determination, and whether or not the data are smoothed before analysis. This version is a development version, from which the user could prepare an embedded version not requiring operator intervention. In any case, the core program of peak identification, fitting, and interference correction is the same.less

  2. Texas Natural Gas Number of Residential Consumers (Number of...

    Gasoline and Diesel Fuel Update (EIA)

    Residential Consumers (Number of Elements) Texas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  3. Texas Natural Gas Number of Commercial Consumers (Number of Elements...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Commercial Consumers (Number of Elements) Texas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  4. Connecticut Natural Gas Number of Commercial Consumers (Number...

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

    Commercial Consumers (Number of Elements) Connecticut Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  5. Connecticut Natural Gas Number of Residential Consumers (Number...

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

    Residential Consumers (Number of Elements) Connecticut Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  6. North Carolina Natural Gas Number of Commercial Consumers (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Commercial Consumers (Number of Elements) North Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  7. New York Natural Gas Number of Commercial Consumers (Number of...

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

    Commercial Consumers (Number of Elements) New York Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  8. New York Natural Gas Number of Residential Consumers (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Residential Consumers (Number of Elements) New York Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  9. Indiana Natural Gas Number of Industrial Consumers (Number of...

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

    Industrial Consumers (Number of Elements) Indiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  10. Evidence is growing on demand side of an oil peak

    SciTech Connect (OSTI)

    2009-07-15

    After years of continued growth, the number of miles driven by Americans started falling in December 2007. Not only are the number of miles driven falling, but as cars become more fuel efficient, they go further on fewer gallons - further reducing demand for gasoline. This trend is expected to accelerate. Drivers include, along with higher-efficiency cars, mass transit, reversal in urban sprawl, biofuels, and plug-in hybrid vehicles.

  11. New runners to boost peak output at Niagara Falls

    SciTech Connect (OSTI)

    Reason, J.

    1990-01-01

    Retrofitted Francis turbines will improve the value of power generated from Niagara Falls by increasing the peak output of the hydroturbine units at the Robert Moses hydroelectric plant. The computer-designed runners are expected to add 330 MW to the peak capacity of the 28-yr-old plant and significantly increase the efficiency at high flow rates. Next year, the first new runner will be retrofit to the highly instrumented Unit 4. If the retrofit unit meets it increased-performance expectations, the other 12 units will be upgraded between 1993 and 1998. The work is part of an overall expansion of the Niagara Power Project designed to made better use of the power value of Niagara river water, within the constraints of a treaty with Canada and the scenic value of the falls. These constraints, together with varying flows and heads, introduced enormous complexities into the selection and design of the new runners. The alterations being made to Unit 4, in addition to replacing the turbine runner, include modifying the draft tube-liners, increasing the wicket-gate stroke, replacing the turbine discharge ring (to accommodate longer blades), making various electrical modifications to the generator, and replacing the transformer. But the key to the retrofit is the computer-designed runner. Charles Grose, senior project manager, New York Power Authority, White Plains, NY, emphasizes that such computer design techniques were not available a few years ago; neither were the computer-controlled machining techniques necessary to manufacture the new runners. Other aspects of the upgrading that were analyzed include runner stability, resonance, shaft torsional stress, and runaway speed.

  12. Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction

    SciTech Connect (OSTI)

    Fuller, Jason C.; Vyakaranam, Bharat GNVSR; Prakash Kumar, Nirupama; Leistritz, Sean M.; Parker, Graham B.

    2012-04-29

    The widespread adoption of demand response enabled appliances and thermostats can result in significant reduction to peak electrical demand and provide potential grid stabilization benefits. GE has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information from the utility grid, often in the form of price. However due to a number of factors, including the number of demand response enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors can be modeled and simulated in open-source software, GridLAB-D, including evaluation of appliance controls, improvement to current algorithms, and development of aggregate control methodologies. This report is the first in a series of three reports describing the potential of GE's demand response enabled appliances to provide benefits to the utility grid. The first report will describe the modeling methodology used to represent the GE appliances in the GridLAB-D simulation environment and the estimated potential for peak demand reduction at various deployment levels. The second and third reports will explore the potential of aggregated group actions to positively impact grid stability, including frequency and voltage regulation and spinning reserves, and the impacts on distribution feeder voltage regulation, including mitigation of fluctuations caused by high penetration of photovoltaic distributed generation and the effects on volt-var control schemes.

  13. Peak Ground Velocities for Seismic Events at Yucca Mountain, Nevada

    SciTech Connect (OSTI)

    K. Coppersmith; R. Quittmeyer

    2005-02-16

    This report describes a scientific analysis to bound credible horizontal peak ground velocities (PGV) for the repository waste emplacement level at Yucca Mountain. Results are presented as a probability distribution for horizontal PGV to represent uncertainties in the analysis. The analysis also combines the bound to horizontal PGV with results of ground motion site-response modeling (BSC 2004 [DIRS 170027]) to develop a composite hazard curve for horizontal PGV at the waste emplacement level. This result provides input to an abstraction of seismic consequences (BSC 2004 [DIRS 169183]). The seismic consequence abstraction, in turn, defines the input data and computational algorithms for the seismic scenario class of the total system performance assessment (TSPA). Planning for the analysis is documented in Technical Work Plan TWP-MGR-GS-000001 (BSC 2004 [DIRS 171850]). The bound on horizontal PGV at the repository waste emplacement level developed in this analysis complements ground motions developed on the basis of PSHA results. In the PSHA, ground motion experts characterized the epistemic uncertainty and aleatory variability in their ground motion interpretations. To characterize the aleatory variability they used unbounded lognormal distributions. As a consequence of these characterizations, as seismic hazard calculations are extended to lower and lower annual frequencies of being exceeded, the ground motion level increases without bound, eventually reaching levels that are not credible (Corradini 2003 [DIRS 171191]). To provide credible seismic inputs for TSPA, in accordance with 10 Code of Federal Regulations (CFR) 63.102(j) [DIRS 156605], this complementary analysis is carried out to determine reasonable bounding values of horizontal PGV at the waste emplacement level for annual frequencies of exceedance as low as 10{sup -8}. For each realization of the TSPA seismic scenario, the results of this analysis provide a constraint on the values sampled from the horizontal PGV hazard curve for the waste emplacement level. The relation of this analysis to other work feeding the seismic consequence abstraction and the TSPA is shown on Figure 1-1. The ground motion hazard results from the PSHA provide the basis for inputs to a site-response model that determines the effect of site materials on the ground motion at a location of interest (e.g., the waste emplacement level). Peak ground velocity values determined from the site-response model for the waste emplacement level are then used to develop time histories (seismograms) that form input to a model of drift degradation under seismic loads potentially producing rockfall. The time histories are also used to carry out dynamic seismic structural response calculations of the drip shield and waste package system. For the drip shield, damage from seismically induced rockfall also is considered. In the seismic consequence abstraction, residual stress results from the structural response calculations are interpreted in terms of the percentage of the component (drip shield, waste package) damaged as a function of horizontal PGV. The composite hazard curve developed in this analysis, which reflects the results of site-response modeling and the bound to credible horizontal PGV at the waste emplacement level, also feeds the seismic consequence abstraction. The composite hazard curve is incorporated into the TSPA sampling process to bound horizontal PGV and related seismic consequences to values that are credible.

  14. Nuclear Hydrogen for Peak Electricity Production and Spinning Reserve

    SciTech Connect (OSTI)

    Forsberg, C.W.

    2005-01-20

    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.

  15. Peak fitting applied to low-resolution enrichment measurements

    SciTech Connect (OSTI)

    Bracken, D.; McKown, T.; Sprinkle, J.K. Jr.; Gunnink, R.; Kartoshov, M.; Kuropatwinski, J.; Raphina, G.; Sokolov, G.

    1998-12-01

    Materials accounting at bulk processing facilities that handle low enriched uranium consists primarily of weight and uranium enrichment measurements. Most low enriched uranium processing facilities draw separate materials balances for each enrichment handled at the facility. The enrichment measurement determines the isotopic abundance of the {sup 235}U, thereby determining the proper strata for the item, while the weight measurement generates the primary accounting value for the item. Enrichment measurements using the passive gamma radiation from uranium were developed for use in US facilities a few decades ago. In the US, the use of low-resolution detectors was favored because they cost less, are lighter and more robust, and don`t require the use of liquid nitrogen. When these techniques were exported to Europe, however, difficulties were encountered. Two of the possible root causes were discovered to be inaccurate knowledge of the container wall thickness and higher levels of minor isotopes of uranium introduced by the use of reactor returns in the enrichment plants. the minor isotopes cause an increase in the Compton continuum under the 185.7 keV assay peak and the observance of interfering 238.6 keV gamma rays. The solution selected to address these problems was to rely on the slower, more costly, high-resolution gamma ray detectors when the low-resolution method failed. Recently, these gamma ray based enrichment measurement techniques have been applied to Russian origin material. The presence of interfering gamma radiation from minor isotopes was confirmed. However, with the advent of fast portable computers, it is now possible to apply more sophisticated analysis techniques to the low-resolution data in the field. Explicit corrections for Compton background, gamma rays from {sup 236}U daughters, and the attenuation caused by thick containers can be part of the least squares fitting routine. Preliminary results from field measurements in Kazakhstan will be discussed.

  16. Peak CO2? China's Emissions Trajectories to 2050

    SciTech Connect (OSTI)

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

    2011-05-01

    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.

  17. Back-Up/ Peak Shaving Fuel Cell System

    SciTech Connect (OSTI)

    Staudt, Rhonda L.

    2008-05-28

    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 Powers 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 Powers 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.

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

    SciTech Connect (OSTI)

    Levene, J. I.

    2007-02-01

    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.

  19. Origin of the narrow, single peak in the fission-fragment mass...

    Office of Scientific and Technical Information (OSTI)

    Origin of the narrow, single peak in the fission-fragment mass distribution for 258Fm Citation Details In-Document Search Title: Origin of the narrow, single peak in the...

  20. Alaska Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Alaska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10 11 8 1990's 8 8 10 11 11 9 202 7 7 9 2000's 9 8 9 9 10 12 11 11 6 3 2010's 3 5 3 3 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of Natural Gas

  1. Hawaii Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Hawaii Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 27 26 29 2000's 28 28 29 29 29 28 26 27 27 25 2010's 24 24 22 22 23 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of Natural Gas Industrial

  2. WE-D-BRF-02: Acoustic Signal From the Bragg Peak for Range Verification in Proton Therapy

    SciTech Connect (OSTI)

    Reinhardt, S; Assmann, W; Fink, A; Thirolf, P; Parodi, K; Kellnberger, S; Omar, M; Ntziachristos, V; Gaebisch, C; Moser, M; Dollinger, G; Sergiadis, G

    2014-06-15

    Purpose: Range verification in ion beam therapy relies to date on nuclear imaging techniques which require complex and costly detector systems. A different approach is the detection of thermoacoustic signals that are generated due to localized energy loss of ion beams. Aim of this work is to study the feasibility of determining the ion range with sub-mm accuracy by use of high frequency ultrasonic (US) transducers and to image the Bragg peak by tomography. Methods: A water phantom was irradiated by a pulsed 20 MeV proton beam with varying pulse intensity, length and repetition rate. The acoustic signal of single proton pulses was measured by different PZT-based US detectors (3.5 MHz and 10 MHz central frequencies). For tomography a 64 channel US detector array was used and moved along the ion track by a remotely controlled motor stage. Results: A clear signal of the Bragg peak was visible for an energy deposition as low as 10{sup 12} eV. The signal amplitude showed a linear increase with particle number per pulse and thus, dose. Range measurements were reproducible within +/? 20 micrometer and agreed well with Geant4 simulations. The tomographic reconstruction does not only allow to measure the ion range but also the beam spot size at the Bragg peak position. Conclusion: Range verification by acoustic means is a promising new technique for treatment modalities where the tumor can be localized by US imaging. Further improvement of sensitivity is required to account for higher attenuation of the US signal in tissue, as well as lower energy density in the Bragg peak in realistic treatment cases due to higher particle energy and larger spot sizes. Nevertheless, the acoustic range verification approach could offer the possibility of combining anatomical US imaging with Bragg Peak imaging in the near future. The work was funded by the DFG cluster of excellence Munich Centre for Advanced Photonics (MAP)

  3. Total Number of Operable Refineries

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

    Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge

  4. Compendium of Experimental Cetane Numbers

    SciTech Connect (OSTI)

    Yanowitz, J.; Ratcliff, M. A.; McCormick, R. L.; Taylor, J. D.; Murphy, M. J.

    2014-08-01

    This report is an updated version of the 2004 Compendium of Experimental Cetane Number Data and presents a compilation of measured cetane numbers for pure chemical compounds. It includes all available single compound cetane number data found in the scientific literature up until March 2014 as well as a number of unpublished values, most measured over the past decade at the National Renewable Energy Laboratory. This Compendium contains cetane values for 389 pure compounds, including 189 hydrocarbons and 201 oxygenates. More than 250 individual measurements are new to this version of the Compendium. For many compounds, numerous measurements are included, often collected by different researchers using different methods. Cetane number is a relative ranking of a fuel's autoignition characteristics for use in compression ignition engines; it is based on the amount of time between fuel injection and ignition, also known as ignition delay. The cetane number is typically measured either in a single-cylinder engine or a constant volume combustion chamber. Values in the previous Compendium derived from octane numbers have been removed, and replaced with a brief analysis of the correlation between cetane numbers and octane numbers. The discussion on the accuracy and precision of the most commonly used methods for measuring cetane has been expanded and the data has been annotated extensively to provide additional information that will help the reader judge the relative reliability of individual results.

  5. Arizona Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Arizona Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 358 344 354 1990's 526 532 532 526 519 530 534 480 514 555 2000's 526 504 488 450 414 425 439 395 383 390 2010's 368 371 379 383 386 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  6. Montana Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Montana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 435 435 428 1990's 457 452 459 462 453 463 466 462 454 397 2000's 71 73 439 412 593 716 711 693 693 396 2010's 384 381 372 372 369 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  7. Nevada Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Nevada Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 93 98 100 1990's 100 113 114 117 119 120 121 93 93 109 2000's 90 90 96 97 179 192 207 220 189 192 2010's 184 177 177 195 218 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016

  8. New Hampshire Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) New Hampshire Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 153 295 376 1990's 364 361 344 334 324 332 367 385 389 417 2000's 432 331 437 550 305 397 421 578 5,298 155 2010's 306 362 466 403 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  9. North Dakota Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) North Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 138 148 151 1990's 165 170 171 174 186 189 206 216 404 226 2000's 192 203 223 234 241 239 241 253 271 279 2010's 307 259 260 266 269 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  10. Rhode Island Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Rhode Island Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,158 1,152 1,122 1990's 1,135 1,107 1,096 1,066 1,064 359 363 336 325 302 2000's 317 283 54 236 223 223 245 256 243 260 2010's 249 245 248 271 266 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  11. South Dakota Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) South Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 261 267 270 1990's 275 283 319 355 381 396 444 481 464 445 2000's 416 402 533 526 475 542 528 548 598 598 2010's 580 556 574 566 575 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  12. Utah Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Utah Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 551 627 550 1990's 1,508 631 783 345 252 713 923 3,379 3,597 3,625 2000's 3,576 3,535 949 924 312 191 274 278 313 293 2010's 293 286 302 323 328 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release

  13. Vermont Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Vermont Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 22 21 14 1990's 15 13 18 20 24 23 27 30 36 37 2000's 38 36 38 41 43 41 35 37 35 36 2010's 38 36 38 13 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages:

  14. Delaware Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Delaware Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241 233 235 1990's 240 243 248 249 252 253 250 265 257 264 2000's 297 316 182 184 186 179 170 185 165 112 2010's 114 129 134 138 141 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  15. Florida Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Florida Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 575 552 460 1990's 452 377 388 433 481 515 517 561 574 573 2000's 520 518 451 421 398 432 475 467 449 607 2010's 581 630 507 528 520 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  16. Idaho Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Idaho Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 219 132 64 1990's 62 65 66 75 144 167 183 189 203 200 2000's 217 198 194 191 196 195 192 188 199 187 2010's 184 178 179 183 189 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016

  17. Maine Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Maine Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 73 73 74 1990's 80 81 80 66 89 74 87 81 110 108 2000's 178 233 66 65 69 69 73 76 82 85 2010's 94 102 108 120 126 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring

  18. West Virginia Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) West Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 463 208 211 1990's 182 198 159 197 191 192 182 173 217 147 2000's 207 213 184 142 137 145 155 114 109 101 2010's 102 94 97 95 92 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next

  19. Wyoming Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Wyoming Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 190 200 230 1990's 284 228 244 194 135 126 170 194 317 314 2000's 308 295 877 179 121 127 133 133 155 130 2010's 120 123 127 132 131 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  20. Departmental Business Instrument Numbering System

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2000-12-05

    To prescribe procedures for assigning identifying numbers to all Department of Energy (DOE), including the National Nuclear Security Administration, business instruments. Cancels DOE 1331.2B. Canceled by DOE O 540.1A.

  1. Departmental Business Instrument Numbering System

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2005-01-27

    The Order prescribes the procedures for assigning identifying numbers to all Department of Energy (DOE) and National Nuclear Security Administration (NNSA) business instruments. Cancels DOE O 540.1. Canceled by DOE O 540.1B.

  2. Document ID Number: RL-721

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ---------------------------------------------------------- Document ID Number: RL-721 REV 4 NEPA REVIEW SCREENING FORM DOE/CX-00066 I. Project Title: Nesting Bird Deterrent Study at the 241-C Tank Farm CX B3.8, "Outdoor Terrestrial Ecological and Environmental Research" II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings,

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

    DOE Patents [OSTI]

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

    2003-01-01

    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.

  4. Alabama Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Alabama Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 53 54,306 55,400 56,822 1990's 56,903 57,265 58,068 57,827 60,320 60,902 62,064 65,919 76,467 64,185 2000's 66,193 65,794 65,788 65,297 65,223 65,294 66,337 65,879 65,313 67,674 2010's 68,163 67,696 67,252 67,136 67,806 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  5. Alabama Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Alabama Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,313 2,293 2,380 1990's 2,431 2,523 2,509 2,458 2,477 2,491 2,512 2,496 2,464 2,620 2000's 2,792 2,781 2,730 2,743 2,799 2,787 2,735 2,704 2,757 3,057 2010's 3,039 2,988 3,045 3,143 3,244 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  6. Alabama Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Alabama Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 656 662,217 668,432 683,528 1990's 686,149 700,195 711,043 730,114 744,394 751,890 766,322 781,711 788,464 775,311 2000's 805,689 807,770 806,389 809,754 806,660 809,454 808,801 796,476 792,236 785,005 2010's 778,985 772,892 767,396 765,957 769,418 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Alaska Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Alaska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 11,484 11,649 11,806 1990's 11,921 12,071 12,204 12,359 12,475 12,584 12,732 12,945 13,176 13,409 2000's 13,711 14,002 14,342 14,502 13,999 14,120 14,384 13,408 12,764 13,215 2010's 12,998 13,027 13,133 13,246 13,399 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  8. Alaska Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Alaska Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 66 67,648 68,612 69,540 1990's 70,808 72,565 74,268 75,842 77,670 79,474 81,348 83,596 86,243 88,924 2000's 91,297 93,896 97,077 100,404 104,360 108,401 112,269 115,500 119,039 120,124 2010's 121,166 121,736 122,983 124,411 126,416 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  9. Arizona Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Arizona Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 46 46,702 46,636 46,776 1990's 47,292 53,982 47,781 47,678 48,568 49,145 49,693 50,115 51,712 53,022 2000's 54,056 54,724 56,260 56,082 56,186 56,572 57,091 57,169 57,586 57,191 2010's 56,676 56,547 56,532 56,585 56,649 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  10. Arizona Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Arizona Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 545 567,962 564,195 572,461 1990's 586,866 642,659 604,899 610,337 635,335 661,192 689,597 724,911 764,167 802,469 2000's 846,016 884,789 925,927 957,442 993,885 1,042,662 1,088,574 1,119,266 1,128,264 1,130,047 2010's 1,138,448 1,146,286 1,157,688 1,172,003 1,186,794 - = No Data Reported; -- = Not

  11. Arkansas Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Arkansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60 60,355 61,630 61,848 1990's 61,530 61,731 62,221 62,952 63,821 65,490 67,293 68,413 69,974 71,389 2000's 72,933 71,875 71,530 71,016 70,655 69,990 69,475 69,495 69,144 69,043 2010's 67,987 67,815 68,765 68,791 69,011 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  12. Arkansas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Arkansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 1,410 1,151 1,412 1990's 1,396 1,367 1,319 1,364 1,417 1,366 1,488 1,336 1,300 1,393 2000's 1,414 1,122 1,407 1,269 1,223 1,120 1,120 1,055 1,104 1,025 2010's 1,079 1,133 990 1,020 1,009 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  13. Arkansas Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Arkansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 475 480,839 485,112 491,110 1990's 488,850 495,148 504,722 513,466 521,176 531,182 539,952 544,460 550,017 554,121 2000's 560,055 552,716 553,192 553,211 554,844 555,861 555,905 557,966 556,746 557,355 2010's 549,970 551,795 549,959 549,764 549,034 - = No Data Reported; -- = Not Applicable; NA =

  14. Massachusetts Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Massachusetts Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 84,636 93,005 92,252 1990's 85,775 88,746 85,873 102,187 92,744 104,453 105,889 107,926 108,832 113,177 2000's 117,993 120,984 122,447 123,006 125,107 120,167 126,713 128,965 242,693 153,826 2010's 144,487 138,225 142,825 144,246 139,556 - = No Data Reported; -- = Not Applicable;

  15. Massachusetts Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Massachusetts Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,626 7,199 13,057 1990's 6,539 5,006 8,723 7,283 8,019 10,447 10,952 11,058 11,245 8,027 2000's 8,794 9,750 9,090 11,272 10,949 12,019 12,456 12,678 36,928 19,208 2010's 12,751 10,721 10,840 11,063 10,946 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  16. Massachusetts Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Massachusetts Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,082,777 1,100,635 1,114,920 1990's 1,118,429 1,127,536 1,137,911 1,155,443 1,179,869 1,180,860 1,188,317 1,204,494 1,212,486 1,232,887 2000's 1,278,781 1,283,008 1,295,952 1,324,715 1,306,142 1,297,508 1,348,848 1,361,470 1,236,480 1,370,353 2010's 1,389,592 1,408,314 1,447,947

  17. Michigan Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Michigan Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 178,469 185,961 191,474 1990's 195,766 198,890 201,561 204,453 207,629 211,817 214,843 222,726 224,506 227,159 2000's 230,558 225,109 247,818 246,123 246,991 253,415 254,923 253,139 252,382 252,017 2010's 249,309 249,456 249,994 250,994 253,127 - = No Data Reported; -- = Not Applicable; NA = Not

  18. Michigan Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Michigan Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10,885 11,117 11,452 1990's 11,500 11,446 11,460 11,425 11,308 11,454 11,848 12,233 11,888 14,527 2000's 11,384 11,210 10,468 10,378 10,088 10,049 9,885 9,728 10,563 18,186 2010's 9,332 9,088 8,833 8,497 8,156 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. Michigan Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Michigan Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,452,554 2,491,149 2,531,304 1990's 2,573,570 2,609,561 2,640,579 2,677,085 2,717,683 2,767,190 2,812,876 2,859,483 2,903,698 2,949,628 2000's 2,999,737 3,011,205 3,110,743 3,140,021 3,161,370 3,187,583 3,193,920 3,188,152 3,172,623 3,169,026 2010's 3,152,468 3,153,895 3,161,033 3,180,349

  20. Minnesota Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Minnesota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 88,789 90,256 92,916 1990's 95,474 97,388 99,707 93,062 102,857 103,874 105,531 108,686 110,986 114,127 2000's 116,529 119,007 121,751 123,123 125,133 126,310 129,149 128,367 130,847 131,801 2010's 132,163 132,938 134,394 135,557 136,382 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  1. Minnesota Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Minnesota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,585 2,670 2,638 1990's 2,574 2,486 2,515 2,477 2,592 2,531 2,564 2,233 2,188 2,267 2000's 2,025 1,996 2,029 2,074 2,040 1,432 1,257 1,146 1,131 2,039 2010's 2,106 1,770 1,793 1,870 1,878 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  2. Minnesota Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Minnesota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 872,148 894,380 911,001 1990's 946,107 970,941 998,201 1,074,631 1,049,263 1,080,009 1,103,709 1,134,019 1,161,423 1,190,190 2000's 1,222,397 1,249,748 1,282,751 1,308,143 1,338,061 1,364,237 1,401,362 1,401,623 1,413,162 1,423,703 2010's 1,429,681 1,436,063 1,445,824 1,459,134 1,472,663 - = No

  3. Mississippi Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Mississippi Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 43,362 44,170 44,253 1990's 43,184 43,693 44,313 45,310 43,803 45,444 46,029 47,311 45,345 47,620 2000's 50,913 51,109 50,468 50,928 54,027 54,936 55,741 56,155 55,291 50,713 2010's 50,537 50,636 50,689 50,153 50,238 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  4. Mississippi Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Mississippi Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,312 1,263 1,282 1990's 1,317 1,314 1,327 1,324 1,313 1,298 1,241 1,199 1,165 1,246 2000's 1,199 1,214 1,083 1,161 996 1,205 1,181 1,346 1,132 1,141 2010's 980 982 936 933 943 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  5. Mississippi Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Mississippi Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 370,094 372,238 376,353 1990's 382,251 386,264 392,155 398,472 405,312 415,123 418,442 423,397 415,673 426,352 2000's 434,501 438,069 435,146 438,861 445,212 445,856 437,669 445,043 443,025 437,715 2010's 436,840 442,479 442,840 445,589 444,423 - = No Data Reported; -- = Not

  6. Missouri Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Missouri Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 96,711 97,939 99,721 1990's 105,164 117,675 125,174 125,571 132,378 130,318 133,445 135,553 135,417 133,464 2000's 133,969 135,968 137,924 140,057 141,258 142,148 143,632 142,965 141,529 140,633 2010's 138,670 138,214 144,906 142,495 143,024 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Missouri Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Missouri Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,832 2,880 3,063 1990's 3,140 3,096 2,989 3,040 3,115 3,033 3,408 3,097 3,151 3,152 2000's 3,094 3,085 2,935 3,115 3,600 3,545 3,548 3,511 3,514 3,573 2010's 3,541 3,307 3,692 3,538 3,497 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  8. Missouri Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Missouri Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,180,546 1,194,985 1,208,523 1990's 1,213,305 1,211,342 1,220,203 1,225,921 1,281,007 1,259,102 1,275,465 1,293,032 1,307,563 1,311,865 2000's 1,324,282 1,326,160 1,340,726 1,343,614 1,346,773 1,348,743 1,353,892 1,354,173 1,352,015 1,348,781 2010's 1,348,549 1,342,920 1,389,910 1,357,740

  9. Montana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Montana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 21,382 22,246 22,219 1990's 23,331 23,185 23,610 24,373 25,349 26,329 26,374 27,457 28,065 28,424 2000's 29,215 29,429 30,250 30,814 31,357 31,304 31,817 32,472 33,008 33,731 2010's 34,002 34,305 34,504 34,909 35,205 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  10. Montana Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Montana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 167,883 171,785 171,156 1990's 174,384 177,726 182,641 188,879 194,357 203,435 205,199 209,806 218,851 222,114 2000's 224,784 226,171 229,015 232,839 236,511 240,554 245,883 247,035 253,122 255,472 2010's 257,322 259,046 259,957 262,122 265,849 - = No Data Reported; -- = Not Applicable; NA = Not

  11. Nebraska Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Nebraska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60,707 61,365 60,377 1990's 60,405 60,947 61,319 60,599 62,045 61,275 61,117 51,661 63,819 53,943 2000's 55,194 55,692 56,560 55,999 57,087 57,389 56,548 55,761 58,160 56,454 2010's 56,246 56,553 56,608 58,005 57,191 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  12. Nebraska Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Nebraska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 675 684 702 1990's 712 718 696 718 766 2,432 2,234 11,553 10,673 10,342 2000's 10,161 10,504 9,156 9,022 8,463 7,973 7,697 7,668 11,627 7,863 2010's 7,912 7,955 8,160 8,495 8,791 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  13. Nevada Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Nevada Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 18,294 18,921 19,924 1990's 20,694 22,124 22,799 23,207 24,521 25,593 26,613 27,629 29,030 30,521 2000's 31,789 32,782 33,877 34,590 35,792 37,093 38,546 40,128 41,098 41,303 2010's 40,801 40,944 41,192 41,710 42,338 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  14. Nevada Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Nevada Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,422 219,981 236,237 1990's 256,119 283,307 295,714 305,099 336,353 364,112 393,783 426,221 458,737 490,029 2000's 520,233 550,850 580,319 610,756 648,551 688,058 726,772 750,570 758,315 760,391 2010's 764,435 772,880 782,759 794,150 808,970 - = No Data Reported; -- = Not Applicable; NA = Not

  15. New Hampshire Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) New Hampshire Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 8,831 9,159 10,237 1990's 10,521 11,088 11,383 11,726 12,240 12,450 12,755 13,225 13,512 13,932 2000's 14,219 15,068 15,130 15,047 15,429 16,266 16,139 16,150 41,332 16,937 2010's 16,645 17,186 17,758 17,298 17,421 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  16. New Hampshire Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) New Hampshire Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60,078 61,969 64,059 1990's 65,310 67,991 69,356 70,938 72,656 74,232 75,175 77,092 78,786 80,958 2000's 82,813 84,760 87,147 88,170 88,600 94,473 94,600 94,963 67,945 96,924 2010's 95,361 97,400 99,738 98,715 99,146 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  17. North Carolina Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) North Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,236 3,196 3,381 1990's 2,802 3,506 3,119 2,664 3,401 3,652 3,973 5,375 6,228 5,672 2000's 5,288 2,962 3,200 3,101 3,021 2,891 2,701 2,991 2,984 2,384 2010's 2,457 2,468 2,525 2,567 2,596 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. North Carolina Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) North Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 435,826 472,928 492,821 1990's 520,140 539,321 575,096 607,388 652,307 678,147 699,159 740,013 777,805 815,908 2000's 858,004 891,227 905,816 953,732 948,283 992,906 1,022,430 1,063,871 1,095,362 1,102,001 2010's 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 - = No Data

  19. North Dakota Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) North Dakota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11,905 12,104 12,454 1990's 12,742 12,082 12,353 12,650 12,944 13,399 13,789 14,099 14,422 15,050 2000's 15,531 15,740 16,093 16,202 16,443 16,518 16,848 17,013 17,284 17,632 2010's 17,823 18,421 19,089 19,855 20,687 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  20. North Dakota Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) North Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 83,517 84,059 84,643 1990's 85,646 87,880 89,522 91,237 93,398 95,818 97,761 98,326 101,930 104,051 2000's 105,660 106,758 108,716 110,048 112,206 114,152 116,615 118,100 120,056 122,065 2010's 123,585 125,392 130,044 133,975 137,972 - = No Data Reported; -- = Not Applicable; NA =

  1. Ohio Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Ohio Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,601 219,257 225,347 1990's 233,075 236,519 237,861 240,684 245,190 250,223 259,663 254,991 258,076 266,102 2000's 269,561 269,327 271,160 271,203 272,445 277,767 270,552 272,555 272,899 270,596 2010's 268,346 268,647 267,793 269,081 269,758 - = No Data Reported; -- = Not Applicable; NA = Not

  2. Ohio Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Ohio Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,929 8,163 8,356 1990's 8,301 8,479 8,573 8,678 8,655 8,650 8,672 7,779 8,112 8,136 2000's 8,267 8,515 8,111 8,098 7,899 8,328 6,929 6,858 6,806 6,712 2010's 6,571 6,482 6,381 6,554 6,526 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  3. Ohio Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Ohio Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,648,972 2,678,838 2,714,839 1990's 2,766,912 2,801,716 2,826,713 2,867,959 2,921,536 2,967,375 2,994,891 3,041,948 3,050,960 3,111,108 2000's 3,178,840 3,195,584 3,208,466 3,225,908 3,250,068 3,272,307 3,263,062 3,273,791 3,262,716 3,253,184 2010's 3,240,619 3,236,160 3,244,274 3,271,074 3,283,869 -

  4. Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 87,824 86,666 86,172 1990's 85,790 86,744 87,120 88,181 87,494 88,358 89,852 90,284 89,711 80,986 2000's 80,558 79,045 80,029 79,733 79,512 78,726 78,745 93,991 94,247 94,314 2010's 92,430 93,903 94,537 95,385 96,004 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,772 2,689 2,877 1990's 2,889 2,840 2,859 2,912 2,853 2,845 2,843 2,531 3,295 3,040 2000's 2,821 3,403 3,438 3,367 3,283 2,855 2,811 2,822 2,920 2,618 2010's 2,731 2,733 2,872 2,958 3,063 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  6. Oklahoma Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Oklahoma Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 809,171 805,107 806,875 1990's 814,296 824,172 832,677 842,130 845,448 856,604 866,531 872,454 877,236 867,922 2000's 859,951 868,314 875,338 876,420 875,271 880,403 879,589 920,616 923,650 924,745 2010's 914,869 922,240 927,346 931,981 937,237 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Oregon Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Oregon Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 40,967 41,998 43,997 1990's 47,175 55,374 50,251 51,910 53,700 55,409 57,613 60,419 63,085 65,034 2000's 66,893 68,098 69,150 74,515 71,762 73,520 74,683 80,998 76,868 76,893 2010's 77,370 77,822 78,237 79,276 80,480 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  8. Oregon Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Oregon Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 676 1,034 738 1990's 699 787 740 696 765 791 799 704 695 718 2000's 717 821 842 926 907 1,118 1,060 1,136 1,075 1,051 2010's 1,053 1,066 1,076 1,085 1,099 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  9. Oregon Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Oregon Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 280,670 288,066 302,156 1990's 326,177 376,166 354,256 371,151 391,845 411,465 433,638 456,960 477,796 502,000 2000's 523,952 542,799 563,744 625,398 595,495 626,685 647,635 664,455 674,421 675,582 2010's 682,737 688,681 693,507 700,211 707,010 - = No Data Reported; -- = Not Applicable; NA = Not

  10. Pennsylvania Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 166,901 172,615 178,545 1990's 186,772 191,103 193,863 198,299 206,812 209,245 214,340 215,057 216,519 223,732 2000's 228,037 225,911 226,957 227,708 231,051 233,132 231,540 234,597 233,462 233,334 2010's 233,751 233,588 235,049 237,922 239,681 - = No Data Reported; -- = Not

  11. Pennsylvania Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,089 6,070 6,023 1990's 6,238 6,344 6,496 6,407 6,388 6,328 6,441 6,492 6,736 7,080 2000's 6,330 6,159 5,880 5,577 5,726 5,577 5,241 4,868 4,772 4,745 2010's 4,624 5,007 5,066 5,024 5,084 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  12. Pennsylvania Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Pennsylvania Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,237,877 2,271,801 2,291,242 1990's 2,311,795 2,333,377 2,363,575 2,386,249 2,393,053 2,413,715 2,431,909 2,452,524 2,493,639 2,486,704 2000's 2,519,794 2,542,724 2,559,024 2,572,584 2,591,458 2,600,574 2,605,782 2,620,755 2,631,340 2,635,886 2010's 2,646,211 2,667,392 2,678,547

  13. Rhode Island Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Rhode Island Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,128 16,096 16,924 1990's 17,765 18,430 18,607 21,178 21,208 21,472 21,664 21,862 22,136 22,254 2000's 22,592 22,815 23,364 23,270 22,994 23,082 23,150 23,007 23,010 22,988 2010's 23,049 23,177 23,359 23,742 23,934 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  14. Rhode Island Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Rhode Island Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 180,656 185,861 190,796 1990's 195,100 196,438 197,926 198,563 200,959 202,947 204,259 212,777 208,208 211,097 2000's 214,474 216,781 219,769 221,141 223,669 224,320 225,027 223,589 224,103 224,846 2010's 225,204 225,828 228,487 231,763 233,786 - = No Data Reported; -- = Not

  15. South Carolina Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) South Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 35,414 37,075 38,856 1990's 39,904 39,999 40,968 42,191 45,487 47,293 48,650 50,817 52,237 53,436 2000's 54,794 55,257 55,608 55,909 56,049 56,974 57,452 57,544 56,317 55,850 2010's 55,853 55,846 55,908 55,997 56,172 - = No Data Reported; -- = Not Applicable; NA = Not Available; W

  16. South Carolina Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) South Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,256 1,273 1,307 1990's 1,384 1,400 1,568 1,625 1,928 1,802 1,759 1,764 1,728 1,768 2000's 1,715 1,702 1,563 1,574 1,528 1,535 1,528 1,472 1,426 1,358 2010's 1,325 1,329 1,435 1,452 1,426 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  17. South Carolina Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) South Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 302,321 313,831 327,527 1990's 339,486 344,763 357,818 370,411 416,773 412,259 426,088 443,093 460,141 473,799 2000's 489,340 501,161 508,686 516,362 527,008 541,523 554,953 570,213 561,196 565,774 2010's 570,797 576,594 583,633 593,286 604,743 - = No Data Reported; -- = Not

  18. South Dakota Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) South Dakota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,480 12,438 12,771 1990's 13,443 13,692 14,133 16,523 15,539 16,285 16,880 17,432 17,972 18,453 2000's 19,100 19,378 19,794 20,070 20,457 20,771 21,149 21,502 21,819 22,071 2010's 22,267 22,570 22,955 23,214 23,591 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  19. South Dakota Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) South Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 101,468 102,084 103,538 1990's 105,436 107,846 110,291 128,029 119,544 124,152 127,269 130,307 133,095 136,789 2000's 142,075 144,310 147,356 150,725 148,105 157,457 160,481 163,458 165,694 168,096 2010's 169,838 170,877 173,856 176,204 179,042 - = No Data Reported; -- = Not

  20. Tennessee Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Tennessee Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 77,104 81,159 84,040 1990's 88,753 89,863 91,999 94,860 97,943 101,561 103,867 105,925 109,772 112,978 2000's 115,691 118,561 120,130 131,916 125,042 124,755 126,970 126,324 128,007 127,704 2010's 127,914 128,969 130,139 131,091 131,001 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  1. Tennessee Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Tennessee Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,206 2,151 2,555 1990's 2,361 2,369 2,425 2,512 2,440 2,393 2,306 2,382 5,149 2,159 2000's 2,386 2,704 2,657 2,755 2,738 2,498 2,545 2,656 2,650 2,717 2010's 2,702 2,729 2,679 2,581 2,595 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  2. Tennessee Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Tennessee Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 534,882 565,856 599,042 1990's 627,031 661,105 696,140 733,363 768,421 804,724 841,232 867,793 905,757 937,896 2000's 969,537 993,363 1,009,225 1,022,628 1,037,429 1,049,307 1,063,328 1,071,756 1,084,102 1,083,573 2010's 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 - = No Data Reported; -- =

  3. Texas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Texas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,852 4,427 13,383 1990's 13,659 13,770 5,481 5,823 5,222 9,043 8,796 5,339 5,318 5,655 2000's 11,613 10,047 9,143 9,015 9,359 9,136 8,664 11,063 5,568 8,581 2010's 8,779 8,713 8,953 8,525 8,406 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  4. Utah Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Utah Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31,329 32,637 32,966 1990's 34,697 35,627 36,145 37,816 39,183 40,101 40,107 40,689 42,054 43,861 2000's 47,201 47,477 50,202 51,063 51,503 55,174 55,821 57,741 59,502 60,781 2010's 61,976 62,885 63,383 64,114 65,134 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. Utah Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Utah Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 414,020 418,569 432,377 1990's 453,023 455,649 467,664 484,438 503,583 523,622 562,343 567,786 588,364 609,603 2000's 641,111 657,728 660,677 678,833 701,255 743,761 754,554 778,644 794,880 810,442 2010's 821,525 830,219 840,687 854,389 869,052 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Vermont Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Vermont Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,447 2,698 2,768 1990's 2,949 3,154 3,198 3,314 3,512 3,649 3,790 3,928 4,034 4,219 2000's 4,316 4,416 4,516 4,602 4,684 4,781 4,861 4,925 4,980 5,085 2010's 5,137 5,256 5,535 5,441 5,589 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  7. Vermont Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Vermont Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,553 16,616 16,920 1990's 18,300 19,879 20,468 21,553 22,546 23,523 24,383 25,539 26,664 27,931 2000's 28,532 29,463 30,108 30,856 31,971 33,015 34,081 34,937 35,929 37,242 2010's 38,047 38,839 39,917 41,152 42,231 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  8. Virginia Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 54,071 54,892 61,012 1990's 63,751 67,997 69,629 70,161 72,188 74,690 77,284 78,986 77,220 80,500 2000's 84,646 84,839 86,328 87,202 87,919 90,577 91,481 93,015 94,219 95,704 2010's 95,401 96,086 96,503 97,499 98,741 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  9. Virginia Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 877 895 895 1990's 929 1,156 1,101 2,706 2,740 2,812 2,822 2,391 2,469 2,984 2000's 1,749 1,261 1,526 1,517 1,217 1,402 1,256 1,271 1,205 1,126 2010's 1,059 1,103 1,132 1,132 1,123 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  10. Virginia Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 550,318 573,731 601,906 1990's 622,883 651,203 664,500 690,061 721,495 753,003 789,985 812,866 847,938 893,887 2000's 907,855 941,582 982,521 996,564 1,029,389 1,066,302 1,085,509 1,101,863 1,113,016 1,124,717 2010's 1,133,103 1,145,049 1,155,636 1,170,161 1,183,894 - = No Data Reported; -- = Not

  11. Washington Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Washington Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 51,365 56,487 55,231 1990's 58,148 60,887 63,391 65,810 68,118 70,781 73,708 75,550 77,770 80,995 2000's 83,189 84,628 85,286 87,082 93,559 92,417 93,628 95,615 97,799 98,965 2010's 99,231 99,674 100,038 100,939 101,730 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  12. Washington Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Washington Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,355 3,564 3,365 1990's 3,428 3,495 3,490 3,448 3,586 3,544 3,587 3,748 3,848 4,040 2000's 4,007 3,898 3,928 3,775 3,992 3,489 3,428 3,630 3,483 3,428 2010's 3,372 3,353 3,338 3,320 3,355 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  13. Washington Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Washington Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 392,469 413,008 425,624 1990's 458,013 492,189 528,913 565,475 604,315 638,603 673,357 702,701 737,208 779,104 2000's 813,319 841,617 861,943 895,800 926,510 966,199 997,728 1,025,171 1,047,319 1,059,239 2010's 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 - = No Data Reported; -- = Not

  14. California Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) California Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 413 404,507 407,435 410,231 1990's 415,073 421,278 412,467 411,648 411,140 411,535 408,294 406,803 588,224 416,791 2000's 413,003 416,036 420,690 431,795 432,367 434,899 442,052 446,267 447,160 441,806 2010's 439,572 440,990 442,708 444,342 443,115 - = No Data Reported; -- = Not Applicable; NA =

  15. California Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) California Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31 44,764 44,680 46,243 1990's 46,048 44,865 40,528 42,748 38,750 38,457 36,613 35,830 36,235 36,435 2000's 35,391 34,893 33,725 34,617 41,487 40,226 38,637 39,134 39,591 38,746 2010's 38,006 37,575 37,686 37,996 37,548 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  16. California Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) California Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,626 7,904,858 8,113,034 8,313,776 1990's 8,497,848 8,634,774 8,680,613 8,726,187 8,790,733 8,865,541 8,969,308 9,060,473 9,181,928 9,331,206 2000's 9,370,797 9,603,122 9,726,642 9,803,311 9,957,412 10,124,433 10,329,224 10,439,220 10,515,162 10,510,950 2010's 10,542,584 10,625,190 10,681,916

  17. Colorado Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Colorado Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 108 109,770 110,769 112,004 1990's 112,661 113,945 114,898 115,924 115,994 118,502 121,221 123,580 125,178 129,041 2000's 131,613 134,393 136,489 138,621 138,543 137,513 139,746 141,420 144,719 145,624 2010's 145,460 145,837 145,960 150,145 150,235 - = No Data Reported; -- = Not Applicable; NA = Not

  18. Colorado Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Colorado Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 896 923 976 1990's 1,018 1,074 1,108 1,032 1,176 1,528 2,099 2,923 3,349 4,727 2000's 4,994 4,729 4,337 4,054 4,175 4,318 4,472 4,592 4,816 5,084 2010's 6,232 6,529 6,906 7,293 7,823 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  19. Colorado Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Colorado Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 925 942,571 955,810 970,512 1990's 983,592 1,002,154 1,022,542 1,044,699 1,073,308 1,108,899 1,147,743 1,183,978 1,223,433 1,265,032 2000's 1,315,619 1,365,413 1,412,923 1,453,974 1,496,876 1,524,813 1,558,911 1,583,945 1,606,602 1,622,434 2010's 1,634,587 1,645,716 1,659,808 1,672,312 1,690,581 -

  20. Connecticut Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Connecticut Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,709 2,818 2,908 1990's 3,061 2,921 2,923 2,952 3,754 3,705 3,435 3,459 3,441 3,465 2000's 3,683 3,881 3,716 3,625 3,470 3,437 3,393 3,317 3,196 3,138 2010's 3,063 3,062 3,148 4,454 4,217 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  1. Delaware Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Delaware Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6 6,180 6,566 7,074 1990's 7,485 7,895 8,173 8,409 8,721 9,133 9,518 9,807 10,081 10,441 2000's 9,639 11,075 11,463 11,682 11,921 12,070 12,345 12,576 12,703 12,839 2010's 12,861 12,931 12,997 13,163 13,352 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  2. Delaware Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Delaware Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 81 82,829 84,328 86,428 1990's 88,894 91,467 94,027 96,914 100,431 103,531 106,548 109,400 112,507 115,961 2000's 117,845 122,829 126,418 129,870 133,197 137,115 141,276 145,010 147,541 149,006 2010's 150,458 152,005 153,307 155,627 158,502 - = No Data Reported; -- = Not Applicable; NA = Not

  3. Florida Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Florida Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 41 42,376 43,178 43,802 1990's 43,674 45,012 45,123 47,344 47,851 46,459 47,578 48,251 46,778 50,052 2000's 50,888 53,118 53,794 55,121 55,324 55,479 55,259 57,320 58,125 59,549 2010's 60,854 61,582 63,477 64,772 67,460 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  4. Florida Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Florida Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 442 444,848 446,690 452,544 1990's 457,648 467,221 471,863 484,816 497,777 512,365 521,674 532,790 542,770 556,628 2000's 571,972 590,221 603,690 617,373 639,014 656,069 673,122 682,996 679,265 674,090 2010's 675,551 679,199 686,994 694,210 703,535 - = No Data Reported; -- = Not Applicable; NA = Not

  5. Georgia Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Georgia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 94 98,809 102,277 106,690 1990's 108,295 109,659 111,423 114,889 117,980 120,122 123,200 123,367 126,050 225,020 2000's 128,275 130,373 128,233 129,867 128,923 128,389 127,843 127,832 126,804 127,347 2010's 124,759 123,454 121,243 126,060 122,573 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Georgia Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Georgia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 3,034 3,144 3,079 1990's 3,153 3,124 3,186 3,302 3,277 3,261 3,310 3,310 3,262 5,580 2000's 3,294 3,330 3,219 3,326 3,161 3,543 3,053 2,913 2,890 2,254 2010's 2,174 2,184 2,112 2,242 2,481 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  7. Georgia Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Georgia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,190 1,237,201 1,275,128 1,308,972 1990's 1,334,935 1,363,723 1,396,860 1,430,626 1,460,141 1,495,992 1,538,458 1,553,948 1,659,730 1,732,865 2000's 1,680,749 1,737,850 1,735,063 1,747,017 1,752,346 1,773,121 1,726,239 1,793,650 1,791,256 1,744,934 2010's 1,740,587 1,740,006 1,739,543 1,805,425

  8. Hawaii Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Hawaii Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,896 2,852 2,842 1990's 2,837 2,786 2,793 3,222 2,805 2,825 2,823 2,783 2,761 2,763 2000's 2,768 2,777 2,781 2,804 2,578 2,572 2,548 2,547 2,540 2,535 2010's 2,551 2,560 2,545 2,627 2,789 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  9. Hawaii Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Hawaii Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 28,502 28,761 28,970 1990's 29,137 29,701 29,805 29,984 30,614 30,492 31,017 30,990 30,918 30,708 2000's 30,751 30,794 30,731 30,473 26,255 26,219 25,982 25,899 25,632 25,466 2010's 25,389 25,305 25,184 26,374 28,919 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  10. Idaho Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Idaho Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 17,482 18,454 18,813 1990's 19,452 20,328 21,145 21,989 22,999 24,150 25,271 26,436 27,697 28,923 2000's 30,018 30,789 31,547 32,274 33,104 33,362 33,625 33,767 37,320 38,245 2010's 38,506 38,912 39,202 39,722 40,229 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  11. Idaho Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Idaho Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 104,824 111,532 113,898 1990's 113,954 126,282 136,121 148,582 162,971 175,320 187,756 200,165 213,786 227,807 2000's 240,399 251,004 261,219 274,481 288,380 301,357 316,915 323,114 336,191 342,277 2010's 346,602 350,871 353,963 359,889 367,394 - = No Data Reported; -- = Not Applicable; NA = Not

  12. Illinois Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Illinois Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241,367 278,473 252,791 1990's 257,851 261,107 263,988 268,104 262,308 264,756 265,007 268,841 271,585 274,919 2000's 279,179 278,506 279,838 281,877 273,967 276,763 300,606 296,465 298,418 294,226 2010's 291,395 293,213 297,523 282,743 294,391 - = No Data Reported; -- = Not Applicable; NA = Not

  13. Illinois Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Illinois Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 19,460 20,015 25,161 1990's 25,991 26,489 27,178 27,807 25,788 25,929 29,493 28,472 28,063 27,605 2000's 27,348 27,421 27,477 26,698 29,187 29,887 26,109 24,000 23,737 23,857 2010's 25,043 23,722 23,390 23,804 23,829 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  14. Illinois Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Illinois Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,170,364 3,180,199 3,248,117 1990's 3,287,091 3,320,285 3,354,679 3,388,983 3,418,052 3,452,975 3,494,545 3,521,707 3,556,736 3,594,071 2000's 3,631,762 3,670,693 3,688,281 3,702,308 3,754,132 3,975,961 3,812,121 3,845,441 3,869,308 3,839,438 2010's 3,842,206 3,855,942 3,878,806 3,838,120

  15. Indiana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Indiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 116,571 119,458 122,803 1990's 124,919 128,223 129,973 131,925 134,336 137,162 139,097 140,515 141,307 145,631 2000's 148,411 148,830 150,092 151,586 151,943 159,649 154,322 155,885 157,223 155,615 2010's 156,557 161,293 158,213 158,965 159,596 - = No Data Reported; -- = Not Applicable; NA = Not

  16. Indiana Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Indiana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,250,476 1,275,401 1,306,747 1990's 1,327,772 1,358,640 1,377,023 1,402,770 1,438,483 1,463,640 1,489,647 1,509,142 1,531,914 1,570,253 2000's 1,604,456 1,613,373 1,657,640 1,644,715 1,588,738 1,707,195 1,661,186 1,677,857 1,678,158 1,662,663 2010's 1,669,026 1,707,148 1,673,132 1,681,841 1,693,267

  17. Iowa Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Iowa Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 80,797 81,294 82,549 1990's 83,047 84,387 85,325 86,452 86,918 88,585 89,663 90,643 91,300 92,306 2000's 93,836 95,485 96,496 96,712 97,274 97,767 97,823 97,979 98,144 98,416 2010's 98,396 98,541 99,113 99,017 99,182 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. Iowa Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Iowa Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,033 1,937 1,895 1990's 1,883 1,866 1,835 1,903 1,957 1,957 2,066 1,839 1,862 1,797 2000's 1,831 1,830 1,855 1,791 1,746 1,744 1,670 1,651 1,652 1,626 2010's 1,528 1,465 1,469 1,491 1,572 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  19. Iowa Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Iowa Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 690,532 689,655 701,687 1990's 706,842 716,088 729,081 740,722 750,678 760,848 771,109 780,746 790,162 799,015 2000's 812,323 818,313 824,218 832,230 839,415 850,095 858,915 865,553 872,980 875,781 2010's 879,713 883,733 892,123 895,414 900,420 - = No Data Reported; -- = Not Applicable; NA = Not

  20. Kansas Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Kansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 82,934 83,810 85,143 1990's 85,539 86,874 86,840 87,735 86,457 88,163 89,168 85,018 89,654 86,003 2000's 87,007 86,592 87,397 88,030 86,640 85,634 85,686 85,376 84,703 84,715 2010's 84,446 84,874 84,673 84,969 85,867 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  1. Kansas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Kansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,440 4,314 4,366 1990's 4,357 3,445 3,296 4,369 3,560 3,079 2,988 7,014 10,706 5,861 2000's 8,833 9,341 9,891 9,295 8,955 8,300 8,152 8,327 8,098 7,793 2010's 7,664 7,954 7,970 7,877 7,429 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  2. Kansas Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Kansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 725,676 733,101 731,792 1990's 747,081 753,839 762,545 777,658 773,357 797,524 804,213 811,975 841,843 824,803 2000's 833,662 836,486 843,353 850,464 855,272 856,761 862,203 858,304 853,125 855,454 2010's 853,842 854,730 854,800 858,572 861,092 - = No Data Reported; -- = Not Applicable; NA = Not

  3. Kentucky Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Kentucky Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 63,024 63,971 65,041 1990's 67,086 68,461 69,466 71,998 73,562 74,521 76,079 77,693 80,147 80,283 2000's 81,588 81,795 82,757 84,110 84,493 85,243 85,236 85,210 84,985 83,862 2010's 84,707 84,977 85,129 85,999 85,318 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  4. Kentucky Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Kentucky Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,391 1,436 1,443 1990's 1,544 1,587 1,608 1,585 1,621 1,630 1,633 1,698 1,864 1,813 2000's 1,801 1,701 1,785 1,695 1,672 1,698 1,658 1,599 1,585 1,715 2010's 1,742 1,705 1,720 1,767 1,780 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  5. Kentucky Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Kentucky Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 596,320 606,106 614,058 1990's 624,477 633,942 644,281 654,664 668,774 685,481 696,989 713,509 726,960 735,371 2000's 744,816 749,106 756,234 763,290 767,022 770,080 770,171 771,047 753,531 754,761 2010's 758,129 759,584 757,790 761,575 760,131 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Louisiana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Louisiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 67,382 66,472 64,114 1990's 62,770 61,574 61,030 62,055 62,184 62,930 62,101 62,270 63,029 62,911 2000's 62,710 62,241 62,247 63,512 60,580 58,409 57,097 57,127 57,066 58,396 2010's 58,562 58,749 63,381 59,147 58,611 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  7. Louisiana Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Louisiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,617 1,503 1,531 1990's 1,504 1,469 1,452 1,592 1,737 1,383 1,444 1,406 1,380 1,397 2000's 1,318 1,440 1,357 1,291 1,460 1,086 962 945 988 954 2010's 942 920 963 916 883 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  8. Maine Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Maine Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,435 3,731 3,986 1990's 4,250 4,455 4,838 4,979 5,297 5,819 6,414 6,606 6,662 6,582 2000's 6,954 6,936 7,375 7,517 7,687 8,178 8,168 8,334 8,491 8,815 2010's 9,084 9,681 10,179 11,415 11,810 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  9. Maine Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Maine Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,134 11,933 11,902 1990's 12,000 12,424 13,766 13,880 14,104 14,917 14,982 15,221 15,646 15,247 2000's 17,111 17,302 17,921 18,385 18,707 18,633 18,824 18,921 19,571 20,806 2010's 21,142 22,461 23,555 24,765 27,047 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  10. Maryland Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Maryland Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 51,252 53,045 54,740 1990's 55,576 61,878 62,858 63,767 64,698 66,094 69,991 69,056 67,850 69,301 2000's 70,671 70,691 71,824 72,076 72,809 73,780 74,584 74,856 75,053 75,771 2010's 75,192 75,788 75,799 77,117 77,846 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  11. Maryland Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Maryland Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,222 5,397 5,570 1990's 5,646 520 514 496 516 481 430 479 1,472 536 2000's 329 795 1,434 1,361 1,354 1,325 1,340 1,333 1,225 1,234 2010's 1,255 1,226 1,163 1,173 1,179 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  12. Maryland Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Maryland Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 755,294 760,754 767,219 1990's 774,707 782,373 894,677 807,204 824,137 841,772 871,012 890,195 901,455 939,029 2000's 941,384 959,772 978,319 987,863 1,009,455 1,024,955 1,040,941 1,053,948 1,057,521 1,067,807 2010's 1,071,566 1,077,168 1,078,978 1,099,272 1,101,292 - = No Data Reported; -- = Not

  13. West Virginia Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) West Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31,283 33,192 33,880 1990's 32,785 32,755 33,289 33,611 33,756 36,144 33,837 33,970 35,362 35,483 2000's 41,949 35,607 35,016 35,160 34,932 36,635 34,748 34,161 34,275 34,044 2010's 34,063 34,041 34,078 34,283 34,339 - = No Data Reported; -- = Not Applicable; NA = Not Available; W

  14. West Virginia Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) West Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 351,024 349,765 349,347 1990's 349,673 350,489 352,463 352,997 352,929 353,629 358,049 362,432 359,783 362,292 2000's 360,471 363,126 361,171 359,919 358,027 374,301 353,292 347,433 347,368 343,837 2010's 344,131 342,069 340,256 340,102 338,652 - = No Data Reported; -- = Not

  15. Wisconsin Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Wisconsin Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 96,760 99,157 102,492 1990's 106,043 109,616 112,761 115,961 119,788 125,539 129,146 131,238 134,651 135,829 2000's 140,370 144,050 149,774 150,128 151,907 155,109 159,074 160,614 163,026 163,843 2010's 164,173 165,002 165,657 166,845 167,901 - = No Data Reported; -- = Not Applicable; NA = Not

  16. Wisconsin Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Wisconsin Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,411 7,218 7,307 1990's 7,154 7,194 7,396 7,979 7,342 6,454 5,861 8,346 9,158 9,756 2000's 9,630 9,864 9,648 10,138 10,190 8,484 5,707 5,999 5,969 6,396 2010's 6,413 6,376 6,581 6,677 7,000 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Wisconsin Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Wisconsin Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,054,347 1,072,585 1,097,514 1990's 1,123,557 1,151,939 1,182,834 1,220,500 1,253,333 1,291,424 1,324,570 1,361,348 1,390,068 1,426,909 2000's 1,458,959 1,484,536 1,514,700 1,541,455 1,569,719 1,592,621 1,611,772 1,632,200 1,646,644 1,656,614 2010's 1,663,583 1,671,834 1,681,001 1,692,891

  18. Wyoming Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Wyoming Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,342 15,093 14,012 1990's 13,767 14,931 15,064 15,315 15,348 15,580 17,036 15,907 16,171 16,317 2000's 16,366 16,027 16,170 17,164 17,490 17,904 18,016 18,062 19,286 19,843 2010's 19,977 20,146 20,387 20,617 20,894 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. Wyoming Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Wyoming Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 113,175 112,126 113,129 1990's 113,598 113,463 114,793 116,027 117,385 119,544 131,910 125,740 127,324 127,750 2000's 129,274 129,897 133,445 135,441 137,434 140,013 142,385 143,644 152,439 153,062 2010's 153,852 155,181 157,226 158,889 160,896 - = No Data Reported; -- = Not Applicable; NA = Not

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

    Open Energy Info (EERE)

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

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

    Open Energy Info (EERE)

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

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

    Open Energy Info (EERE)

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

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

    Broader source: Energy.gov [DOE]

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

  4. 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...

  5. 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...

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

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

    EIA-411 for 2005" ,"Released: September 26, 2007" ,"Next Update: October 2007" ,"Table 3d. April Monthly Peak Hour Demand, Actual and Projected by North American Electric...

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

    Open Energy Info (EERE)

    URI: cleanenergysolutions.orgcontentimpact-smart-grid-technologies-peak-l Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible...

  8. Webinar August 11: Analysis Using Fuel Cell MHE for Shaving Peak...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    on Material Handling Fuel Cells for Building Electric Peak Shaving Applications DOE Announces Webinars on Geography of Alternative Fuels, Wind Siting Considerations, and More...

  9. Insights from Smart Meters: The Potential for Peak Hour Savings from

    Office of Environmental Management (EM)

    Behavior-Based Programs | Department of Energy The Potential for Peak Hour Savings from Behavior-Based Programs Insights from Smart Meters: The Potential for Peak Hour Savings from Behavior-Based Programs This report focuses on one example of the value that analysis of this data can provide: insights into whether BB efficiency programs have the potential to provide peak-hour energy savings. This is important because there is increasing interest in using BB programs as a stand-alone peak

  10. Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography

    SciTech Connect (OSTI)

    Guiochon, Georges A; Shalliker, R. Andrew

    2010-01-01

    An algorithm was developed for 2DHPLC that automated the process of peak recognition, measuring their retention times, and then subsequently plotting the information in a two-dimensional retention plane. Following the recognition of peaks, the software then performed a series of statistical assessments of the separation performance, measuring for example, correlation between dimensions, peak capacity and the percentage of usage of the separation space. Peak recognition was achieved by interpreting the first and second derivatives of each respective one-dimensional chromatogram to determine the 1D retention times of each solute and then compiling these retention times for each respective fraction 'cut'. Due to the nature of comprehensive 2DHPLC adjacent cut fractions may contain peaks common to more than one cut fraction. The algorithm determined which components were common in adjacent cuts and subsequently calculated the peak maximum profile by interpolating the space between adjacent peaks. This algorithm was applied to the analysis of a two-dimensional separation of an apple flesh extract separated in a first dimension comprising a cyano stationary phase and an aqueous/THF mobile phase as the first dimension and a second dimension comprising C18-Hydro with an aqueous/MeOH mobile phase. A total of 187 peaks were detected.

  11. Program Design Analysis using BEopt Building Energy Optimization Software: Defining a Technology Pathway Leading to New Homes with Zero Peak Cooling Demand; Preprint

    SciTech Connect (OSTI)

    Anderson, R.; Christensen, C.; Horowitz, S.

    2006-08-01

    An optimization method based on the evaluation of a broad range of different combinations of specific energy efficiency and renewable-energy options is used to determine the least-cost pathway to the development of new homes with zero peak cooling demand. The optimization approach conducts a sequential search of a large number of possible option combinations and uses the most cost-effective alternatives to generate a least-cost curve to achieve home-performance levels ranging from a Title 24-compliant home to a home that uses zero net source energy on an annual basis. By evaluating peak cooling load reductions on the least-cost curve, it is then possible to determine the most cost-effective combination of energy efficiency and renewable-energy options that both maximize annual energy savings and minimize peak-cooling demand.

  12. Origin of the narrow, single peak in the fission-fragment mass distribution

    Office of Scientific and Technical Information (OSTI)

    for 258Fm (Journal Article) | SciTech Connect SciTech Connect Search Results Journal Article: Origin of the narrow, single peak in the fission-fragment mass distribution for 258Fm Citation Details In-Document Search Title: Origin of the narrow, single peak in the fission-fragment mass distribution for 258Fm We discuss the origin of the narrowness of the single peak at mass-symmetric division in the fragment mass-yield curve for spontaneous fission of {sup 258}Fm. For this purpose, we employ

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

    Office of Scientific and Technical Information (OSTI)

    (Dataset) | SciTech Connect Dataset: Stimulation at Desert Peak -modeling with the coupled THM code FEHM Citation Details In-Document Search Title: Stimulation at Desert Peak -modeling with the coupled THM code FEHM Numerical modeling of the 2011 shear stimulation at the Desert Peak well 27-15. This submission contains the FEHM executable code for a 64-bit PC Windows-7 machine, and the input and output files for the results presented in the included paper from ARMA-213 meeting. Authors:

  14. Building America Top Innovations 2012: High-Performance with Solar Electric Reduced Peak Demand

    SciTech Connect (OSTI)

    none,

    2013-01-01

    This Building America Top Innovations profile describes Building America solar home research that has demonstrated the ability to reduce peak demand by 75%. Numerous field studies have monitored power production and system effectiveness.

  15. Method and apparatus for clockless analog-to-digital conversion and peak detection

    DOE Patents [OSTI]

    DeGeronimo, Gianluigi

    2007-03-06

    An apparatus and method for analog-to-digital conversion and peak detection includes at least one stage, which includes a first switch, second switch, current source or capacitor, and discriminator. The discriminator changes state in response to a current or charge associated with the input signal exceeding a threshold, thereby indicating whether the current or charge associated with the input signal is greater than the threshold. The input signal includes a peak or a charge, and the converter includes a peak or charge detect mode in which a state of the switch is retained in response to a decrease in the current or charge associated with the input signal. The state of the switch represents at least a portion of a value of the peak or of the charge.

  16. Desert Peak to Humboldt House and Winnemucca, in: Lane, M.A....

    Open Energy Info (EERE)

    to Humboldt House and Winnemucca, in: Lane, M.A., (ed) Nevada geothermal areas: Desert Peak, Humboldt House, Beoware: Guidebook for field trip Jump to: navigation, search OpenEI...

  17. Webinar: Analysis Using Fuel Cell MHE for Shaving Peak Building Energy

    Broader source: Energy.gov [DOE]

    The Fuel Cell Technologies Office will present a live webinar entitled "Analysis Using Fuel Cell MHE for Shaving Peak Building Energy" on Tuesday, August 11, from 12 to 1 p.m. EDT.

  18. How are flat demand charges based on the highest peak over the...

    Open Energy Info (EERE)

    How are flat demand charges based on the highest peak over the past 12 months designated in the database (LADWP does this) Home > Groups > Utility Rate Submitted by Marcroper on 11...

  19. NREL's Energy-Saving Technology for Air Conditioning Cuts Peak Power Loads Without Using Harmful Refrigerants (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2012-07-01

    This fact sheet describes how the DEVAP air conditioner was invented, explains how the technology works, and why it won an R&D 100 Award. Desiccant-enhanced evaporative (DEVAP) air-conditioning will provide superior comfort for commercial buildings in any climate at a small fraction of the electricity costs of conventional air-conditioning equipment, releasing far less carbon dioxide and cutting costly peak electrical demand by an estimated 80%. Air conditioning currently consumes about 15% of the electricity generated in the United States and is a major contributor to peak electrical demand on hot summer days, which can lead to escalating power costs, brownouts, and rolling blackouts. DEVAP employs an innovative combination of air-cooling technologies to reduce energy use by up to 81%. DEVAP also shifts most of the energy needs to thermal energy sources, reducing annual electricity use by up to 90%. In doing so, DEVAP is estimated to cut peak electrical demand by nearly 80% in all climates. Widespread use of this cooling cycle would dramatically cut peak electrical loads throughout the country, saving billions of dollars in investments and operating costs for our nation's electrical utilities. Water is already used as a refrigerant in evaporative coolers, a common and widely used energy-saving technology for arid regions. The technology cools incoming hot, dry air by evaporating water into it. The energy absorbed by the water as it evaporates, known as the latent heat of vaporization, cools the air while humidifying it. However, evaporative coolers only function when the air is dry, and they deliver humid air that can lower the comfort level for building occupants. And even many dry climates like Phoenix, Arizona, have a humid season when evaporative cooling won't work well. DEVAP extends the applicability of evaporative cooling by first using a liquid desiccant-a water-absorbing material-to dry the air. The dry air is then passed to an indirect evaporative cooling stage, in which the incoming air is in thermal contact with a moistened surface that evaporates the water into a separate air stream. As the evaporation cools the moistened surface, it draws heat from the incoming air without adding humidity to it. A number of cooling cycles have been developed that employ indirect evaporative cooling, but DEVAP achieves a superior efficiency relative to its technological siblings.

  20. Verification Challenges at Low Numbers

    SciTech Connect (OSTI)

    Benz, Jacob M.; Booker, Paul M.; McDonald, Benjamin S.

    2013-06-01

    Many papers have dealt with the political difficulties and ramifications of deep nuclear arms reductions, and the issues of “Going to Zero”. Political issues include extended deterrence, conventional weapons, ballistic missile defense, and regional and geo-political security issues. At each step on the road to low numbers, the verification required to ensure compliance of all parties will increase significantly. Looking post New START, the next step will likely include warhead limits in the neighborhood of 1000 . Further reductions will include stepping stones at1000 warheads, 100’s of warheads, and then 10’s of warheads before final elimination could be considered of the last few remaining warheads and weapons. This paper will focus on these three threshold reduction levels, 1000, 100’s, 10’s. For each, the issues and challenges will be discussed, potential solutions will be identified, and the verification technologies and chain of custody measures that address these solutions will be surveyed. It is important to note that many of the issues that need to be addressed have no current solution. In these cases, the paper will explore new or novel technologies that could be applied. These technologies will draw from the research and development that is ongoing throughout the national laboratory complex, and will look at technologies utilized in other areas of industry for their application to arms control verification.

  1. Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Demonstration...

    Office of Scientific and Technical Information (OSTI)

    Sponsoring Org: USDOE; NASA, Washington, DC (United States) Country of Publication: United States Language: English Subject: 21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 30 ...

  2. Five Kilowatt Fuel Cell Demonstration for Remote Power Applications

    SciTech Connect (OSTI)

    Dennis Witmer; Tom Johnson; Jack Schmid

    2008-12-31

    While most areas of the US are serviced by inexpensive, dependable grid connected electrical power, many areas of Alaska are not. In these areas, electrical power is provided with Diesel Electric Generators (DEGs), at much higher cost than in grid connected areas. The reasons for the high cost of power are many, including the high relative cost of diesel fuel delivered to the villages, the high operational effort required to maintain DEGs, and the reverse benefits of scale for small utilities. Recent progress in fuel cell technologies have lead to the hope that the DEGs could be replaced with a more efficient, reliable, environmentally friendly source of power in the form of fuel cells. To this end, the University of Alaska Fairbanks has been engaged in testing early fuel cell systems since 1998. Early tests were conducted on PEM fuel cells, but since 2001, the focus has been on Solid Oxide Fuel Cells. In this work, a 5 kW fuel cell was delivered to UAF from Fuel Cell Technologies of Kingston, Ontario. The cell stack is of a tubular design, and was built by Siemens Westinghouse Fuel Cell division. This stack achieved a run of more than 1 year while delivering grid quality electricity from natural gas with virtually no degradation and at an electrical efficiency of nearly 40%. The project was ended after two control system failures resulted in system damage. While this demonstration was successful, considerable additional product development is required before this technology is able to provide electrical energy in remote Alaska. The major issue is cost, and the largest component of system cost currently is the fuel cell stack cost, although the cost of the balance of plant is not insignificant. While several manufactures are working on schemes for significant cost reduction, these systems do not as yet provide the same level of performance and reliability as the larger scale Siemens systems, or levels that would justify commercial deployment.

  3. Vehicle Technologies Office Merit Review 2015: 88 Kilowatt Automotive...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Technologies Office Merit Review 2015: Advanced Low-Cost SiC and GaN Wide Bandgap Inverters for Under-the-Hood Electric Vehicle Traction Drives Vehicle Technologies Office...

  4. Five Kilowatt Solid Oxide Fuel Cell/Diesel Reformer

    SciTech Connect (OSTI)

    Dennis Witmer; Thomas Johnson

    2008-12-31

    Reducing fossil fuel consumption both for energy security and for reduction in global greenhouse emissions has been a major goal of energy research in the US for many years. Fuel cells have been proposed as a technology that can address both these issues--as devices that convert the energy of a fuel directly into electrical energy, they offer low emissions and high efficiencies. These advantages are of particular interest to remote power users, where grid connected power is unavailable, and most electrical power comes from diesel electric generators. Diesel fuel is the fuel of choice because it can be easily transported and stored in quantities large enough to supply energy for small communities for extended periods of time. This projected aimed to demonstrate the operation of a solid oxide fuel cell on diesel fuel, and to measure the resulting efficiency. Results from this project have been somewhat encouraging, with a laboratory breadboard integration of a small scale diesel reformer and a Solid Oxide Fuel Cell demonstrated in the first 18 months of the project. This initial demonstration was conducted at INEEL in the spring of 2005 using a small scale diesel reformer provided by SOFCo and a fuel cell provided by Acumentrics. However, attempts to integrate and automate the available technology have not proved successful as yet. This is due both to the lack of movement on the fuel processing side as well as the rather poor stack lifetimes exhibited by the fuel cells. Commercial product is still unavailable, and precommercial devices are both extremely expensive and require extensive field support.

  5. Reducing Residential Peak Electricity Demand with Mechanical Pre-Cooling of Building Thermal Mass

    SciTech Connect (OSTI)

    Turner, Will; Walker, Iain; Roux, Jordan

    2014-08-01

    This study uses an advanced airflow, energy and humidity modelling tool to evaluate the potential for residential mechanical pre-cooling of building thermal mass to shift electricity loads away from the peak electricity demand period. The focus of this study is residential buildings with low thermal mass, such as timber-frame houses typical to the US. Simulations were performed for homes in 12 US DOE climate zones. The results show that the effectiveness of mechanical pre-cooling is highly dependent on climate zone and the selected pre-cooling strategy. The expected energy trade-off between cooling peak energy savings and increased off-peak energy use is also shown.

  6. Estimating coal production peak and trends of coal imports in China

    SciTech Connect (OSTI)

    Bo-qiang Lin; Jiang-hua Liu

    2010-01-15

    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.

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

    SciTech Connect (OSTI)

    Caballero, I.; Barragan, L.; Wilms, J.; Kreykenbohm, I.; Ferrigno, C.; Klochkov, D.; Suchy, S.; Santangelo, A.; Staubert, R.; Zurita Heras, J. A.; Kretschmar, P.; Fuerst, F.; Rothschild, R.; Finger, M. H.; Camero-Arranz, A.; Makishima, K.; Enoto, T.; Iwakiri, W.; and others

    2013-02-20

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

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

    SciTech Connect (OSTI)

    Neugebauer, Marcia

    2012-05-01

    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.

  9. DOUBLETS AND DOUBLE PEAKS: LATE-TIME [O I] lambdalambda6300, 6364 LINE

    Office of Scientific and Technical Information (OSTI)

    PROFILES OF STRIPPED-ENVELOPE, CORE-COLLAPSE SUPERNOVAE (Journal Article) | SciTech Connect DOUBLETS AND DOUBLE PEAKS: LATE-TIME [O I] lambdalambda6300, 6364 LINE PROFILES OF STRIPPED-ENVELOPE, CORE-COLLAPSE SUPERNOVAE Citation Details In-Document Search Title: DOUBLETS AND DOUBLE PEAKS: LATE-TIME [O I] lambdalambda6300, 6364 LINE PROFILES OF STRIPPED-ENVELOPE, CORE-COLLAPSE SUPERNOVAE We present optical spectra of SN 2007gr, SN 2007rz, SN 2007uy, SN 2008ax, and SN 2008bo obtained in the

  10. Microsoft Word - BUGS_The Next Smart Grid Peak Resource Final 4_19.docx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    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

  11. California's Efforts for Advancing Ultrafine Particle Number...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel Exhaust California's Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel...

  12. Identification of Export Control Classification Number - ITER

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Identification of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for...

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

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    kelkar, sharad

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

  14. First Tracer Test After Circulation in Desert Peak 27-15

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Rose, Peter

    2013-11-16

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

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

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    kelkar, sharad

    2013-04-30

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

  16. Webinar August 11: Analysis Using Fuel Cell MHE for Shaving Peak Building Energy

    Broader source: Energy.gov [DOE]

    The Fuel Cell Technologies Office will present a live webinar entitled "Analysis Using Fuel Cell MHE for Shaving Peak Building Energy" on Tuesday, August 11, from 12 to 1 p.m. EDT. This webinar will explore the synergy between a facility's use of hydrogen fuel cell forklifts and its reduction of electric grid time of use energy charges.

  17. First Tracer Test After Circulation in Desert Peak 27-15

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Rose, Peter

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

  18. Cosmology Constraints from the Weak Lensing Peak Counts and the Power Spectrum in CFHTLenS

    SciTech Connect (OSTI)

    Liu, Jia; May, Morgan; Petri, Andrea; Haiman, Zoltan; Hui, Lam; Kratochvil, Jan M.

    2015-03-04

    Lensing peaks have been proposed as a useful statistic, containing cosmological information from non-Gaussianities that is inaccessible from traditional two-point statistics such as the power spectrum or two-point correlation functions. Here we examine constraints on cosmological parameters from weak lensing peak counts, using the publicly available data from the 154 deg2 CFHTLenS survey. We utilize a new suite of ray-tracing N-body simulations on a grid of 91 cosmological models, covering broad ranges of the three parameters Ωm, σ8, and w, and replicating the galaxy sky positions, redshifts, and shape noise in the CFHTLenS observations. We then build an emulator that interpolates the power spectrum and the peak counts to an accuracy of ≤ 5%, and compute the likelihood in the three-dimensional parameter space (Ωm, σ8, w) from both observables. We find that constraints from peak counts are comparable to those from the power spectrum, and somewhat tighter when different smoothing scales are combined. Neither observable can constrain w without external data. When the power spectrum and peak counts are combined, the area of the error “banana” in the (Ωm, σ8) plane reduces by a factor of ≈ two, compared to using the power spectrum alone. For a flat Λ cold dark matter model, combining both statistics, we obtain the constraint σ8m/0.27)0.63 = 0.85+0.03-0.03.

  19. Cosmology Constraints from the Weak Lensing Peak Counts and the Power Spectrum in CFHTLenS

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Liu, Jia; May, Morgan; Petri, Andrea; Haiman, Zoltan; Hui, Lam; Kratochvil, Jan M.

    2015-03-04

    Lensing peaks have been proposed as a useful statistic, containing cosmological information from non-Gaussianities that is inaccessible from traditional two-point statistics such as the power spectrum or two-point correlation functions. Here we examine constraints on cosmological parameters from weak lensing peak counts, using the publicly available data from the 154 deg2 CFHTLenS survey. We utilize a new suite of ray-tracing N-body simulations on a grid of 91 cosmological models, covering broad ranges of the three parameters Ωm, σ8, and w, and replicating the galaxy sky positions, redshifts, and shape noise in the CFHTLenS observations. We then build an emulator thatmore » interpolates the power spectrum and the peak counts to an accuracy of ≤ 5%, and compute the likelihood in the three-dimensional parameter space (Ωm, σ8, w) from both observables. We find that constraints from peak counts are comparable to those from the power spectrum, and somewhat tighter when different smoothing scales are combined. Neither observable can constrain w without external data. When the power spectrum and peak counts are combined, the area of the error “banana” in the (Ωm, σ8) plane reduces by a factor of ≈ two, compared to using the power spectrum alone. For a flat Λ cold dark matter model, combining both statistics, we obtain the constraint σ8(Ωm/0.27)0.63 = 0.85+0.03-0.03.« less

  20. Momentum-resolved photoemission of the Kondo peak in an ordered Ce-containing alloy

    SciTech Connect (OSTI)

    Garnier, M.; Purdie, D.; Breuer, K.; Hengsberger, M.; Baer, Y.

    1997-11-01

    A comparison of uv-photoemission spectra recorded from the surface alloys Pt(111)({radical}(3){times}{radical}(3))R30{degree}Ce and Pt(111)(2{times}2)La allows the contribution from the 4f electrons to be seen easily. The valence-band structure of these two surfaces is very similar, and the most obvious 4f contribution in high-resolution photoemission spectra of the Ce-containing alloy is the tail of the Kondo peak cut at E{sub F}. Within the limits of our measurement, no dispersion of this feature in the occupied regime is detected. The Kondo peak displays a marked intensity dependence on the emission angle, suggesting that hybridization is present in only a limited part of reciprocal space. The temperature dependence of this near-E{sub F} feature supports this interpretation. {copyright} {ital 1997} {ital The American Physical Society}

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

    SciTech Connect (OSTI)

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

    2010-09-29

    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.

  2. Dose ratio proton radiography using the proximal side of the Bragg peak

    SciTech Connect (OSTI)

    Doolan, P. J. Royle, G.; Gibson, A.; Lu, H.-M.; Prieels, D.; Bentefour, E. H.

    2015-04-15

    Purpose: In recent years, there has been a movement toward single-detector proton radiography, due to its potential ease of implementation within the clinical environment. One such single-detector technique is the dose ratio method in which the dose maps from two pristine Bragg peaks are recorded beyond the patient. To date, this has only been investigated on the distal side of the lower energy Bragg peak, due to the sharp falloff. The authors investigate the limits and applicability of the dose ratio method on the proximal side of the lower energy Bragg peak, which has the potential to allow a much wider range of water-equivalent thicknesses (WET) to be imaged. Comparisons are made with the use of the distal side of the Bragg peak. Methods: Using the analytical approximation for the Bragg peak, the authors generated theoretical dose ratio curves for a range of energy pairs, and then determined how an uncertainty in the dose ratio would translate to a spread in the WET estimate. By defining this spread as the accuracy one could achieve in the WET estimate, the authors were able to generate lookup graphs of the range on the proximal side of the Bragg peak that one could reliably use. These were dependent on the energy pair, noise level in the dose ratio image and the required accuracy in the WET. Using these lookup graphs, the authors investigated the applicability of the technique for a range of patient treatment sites. The authors validated the theoretical approach with experimental measurements using a complementary metal oxide semiconductor active pixel sensor (CMOS APS), by imaging a small sapphire sphere in a high energy proton beam. Results: Provided the noise level in the dose ratio image was 1% or less, a larger spread of WETs could be imaged using the proximal side of the Bragg peak (max 5.31 cm) compared to the distal side (max 2.42 cm). In simulation, it was found that, for a pediatric brain, it is possible to use the technique to image a region with a square field equivalent size of 7.6 cm{sup 2}, for a required accuracy in the WET of 3 mm and a 1% noise level in the dose ratio image. The technique showed limited applicability for other patient sites. The CMOS APS demonstrated a good accuracy, with a root-mean-square-error of 1.6 mm WET. The noise in the measured images was found to be ? = 1.2% (standard deviation) and theoretical predictions with a 1.96? noise level showed good agreement with the measured errors. Conclusions: After validating the theoretical approach with measurements, the authors have shown that the use of the proximal side of the Bragg peak when performing dose ratio imaging is feasible, and allows for a wider dynamic range than when using the distal side. The dynamic range available increases as the demand on the accuracy of the WET decreases. The technique can only be applied to clinical sites with small maximum WETs such as for pediatric brains.

  3. Flexible Coal: Evolution from Baseload to Peaking Plant (Brochure), 21st Century Power Partnership

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Accelerating 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 located in North America referred to here as the CGS plant. For commercial reasons, the station has not been identified. Jaquelin Cochran, a Debra Lew, a Nikhil Kumar b a National Renewable Energy Laboratory, b Intertek Summary for Policymakers: Key Findings from a North American Coal Generating Station (CGS)

  4. Method and apparatus for reducing rotor blade deflections, loads, and/or peak rotational speed

    DOE Patents [OSTI]

    Moroz, Emilian Mieczyslaw; Pierce, Kirk Gee

    2006-10-17

    A method for reducing at least one of loads, deflections of rotor blades, or peak rotational speed of a wind turbine includes storing recent historical pitch related data, wind related data, or both. The stored recent historical data is analyzed to determine at least one of whether rapid pitching is occurring or whether wind speed decreases are occurring. A minimum pitch, a pitch rate limit, or both are imposed on pitch angle controls of the rotor blades conditioned upon results of the analysis.

  5. Method for the Production of Ultrashort Peak Power Laser Pulses and System

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    For Putting Into Practice This Method Inventors Gerard Mourou, Nathaniel J. Fisch, Vladimir M. Malkin, and Zeev Toroker | Princeton Plasma Physics Lab Method for the Production of Ultrashort Peak Power Laser Pulses and System For Putting Into Practice This Method Inventors Gerard Mourou, Nathaniel J. Fisch, Vladimir M. Malkin, and Zeev Toroker Laser-pulse intensities have grown remarkably in recent years, primarily through the method of chirped pulse amplification. Much higher powers are

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

    SciTech Connect (OSTI)

    Yin, Rongxin; Kiliccote, Sila; Piette, Mary Ann; Parrish, Kristen

    2010-05-14

    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.

  7. CONSTRAINING PHYSICAL PROPERTIES OF TYPE IIn SUPERNOVAE THROUGH RISE TIMES AND PEAK LUMINOSITIES

    SciTech Connect (OSTI)

    Moriya, Takashi J.; Maeda, Keiichi

    2014-08-01

    We investigate the diversity in the wind density, supernova ejecta energy, and ejecta mass in Type IIn supernovae based on their rise times and peak luminosities. We show that the wind density and supernova ejecta properties can be estimated independently if both the rise time and peak luminosity are observed. The peak luminosity is mostly determined by the supernova properties and the rise time can be used to estimate the wind density. We find that the ejecta energies of Type IIn supernovae need to vary by factors of 0.2-5 from the average if their ejecta masses are similar. The diversity in the observed rise times indicates that their wind densities vary by factors of 0.2-2 from the average. We show that Type IIn superluminous supernovae should have not only large wind density but also large ejecta energy and/or small ejecta mass to explain their large luminosities and the rise times at the same time. We also note that shock breakout does not necessarily occur in the wind even if it is optically thick, except for the case of superluminous supernovae, and we analyze the observational data both with and without assuming that the shock breakout occurs in the dense wind of Type IIn supernovae.

  8. Climate Zone Number 5 | Open Energy Information

    Open Energy Info (EERE)

    Climate Zone Number 5 Jump to: navigation, search A type of climate defined in the ASHRAE 169-2006 standard. Climate Zone Number 5 is defined as Cool- Humid(5A) with IP Units 5400...

  9. Ice Thermal Storage Systems for LWR Supplemental Cooling and Peak Power Shifting

    SciTech Connect (OSTI)

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

    2010-06-01

    Availability of enough 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. The issues become more severe due to the new round of nuclear power expansion and global warming. During hot summer days, cooling water leaving a power plant may become too hot to threaten aquatic life so that environmental regulations may force the plant to reduce power output or even temporarily to be shutdown. For new nuclear power plants to be built at areas without enough cooling water, dry cooling can be used to remove waste heat directly into the atmosphere. However, dry cooling will result in much lower thermal efficiency when the weather is hot. One potential solution for the above mentioned issues is to use ice thermal storage systems (ITS) 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 those ice for supplemental cooling during peak demand time. ITS is suitable for supplemental cooling storage due to its very high energy storage density. ITS also provides a way to shift large amount of electricity from off peak time to peak time. Some gas turbine plants already use ITS to increase thermal efficiency during peak hours in summer. ITSs have also been widely used for building cooling to save energy cost. Among three cooling methods for LWR applications: once-through, wet cooling tower, and dry cooling tower, once-through cooling plants near a large water body like an ocean or a large lake and wet cooling plants can maintain the designed turbine backpressure (or condensation temperature) during 99% of the time; therefore, adding ITS to those plants will not generate large benefits. For once-through cooling plants near a limited water body like a river or a small lake, 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 ice thermal storage systems can effectively reduce the efficiency loss and water consumption during hot weather so that new LWRs could be considered in regions without enough cooling water. \\ This paper presents the feasibility study of using ice thermal storage systems for LWR supplemental cooling and peak power shifting. LWR cooling issues and ITS application status will be reviewed. Two ITS application case studies will be presented and compared with alternative options: one for once-through cooling without enough cooling for short time, and the other with dry cooling. Because capital cost, especially the ice storage structure/building cost, is the major cost for ITS, two different cost estimation models are developed: one based on scaling method, and the other based on a preliminary design using Building Information Modeling (BIM), an emerging technology in Architecture/Engineering/Construction, which enables design options, performance analysis and cost estimating in the early design stage.

  10. Have We Run Out of Oil Yet? Oil Peaking Analysis from an Optimist's Perspective

    SciTech Connect (OSTI)

    Greene, David L; Hopson, Dr Janet L; Li, Jia

    2005-01-01

    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 environmental constraints are allowed to hinder oil production, geological constraints on the rates at which oil can be produced are not represented, and when USGS resource estimates are used, more than the mean estimate of ultimately recoverable resources is assumed to exist. The issue is framed not as a question of "running out" of conventional oil, but in terms of the timing and rate of transition from conventional to unconventional oil resources. Unconventional oil is chosen because production from Venezuela's heavy-oil fields and Canada's Athabascan oil sands is already underway on a significant scale and unconventional oil is most consistent with the existing infrastructure for producing, refining, distributing and consuming petroleum. However, natural gas or even coal might also prove to be economical sources of liquid hydrocarbon fuels. These results indicate a high probability that production of conventional oil from outside of the Middle East region will peak, or that the rate of increase of production will become highly constrained before 2025. If world consumption of hydrocarbon fuels is to continue growing, massive development of unconventional resources will be required. While there are grounds for pessimism and optimism, it is certainly not too soon for extensive, detailed analysis of transitions to alternative energy sources.

  11. Microsoft Word - Rockwood _CFC_ Silver Peak Area Final EA V4 _dec date_.docx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    the Interior Bureau of Land Management Environmental Assessment # DOI-BLM-NV-B020-2012-0214-EA DOE/EA-1921 DATE: December 2012 Silver Peak Area Geothermal Exploration Project ENVIRONMENTAL ASSESSMENT Geothermal Lease: N-87008 Tonopah Field Office P.O. Box 911 1553 S. Main Street Tonopah, NV 89049 Phone: 775-482-7800 Fax: 775-482-7810 BLM Mission Statement It is the mission of the Bureau of Land Management to sustain the health, diversity, and productivity of the public lands for the use and

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

    Energy Savers [EERE]

    1 OG&E Uses Time-Based Rate Program to Reduce Peak Demand As part of its Smart Grid Investment Grant (SGIG) project for the U.S. Department of Energy's (DOE) Office of Electricity Delivery and Energy Reliability (OE), Oklahoma Gas and Electric Company (OG&E) has successfully tested over a two-year period a new time-based rate, which provided about 4,670 participating customers with pric es that varied daily in order to induce a change in their patterns of electricity consumption and a

  13. AVTA: EVSE Charging Protocol for On and Off-Peak Demand

    Broader source: Energy.gov [DOE]

    The Vehicle Technologies Office's Advanced Vehicle Testing Activity carries out testing on a wide range of advanced vehicles and technologies on dynamometers, closed test tracks, and on-the-road. These results provide benchmark data that researchers can use to develop technology models and guide future research and development. The following report is a description of development of a charge protocol to take advantage of off and on-peak demand economics at facilities, as informed by the AVTA's testing on plug-in electric vehicle charging equipment. This research was conducted by Idaho National Laboratory.

  14. Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  15. Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  16. Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  17. Texas Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Texas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  18. Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Gas and Gas Condensate Wells (Number of Elements) Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  19. Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Gas and Gas Condensate Wells (Number of Elements) Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  20. Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  1. U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  2. Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  3. Utah Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Gas and Gas Condensate Wells (Number of Elements) Utah Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  4. ARM - Measurement - Cloud particle number concentration

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    number concentration ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Cloud particle number concentration The total number of cloud particles present in any given volume of air. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available

  5. Calculating Atomic Number Densities for Uranium

    Energy Science and Technology Software Center (OSTI)

    1993-01-01

    Provides method to calculate atomic number densities of selected uranium compounds and hydrogenous moderators for use in nuclear criticality safety analyses at gaseous diffusion uranium enrichment facilities.

  6. OMB Control Number: 1910-5165

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    damages assessed under Contract Work Hours and Safety Standards Act: Page 1 OMB Control Number: 1910-5165 Expires: 04302015 SEMI-ANNUAL DAVIS-BACON ENFORCEMENT REPORT...

  7. Petrology and geochemistry of Alto Peak, a vapor-cored hydrothermal system, Leyte Province, Philippines

    SciTech Connect (OSTI)

    Reyes, A.G.; Giggenbach, W.F.; Saleras, J.R.M.; Salonga, N.D.; Vergara, M.C.

    1993-10-01

    Based on detailed petrological information on secondary mineral assemblages and the composition of fluids trapped in inclusions and discharged from five wells, the Alto Peak geothermal field was found to represent a combined vapor and liquid-dominated system. A central core or chimney, with a diameter of about 1 km, a height of some 3 km and occupied by a high gas vapor (1.1 to 5.6 molal CO{sub 2}), is surrounded by an envelope of intermediate salinity water (7,000 mg/kg Cl) with temperatures between 250 and 350 C. The transition from purely vapor-dominated to liquid-dominated zones takes place via two-phase zones occupied by fluid mixtures of highly variable compositions. Much of the lower temperature, mature neutral pH Cl water is likely to have formed during an earlier stage in the evolution of the system. High temperatures of > 300 C, and associated alteration, are limited to wells AP-1D and the lower parts of AP-2D and are ascribed to re-heating by recent magmatic intrusions. The isotopic composition of the well discharges suggests that they contain some 40 to 50% of magmatic water. Alto Peak is considered a typical example of hydrothermal systems associated with many dormant volcanoes.

  8. Low Mach Number Models in Computational Astrophysics

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Ann Almgren Low Mach Number Models in Computational Astrophysics February 4, 2014 Ann Almgren. Berkeley Lab Downloads Almgren-nug2014.pdf | Adobe Acrobat PDF file Low Mach Number Models in Computational Astrophysics - Ann Almgren, Berkeley Lab Last edited: 2016-02-01 08:06:52

  9. Compendium of Experimental Cetane Number Data

    SciTech Connect (OSTI)

    Murphy, M. J.; Taylor, J. D.; McCormick, R. L.

    2004-09-01

    In this report, we present a compilation of reported cetane numbers for pure chemical compounds. The compiled database contains cetane values for 299 pure compounds, including 156 hydrocarbons and 143 oxygenates. Cetane number is a relative ranking of fuels based on the amount of time between fuel injection and ignition. The cetane number is typically measured either in a combustion bomb or in a single-cylinder research engine. This report includes cetane values from several different measurement techniques - each of which has associated uncertainties. Additionally, many of the reported values are determined by measuring blending cetane numbers, which introduces significant error. In many cases, the measurement technique is not reported nor is there any discussion about the purity of the compounds. Nonetheless, the data in this report represent the best pure compound cetane number values available from the literature as of August 2004.

  10. Control system analysis for off-peak auxiliary heating of passive solar systems

    SciTech Connect (OSTI)

    Murray, H.S.; Melsa, J.L.; Balcomb, J.D.

    1980-01-01

    A computer simulation method is presented for the design of an electrical auxiliary energy system for passive solar heated structures. The system consists of electrical mats buried in the ground underneath the structure. Energy is stored in the ground during utility off-peak hours and released passively to the heated enclosure. An optimal control strategy is used to determine the system design parameters of depth of mat placement and minimum instaled electrical heating capacity. The optimal control applies combinations of fixed duration energy pulses to the heater, which minimize the room temperature error-squared for each day, assuming advance knowledge of the day's weather. Various realizable control schemes are investigated in an attempt to find a system that approaches the performance of the optimal control system.

  11. In-situ single-grain peak profile measurements on Ti-7Al during tensile deformation.

    SciTech Connect (OSTI)

    Lienert, U.; Brandes, M. C.; Bernier, J. V.; Weiss, J.; Shastri, S. D.; Mills, M. J.; Miller, M. P.; US Naval Research Lab.; LLNL; Mechanical Solutions, Inc.; Ohio State Univ.; Cornell Univ.

    2009-10-25

    High-energy three-dimensional X-ray diffraction with medium and high reciprocal space resolution was applied to study in situ tensile deformation of Ti-7Al specimens. Samples with planar and random dislocation microstructures were prepared and characterized by electron microscopy. Stress tensors of individual grains were obtained at several loads up to 2% deformation. The stress tensors were found to rotate, and resolved shear stresses were calculated. High-resolution reciprocal space maps of selected grains were recorded. Azimuthal and radial distributions were visualized and discussed in terms of idealized dislocation structures. Heterogeneous grain rotations were observed for the planar microstructure and found to be consistent with activation of the highest stressed basal slip system. Intra-granular strain gradients were detected in excess of the intrinsic radial dislocation peak broadening. The potential of combining the applied techniques with modeling to obtain multiple length-scale information during deformation of bulk specimens is discussed.

  12. Method and device for remotely monitoring an area using a low peak power optical pump

    DOE Patents [OSTI]

    Woodruff, Steven D.; Mcintyre, Dustin L.; Jain, Jinesh C.

    2014-07-22

    A method and device for remotely monitoring an area using a low peak power optical pump comprising one or more pumping sources, one or more lasers; and an optical response analyzer. Each pumping source creates a pumping energy. The lasers each comprise a high reflectivity mirror, a laser media, an output coupler, and an output lens. Each laser media is made of a material that emits a lasing power when exposed to pumping energy. Each laser media is optically connected to and positioned between a corresponding high reflectivity mirror and output coupler along a pumping axis. Each output coupler is optically connected to a corresponding output lens along the pumping axis. The high reflectivity mirror of each laser is optically connected to an optical pumping source from the one or more optical pumping sources via an optical connection comprising one or more first optical fibers.

  13. High peak-power kilohertz laser system employing single-stage multi-pass amplification

    DOE Patents [OSTI]

    Shan, Bing; Wang, Chun; Chang, Zenghu

    2006-05-23

    The present invention describes a technique for achieving high peak power output in a laser employing single-stage, multi-pass amplification. High gain is achieved by employing a very small "seed" beam diameter in gain medium, and maintaining the small beam diameter for multiple high-gain pre-amplification passes through a pumped gain medium, then leading the beam out of the amplifier cavity, changing the beam diameter and sending it back to the amplifier cavity for additional, high-power amplification passes through the gain medium. In these power amplification passes, the beam diameter in gain medium is increased and carefully matched to the pump laser's beam diameter for high efficiency extraction of energy from the pumped gain medium. A method of "grooming" the beam by means of a far-field spatial filter in the process of changing the beam size within the single-stage amplifier is also described.

  14. Structural geology of the French Peak accommodation zone, Nevada Test Site, southwestern Nevada

    SciTech Connect (OSTI)

    Hudson, M.R.

    1997-12-31

    The French Peak accommodation zone (FPAZ) forms an east-trending bedrock structural high in the Nevada Test Site region of southwestern Nevada that formed during Cenozoic Basin and Range extension. The zone separates areas of opposing directions of tilt and downthrow on faults in the Yucca Flat and Frenchman Flat areas. Paleomagnetic data show that rocks within the accommodation zone adjacent to Yucca Flat were not strongly affected by vertical-axis rotation and thus that the transverse strikes of fault and strata formed near their present orientation. Both normal- and oblique strike-slip faulting in the FPAZ largely occurred under a normal-fault stress regime, with least principal stress oriented west-northwest. The normal and sinistral faults in the Puddle Peka segment transfers extension between the Plutonium Valley normal fault zone and the Cane Spring sinistral fault. Recognition of sinistral shear across the Puddle Peak segment allows the Frenchman Flat basin to be interpreted as an asymmetric pull-apart basin developed between the FPAZ and a zone of east-northeast-striking faults to the south that include the Rock Valley fault. The FPAZ has the potential to influence ground-water flow in the region in several ways. Fracture density and thus probably fracture conductivity is high within the FPAZ due to the abundant fault splays present. Moreover,, fractures oriented transversely to the general southward flow of ground water through Yucca Flat area are significant and have potential to laterally divert ground water. Finally, the FPAZ forms a faulted structural high whose northern and southern flanks may permit intermixing of ground waters from different aquifer levels, namely the lower carbonate, welded tuff, and alluvial aquifers. 42 refs.

  15. Impacts of Climate Change on Energy Consumption and Peak Demand in Buildings: A Detailed Regional Approach

    SciTech Connect (OSTI)

    Dirks, James A.; Gorrissen, Willy J.; Hathaway, John E.; Skorski, Daniel C.; Scott, Michael J.; Pulsipher, Trenton C.; Huang, Maoyi; Liu, Ying; Rice, Jennie S.

    2015-01-01

    This paper presents the results of numerous commercial and residential building simulations, with the purpose of examining the impact of climate change on peak and annual building energy consumption over the portion of the Eastern Interconnection (EIC) located in the United States. The climate change scenario considered (IPCC A2 scenario as downscaled from the CASCaDE data set) has changes in mean climate characteristics as well as changes in the frequency and duration of intense weather events. This investigation examines building energy demand for three annual periods representative of climate trends in the CASCaDE data set at the beginning, middle, and end of the century--2004, 2052, and 2089. Simulations were performed using the Building ENergy Demand (BEND) model which is a detailed simulation platform built around EnergyPlus. BEND was developed in collaboration with the Platform for Regional Integrated Modeling and Analysis (PRIMA), a modeling framework designed to simulate the complex interactions among climate, energy, water, and land at decision-relevant spatial scales. Over 26,000 building configurations of different types, sizes, vintages, and, characteristics which represent the population of buildings within the EIC, are modeled across the 3 EIC time zones using the future climate from 100 locations within the target region, resulting in nearly 180,000 spatially relevant simulated demand profiles for each of the 3 years. In this study, the building stock characteristics are held constant based on the 2005 building stock in order to isolate and present results that highlight the impact of the climate signal on commercial and residential energy demand. Results of this analysis compare well with other analyses at their finest level of specificity. This approach, however, provides a heretofore unprecedented level of specificity across multiple spectrums including spatial, temporal, and building characteristics. This capability enables the ability to perform detailed hourly impact studies of building adaptation and mitigation strategies on energy use and electricity peak demand within the context of the entire grid and economy.

  16. Offsets between the X-ray and the Sunyaev-Zel'Dovich-effect peaks in merging galaxy clusters and their cosmological implications

    SciTech Connect (OSTI)

    Zhang, Congyao; Yu, Qingjuan; Lu, Youjun

    2014-12-01

    Observations reveal that the peaks of the X-ray map and the Sunyaev-Zel'dovich (SZ) effect map of some galaxy clusters are offset from each other. In this paper, we perform a set of hydrodynamical simulations of mergers of two galaxy clusters to investigate the spatial offset between the maxima of the X-ray and the SZ surface brightness of the merging clusters. We find that significantly large SZ-X-ray offsets (>100 kpc) can be produced during the major mergers of galaxy clusters (with mass > 1 10{sup 14} M {sub ?}). The significantly large offsets are mainly caused by a 'jump effect' that occurs between the primary and secondary pericentric passages of the two merging clusters, during which the X-ray peak may jump to the densest gas region located near the center of the small cluster, but the SZ peak remains near the center of the large one. Our simulations show that merging systems with higher masses and larger initial relative velocities may result in larger offset sizes and longer offset time durations; and only nearly head-on mergers are likely to produce significantly large offsets. We further investigate the statistical distribution of the SZ-X-ray offset sizes and find that (1) the number distribution of the offset sizes is bimodal with one peak located at low offsets ?0 and the other at large offsets ?350-450 h {sup 1} kpc, but the objects with intermediate offsets are scarce; and (2) the probabilities of the clusters in the mass range higher than 2 10{sup 14} h {sup 1} M {sub ?} that have offsets larger than 20, 50, 200, 300, and 500 h {sup 1} kpc are 34.0%, 11.1%, 8.0%, 6.5%, and 2.0%, respectively, at z = 0.7. The probability is sensitive to the underlying pairwise velocity distribution and the merger rate of clusters. We suggest that the SZ-X-ray offsets provide a probe to the cosmic velocity fields on the cluster scale and the cluster merger rate, and future observations on the SZ-X-ray offsets for a large number of clusters may put strong constraints on them. Our simulation results suggest that the SZ-X-ray offset in the Bullet Cluster, together with the mass ratio of the two merging clusters, requires a relative velocity larger than 3000 km s{sup 1} at an initial separation 5 Mpc. The cosmic velocity distribution at the high-velocity end is expected to be crucial in determining whether there exists an incompatibility between the existence of the Bullet Cluster and the prediction of a ?CDM model.

  17. Particle Number & Particulate Mass Emissions Measurements on...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    on a 'Euro VI' Heavy-duty Engine using the PMP Methodologies Particle Number & Particulate Mass Emissions Measurements on a 'Euro VI' Heavy-duty Engine using the PMP ...

  18. Supernovae with two peaks in the optical light curve and the signature of progenitors with low-mass extended envelopes

    SciTech Connect (OSTI)

    Nakar, Ehud [Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978 (Israel); Piro, Anthony L. [Theoretical Astrophysics, California Institute of Technology, 1200 East California Boulevard, M/C 350-17, Pasadena, CA 91125 (United States)

    2014-06-20

    Early observations of supernova light curves are powerful tools for shedding light on the pre-explosion structures of their progenitors and their mass-loss histories just prior to explosion. Some core-collapse supernovae that are detected during the first days after the explosion prominently show two peaks in the optical bands, including the R and I bands, where the first peak appears to be powered by the cooling of shocked surface material and the second peak is clearly powered by radioactive decay. Such light curves have been explored in detail theoretically for SN 1993J and 2011dh, where it was found that they may be explained by progenitors with extended, low-mass envelopes. Here, we generalize these results. We first explore whether any double-peaked light curve of this type can be generated by a progenitor with a 'standard' density profile, such as a red supergiant or a Wolf-Rayet star. We show that a standard progenitor (1) cannot produce a double-peaked light curve in the R and I bands and (2) cannot exhibit a fast drop in the bolometric luminosity as is seen after the first peak. We then explore the signature of a progenitor with a compact core surrounded by extended, low-mass material. This may be a hydrostatic low-mass envelope or material ejected just prior to the explosion. We show that it naturally produces both of these features. We use this result to provide simple formulae to estimate (1) the mass of the extended material from the time of the first peak, (2) the extended material radius from the luminosity of the first peak, and (3) an upper limit on the core radius from the luminosity minimum between the two peaks.

  19. Identification of Export Control Classification Number - ITER

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Identification of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for the products (equipment, components and/or materials) and if applicable the nonproprietary associated installation/maintenance documentation that will be shipped from the United States to the ITER International Organization in Cadarache, France or to ITER Members worldwide on behalf of the Company. In rare

  20. Stockpile Stewardship Quarterly Volume 1, Number 4

    National Nuclear Security Administration (NNSA)

    1, Number 4 * February 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 1, Number 4 Inside this Issue 2 Applying Advanced Simulation Models to Neutron Tube Ion Extraction 3 Advanced Optical Cavities for Subcritical and Hydrodynamic Experiments 5 Progress Toward Ignition on the National Ignition Facility 7 Commissioning URSA Minor: The First LTD-Based Accelerator for Radiography 8 Publication

  1. Methods, systems and apparatus for approximation of peak summed fundamental and third harmonic voltages in a multi-phase machine

    DOE Patents [OSTI]

    Ransom, Ray M. (Big Bear City, CA); Gallegos-Lopez, Gabriel (Torrance, CA); Kinoshita, Michael H. (Redondo Beach, CA)

    2012-07-31

    Methods, system and apparatus are provided for quickly approximating a peak summed magnitude (A) of a phase voltage (Vph) waveform in a multi-phase system that implements third harmonic injection.

  2. High-Performance with Solar Electric Reduced Peak Demand: Premier Homes Rancho Cordoba, CA- Building America Top Innovation

    Broader source: Energy.gov [DOE]

    This Building America Innovations profile describes Building America solar home research that has demonstrated the ability to reduce peak demand by 75%. Numerous field studies have monitored power production and system effectiveness.

  3. Project title: Stimulation at Desert Peak and Bradys reservoirs: modeling with the coupled THM code FEHM

    Broader source: Energy.gov [DOE]

    Project title: Stimulation at Desert Peak and Bradys reservoirs: modeling with the coupled THM code FEHM presentation at the April 2013 peer review meeting held in Denver, Colorado.

  4. Light, alpha, and Fe-peak element abundances in the galactic bulge

    SciTech Connect (OSTI)

    Johnson, Christian I.; Rich, R. Michael; Kobayashi, Chiaki; Kunder, Andrea; Koch, Andreas E-mail: rmr@astro.ucla.edu E-mail: akunder@aip.de

    2014-10-01

    We present radial velocities and chemical abundances of O, Na, Mg, Al, Si, Ca, Cr, Fe, Co, Ni, and Cu for a sample of 156 red giant branch stars in two Galactic bulge fields centered near (l, b) = (+5.25,3.02) and (0,12). The (+5.25,3.02) field also includes observations of the bulge globular cluster NGC 6553. The results are based on high-resolution (R ? 20,000), high signal-to-noise ration (S/N ? 70) FLAMES-GIRAFFE spectra obtained through the European Southern Observatory archive. However, we only selected a subset of the original observations that included spectra with both high S/N and that did not show strong TiO absorption bands. This work extends previous analyses of this data set beyond Fe and the ?-elements Mg, Si, Ca, and Ti. While we find reasonable agreement with past work, the data presented here indicate that the bulge may exhibit a different chemical composition than the local thick disk, especially at [Fe/H] ? 0.5. In particular, the bulge [?/Fe] ratios may remain enhanced to a slightly higher [Fe/H] than the thick disk, and the Fe-peak elements Co, Ni, and Cu appear enhanced compared to the disk. There is also some evidence that the [Na/Fe] (but not [Al/Fe]) trends between the bulge and local disk may be different at low and high metallicity. We also find that the velocity dispersion decreases as a function of increasing [Fe/H] for both fields, and do not detect any significant cold, high-velocity populations. A comparison with chemical enrichment models indicates that a significant fraction of hypernovae may be required to explain the bulge abundance trends, and that initial mass functions that are steep, top-heavy (and do not include strong outflow), or truncated to avoid including contributions from stars >40 M {sub ?} are ruled out, in particular because of disagreement with the Fe-peak abundance data. For most elements, the NGC 6553 stars exhibit abundance trends nearly identical to comparable metallicity bulge field stars. However, the star-to-star scatter and mean [Na/Fe] ratios appear higher in the cluster, perhaps indicating additional self-enrichment.

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

    SciTech Connect (OSTI)

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

    2014-03-25

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

  6. Probing lepton number violation on three frontiers

    SciTech Connect (OSTI)

    Deppisch, Frank F. [Department of Physics and Astronomy, University College London (United Kingdom)

    2013-12-30

    Neutrinoless double beta decay constitutes the main probe for lepton number violation at low energies, motivated by the expected Majorana nature of the light but massive neutrinos. On the other hand, the theoretical interpretation of the (non-)observation of this process is not straightforward as the Majorana neutrinos can destructively interfere in their contribution and many other New Physics mechanisms can additionally mediate the process. We here highlight the potential of combining neutrinoless double beta decay with searches for Tritium decay, cosmological observations and LHC physics to improve the quantitative insight into the neutrino properties and to unravel potential sources of lepton number violation.

  7. Battling bird flu by the numbers

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Battling bird flu by the numbers Battling bird flu by the numbers Lab theorists have developed a mathematical tool that could help health experts and crisis managers determine in real time whether an emerging infectious disease such as avian influenza H5N1 is poised to spread globally. May 27, 2008 Los Alamos National Laboratory sits on top of a once-remote mesa in northern New Mexico with the Jemez mountains as a backdrop to research and innovation covering multi-disciplines from bioscience,

  8. WIPP Documents - All documents by number

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Note: Documents that do not have document numbers are not included in this listing. Large file size alert This symbol means the document may be a large file size. All documents by number Common document prefixes DOE/CAO DOE/TRU DOE/CBFO DOE/WIPP DOE/EA NM DOE/EIS Other DOE/CAO Back to top DOE/CAO 95-1095, Oct. 1995 Remote Handled Transuranic Waste Study This study was conducted to satisfy the requirements defined by the WIPP Land Withdrawal Act and considered by DOE to be a prudent exercise in

  9. Flux harmonics in large SFR cores in relation with core characteristics such as power peaks

    SciTech Connect (OSTI)

    Rimpault, G.; Buiron, L.; Fontaine, B.; Sciora, P.; Tommasi, J.

    2013-07-01

    Designing future Sodium Fast Reactors (SFR) requires enhancing their operational performance and reducing the probability to go into core disruption. As a consequence of these constraints, these novel reactors exhibit rather unusual features compared to past designs. The cores are much larger with rather flat shape. The consequences of that shape on the core characteristics deserve to be studied. The approach taken in this paper is to calculate the eigenvalue associated to the first harmonic and its associated flux. It is demonstrated that these values are linked to some core features, in particular, those sensitive to spatial effects such as power peaks induced by the movement of control rods. The uncertainty associated to these characteristics is being tentatively studied and guidelines for further studied are being identified. In the development strategy of these new SFR designs, a first demonstration plant of limited installed power (around 1500 MWth) will have to be built first. Identifying the possibility of going later to higher power plants (around 3600 MWth) without facing new challenges is an important criterion for designing such a plant. That strategy is being studied, in this paper, focusing on some rather frequent initiator such as the inadvertent control rod withdrawal for different core sizes with the help of the perturbation theory and the flux harmonics. (authors)

  10. Daily air pollution effects on children's respiratory symptoms and peak expiratory flow

    SciTech Connect (OSTI)

    Vedal, S.; Schenker, M.B.; Munoz, A.; Samet, J.M.; Batterman, S.; Speizer, F.E.

    1987-06-01

    To identify acute respiratory health effects associated with air pollution due to coal combustion, a subgroup of elementary school-aged children was selected from a large cross-sectional study and followed daily for eight months. Children were selected to obtain three equal-sized groups: one without respiratory symptoms, one with symptoms of persistent wheeze, and one with cough or phlegm production but without persistent wheeze. Parents completed a daily diary of symptoms from which illness constellations of upper respiratory illness (URI) and lower respiratory illness (LRI) and the symptom of wheeze were derived. Peak expiratory flow rate (PEFR) was measured daily for nine consecutive weeks during the eight-month study period. Maximum hourly concentrations of sulfur dioxide, nitrogen dioxide, ozone, and coefficient of haze for each 24-hour period, as well as minimum hourly temperature, were correlated with daily URI, LRI, wheeze, and PEFR using multiple regression models adjusting for illness occurrence or level of PEFR on the immediately preceding day. Respiratory illness on the preceding day was the most important predictor of current illness. A drop in temperature was associated with increased URI and LRI but not with increased wheeze or with a decrease in level of PEFR. No air pollutant was strongly associated with respiratory illness or with level of PEFR, either in the group of children as a whole, or in either of the symptomatic subgroups; the pollutant concentrations observed, however, were uniformly lower than current ambient air quality standards.

  11. Development of a Dispatchable PV Peak Shainv System. PV: Bonus Program - Phase 1 Report. Volume 1

    SciTech Connect (OSTI)

    1995-10-01

    This report summarizes the work performed by Delmarva Power and Light and its subcontractors in Phase 1 of the US Department of Energy's PV:BONUS Program. The purpose of the program is to develop products and systems for buildings which utilize photovoltaic (N) technology. Beginning with a cooperative research effort with the University of Delaware's Center for Energy and Environmental Policy Research Delmarva Power developed and demonstrated the concept of Dispatchable PV Peak Shaving. This concept and the system which resulted horn the development work are unique from other grid-connected PV systems because it combines a PV, battery energy storage, power conversion and control technologies into an integrated package. Phase 1 began in July 1993 with the installation of a test and demonstration system at Delmarva's Northern Division General Office building near Newark, Delaware. Following initial testing throughout the summer and fall of 1993, significant modifications were made under an amendment to the DOE contract. Work on Phase 1 concluded in the early spring of 1995. Significant progress towards the goal of commercializing the system was made during Phase 1, and is summarized. Based on progress in Phase 1, a proposal to continue the work in Phase 2 was submitted to the US DOE in May 1995. A contract amendment and providing funds for the Phase 2 work is expected in July 1995.

  12. EIS No. 20100312 EIS Comanche Peak Nuclear Power Plant Units 3 and 4

    SciTech Connect (OSTI)

    Bjornstad, David J

    2010-08-01

    In accordance with Section 309(a) of the Clean Air Act, EPA is required to make its comments on EISs issued by other Federal agencies public. Historically, EPA has met this mandate by publishing weekly notices of availability of EPA comments, which includes a brief summary of EPA's comment letters, in the Federal Register. Since February 2008, EPA has been including its comment letters on EISs on its Web site at: http://www.epa.gov/compliance/nepa/eisdata.html. Including the entire EIS comment letters on the Web site satisfies the Section 309(a) requirement to make EPA's comments on EISs available to the public. Accordingly, on March 31, 2010, EPA discontinued the publication of the notice of availability of EPA comments in the Federal Register. EIS No. 20100312, Draft EIS, NRC, TX, Comanche Peak Nuclear Power Plant Units 3 and 4, Application for Combined Licenses (COLs) for Construction Permits and Operating Licenses, (NUREG-1943), Hood and Somervell Counties, TX, Comment Period Ends: 10/26/2010.

  13. Implications of 'peak oil' for atmospheric CO{sub 2} and climate - article no. GB3012

    SciTech Connect (OSTI)

    Kharecha, P.A.; Hansen, J.E.

    2008-08-15

    Unconstrained CO{sub 2} emission from fossil fuel burning has been the dominant cause of observed anthropogenic global warming. The amounts of 'proven' and potential fossil fuel reserves are uncertain and debated. Regardless of the true values, society has flexibility in the degree to which it chooses to exploit these reserves, especially unconventional fossil fuels and those located in extreme or pristine environments. If conventional oil production peaks within the next few decades, it may have a large effect on future atmospheric CO{sub 2} and climate change, depending upon subsequent energy choices. Assuming that proven oil and gas reserves do not greatly exceed estimates of the Energy Information Administration, and recent trends are toward lower estimates, we show that it is feasible to keep atmospheric CO{sub 2} from exceeding about 450 ppm by 2100, provided that emissions from coal, unconventional fossil fuels, and land use are constrained. Coal-fired power plants without sequestration must be phased out before midcentury to achieve this CO{sub 2} limit. It is also important to 'stretch' conventional oil reserves via energy conservation and efficiency, thus averting strong pressures to extract liquid fuels from coal or unconventional fossil fuels while clean technologies are being developed for the era 'beyond fossil fuels'. We argue that a rising price on carbon emissions is needed to discourage conversion of the vast fossil resources into usable reserves, and to keep CO{sub 2} beneath the 450 ppm ceiling.

  14. The 17 GHz active region number

    SciTech Connect (OSTI)

    Selhorst, C. L.; Pacini, A. A.; Costa, J. E. R.; Gimnez de Castro, C. G.; Valio, A.; Shibasaki, K.

    2014-08-01

    We report the statistics of the number of active regions (NAR) observed at 17 GHz with the Nobeyama Radioheliograph between 1992, near the maximum of cycle 22, and 2013, which also includes the maximum of cycle 24, and we compare with other activity indexes. We find that NAR minima are shorter than those of the sunspot number (SSN) and radio flux at 10.7 cm (F10.7). This shorter NAR minima could reflect the presence of active regions generated by faint magnetic fields or spotless regions, which were a considerable fraction of the counted active regions. The ratio between the solar radio indexes F10.7/NAR shows a similar reduction during the two minima analyzed, which contrasts with the increase of the ratio of both radio indexes in relation to the SSN during the minimum of cycle 23-24. These results indicate that the radio indexes are more sensitive to weaker magnetic fields than those necessary to form sunspots, of the order of 1500 G. The analysis of the monthly averages of the active region brightness temperatures shows that its long-term variation mimics the solar cycle; however, due to the gyro-resonance emission, a great number of intense spikes are observed in the maximum temperature study. The decrease in the number of these spikes is also evident during the current cycle 24, a consequence of the sunspot magnetic field weakening in the last few years.

  15. Pennsylvania Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 618 606 604 540 627 666 1967-2014 Industrial Number of Consumers 4,745 4,624 5,007 5,066 5,024 5,084 1987-2014...

  16. The New Element Curium (Atomic Number 96)

    DOE R&D Accomplishments [OSTI]

    Seaborg, G. T.; James, R. A.; Ghiorso, A.

    1948-00-00

    Two isotopes of the element with atomic number 96 have been produced by the helium-ion bombardment of plutonium. The name curium, symbol Cm, is proposed for element 96. The chemical experiments indicate that the most stable oxidation state of curium is the III state.

  17. Washington Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    059,239 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1987-2014 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1997-2014 Commercial Number of Consumers 98,965 99,231...

  18. Minnesota Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    1,436,063 1,445,824 1,459,134 1,472,663 1997-2014 Commercial Number of Consumers 131,801 132,163 132,938 134,394 135,557 136,382 1987-2014 Sales 131,986 132,697 134,165 135,235...

  19. West Virginia Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    343,837 344,131 342,069 340,256 340,102 338,652 1987-2014 Sales 344,125 342,063 340,251 340,098 338,649 1997-2014 Transported 6 6 5 4 3 1997-2014 Commercial Number of Consumers...

  20. Connecticut Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    489,349 490,185 494,970 504,138 513,492 522,658 1986-2014 Sales 489,380 494,065 503,241 512,110 521,460 1997-2014 Transported 805 905 897 1,382 1,198 1997-2014 Commercial Number of...

  1. North Carolina Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,102,001 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1987-2014 Sales 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1997-2014 Commercial Number of Consumers 113,630...

  2. Climate Zone Number 1 | Open Energy Information

    Open Energy Info (EERE)

    Zone Number 1 is defined as Very Hot - Humid(1A) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C Dry(1B) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C...

  3. Maine Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    20,806 21,142 22,461 23,555 24,765 27,047 1987-2014 Sales 21,141 22,461 23,555 24,765 27,047 1997-2014 Transported 1 0 0 0 0 2010-2014 Commercial Number of Consumers 8,815 9,084...

  4. South Dakota Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    173,856 176,204 179,042 1997-2014 Commercial Number of Consumers 22,071 22,267 22,570 22,955 23,214 23,591 1987-2014 Sales 22,028 22,332 22,716 22,947 23,330 1998-2014...

  5. Rhode Island Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    24,846 225,204 225,828 228,487 231,763 233,786 1987-2014 Sales 225,204 225,828 228,487 231,763 233,786 1997-2014 Commercial Number of Consumers 22,988 23,049 23,177 23,359 23,742 23,934 1987-2014 Sales 21,507 21,421 21,442 21,731 21,947 1998-2014 Transported 1,542 1,756 1,917 2,011 1,987 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 467 454 468 432 490 551 1967-2014 Industrial Number of Consumers 260 249 245 248 271 266 1987-2014 Sales 57 53 56 62 62 1998-2014 Transported 192

  6. South Carolina Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    565,774 570,797 576,594 583,633 593,286 604,743 1987-2014 Sales 570,797 576,594 583,633 593,286 604,743 1997-2014 Commercial Number of Consumers 55,850 55,853 55,846 55,908 55,997 56,172 1987-2014 Sales 55,776 55,760 55,815 55,902 56,074 1998-2014 Transported 77 86 93 95 98 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 393 432 396 383 426 452 1967-2014 Industrial Number of Consumers 1,358 1,325 1,329 1,435 1,452 1,426 1987-2014 Sales 1,139 1,137 1,215 1,223 1,199 1998-2014

  7. Tennessee Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ,083,573 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 1987-2014 Sales 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 1997-2014 Commercial Number of Consumers 127,704 127,914 128,969 130,139 131,091 131,001 1987-2014 Sales 127,806 128,866 130,035 130,989 130,905 1998-2014 Transported 108 103 104 102 96 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 406 439 404 345 411 438 1967-2014 Industrial Number of Consumers 2,717 2,702 2,729 2,679 2,581 2,595 1987-2014 Sales 2,340

  8. Texas Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    4,248,613 4,288,495 4,326,156 4,370,057 4,424,103 4,469,282 1987-2014 Sales 4,287,929 4,326,076 4,369,990 4,424,037 4,469,220 1997-2014 Transported 566 80 67 66 62 1997-2014 Commercial Number of Consumers 313,384 312,277 314,041 314,811 314,036 317,217 1987-2014 Sales 310,842 312,164 312,574 311,493 313,971 1998-2014 Transported 1,435 1,877 2,237 2,543 3,246 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 534 605 587 512 553 583 1967-2014 Industrial Number of Consumers 8,581

  9. Kentucky Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    754,761 758,129 759,584 757,790 761,575 760,131 1987-2014 Sales 728,940 730,602 730,184 736,011 735,486 1997-2014 Transported 29,189 28,982 27,606 25,564 24,645 1997-2014 Commercial Number of Consumers 83,862 84,707 84,977 85,129 85,999 85,318 1987-2014 Sales 80,541 80,392 80,644 81,579 81,026 1998-2014 Transported 4,166 4,585 4,485 4,420 4,292 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 423 435 407 361 435 469 1967-2014 Industrial Number of Consumers 1,715 1,742 1,705 1,720

  10. Louisiana Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    889,570 893,400 897,513 963,688 901,635 899,378 1987-2014 Sales 893,400 897,513 963,688 901,635 899,378 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 58,396 58,562 58,749 63,381 59,147 58,611 1987-2014 Sales 58,501 58,685 63,256 58,985 58,438 1998-2014 Transported 61 64 125 162 173 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 405 461 441 415 488 532 1967-2014 Industrial Number of Consumers 954 942 920 963 916 883 1987-2014 Sales 586 573 628 570 546

  11. Maryland Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    067,807 1,071,566 1,077,168 1,078,978 1,099,272 1,101,292 1987-2014 Sales 923,870 892,844 867,627 852,555 858,352 1997-2014 Transported 147,696 184,324 211,351 246,717 242,940 1997-2014 Commercial Number of Consumers 75,771 75,192 75,788 75,799 77,117 77,846 1987-2014 Sales 54,966 53,778 52,383 52,763 53,961 1998-2014 Transported 20,226 22,010 23,416 24,354 23,885 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 912 898 891 846 923 961 1967-2014 Industrial Number of Consumers

  12. Mississippi Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    437,715 436,840 442,479 442,840 445,589 444,423 1987-2014 Sales 436,840 439,511 440,171 442,974 444,423 1997-2014 Transported 0 2,968 2,669 2,615 0 2010-2014 Commercial Number of Consumers 50,713 50,537 50,636 50,689 50,153 50,238 1987-2014 Sales 50,503 50,273 50,360 49,829 50,197 1998-2014 Transported 34 363 329 324 41 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 377 419 400 352 388 442 1967-2014 Industrial Number of Consumers 1,141 980 982 936 933 943 1987-2014 Sales 860 853

  13. Missouri Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    348,781 1,348,549 1,342,920 1,389,910 1,357,740 1,363,286 1987-2014 Sales 1,348,549 1,342,920 1,389,910 1,357,740 1,363,286 1997-2014 Transported 0 0 0 0 0 2010-2014 Commercial Number of Consumers 140,633 138,670 138,214 144,906 142,495 143,024 1987-2014 Sales 137,342 136,843 143,487 141,047 141,477 1998-2014 Transported 1,328 1,371 1,419 1,448 1,547 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 437 441 451 378 453 510 1967-2014 Industrial Number of Consumers 3,573 3,541 3,307

  14. Montana Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    255,472 257,322 259,046 259,957 262,122 265,849 1987-2014 Sales 256,841 258,579 259,484 261,637 265,323 1997-2014 Transported 481 467 473 485 526 2005-2014 Commercial Number of Consumers 33,731 34,002 34,305 34,504 34,909 35,205 1987-2014 Sales 33,652 33,939 33,967 34,305 34,558 1998-2014 Transported 350 366 537 604 647 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 699 602 651 557 601 612 1967-2014 Industrial Number of Consumers 396 384 381 372 372 369 1987-2014 Sales 312 304

  15. Utah Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    810,442 821,525 830,219 840,687 854,389 869,052 1987-2014 Sales 821,525 830,219 840,687 854,389 869,052 1997-2014 Commercial Number of Consumers 60,781 61,976 62,885 63,383 64,114 65,134 1987-2014 Sales 61,929 62,831 63,298 63,960 64,931 1998-2014 Transported 47 54 85 154 203 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 609 621 643 558 646 586 1967-2014 Industrial Number of Consumers 293 293 286 302 323 328 1987-2014 Sales 205 189 189 187 178 1998-2014 Transported 88 97 113

  16. Vermont Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    37,242 38,047 38,839 39,917 41,152 42,231 1987-2014 Sales 38,047 38,839 39,917 41,152 42,231 1997-2014 Commercial Number of Consumers 5,085 5,137 5,256 5,535 5,441 5,589 1987-2014 Sales 5,137 5,256 5,535 5,441 5,589 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 488 464 472 418 873 864 1967-2014 Industrial Number of Consumers 36 38 36 38 13 13 1987-2014 Sales 37 35 38 13 13 1998-2014 Transported 1 1 0 0 0 1999-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 80,290

  17. Virginia Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    1,124,717 1,133,103 1,145,049 1,155,636 1,170,161 1,183,894 1987-2014 Sales 1,076,080 1,081,581 1,088,340 1,102,646 1,114,224 1997-2014 Transported 57,023 63,468 67,296 67,515 69,670 1997-2014 Commercial Number of Consumers 95,704 95,401 96,086 96,503 97,499 98,741 1987-2014 Sales 85,521 85,522 85,595 86,618 87,470 1998-2014 Transported 9,880 10,564 10,908 10,881 11,271 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 707 722 669 624 699 731 1967-2014 Industrial Number of

  18. Washington Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    059,239 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1987-2014 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1997-2014 Commercial Number of Consumers 98,965 99,231 99,674 100,038 100,939 101,730 1987-2014 Sales 99,166 99,584 99,930 100,819 101,606 1998-2014 Transported 65 90 108 120 124 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 517 567 534 553 535 1967-2014 Industrial Number of Consumers 3,428 3,372 3,353 3,338 3,320 3,355 1987-2014 Sales 3,056 3,031

  19. Wisconsin Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    656,614 1,663,583 1,671,834 1,681,001 1,692,891 1,705,907 1987-2014 Sales 1,663,583 1,671,834 1,681,001 1,692,891 1,705,907 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 163,843 164,173 165,002 165,657 166,845 167,901 1987-2014 Sales 163,060 163,905 164,575 165,718 166,750 1998-2014 Transported 1,113 1,097 1,082 1,127 1,151 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 558 501 528 465 596 637 1967-2014 Industrial Number of Consumers 6,396 6,413 6,376

  20. Wyoming Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    153,062 153,852 155,181 157,226 158,889 160,896 1987-2014 Sales 117,735 118,433 118,691 117,948 118,396 1997-2014 Transported 36,117 36,748 38,535 40,941 42,500 1997-2014 Commercial Number of Consumers 19,843 19,977 20,146 20,387 20,617 20,894 1987-2014 Sales 14,319 14,292 14,187 14,221 14,452 1998-2014 Transported 5,658 5,854 6,200 6,396 6,442 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 523 558 580 514 583 583 1967-2014 Industrial Number of Consumers 130 120 123 127 132 131

  1. Nebraska Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    512,551 510,776 514,481 515,338 527,397 522,408 1987-2014 Sales 442,413 446,652 447,617 459,712 454,725 1997-2014 Transported 68,363 67,829 67,721 67,685 67,683 1997-2014 Commercial Number of Consumers 56,454 56,246 56,553 56,608 58,005 57,191 1987-2014 Sales 40,348 40,881 41,074 42,400 41,467 1998-2014 Transported 15,898 15,672 15,534 15,605 15,724 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 569 568 468 555 567 1967-2014 Industrial Number of Consumers 7,863 7,912 7,955

  2. Nevada Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    760,391 764,435 772,880 782,759 794,150 808,970 1987-2014 Sales 764,435 772,880 782,759 794,150 808,970 1997-2014 Commercial Number of Consumers 41,303 40,801 40,944 41,192 41,710 42,338 1987-2014 Sales 40,655 40,786 41,023 41,536 42,163 1998-2014 Transported 146 158 169 174 175 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 715 722 751 704 748 687 1967-2014 Industrial Number of Consumers 192 184 177 177 195 218 1987-2014 Sales 152 147 146 162 183 1998-2014 Transported 32 30 31

  3. New Hampshire Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    96,924 95,361 97,400 99,738 98,715 99,146 1987-2014 Sales 95,360 97,400 99,738 98,715 99,146 1997-2014 Transported 1 0 0 0 0 2010-2014 Commercial Number of Consumers 16,937 16,645 17,186 17,758 17,298 17,421 1987-2014 Sales 15,004 15,198 15,429 14,685 14,527 1998-2014 Transported 1,641 1,988 2,329 2,613 2,894 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 587 505 517 458 532 540 1967-2014 Industrial Number of Consumers 155 306 362 466 403 326 1987-2014 Sales 31 25 30 35 45

  4. New Mexico Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    560,479 559,852 570,637 561,713 572,224 614,313 1987-2014 Sales 559,825 570,592 561,652 572,146 614,231 1997-2014 Transported 27 45 61 78 82 1997-2014 Commercial Number of Consumers 48,846 48,757 49,406 48,914 50,163 55,689 1987-2014 Sales 45,679 46,104 45,298 46,348 51,772 1998-2014 Transported 3,078 3,302 3,616 3,815 3,917 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 506 516 507 509 534 461 1967-2014 Industrial Number of Consumers 471 438 360 121 123 116 1987-2014 Sales 390

  5. North Dakota Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    22,065 123,585 125,392 130,044 133,975 137,972 1987-2014 Sales 123,585 125,392 130,044 133,975 137,972 1997-2014 Transported 0 0 0 0 0 2004-2014 Commercial Number of Consumers 17,632 17,823 18,421 19,089 19,855 20,687 1987-2014 Sales 17,745 18,347 19,021 19,788 20,623 1998-2014 Transported 78 74 68 67 64 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 623 578 596 543 667 677 1967-2014 Industrial Number of Consumers 279 307 259 260 266 269 1987-2014 Sales 255 204 206 211 210

  6. Oklahoma Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    924,745 914,869 922,240 927,346 931,981 937,237 1987-2014 Sales 914,869 922,240 927,346 931,981 937,237 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 94,314 92,430 93,903 94,537 95,385 96,004 1987-2014 Sales 88,217 89,573 90,097 90,861 91,402 1998-2014 Transported 4,213 4,330 4,440 4,524 4,602 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 439 452 430 382 464 489 1967-2014 Industrial Number of Consumers 2,618 2,731 2,733 2,872 2,958 3,063 1987-2014

  7. Oregon Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    675,582 682,737 688,681 693,507 700,211 707,010 1987-2014 Sales 682,737 688,681 693,507 700,211 707,010 1997-2014 Commercial Number of Consumers 76,893 77,370 77,822 78,237 79,276 80,480 1987-2014 Sales 77,351 77,793 78,197 79,227 80,422 1998-2014 Transported 19 29 40 49 58 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 387 352 390 368 386 353 1967-2014 Industrial Number of Consumers 1,051 1,053 1,066 1,076 1,085 1,099 1987-2014 Sales 821 828 817 821 839 1998-2014 Transported

  8. Sensitivity in risk analyses with uncertain numbers.

    SciTech Connect (OSTI)

    Tucker, W. Troy; Ferson, Scott

    2006-06-01

    Sensitivity analysis is a study of how changes in the inputs to a model influence the results of the model. Many techniques have recently been proposed for use when the model is probabilistic. This report considers the related problem of sensitivity analysis when the model includes uncertain numbers that can involve both aleatory and epistemic uncertainty and the method of calculation is Dempster-Shafer evidence theory or probability bounds analysis. Some traditional methods for sensitivity analysis generalize directly for use with uncertain numbers, but, in some respects, sensitivity analysis for these analyses differs from traditional deterministic or probabilistic sensitivity analyses. A case study of a dike reliability assessment illustrates several methods of sensitivity analysis, including traditional probabilistic assessment, local derivatives, and a ''pinching'' strategy that hypothetically reduces the epistemic uncertainty or aleatory uncertainty, or both, in an input variable to estimate the reduction of uncertainty in the outputs. The prospects for applying the methods to black box models are also considered.

  9. Colorado Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,622,434 1,634,587 1,645,716 1,659,808 1,672,312 1,690,581 1986-2014 Sales 1,634,582 1,645,711 1,659,803 1,672,307 1,690,576 1997-2014 Transported 5 5 5 5 5 1997-2014 Commercial Number of Consumers 145,624 145,460 145,837 145,960 150,145 150,235 1986-2014 Sales 145,236 145,557 145,563 149,826 149,921 1998-2014 Transported 224 280 397 319 314 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 429 396 383 355 392 386 1967-2014 Industrial Number of Consumers 5,084 6,232 6,529 6,906

  10. Delaware Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    9,006 150,458 152,005 153,307 155,627 158,502 1986-2014 Sales 150,458 152,005 153,307 155,627 158,502 1997-2014 Commercial Number of Consumers 12,839 12,861 12,931 12,997 13,163 13,352 1986-2014 Sales 12,706 12,656 12,644 12,777 12,902 1998-2014 Transported 155 275 353 386 450 1999-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 910 948 810 772 849 890 1967-2014 Industrial Number of Consumers 112 114 129 134 138 141 1987-2014 Sales 40 35 29 28 28 1998-2014 Transported 74 94 105 110

  11. Florida Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    674,090 675,551 679,199 686,994 694,210 703,535 1986-2014 Sales 661,768 664,564 672,133 679,191 687,766 1997-2014 Transported 13,783 14,635 14,861 15,019 15,769 1997-2014 Commercial Number of Consumers 59,549 60,854 61,582 63,477 64,772 67,460 1986-2014 Sales 41,750 41,068 41,102 40,434 41,303 1998-2014 Transported 19,104 20,514 22,375 24,338 26,157 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 846 888 869 861 926 929 1967-2014 Industrial Number of Consumers 607 581 630 507 528

  12. Georgia Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    1,744,934 1,740,587 1,740,006 1,739,543 1,805,425 1,755,847 1986-2014 Sales 321,290 321,515 319,179 377,652 315,562 1997-2014 Transported 1,419,297 1,418,491 1,420,364 1,427,773 1,440,285 1997-2014 Commercial Number of Consumers 127,347 124,759 123,454 121,243 126,060 122,573 1986-2014 Sales 32,318 32,162 31,755 36,556 31,845 1998-2014 Transported 92,441 91,292 89,488 89,504 90,728 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 421 482 458 428 454 482 1967-2014 Industrial Number

  13. Hawaii Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    25,466 25,389 25,305 25,184 26,374 28,919 1987-2014 Sales 25,389 25,305 25,184 26,374 28,919 1998-2014 Commercial Number of Consumers 2,535 2,551 2,560 2,545 2,627 2,789 1987-2014 Sales 2,551 2,560 2,545 2,627 2,789 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 691 697 691 727 713 692 1980-2014 Industrial Number of Consumers 25 24 24 22 22 23 1997-2014 Sales 24 24 22 22 23 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 13,753 14,111 15,087 16,126 17,635 17,

  14. Idaho Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    42,277 346,602 350,871 353,963 359,889 367,394 1987-2014 Sales 346,602 350,871 353,963 359,889 367,394 1997-2014 Commercial Number of Consumers 38,245 38,506 38,912 39,202 39,722 40,229 1987-2014 Sales 38,468 38,872 39,160 39,681 40,188 1998-2014 Transported 38 40 42 41 41 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 412 390 433 404 465 422 1967-2014 Industrial Number of Consumers 187 184 178 179 183 189 1987-2014 Sales 108 103 105 109 115 1998-2014 Transported 76 75 74 74 74

  15. Iowa Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    875,781 879,713 883,733 892,123 895,414 900,420 1987-2014 Sales 879,713 883,733 892,123 895,414 900,420 1997-2014 Commercial Number of Consumers 98,416 98,396 98,541 99,113 99,017 99,182 1987-2014 Sales 96,996 97,075 97,580 97,334 97,409 1998-2014 Transported 1,400 1,466 1,533 1,683 1,773 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 576 525 526 442 572 579 1967-2014 Industrial Number of Consumers 1,626 1,528 1,465 1,469 1,491 1,572 1987-2014 Sales 1,161 1,110 1,042 1,074 1,135

  16. Kansas Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    855,454 853,842 854,730 854,800 858,572 861,092 1987-2014 Sales 853,842 854,730 854,779 858,546 861,066 1997-2014 Transported 0 0 21 26 26 2004-2014 Commercial Number of Consumers 84,715 84,446 84,874 84,673 84,969 85,867 1987-2014 Sales 78,310 78,559 78,230 78,441 79,231 1998-2014 Transported 6,136 6,315 6,443 6,528 6,636 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 384 377 378 301 391 425 1967-2014 Industrial Number of Consumers 7,793 7,664 7,954 7,970 7,877 7,429 1987-2014

  17. Alabama Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    785,005 778,985 772,892 767,396 765,957 769,418 1986-2014 Sales 778,985 772,892 767,396 765,957 769,418 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 67,674 68,163 67,696 67,252 67,136 67,806 1986-2014 Sales 68,017 67,561 67,117 67,006 67,677 1998-2014 Transported 146 135 135 130 129 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 359 397 371 320 377 406 1967-2014 Industrial Number of Consumers 3,057 3,039 2,988 3,045 3,143 3,244 1986-2014 Sales 2,758

  18. Alaska Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    120,124 121,166 121,736 122,983 124,411 126,416 1986-2014 Sales 121,166 121,736 122,983 124,411 126,416 1997-2014 Commercial Number of Consumers 13,215 12,998 13,027 13,133 13,246 13,399 1986-2014 Sales 12,673 12,724 13,072 13,184 13,336 1998-2014 Transported 325 303 61 62 63 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 1,258 1,225 1,489 1,515 1,411 1,338 1967-2014 Industrial Number of Consumers 3 3 5 3 3 1 1987-2014 Sales 2 2 3 2 1 1998-2014 Transported 1 3 0 1 0 1998-2014

  19. Arizona Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,130,047 1,138,448 1,146,286 1,157,688 1,172,003 1,186,794 1986-2014 Sales 1,138,448 1,146,280 1,157,682 1,171,997 1,186,788 1997-2014 Transported 0 6 6 6 6 1997-2014 Commercial Number of Consumers 57,191 56,676 56,547 56,532 56,585 56,649 1986-2014 Sales 56,510 56,349 56,252 56,270 56,331 1998-2014 Transported 166 198 280 315 318 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 564 577 558 581 538 1967-2014 Industrial Number of Consumers 390 368 371 379 383 386 1987-2014

  20. Arkansas Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    557,355 549,970 551,795 549,959 549,764 549,034 1986-2014 Sales 549,970 551,795 549,959 549,764 549,034 1997-2014 Commercial Number of Consumers 69,043 67,987 67,815 68,765 68,791 69,011 1986-2014 Sales 67,676 67,454 68,151 68,127 68,291 1998-2014 Transported 311 361 614 664 720 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 527 592 590 603 692 734 1967-2014 Industrial Number of Consumers 1,025 1,079 1,133 990 1,020 1,009 1986-2014 Sales 580 554 523 513 531 1998-2014 Transported

  1. Multiple current peaks in room-temperature atmospheric pressure homogenous dielectric barrier discharge plasma excited by high-voltage tunable nanosecond pulse in air

    SciTech Connect (OSTI)

    Yang, De-Zheng; Wang, Wen-Chun; Zhang, Shuai; Tang, Kai; Liu, Zhi-jie; Wang, Sen

    2013-05-13

    Room temperature homogenous dielectric barrier discharge plasma with high instantaneous energy efficiency is acquired by using nanosecond pulse voltage with 20-200 ns tunable pulse width. Increasing the voltage pulse width can lead to the generation of regular and stable multiple current peaks in each discharge sequence. When the voltage pulse width is 200 ns, more than 5 organized current peaks can be observed under 26 kV peak voltage. Investigation also shows that the organized multiple current peaks only appear in homogenous discharge mode. When the discharge is filament mode, organized multiple current peaks are replaced by chaotic filament current peaks.

  2. Volume, Number of Shipments Surpass Goals

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    shatters records in first year of accelerated shipping effort October 3, 2012 Los Alamos National Laboratory shatters records in first year of accelerated shipping effort Volume, Number of Shipments Surpass Goals LOS ALAMOS, NEW MEXICO, October 3, 2012-In the first year of an effort to accelerate shipments of transuranic (TRU) waste to the Waste Isolation Pilot Plant (WIPP), Los Alamos National Laboratory shattered its own record with 59 more shipments than planned, and became one of the largest

  3. Low Mach Number Models in Computational Astrophysics

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    In memoriam: Michael Welcome 1957 - 2014 RIP Almgren CCSE Low Mach Number Models in Computational Astrophysics Ann Almgren Center for Computational Sciences and Engineering Lawrence Berkeley National Laboratory NUG 2014: NERSC@40 February 4, 2014 Collaborators: John Bell, Chris Malone, Andy Nonaka, Stan Woosley, Michael Zingale Almgren CCSE Introduction We often associate astrophysics with explosive phenomena: novae supernovae gamma-ray bursts X-ray bursts Type Ia Supernovae Largest

  4. Notices Total Estimated Number of Annual

    Energy Savers [EERE]

    372 Federal Register / Vol. 78, No. 181 / Wednesday, September 18, 2013 / Notices Total Estimated Number of Annual Burden Hours: 10,128. Abstract: Enrollment in the Federal Student Aid (FSA) Student Aid Internet Gateway (SAIG) allows eligible entities to securely exchange Title IV, Higher Education Act (HEA) assistance programs data electronically with the Department of Education processors. Organizations establish Destination Point Administrators (DPAs) to transmit, receive, view and update

  5. Stockpile Stewardship Quarterly, Volume 2, Number 1

    National Nuclear Security Administration (NNSA)

    1 * May 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 2, Number 1 Inside this Issue 2 LANL and ANL Complete Groundbreaking Shock Experiments at the Advanced Photon Source 3 Characterization of Activity-Size-Distribution of Nuclear Fallout 5 Modeling Mix in High-Energy-Density Plasma 6 Quality Input for Microscopic Fission Theory 8 Fiber Reinforced Composites Under Pressure: A Case Study in

  6. U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number

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

    of Elements) Acquifers Capacity (Number of Elements) U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49 2000's 49 39 38 43 43 44 44 43 43 43 2010's 43 43 44 47 46 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of

  7. Method for reducing peak phase current and decreasing staring time for an internal combustion engine having an induction machine

    DOE Patents [OSTI]

    Amey, David L. (Birmingham, MI); Degner, Michael W. (Farmington Hills, MI)

    2002-01-01

    A method for reducing the starting time and reducing the peak phase currents for an internal combustion engine that is started using an induction machine starter/alternator. The starting time is reduced by pre-fluxing the induction machine and the peak phase currents are reduced by reducing the flux current command after a predetermined period of time has elapsed and concurrent to the application of the torque current command. The method of the present invention also provides a strategy for anticipating the start command for an internal combustion engine and determines a start strategy based on the start command and the operating state of the internal combustion engine.

  8. ,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, "

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

    2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, " ,"2008 and Projected 2009 through 2013 " ,"(Megawatts and 2008 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid" ,"Projected Year Base","Year",,"FRCC"," MRO (U.S.) ","NPCC (U.S.)

  9. Atmospheric Radiation Measurement (ARM) Data from Steamboat Springs, Colorado, for the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    In October 2010, the initial deployment of the second ARM Mobile Facility (AMF2) took place at Steamboat Springs, Colorado, for the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX). The objective of this field campaign was to obtain data about liquid and mixed-phase clouds using AMF2 instruments in conjunction with Storm Peak Laboratory (located at an elevation of 3220 meters on Mt. Werner), a cloud and aerosol research facility operated by the Desert Research Institute. STORMVEX datasets are freely available for viewing and download. Users are asked to register with the ARM Archive; the user's email address is used from that time forward as the login name.

  10. Property:Number of Plants Included in Planned Estimate | Open...

    Open Energy Info (EERE)

    Number of Plants Included in Planned Estimate Jump to: navigation, search Property Name Number of Plants Included in Planned Estimate Property Type String Description Number of...

  11. Property:NumberOfLEDSTools | Open Energy Information

    Open Energy Info (EERE)

    Name NumberOfLEDSTools Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLEDSTools&oldid322418" Feedback Contact needs updating Image...

  12. Property:Number of Color Cameras | Open Energy Information

    Open Energy Info (EERE)

    Color Cameras Jump to: navigation, search Property Name Number of Color Cameras Property Type Number Pages using the property "Number of Color Cameras" Showing 25 pages using this...

  13. Health Code Number (HCN) Development Procedure

    SciTech Connect (OSTI)

    Petrocchi, Rocky; Craig, Douglas K.; Bond, Jayne-Anne; Trott, Donna M.; Yu, Xiao-Ying

    2013-09-01

    This report provides the detailed description of health code numbers (HCNs) and the procedure of how each HCN is assigned. It contains many guidelines and rationales of HCNs. HCNs are used in the chemical mixture methodology (CMM), a method recommended by the department of energy (DOE) for assessing health effects as a result of exposures to airborne aerosols in an emergency. The procedure is a useful tool for proficient HCN code developers. Intense training and quality assurance with qualified HCN developers are required before an individual comprehends the procedure to develop HCNs for DOE.

  14. The numbers will follow | Jefferson Lab

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The numbers will follow September 26, 2008 As all of you well know, the safety performance of Jefferson Lab, our laboratory, has been nothing short of stellar over the past couple of years. To cap it all, you were subjected to what is usually rated as the toughest of the sit-down examinations, the HSS audit. Not only did you exceed expectations, but you did so by a large margin. A basis for this great result, as documented by the HSS team, was the engagement and commitment of the workforce, the

  15. Mo Year Report Period: EIA ID NUMBER:

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Mo Year Report Period: EIA ID NUMBER: http://www.eia.gov/survey/form/eia_14/instructions.pdf Mailing Address: Secure File Transfer option available at: (e.g., PO Box, RR) https://signon.eia.doe.gov/upload/noticeoog.jsp Electronic Transmission: The PC Electronic Zip Code - Data Reporting Option (PEDRO) is available. If interested in software, call (202) 586-9659. Email form to: OOG.SURVEYS@eia.doe.gov - - - - Fax form to: (202) 586-9772 Mail form to: Oil & Gas Survey Email address: U.S.

  16. Experimental Stations by Number | Stanford Synchrotron Radiation

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Lightsource Experimental Stations by Number Beam Line by Techniques Photon Source Parameters Station Type Techniques Energy Range Contact Person Experimental Station 1-5 X-ray Materials Small-angle X-ray Scattering (SAXS) focused 4600-16000 eV Christopher J. Tassone Tim J. Dunn Experimental Station 2-1 X-ray Powder diffraction Thin film diffraction Focused 5000 - 14500 eV Apurva Mehta Charles Troxel Jr Experimental Station 2-2 X-ray X-ray Absorption Spectroscopy 1000-40000 eV Ryan Davis

  17. OMB Control Number: 1910-5165

    Energy Savers [EERE]

    OMB Control Number: 1910-5165 Expires: xx/xx/201x SEMI-ANNUAL DAVIS-BACON ENFORCEMENT REPORT Please submit this Semi-Annual Davis-Bacon Enforcement Report to your site DOE/NNSA Contractor Human Resource Division (CHRD) Office. If you do not have a DOE/NNSA CHRD Office, please submit the report to: DBAEnforcementReports@hq.doe.gov. The following questions regarding enforcement activity (Davis-Bacon and Related Acts) by this Agency are required by 29 CFR, Part 5.7(b), and Department of Labor, All

  18. What's Behind the Numbers? | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    What's Behind the Numbers? Dr. Richard Newell Dr. Richard Newell What does this mean for me? New website shows data on the why's, when's and how's of crude oil prices. Among the most visible prices that consumers may see on a daily basis are the ones found on the large signs at the gasoline stations alongside our streets and highways. The biggest single factor affecting gasoline prices is the cost of crude oil, the main raw material for gasoline production, which accounts for well over half the

  19. Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 1990's 5 6 6 6 6 7 7 8 8 8 2000's 9 8 7 9 6 6 7 7 6 6 2010's 5 5 5 5 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages:

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

    SciTech Connect (OSTI)

    DeForest, Nicholas; Mendes, Goncalo; Stadler, Michael; Feng, Wei; Lai, Judy; Marnay, Chris

    2013-06-02

    In much of the developed world, air-conditioning in buildings is the dominant driver of summer peak electricity demand. In the developing world a steadily increasing utilization of air-conditioning places additional strain on already-congested grids. This common thread represents a large and growing threat to the reliable delivery of electricity around the world, requiring capital-intensive expansion of capacity and draining available investment resources. Thermal energy storage (TES), in the form of ice or chilled water, may be one of the few technologies currently capable of mitigating this problem cost effectively and at scale. The installation of TES capacity allows a building to meet its on-peak air conditioning load without interruption using electricity purchased off-peak and operating with improved thermodynamic efficiency. In this way, TES has the potential to fundamentally alter consumption dynamics and reduce impacts of air conditioning. This investigation presents a simulation study of a large office building in four distinct geographical contexts: Miami, Lisbon, Shanghai, and Mumbai. The optimization tool DER-CAM (Distributed Energy Resources Customer Adoption Model) is applied to optimally size TES systems for each location. Summer load profiles are investigated to assess the effectiveness and consistency in reducing peak electricity demand. Additionally, annual energy requirements are used to determine system cost feasibility, payback periods and customer savings under local utility tariffs.

  1. ,"Table 2. Noncoincident Peak Load, by North American Electric Reliability Corporation Assessment Area,"

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

    2. Noncoincident Peak Load, by North American Electric Reliability Corporation Assessment Area," ,"1990-2010 Actual, 2011-2015 Projected" ,"(Megawatts)" ,"Interconnection","NERC Regional Assesment Area","Summer" ,,,"Actual",,,,,,,,,,,,,,,,,,,,,"Projected"

  2. Michigan Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    3,169,026 3,152,468 3,153,895 3,161,033 3,180,349 3,192,807 1987-2014 Sales 2,952,550 2,946,507 2,939,693 2,950,315 2,985,315 1997-2014 Transported 199,918 207,388 221,340 230,034 207,492 1997-2014 Commercial Number of Consumers 252,017 249,309 249,456 249,994 250,994 253,127 1987-2014 Sales 217,325 213,995 212,411 213,532 219,240 1998-2014 Transported 31,984 35,461 37,583 37,462 33,887 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 649 611 656 578 683 736 1967-2014 Industrial

  3. New Jersey Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    2,635,324 2,649,282 2,659,205 2,671,308 2,686,452 2,705,274 1987-2014 Sales 2,556,514 2,514,492 2,467,520 2,428,664 2,482,281 1997-2014 Transported 92,768 144,713 203,788 257,788 222,993 1997-2014 Commercial Number of Consumers 234,125 234,158 234,721 237,602 236,746 240,083 1987-2014 Sales 200,680 196,963 192,913 185,030 186,591 1998-2014 Transported 33,478 37,758 44,689 51,716 53,492 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 771 775 817 735 726 842 1967-2014 Industrial

  4. Ohio Number of Natural Gas Consumers

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    3,253,184 3,240,619 3,236,160 3,244,274 3,271,074 3,283,869 1987-2014 Sales 1,418,217 1,352,292 855,055 636,744 664,015 1997-2014 Transported 1,822,402 1,883,868 2,389,219 2,634,330 2,619,854 1997-2014 Commercial Number of Consumers 270,596 268,346 268,647 267,793 269,081 269,758 1987-2014 Sales 92,621 85,877 51,308 35,966 37,035 1998-2014 Transported 175,725 182,770 216,485 233,115 232,723 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 594 583 601 543 625 679 1967-2014

  5. California Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    0,510,950 10,542,584 10,625,190 10,681,916 10,754,908 10,781,720 1986-2014 Sales 10,469,734 10,545,585 10,547,706 10,471,814 10,372,973 1997-2014 Transported 72,850 79,605 134,210 283,094 408,747 1997-2014 Commercial Number of Consumers 441,806 439,572 440,990 442,708 444,342 443,115 1986-2014 Sales 399,290 390,547 387,760 387,806 385,878 1998-2014 Transported 40,282 50,443 54,948 56,536 57,237 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 561 564 558 572 574 536 1967-2014

  6. Illinois Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,839,438 3,842,206 3,855,942 3,878,806 3,838,120 3,868,501 1987-2014 Sales 3,568,120 3,594,047 3,605,796 3,550,217 3,570,339 1997-2014 Transported 274,086 261,895 273,010 287,903 298,162 1997-2014 Commercial Number of Consumers 294,226 291,395 293,213 297,523 282,743 294,391 1987-2014 Sales 240,197 241,582 244,480 225,913 235,097 1998-2014 Transported 51,198 51,631 53,043 56,830 59,294 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 757 680 735 632 816 837 1967-2014 Industrial

  7. Contractor: Contract Number: Contract Type: Total Estimated

    Office of Environmental Management (EM)

    Contract Number: Contract Type: Total Estimated Contract Cost: Performance Period Total Fee Paid FY2004 $294,316 FY2005 $820,074 FY2006 $799,449 FY2007 $877,898 FY2008 $866,608 FY2009 $886,404 FY2010 $800,314 FY2011 $871,280 FY2012 $824,517 FY2013 Cumulative Fee Paid $7,040,860 $820,074 $799,449 $877,898 $916,130 $886,608 Computer Sciences Corporation DE-AC06-04RL14383 $895,358 $899,230 $907,583 Cost Plus Award Fee $134,100,336 $8,221,404 Fee Available Contract Period: Fee Information Minimum

  8. Word Pro - Untitled1

    Gasoline and Diesel Fuel Update (EIA)

    7 Table 10.8 Photovoltaic Cell and Module Shipments by Type, Trade, and Prices, 1982-2010 Year U.S. Companies Reporting Shipments Shipments Trade Prices 1 Crystalline Silicon Thin-Film Total 2 Imports Exports Cells Modules Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Number Peak Kilowatts 3 Dollars 4 per Peak Watt 3 1982 19 NA NA NA NA 6,897 NA NA NA NA NA NA NA 1983 18 NA NA NA NA

  9. Table 10.8 Photovoltaic Cell and Module Shipments by Type, Trade, and Prices, 1982-2010

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

    Photovoltaic Cell and Module Shipments by Type, Trade, and Prices, 1982-2010 Year U.S. Companies Reporting Shipments Shipments Trade Prices 1 Crystalline Silicon Thin-Film Total 2 Imports Exports Cells Modules Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Cells and Modules Modules Only Number Peak Kilowatts 3 Dollars 4 per Peak Watt 3 1982 19 NA NA NA NA 6,897 NA NA NA NA NA NA NA 1983 18 NA NA NA NA 12,620 NA NA NA

  10. U.S. Natural Gas Number of Commercial Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) - Sales (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 4,823,842 4,599,494 2000's 4,576,873 4,532,034 4,588,964 4,662,853 4,644,363 4,698,626 4,733,822 2010's 4,584,884 4,556,220 4,518,745 4,491,326 4,533,729 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  11. U.S. Natural Gas Number of Commercial Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 220,655 410,695 2000's 433,944 464,412 475,420 489,324 495,586 499,402 539,557 2010's 716,692 763,597 837,652 881,196 885,257 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release

  12. U.S. Natural Gas Number of Industrial Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 182,424 157,050 2000's 157,806 152,974 143,177 142,816 151,386 146,450 135,070 2010's 129,119 124,552 121,821 123,124 122,182 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  13. U.S. Natural Gas Number of Industrial Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49,014 71,281 2000's 75,826 64,052 62,738 62,698 57,672 59,773 58,760 2010's 63,611 64,749 67,551 69,164 69,953 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  14. U.S. Natural Gas Number of Residential Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 55,934,175 56,520,482 56,023,710 2000's 56,261,031 56,710,548 57,267,445 57,815,669 58,524,797 59,787,524 60,129,047 2010's 60,267,648 60,408,842 60,010,723 59,877,464 60,222,681 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  15. U.S. Natural Gas Number of Residential Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 252,783 801,264 2,199,519 2000's 2,978,319 3,576,181 3,839,809 4,055,781 3,971,337 3,829,303 4,037,233 2010's 5,274,697 5,531,680 6,364,411 6,934,929 7,005,081 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  16. Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,700 1990's 2,607 2,802 2,890 3,075 2,940 2,918 2,990 3,071 3,423 3,634 2000's 3,321 4,331 4,544 4,539 4,971 5,751 6,578 6,925 7,095 7,031 2010's 6,059 6,477 6,240 5,754 5,754 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure

  17. New Jersey Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) New Jersey Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 200,387 206,261 212,496 1990's 217,548 215,408 212,726 215,948 219,061 222,632 224,749 226,714 234,459 232,831 2000's 243,541 212,726 214,526 223,564 223,595 226,007 227,819 230,855 229,235 234,125 2010's 234,158 234,721 237,602 236,746 240,083 - = No Data Reported; -- = Not Applicable; NA = Not

  18. New Jersey Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New Jersey Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,265 6,123 6,079 1990's 5,976 8,444 11,474 11,224 10,608 10,362 10,139 17,625 16,282 10,089 2000's 9,686 9,247 8,473 9,027 8,947 8,500 8,245 8,036 7,680 7,871 2010's 7,505 7,391 7,290 7,216 7,157 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  19. New Jersey Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) New Jersey Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,869,903 1,918,185 1,950,165 1990's 1,982,136 2,005,020 2,032,115 2,060,511 2,089,911 2,123,323 2,147,622 2,193,629 2,252,248 2,245,904 2000's 2,364,058 2,466,771 2,434,533 2,562,856 2,582,714 2,540,283 2,578,191 2,609,788 2,601,051 2,635,324 2010's 2,649,282 2,659,205 2,671,308 2,686,452

  20. New Mexico Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) New Mexico Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 36,444 36,940 36,960 1990's 38,026 38,622 40,312 40,166 39,846 38,099 37,796 38,918 42,067 43,834 2000's 44,164 44,306 45,469 45,491 45,961 47,745 47,233 48,047 49,235 48,846 2010's 48,757 49,406 48,914 50,163 55,689 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  1. New Mexico Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) New Mexico Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 17,087 1990's 17,124 20,021 18,040 20,846 23,292 23,510 24,134 27,421 28,200 26,007 2000's 33,948 35,217 35,873 37,100 38,574 40,157 41,634 42,644 44,241 44,784 2010's 44,748 32,302 28,206 27,073 27,957 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  2. New Mexico Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New Mexico Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,703 1,668 1,653 1990's 1,407 1,337 141 152 1,097 1,065 1,365 1,366 1,549 1,482 2000's 1,517 1,875 1,356 1,270 1,164 988 1,062 470 383 471 2010's 438 360 121 123 116 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  3. New Mexico Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) New Mexico Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 348,759 356,192 361,521 1990's 369,451 379,472 389,063 397,681 409,095 421,896 428,621 443,167 454,065 473,375 2000's 479,894 485,969 496,577 498,852 509,119 530,277 533,971 547,512 556,905 560,479 2010's 559,852 570,637 561,713 572,224 614,313 - = No Data Reported; -- = Not Applicable; NA = Not

  4. New York Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New York Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 23,276 24,654 27,426 1990's 25,008 28,837 28,198 23,833 21,833 22,484 15,300 23,099 5,294 6,136 2000's 6,553 6,501 3,068 2,984 2,963 3,752 3,642 7,484 7,080 6,634 2010's 6,236 6,609 5,910 6,311 6,313 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  5. U.S. Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) U.S. Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,013,040 4,124,745 4,168,048 1990's 4,236,280 4,357,252 4,409,699 4,464,906 4,533,905 4,636,500 4,720,227 4,761,409 5,044,497 5,010,189 2000's 5,010,817 4,996,446 5,064,384 5,152,177 5,139,949 5,198,028 5,273,379 5,308,785 5,444,335 5,322,332 2010's 5,301,576 5,319,817 5,356,397 5,372,522 5,418,986 - =

  6. U.S. Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) U.S. Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 195,544 199,041 225,346 1990's 218,341 216,529 209,616 209,666 202,940 209,398 206,049 234,855 226,191 228,331 2000's 220,251 217,026 205,915 205,514 209,058 206,223 193,830 198,289 225,044 207,624 2010's 192,730 189,301 189,372 192,288 192,135 - = No Data Reported; -- = Not Applicable; NA = Not

  7. U.S. Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) U.S. Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 47,710,444 48,474,449 49,309,593 1990's 50,187,178 51,593,206 52,331,397 52,535,411 53,392,557 54,322,179 55,263,673 56,186,958 57,321,746 58,223,229 2000's 59,252,728 60,286,364 61,107,254 61,871,450 62,496,134 63,616,827 64,166,280 64,964,769 65,073,996 65,329,582 2010's 65,542,345 65,940,522

  8. California Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) California Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,214 1990's 1,162 1,377 1,126 1,092 1,261 997 978 930 847 1,152 2000's 1,169 1,244 1,232 1,249 1,272 1,356 1,451 1,540 1,645 1,643 2010's 1,580 1,308 1,423 1,335 1,118 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  9. District of Columbia Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) District of Columbia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 14,683 11,370 11,354 1990's 11,322 11,318 11,206 11,133 11,132 11,089 10,952 10,874 10,658 12,108 2000's 11,106 10,816 10,870 10,565 10,406 10,381 10,410 9,915 10,024 10,288 2010's 9,879 10,050 9,771 9,963 10,049 - = No Data Reported; -- = Not Applicable; NA = Not

  10. District of Columbia Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) District of Columbia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 134 130,748 134,758 134,837 1990's 136,183 136,629 136,438 135,986 135,119 135,299 135,215 134,807 132,867 137,206 2000's 138,252 138,412 143,874 136,258 138,134 141,012 141,953 142,384 142,819 143,436 2010's 144,151 145,524 145,938 146,712 147,877 - = No Data Reported; --

  11. Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 13,935 1990's 16,980 17,948 18,400 19,472 19,365 22,020 21,388 21,500 21,000 17,568 2000's 15,206 15,357 16,957 17,387 18,120 18,946 19,713 19,713 17,862 21,243 2010's 22,145 25,758 24,697 23,792 24,354 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  12. Validation Methodology to Allow Simulated Peak Reduction and Energy Performance Analysis of Residential Building Envelope with Phase Change Materials: Preprint

    SciTech Connect (OSTI)

    Tabares-Velasco, P. C.; Christensen, C.; Bianchi, M.

    2012-08-01

    Phase change materials (PCM) represent a potential technology to reduce peak loads and HVAC energy consumption in residential buildings. This paper summarizes NREL efforts to obtain accurate energy simulations when PCMs are modeled in residential buildings: the overall methodology to verify and validate Conduction Finite Difference (CondFD) and PCM algorithms in EnergyPlus is presented in this study. It also shows preliminary results of three residential building enclosure technologies containing PCM: PCM-enhanced insulation, PCM impregnated drywall and thin PCM layers. The results are compared based on predicted peak reduction and energy savings using two algorithms in EnergyPlus: the PCM and Conduction Finite Difference (CondFD) algorithms.

  13. Interaction-powered supernovae: rise-time versus peak-luminosity correlation and the shock-breakout velocity

    SciTech Connect (OSTI)

    Ofek, Eran O.; Arcavi, Iair; Tal, David; Gal-Yam, Avishay; Ben-Ami, Sagi; De Cia, Annalisa; Yaron, Ofer; Sullivan, Mark; Kulkarni, Shrinivas R.; Cao, Yi; Nugent, Peter E.; Bersier, David; Cenko, S. Bradley; Filippenko, Alexei V.; Fransson, Claes; Kasliwal, Mansi M.; Laher, Russ; Surace, Jason; Quimby, Robert

    2014-06-20

    Interaction of supernova (SN) ejecta with the optically thick circumstellar medium (CSM) of a progenitor star can result in a bright, long-lived shock-breakout event. Candidates for such SNe include Type IIn and superluminous SNe. If some of these SNe are powered by interaction, then there should be a specific relation between their peak luminosity, bolometric light-curve rise time, and shock-breakout velocity. Given that the shock velocity during shock breakout is not measured, we expect a correlation, with a significant spread, between the rise time and the peak luminosity of these SNe. Here, we present a sample of 15 SNe IIn for which we have good constraints on their rise time and peak luminosity from observations obtained using the Palomar Transient Factory. We report on a possible correlation between the R-band rise time and peak luminosity of these SNe, with a false-alarm probability of 3%. Assuming that these SNe are powered by interaction, combining these observables and theory allows us to deduce lower limits on the shock-breakout velocity. The lower limits on the shock velocity we find are consistent with what is expected for SNe (i.e., ?10{sup 4} km s{sup 1}). This supports the suggestion that the early-time light curves of SNe IIn are caused by shock breakout in a dense CSM. We note that such a correlation can arise from other physical mechanisms. Performing such a test on other classes of SNe (e.g., superluminous SNe) can be used to rule out the interaction model for a class of events.

  14. Project Registration Number Assignments (Active) | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Project Registration Number Assignments (Active) Project Registration Number Assignments (Active) As of: October 2015 Provides a table of Project Registration Number Assignments (Active) PDF icon Project Registration Number Assignment (Active) More Documents & Publications Project Registration Number Assignments (Completed) All Active DOE Technical Standards Document Active Project Justification Statement For Additional Information Contact: Jeffrey Feit phone: 301-903-0471 e-mail:

  15. Project Registration Number Assignments (Completed) | Department of Energy

    Energy Savers [EERE]

    Registration Number Assignments (Completed) Project Registration Number Assignments (Completed) As of: March 2016 Provides a table of Project Registration Number Assignments (Completed) PDF icon Project Registration Number Assignments (Completed) More Documents & Publications All Active DOE Technical Standards Document Project Registration Number Assignments (Active) The Proposed Reaffirmations and Cancellations For Additional Information Contact: Jeffrey Feit phone: 301-903-0471 e-mail:

  16. Observed Temperature Effects on Hourly Residential Electric LoadReduction in Response to an Experimental Critical Peak PricingTariff

    SciTech Connect (OSTI)

    Herter, Karen B.; McAuliffe, Patrick K.; Rosenfeld, Arthur H.

    2005-11-14

    The goal of this investigation was to characterize themanual and automated response of residential customers to high-price"critical" events dispatched under critical peak pricing tariffs testedin the 2003-2004 California Statewide Pricing Pilot. The 15-monthexperimental tariff gave customers a discounted two-price time-of-userate on 430 days in exchange for 27 critical days, during which the peakperiod price (2 p.m. to 7 p.m.) was increased to about three times thenormal time-of-use peak price. We calculated response by five-degreetemperature bins as the difference between peak usage on normal andcritical weekdays. Results indicatedthat manual response to criticalperiods reached -0.23 kW per home (-13 percent) in hot weather(95-104.9oF), -0.03 kW per home (-4 percent) in mild weather (60-94.9oF),and -0.07 kW per home (-9 percent) during cold weather (50-59.9oF).Separately, we analyzed response enhanced by programmable communicatingthermostats in high-use homes with air-conditioning. Between 90oF and94.9oF, the response of this group reached -0.56 kW per home (-25percent) for five-hour critical periods and -0.89 kW/home (-41 percent)for two-hour critical periods.

  17. Base-Load and Peak Electricity from a Combined Nuclear Heat and Fossil Combined-Cycle Plant

    SciTech Connect (OSTI)

    Conklin, James C.; Forsberg, Charles W.

    2007-07-01

    A combined-cycle power plant is proposed that uses heat from a high-temperature reactor and fossil fuel to meet base-load and peak electrical demands. The high temperature gas turbine produces shaft power to turn an electric generator. The hot exhaust is then fed to a heat recovery steam generator (HRSG) that provides steam to a steam turbine for added electrical power production. A simplified computational model of the thermal power conversion system was developed in order to parametrically investigate two different steady-state operation conditions: base load nuclear heat only from an Advanced High Temperature Reactor (AHTR), and combined nuclear heat with fossil heat to increase the turbine inlet temperature. These two cases bracket the expected range of power levels, where any intermediate power level can result during electrical load following. The computed results indicate that combined nuclear-fossil systems have the potential to offer both low-cost base-load electricity and lower-cost peak power relative to the existing combination of base-load nuclear plants and separate fossil-fired peak-electricity production units. In addition, electric grid stability, reduced greenhouse gases, and operational flexibility can also result with using the conventional technology presented here for the thermal power conversion system coupled with the AHTR. (authors)

  18. Base-Load and Peak Electricity from a Combined Nuclear Heat and Fossil Combined-Cycle Plant

    SciTech Connect (OSTI)

    Conklin, Jim; Forsberg, Charles W

    2007-01-01

    A combined-cycle power plant is proposed that uses heat from a high-temperature reactor and fossil fuel to meet base-load and peak electrical demands. The high-temperature gas turbine produces shaft power to turn an electric generator. The hot exhaust is then fed to a heat recovery steam generator (HRSG) that provides steam to a steam turbine for added electrical power production. A simplified computational model of the thermal power conversion system was developed in order to parametrically investigate two different steady-state operation conditions: base load nuclear heat only from an Advanced High Temperature Reactor (AHTR), and combined nuclear heat with fossil heat to increase the turbine inlet temperature. These two cases bracket the expected range of power levels, where any intermediate power level can result during electrical load following. The computed results indicate that combined nuclear-fossil systems have the potential to offer both low-cost base-load electricity and lower-cost peak power relative to the existing combination of base-load nuclear plants and separate fossil-fired peak-electricity production units. In addition, electric grid stability, reduced greenhouse gases, and operational flexibility can also result with using the conventional technology presented here for the thermal power conversion system coupled with the AHTR.

  19. Property:NEPA SerialNumber | Open Energy Information

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