National Library of Energy BETA

Sample records for aeo forecast evaluations

  1. Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets

    SciTech Connect (OSTI)

    Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

    2005-02-09

    This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.

  2. Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

    On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

  3. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

    On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

  4. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

    On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we once again find that the AEO 2007 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. Specifically, the NYMEX-AEO 2007 premium is $0.73/MMBtu levelized over five years. In other words, on average, one would have had to pay $0.73/MMBtu more than the AEO 2007 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

  5. Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

    On December 9, the reference case projections from ''Annual Energy Outlook 2005 (AEO 2005)'' were posted on the Energy Information Administration's (EIA) web site. As some of you may be aware, we at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past four years, forward natural gas contracts (e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past four years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation (presuming that long-term price stability is valued). In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2005. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or, more recently (and briefly), http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past four AEO releases (AEO 2001-AE0 2004), we once again find that the AEO 2005 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEXAEO 2005 reference case comparison yields by far the largest premium--$1.11/MMBtu levelized over six years--that we have seen over the last five years. In other words, on average, one would have to pay $1.11/MMBtu more than the AEO 2005 reference case natural gas price forecast in order to lock in natural gas prices over the coming six years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation. Fixed-price renewables obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of six years.

  6. Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

    On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired or nuclear generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers; and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal, uranium, and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

  7. Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark A; Bolinger, Mark; Wiser, Ryan

    2008-01-07

    On December 12, 2007, the reference-case projections from Annual Energy Outlook 2008 (AEO 2008) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof) or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers (though its appeal has diminished somewhat as prices have increased); and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

  8. Energy Technologies on the Horizon (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    A key issue in mid-term forecasting is the representation of changing and developing technologies. How existing technologies will evolve, and what new technologies might emerge, cannot be known with certainty. The issue is of particular importance in Annual Energy Outlook 2006 (AEO), the first AEO with projections out to 2030.

  9. AEO2015 BWG

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

    Behjat Hojjati Kevin Jarzomski David Peterson Steve Wade Owen Comstock (currently on detail) August 7, 2014 AEO2015 Model Updates Discussion purposes only - do not cite or circulate Overview AEO2015 Builldings Working Group Washington, D.C., August 7, 2014 2 * Shorter AEO this year * Federal standards * End-use technology characterizations * Historical updates * Discussion Discussion purposes only - do not cite or circulate Federal standards AEO2015 Builldings Working Group Washington, D.C.,

  10. World Oil Prices in AEO2007 (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    Over the long term, the Annual Energy Outlook 2007 (AEO) projection for world oil prices -- defined as the average price of imported low-sulfur, light crude oil to U.S. refiners -- is similar to the AEO2006 projection. In the near term, however, AEO2007 projects prices that are $8 to $10 higher than those in AEO2006.

  11. AEO2014 results and status updates for the AEO2015

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

    ... modeling updates for AEO2015 * Modeling changes planned for AEO 2015 - Cross State Air Pollution Rule (Transport RuleCSAPR) - U.S. Supreme Court overturned D.C. Circuit Court's ...

  12. World Oil Prices in AEO2006 (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    World oil prices in the Annual Energy Outlook 2006 (AEO) reference case are substantially higher than those in the AEO2005 reference case. In the AEO2006 reference case, world crude oil prices, in terms of the average price of imported low-sulfur, light crude oil to U.S. refiners, decline from current levels to about $47 per barrel (2004 dollars) in 2014, then rise to $54 per barrel in 2025 and $57 per barrel in 2030. The price in 2025 is approximately $21 per barrel higher than the corresponding price projection in the AEO2005 reference case.

  13. Industrial Plans for AEO2014

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

    - Replace energy consumption based on engineering judgment with specific technology or ... for AEO2014 - Glass (defaulted) - Food (not a process flow model; revise on more ...

  14. Metrics for Evaluating the Accuracy of Solar Power Forecasting (Presentation)

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B.; Florita, A.; Lu, S.; Hamann, H.; Banunarayanan, V.

    2013-10-01

    This presentation proposes a suite of metrics for evaluating the performance of solar power forecasting.

  15. Industrial Plans for AEO2014

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

    30, 2013 | Washington, DC WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Industrial team plans for AEO2014 Overview -- AEO2014 * Process flow status & updates * Other model updates * Major data updates * CHP updates 2 Industrial Team Washington DC, July 30, 2013 WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Process flow models * General: - Replace energy consumption based on

  16. AEO2016 Electricity Working Group

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

    Office of Electricity, Coal, Nuclear, and Renewables Analysis December 8, 2015 | Washington, DC AEO2016 Electricity Working Group WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE What to look for: Electricity sector in AEO2016 * Inclusion of EPA final Clean Power Plan in Reference Case * Updated cost estimates for new generating technologies * Major data update on existing coal plant status: MATS- compliant technology or retirement

  17. Efficiency and Intensity in the AEO 2010

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

    the sources of efficiency in the AEO 2010? * What is the contribution of energy efficiency to projected U.S. energy intensity? * How do AEO scenarios relate to technical potential? ...

  18. Renewable Electricity in the Annual Energy Outlook (AEO)

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

    For Renewable Electricity Working Group July 24, 2014 Christopher Namovicz and Gwen Bredehoeft Renewable Electricity Analysis Team AEO2014 results and status updates for the AEO2015 Agenda Renewable Electricity Analysis Team July 24, 2014 2 * Review of AEO2014 - Changes made for AEO2014 - Review of Results * Status of AEO2015 * Updates planned for AEO2015 WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Updates included in the AEO2014

  19. Industrial Team Plans for AEO2015

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

    24, 2014 | Washington, DC WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Industrial team plans for AEO2015 AEO2015 lite year additions * Process flow status (complete AEO2016) * Data updates * Regulation changes * Ethane / propane price modeling 2 Industrial Team Washington DC, July 24, 2014 WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Process flow models * General: - Replace

  20. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Florita, A.; Lu, S.; Hamann, H. F.; Banunarayanan, V.

    2013-10-01

    Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    202-586-6419 Vishakh Mantri, Ph.D, P.E. Chemical Engineer, Energy Information ... tcapehart@ers.usda.gov 202-694-5313 Chemical Production in the AEO Peter Gross Energy ...

  2. File:AEO2012earlyrelease.pdf | Open Energy Information

    Open Energy Info (EERE)

    AEO2012earlyrelease.pdf Jump to: navigation, search File File history File usage File:AEO2012earlyrelease.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  3. A sensitivity analysis of the treatment of wind energy in the AEO99 version of NEMS

    SciTech Connect (OSTI)

    Osborn, Julie G; Wood, Frances; Richey, Cooper; Sanders, Sandy; Short, Walter; Koomey, Jonathan

    2001-01-01

    Each year, the U.S. Department of Energy's Energy Information Administration (EIA) publishes a forecast of the domestic energy economy in the Annual Energy Outlook (AEO). During the forecast period of the AEO (currently through 2020), renewable energy technologies have typically not achieved significant growth. The contribution of renewable technologies as electric generators becomes more important, however, in scenarios analyzing greenhouse gas emissions reductions or significant technological advancements. We examined the economic assumptions about wind power used for producing forecasts with the National Energy Modeling System (NEMS) to determine their influence on the projected capacity expansion of this technology. This analysis should help illustrate to policymakers what types of issues may affect wind development, and improve the general understanding of the NEMS model itself. Figure 1 illustrates the model structure and factors relevant to wind deployment. We found that NEMS uses various cost multipliers and constraints to represent potential physical and economic limitations to growth in wind capacity, such as resource depletion, costs associated with rapid manufacturing expansion, and grid stability with high levels of capacity from intermittent resources. The model's flexibility allows the user to make alternative assumptions about the magnitude of these factors. While these assumptions have little effect on the Reference Case forecast for the 1999 edition of the AEO, they can make a dramatic difference when wind is more attractive, such as under a carbon permit trading system. With $100/ton carbon permits, the wind capacity projection for 2020 ranges from 15 GW in the unaltered model (AEO99 Reference Case) to 168 GW in the extreme case when all the multipliers and constraints examined in this study are removed. Furthermore, if modifications are made to the model allowing inter-regional transmission of electricity, wind capacity is forecast to reach 214 GW when all limitations are removed. The figures in the upper end of these ranges are not intended to be viewed as reasonable projections, but their magnitude illustrates the importance of the parameters governing the growth of wind capacity and resource availability in forecasts using NEMS. In addition, many uncertainties exist regarding these assumptions that potentially affect the growth of wind power. We suggest several areas in which to focus future research in order to better model the potential development of this resource. Because many of the assumptions related to wind in the model are also used for other renewable technologies, these suggestions could be applied to other renewable resources as well.

  4. Summary of AEO2015 Renewable Electricity Working Group Meeting

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

    August 13, 2014 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Renewable Electricity Analysis Team SUBJECT: Summary of AEO2015 Renewable Electricity Working Group Meeting held on July 24, 2014 Presenters: Chris Namovicz, Gwen Bredehoeft Topics included AEO2014 model and data updates, a summary of AEO2014 model results,

  5. Summary of AEO2016 Electricity Working Group Meeting

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

    ... emissions from affected plants and increase generation from clean sources. ... a 111(b) new source compliant coal generating technology in AEO2016. EIA responded that the ...

  6. AEO 2015 Electricity, Coal, Nuclear and Renewables Preliminary...

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

    Electricity, Coal, Nuclear and Renewables Preliminary Results For Joint Electricity, Coal, Nuclear, and Renewables AEO2015 Working Group September 15, 2014 | Washington, DC By EIA, ...

  7. AEO2015 Liquid Fuels Markets Working Group Presentation

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

    * Cellulosic ethanol and pyrolysis: decrease nameplate capacity and revise overnight capital costs * RFS: Planning no change from AEO2014 - Awaiting EPA release of final 2014 ...

  8. World Oil Prices and Production Trends in AEO2009 (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    The oil prices reported in Annual Energy Outlook 2009 (AEO) represent the price of light, low-sulfur crude oil in 2007 dollars. Projections of future supply and demand are made for "liquids," a term used to refer to those liquids that after processing and refining can be used interchangeably with petroleum products. In AEO2009, liquids include conventional petroleum liquids -- such as conventional crude oil and natural gas plant liquids -- in addition to unconventional liquids, such as biofuels, bitumen, coal-to-liquids (CTL), gas-to-liquids (GTL), extra-heavy oils, and shale oil.

  9. World Oil Prices and Production Trends in AEO2008 (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    Annual Energy Outlook 2008 (AEO) defines the world oil price as the price of light, low-sulfur crude oil delivered in Cushing, Oklahoma. Since 2003, both "above ground" and "below ground" factors have contributed to a sustained rise in nominal world oil prices, from $31 per barrel in 2003 to $69 per barrel in 2007. The AEO2008 reference case outlook for world oil prices is higher than in the AEO2007 reference case. The main reasons for the adoption of a higher reference case price outlook include continued significant expansion of world demand for liquids, particularly in non-OECD (Organization for Economic Cooperation and Development) countries, which include China and India; the rising costs of conventional non-OPEC (Organization of the Petroleum Exporting Countries) supply and unconventional liquids production; limited growth in non-OPEC supplies despite higher oil prices; and the inability or unwillingness of OPEC member countries to increase conventional crude oil production to levels that would be required for maintaining price stability. The Energy Information Administration will continue to monitor world oil price trends and may need to make further adjustments in future AEOs.

  10. Clean Air Interstate Rule: Changes and Modeling in AEO2010 (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    On December 23, 2008, the D.C. Circuit Court remanded but did not vacate the Clean Air Interstate Rule (CAIR), overriding its previous decision on February 8, 2008, to remand and vacate CAIR. The December decision, which is reflected in Annual Energy Outlook 2010 (AEO) , allows CAIR to remain in effect, providing time for the Environmental Protection Agency to modify the rule in order to address objections raised by the Court in its earlier decision. A similar rule, referred to as the Clean Air Mercury Rule (CAMR), which was to set up a cap-and-trade system for reducing mercury emissions by approximately 70%, is not represented in the AEO2010 projections, because it was vacated by the D.C. Circuit Court in February 2008.

  11. AEO2011: Energy Consumption by Sector and Source - Mountain ...

    Open Energy Info (EERE)

    comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 8, and contains only the reference...

  12. AEO2011:Total Energy Supply, Disposition, and Price Summary ...

    Open Energy Info (EERE)

    case. The dataset uses quadrillion Btu and the U.S. Dollar. The data is broken down into production, imports, exports, consumption and price. Data and Resources AEO2011:Total...

  13. Summary of Second AEO 2015 Working Group Meeting

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

    November 7, 2014 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Office of Electricity, Coal, Nuclear, and Renewables Analysis SUBJECT: Summary of Second AEO 2015 Working Group Meeting held on September 15, 2014 ATTENDEES: 21 EIA, 68 external (list provided following meeting summary) Presentation topics included a review of the AEO2015 publication schedule and contents, an overview of model assumptions updates in

  14. EPACT2005: Status of Provisions (Update) (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The Energy Policy Act 2005 (EPACT) was signed into law by President Bush on August 8, 2005, and became Public Law 109-058. A number of provisions from EPACT2005 were included in the Annual Energy Outlook 2006 (AEO) projections. Many others were not considered in AEO2006particularly, those that require funding appropriations or further specification by federal agencies or Congress before implementation.

  15. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

    Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

  16. California's Move Toward E10 (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    In Annual Energy Outlook 2009, (AEO) E10–a gasoline blend containing 10% ethanol–is assumed to be the maximum ethanol blend allowed in California erformulated gasoline (RFG), as opposed to the 5.7% blend assumed in earlier AEOs. The 5.7% blend had reflected decisions made when California decided to phase out use of the additive methyl tertiary butyl ether in its RFG program in 2003, opting instead to use ethanol in the minimum amount that would meet the requirement for 2.0% oxygen content under the Clean Air Act provisions in effect at that time.

  17. Microsoft Word - AEO2012 SENR final markup 1 31 12 _2_.docx

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

    nonpetroleum liquids, net petroleum imports make up a smaller share of total liquids consumption: U.S. dependence on imported petroleum liquids declines in the AEO2012 Reference...

  18. State Renewable Energy Requirements and Goals: Update Through 2007 (Update) (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    In recent years, the Annual Energy Outlook (AEO) has tracked the growing number of states that have adopted requirements or goals for renewable energy. While there is no federal renewable generation mandate, the states have been adopting such standards for some time. AEO2005 provided a summary of all existing programs in effect at that time, and subsequent AEOs have examined new policies or changes to existing ones. Since the publication of AEO2007, four states have enacted new renewable portfolio standards (RPS) legislation, and five others have strengthened their existing RPS programs. In total, 25 states and the District of Columbia.

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    2015 Domestic Uranium Production Report May 2016 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as

  20. Federal Fuels Taxes and Tax Credits (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The Annual Energy Outlook 2007 (AEO) reference case and alternative cases generally assume compliance with current laws and regulations affecting the energy sector. Some provisions of the U.S. Tax Code are scheduled to expire, or may be subject to adjustment, before the end of the projection period. In general, scheduled expirations and adjustments provided in legislation or regulations are assumed to occur, unless there is significant historical evidence to support an alternative assumption. This section examines the AEO2007 treatment of three provisions that could have significant impacts on U.S. energy markets: the gasoline excise tax, biofuel (ethanol and biodiesel) tax credits, and the production tax credit for electricity generation from certain renewable resources.

  1. Second AEO2014 Buildings Sector Working Group Meeting

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

    25, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION AND EFFICIENCY ANALYSIS FROM: BUILDINGS CONSUMPTION & EFFICIENCY ANALYSIS TEAM SUBJECT: Second AEO2014 Buildings Sector Working Group Meeting Summary (presented on 09-26-2013) Attendees: James Berry (EIA OES) Stephanie Burns (IMT) Gwendolyn Bredehoeft (EIA OEA) Colin McMillan (NREL) Bill McNary (EIA OES)

  2. Second AEO2014 Macro-Industrial Working Group Meeting Summary

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

    7, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ANALYSIS FROM: MACROECONOMIC & INDUSTRIAL ENERGY CONSUMPTION & EFFICIENCY ANALYSIS TEAMS SUBJECT: Second AEO2014 Macro-Industrial Working Group Meeting Summary (presented on 09-26-2013) Attendees: Bob Adler (EIA) Robert

  3. Second AEO2014 Oil and Gas Working Group Meeting Summary

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

    7 November 12, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS FROM: ANGELINA LAROSE TEAM LEAD NATURAL GAS MARKETS TEAM JOHN STAUB TEAM LEAD EXPLORATION AND PRODUCTION ANALYSIS TEAM EXPLORATION AND PRODUCTION and NATURAL GAS MARKETS TEAMS SUBJECT: Second AEO2014 Oil and Gas Working Group Meeting Summary (presented September 26, 2013) Attendees: Robert Anderson (DOE) Peter Balash (NETL)* David Bardin (self) Joe Benneche (EIA) Philip Budzik (EIA) Kara Callahan

  4. Second AEO2014 Transportation Working Group Meeting Summary

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

    , 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JIM TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION AND EFFICIENCY ANALYSIS FROM: TRANSPORTATION CONSUMPTION & EFFICIENCY ANALYSIS TEAM SUBJECT: Second AEO2014 Transportation Working Group Meeting Summary (presented on 09-25-2013) Attendees: Nicholas Chase (EIA/OECEA) Carrie Hughes-Cromwick (EIA/OES) Paul Holtberg (EIA/OEA) Trisha Hutchins (EIA/OECEA) Jim Kliesch

  5. Second AEO2015 Macro-Industrial Workiing Group Meeting Summary

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

    6, 2014 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ANALYSIS FROM: MACROECONOMIC & INDUSTRIAL ENERGY CONSUMPTION & EFFICIENCY ANALYSIS TEAMS SUBJECT: Second AEO2015 Macro-Industrial Working Group Meeting Summary, presented on 09-29-2014 Attendees: Gary Ambach (Michaels

  6. Second AEO2016 Macro-Induistrial Working Group Meeting summary

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

    March 21, 2016 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ANALYSIS FROM: MACROECONOMIC & INDUSTRIAL ENERGY CONSUMPTION & EFFICIENCY ANALYSIS TEAMS SUBJECT: Second AEO2016 Macro-Industrial Working Group Meeting Summary, presented on 02-18-2016 Attendees: Nate Aden

  7. Summary of AEO2016 Electricity Working Group Meeting

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

    WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE February 10, 2016 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Chris Namovicz Acting Team Leader for Electricity Analysis Team SUBJECT: Summary of AEO2016 Electricity Working Group Meeting held on February 10, 2016

  8. Summary of First AEO2014 Electricity Working Group Meeting

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

    9, 2013 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Alan Beamon Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Electricity Analysis Team SUBJECT: Summary of First AEO 2014 Electricity Working Group Meeting held on July 24, 2013 ATTENDEES: Diefenderfer, Jim Aniti, Lori Milton, Carrie Jones, Jeff Martin, Laura Bredehoeft, Gwendolyn Eynon, Bob Leff, Mike Mellish, Mike Kearney, Diane

  9. Summary of First AEO2015 Electricity Working Group Meeting

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

    August 8, 2014 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Electricity Analysis Team SUBJECT: Summary of First AEO 2015 Electricity Working Group Meeting held on July 31, 2014 ATTENDEES: Krall, Eric Diefenderfer, Jim †Aniti, Lori Bowman, Michelle Hodge, Tyler Mellish, Mike Slater-Thompson, Nancy Marcy, Cara

  10. Summary of Second AEO 2014 Electricity Working Group Meeting

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

    7, 2013 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Alan Beamon Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Electricity Analysis Team SUBJECT: Summary of Second AEO 2014 Electricity Working Group Meeting held on September 25, 2013 ATTENDEES: Adams, Greg (EIA OEA) Aniti, Lori (EIA OEA) Bredehoeft, Gwendolyn (EIA OEA) Crozat, Matthew P. (US DOE: Office of Nuclear Energy)

  11. First AEO2014 Buildings Sector Working Group Meeting Summary

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

    0, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION AND EFFICIENCY ANALYSIS FROM: BUILDINGS CONSUMPTION & EFFICIENCY ANALYSIS TEAM SUBJECT: First AEO2014 Buildings Sector Working Group Meeting Summary (presented on 07-22-2013) Attendees: James Berry (EIA OES) Stephanie Burns (IMT) Gwendolyn Bredehoeft (EIA OEA) Paul Holtberg (EIA OEA) Colin McMillan (NREL)

  12. First AEO2014 Macro-Industrial Working Group Meeting Summary

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

    3 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ANALYSIS FROM: MACROECONOMIC & INDUSTRIAL ENERGY CONSUMPTION & EFFICIENCY ANALYSIS TEAMS SUBJECT: First AEO2014 Macro-Industrial Working Group Meeting Summary (presented on 07-30-2013) Attendees: Tom Lorenz (EIA) Bob Adler

  13. First AEO2014 Transportation Working Group Meeting Summary

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

    1, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JIM TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION AND EFFICIENCY ANALYSIS FROM: TRANSPORTATION CONSUMPTION & EFFICIENCY ANALYSIS TEAM SUBJECT: First AEO2014 Transportation Working Group Meeting Summary (presented on 07-23-2013) Attendees: Shirley Neff (EIA/AO) Jim Turnure (EIA/OECEA) Jade Jenkins (EIA/OECEA) Ken Katz (DOT/NHTSA) Pete Whitman (DOE/PI) Tien Nguyen

  14. First AEO2015 Liquid Fuels Markets Working Group Meeting

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

    July 21, 2014 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSYS JOHN POWELL TEAM LEADER, LIQUID FUELS MARKET TEAM MICHAEL SCHAAL DIRECTOR, OFFICE OF ENERGY ANALYSIS FROM: LIQUID FUELS MARKET TEAM SUBJECT: First AEO2015 Liquid Fuels Markets Working Group Meeting Summary (presented on 07-17-2014) Attendees: (EIA) John Powell, Mindi Farber-DeAnda, Mike Cole, Adrian Geagla, Arup Mallik, David Manowitz, Vishakh Mantri, Beth May, Terry Yen, John Conti, Michael Schaal Bryan Just

  15. First AEO2015 Macro-Industrial Working Group Meeting Summary

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

    4 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ANALYSIS FROM: MACROECONOMIC & INDUSTRIAL ENERGY CONSUMPTION & EFFICIENCY ANALYSIS TEAMS SUBJECT: First AEO2015 Macro-Industrial Working Group Meeting Summary, presented on 07-24-2014 Attendees: Bob Adler (EIA) Gary Ambach

  16. First AEO2015 Oil and Gas Working Group Meeting Summary

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

    5 August 8, 2014 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS FROM: ANGELINA LAROSE TEAM LEAD NATURAL GAS MARKETS TEAM JOHN STAUB TEAM LEAD EXPLORATION AND PRODUCTION ANALYSIS TEAM EXPLORATION AND PRODUCTION and NATURAL GAS MARKETS TEAMS SUBJECT: First AEO2015 Oil and Gas Working Group Meeting Summary (presented on August 7, 2014) Attendees: Tien Nguyen (DOE) Joseph Benneche (EIA) Dana Van Wagener (EIA)* Troy Cook (EIA)* Angelina LaRose (EIA) Laura Singer (EIA) Michael

  17. Workshop on Biofuels Projections in AEO Attendance List

    Gasoline and Diesel Fuel Update (EIA)

    Attendance List 1 March 2013 Workshop on Biofuels Projections in AEO Attendee list In person attendees Mia Adelberg Abengoa Bioenergy Michael Bredehoeft EIA Tom Capehart USDA Terry Carter Biofuels Center of North Carolina Adam Christensen Johns Hopkins University Michael Cole EIA John Conti EIA Lauren Cooper Center for Climate and Energy Solutions Mindi Farber-DeAnda EIA Denise Gerber Fiberight Steve Gerber Fiberight Ryan Graf Policy Navigation Group David L. Greene Oak Ridge National Laboratory

  18. Workshop on Biofuels Projections in AEO Presenters Biographies

    Gasoline and Diesel Fuel Update (EIA)

    Presenters' Biographies 1 March 2013 Workshop on Biofuels Projections in AEO Presenters' Biographies (by presentation order) John Conti John J. Conti is the Assistant Administrator for Energy Analysis at EIA. Mr. Conti analyzes energy supply, demand, and prices including the impact of financial markets on energy markets; prepares reports on current and future energy use; analyzes the impact of energy policies; and develops advanced techniques for conducting energy information analyses. He also

  19. Federal Fuels Taxes and Tax Credits (Update) (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    The Annual Energy Outlook 2008 (AEO) reference case incorporates current regulations that pertain to the energy industry. This section describes the handling of federal taxes and tax credits in AEO2008, focusing primarily on areas where regulations have changed or the handling of taxes or tax credits has been updated.

  20. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    DOCUMENT BEING CONTINUED AEO COTIUTINSHE DE-AC27-08RV14800/044 2AG OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) Account code: ARRA Appr Year 2009 Allottee 3 Reporting Entity 421301 Object Class 31003 Program 11113 70 Project 2002110 WFO 0000000 Local Use 0420660 TAS Agency Code 89 TAS Account Code 0253 TAS Subaccount Code Amount: -$100,000.00 Delivery Location Code: 010601 Richland

  1. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    DOCUMENT BEING CONTINUED AEO COTIUAIN IET DE-AC27--08RV14800/046 2G OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) ORP-00014 TOO Funds Fund 01250 Appr Year 2010 Allottee 34 Reportng Enity 4231.11 Object Class 25200 Program 1111412 Project 0004262 WFO 0000000 Local Use 0000000 Amount: $1,200,000.00 ORP 0014 TOO Fund 01250 AppL Ye~ir 2010 Reporting Entity 421301 Object Class 25200

  2. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    NO. OF DOCUMENT BEING CONTINUED AEO COTIUAIN HETDE-AC27-08RV14800/052 2A OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIES/SERVICES QUANTITY UNITI UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) Fund 01250 Appr Year 2010 Allottee 34 Reporting Entity 421301 Object Class 25200 Program 1110462 Project 0001539 WFO 0000000 Local Use 0420149 Amount: $10,214.00 Delivery Location Code: 00601 RichandOperations Office U.S Dep~artment of Energy Richland Operations

  3. CONTINUATION S EFIIERENCE NO OF DOCUMENT BEING CONTINUED AEO

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

    CONTINUATION S EFIIERENCE NO OF DOCUMENT BEING CONTINUED AEO CONINUTIO SHETDE-AC27-08RV148OO/095 rG NAME OF OFFEROR OR CONTRACTOR WASH-INGTON RIVER PROTECTION SOLUTIONS LLC- ITEM NO SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT (A) (B) (C) (D) )/F New Total Amount for this Award: $7,094,451,000.00 Obligated Amount for this Modification: $30, 952, 500.00 New Total Obligated Amount for this Award: $1, 353,766,560.39 Incremental Funded Amount changed: from $1,293,125,180.69 to $1,323,766,560.39

  4. CONTINUATON SHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    CONTINUATON SHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO COTNUTO SETDE-AC27-08RV14800/070 2AG OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) De-obligating WEPS TDD funds for ATL Aluminum Solubility Sample Analysis Fund 01250 Appr Year 2009 Allottee 34 Reporting Entity 421301 Object Class 25200 Program 1110676 Project 0004022 WFO 0000000 Local Use 0000000 Amount: -$3,155.93

  5. CONTIUATIN SHET IREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    CONTIUATIN SHET IREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO COTNUTO SETDE-AC27-08RVI4800/055 2AG OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIESISERVICES QUANTITY NIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) Total Amount changed from $7,066,503,000.00 to $7,066,500,000.00 Obligated Amount for this modification: $140, 000.00 Incremental Funded Amount changed from $1,102, 822,315.05 to $1,102,962,315.05 NEW ACCOUNTING CODE ADDED: Account code: WTP

  6. AEO2014 - Issues in Focus articles - U.S. Energy Information...

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

    Past AEO analyses that remain relevant 2013 2012 2011 U.S. reliance on imported liquid fuels in alternative scenarios Competition between coal and natural gas in the electric...

  7. Energy Independence and Security Act of 2007: Summary of Provisions (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    The Energy Independence and Security Act of 2007 was signed into law on December 19, 2007, and became Public Law 110-140. Provisions in EISA2007 that require funding appropriations to be implemented, whose impact is highly uncertain, or that require further specification by federal agencies or Congress are not included in Annual Energy Outlook 2008 (AEO). For example, the Energy Information Administration (EIA) does not try to anticipate policy responses to the many studies required by EISA2007, nor to predict the impact of research and development (R&D) funding authorizations included in the bill. Moreover, AEO2008 does not include any provision that addresses a level of detail beyond that modeled in the National Energy Modeling System (NEMS), which was used to develop the AEO2008 projections. AEO2008 addresses only those provisions in EISA2007 that establish specific tax credits, incentives, or standards.

  8. Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

    2012-09-01

    Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

  9. Changing Trends in the Bulk Chemicals and Pulp and Paper Industries (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    Compared with the experience of the 1990s, rising energy prices in recent years have led to questions about expectations of growth in industrial output, particularly in energy-intensive industries. Given the higher price trends, a review of expected growth trends in selected industries was undertaken as part of the production of Annual Energy Outlook 2005 (AEO). In addition, projections for the industrial value of shipments, which were based on the Standard Industrial Classification (SIC) system in AEO2004, are based on the North American Industry Classification System (NAICS) in AEO2005. The change in industrial classification leads to lower historical growth rates for many industrial sectors. The impacts of these two changes are highlighted in this section for two of the largest energy-consuming industries in the U.S. industrial sector-bulk chemicals and pulp and paper.

  10. State Renewable Energy Requirements and Goals: Update through 2009 (Update) (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    To the extent possible,Annual Energy Outlook 2010 (AEO) incorporates the impacts of state laws requiring the addition of renewable generation or capacity by utilities doing business in the states. Currently, 30 states and the District of Columbia have enforceable renewable portfolio standards (RPS) or similar laws). Under such standards, each state determines its own levels of generation, eligible technologies, and noncompliance penalties. AEO2010 includes the impacts of all laws in effect as of September 2009 (with the exception of Hawaii, because the National Energy Modeling System provides electricity market projections for the continental United States only).

  11. Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models

    SciTech Connect (OSTI)

    Hannay, C; Williamson, D L; Hack, J J; Kiehl, J T; Olson, J G; Klein, S A; Bretherton, C S; K?hler, M

    2008-01-24

    We examine forecasts of Southeast Pacific stratocumulus at 20S and 85W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3 days to determine the differences with the EPIC field data. Observations during the EPIC cruise show a stable and well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the four models is that the forecasted planetary boundary layer (PBL) height is too low when compared to EPIC observations. All the models produce a strong diurnal cycle in the Liquid Water Path (LWP) but there are large differences in the amplitude and the phase compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface. There is a large spread in the surface energy budget terms amongst the models and large discrepancies with observational estimates. Single Column Model (SCM) experiments with the CAM show that the vertical pressure velocity has a large impact on the PBL height and LWP. Both the amplitude of the vertical pressure velocity field and its vertical structure play a significant role in the collapse or the maintenance of the PBL.

  12. Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

    SciTech Connect (OSTI)

    Zack, J; Natenberg, E J; Knowe, G V; Manobianco, J; Waight, K; Hanley, D; Kamath, C

    2011-09-13

    The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.

  13. Industrial Sector Energy Demand: Revisions for Non-Energy-Intensive Manufacturing (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    For the industrial sector, the Energy Information Administration's (EIA) analysis and projection efforts generally have focused on the energy-intensive industriesfood, bulk chemicals, refining, glass, cement, steel, and aluminumwhere energy cost averages 4.8% of annual operating cost. Detailed process flows and energy intensity indicators have been developed for narrowly defined industry groups in the energy-intensive manufacturing sector. The non-energy-intensive manufacturing industries, where energy cost averages 1.9% of annual operating cost, previously have received somewhat less attention, however. In Annual Energy Outlook 2006 (AEO), energy demand projections were provided for two broadly aggregated industry groups in the non-energy-intensive manufacturing sector: metal-based durables and other non-energy-intensive. In the AEO2006 projections, the two groups accounted for more than 50% of the projected increase in industrial natural gas consumption from 2004 to 2030.

  14. Second AEO2-015 Liquid Fuels Markets Working Group Meeting Summary

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

    September 24, 2014 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSYS MICHAEL SCHAAL DIRECTOR, OFFICE OF ENERGY ANALYSIS JOHN POWELL TEAM LEADER, LIQUID FUELS MARKET TEAM FROM: LIQUID FUELS MARKET TEAM SUBJECT: Second AEO2015 Liquid Fuels Markets Working Group Meeting Summary (presented on 09-24-2014) Attendees: (EIA) John Powell, Mindi Farber-DeAnda, Mike Cole, Adrian Geagla, David Manowitz, Beth May Seth Meyer (USDA) Austin Brown (NREL) Robert Smith (US DOE) Ben Salisbury

  15. Summary of AEO2016 Electricity Working Group Meeting held on December 8, 2015

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

    January7, 2016 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team Office of Integrated and International Energy Analysis FROM: Chris Namovicz Team Leader for Electricity Analysis (acting) And Thad Huetteman, Electricity Analysis Team SUBJECT: Summary of AEO2016 Electricity Working Group Meeting held on December 8, 2015 Presenters: Chris

  16. WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES. DO NOT QUOTE OR CITE AS AEO2016

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

    February 1, 2016 MEMORANDUM TO: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis FROM: Coal and Uranium Analysis Team SUBJECT: Notes from the First AEO2016 Coal Working Group Meeting workshop held on December 1, 2015 Attendees (47) Name Affiliation Ross, Joey Alliance Resource Partners, L.P. Alfaro, Jose L. Alpha Natural Resources Blumenfeld, Andy Arch Coal, Inc. Lewandowski, David Clean Energy James,

  17. WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES. DO NOT QUOTE OR CITE AS AEO2016

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

    March 10, 2016 MEMORANDUM TO: John Conti Assistant Administrator for Energy Analysis Jim Diefenderfer Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis FROM: Coal and Uranium Analysis Team SUBJECT: Notes from the Second AEO2016 Coal Working Group Meeting workshop held on February 9, 2016 Attendees (30) Name Affiliation Adams, Greg U.S. DOE: EIA Coleman, Leslie National Mining Association Diefenderfer, Jim U.S. DOE: EIA DiGiantommaso, Jennifer U.S. Department of Labor

  18. Solar Forecasting

    Broader source: Energy.gov [DOE]

    On December 7, 2012, DOE announced $8 million to fund two solar projects that are helping utilities and grid operators better forecast when, where, and how much solar power will be produced at U.S....

  19. Forecast Change

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

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,153 3,143 -0.3% Price (centskWh) 12.06 12.09 12.58 13.04 12.95 12.96 ...

  20. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

    Lew, D.; Milligan, M.; Jordan, G.; Piwko, R.

    2011-04-01

    This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts.

  1. Update to industrial drivers in the AEO2015 as a result of new input-output data

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

    Update to industrial drivers in the AEO2015 as a result of new input-output data Elizabeth Sendich May 4, 2015 Independent Statistics & Analysis www.eia.gov U.S. Energy Information Administration Washington, DC 20585 This paper is released to encourage discussion and critical comment. The analysis and conclusions expressed here are those of the authors and not necessarily those of the U.S. Energy Information Administration. WORKING PAPER SERIES April 2015 Elizabeth Sendich | U.S. Energy

  2. probabilistic energy production forecasts

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

    energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary ...

  3. Wind Power Forecasting Data

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

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  4. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability ... that measure feedstock production, water quality, water quantity, and biodiversity. ...

  5. Wind Power Forecasting

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

    data Presentations BPA Super Forecast Methodology Related Links Near Real-time Wind Animation Meteorological Data Customer Supplied Generation Imbalance Dynamic Transfer Limits...

  6. A critical evaluation of the upper ocean heat budget in the Climate Forecast System Reanalysis data for the south central equatorial Pacific

    SciTech Connect (OSTI)

    Liu H.; Lin W.; Liu, X.; Zhang, M.

    2011-08-26

    Coupled ocean-atmospheric models suffer from the common bias of a spurious rain belt south of the central equatorial Pacific throughout the year. Observational constraints on key processes responsible for this bias are scarce. The recently available reanalysis from a coupled model system for the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data is a potential benchmark for climate models in this region. Its suitability for model evaluation and validation, however, needs to be established. This paper examines the mixed layer heat budget and the ocean surface currents - key factors for the sea surface temperature control in the double Inter-Tropical Convergence Zone in the central Pacific - from 5{sup o}S to 10{sup o}S and 170{sup o}E to 150{sup o}W. Two independent approaches are used. The first approach is through comparison of CFSR data with collocated station observations from field experiments; the second is through the residual analysis of the heat budget of the mixed layer. We show that the CFSR overestimates the net surface flux in this region by 23 W m{sup -2}. The overestimated net surface flux is mainly due to an even larger overestimation of shortwave radiation by 44 W m{sup -2}, which is compensated by a surface latent heat flux overestimated by 14 W m{sup -2}. However, the quality of surface currents and the associated oceanic heat transport in CFSR are not compromised by the surface flux biases, and they agree with the best available estimates. The uncertainties of the observational data from field experiments are also briefly discussed in the present study.

  7. Using Wikipedia to forecast disease

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

    Using Wikipedia to forecast disease Using Wikipedia to forecast disease Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. December 22, 2014 Using Wikipedia to forecast disease Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505) 667-0471 Email "A global disease-forecasting system will improve

  8. NREL: Transmission Grid Integration - Forecasting

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

    Forecasting NREL researchers use solar and wind resource assessment and forecasting techniques to develop models that better characterize the potential benefits and impacts of ...

  9. Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Florita, A.; Hodge, B. M.; Freedman, J.

    2014-05-01

    The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

  10. Acquisition Forecast | Department of Energy

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

    Acquisition Forecast Acquisition Forecast Acquisition Forecast It is the policy of the U.S. Department of Energy (DOE) to provide timely information to the public regarding DOE's forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department's major site and facilities management contractors. This forecast has been expanded to also provide timely status information for ongoing prime contracting actions that are valued in excess of the

  11. The forecast calls for flu

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

    The forecast calls for flu Science on the Hill: The forecast calls for flu Using mathematics, computer programs, statistics and information about how disease develops and spreads, a research team at Los Alamos National Laboratory found a way to forecast the flu season and even next week's sickness trends. January 15, 2016 Forecasting flu A team from Los Alamos has developed a method to predict flu outbreaks based in part on influenza-related searches of Wikipedia. The forecast calls for flu

  12. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

    This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation.

  13. Final Report- Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Broader source: Energy.gov [DOE]

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California independent system operator’s load forecasts by integrating behind-the-meter photovoltaic forecasts.

  14. Evaluation Framework and Tools for Distributed Energy Resources

    SciTech Connect (OSTI)

    Gumerman, Etan Z.; Bharvirkar, Ranjit R.; LaCommare, Kristina Hamachi; Marnay , Chris

    2003-02-01

    The Energy Information Administration's (EIA) 2002 Annual Energy Outlook (AEO) forecast anticipates the need for 375 MW of new generating capacity (or about one new power plant) per week for the next 20 years, most of which is forecast to be fueled by natural gas. The Distributed Energy and Electric Reliability Program (DEER) of the Department of Energy (DOE), has set a national goal for DER to capture 20 percent of new electric generation capacity additions by 2020 (Office of Energy Efficiency and Renewable Energy 2000). Cumulatively, this amounts to about 40 GW of DER capacity additions from 2000-2020. Figure ES-1 below compares the EIA forecast and DEER's assumed goal for new DER by 2020 while applying the same definition of DER to both. This figure illustrates that the EIA forecast is consistent with the overall DEER DER goal. For the purposes of this study, Berkeley Lab needed a target level of small-scale DER penetration upon which to hinge consideration of benefits and costs. Because the AEO2002 forecasted only 3.1 GW of cumulative additions from small-scale DER in the residential and commercial sectors, another approach was needed to estimate the small-scale DER target. The focus here is on small-scale DER technologies under 500 kW. The technology size limit is somewhat arbitrary, but the key results of interest are marginal additional costs and benefits around an assumed level of penetration that existing programs might achieve. Berkeley Lab assumes that small-scale DER has the same growth potential as large scale DER in AEO2002, about 38 GW. This assumption makes the small-scale goal equivalent to 380,000 DER units of average size 100 kW. This report lays out a framework whereby the consequences of meeting this goal might be estimated and tallied up. The framework is built around a list of major benefits and a set of tools that might be applied to estimate them. This study lists some of the major effects of an emerging paradigm shift away from central station power and towards a more dispersed and heterogeneous power system. Seventeen societal effects of small-scale DER are briefly summarized. Each effect is rated as high, medium or low, on three different scales that will help determine the optimal social investment. The three scales are: the magnitude of the economic benefit; the likelihood that the benefit can be monetized in efficient markets, i.e. internalized; and how tractable it might be to quantify each benefit analytically. Some of the modeling tools that may be used to estimate these effects are described in the Appendix.

  15. Annual Energy Outlook Retrospective Review: Evaluation of 2014 and Prior Reference Case Projections

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

    Annual Energy Outlook Retrospective Review: Evaluation of 2014 and Prior Reference Case Projections March 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2014 and Prior Reference Case Projections i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's

  16. Using Wikipedia to forecast diseases

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

    Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. November 13, 2014 Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505)

  17. Supply Forecast and Analysis (SFA)

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

    Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies ...

  18. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

    This presentation describes the importance of good forecasting for variable generation, the different approaches used by industry, and the importance of validated high-quality data.

  19. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    Zip: 94965 Region: Bay Area Sector: Services Product: Intelligent Monitoring and Forecasting Services Year Founded: 2010 Website: www.forecastenergy.net Coordinates:...

  20. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast AgencyCompany Organization: United States Department of Energy Sector:...

  1. Module 6 - Metrics, Performance Measurements and Forecasting...

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

    6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement ...

  2. Intermediate future forecasting system

    SciTech Connect (OSTI)

    Gass, S.I.; Murphy, F.H.; Shaw, S.H.

    1983-12-01

    The purposes of the Symposium on the Department of Energy's Intermediate Future Forecasting System (IFFS) were: (1) to present to the energy community details of DOE's new energy market model IFFS; and (2) to have an open forum in which IFFS and its major elements could be reviewed and critiqued by external experts. DOE speakers discussed the total system, its software design, and the modeling aspects of oil and gas supply, refineries, electric utilities, coal, and the energy economy. Invited experts critiqued each of these topics and offered suggestions for modifications and improvement. This volume documents the proceedings (papers and discussion) of the Symposium. Separate abstracts have been prepared for each presentation for inclusion in the Energy Data Base.

  3. Science on Tap - Forecasting illness

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

    Science on Tap - Forecasting illness Science on Tap - Forecasting illness WHEN: Mar 17, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Mark your calendars for this event held every third Thursday from 5:30 to 7 p.m. A short presentation is followed by a lively discussion on a different subject each month. Forecasting the flu (and other

  4. AEO2015 BWG

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

    fans: effective 2019 - external power supplies: effective 2016 - set-top boxes ... equipment, space cooling equipment, water heaters * Residential space heating ...

  5. AEO2014 Preliminary Results

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

    ...Freezers PCs, Laptop Medical Imaging Equipment PCs, Desktop Video Displays Monitors (i.e. desktop PC monitors) Video Boards Audio Equipment Security Systems Portable Electric Spas ...

  6. Acquisition Forecast Download | Department of Energy

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

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. File Acquisition-Forecast-2016-05-06.xlsx More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Small Business Program Manager Directory

  7. Selected papers on fuel forecasting and analysis

    SciTech Connect (OSTI)

    Gordon, R.L.; Prast, W.G.

    1983-05-01

    Of the 19 presentations at this seminar, covering coal, uranium, oil, and gas issues as well as related EPRI research projects, eleven papers are published in this volume. Nine of the papers primarily address coal-market analysis, coal transportation, and uranium supply. Two additional papers provide an evaluation and perspective on the art and use of coal-supply forecasting models and on the relationship between coal and oil prices. The authors are energy analysts and EPRI research contractors from academia, the consulting profession, and the coal industry. A separate abstract was prepared for each of the 11 papers.

  8. Introduction to the BTO Goals Framework

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

    uses - EUI trends regularly reported and forecast by EIA * Tiered Goals: - Building Sector ... All numbers are relative to the 2015 AEO Reference Case Forecast. 11 Are BTO's Goals ...

  9. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

  10. Wind Forecast Improvement Project Southern Study Area Final Report...

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

    Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report PDF icon Wind Forecast Improvement Project ...

  11. Uncertainty Reduction in Power Generation Forecast Using Coupled...

    Office of Scientific and Technical Information (OSTI)

    quantify the forecast uncertainty by reducing prediction intervals of forecasts. ... means, e.g., using weather-based models, and reduce forecast errors prediction intervals. ...

  12. Picture of the Week: Forecasting Flu

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

    3 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? March 6, 2016 flu epidemics modellled using social media Watch the video on YouTube. Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del

  13. Impacts of Increased Access to Oil & Natural Gas Resources in the Lower 48 Federal Outer Continental Shelf (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    This analysis was updated for Annual Energy Outlook 2009 (AEO): Impact of Limitations on Access to Oil and Natural Gas Resources in the Federal Outer Continental Shelf (OCS). The OCS is estimated to contain substantial resources of crude oil and natural gas; however, some areas of the OCS are subject to drilling restrictions. With energy prices rising over the past several years, there has been increased interest in the development of more domestic oil and natural gas supply, including OCS resources. In the past, federal efforts to encourage exploration and development activities in the deep waters of the OCS have been limited primarily to regulations that would reduce royalty payments by lease holders. More recently, the states of Alaska and Virginia have asked the federal government to consider leasing in areas off their coastlines that are off limits as a result of actions by the President or Congress. In response, the Minerals Management Service (MMS) of the U.S. Department of the Interior has included in its proposed 5-year leasing plan for 2007-2012 sales of one lease in the Mid-Atlantic area off the coastline of Virginia and two leases in the North Aleutian Basin area of Alaska. Development in both areas still would require lifting of the current ban on drilling.

  14. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

  15. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

  16. EIA lowers forecast for summer gasoline prices

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

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be ... according to the new monthly forecast from the U.S. Energy Information Administration. ...

  17. Wind Forecasting Improvement Project | Department of Energy

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

    Forecasting Improvement Project Wind Forecasting Improvement Project October 3, 2011 - 12:12pm Addthis This is an excerpt from the Third Quarter 2011 edition of the Wind Program R&D Newsletter. In July, the Department of Energy launched a $6 million project with the National Oceanic and Atmospheric Administration (NOAA) and private partners to improve wind forecasting. Wind power forecasting allows system operators to anticipate the electrical output of wind plants and adjust the electrical

  18. NREL: Resource Assessment and Forecasting Home Page

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

    NREL's resource assessment and forecasting research supports industry, government, and academia by providing renewable energy resource measurements, models, maps, and support services. These resources are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research: Capabilities Facilities Research staff Data and resources. Resource assessment and forecasting research is primarily performed at

  19. Assumptions and Expectations for Annual Energy Outlook 2015: Oil and Gas Working Group

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

    Assumptions and Expectations for Annual Energy Outlook 2016: Oil and Gas Working Group AEO2016 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis December 1, 2015| Washington, DC http://www.eia.gov/forecasts/aeo/workinggroup/ WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE We welcome feedback on our assumptions and documentation * The AEO Assumptions report http://www.eia.gov/forecasts/aeo/assumptions/

  20. Thirty-Year Solid Waste Generation Maximum and Minimum Forecast for SRS

    SciTech Connect (OSTI)

    Thomas, L.C.

    1994-10-01

    This report is the third phase (Phase III) of the Thirty-Year Solid Waste Generation Forecast for Facilities at the Savannah River Site (SRS). Phase I of the forecast, Thirty-Year Solid Waste Generation Forecast for Facilities at SRS, forecasts the yearly quantities of low-level waste (LLW), hazardous waste, mixed waste, and transuranic (TRU) wastes generated over the next 30 years by operations, decontamination and decommissioning and environmental restoration (ER) activities at the Savannah River Site. The Phase II report, Thirty-Year Solid Waste Generation Forecast by Treatability Group (U), provides a 30-year forecast by waste treatability group for operations, decontamination and decommissioning, and ER activities. In addition, a 30-year forecast by waste stream has been provided for operations in Appendix A of the Phase II report. The solid wastes stored or generated at SRS must be treated and disposed of in accordance with federal, state, and local laws and regulations. To evaluate, select, and justify the use of promising treatment technologies and to evaluate the potential impact to the environment, the generic waste categories described in the Phase I report were divided into smaller classifications with similar physical, chemical, and radiological characteristics. These smaller classifications, defined within the Phase II report as treatability groups, can then be used in the Waste Management Environmental Impact Statement process to evaluate treatment options. The waste generation forecasts in the Phase II report includes existing waste inventories. Existing waste inventories, which include waste streams from continuing operations and stored wastes from discontinued operations, were not included in the Phase I report. Maximum and minimum forecasts serve as upper and lower boundaries for waste generation. This report provides the maximum and minimum forecast by waste treatability group for operation, decontamination and decommissioning, and ER activities.

  1. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  2. ARM - CARES - Tracer Forecast for CARES

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

    CampaignsCarbonaceous Aerosols and Radiative Effects Study (CARES)Tracer Forecast for CARES Related Links CARES Home AAF Home ARM Data Discovery Browse Data Post-Campaign Data Sets Field Updates CARES Wiki Campaign Images Experiment Planning Proposal Abstract and Related Campaigns Science Plan Operations Plan Measurements Forecasts News News & Press Backgrounder (PDF, 1.45MB) G-1 Aircraft Fact Sheet (PDF, 1.3MB) Contacts Rahul Zaveri, Lead Scientist Tracer Forecasts for CARES This webpage

  3. Solar Forecast Improvement Project | Department of Energy

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

    NOAA also will provide advanced satellite products. INNOVATIONS NOAA is providing numerical weather prediction (NWP) modeling with new information that will help solar forecasts. ...

  4. Development and Demonstration of Advanced Forecasting, Power...

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

    Management Tools and Best Practices Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices Development ...

  5. NREL: Resource Assessment and Forecasting - Webmaster

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

    email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home Capabilities Facilities Working with Us Research Staff Data & Resources Did...

  6. Forecast and Funding Arrangements - Hanford Site

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

    Annual Waste Forecast and Funding Arrangements About Us Hanford Site Solid Waste Acceptance Program What's New Acceptance Criteria Acceptance Process Becoming a new Hanford...

  7. Data Collection and Comparison with Forecasted Unit Sales of...

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

    Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection ...

  8. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in ...

  9. The Wind Forecast Improvement Project (WFIP): A Public/Private...

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

    Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits ... Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits ...

  10. Modeling and forecasting the distribution of Vibrio vulnificus...

    Office of Scientific and Technical Information (OSTI)

    Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay Citation Details In-Document Search Title: Modeling and forecasting the distribution of Vibrio ...

  11. Improving the Accuracy of Solar Forecasting Funding Opportunity...

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

    Improving the Accuracy of Solar Forecasting Funding Opportunity Improving the Accuracy of Solar Forecasting Funding Opportunity Through the Improving the Accuracy of Solar ...

  12. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

    This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

  13. Energy Intensity Trends in AEO2010 (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Energy intensity (energy consumption per dollar of real GDP) indicates how much energy a country uses to produce its goods and services. From the early 1950s to the early 1970s, U.S. total primary energy consumption and real GDP increased at nearly the same annual rate. During that period, real oil prices remained virtually flat. In contrast, from the mid-1970s to 2008, the relationship between energy consumption and real GDP growth changed, with primary energy consumption growing at less than one-third the previous average rate and real GDP growth continuing to grow at its historical rate. The decoupling of real GDP growth from energy consumption growth led to a decline in energy intensity that averaged 2.8% per year from 1973 to 2008. In the Annual Energy Outlook 2010 Reference case, energy intensity continues to decline, at an average annual rate of 1.9% from 2008 to 2035.

  14. The 2008/2009 World Economic Crisis: What It Means for U.S. Agricultur...

    Gasoline and Diesel Fuel Update (EIA)

    Biofuels in AEO2013 Workshop U.S. Energy Information Administration March 20, 2013 ... not forecasts - Conditional, long-run scenario - Neutral assumptions for macro, policy, ...

  15. Distribution of Wind Power Forecasting Errors from Operational Systems (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Ela, E.; Milligan, M.

    2011-10-01

    This presentation offers new data and statistical analysis of wind power forecasting errors in operational systems.

  16. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Offshore Lubricants Market Forecast Home There are currently no posts in this category. Syndicate...

  17. Coal Fired Power Generation Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Coal Fired Power Generation Market Forecast Home There are currently no posts in this category. Syndicate...

  18. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

    The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030....

  19. energy data + forecasting | OpenEI Community

    Open Energy Info (EERE)

    energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in...

  20. Forecast of transportation energy demand through the year 2010

    SciTech Connect (OSTI)

    Mintz, M.M.; Vyas, A.D.

    1991-04-01

    Since 1979, the Center for Transportation Research (CTR) at Argonne National Laboratory (ANL) has produced baseline projections of US transportation activity and energy demand. These projections and the methodologies used to compute them are documented in a series of reports and research papers. As the lastest in this series of projections, this report documents the assumptions, methodologies, and results of the most recent projection -- termed ANL-90N -- and compares those results with other forecasts from the current literature, as well as with the selection of earlier Argonne forecasts. This current forecast may be used as a baseline against which to analyze trends and evaluate existing and proposed energy conservation programs and as an illustration of how the Transportation Energy and Emission Modeling System (TEEMS) works. (TEEMS links disaggregate models to produce an aggregate forecast of transportation activity, energy use, and emissions). This report and the projections it contains were developed for the US Department of Energy's Office of Transportation Technologies (OTT). The projections are not completely comprehensive. Time and modeling effort have been focused on the major energy consumers -- automobiles, trucks, commercial aircraft, rail and waterborne freight carriers, and pipelines. Because buses, rail passengers services, and general aviation consume relatively little energy, they are projected in the aggregate, as other'' modes, and used primarily as scaling factors. These projections are also limited to direct energy consumption. Projections of indirect energy consumption, such as energy consumed in vehicle and equipment manufacturing, infrastructure, fuel refining, etc., were judged outside the scope of this effort. The document is organized into two complementary sections -- one discussing passenger transportation modes, and the other discussing freight transportation modes. 99 refs., 10 figs., 43 tabs.

  1. NREL: Resource Assessment and Forecasting - Research Staff

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

    Research Staff NREL's resource assessment and forecasting research staff provides expertise in renewable energy measurement and instrumentation through NREL's Power Systems Engineering Center. Photo not available Linda Crow - Administrative Associate B.S. Environmental Studies, The Evergreen State College Linda currently works for the Resource Assessment and Forecasting group as their administrative support. She has worked with scientists at the Office of Science at the Air Force Academy and at

  2. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  3. AEO2012 Early Release Overview

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

    Mellish, Carrie Milton, Brian Murphy, Kelly Perl, David Peterson, John Powell, Nancy Slater-Thompson, Kay A. Smith, John Staub, Charles L. Smith, Craig Federhen, and Peggy Wells. ...

  4. AEO Early Release 2013 - oil

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

    Growing U.S. oil output and rising vehicle fuel economy to cut U.S. reliance on foreign oil The United States is expected to continue cutting its dependence on petroleum and liquid fuels imports over the rest of this decade because of growing domestic crude oil production and more fuel-efficient vehicles on America's highways. The new long-term outlook from the U.S. Energy Information Administration shows America's dependence on imported petroleum and liquid fuels will decline from 45 percent of

  5. AEO2016 Electricity Working Group

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

    Address improving macro fundamentals: lower interest rates ... Four building blocks (heat rate improvement, switching to ... * In response to the transfer of pollutants from air to ...

  6. AEO2014: Preliminary Industrial Output

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

    are run for the ratio of gross output (production) and demand computed from Input-Output basis * Major drivers: capacity utilization, interest rates, relative prices, ...

  7. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

    Templeton, K.J.; Clary, J.L.

    1994-09-01

    This report describes a 30-year forecast of the solid waste volumes by container type. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste. These volumes and their associated container types will be generated or received at the US Department of Energy Hanford Site for storage, treatment, and disposal at Westinghouse Hanford Company`s Solid Waste Operations Complex (SWOC) during a 30-year period from FY 1994 through FY 2023. The forecast data for the 30-year period indicates that approximately 307,150 m{sup 3} of LLMW and TRU/TRUM waste will be managed by the SWOC. The main container type for this waste is 55-gallon drums, which will be used to ship 36% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of 55-gallon drums is Past Practice Remediation. This waste will be generated by the Environmental Restoration Program during remediation of Hanford`s past practice sites. Although Past Practice Remediation is the primary generator of 55-gallon drums, most waste generators are planning to ship some percentage of their waste in 55-gallon drums. Long-length equipment containers (LECs) are forecasted to contain 32% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of LECs is the Long-Length Equipment waste generator, which is responsible for retrieving contaminated long-length equipment from the tank farms. Boxes are forecasted to contain 21% of the waste. These containers are primarily forecasted for use by the Environmental Restoration Operations--D&D of Surplus Facilities waste generator. This waste generator is responsible for the solid waste generated during decontamination and decommissioning (D&D) of the facilities currently on the Surplus Facilities Program Plan. The remaining LLMW and TRU/TRUM waste volume is planned to be shipped in casks and other miscellaneous containers.

  8. A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast

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

    System A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast System Morcrette, Jean-Jacques European Centre for Medium-Range Weather Forecasts Category: Radiation A new processor for evaluating the UV-B and UV-A radiation at the surface, based on modifications to the current shortwave radiation scheme of the ECMWF forecast system is described. Sensitivity studies of the UV surface irradiance and Erythemal Dose Rate to spectral resolution, representation and atmospheric

  9. Solar Forecasting Gets a Boost from Watson, Accuracy Improved...

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

    Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM ...

  10. PBL FY 2003 Second Quarter Review Forecast of Generation Accumulated...

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

    the rate period (i.e., FY 2002-2006), a forecast of that end-of-year Accumulated Net Revenue (ANR) will be completed. If the ANR at the end of the forecast year falls below the...

  11. Combined Heat And Power Installation Market Forecast | OpenEI...

    Open Energy Info (EERE)

    Combined Heat And Power Installation Market Forecast Home There are currently no posts in this category. Syndicate...

  12. DOE Taking Wind Forecasting to New Heights | Department of Energy

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

    Taking Wind Forecasting to New Heights DOE Taking Wind Forecasting to New Heights May 18, 2015 - 3:24pm Addthis A 2013 study conducted for the U.S. Department of Energy (DOE) by the National Oceanic and Atmospheric Administration (NOAA), AWS Truepower, and WindLogics in the Great Plains and Western Texas, demonstrated that wind power forecasts can be improved substantially using data collected from tall towers, remote sensors, and other devices, and incorporated into improved forecasting models

  13. Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01

    This presentation presents some statistical analysis of wind power forecast errors and error distributions, with examples using ERCOT data.

  14. Wind power forecasting in U.S. electricity markets.

    SciTech Connect (OSTI)

    Botterud, A.; Wang, J.; Miranda, V.; Bessa, R. J.; Decision and Information Sciences; INESC Porto

    2010-04-01

    Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts.

  15. Wind power forecasting in U.S. Electricity markets

    SciTech Connect (OSTI)

    Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro; Bessa, Ricardo J.

    2010-04-15

    Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. (author)

  16. Uncertainty Reduction in Power Generation Forecast Using Coupled

    Office of Scientific and Technical Information (OSTI)

    Wavelet-ARIMA (Conference) | SciTech Connect Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA Citation Details In-Document Search Title: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of

  17. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

    SciTech Connect (OSTI)

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-08-15

    Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models includes the characterization of patient-specific response with implications for treatment management and research study design.

  18. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

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

    Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting Preprint Jie Zhang 1 , Bri-Mathias Hodge 1 , Siyuan Lu 2 , Hendrik F. Hamann 2 , Brad Lehman 3 , Joseph Simmons 4 , Edwin Campos 5 , and Venkat Banunarayanan 6 1 National Renewable Energy Laboratory 2 IBM TJ Watson Research Center 3 Northeastern University 4 University of Arizona 5 Argonne National Laboratory 6 U.S. Department of Energy Presented at the IEEE Power and Energy Society General Meeting Denver,

  19. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    SciTech Connect (OSTI)

    Wilczak, James M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  20. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  1. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

    This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

  2. Evaluation

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

    Savings Portfolio (122013) Energy Smart Grocer Impact Evaluation (102013) Energy Smart Industrial - Energy Management Pilot Impact Evaluation (22013) Clark PUD Home...

  3. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

    Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

    2014-12-30

    A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

  4. Global disease monitoring and forecasting with Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

  5. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

  6. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  7. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  8. Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

    2012-09-01

    The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

  9. Industrial end-use forecasting that incorporates DSM and air quality

    SciTech Connect (OSTI)

    Tutt, T.; Flory, J.

    1995-05-01

    The California Energy Commission (CEC) and major enregy utilities in California have generally depended on simple aggregate intensity or economic models to forecast energy use in the process industry sector (which covers large industries employing basic processes to transform raw materials, such as paper mills, glass plants, and cement plants). Two recent trends suggests that the time has come to develop a more disaggregate process industry forecasting model. First, recent efforts to improve air quality, especially by the South Coast Air Quality Management District (SCAQMD), could significantly affect energy use by the process industry by altering the technologies and processes employed in order to reduce emissions. Second, there is a renewed interest in Demand-Side Management (DSM), not only for utility least-cost planning, but also for improving the economic competitiveness and environmental compliance of the pro{minus}cess industries. A disaggregate forecasting model is critical to help the CEC and utilities evaluate both the air quality and DSM impacts on energy use. A crucial obstacle to the development and use of these detailed process industry forecasting models is the lack of good data about disaggregate energy use in the sector. The CEC is nearing completion of a project to begin to overcome this lack of data. The project is testing methds of developing detailed energy use data, collecting an initial database for a large portion of southern California, and providing recommendations and direction for further data collection efforts.

  10. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

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

    Complex Terrain | Department of Energy for Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am Addthis On February 11, 2014 the Wind Program announced a Notice of Intent to issue a funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex terrain, this funding would

  11. DOE Benefits Forecasts: Report of the External Peer Review Panel |

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

    Department of Energy Benefits Forecasts: Report of the External Peer Review Panel DOE Benefits Forecasts: Report of the External Peer Review Panel A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts. PDF icon Report of the External Peer Review Panel More Documents & Publications Industrial Technologies Funding Profile by Subprogram Survey of Emissions Models for Distributed Combined Heat and Power

  12. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical

    Office of Scientific and Technical Information (OSTI)

    Modelling Approach (Journal Article) | SciTech Connect Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach Citation Details In-Document Search Title: Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the

  13. Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Office of Scientific and Technical Information (OSTI)

    (BNL) Field Campaign Report (Technical Report) | SciTech Connect SciTech Connect Search Results Technical Report: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report Citation Details In-Document Search Title: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf]

  14. Energy Conservation Program: Data Collection and Comparison with Forecasted

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

    Unit Sales for Five Lamp Types, Notice of Data Availability | Department of Energy Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability This document is the notice of data availability for Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types. PDF icon

  15. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.

    2012-09-01

    Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

  16. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    Title: 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory When considering the amount of shortwave radiation incident on a photovoltaic solar array and, ...

  17. Improving the Accuracy of Solar Forecasting Funding Opportunity...

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

    These projects aim to improve the accuracy of solar forecasting that could increase penetration of solar power by enabling more certainty in power prediction from solar power ...

  18. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic...

    Office of Scientific and Technical Information (OSTI)

    Title: Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates ...

  19. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    SciTech Connect (OSTI)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  20. DOE Announces Webinars on Solar Forecasting Metrics, the DOE...

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

    DOE Announces Webinars on Solar Forecasting Metrics, the DOE ... from adopting the latest energy efficiency and renewable ... to liquids technology, advantages of using natural gas, ...

  1. FY 2004 Second Quarter Review Forecast of Generation Accumulated...

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

    Bonneville Power Administration Power Business Line Generation (PBL) Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  2. PBL FY 2003 Third Quarter Review Forecast of Generation Accumulated...

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

    2003 Bonneville Power Administration Power Business Line Generation Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  3. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

    Bird, L.; Holt, E.; Sumner, J.; Kreycik, C.

    2010-05-01

    Various factors influence the development of the voluntary 'green' power market--the market in which consumers purchase or produce power from non-polluting, renewable energy sources. These factors include climate policies, renewable portfolio standards (RPS), renewable energy prices, consumers' interest in purchasing green power, and utilities' interest in promoting existing programs and in offering new green options. This report presents estimates of voluntary market demand for green power through 2015 that were made using historical data and three scenarios: low-growth, high-growth, and negative-policy impacts. The resulting forecast projects the total voluntary demand for renewable energy in 2015 to range from 63 million MWh annually in the low case scenario to 157 million MWh annually in the high case scenario, representing an approximately 2.5-fold difference. The negative-policy impacts scenario reflects a market size of 24 million MWh. Several key uncertainties affect the results of this forecast, including uncertainties related to growth assumptions, the impacts that policy may have on the market, the price and competitiveness of renewable generation, and the level of interest that utilities have in offering and promoting green power products.

  4. Why Models Don%3CU%2B2019%3Et Forecast.

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01

    The title of this paper, Why Models Don't Forecast, has a deceptively simple answer: models don't forecast because people forecast. Yet this statement has significant implications for computational social modeling and simulation in national security decision making. Specifically, it points to the need for robust approaches to the problem of how people and organizations develop, deploy, and use computational modeling and simulation technologies. In the next twenty or so pages, I argue that the challenge of evaluating computational social modeling and simulation technologies extends far beyond verification and validation, and should include the relationship between a simulation technology and the people and organizations using it. This challenge of evaluation is not just one of usability and usefulness for technologies, but extends to the assessment of how new modeling and simulation technologies shape human and organizational judgment. The robust and systematic evaluation of organizational decision making processes, and the role of computational modeling and simulation technologies therein, is a critical problem for the organizations who promote, fund, develop, and seek to use computational social science tools, methods, and techniques in high-consequence decision making.

  5. Assumptions to the Annual Energy Outlook 2015

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

    00 Appendix A: Handling of federal and selected state legislation and regulations in the AEO Residential sector Legislation Brief description AEO handling Basis A. National Appliance Energy Conservation Act of 1987 (NAECA87) Requires Secretary of Energy to set minimum efficiency standards for various appliance categories with periodic updates Include categories represented in the AEO residential sector forecast Public Law 100-12 a. Room air conditioners Sets standards for room air conditioners

  6. Evaluation

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

    of Array Irradiance Models at Locations across the United States Matthew Lave, Member, IEEE, William Hayes, Andrew Pohl, and Clifford W. Hansen Abstract-We report an evaluation of...

  7. Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.

    2012-07-01

    The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

  8. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership

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

    for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations | Department of Energy The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The Wind Forecast Improvement

  9. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    Reports and Publications (EIA)

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  10. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    Open Energy Info (EERE)

    TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name: 3TIER Environmental Forecast Group Inc (3TIER) Place: Seattle, Washington Zip: 98121 Sector: Renewable...

  11. Forecasting the Magnitude of Sustainable Biofeedstock Supplies: the Challenges and the Rewards

    SciTech Connect (OSTI)

    Graham, Robin Lambert

    2007-01-01

    Forecasting the magnitude of sustainable biofeedstock supplies is challenging because of 1) the myriad of potential feedstock types and their management 2) the need to account for the spatial variation of both the supplies and their environmental and economic consequences, and 3) the inherent challenges of optimizing across economic and environmental considerations. Over the last two decades U.S. biomass forecasts have become increasingly complex and sensitive to environmental and economic considerations. More model development and research is needed however, to capture the landscape and regional tradeoffs of differing biofeedstock supplies especially with regards water quality concerns and wildlife/biodiversity. Forecasts need to be done in the context of the direction of change and what the probable land use and attendant environmental and economic outcomes would be if biofeedstocks were not being produced. To evaluate sustainability, process-oriented models need to be coupled or used to inform sector models and more work needs to be done on developing environmental metrics that are useful for evaluating economic and environmental tradeoffs. These challenges are exciting and worthwhile as they will enable the bioenergy industry to capture environmental and social benefits of biofeedstock production and reduce risks.

  12. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Chakrabarti, Bhujanga B.; Subbarao, Krishnappa; Loutan, Clyde; Guttromson, Ross T.

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

  13. Development and testing of improved statistical wind power forecasting methods.

    SciTech Connect (OSTI)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  14. U.S. Regional Demand Forecasts Using NEMS and GIS

    SciTech Connect (OSTI)

    Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

    2005-07-01

    The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of demand.

  15. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  16. Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

    Broader source: Energy.gov [DOE]

    Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

  17. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Solar Trackers Market Forecast Home John55364's picture Submitted by John55364(100) Contributor 12 May, 2015 - 03:54 Solar Trackers Market - Global Industry Analysis, Size, Share,...

  18. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT...

    Open Energy Info (EERE)

    Tool (EFFECT) EFFECT is an open, Excel-based modeling tool used to forecast greenhouse gas emissions from a range of development scenarios at the regional and national levels....

  19. Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

    Reports and Publications (EIA)

    2003-01-01

    This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

  20. ARM - PI Product - CCPP-ARM Parameterization Testbed Model Forecast...

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

    love to hear from you Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : CCPP-ARM Parameterization Testbed Model Forecast Data Dataset contains the NCAR...

  1. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    1995-05-01

    This report presents information from the Energy Information Administration (EIA). Articles are included on international energy forecasting data, data on the use of home appliances, gasoline prices, household energy use, and EIA information products and dissemination avenues.

  2. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect (OSTI)

    Hodge, B. M.; Florita, A.; Sharp, J.; Margulis, M.; Mcreavy, D.

    2015-02-01

    This report summarizes an assessment of improved short-term wind power forecasting in the California Independent System Operator (CAISO) market and provides a quantification of its potential value.

  3. Summer gasoline price forecast slightly higher, but drivers still...

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

    In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average 2.21 per gallon this summer. While that's 17 ...

  4. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  5. Radar Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    1) To provide profiles of the horizontal wind to be used to test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical ...

  6. PBL FY 2002 Second Quarter Review Forecast of Generation Accumulated...

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

    Slice true-ups, and actual expense levels. Any variation of these can change the net revenue situation. FY 2002 Forecasted Second Quarter Results 170 (418) FY 2002 Unaudited...

  7. Forecasting the oil-gasoline price relationship: should we care...

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

    (2007, EE) obtain similar results on a panel of 15 OECD countries, with annual data ... Results Point forecasts of the N.Y. gasoline price 26 Panel (a): daily data Model MSFE ...

  8. New Forecasting Tools Enhance Wind Energy Integration In Idaho...

    Office of Environmental Management (EM)

    New Forecasting Tools Enhance Wind Energy Integration in Idaho and Oregon Page 1 Under the ... (RIT) that enables grid operators to use wind energy more cost-effectively to serve ...

  9. Modeling and forecasting the distribution of Vibrio vulnificus in

    Office of Scientific and Technical Information (OSTI)

    Chesapeake Bay (Journal Article) | SciTech Connect Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay Citation Details In-Document Search Title: Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters

  10. Forecasting neutrino masses from combining KATRIN and the CMB observations:

    Office of Scientific and Technical Information (OSTI)

    Frequentist and Bayesian analyses (Journal Article) | SciTech Connect SciTech Connect Search Results Journal Article: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses Citation Details In-Document Search Title: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses We present a showcase for deriving bounds on the neutrino masses from laboratory experiments and cosmological

  11. Expert Panel: Forecast Future Demand for Medical Isotopes | Department of

    Energy Savers [EERE]

    Energy Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and

  12. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National

    Office of Scientific and Technical Information (OSTI)

    Laboratory (Technical Report) | SciTech Connect 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory Citation Details In-Document Search Title: 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to

  13. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

  14. National Oceanic and Atmospheric Administration Provides Forecasting Support for CLASIC and CHAPS 2007

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

    NOAA Provides Forecasting Support for CLASIC and CHAPS 2007 Forecasting Challenge While weather experiments in the heart of Tornado Alley typically focus on severe weather, the CLASIC and CHAPS programs will have different emphases. Forecasters from the National Oceanic and Atmospheric Administration in Norman, Okla. will provide weather forecasting support to these two Department of Energy experiments based in the state. Forecasting support for meteorological research field programs usually

  15. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

    Davis, J.I.; Spaeth, M.L.

    1986-01-01

    The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

  16. 2017 Levelized Costs AEO 2012 Early Release

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

    Report," collects the cost and quality of fossil fuel purchases made by electric ... a reduction of approximately 9 percent of natural gas purchases, cost, and quality data. ...

  17. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    ... with specific circumstances, such a tanker might cost around 7 cents per gallon for a round trip. ATB's would be less efficient than tankers, but more efficient than a towed barge. ...

  18. 2017 Levelized Costs AEO 2012 Early Release

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

    Residential Energy Consumption Survey (RECS) End-Use Models FAQs 1 February 2013 Residential Energy Consumption Survey (RECS) End-Use Models FAQs What is an end-use model? An end-use model is a set of equations designed to disaggregate a RECS sample household's total annual fuel consumption into end uses such as space heating, air conditioning, water heating, refrigeration, and so on. These disaggregated values are then weighted up to produce population estimates of total and average energy end

  19. AEO Early Release 2013 - renewable generation

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

    Renewables account for a bigger share of U.S. electricity generation in decades ahead The United States will generate a bigger share of its electricity from renewable sources such as solar, wind, and biomass energy in the decades ahead, according to the new long-term outlook just released by the U.S. Energy Information Administration. EIA says that lower costs are making renewable electricity more economical, and along with federal and state policies that promote renewables, EIA projects that

  20. 2017 Levelized Costs AEO 2012 Early Release

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

    be revised lower on weak manufacturing data from China and Europe in November, leaving global ... experienced colder-than-expected weather, which helped push PADD 2 distillate ...

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Additionally, weaker-than-expected manufacturing data from China and Europe also contributed to ... With cold weather in the Northeast and Midwest subsiding in March, domestic heating ...

  2. 2017 Levelized Costs AEO 2012 Early Release

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

    Recent manufacturing data for the United States and China were above expectations, supporting demand for ... in late November as colder weather raised prospects of higher demand ...

  3. AEO Early Release 2013 - LNG exports

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

    Some of that gas will be sent overseas in huge ocean-going tankers carrying super-cooled liquefied natural gas, or LNG. U.S. exports of liquefied natural gas are expected to reach ...

  4. 2017 Levelized Costs AEO 2012 Early Release

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

    Crude oil imports into the United States were relatively constant from September to October, and with U.S. refineries currently undergoing planned maintenance, crude oil ...

  5. 2017 Levelized Costs AEO 2012 Early Release

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

    The four-week average of commercial crude oil imports for PADD 3 dropped to 3.18 MMbbld for the week ending May 16, its lowest level since 1992. With the recent narrowing of the ...

  6. AEO2016 Preliminary Industrial Output Results

    Gasoline and Diesel Fuel Update (EIA)

    - Enhancements of the industrial output model to incorporate additional detail of chemical, glass, and paper industries. - The extension of the supply matrices allowing for ...

  7. 2017 Levelized Costs AEO 2012 Early Release

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

    From a market perspective, commodity buyers do not typically care about the source of a product as long as its chemical composition meets specifications. We are proposing to rework ...

  8. AEO2015 Transportation Working Group Meeting

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

    (IHS) Dallas Burkholder (EPA) Don Pickrell (Volpe) Ed Coe (EPA) Erik Herzog (EPA) Frances Wood (On Location) Jarrod Brown (EPA) Siddiq Khan (ACEEE) Stephen Zoepf (DOT) Therese ...

  9. Comparing Efficiency Projections (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Realized improvements in energy efficiency generally rely on a combination of technology and economics. The figure below illustrates the role of technology assumptions in the Annual Energy Outlook 2010 projections for energy efficiency in the residential and commercial buildings sector. Projected energy consumption in the Reference case is compared with projections in the Best Available Technology, High Technology, and 2009 Technology cases and an estimate based on an assumption of no change in efficiency for building shells and equipment.

  10. State Appliance Standards (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    State appliance standards have existed for decades, starting with Californias enforcement of minimum efficiency requirements for refrigerators and several other products in 1979. In 1987, recognizing that different efficiency standards for the same products in different states could create problems for manufacturers, Congress enacted the National Appliance Energy Conservation Act (NAECA), which initially covered 12 products. The Energy Policy Act of 1992 (EPACT92), EPACT2005, and EISA2007 added additional residential and commercial products to the 12 products originally specified under NAECA.

  11. Energy Demand (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Growth in U.S. energy use is linked to population growth through increases in demand for housing, commercial floorspace, transportation, manufacturing, and services. This affects not only the level of energy use, but also the mix of fuels and consumption by sector.

  12. CONTINATIONSHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    COTIUTINSHE DE-AC27-08RV14800/067 2G OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) -$1,000,000.00 New Total Obligated Amount tor this Award: $1, 180,251,170.41 incremental Funded Amount changed: from $1,181,251,170.41 to $1, 180,251,170.41 Account code: Reforming Treatability Fund 01250 Appr Year 2010 Allottee 34 Reporting Entity 421301 Object Class 25200 Program 1111412 Project

  13. CONTINATIONSHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

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

    COTNUTO SETDE-AC27-08RV14800/048 rAG OF NAME OF OFFEROR OR CONTRACTOR WASHINGTON RIVER PROTECTION SOLUTIONS LLC ITEM NO. SUPPLIESISERVICES QUANTITY JNIT UNIT PRICE AMOUNT (A) (B) (C) (D) (E) (F) WRPS Operations (FY 2010) Fund 01250 Appr Year 2010 Aliottee 34 Reporting Entity 421301 Object Class 25200 Progrl~am~ 1110909 Project 0001481 WFO 0000000 Local Use 0000000 Amount: $70,000,000.00 Delivery Location Code: 00601 Richland Operations Office U.S. Department of Energy Richland Operations Office

  14. AEO2015 Coal Working Group Meeting Summary

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

    ... the use of carbon capture and sequestration (CCS) technology at new coal-fired generating. ... indicated that EPA's draft rule (Clean Power Plan) for reducing CO 2 ...

  15. CAFE Standards (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Pursuant to the Presidents announcement of a National Fuel Efficiency Policy, the National Highway Traffic Safety Administration (NHTSA) and the EPA have promulgated nationally coordinated standards for tailpipe Carbon Dioxide (CO2)-equivalent emissions and fuel economy for light-duty vehicles (LDVs), which includes both passenger cars and light-duty trucks. In the joint rulemaking, the Environmental Protection Agency is enacting CO2-equivalent emissions standards under the Clean Air Act (CAA), and NHTSA is enacting companion Corporate Average Fuel Economy standards under the Energy Policy and Conservation Act, as amended by the Energy Independence and Security Act of 2007.

  16. Microsoft Word - macroeconomic_aeo2012.docx

    Gasoline and Diesel Fuel Update (EIA)

    Gross State Product The MAM projects regional gross regional product in real per capita terms. The equations are in log form. There is an estimated equation for each of the nine...

  17. AEO2014 Renewables Working Group Meeting

    Gasoline and Diesel Fuel Update (EIA)

    The meeting was held in the Forrestal Building, and included participation via a teleconference and web-cast. Chris Namovicz of EIA led off the meeting by reviewing the roll-out ...

  18. AEO 2013 Liquid Fuels Markets Working Group

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

    ... A: Low due to capital cost. A tax is still likely to be collected in the form of a mileage tax. Slide 8 Most comments here were to keep RFS as is in the model There was discussion ...

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    ... Those schedules include; * Schedule 2, General Information and Energy Sources and ... EIA-861. These schedules include Schedule 2C Green Pricing and Schedule 2D Net Metering. ...

  20. Coal Transportation Issues (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    Most of the coal delivered to U.S. consumers is transported by railroads, which accounted for 64% of total domestic coal shipments in 2004. Trucks transported approximately 12% of the coal consumed in the United States in 2004, mainly in short hauls from mines in the East to nearby coal-fired electricity and industrial plants. A number of minemouth power plants in the West also use trucks to haul coal from adjacent mining operations. Other significant modes of coal transportation in 2004 included conveyor belt and slurry pipeline (12%) and water transport on inland waterways, the Great Lakes, and tidewater areas (9%).

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices continued upward and reached the highest levels of the year. The North Sea Brent front-month futures price rose $6.34 per barrel (b) from April 1 to settle at $45.01/b on May 5 (Figure 1). The West Texas Intermediate (WTI) front-month futures price rose $7.53/b and settled at $44.32/b over the same period. Early data on petroleum product consumption in 2016 suggest that last year's strong growth may continue this year. U.S.

  2. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    5 1 April 2015 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: After increasing in February, global crude oil prices declined in March. The North Sea Brent front month futures price settled at $54.95/bbl on April 2, a decline of $4.59/bbl since the close on March 2 (Figure 1). The West Texas Intermediate (WTI) front month futures price declined by $0.45/bbl over the same period to settle at $49.14/bbl on April 2. The average Brent price for March was 3.2% lower

  3. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Outlook Market Prices and Uncertainty Report Crude Oil Prices: After increasing at the start of March, crude oil prices stabilized and traded within a relatively narrow range through the first week of April. The North Sea Brent front month futures price rose $2.62 per barrel (b) from March 1 to settle at $39.43/b on April 7 (Figure 1). The West Texas Intermediate (WTI) front month futures price rose $2.86/b and settled at $37.26 over the same period. The increase in crude oil prices alongside

  4. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Front month futures prices for both the Brent and WTI crude oil benchmarks rose over the last month, with WTI rising faster than Brent to sharply narrow the spread between the two benchmarks. Since July 1, Brent has increased by $6.54 per barrel to settle at $109.54 per barrel on August 1 (Figure 1). Over the same time period, WTI increased by $9.90 per barrel to settle at $107.89. While the August 1 settle was the highest price for Brent

  5. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices, which reached their highest point of the year in June, fell to their lowest levels of the year in early August. The North Sea Brent front month futures price settled at $105.44/barrel on August 7, a decrease of $6.85/barrel from July 1 (Figure 1). The front month West Texas Intermediate (WTI) contract also fell, settling at $97.34/barrel on August 7, $8.00/barrel lower than on July 1. A further easing of

  6. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices moved lower through much of July and early August. The North Sea Brent front month futures price declined $12.49 per barrel (b) since July 1 to settle at $49.52/b on August 6 (Figure 1). The West Texas Intermediate (WTI) front month futures price declined $12.30/b over the same time, settling at $44.66/b on August 6. Both benchmarks recorded their largest month-over-month decline since January 2015. One of the factors that

  7. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices in November declined to the lowest levels since August. The North Sea Brent front month futures price settled at $43.84 per barrel (b) on December 3, a decrease of $4.95/b since November 2 (Figure 1). The West Texas Intermediate (WTI) front month futures price settled at $41.08/b on December 3, declining $5.06/b over the same period. The prospect of an oversupplied crude oil market continuing in the near term weighed on

  8. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    February 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices were relatively stable to start the year. The North Sea Brent front month futures price settled at $107.19 per barrel (bbl) on February 6, a decline of less than $1/bbl from its settle price on January 2 (Figure 1). Over the same period, the West Texas Intermediate (WTI) front month futures contract rose $2.40/bbl, settling at $97.84/bbl on February 6. Crude oil has so

  9. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices moved higher toward the end of January and into the first week of February. The North Sea Brent front month futures price settled at $56.57/bbl on February 5, an increase of $0.15/bbl from January 2 (Figure 1). The front month West Texas Intermediate (WTI) contract price settled at $50.48/bbl on February 5, $2.21/bbl lower than at the start of January. These changes were relatively small compared to an average

  10. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: The North Sea Brent front month futures price settled at $34.46/b on February 4 $2.76 per barrel (b) below its January 4 level (Figure 1). The West Texas Intermediate (WTI) front month futures price settled at $31.72, a decrease of $5.04/b over the same period. On January 20, both Brent and WTI were at their lowest levels since 2003. During the first three weeks of January, Brent and WTI front month futures prices declined 25% and 28%,

  11. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices were relatively stable throughout December before declining at the beginning of January, while U.S. domestic prices moved higher in December. The North Sea Brent front month futures price settled at $107.78 per barrel (bbl) on January 2, a decline of $3.67/bbl from its close on December 2 (Figure 1). Over the same period, the West Texas Intermediate (WTI) front month futures contract rose $1.62/bbl, settling

  12. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil markets continue to search for a bottom as prices declined again in December and the first week of January. The North Sea Brent front month futures price settled at $50.96/bbl on January 8, a decline of $21.58/bbl from December 1 (Figure 1). The front month West Texas Intermediate (WTI) contract price settled at $48.79/bbl on January 8, decreasing by $20.21/bbl since the start of December. Crude oil prices now have declined more

  13. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil futures prices reached the lowest level in 12 years in December and early January. The North Sea Brent front month futures price settled at $33.75 per barrel (b) on January 7, $10.69/b lower than the close on December 1 (Figure 1). The West Texas Intermediate (WTI) front month futures price settled at $33.27, a decrease of $8.58/b over the same period. Global crude oil prices declined after the December 4 Organization of Petroleum

  14. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: After an upward move in mid-June, crude oil prices retreated close to previous levels. The North Sea Brent front month futures price settled at $111/barrel on July 3, an increase of $2.17/barrel from June 2 (Figure 1). The front month West Texas Intermediate (WTI) contract also rose, settling at $104.06/barrel on July 3, $1.59/barrel higher than on June 2. Tensions in Iraq were the primary driver of the crude oil price increase in mid-June.

  15. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Global and domestic crude oil prices traded in a narrow range in June. The North Sea Brent front month futures price declined $2.87 per barrel (b) since June 1 to settle at $62.01/b on July 1 (Figure 1). The West Texas Intermediate (WTI) front month futures price declined $3.24/b over the month, settling at $56.96/b on July 1. As global crude oil supply remains robust, demand-side factors are likely contributing to renewed price stability

  16. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices declined in May and in the first week of June while domestic crude oil prices stayed relatively stable. The North Sea Brent front month futures declined $4.43 per barrel (b) since May 1 to settle at $62.03/b on June 4 (Figure 1). The West Texas Intermediate (WTI) front month futures price decreased $1.15/b over the same period to settle at $58/b on June 4. Elevated crude oil production from members of The

  17. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil futures prices rose over the previous month but remained within the recent, and relatively narrow, trading range. The North Sea Brent front month futures price settled at $108.10 per barrel (bbl) on March 6, an increase of $2.06/bbl from February 3 (Figure 1). Over the same period, the West Texas Intermediate (WTI) front month futures contract rose $5.13/bbl, settling at $101.56/bbl on March 6. The brief uptick in

  18. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    March 2015 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: Over the past month, international crude oil prices recorded the first month- over-month increase since June 2014. The North Sea Brent front month futures price settled at $60.48/bbl on March 5, an increase of $5.73/bbl from February 2 (Figure 1). In the U.S. market, domestic crude oil prices continued to lag behind international benchmarks. The front month West Texas Intermediate (WTI) contract price

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: The North Sea Brent front month futures price rose $2.83 per barrel (b) from February 1 to settle at $37.07/b on March 3 (Figure 1). The West Texas Intermediate (WTI) front month futures price rose $2.95/b and settled at $34.57 over the same period. Crude oil prices began to increase during the second half of February in response to potential future supply reductions and better economic data in the United States. Discussion of a potential

  20. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil futures prices rebounded in April and approached the top of their recent trading range. The North Sea Brent front month futures price settled at $107.76 per barrel (bbl) on May 1, an increase of $2.14/bbl from April 1 (Figure 1). West Texas Intermediate (WTI) prices at the start of May were near the same levels as the beginning of April. The front month WTI contract settled at $99.42/bbl on May 1, a slight decrease

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices continued to rise in April and reached their highest levels of the year. The North Sea Brent front month futures price settled at $65.54 per barrel (b) on May 7, an increase of $8.44/b since the close on April 1 (Figure 1). The West Texas Intermediate (WTI) front month futures price rose by $8.85/b over the same period to settle at $58.94/b on May 7. Although current oil market conditions still show production outpacing

  2. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: North Sea Brent and West Texas Intermediate (WTI) front month futures contracts continued their recent decline in October and the first week of November as a larger-than-normal seasonal decrease in global refinery runs from August through October lessened demand for crude oil. The Brent contract settled at $103.46 per barrel on November 7, a decline of $4.48 per barrel compared to October 1 (Figure 1). The decreases in WTI futures prices

  3. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices remained within the range established over the previous three months. The North Sea Brent front month futures price settled at $47.98 per barrel (b) on November 5, an increase of $0.29/b since October 1 (Figure 1). The West Texas Intermediate (WTI) front month futures price settled at $45.20/b on November 5, rising by 46 cents/b over the same time. Although prices were relatively stable, large uncertainty remains in the

  4. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    3 1 October 2013 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: Front month futures prices for the Brent and West Texas Intermediate (WTI) crude oil benchmarks fell in September. The Brent contract settled at $109.00 per barrel on October 3, a decline of $6.68 per barrel since September 3, and WTI settled at $103.31 per barrel on October 3, falling by $5.23 per barrel over the same period (Figure 1). These changes marked the first month-over-month declines in

  5. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    4 1 October 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices continued on a downward trajectory in September, falling under $100 per barrel (bbl) for the first time since June 2012. The North Sea Brent front month futures price settled at $93.42/bbl on October 2, a decrease of $6.92/bbl from September 2 (Figure 1). U.S. domestic crude oil benchmarks also declined, with the front month West Texas Intermediate (WTI) contract price

  6. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: The front month futures price for Brent, the world waterborne crude benchmark, increased by $5.72 per barrel to settle at $115.26 per barrel on September 5 (Figure 1). Front month futures prices for West Texas Intermediate (WTI) crude oil also increased over the same time period but by a lesser amount, to settle at $108.37 per barrel on September 5. The primary drivers of higher crude oil prices over the past five weeks included an uptick

  7. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices fell in August and remain near their lowest levels of 2014. The North Sea Brent front month futures price settled at $101.83/barrel on September 4, a decrease of $3.01/barrel from August 1 (Figure 1). The front month West Texas Intermediate (WTI) contract price fell by $3.43/barrel over the same period, settling at $94.45/barrel on September 4. Although the U.S. economy showed robust growth in the second

  8. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update (EIA)

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices declined through most of August before rising at the end of the month and in the first week of September. The North Sea Brent front month futures price rose $1.16 per barrel (b) since August 3 to settle at $50.68/b on September 3 (Figure 1). The West Texas Intermediate (WTI) front month futures price increased $1.58/b over the same period to settle at $46.75/b. In contrast to July, when crude oil prices may have responded

  9. 2017 Levelized Costs AEO 2012 Early Release

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

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices continued upward and reached the highest levels of the year. The North Sea Brent front-month futures price rose $6.34 per barrel (b) from April 1 to settle at $45.01/b on May 5 (Figure 1). The West Texas Intermediate (WTI) front-month futures price rose $7.53/b and settled at $44.32/b over the same period. Early data on petroleum product consumption in 2016 suggest that last year's strong growth may continue this year. U.S.

  10. 2017 Levelized Costs AEO 2012 Early Release

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

    October 2014 1 October 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: International crude oil prices continued on a downward trajectory in September, falling under $100 per barrel (bbl) for the first time since June 2012. The North Sea Brent front month futures price settled at $93.42/bbl on October 2, a decrease of $6.92/bbl from September 2 (Figure 1). U.S. domestic crude oil benchmarks also declined, with the front month West Texas Intermediate (WTI)

  11. 2017 Levelized Costs AEO 2012 Early Release

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

    Winter Fuels Explanatory Notes 1 October 2014 Winter Fuels Explanatory Notes Prices The residential No. 2 heating oil and propane prices (excluding taxes) for a given state are based on the results of two independent telephone surveys of marketers and refiners, one for each of the two products. Data are collected by State Energy Offices under the U.S. Energy Information Administration (EIA) State Heating Oil and Propane Program (SHOPP). Sampling methodology and estimation procedures for

  12. 2017 Levelized Costs AEO 2012 Early Release

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

    2013 AFV Definitions, Sources, and Explanatory Notes 1 March 2013 Alternative Fuel Vehicle Data Definitions, Sources, and Explanatory Notes Definitions Key Terms Definitions Aftermarket Vehicle Converter An entity (company or organization) that converts vehicles from operating on a traditional fuel (gasoline or petroleum-based diesel) to operate on an alternative fuel or from one alternative fuel to another alternative fuel. The converted vehicle may operate exclusively on the fuel or power

  13. Nonconventional Liquid Fuels (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    Higher prices for crude oil and refined petroleum products are opening the door for nonconventional liquids to displace petroleum in the traditional fuel supply mix. Growing world demand for diesel fuel is helping to jump-start the trend toward increasing production of nonconventional liquids, and technological advances are making the nonconventional alternatives more viable commercially. Those trends are reflected in the Annual Energy Outlook 2006 projections.

  14. Supplement to the Annual Energy Outlook 1993

    SciTech Connect (OSTI)

    Not Available

    1993-02-17

    The Supplement to the Annual Energy Outlook 1993 is a companion document to the Energy Information Administration`s (EIA) Annual Energy Outlook 1993 (AEO). Supplement tables provide the regional projections underlying the national data and projections in the AEO. The domestic coal, electric power, commercial nuclear power, end-use consumption, and end-use price tables present AEO forecasts at the 10 Federal Region level. World coal tables provide data and projections on international flows of steam coal and metallurgical coal, and the oil and gas tables provide the AEO oil and gas supply forecasts by Oil and Gas Supply Regions and by source of supply. All tables refer to cases presented in the AEO, which provides a range of projections for energy markets through 2010.

  15. Survey of Variable Generation Forecasting in the West: August 2011 - June 2012

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

    This report surveyed Western Interconnection Balancing Authorities regarding their implementation of variable generation forecasting, the lessons learned to date, and recommendations they would offer to other Balancing Authorities who are considering variable generation forecasting. Our survey found that variable generation forecasting is at an early implementation stage in the West. Eight of the eleven Balancing Authorities interviewed began forecasting in 2008 or later. It also appears that less than one-half of the Balancing Authorities in the West are currently utilizing variable generation forecasting, suggesting that more Balancing Authorities in the West will engage in variable generation forecasting should more variable generation capacity be added.

  16. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

    This report presents an overview of forecasts of domestic energy consumption, production, and prices for the year 1990. These results are selected from more detailed projections prepared and published in Volume 3 of the Energy Information Administration 1980 Annual Report to Congress. This report focuses specifically upon the 1980's and concentrates upon similarities and differences in the domestic energy system, as forecast, compared to the national experience in the years immediately following the 1973--1974 oil embargo. Interest in the 1980's stems not only from its immediacy in time, but also from its importance as a time in which certain adjustments to higher energy prices are expected to take place. The forecasts presented do not attempt to account for all of this wide range of potentially important forces that could conceivably alter the energy situation. Instead, the projections are based on a particular set of assumptions that seems reasonable in light of what is currently known. 9 figs., 25 tabs.

  17. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  18. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

    2008-01-15

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  19. Waste Treatment Plant Liquid Effluent Treatability Evaluation

    SciTech Connect (OSTI)

    LUECK, K.J.

    2001-06-07

    Bechtel National, Inc. (BNI) provided a forecast of the radioactive, dangerous liquid effluents expected to be generated by the Waste Treatment Plant (WTP). The forecast represents the liquid effluents generated from the processing of 25 distinct batches of tank waste through the WTP. The WTP liquid effluents will be stored, treated, and disposed of in the Liquid Effluent Retention Facility (LERF) and the Effluent Treatment Facility (ETF). Fluor Hanford, Inc. (FH) evaluated the treatability of the WTP liquid effluents in the LERFIETF. The evaluation was conducted by comparing the forecast to the LERFIETF treatability envelope, which provides information on the items that determine if a liquid effluent is acceptable for receipt and treatment at the LERFIETF. The WTP liquid effluent forecast is outside the current LERFlETF treatability envelope. There are several concerns that must be addressed before the WTP liquid effluents can be accepted at the LERFIETF.

  20. DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting

    Broader source: Energy.gov [DOE]

    DOE has published a new report forecasting the energy savings of LED white-light sources compared with conventional white-light sources. The sixth iteration of the Energy Savings Forecast of Solid...

  1. Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

    This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

  2. Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

    Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

  3. Beyond "Partly Sunny": A Better Solar Forecast | Department of Energy

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

    Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during

  4. A Public-Private-Academic Partnership to Advance Solar Power Forecasting |

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

    Department of Energy A Public-Private-Academic Partnership to Advance Solar Power Forecasting A Public-Private-Academic Partnership to Advance Solar Power Forecasting UCAR logo2.jpg The University Corporation for Atmospheric Research (UCAR) will develop a solar power forecasting system that advances the state of the science through cutting-edge research. APPROACH UCAR value chain.png The team will develop a solar power forecasting system that advances the state of the science through

  5. Data Collection and Comparison with Forecasted Unit Sales of Five Lamp

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

    Types | Department of Energy Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types More Documents & Publications Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability CX-100584 Categorical Exclusion Determination ISSUANCE

  6. Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01

    The report addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America.

  7. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect (OSTI)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  8. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center (OSTI)

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

  9. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V.

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

  10. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  11. Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

    The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

  12. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Gomez-Lazaro, E.; Lovholm, A. L.; Berge, E.; Miettinen, J.; Holttinen, H.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Dobschinski, J.

    2013-10-01

    One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

  13. Supplement to the annual energy outlook 1994

    SciTech Connect (OSTI)

    1994-03-01

    This report is a companion document to the Annual Energy Outlook 1994 (AEO94), (DOE/EIA-0383(94)), released in Jan. 1994. Part I of the Supplement presents the key quantitative assumptions underlying the AEO94 projections, responding to requests by energy analysts for additional information on the forecasts. In Part II, the Supplement provides regional projections and other underlying details of the reference case projections in the AEO94. The AEO94 presents national forecasts of energy production, demand and prices through 2010 for five scenarios, including a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. These forecasts are used by Federal, State, and local governments, trade associations, and other planners and decisionmakers in the public and private sectors.

  14. Roel Neggers European Centre for Medium-range Weather Forecasts

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

    transition from shallow to deep convection using a dual mass flux boundary layer scheme Roel Neggers European Centre for Medium-range Weather Forecasts Introduction ! " #" $ % % & # % " " " ' % ' ( ) * + " % ( , - . / 0 / " 0 . * 0 . * . . " 0 References A short model description Sensitivity tests Results Tropospheric humidity # " humidity 1 % 2 % ' 3 " % + 1 % 2 % % 3 % Updraft entrainment ' + % " 3 % 4 # " + %' 5 6)( . % ' 1 % .7

  15. NREL: Resource Assessment and Forecasting - Data and Resources

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

    Data and Resources National Solar Radiation Database NREL resource assessment and forecasting research information is available from the following sources. Renewable Resource Data Center (RReDC) Provides information about biomass, geothermal, solar, and wind energy resources. Measurement and Instrumentation Data Center Provides irradiance and meteorological data from stations throughout the United States. Baseline Measurement System (BMS) Provides live solar radiation data from approximately 70

  16. NREL: Energy Analysis - Energy Forecasting and Modeling Staff

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

    Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Elizabeth Torres Clayton Barrows Dave Bielen Aaron Bloom Greg Brinkman Brian W Bush Stuart Cohen Wesley Cole Paul Denholm Nicholas DiOrio Aron Dobos Kelly Eurek Janine Freeman Bethany Frew Pieter Gagnon Elaine Hale

  17. Towards a Science of Tumor Forecast for Clinical Oncology

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoplymore » of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.« less

  18. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect (OSTI)

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.

  19. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

    This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

  20. World Oil Prices and Production Trends in AEO2010 (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    In Annual Energy Outlook 2010, the price of light, low-sulfur (or "sweet") crude oil delivered at Cushing, Oklahoma, is tracked to represent movements in world oil prices. The Energy Information Administration makes projections of future supply and demand for "total liquids,"" which includes conventional petroleum liquids -- such as conventional crude oil, natural gas plant liquids, and refinery gain -- in addition to unconventional liquids, which include biofuels, bitumen, coal-to-liquids (CTL), gas-to-liquids (GTL), extra-heavy oils, and shale oil.

  1. Natural Gas and Crude Oil Prices in AEO (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    If oil and natural gas were perfect substitutes in all markets where they are used, market forces would be expected to drive their delivered prices to near equality on an energy-equivalent basis. The price of West Texas Intermediate (WTI) crude oil generally is denominated in terms of barrels, where 1 barrel has an energy content of approximately 5.8 million Btu. The price of natural gas (at the Henry Hub), in contrast, generally is denominated in million Btu. Thus, if the market prices of the two fuels were equal on the basis of their energy contents, the ratio of the crude oil price (the spot price for WTI, or low-sulfur light, crude oil) to the natural gas price (the Henry Hub spot price) would be approximately 6.0. From 1990 through 2007, however, the ratio of natural gas prices to crude oil prices averaged 8.6; and in the Annual Energy Outlook 2009 projections from 2008 through 2030, it averages 7.7 in the low oil price case, 14.6 in the reference case, and 20.2 in the high oil price case.

  2. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Coal AEO2016 Meetings First AEO2016 Meeting (December 1, 2015) Summary of meeting Presentation Second AEO2016 Meeting (February 9, 2016) Summary of meeting Presentation Future Operating and Maintenance Considerations for the Existing Fleet of Coal-fired Power Plants Workshop (June 16, 2015) Summary of meeting Presentation 1: Coal Baseload Asset Aging, Evaluating Impacts on Capacity Factors Presentation 2: Future Operating and Maintenance Considerations for Existing Coal-Fired Power Plants

  3. Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition

    SciTech Connect (OSTI)

    Rogers, J.; Porter, K.

    2011-03-01

    The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

  4. Report of the external expert peer review panel: DOE benefits forecasts

    SciTech Connect (OSTI)

    None, None

    2006-12-20

    A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts.

  5. Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

    SciTech Connect (OSTI)

    Hodge, B.

    2013-12-01

    Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

  6. The Value of Improved Short-Term Wind Power Forecasting

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

    The Value of Improved Short- Term Wind Power Forecasting B.-M. Hodge and A. Florita National Renewable Energy Laboratory J. Sharp Sharply Focused, LLC M. Margulis and D. Mcreavy Lockheed Martin Technical Report NREL/TP-5D00-63175 February 2015 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL)

  7. Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.

  8. Assumptions and Expectations for Annual Energy Outlook 2015: Oil and Gas Working Group

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

    5: Oil and Gas Working Group AEO2015 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis August 7, 2014 | Washington, DC http://www.eia.gov/forecasts/aeo/workinggroup/ WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Changes in release cycles for EIA's AEO and IEO * To focus more resources on rapidly changing energy markets and how they might evolve over the next few years, the U.S. Energy Information

  9. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  10. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  11. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B.; Miettinen, J.; Holttinen, H.; Gomez-Lozaro, E.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Lovholm, A.; Berge, E.; Dobschinski, J.

    2013-10-01

    This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.

  12. Annual Energy Outlook 2016: Electricity Sector Preliminary Results

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

    Electricity Sector Preliminary Results For Electricity AEO2016 Working Group February 10, 2016| Washington, DC By EIA, Office of Electricity, Coal, Nuclear & Renewables Analysis WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Summary 2 Electricity Analysis Team February 10, 2016 * Address issues raised by stakeholders * Discuss recent developments- updates to generator status and capital costs * Present preliminary AEO2016 forecast

  13. Assumptions and Expectations for Annual Energy Outlook 2014: Oil and Gas Working Group

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

    4: Oil and Gas Working Group AEO2014 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis July 25, 2013 | Washington, DC http://www.eia.gov/forecasts/aeo/workinggroup/ WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Introduction/Background Office of Petroleum, Gas, and Biofuels Analysis Working Group Presentation for Discussion Purposes Washington, DC, July 25, 2013 DO NOT QUOTE OR CITE as results are

  14. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center (OSTI)

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improvesmore » WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.« less

  15. Betting on the Future: The authors compare natural gas forecaststo futures buys

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-01-20

    On December 12, 2005, the reference case projections from Annual Energy Outlook 2006 (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market. The goal is better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. Below is a discussion of our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this article we update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic. As was the case in the past five AEO releases (AEO 2001-AEO 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years. Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years

  16. An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study

    SciTech Connect (OSTI)

    De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

    2011-01-17

    This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

  17. Review of Variable Generation Forecasting in the West: July 2013 - March 2014

    SciTech Connect (OSTI)

    Widiss, R.; Porter, K.

    2014-03-01

    This report interviews 13 operating entities (OEs) in the Western Interconnection about their implementation of wind and solar forecasting. The report updates and expands upon one issued by NREL in 2012. As in the 2012 report, the OEs interviewed vary in size and character; the group includes independent system operators, balancing authorities, utilities, and other entities. Respondents' advice for other utilities includes starting sooner rather than later as it can take time to plan, prepare, and train a forecast; setting realistic expectations; using multiple forecasts; and incorporating several performance metrics.

  18. World oil inventories forecast to grow significantly in 2016 and 2017

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

    World oil inventories forecast to grow significantly in 2016 and 2017 Global oil inventories are expected to continue strong growth over the next two years which should keep oil prices low. In its new monthly forecast, the U.S. Energy Information Administration said world oil stocks are likely to increase by 1.6 million barrels per day this year and by 600,000 barrels per day next year. The higher forecast for inventory builds are the result of both higher global oil production and less oil

  19. WASTE TREATMENT PLANT (WTP) LIQUID EFFLUENT TREATABILITY EVALUATION

    SciTech Connect (OSTI)

    LUECK, K.J.

    2004-10-18

    A forecast of the radioactive, dangerous liquid effluents expected to be produced by the Waste Treatment Plant (WTP) was provided by Bechtel National, Inc. (BNI 2004). The forecast represents the liquid effluents generated from the processing of Tank Farm waste through the end-of-mission for the WTP. The WTP forecast is provided in the Appendices. The WTP liquid effluents will be stored, treated, and disposed of in the Liquid Effluent Retention Facility (LERF) and the Effluent Treatment Facility (ETF). Both facilities are located in the 200 East Area and are operated by Fluor Hanford, Inc. (FH) for the US. Department of Energy (DOE). The treatability of the WTP liquid effluents in the LERF/ETF was evaluated. The evaluation was conducted by comparing the forecast to the LERF/ETF treatability envelope (Aromi 1997), which provides information on the items which determine if a liquid effluent is acceptable for receipt and treatment at the LERF/ETF. The format of the evaluation corresponds directly to the outline of the treatability envelope document. Except where noted, the maximum annual average concentrations over the range of the 27 year forecast was evaluated against the treatability envelope. This is an acceptable approach because the volume capacity in the LERF Basin will equalize the minimum and maximum peaks. Background information on the LERF/ETF design basis is provided in the treatability envelope document.

  20. Material World: Forecasting Household Appliance Ownership in a Growing Global Economy

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

    Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

  1. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Broader source: Energy.gov [DOE]

    This project will address the need for a more accurate approach to forecasting net utility load by taking into consideration the contribution of customer-sited PV energy generation. Tasks within...

  2. Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain

    Broader source: Energy.gov [DOE]

    On April 4, 2014 the U.S. Department of Energy announced a $2.5 million funding opportunity entitled “Wind Forecasting Improvement Project in Complex Terrain.” By researching the physical processes...

  3. Ramping Effect on Forecast Use: Integrated Ramping as a Mitigation Strategy; NREL (National Renewable Energy Laboratory)

    SciTech Connect (OSTI)

    Diakov, Victor; Barrows, Clayton; Brinkman, Gregory; Bloom, Aaron; Denholm, Paul

    2015-06-23

    Power generation ramping between forecasted (net) load set-points shift the generation (MWh) from its scheduled values. The Integrated Ramping is described as a method that mitigates this problem.

  4. Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology

    Broader source: Energy.gov [DOE]

    As part of this project, new solar forecasting technology will be developed that leverages big data processing, deep machine learning, and cloud modeling integrated in a universal platform with an...

  5. U.S. diesel fuel price forecast to be 1 penny lower this summer...

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

    That's down 12 percent from last summer's record exports. Biodiesel production, which averaged 68,000 barrels a day last summer, is forecast to jump to 82,000 barrels a day this ...

  6. A comparison of model short-range forecasts and the ARM Microbase...

    Office of Scientific and Technical Information (OSTI)

    the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. ...

  7. Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Lew, D.; Milligan, M.

    2013-01-01

    Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

  8. Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Orwig, K.; Milligan, M.

    2012-06-01

    In this paper, we examine the parameters associated with the calculation of the Renyi entropy in order to further the understanding of its application to assessing wind power forecasting errors.

  9. U.S. oil production forecast update reflects lower rig count

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

    U.S. oil production forecast update reflects lower rig count Lower oil prices and fewer rigs drilling for crude oil are expected to slow U.S. oil production growth this year and in ...

  10. Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

    2013-11-01

    As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

  11. Resource Information and Forecasting Group; Electricity, Resources, & Building Systems Integration (ERBSI) (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2009-11-01

    Researchers in the Resource Information and Forecasting group at NREL provide scientific, engineering, and analytical expertise to help characterize renewable energy resources and facilitate the integration of these clean energy sources into the electricity grid.

  12. U.S. Crude Oil Production Forecast-Analysis of Crude Types

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

    of Energy Washington, DC 20585 U.S. Energy Information Administration | U.S. Crude Oil Production Forecast-Analysis of Crude Types i This report was prepared by the U.S....

  13. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01

    This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

  14. A Comparison of Water Vapor Quantities from Model Short-Range Forecasts and

    Office of Scientific and Technical Information (OSTI)

    ARM Observations (Technical Report) | SciTech Connect Water Vapor Quantities from Model Short-Range Forecasts and ARM Observations Citation Details In-Document Search Title: A Comparison of Water Vapor Quantities from Model Short-Range Forecasts and ARM Observations (in English; Croatian) Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was

  15. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  16. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  17. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect (OSTI)

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  18. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  19. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    1995-01-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  20. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Florita, A.

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  1. State Energy Efficiency Program Evaluation Inventory

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

    State Energy Efficiency Program Evaluation Inventory July 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | State Energy Efficiency Program Evaluation Inventory i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other

  2. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect (OSTI)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  3. HOW TO DEAL WITH WASTE ACCEPTANCE UNCERTAINTY USING THE WASTE ACCEPTANCE CRITERIA FORECASTING AND ANALYSIS CAPABILITY SYSTEM (WACFACS)

    SciTech Connect (OSTI)

    Redus, K. S.; Hampshire, G. J.; Patterson, J. E.; Perkins, A. B.

    2002-02-25

    The Waste Acceptance Criteria Forecasting and Analysis Capability System (WACFACS) is used to plan for, evaluate, and control the supply of approximately 1.8 million yd3 of low-level radioactive, TSCA, and RCRA hazardous wastes from over 60 environmental restoration projects between FY02 through FY10 to the Oak Ridge Environmental Management Waste Management Facility (EMWMF). WACFACS is a validated decision support tool that propagates uncertainties inherent in site-related contaminant characterization data, disposition volumes during EMWMF operations, and project schedules to quantitatively determine the confidence that risk-based performance standards are met. Trade-offs in schedule, volumes of waste lots, and allowable concentrations of contaminants are performed to optimize project waste disposition, regulatory compliance, and disposal cell management.

  4. Wind power forecasting : state-of-the-art 2009.

    SciTech Connect (OSTI)

    Monteiro, C.; Bessa, R.; Miranda, V.; Botterud, A.; Wang, J.; Conzelmann, G.; Decision and Information Sciences; INESC Porto

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

  5. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

    SciTech Connect (OSTI)

    Freedman, Jeffrey M.; Manobianco, John; Schroeder, John; Ancell, Brian; Brewster, Keith; Basu, Sukanta; Banunarayanan, Venkat; Hodge, Bri-Mathias; Flores, Isabel

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  6. Annual energy outlook 1995, with projections to 2010

    SciTech Connect (OSTI)

    1995-01-01

    The Annual Energy Outlook 1995 (AEO95) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projections and analyses of energy supply, demand, and prices through 2010, based on results from the National Energy Modeling System (NEMS). Quarterly forecasts of energy supply and demand for 1995 and 1996 are published in the Short-Term Energy Outlook (February 1995). Forecast tables for the five cases examined in the AEO95 are provided in Appendixes A through C. Appendix A gives historical data and forecasts for selected years from 1992 through 2010 for the reference case. Appendix B presents two additional cases, which assume higher and lower economic growth than the reference case. Appendix C presents two cases that assume higher and lower world oil prices. Appendix D presents a summary of the forecasts in units of oil equivalence. Appendix E presents a summary of household energy expenditures. Appendix F provides detailed comparisons of the AEO95 forecasts with those of other organizations. Appendix G briefly describes NEMS and the major AEO95 forecast assumptions. Appendix H presents a stand-alone high electricity demand case. Appendix 1 provides a table of energy conversion factors and a table of metric conversion factors. 89 figs., 23 tabs.

  7. The Value of Improved Wind Power Forecasting in the Western Interconnection (Poster), NREL (National Renewable Energy Laboratory)

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

    outcome of this research will facilitate a better functional understanding of wind forecasting accuracy and power system operations at various spatial and temporal scales.* Of particular interest are: 1. Correlated behavior among variables (e.g., changes in dispatch stacks, production costs, or generation by type as a function of forecasting accuracy); 2. The relative reduction in wind curtailment with improved forecasting accuracy; and 3. The value of information (e.g., which subset of

  8. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect (OSTI)

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  9. Distributed Generation Potential of the U.S. CommercialSector

    SciTech Connect (OSTI)

    LaCommare, Kristina Hamachi; Edwards, Jennifer L.; Gumerman,Etan; Marnay, Chris

    2005-06-01

    Small-scale (100 kW-5 MW) on-site distributed generation (DG) economically driven by combined heat and power (CHP) applications and, in some cases, reliability concerns will likely emerge as a common feature of commercial building energy systems in developed countries over the next two decades. In the U.S., private and public expectations for this technology are heavily influenced by forecasts published by the Energy Information Administration (EIA), most notably the Annual Energy Outlook (AEO). EIA's forecasts are typically made using the National Energy Modeling System (NEMS), which has a forecasting module that predicts the penetration of several possible commercial building DG technologies over the period 2005-2025. Annual penetration is forecast by estimating the payback period for each technology, for each of a limited number of representative building types, for each of nine regions. This process results in an AEO2004 forecast deployment of about a total 3 GW of DG electrical generating capacity by 2025, which is only 0.25 percent of total forecast U.S. capacity. Analyses conducted using both the AEO2003 and AEO2004 versions of NEMS changes the baseline costs and performance characteristics of DG to reflect a world without U.S. Department of Energy (DOE) research into several thermal DG technologies, which is then compared to a case with enhanced technology representative of the successful achievement of DOE research goals. The net difference in 2025 DG penetration is dramatic using the AEO2003 version of NEMS, but much smaller in the AEO2004 version. The significance and validity of these contradictory results are discussed, and possibilities for improving estimates of commercial U.S. DG potential are explored.

  10. Enhancing Cloud Radiative Processes and Radiation Efficiency in the Advanced Research Weather Research and Forecasting (WRF) Model

    SciTech Connect (OSTI)

    Iacono, Michael J.

    2015-03-09

    The objective of this research has been to evaluate and implement enhancements to the computational performance of the RRTMG radiative transfer option in the Advanced Research version of the Weather Research and Forecasting (WRF) model. Efficiency is as essential as accuracy for effective numerical weather prediction, and radiative transfer is a relatively time-consuming component of dynamical models, taking up to 30-50 percent of the total model simulation time. To address this concern, this research has implemented and tested a version of RRTMG that utilizes graphics processing unit (GPU) technology (hereinafter RRTMGPU) to greatly improve its computational performance; thereby permitting either more frequent simulation of radiative effects or other model enhancements. During the early stages of this project the development of RRTMGPU was completed at AER under separate NASA funding to accelerate the code for use in the Goddard Space Flight Center (GSFC) Goddard Earth Observing System GEOS-5 global model. It should be noted that this final report describes results related to the funded portion of the originally proposed work concerning the acceleration of RRTMG with GPUs in WRF. As a k-distribution model, RRTMG is especially well suited to this modification due to its relatively large internal pseudo-spectral (g-point) dimension that, when combined with the horizontal grid vector in the dynamical model, can take great advantage of the GPU capability. Thorough testing under several model configurations has been performed to ensure that RRTMGPU improves WRF model run time while having no significant impact on calculated radiative fluxes and heating rates or on dynamical model fields relative to the RRTMG radiation. The RRTMGPU codes have been provided to NCAR for possible application to the next public release of the WRF forecast model.

  11. Baseline data for the residential sector and development of a residential forecasting database

    SciTech Connect (OSTI)

    Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

    1994-05-01

    This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

  12. Second AEO2014 Liquids Fuels Markets Working Group Meeting Summary

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

    JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS JOHN POWELL TEAM LEADER LIQUID ... - The California LCFS draws low carbon intensity biofuels to California - corn ethanol, ...

  13. AEO 2013 Liquid Fuels Markets Working Group 2

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

    Attendance (WebEx) Mac Statton, Dave Schmalzer, Jarrod Brown, John Prydol, Russ Smith, Rodney Geisbrecht, Dallas Burkholder, Kristen King Notes by Slide Slide 2 The reference case ...

  14. Liquefied Natural Gas: Global Challenges (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    U.S. imports of liquefied natural gas (LNG) in 2007 were more than triple the 2000 total, and they are expected to grow in the long term as North Americas conventional natural gas production declines. With U.S. dependence on LNG imports increasing, competitive forces in the international markets for natural gas in general and LNG in particular will play a larger role in shaping the U.S. market for LNG. Key factors currently shaping the future of the global LNG market include the evolution of project economics, worldwide demand for natural gas, government policies that affect the development and use of natural resources in countries with LNG facilities, and changes in seasonal patterns of LNG trade.

  15. REFERENCE NO. OF DOCUMENT BEING CONTINUED AEO CONTINUATION SHEET...

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

    2010 Allottee 34 Reporting Entity 421301 Amount: 43,752,060.00 Account code: P&B Rocky Flats Post Retirement Benefits Fund 01050 Appr Year 2010 Allottee 34 Reporting Entity...

  16. Second AEO2014 Macro-Industrial Working Group Meeting Summary

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

    ... Shouldn't industrial output level off? a. Natural gas intensive industrial output does level off, as the bulk chemical output shows. Roughly half of the output in energy intensive ...

  17. Summary of AEO2016 Electricity Working Group Meeting held on...

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

    MWH Global Coleman, Leslie National Mining Association Roche, Madelyn NRECA Wood, Frances OnLocation Wright, Evelyn Sustainable Energy Economics Luckow, Patrick Synapse Energy ...

  18. Summary of Second AEO 2014 Electricity Working Group Meeting

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

    Michael (Covanta Energy) Williams, Emily (American Wind Energy Association) Wood, Frances (OnLocation) Wright, Evelyn (DecisionWare) Zelek, Charles (US DOE: Office of Fossil ...

  19. AEO 2014 Renewable Electricity Working Group Meeting Summary

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

    EERE) Shirley Neff Tyler Hodge Phone Becky Campbell * (SEPA) Elyse Steiner* (EPA) Frances Wood* (On Location) Justin Baca*(SEIA) Liz Salerno* (AWEA) *Non-EIA Attendees Date: ...

  20. Overview of Levelized Cost of Energy in the AEO

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

    Presented to the EIA Energy Conference June 17, 2013 Chris Namovicz Assessing the Economic Value of New Utility-Scale Renewable Generation Projects Overview * Levelized cost of energy (LCOE) has been used by planners, analysts, policymakers, advocates and others to assess the economic competitiveness of technology options in the electric power sector * While of limited usefulness in the analysis of "conventional" utility systems, this approach is not generally appropriate when

  1. Climate Stewardship Act of 2004 (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    The Climate Stewardship Act of 2004 would establish a system of tradable allowances to reduce greenhouse gas emissions. The bill includes requirements for mandatory emissions reporting by covered entities and for voluntary reporting of emissions reduction activities by noncovered entities; a national greenhouse gas database and registry of reductions; and a research program on climate change and related activities.

  2. AEO2014 Liquid Fuels Markets Working Group Meeting 1

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

    (Argonne National Laboratory), Donald Hanson (Argonne National Laboratory), Wyatt Thompson (FAPRI, University of Missouri), Jarrett Whistance (FAPRI, University of Missouri), ...

  3. Clean Air Mercury Rule (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    On February 8, 2008, a three-judge panel on the D.C. Circuit of the U.S. Court of Appeals issued a decision to vacate the Clean Air Mercury Rule (CAMR). In its ruling, the panel cited the history of hazardous air pollutant regulation under Section 112 of the Clean Air Act (CAA). Section 112, as written by Congress, listed emitted mercury as a hazardous air pollutant that must be subject to regulation unless it can be proved harmless to public welfare and the environment. In 2000, the Environmental Protection Agency ruled that mercury was indeed hazardous and must be regulated under Section 112 and, therefore, subjected to the best available control technology for mitigation.

  4. Distributed Generation in Buildings (released in AEO2005)

    Reports and Publications (EIA)

    2008-01-01

    Currently, distributed generation provides a very small share of residential and commercial electricity requirements in the United States. The Annual Energy Outlook 2005 reference case projects a significant increase in electricity generation in the buildings sector, but distributed generation is expected to remain a small contributor to the sectors energy needs. Although the advent of higher energy prices or more rapid improvement in technology could increase the use of distributed generation relative to the reference case projection, the vast majority of electricity used in buildings is projected to continue to be purchased from the grid.

  5. Mercury Emissions Control Technologies (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    The Annual Energy Outlook 2006 reference case assumes that states will comply with the requirements of the Environmental Protection Agency's new Clean Air Mercury Rule (CAMR) regulation. CAMR is a two-phase program, with a Phase I cap of 38 tons of mercury emitted from all U.S. power plants in 2010 and a Phase II cap of 15 tons in 2018. Mercury emissions in the electricity generation sector in 2003 are estimated at around 50 tons. Generators have a variety of options to meet the mercury limits, such as: switching to coal with a lower mercury content, relying on flue gas desulfurization or selective catalytic reduction equipment to reduce mercury emissions, or installing conventional activated carbon injection (ACI) technology.

  6. Updated State Air Emissions Regulations (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    The Regional Greenhouse Gas Initiative (RGGI) is a program that includes 10 Northeast states that have agreed to curtail and reverse growth in their carbon dioxide (CO2) emissions. The RGGI program includes all electricity generating units with a capacity of at least 25 megawatts and requires an allowance for each ton of CO2 emitted. The first year of mandatory compliance was in 2009.

  7. Energy Policy Act 2005 Summary (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    The U.S. House of Representatives passed H.R. 6 EH, the Energy Policy Act of 2005, on April 21, 2005, and the Senate passed H.R. 6 EAS on June 28, 2005. A conference committee was convened to resolve differences between the two bills, and a report was approved and issued on July 27, 2005. The House approved the conference report on July 28, 2005, and the Senate followed on July 29, 2005. EPACT2005 was signed into law by President Bush on August 8, 2005, and became Public Law 109-058.

  8. Restricted Natural Gas Supply Case (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    The restricted natural gas supply case provides an analysis of the energy-economic implications of a scenario in which future gas supply is significantly more constrained than assumed in the reference case. Future natural gas supply conditions could be constrained because of problems with the construction and operation of large new energy projects, and because the future rate of technological progress could be significantly lower than the historical rate. Although the restricted natural gas supply case represents a plausible set of constraints on future natural gas supply, it is not intended to represent what is likely to happen in the future.

  9. No Sunset and Extended Policies Cases (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    The Annual Energy Outlook 2010 Reference case is best described as a current laws and regulations case, because it generally assumes that existing laws and fully promulgated regulations will remain unchanged throughout the projection period, unless the legislation establishing them specifically calls for them to end or change. The Reference case often serves as a starting point for the analysis of proposed legislative or regulatory changes, a task that would be difficult if the Reference case included projected legislative or regulatory changes.

  10. Federal Fuels Taxes and Tax Credits (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Provides a review and update of the handling of federal fuels taxes and tax credits, focusing primarily on areas for which regulations have changed or the handling of taxes or credits has been updated in Annual Energy Outlook 2009.

  11. Liquid Fuels Taxes and Credits (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Provides a review of the treatment of federal fuels taxes and tax credits in Annual Energy Outlook 2010.

  12. EPACT2005 Loan Guarantee Program (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    Title XVII of the Energy Policy Act 2005 (EPACT) authorized the Department of Energy (DOE) to issue loan guarantees for projects involving new or improved technologies to avoid, reduce, or sequester greenhouse gases (GHGs). The law specified that the amount of the guarantee would be up to 80% of a project's cost. EPACT2005 also specified that DOE must receive funds equal to the subsidy cost either through the federal appropriations process or from the firm receiving the guarantee. As discussed in Annual Energy Outlook 2007, this program, by lowering borrowing costs, can have a major impact on the economics of capital-intensive technologies.

  13. American Jobs Creation Act of 2004 (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    The American Jobs Creation Act of 2004 was signed into law on October 22, 2004. Most of the 650 pages of the Act are related to tax legislation. Provisions pertaining to energy are detailed in this analysis.

  14. New NHTSA CAFE Standards (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    EISA2007 requires the National Highway Traffic Safety Administration (NHTSA) to raise the Corporate Average Fuel Economy (CAFE) standards for passenger cars and light trucks to ensure that the average tested fuel economy of the combined fleet of all new passenger cars and light trucks sold in the United States in model year (MY) 2020 equals or exceeds 35 mpg, 34% above the current fleet average of 26.4 mpg. Pursuant to this legislation, NHTSA recently proposed revised CAFE standards that substantially increase the minimum fuel economy requirements for passenger cars and light trucks for MY 2011 through MY 2015.

  15. AEO2012 Preliminary Assumptions: Oil and Gas Supply

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

    ... 182 2009 2016 MC562 Isabela 6535 45 2007 2012* WR508 Stones 9556 89 2005 2016 MC563 Santa Cruz 6515 2009 2012* MC948 GunflintFreedom 6095 691 2008 2016 MC519 Santiago 6500 2011 ...

  16. Microsoft Word - Final AEO2007 Commercial Doc.doc

    Gasoline and Diesel Fuel Update (EIA)

    the State Energy Data System (SEDS) historical commercial sector consumption, applying an additive correction term to ensure that simulated model results correspond to published...

  17. Microsoft Word - Final Industrial Documentation AEO2008 _6-12...

    Gasoline and Diesel Fuel Update (EIA)

    factors are multiplicative for all fuels which have values greater than zero and are additive otherwise. ( ) ( ) ( ) ( ) ( ) ( ) - - - max...

  18. AEO2014 - Legislation and Regulations articles - U.S. Energy...

    Gasoline and Diesel Fuel Update (EIA)

    and diesel fuel sold. There are four interrelated requirements, for cellulosic biofuels, biomass-based diesel, advanced biofuels, and total renewable fuels. State renewable...

  19. Mobile Source Air Toxics Rule (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    On February 9, 2007, the Environmental Protection Agency (EPA) released its MSAT2 rule, which will establish controls on gasoline, passenger vehicles, and portable fuel containers. The controls are designed to reduce emissions of benzene and other hazardous air pollutants. Benzene is a known carcinogen, and the EPA estimates that mobile sources produced more than 70% of all benzene emissions in 1999. Other mobile source air toxics, including 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, and naphthalene, also are thought to increase cancer rates or contribute to other serious health problems.

  20. EIA - Annual Energy Outlook (AEO) 2013 Data Tables

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

    Table 55.2. Electric Power Projections by Electricity Market Module Region - Florida Reliability Coordinating Council XLS Table 55.3. Electric Power Projections by Electricity...

  1. AEO2014 Coal Working Group Meeting I Summary

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

    ... No feedback was provided on this question. One participant responded by stating that it was very important to make some sort of assumption regarding the future exchange rate even ...

  2. Electricity Plant Cost Uncertainties (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Construction costs for new power plants have increased at an extraordinary rate over the past several years. One study, published in mid-2008, reported that construction costs had more than doubled since 2000, with most of the increase occurring since 2005. Construction costs have increased for plants of all types, including coal, nuclear, natural gas, and wind.

  3. Second AEO2016 Macro-Induistrial Working Group Meeting summary

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

    ... Answer: It is assumed that the current build-up and on-shoring of nitrogenous fertilizer (ammoniaurea) and methanol plants will continue into the mid-term with moderate growth in ...

  4. Multi-Pollutant Legislation and Regulations (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    The 108th Congress proposed and debated a variety of bills addressing pollution control at electric power plants but did not pass any of them into law. In addition, the Environmental Protection Agency (EPA) currently is preparing two regulations-a proposed Clean Air Interstate Rule (pCAIR) and a Clean Air Mercury Rule (CAMR)-to address emissions from coal-fired power plants. Several states also have taken legislative actions to limit pollutants from power plants in their jurisdictions. This section discusses three Congressional air pollution bills and the EPA's pCAIR and CAMR regulations.

  5. Federal Air Emissions Regulations (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    In 2005, the Environmental Protection Agency (EPA) finalized two regulations, the Clean Air Interstate Rule (CAIR) and the Clean Air Mercury Rule CAMR, that would reduce emissions from coal-fired power plants in the United States. Both CAIR and CAMR are included in the Annual Energy Outlook 2006 reference case. The EPA has received 11 petitions for reconsideration of CAIR and has provided an opportunity for public comment on reconsidering certain aspects of CAIR. Public comments were accepted until January 13, 2006. The EPA has also received 14 petitions for reconsideration of CAMR and is willing to reconsider certain aspects of the rule. Public comments were accepted for 45 days after publication of the reconsideration notice in the Federal Register. Several states and organizations have filed lawsuits against CAMR. The ultimate decision of the courts will have a significant impact on the implementation of CAMR.

  6. EIA - Annual Energy Outlook (AEO) 2013 Data Tables

    Gasoline and Diesel Fuel Update (EIA)

    Information Administration Drivers of U.S. Household Energy Consumption, 1980-2009 Release date: February 3, 2015 Introduction In 2012, the residential sector accounted for 21% of total primary energy consumption and about 20% of carbon dioxide emissions in the United States (computed from EIA 2013). Because of the impacts of residential sector energy use on the environment and the economy, this study was undertaken to help provide a better understanding of the factors affecting energy

  7. Electricity Prices in Transition (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The push by some states to restructure electricity markets progressed rapidly throughout the late 1990s. Although the energy crisis in California during 2000 and 2001 slowed the momentum, 19 states and the District of Columbia currently have some form of restructuring in place. In addition, Washington State, which has not restructured its electricity market, allows its largest industrial customers to choose their suppliers.

  8. Tax Credits and Renewable Generation (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Tax incentives have been an important factor in the growth of renewable generation over the past decade, and they could continue to be important in the future. The Energy Tax Act of 1978 (Public Law 95-618) established ITCs for wind, and EPACT92 established the Renewable Electricity Production Credit (more commonly called the PTC) as an incentive to promote certain kinds of renewable generation beyond wind on the basis of production levels. Specifically, the PTC provided an inflation-adjusted tax credit of 1.5 cents per kilowatthour for generation sold from qualifying facilities during the first 10 years of operation. The credit was available initially to wind plants and facilities that used closed-loop biomass fuels and were placed in service after passage of the Act and before June 1999.

  9. Clean Air Interstate Rule (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Clean Air Interstate Rule (CAIR) is a cap-and-trade program promulgated by the Environmental Protection Agency in 2005, covering 28 eastern U.S. states and the District of Columbia. It was designed to reduce sulfur dioxide (SO2) and nitrogen oxide (NOx) emissions in order to help states meet their National Ambient Air Quality Standards (NAAQS) for ozone and particulate matter (PM2.5) and to further emissions reductions already achieved through the Acid Rain Program and the NOx State Implementation Plan call program. The rule was set to commence in 2009 for seasonal and annual NOx emissions and in 2010 for SO2 emissions.

  10. Clean Air Nonroad Diesel Rule (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    On June 29, 2004, the Environmental Protection Agency issued a comprehensive final rule regulating emissions from nonroad diesel engines and sulfur content in nonroad diesel fuel. The nonroad fuel market makes up more than 18% of the total distillate pool. The rule applies to new equipment covering a broad range of engine sizes, power ratings, and equipment types. There are currently about 6 million pieces of nonroad equipment operating in the United States, and more than 650,000 new units are sold each year.

  11. AEO2013 Early Release Base Overnight Project Technological Total Overnight

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

    3 Early Release Base Overnight Project Technological Total Overnight Variable Fixed Heatrate 6 nth-of-a- kind Online Size Lead time Cost in 2012 Contingency Optimism Cost in 2012 4 O&M 5 O&M in 2012 Heatrate Technology Year 1 (MW) (years) (2011 $/kW) Factor 2 Factor 3 (2011 $/kW) (2011 $/MWh) (2011$/kW) (Btu/kWh) (Btu/kWh) Scrubbed Coal New 7 2016 1300 4 2,694 1.07 1.00 2,883 4.39 30.64 8,800 8,740 Integrated Coal-Gasification Comb Cycle (IGCC) 7 2016 1200 4 3,475 1.07 1.00 3,718 7.09

  12. Annual Energy Outlook (AEO) 2006 - Supplemental Tables - All Tables

    SciTech Connect (OSTI)

    2009-01-18

    Tables describing regional energy consumption and prices by sector; residential, commercial, and industrial demand sector data; transportation demand sector; electricity and renewable fuel; and petroleum, natural gas, and coal data.

  13. Alaskan Natural Gas Pipeline Developments (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The Annual Energy Outlook 2007 reference case projects that an Alaska natural gas pipeline will go into operation in 2018, based on the Energy Information Administration's current understanding of the projects time line and economics. There is continuing debate, however, about the physical configuration and the ownership of the pipeline. In addition, the issue of Alaskas oil and natural gas production taxes has been raised, in the context of a current market environment characterized by rising construction costs and falling natural gas prices. If rates of return on investment by producers are reduced to unacceptable levels, or if the project faces significant delays, other sources of natural gas, such as unconventional natural gas production and liquefied natural gas imports, could fulfill the demand that otherwise would be served by an Alaska pipeline.

  14. Expectations for Oil Shale Production (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Oil shales are fine-grained sedimentary rocks that contain relatively large amounts of kerogen, which can be converted into liquid and gaseous hydrocarbons (petroleum liquids, natural gas liquids, and methane) by heating the rock, usually in the absence of oxygen, to 650 to 700 degrees Fahrenheit (in situ retorting) or 900 to 950 degrees Fahrenheit (surface retorting). (Oil shale is, strictly speaking, a misnomer in that the rock is not necessarily a shale and contains no crude oil.) The richest U.S. oil shale deposits are located in Northwest Colorado, Northeast Utah, and Southwest Wyoming. Currently, those deposits are the focus of petroleum industry research and potential future production. Among the three states, the richest oil shale deposits are on federal lands in northwest Colorado.

  15. Changing Trends in the Refining Industry (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    There have been some major changes in the U.S. refining industry recently, prompted in part by a significant decline in the quality of imported crude oil and by increasing restrictions on the quality of finished products. As a result, high-quality crudes, such as the West Texas Intermediate (WTI) crude that serves as a benchmark for oil futures on the New York Mercantile Exchange (NYMEX), have been trading at record premiums to the OPEC (Organization of the Petroleum Exporting Countries) Basket price.

  16. World Oil Price Cases (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    World oil prices in Annual Energy Outlook 2005 are set in an environment where the members of OPEC (Organization of the Petroleum Exporting Countries) are assumed to act as the dominant producers, with lower production costs than other supply regions or countries. Non-OPEC oil producers are assumed to behave competitively, producing as much oil as they can profitability extract at the market price for oil. As a result, the OPEC member countries will be able effectively to set the price of oil when they can act in concert by varying their aggregate production. Alternatively, OPEC members could target a fixed level of production and let the world market determine the price.

  17. Economic Effects of High Oil Prices (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    The Annual Energy Outlook 2006 projections of future energy market conditions reflect the effects of oil prices on the macroeconomic variables that affect oil demand, in particular, and energy demand in general. The variables include real gross domestic product (GDP) growth, inflation, employment, exports and imports, and interest rates.

  18. AEO2014 Oil and Gas Working Group Meeting Summary

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

    Ron Gecan (CBO) Matthew Gilstrap (INTEK) Paul Holtberg (EIA) Ozge Kaplan (EPA)* Robert King (EIA) Steven Koptis (Douglas-Westwood)* Angelina Larose (EIA) Geoffrey Lyon (DOE) ...

  19. Annual energy outlook 1994: With projections to 2010

    SciTech Connect (OSTI)

    Not Available

    1994-01-01

    The Annual Energy Outlook 1994 (AEO94) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projects and analyses of energy supply, demand, and prices through 2010, based for the first time on results from the National Energy Modeling System (NEMS). NEMS is the latest in a series of computer-based energy modeling systems used over the past 2 decades by EIA and its predecessor organization, the Federal Energy Administration, to analyze and forecast energy consumption and supply in the midterm period (about 20 years). Quarterly forecasts of energy supply and demand for 1994 and 1995 are published in the Short-Term Energy Outlook (February 1994). Forecast tables for 2000, 2005, and 2010 for each of the five scenarios examined in the AEO94 are provided in Appendices A through E. The five scenarios include a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. Appendix F provides detailed comparisons of the AEO94 forecasts with those of other organizations. Appendix G briefly described the NEMS and the major AEO94 forecast assumptions. Appendix H summarizes the key results for the five scenarios.

  20. Gasoline price forecast to stay below 3 dollar a gallon in 2015

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

    Gasoline price forecast to stay below $3 a gallon in 2015 The national average pump price of gasoline is expected to stay below $3 per gallon during 2015. In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular gasoline should average $2.33 per gallon this year. The price of gasoline increased in early February after falling for 17 weeks in a row. But gasoline prices will continue to remain low in 2015 when compared with pump prices in recent

  1. A Comparison of Model Short-Range Forecasts and the ARM Microbase Data

    Office of Scientific and Technical Information (OSTI)

    Fourth Quarter ARM Science Metric (Technical Report) | SciTech Connect Model Short-Range Forecasts and the ARM Microbase Data Fourth Quarter ARM Science Metric Citation Details In-Document Search Title: A Comparison of Model Short-Range Forecasts and the ARM Microbase Data Fourth Quarter ARM Science Metric For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the "Microbase" value added product (referred to as OBS, within

  2. EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy

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

    Improved by 30% | Department of Energy Forecasting Gets a Boost from Watson, Accuracy Improved by 30% EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM Youtube Video | Courtesy of IBM Remember when IBM's super computer Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter? With funding from the U.S. Department of Energy SunShot Initiative, IBM researchers are using Watson-like technology to improve solar

  3. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2003 THRU FY2046 VERSION 2003.1 VOLUME 2 [SEC 1 & 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

    This report includes data requested on September 10, 2002 and includes radioactive solid waste forecasting updates through December 31, 2002. The FY2003.0 request is the primary forecast for fiscal year FY 2003.

  4. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect (OSTI)

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

  5. Expectations models of electric utilities' forecasts: a case study of econometric estimation with influential data points

    SciTech Connect (OSTI)

    Vellutini, R. de A.S.; Mount, T.D.

    1983-01-01

    This study develops an econometric model for explaining how electric utilities revise their forecasts of future electricity demand each year. The model specification is developed from the adaptive expectations hypothesis and it relates forecasted growth rates to actual lagged growth rates of electricity demand. Unlike other studies of the expectation phenomenon, expectations of future demand levels constitute an observable variable and thus can be incorporated explicitly into the model. The data used for the analysis were derived from the published forecasts of the nine National Electric Reliability Councils in the US for the years 1974 to 1980. Three alternative statistical methods are used for estimation purposes: ordinary least-squares, robust regression and a diagnostic analysis to identify influential observations. The results obtained with the first two methods are very similar, but are both inconsistent with the underlying economic logic of the model. The estimated model obtained from the diagnostics approach after deleting two aberrant observations is consistent with economic logic, and supports the hypothesis that the low growth demand experienced immediately following the oil embargo in 1973 were disregarded by the industry for forecasting purposes. The model includes transitory effects associated with the oil embargo that gradually disappear over time, the estimated coefficients for the lagged values of actual growth approach a structure with declining positive weights. The general shape of this asymptotic structure is similar to the findings in many economic applications using distributed lag models.

  6. Contact Us - U.S. Energy Information Administration (EIA) - U.S. Energy

    Gasoline and Diesel Fuel Update (EIA)

    Information Administration (EIA) Forecasting & Analysis Short-Term (STEO) Energy Forecast Experts Long-Term (AEO) Energy Forecast Experts International (IEO) Energy Forecast Experts Renewable Energy Forecast Experts Short-Term (STEO) Analysis and Forecasting Experts Overview Timothy Hess 202-586-4212 timothy.hess@eia.gov World Oil Prices/International Petroleum Eric Kreil 202-586-6573 erik.kreil@eia.gov Energy Prices Sean Hill 202-586-4247 sean.hill@eia.gov Futures Markets and Energy

  7. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect (OSTI)

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  8. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    SciTech Connect (OSTI)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.

  9. State Energy Efficiency Program Evaluation Inventory - Energy Information

    Gasoline and Diesel Fuel Update (EIA)

    State Energy Efficiency Program Evaluation Inventory July 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | State Energy Efficiency Program Evaluation Inventory i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other

  10. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications (EIA)

    1998-01-01

    The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

  11. EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day

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

    EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day The forecast for U.S. crude oil production keeps going higher. The U.S. Energy Information Administration revised upward its projection for crude oil output in 2013 by 70,000 barrels per day and for next year by 190,000 barrels per day. U.S. oil production is now on track to average 7.5 million barrels per day this year and rise to 8.4 million barrels per day in 2014, according to EIA's latest monthly forecast.

  12. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Residential/Commercial Buildings AEO2016 Meetings First AEO2016 Meeting (December 8, 2015) Summary of meeting Presentation AEO2015 Meetings First AEO2015 Meeting (August 7, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 22, 2013) Summary of meeting Presentation Second AEO2014 Meeting (September 26, 2013) Summary of meeting Presentation AEO2013 Meetings First AEO2013 Assumptions Meeting (July 23, 2012) Summary of meeting Presentation Second AEO2013 Preliminary

  13. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Renewables AEO2016 Meetings First AEO2016 Meeting (December 23, 2015) Summary of meeting Presentation Second AEO2016 Meeting (February 9, 2016) Presentation AEO2015 Meetings First AEO2015 Meeting (July 24, 2014) Summary of meeting Presentation Second AEO2015 Meeting (September 15, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 9, 2013) Summary of meeting Presentation Second AEO2014 Meeting (September 26, 2013) Summary of meeting Presentation AEO2013 Meetings

  14. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Transportation AEO2016 Meetings First AEO2016 Meeting (December 15, 2015) Summary of meeting Presentation Second AEO2016 Meeting (March 9, 2016) Summary of meeting Presentation AEO2015 Meetings First AEO2015 Meeting (July 30, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 23, 2013) Summary of meeting Presentation Second AEO2014 Meeting (September 26, 2013) Summary of meeting Presentation AEO2013 Meetings First AEO2013 Meeting (August 14, 2012) Summary of

  15. Analysis methods for fast impurity ion dynamics data

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

    Residential/Commercial Buildings AEO2016 Meetings First AEO2016 Meeting (December 8, 2015) Summary of meeting Presentation AEO2015 Meetings First AEO2015 Meeting (August 7, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 22, 2013) Summary of meeting Presentation Second AEO2014 Meeting (September 26, 2013) Summary of meeting Presentation AEO2013 Meetings First AEO2013 Assumptions Meeting (July 23, 2012) Summary of meeting Presentation Second AEO2013 Preliminary

  16. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

    SciTech Connect (OSTI)

    Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M.

    2009-10-09

    We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

  17. 2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    2006-07-01

    This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

  18. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation’s low-income households by primary heating fuel type, nationally and by Census Region. The statistics are intended for the use of policymakers in the Department of Energy’s Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2006 fiscal year.

  19. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

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

    4 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ Bartholomew S Giangrande March 2016 DISCLAIMER This report was prepared as an account of work sponsored by the U.S. Government. Neither the United States nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or

  20. Validation of Global Weather Forecast and Climate Models Over the North

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

    Slope of Alaska Validation of Global Weather Forecast and Climate Models Over the North Slope of Alaska Xie, Shaocheng Lawrence Livermore National Laboratory Klein, Stephen Lawrence Livermore National Laboratory Boyle, Jim Lawrence Livermore National Laboratory Fiorino, Michael DOE/Lawrence Livermore National Laboratory Hnilo, Justin DOE/Lawrence Livermore National Laboratory Phillips, Thomas PCMDI/LLNL Potter, Gerald Lawrence Livermore National Laboratory Beljaars, Anton ECMWF Category:

  1. Are there Gains from Pooling Real-Time Oil Price Forecasts?

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

    Are there Gains from Pooling Real- Time Oil Price Forecasts? Christiane Baumeister, Bank of Canada Lutz Kilian, University of Michigan Thomas K. Lee, U.S. Energy Information Administration February 12, 2014 Independent Statistics & Analysis www.eia.gov U.S. Energy Information Administration Washington, DC 20585 This paper is released to encourage discussion and critical comment. The analysis and conclusions expressed here are those of the authors and not necessarily those of the U.S. Energy

  2. doe sc arm 16 025 The Radar Wind Profiler for Cloud Forecasting at BNL_formatted

    Office of Scientific and Technical Information (OSTI)

    5 Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report MP Jensen SE Giangrande MJ Bartholomew April 2016 CLIMATE RESEARCH FACILITY DISCLAIMER This report was prepared as an account of work sponsored by the U.S. Government. Neither the United States nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any

  3. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equationsmore » at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.« less

  4. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect (OSTI)

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  5. Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

    SciTech Connect (OSTI)

    Jacobs, John M.; Rhodes, M.; Brown, C. W.; Hood, Raleigh R.; Leight, A.; Long, Wen; Wood, R.

    2014-11-01

    The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.

  6. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    SciTech Connect (OSTI)

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-06-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  7. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    SciTech Connect (OSTI)

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.

  8. Annual energy outlook 1999, with projections to 2020

    SciTech Connect (OSTI)

    1998-12-01

    The Annual Energy Outlook 1999 (AEO99) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA`s National Energy Modeling System (NEMS). The report begins with an Overview summarizing the AEO99 reference case. The next section, Legislation and Regulations, describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. Issues in Focus discusses current energy issues--the economic decline in East Asia, growth in demand for natural gas, vehicle emissions standards, competitive electricity pricing, renewable portfolio standards, and carbon emissions. It is followed by the analysis of energy market trends. The analysis in AEO99 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present a summary of the reference case forecasts in units of oil equivalence and household energy expenditures. The AEO99 projections are based on Federal, State, and local laws and regulations in effect on July 1, 1998. Pending legislation and sections of existing legislation for which funds have not been appropriated are not reflected in the forecasts. Historical data used for the AEOI99 projections were the most current available as of July 31, 1998, when most 1997 data but only partial 1998 data were available.

  9. FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters. The range is primarily due to uncertainties associated with the Tank Waste Remediation System (TWRS) program, including uncertainties regarding retrieval of long-length equipment, scheduling, and tank retrieval technologies.

  10. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect (OSTI)

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  11. Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

    SciTech Connect (OSTI)

    B. Faybishenko

    2006-09-11

    At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing them with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.

  12. FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

  13. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

    SciTech Connect (OSTI)

    Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

    2013-06-15

    Highlights: ► We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ► The model is robust at multiple time scales with the anticipated accuracy. ► At month-scale, the SARIMA model shows good representation for monthly MSW generation. ► At medium-term time scale, grey relational analysis could yield the MSW generation. ► At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 – 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 – 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term.

  14. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Office of Scientific and Technical Information (OSTI)

    U.S. DEPARTMENT OF HP IENERGY Office of Science DOE/SC-ARM-15-024 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ Bartholomew S Giangrande March 2016 CLIMATE RESEARCH FACILITY DISCLAIMER This report was prepared as an account of work sponsored by the U.S. Government. Neither the United States nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy,

  15. Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

    SciTech Connect (OSTI)

    none,

    2014-08-29

    With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

  16. RACORO Forecasting

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

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  17. Forecasting Flu

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

    introduction in 1992 of an American-made truck with a fully factory-installed/war- ranted liquefied petroleum gas (LPG) engine represents another "Ford first" in the alternative fuel arena. Now the company has introduced an LPG- powered F-700, a medium/heavy- duty truck. According to Tom Steckel, Ford's medium-duty marketing man- ager, Ford's latest sales figures already prove the alternative fuel F-700's popularity. With a little more than 10 months of the model year finished, Ford

  18. Annual Energy Outlook Retrospective Review - Energy Information

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

    Administration Annual Energy Outlook Retrospective Review Release Date: March 25, 2015 | Next Release Date: April 2017 | Report Number: DOE/EIA-0640(2014) Evaluation of 2014 and Prior Reference Case Projections The U.S. Energy Information Administration (EIA) produces projections of energy production, consumption and prices each year in the Annual Energy Outlook (AEO). Each year, EIA also produces an AEO Retrospective Review document, which presents a comparison between realized energy

  19. Average Depth of Crude Oil and Natural Gas Wells

    Gasoline and Diesel Fuel Update (EIA)

    Administration ‹ Analysis & Projections Annual Energy Outlook Retrospective Review Release Date: March 25, 2015 | Next Release Date: April 2017 | Report Number: DOE/EIA-0640(2014) Evaluation of 2014 and Prior Reference Case Projections The U.S. Energy Information Administration (EIA) produces projections of energy production, consumption and prices each year in the Annual Energy Outlook (AEO). Each year, EIA also produces an AEO Retrospective Review document, which presents a

  20. Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

  1. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Electricity AEO2016 Meetings First AEO2016 Meeting (December 8, 2015) Summary of meeting Presentation Second AEO2016 Meeting (February 10, 2016) Summary of meeting Presentation AEO2015 Meetings First AEO2015 Meeting (July 31, 2015) Summary of meeting Presentation Second AEO2015 Meeting (September 15, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 24, 2013) Summary of meeting Presentation Second AEO2014 Meeting (September 25, 2013) Summary of meeting

  2. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Liquid Fuels Markets AEO2016 Meetings First AEO2016 Meeting (November 19, 2015) Summary of meeting Presentation Second AEO2016 Meeting (February 24, 2016) Summary of meeting Presentation AEO2015 Meetings First AEO2015 Meeting (July 17, 2014) Summary of meeting Presentation Second AEO2015 Meeting (September 24, 2014) Summary of meeting Presentation AEO2014 Meetings First AEO2014 Meeting (July 24, 2013) Summary of meeting Presentation Second AEO2014 Meeting (November 5, 2013) Summary of meeting

  3. Crude oil and alternate energy production forecasts for the twenty-first century: The end of the hydrocarbon era

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

    Predictions of production rates and ultimate recovery of crude oil are needed for intelligent planning and timely action to ensure the continuous flow of energy required by the world`s increasing population and expanding economies. Crude oil will be able to supply increasing demand until peak world production is reached. The energy gap caused by declining conventional oil production must then be filled by expanding production of coal, heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources (solar, wind, and geothermal). Declining oil production forecasts are based on current estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for the United States and close to 3 trillion barrels for the world. Peak world crude oil production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil production in the United States is forecast to terminate by about 2090, and world production will be close to exhaustion by 2100.

  4. Solid waste integrated forecast technical (SWIFT) report: FY1997 to FY 2070, Revision 1

    SciTech Connect (OSTI)

    Valero, O.J.; Templeton, K.J.; Morgan, J.

    1997-01-07

    This web site provides an up-to-date report on the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons with previous forecasts and with other national data sources. This web site does not include: liquid waste (current or future generation); waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); or waste that has been received by the WM Project to date (i.e., inventory waste). The focus of this web site is on low-level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this web site is reporting data th at was requested on 10/14/96 and submitted on 10/25/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program's life cycle. Therefore, these data represent revisions from the previous FY97.0 Data Version, due primarily to revised estimates from PNNL. There is some useful information about the structure of this report in the SWIFT Report Web Site Overview.

  5. Better Buildings Residential Network Data & Evaluation Peer Exchange...

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

    Analysis 9 Custom database tools * Forecast vs. actual-YTD and weekly * Direct mail ... * Workforce Data * Neighborhood and geographic data * Conversion rates 1 0 Forecast vs. ...

  6. Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts

    SciTech Connect (OSTI)

    Reno Harnish

    2011-08-16

    The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

  7. Solid Waste Integrated Forecast Technical (SWIFT) Report FY2001 to FY2046 Volume 1

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons to previous forecasts and other national data sources. This report does not include: waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); waste that has been received by the WM Project to date (i.e., inventory waste); mixed low-level waste that will be processed and disposed by the River Protection Program; and liquid waste (current or future generation). Although this report currently does not include liquid wastes, they may be added as information becomes available.

  8. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  9. HONEYWELL - KANSAS CITY PLANT FISCAL YEARS 2009 THRU 2015 SMALL BUSINESS PROGRAM RESULTS & FORECAST

    National Nuclear Security Administration (NNSA)

    HONEYWELL - KANSAS CITY PLANT FISCAL YEARS 2009 THRU 2015 SMALL BUSINESS PROGRAM RESULTS & FORECAST CATEGORY Total Procurement Total SB Small Disad. Bus Woman-Owned SB Hub-Zone SB Veteran-Owned SB Service Disabled Vet. SB FY 2009 Dollars Goal (projected) $183,949,920 $82,690,000 $4,550,000 $8,829,596 $3,370,000 $5,025,000 $460,000 FY 2009 Dollars Accomplished $143,846,731 $68,174,398 $9,247,214 $11,333,905 $4,979,858 $6,713,791 $1,612,136 FY 2009 % Goal 45.0% 2.5% 4.8% 1.8% 2.7% 0.25% FY

  10. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; et al

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

  11. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

    SciTech Connect (OSTI)

    Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; Lancaster, Nick; Marani, Marco; Merritts, Dorothy J.; Moore, Laura J.; Pederson, Joel L.; Poulos, Michael J.; Rittenour, Tammy M.; Rowland, Joel C.; Ruggiero, Peter; Ward, Dylan J.; Wickert, Andrew D.; Yager, Elowyn M.

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we have the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.

  12. USE OF AN EQUILIBRIUM MODEL TO FORECAST DISSOLUTION EFFECTIVENESS, SAFETY IMPACTS, AND DOWNSTREAM PROCESSABILITY FROM OXALIC ACID AIDED SLUDGE REMOVAL IN SAVANNAH RIVER SITE HIGH LEVEL WASTE TANKS 1-15

    SciTech Connect (OSTI)

    KETUSKY, EDWARD

    2005-10-31

    This thesis details a graduate research effort written to fulfill the Magister of Technologiae in Chemical Engineering requirements at the University of South Africa. The research evaluates the ability of equilibrium based software to forecast dissolution, evaluate safety impacts, and determine downstream processability changes associated with using oxalic acid solutions to dissolve sludge heels in Savannah River Site High Level Waste (HLW) Tanks 1-15. First, a dissolution model is constructed and validated. Coupled with a model, a material balance determines the fate of hypothetical worst-case sludge in the treatment and neutralization tanks during each chemical adjustment. Although sludge is dissolved, after neutralization more is created within HLW. An energy balance determines overpressurization and overheating to be unlikely. Corrosion induced hydrogen may overwhelm the purge ventilation. Limiting the heel volume treated/acid added and processing the solids through vitrification is preferred and should not significantly increase the number of glass canisters.

  13. Stochastic Energy Deployment System (SEDS) World Oil Model (WOM)

    SciTech Connect (OSTI)

    2009-08-07

    The function of the World Oil Market Model (WOMM) is to calculate a world oil price. SEDS will set start and end dates for the forecast period, and a time increment (assumed to be 1 year in the initial version). The WOMM will then randomly select an Annual Energy Outlook (AEO) oil price case and calibrate itself to that case. As it steps through each year, the WOMM will generate a stochastic supply shock to OPEC output and accept a new estimate of U.S. petroleum demand from SEDS. The WOMM will then calculate a new oil market equilibrium for the current year. The world oil price at the new equilibrium will be sent back to SEDS. When the end year is reached, the process will begin again with the selection of a new AEO forecast. Iterations over forecasts will continue until SEDS has completed all its simulation runs.

  14. Stochastic Energy Deployment System (SEDS) World Oil Model (WOM)

    Energy Science and Technology Software Center (OSTI)

    2009-08-07

    The function of the World Oil Market Model (WOMM) is to calculate a world oil price. SEDS will set start and end dates for the forecast period, and a time increment (assumed to be 1 year in the initial version). The WOMM will then randomly select an Annual Energy Outlook (AEO) oil price case and calibrate itself to that case. As it steps through each year, the WOMM will generate a stochastic supply shock tomore » OPEC output and accept a new estimate of U.S. petroleum demand from SEDS. The WOMM will then calculate a new oil market equilibrium for the current year. The world oil price at the new equilibrium will be sent back to SEDS. When the end year is reached, the process will begin again with the selection of a new AEO forecast. Iterations over forecasts will continue until SEDS has completed all its simulation runs.« less

  15. Hawaii Energy Strategy: Program guide. [Contains special sections on analytical energy forecasting, renewable energy resource assessment, demand-side energy management, energy vulnerability assessment, and energy strategy integration

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

    The Hawaii Energy Strategy program, or HES, is a set of seven projects which will produce an integrated energy strategy for the State of Hawaii. It will include a comprehensive energy vulnerability assessment with recommended courses of action to decrease Hawaii's energy vulnerability and to better prepare for an effective response to any energy emergency or supply disruption. The seven projects are designed to increase understanding of Hawaii's energy situation and to produce recommendations to achieve the State energy objectives of: Dependable, efficient, and economical state-wide energy systems capable of supporting the needs of the people, and increased energy self-sufficiency. The seven projects under the Hawaii Energy Strategy program include: Project 1: Develop Analytical Energy Forecasting Model for the State of Hawaii. Project 2: Fossil Energy Review and Analysis. Project 3: Renewable Energy Resource Assessment and Development Program. Project 4: Demand-Side Management Program. Project 5: Transportation Energy Strategy. Project 6: Energy Vulnerability Assessment Report and Contingency Planning. Project 7: Energy Strategy Integration and Evaluation System.

  16. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  17. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  18. Demand forecasting and revenue requirements, with implications for consideration in British Columbia

    SciTech Connect (OSTI)

    Acton, J.P.

    1983-05-01

    This paper was filed as an exhibit on behalf of The Consumers' Association of Canada (B.C. Branch), The Federated Anti-Poverty Groups of B.C., The Sierra Club of Western Canada, and the B.C. Old Age Pensioners' Organization. It was subjected to cross-examination on October 29, 1982, during Phase I of the hearings. The Utilities Commission had designated Phase I for consideration of (1) demand, (2) assets in service, (3) revenue requirements excluding return, and (4) financing and capital requirements. This paper presents a general discussion of the elements of a rate structure and their relationship to the demand for electricity, a systematic review of some 50 empirical studies of the demand for electricity as a function of price and other factors by the three principal classes of customers, and a discussion of the notion of revenue requirements. The paper should be of interest to utility regulators, rate specialists, and forecasters for its review of demand models and to academics concerned with the study of energy demand.

  19. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect (OSTI)

    Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

  20. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    SciTech Connect (OSTI)

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  1. Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results

    SciTech Connect (OSTI)

    Koomey, J.G.; Brown, R.E.; Richey, R.

    1995-12-01

    This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

  2. The Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation), NREL (National Renewable Energy Laboratory)

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

    Improved Solar Forecasts on Bulk Power System Operations in ISO-NE 4 th Solar Integration Workshop Carlo Brancucci Martinez-Anido, Anthony Florita, and Bri-Mathias Hodge Berlin, Germany November 10, 2014 NREL/PR-5000-63082 2 Motivation and Scope * The economic benefits from renewable energy forecasting are largely unquantified in the power community o Current renewable energy penetration levels in the United States are often too low to appreciably quantify the value of improving renewable energy

  3. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    SciTech Connect (OSTI)

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

  4. Recent program evaluations: Implications for long-run planning

    SciTech Connect (OSTI)

    Baxter, L.W.; Schultz, D.K.

    1994-06-08

    Demand-side management (DSM) remains the centerpiece of California`s energy policy. Over the coming decade, California plans to meet 30 percent of the state`s incremental electricity demand and 50 percent of its peak demand with (DSM) programs. The major investor-owned utilities in California recently completed the first round of program impact studies for energy efficiency programs implemented in 1990 and 1991. The central focus of this paper is to assess the resource planning and policy implications of Pacific Gas and Electric (PG&E) Company`s recent program evaluations. The paper has three goals. First, we identify and discuss major issues that surfaced from our attempt to apply evaluation results to forecasting and planning questions. Second, we review and summarize the evaluation results for PG&E`s primary energy efficiency programs. Third, we change long-run program assumptions, based on our assessment in the second task, and then examine the impacts of these changes on a recent PG&E demand-side management forecast and resource plan.

  5. Nondestructive evaluation

    SciTech Connect (OSTI)

    Martz, H.E.

    1997-02-01

    Research reported in the thrust area of nondestructive evaluation includes: advanced 3-D imaging technologies; new techniques in laser ultrasonic testing; infrared computed tomography for thermal NDE of materials, structures, sources, and processes; automated defect detection for large laser optics; multistatic micropower impulse radar imaging for nondestructive evaluation; and multi-modal NDE for AVLIS pod shielding components.

  6. Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1

    SciTech Connect (OSTI)

    Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

    1994-05-01

    This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

  7. ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS

    SciTech Connect (OSTI)

    Chiswell, S

    2009-01-11

    Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-time level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.

  8. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  9. Macro-Industrial Working Group: meeting 1

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

    30 2013 Macroeconomic team: Kay Smith, Russ Tarver, Elizabeth Sendich and Vipin Arora Briefing on Macroeconomic Reference Case for the Annual Energy Outlook 2014 Macroeconomic/Industrial Working Group Meeting Presentation Goals * The Reference Case presented is the GI May Long-term Trend forecast. Due to the comprehensive revision of the GDP accounts, released at the end of July, we will not update the macro projection to an August baseline in order to keep on the AEO2014 schedule. The GI August

  10. Analysis & Projections - Pub - U.S. Energy Information Administration (EIA)

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

    Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly Short-Term Forecasts to 2012 Annual Projections to 2035 International Projections Analysis & Projections Most Requested All Reports Models & Documentation AEO Working Groups Purpose of Working Groups The Annual Energy Outlook, prepared by the U.S. Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices, based on results from EIA's National Energy Modeling

  11. Analysis Insights, August 2015: Sustainable Transportation (Brochure), NREL (National Renewable Energy Laboratory)

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

    Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly Short-Term Forecasts to 2012 Annual Projections to 2035 International Projections Analysis & Projections Most Requested All Reports Models & Documentation AEO Working Groups Purpose of Working Groups The Annual Energy Outlook, prepared by the U.S. Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices, based on results from EIA's National Energy Modeling

  12. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect (OSTI)

    Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  13. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2005 THRU FY2035 2005.0 VOLUME 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2005-08-17

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: (1) an overview of Hanford-wide solid waste to be managed by the WM Project; (2) multi-level and waste class-specific estimates; (3) background information on waste sources; and (4) comparisons to previous forecasts and other national data sources. The focus of this report is low-level waste (LLW), mixed low-level waste (MLLW), and transuranic waste, both non-mixed and mixed (TRU(M)). Some details on hazardous waste are also provided, however, this information is not considered comprehensive. This report includes data requested in December, 2004 with updates through March 31,2005. The data represent a life cycle forecast covering all reported activities from FY2005 through the end of each program's life cycle and are an update of the previous FY2004.1 data version.

  14. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    SciTech Connect (OSTI)

    Anggraeni, Novia Antika

    2015-04-24

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.

  15. Annual energy outlook 1997 with projections to 2015

    SciTech Connect (OSTI)

    1996-12-01

    The Annual Energy Outlook 1997 (AEO97) presents midterm forecasts of energy supply, demand, and prices through 2015 prepared by the Energy Information Administration (EIA). These projections are based on results of EIA`s National Energy Modeling System (NEMS). This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving legislative and regulatory issues. ``Issues in Focus`` discusses emerging energy issues and other topics of particular interest. It is followed by the analysis of energy market trends. The analysis in AEO97 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present summaries of the reference case forecasts in units of oil equivalence and household energy expenditures. Twenty-three other cases explore the impacts of varying key assumptions in NEMS--generally, technology penetration, with the major results shown in Appendix F. Appendix G briefly describes NEMS and the major AEO97 assumptions, with a summary table. 114 figs., 22 tabs.

  16. ARM Data Used to Evaluate Reanalysis Results

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

    models (Xie et al. 2004; Kennedy et al. 2011), investigating extreme weather and climatic events (Dong et al. 2011 and 2014), and examining forecast skill. These...

  17. Loan Guarantees and the Economics of Electricity Generating Technologies (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The loan guarantee program authorized in Title XVII of EPACT2005 is not included in the Annual Energy Outlook 2007, because the Federal Credit Reform Act of 1990 requires congressional authorization of loan guarantees in an appropriations act before a federal agency can make a binding loan guarantee agreement. As of October 2006, Congress had not provided the legislation necessary for the Department of Energy (DOE) to implement the loan guarantee program (see Legislation and Regulations). In August 2006, however, DOE invited firms to submit pre-applications for the first $2 billion in potential loan guarantees.

  18. Fuel Economy Standards for New Light Trucks (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    In March 2006, the National Highway Traffic Safety Administration (NHTSA) finalized Corporate Average Fuel Economy (CAFE) standards requiring higher fuel economy performance for light-duty trucks in model year (MY) 2008 through 2011. Unlike the proposed CAFE standards discussed in Annual Energy Outlook 2006, which would have established minimum fuel economy requirements by six footprint size classes, the final reformed CAFE standards specify a continuous mathematical function that determines minimum fuel economy requirements by vehicle footprint, defined as the wheelbase (the distance from the front axle to the center of the rear axle) times the average track width (the distance between the center lines of the tires) of the vehicle in square feet.

  19. Proposed Revisions to Light Truck Fuel Economy Standard (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    In August 2005, the National Highway Traffic Safety Administration (NHTSA) published proposed reforms to the structure of CAFE standards for light trucks and increases in light truck Corporate Average Fuel Economy (CAFE) standards for model years 2008 through 201. Under the proposed new structure, NHTSA would establish minimum fuel economy levels for six size categories defined by the vehicle footprint (wheelbase multiplied by track width), as summarized in Table 3. For model years 2008 through 2010, the new CAFE standards would provide manufacturers the option of complying with either the standards defined for each individual footprint category or a proposed average light truck fleet standard of 22.5 miles per gallon in 2008, 23.1 miles per gallon in 2009, and 23.5 miles per gallon in 2010. All light truck manufacturers would be required to meet an overall standard based on sales within each individual footprint category after model year 2010.

  20. Importance of Low Permeability Natural Gas Reservoirs (released in AEO2010)

    Reports and Publications (EIA)

    2010-01-01

    Production from low-permeability reservoirs, including shale gas and tight gas, has become a major source of domestic natural gas supply. In 2008, low-permeability reservoirs accounted for about 40% of natural gas production and about 35% of natural gas consumption in the United States. Permeability is a measure of the rate at which liquids and gases can move through rock. Low-permeability natural gas reservoirs encompass the shale, sandstone, and carbonate formations whose natural permeability is roughly 0.1 millidarcies or below. (Permeability is measured in darcies.)

  1. Military Construction Appropriations and Emergency Hurricane Supplemental Appropriations Act, 2005 (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    H.R. 4837, The Military Construction Appropriations and Emergency Hurricane Supplemental Appropriations Act, 2005, was signed into law on October 13, 2004. The Act provides for construction to support the operations of the U.S. Armed Forces and for military family housing. It also provides funds to help citizens in Florida and elsewhere in the aftermath of multiple hurricanes and other natural disasters. In addition, it authorizes construction of an Alaska Natural Gas Pipeline.

  2. FE LNG Exports-v1-aeo2014_8_29_14.xlsx

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

    baseline 12 Bcf 16 Bcf 20 Bcf Alt 20 Bcf baseline 12 Bcf 16 Bcf 20 Bcf baseline 12 Bcf 16 Bcf 20 Bcf baseline 12 Bcf 16 Bcf 20 Bcf baseline 12 Bcf 16 Bcf 20 Bcf NATURAL GAS VOLUMES (Tcf) Net Exports 3.6 5.1 6.1 7.0 6.3 4.9 4.9 5.9 6.8 1.8 4.1 5.0 5.8 3.3 5.0 6.0 7.0 3.2 5.0 6.0 7.0 gross imports 2.2 2.3 2.3 2.3 2.3 2.4 2.5 2.4 2.3 2.6 3.0 3.0 3.1 2.3 2.4 2.4 2.4 2.3 2.4 2.4 2.4 gross exports 5.8 7.5 8.5 9.3 8.6 7.3 7.4 8.3 9.2 4.4 7.0 8.0 8.9 5.6 7.4 8.4 9.3 5.5 7.4 8.4 9.3 Dry Production 32.5

  3. Price Responsiveness in the AEO2003 NEMS Residential and Commercial Buildings Sector Models

    Reports and Publications (EIA)

    2003-01-01

    This paper describes the demand responses to changes in energy prices in the Annual Energy Outlook 2003 versions of the Residential and Commercial Demand Modules of the National Energy Modeling System (NEMS). It updates a similar paper completed for the Annual Energy Outlook 1999 version of the NEMS.

  4. Update on Transition to Ultra-Low-Sulfur Diesel Fuel (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    On November 8, 2005, the Environmental Protection Agency (EPA) Administrator signed a direct final rule that will shift the retail compliance date for offering ultra-low sulfur diesel (ULSD) for highway use from September 1, 2006, to October 15, 2006. The change will allow more time for retail outlets and terminals to comply with the new 15 parts per million (ppm) sulfur standard, providing time for entities in the diesel fuel distribution system to flush higher sulfur fuel out of the system during the transition. Terminals will have until September 1, 2006, to complete their transitions to ULSD. The previous deadline was July 15, 2006.

  5. U.S. Greenhouse Gas Intensity and the Global Climate Change Initiative (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    On February 14, 2002, President Bush announced the Administrations Global Climate Change Initiative. A key goal of the Climate Change Initiative is to reduce U.S. greenhouse gas intensity by 18% over the 2002 to 2012 time frame. For the purposes of the initiative, greenhouse gas intensity is defined as the ratio of total U.S. greenhouse gas emissions to economic output.

  6. U.S. Greenhouse Gas Intensity and the Global Climate Change Initiative (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    On February 14, 2002, President Bush announced the Administrations Global Climate Change Initiative. A key goal of the Climate Change Initiative is to reduce U.S. greenhouse gas (GHG) intensity-defined as the ratio of total U.S. GHG emissions to economic output-by 18% over the 2002 to 2012 time frame.

  7. Greenhouse Gas Concerns and Power Sector Planning (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Concerns about potential climate change driven by rising atmospheric concentrations of Greenhouse Gases (GHG) have grown over the past two decades, both domestically and abroad. In the United States, potential policies to limit or reduce GHG emissions are in various stages of development at the state, regional, and federal levels. In addition to ongoing uncertainty with respect to future growth in energy demand and the costs of fuel, labor, and new plant construction, U.S. electric power companies must consider the effects of potential policy changes to limit or reduce GHG emissions that would significantly alter their planning and operating decisions. The possibility of such changes may already be affecting planning decisions for new generating capacity.

  8. Biofuels in the U.S. Transportation Sector (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    Sustained high world oil prices and the passage of the Energy Policy Act 2005 (EPACT) have encouraged the use of agriculture-based ethanol and biodiesel in the transportation sector; however, both the continued growth of the biofuels industry and the long-term market potential for biofuels depend on the resolution of critical issues that influence the supply of and demand for biofuels. For each of the major biofuelscorn-based ethanol, cellulosic ethanol, and biodieselresolution of technical, economic, and regulatory issues remains critical to further development of biofuels in the United States.

  9. Federal and State Ethanol and Biodiesel Requirements (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    The Energy Policy Act 2005 requires that the use of renewable motor fuels be increased from the 2004 level of just over 4 billion gallons to a minimum of 7.5 billion gallons in 2012, after which the requirement grows at a rate equal to the growth of the gasoline pool. The law does not require that every gallon of gasoline or diesel fuel be blended with renewable fuels. Refiners are free to use renewable fuels, such as ethanol and biodiesel, in geographic regions and fuel formulations that make the most sense, as long as they meet the overall standard. Conventional gasoline and diesel can be blended with renewables without any change to the petroleum components, although fuels used in areas with air quality problems are likely to require adjustment to the base gasoline or diesel fuel if they are to be blended with renewables.

  10. Loan Guarantee Program Established in EPACT2005 (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    Title XVII of EPACT2005 [20] authorized the Department of Energy (DOE) to issue loan guarantees to new or improved technology projects that avoid, reduce, or sequester greenhouse gases. In 2006, DOE issued its first solicitation for $4 billion in loan guarantees for non-nuclear technologies. The issue of the size of the program was addressed subsequently in the Consolidated Appropriation Act of 2008 (the FY08 Appropriations Act) passed in December 2008, which limited future solicitations to $38.5 billion and stated that authority to make the guarantees would end on September 30, 2009. The legislation also allocated the $38.5 billion cap as follows: $18.5 billion for nuclear plants; $6 billion for CCS technologies; $2 billion for advanced coal gasification units; $2 billion for advanced nuclear facilities for the front end of the nuclear fuel cycle; and $10 billion for renewable, conservation, distributed energy, and transmission/ distribution technologies. DOE also was required to submit all future solicitations to both the House and Senate Appropriations Committees for approval.

  11. Advanced Technologies for Light-Duty Vehicles (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    A fundamental concern in projecting the future attributes of light-duty vehicles-passenger cars, sport utility vehicles, pickup trucks, and minivans-is how to represent technological change and the market forces that drive it. There is always considerable uncertainty about the evolution of existing technologies, what new technologies might emerge, and how consumer preferences might influence the direction of change. Most of the new and emerging technologies expected to affect the performance and fuel use of light-duty vehicles over the next 25 years are represented in the National Energy Modeling System (NEMS); however, the potential emergence of new, unforeseen technologies makes it impossible to address all the technology options that could come into play. The previous section of Issues in Focus discussed several potential technologies that currently are not represented in NEMS. This section discusses some of the key technologies represented in NEMS that are expected to be implemented in light-duty vehicles over the next 25 years.

  12. Maximum Achievable Control Technology for New Industrial Boilers (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    As part of Clean Air Act 90 (CAAA90, the EPA on February 26, 2004, issued a final rulethe National Emission Standards for Hazardous Air Pollutants (NESHAP) to reduce emissions of hazardous air pollutants (HAPs) from industrial, commercial, and institutional boilers and process heaters. The rule requires industrial boilers and process heaters to meet limits on HAP emissions to comply with a Maximum Achievable Control Technology (MACT) floor level of control that is the minimum level such sources must meet to comply with the rule. The major HAPs to be reduced are hydrochloric acid, hydrofluoric acid, arsenic, beryllium, cadmium, and nickel. The EPA predicts that the boiler MACT rule will reduce those HAP emissions from existing sources by about 59,000 tons per year in 2005.

  13. Regulation of Emissions from Stationary Diesel Engines (released in AEO2007)

    Reports and Publications (EIA)

    2007-01-01

    On July 11, 2006, the Environmental Protection Agency (EPA) issued regulations covering emissions from stationary diesel engines New Source Performance Standards that limit emissions of NOx, particulate matter, SO2, carbon monoxide, and hydrocarbons to the same levels required for nonroad diesel engines. The regulation affects new, modified, and reconstructed diesel engines. Beginning with model year 2007, engine manufacturers must specify that new engines less than 3,000 horsepower meet the same emissions standard as nonroad diesel engines. For engines greater than 3,000 horsepower, the standard will be fully effective in 2011. Stationary diesel engine fuel will also be subject to the same standard as nonroad diesel engine fuel, which reduces the sulfur content of the fuel to 500 parts per million by mid-2007 and 15 parts per million by mid-2010.

  14. California Greenhouse Gas Emissions Standards for Light-Duty Vehicles (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    In July 2002, California Assembly Bill 1493 (A.B. 1493) was signed into law. The law requires that the California Air Resources Board (CARB) develop and adopt, by January 1, 2005, greenhouse gas emission standards for light-duty vehicles that provide the maximum feasible reduction in emissions. In estimating the feasibility of the standard, CARB is required to consider cost-effectiveness, technological capability, economic impacts, and flexibility for manufacturers in meeting the standard.

  15. Energy Improvement and Extension Act of 2008: Summary of Provisions (released in AEO2009)

    Reports and Publications (EIA)

    2009-01-01

    The Emergency Economic Stabilization Act of 2008 (Public Law 110-343), which was signed into law on October 3, 2008, incorporates EIEA2008 in Division B. Provisions in EIEA2008 that require funding appropriations to be implemented, whose impact is highly uncertain or that require further specification by federal agencies or Congress, are not included in Annual Energy Outlook 2009.

  16. Limited Electricity Generation Supply and Limited Natural Gas Supply Cases (released in AEO2008)

    Reports and Publications (EIA)

    2008-01-01

    Development of U.S. energy resources and the permitting and construction of large energy facilities have become increasingly difficult over the past 20 years, and they could become even more difficult in the future. Growing public concern about global warming and CO2 emissions also casts doubt on future consumption of fossil fuels -- particularly coal, which releases the largest amount of CO2 per unit of energy produced. Even without regulations to limit greenhouse gas emissions in the United States, the investment community may already be limiting the future use of some energy options. In addition, there is considerable uncertainty about the future availability of, and access to, both domestic and foreign natural gas resources.

  17. State Renewable Energy Requirements and Goals: Update Through 2005 (Update) (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    Annual Energy Outlook 2005 provided a summary of 17 state renewable energy programs in existence as of December 31, 2003, in 15 states.

  18. Volumetric Excise Tax Credit for Alternative Fuels (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    On August 10, 2005, President Bush signed into law the Safe, Accountable, Flexible, and Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU)]. The act includes authorization for a multitude of transportation infrastructure projects, establishes highway safety provisions, provides for research and development, and includes a large number of miscellaneous provisions related to transportation, most of which are not included in Annual Energy Outlook 2006 because their energy impacts are vague or undefined.

  19. State Restrictions on Methyl Tertiary Butyl Ether (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    By the end of 2005, 25 states had barred, or passed laws banning, any more than trace levels of methyl tertiary butyl ether (MTBE) in their gasoline supplies, and legislation to ban MTBE was pending in 4 others. Some state laws address only MTBE; others also address ethers such as ethyl tertiary butyl ether (ETBE) and tertiary amyl methyl ether (TAME). Annual Energy Outlook 2006 assumes that all state MTBE bans prohibit the use of all ethers for gasoline blending.

  20. State Renewable Energy Requirements and Goals: Update Through 2003 (released in AEO2005)

    Reports and Publications (EIA)

    2005-01-01

    As of the end of 2003, 15 states had legislated programs to encourage the development of renewable energy for electricity generation. Of the 17 programs (two states have multiple programs), 9 are renewable portfolio standards (RPS), 4 are renewable energy mandates, and 4 are renewable energy goals.