National Library of Energy BETA

Sample records for aeo forecast evaluations

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

    SciTech Connect

    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

    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

    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

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

    SciTech Connect

    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

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

    SciTech Connect

    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

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

    SciTech Connect

    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

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

    SciTech Connect

    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

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

    Reports and Publications

    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

    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. Second AEO2014 Macro-Industrial Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    In comparing the AEO2014 macro industrial forecast with the ... of robust supply and low price) and how this influenced ... allow multi-channel burners (burners of more than one fuel). ...

  11. AEO2016 Electricity Working Group

    Gasoline and Diesel Fuel Update

    in Reference Case: coal ash, cooling water intake, effluent limits (under ... Regulation AEO2015 Assumption AEO2016 Assumption Comment Cooling Water Intakes (Clean ...

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

    Reports and Publications

    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.

  13. AEO2014 results and status updates for the AEO2015

    Energy Information Administration (EIA) (indexed site)

    For AEO Electricity Working Group July 31, 2014, 1:00 PM Electricity Analysis Team Office of Electricity, Coal, Nuclear, and Renewables Analysis Electric Power in the Annual Energy Outlook AEO2014 results and status updates for the AEO2015 Working group presentation for discussion purposes. Do not quote or cite, as preliminary AEO2015 data and results are subject to change. Agenda Office of Electricity, Coal, Nuclear and Renewables Analysis | July 31, 2014 | Working group presentation for

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

    SciTech Connect

    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. World Oil Prices in AEO2006 (released in AEO2006)

    Reports and Publications

    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.

  16. AEO2014 Preliminary Results

    Energy Information Administration (EIA) (indexed site)

    September 26, 2013 AEO2014 Preliminary Results For discussion purposes only Not for citation Overview 2 * Residential projects - RECS update - Housing stock formation and decay - Lighting model - ENERGY STAR homes benchmarking - Weather elasticities * Commercial projects - Major end-use capacity factors - Data center servers - ENERGY STAR buildings - Hurdle rate floor * Both sectors - Usual annual updates - Miscellaneous end-use technology assumptions updates - Distributed generation * Contract

  17. AEO2016 Electricity Working Group

    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

  18. Industrial Plans for AEO2014

    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

  19. AEO2017 Industrial Working Group Meeting 1: Preview of Updates

    Energy Information Administration (EIA) (indexed site)

    1: preview of updates Industrial Working Group Industrial Team: Kelly Perl, Team Leader; Peter Gross, Susan Hicks, Paul Otis, & Matt Skelton August 16, 2016| Washington, DC Preliminary Results. Do not Disseminate. Macro-Industrial Working Group has split in two! * Macro working group had meeting July 28; materials available on http://www.eia.gov/forecasts/aeo/workinggroup/macroindustrial/ * Industrial working group will have two meetings this year - Second date: September 22, 2016: 1:30-3:00

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

    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

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

    SciTech Connect

    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.

  2. Industrial Team Plans for AEO2015

    Energy Information Administration (EIA) (indexed site)

    Process flow models * General: - Replace energy consumption based on engineering judgment ... drivers * AEO2015 new model - DLM joint pricing model of ethane and propane - Automated ...

  3. Industrial Team Plans for AEO2015

    Gasoline and Diesel Fuel Update

    CHANGE Data updates & regulation * Data - CHP historical data - Benchmarking total ... for AEO2015; may not be applicable to CHP * Size: greater than or equal to 25MW ...

  4. AEO2017 Liquid Fuels Markets Working Group Presentation

    Energy Information Administration (EIA) (indexed site)

    Liquid Fuels Markets Working Group AEO2017 Liquid Fuels Markets Working Group Meeting #1 Office of Petroleum, Natural Gas, and Biofuels Analysis August 25, 2016 | Washington, DC WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Discussion topics * "Short" AEO2017 * World Oil Price Path * Assumptions/changes for AEO2017 - petroleum * Assumptions/changes for AEO2017 - biofuels/non-petroleum 2 AEO2017 LFMM/IEM Working Group Meeting

  5. AEO2015 Transportation Working Group Meeting

    Gasoline and Diesel Fuel Update

    ... Vehicles with these technology options are not connected to the electrical grid. 7. Has EIA made any improvements to the VMT modeling? a. For AEO2014, EIA updated the population, ...

  6. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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 ...

  7. AEO2015 Coal Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    AS AEO2015 MODELING ASSUMPTIONS AND INPUTS ARE SUBJECT TO CHANGE. 1 July 30, 2014 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: AEO2015 Coal Working Group Meeting I Summary Attendees (39) Name Affiliation Greg Adams (Moderator) US DOE: EIA Jim Diefenderfer Tyler Hodge Elias Johnson Ayaka Jones Eric Krall Laura Martin Mike Mellish Kate

  8. AEO2016 Preliminary Industrial Output Results

    Energy Information Administration (EIA) (indexed site)

    1: Preliminary macroeconomic results For Macro-Industrial Working Group December 3, 2015 | Washington, DC By Kay Smith, Macro Team Leader, Elizabeth Sendich, Russ Tarver, and Vipin Arora DO NOT CITE OR DISTRIBUTE Macro Team's AEO2016 Briefing Plans * Review incorporation of completed AEO macroeconomic initiatives - Revised commercial floorspace model using indices rather than levels so that EIA customers won't have to incur extra data costs to compensate Dodge - Enhancements of the industrial

  9. Coal Working Group - AEO2017 Summary Notes

    Energy Information Administration (EIA) (indexed site)

    AEO2017 MODELING ASSUMPTIONS AND NOTE INPUTS ARE SUBJECT TO CHANGE. 1 September 7, 2016 MEMORANDUM TO: Dr. Ian Mead 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 AEO2017 1 st Coal Working Group held on August 31, 2016 Attendees (62) *Indicates participation via WebEx. In an effort to solicit feedback each year, the Coal and Uranium Analysis Team

  10. Microsoft PowerPoint - AEO2017 1st Electricity Working Group Presentation_v5

    Energy Information Administration (EIA) (indexed site)

    Electricity Sector Working Group Policy Assumptions and Key Model Updates For Electricity Working Group September 1, 2016 By Thad Huetteman, Team Lead, Electricity Team Office of Electricity, Coal, Nuclear, and Renewables Analysis What to look for re: Electricity in AEO2017 * Evolution of new longer-term forecast horizon (extend Reference Case to 2050) including: - Renewables: integration/ distributed generation - Nuclear: retirements/uprates/plant life extension - Continued updates: generating

  11. Second AEO2016 Macro-Induistrial Working Group Meeting summary

    Gasoline and Diesel Fuel Update

    ... liquid feedstocks. - Projections of CHP generation in the AEO2016 are lower than in the AEO2015 reference case due to updates in historical CHP data and the new process flow models ...

  12. File:AEO2012earlyrelease.pdf | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

  13. AEO2014: Preliminary Industrial Output

    Annual Energy Outlook

    Insights' (GI) May Long-term Trend forecast and this year's ... demand is driven by the price level, income, wealth, ... NG 322 Pulp & paper Fuel Index 32511a9 Bulk ...

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

    SciTech Connect

    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

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

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Metrics for Evaluating the Accuracy of Solar Power Forecasting Preprint J. Zhang, B.-M. Hodge, and A. Florita National Renewable Energy Laboratory S. Lu and H. F. Hamann IBM TJ Watson Research Center V. Banunarayanan U.S. Department of Energy To be presented at 3rd International Workshop on Integration of Solar Power into Power Systems London, England October 21 - 22, 2013 Conference Paper NREL/CP-5500-60142 October 2013 NOTICE The submitted manuscript has been offered by an employee of the

  16. Summary of AEO2015 Renewable Electricity Working Group Meeting

    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,

  17. AEO 2015 Electricity, Coal, Nuclear and Renewables Preliminary Results

    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, Office of Electricity, Coal, Nuclear & Renewables Analysis WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE The AEO2015 will be abridged compared to AEO2014 * The U.S. Energy Information Administration is revising the schedule for production of the

  18. AEO2017 Modeling updates in the transportation sector

    Energy Information Administration (EIA) (indexed site)

    7 For AEO2017 Transportation Working Group August 31, 2016 | Washington, DC By Melissa Lynes, John Maples, Mark Schipper, and David Stone Office of Energy Consumption and Efficiency Analysis Modeling updates in the transportation sector Updates to the Annual Energy Outlook 2017 * Transportation demand model highlights - 10-year extension of last-year projection, AEO2016 is 2040 and AEO2017 is 2050 - Battery costs for electric vehicles - Phase 2 greenhouse gas and fuel efficiency standards for

  19. AEO2017 Liquid Fuels Markets Working Group Presentation

    Energy Information Administration (EIA) (indexed site)

    ... assumptionschanges * Key assumptions maintained from AEO2016 - Ethanol and biodiesel tax credits are not extended beyond current end dates - GTL, CTL, BTL (xTL ...

  20. First AEO2015 Oil and Gas Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    TEAM EXPLORATION AND PRODUCTION and NATURAL GAS MARKETS TEAMS SUBJECT: First AEO2015 Oil and Gas Working Group ... to High Resource case * World oil price outlooks based on ...

  1. Second AEO2017 Industrial Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    ONLY. DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE. October 4, 2016 MEMORANDUM FOR: Ian Mead Assistant Administrator for Energy Analysis James Turnure Director, Office of Energy Consumption & Efficiency Analysis Paul Holtberg Team Leader, Analysis Integration Team FROM: Industrial Energy Consumption & Efficiency Analysis Team Subject: Second AEO2017 Industrial Working Group Meeting held on September 22, 2016 The Energy Consumption & Efficiency Analysis Team led a discussion

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

    Reports and Publications

    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.

  3. Buildings Working Group Meeting AEO2016 Preliminary Results

    Energy Information Administration (EIA) (indexed site)

    Buildings Working Group Meeting Office of Energy Consumption and Efficiency Analysis February 18, 2016 | Washington, DC By Buildings Energy Analysis Team AEO2016 Preliminary Results Discussion purposes only - do not cite or circulate Overview * Key policies - Clean Power Plan - Federal standards and ENERGY STAR specifications * Sector drivers - Fuel prices - Weather - Commercial floorspace * Distributed generation * Residential and commercial consumption AEO2016 Buildings Working Group,

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

  8. Summary of AEO2017 Renewables Working Group Meeting

    Gasoline and Diesel Fuel Update

    A separate working group will be held at a later date to present preliminary AEO2017 model results. Solar PV Load Shapes After EIA presented solar PV load shapes as one of the key ...

  9. Summary of Second AEO 2015 Working Group Meeting

    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

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

    Reports and Publications

    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.

  11. First AEO2017 Liquid Fuels Markets Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    AEO2017 MODELING ASSUMPTIONS AND NOTE INPUTS ARE SUBJECT TO CHANGE. 1 September 13, 2016 MEMORANDUM FOR: IAN MEAD ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSYS FROM: JOHN STAUB TEAM LEADER, EXPLORATION AND PRODUCTION TEAM ACTING TEAM LEADER, LIQUID FUELS MARKET TEAM SUBJECT: First AEO2017 Liquid Fuels Markets Working Group Meeting Summary (presented on 08-25-2016) Attendees: (EIA) John Staub, Mindi Farber-DeAnda, Adrian Geagla, Beth May, John Powell (DOE) Attending by Phone: Dale Nesbitt

  12. Handling Key AEO2017 Electric Sector Policy Assumptions and Key Model Updates

    Energy Information Administration (EIA) (indexed site)

    Key AEO2017 Renewable Electricity Key Model Updates For EIA Renewables Working Group September 1, 2016 By Chris Namovicz Team Leader for Renewable Electricity Analysis Summary of key changes from AEO2016 * AEO 2017 will be a limited release year - Very few side cases and limited analytic write-up (similar to AEO 2015) * We plan on extending the projections to 2050 * Pending any relevant court rulings, the Clean Power Plan will continue to be in the Reference case. * This presentation will focus

  13. Issues in midterm analysis and forecasting 1998

    SciTech Connect

    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.

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

    Reports and Publications

    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.

  15. First AEO2017 Oil and Gas Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE. September 12, 2016 MEMORANDUM FOR: Ian Mead Assistant Administrator for Energy Analysis FROM: John Staub Team Lead, Exploration and Production Analysis Mindi Farber-DeAnda Acting Team Lead, Natural Gas Markets Subject: First AEO2017 Oil and Gas Working Group Meeting held on August 25, 2016 The meeting began with an overview of the areas under focus for the AEO2017 in the Oil and Gas Supply Module (OGSM) and the Natural Gas Transmission and

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

    Reports and Publications

    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.

  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. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    012 Short-Term Energy Outlook August 7, 2012 Notice of change to electricity generation and renewables forecast tables The U.S. Energy Information Administration (EIA) has changed the format of the Short-Term Energy Outlook tables for electricity industry overview (Table 7a), electricity generation (Table 7d), electricity generation fuel consumption (Table 7e), and renewable energy (Table 8). Electricity Generation and Fuel Consumption The new electricity generation and fuel consumption tables

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

    Reports and Publications

    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.

  20. Second AEO2014 Macro-Industrial Working Group Meeting Summary

    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

  1. Second AEO2014 Oil and Gas Working Group Meeting Summary

    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

  2. Second AEO2015 Macro-Industrial Workiing Group Meeting Summary

    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

  3. Second AEO2016 Buildings Sector Workingb Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE June 10, 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 FROM: Buildings Consumption & Efficiency Analysis Team Subject: Second AEO2016 Buildings Sector Working Group Meeting Summary, workshop held on February 18, 2016

  4. Second AEO2016 Macro-Induistrial Working Group Meeting summary

    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

  5. Second AEO2016 Oil and Gas Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    April 8, 2016 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS FROM: MINDI FARBER-DEANDA ACTING 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 AEO2016 Oil and Gas Working Group Meeting Summary (presented on February 29, 2016) Attendees: Joseph Benneche (EIA) Katie Dyl (EIA) Terry Yen (EIA) Danya Murali (EIA) Laura Singer (EIA) Faouzi Aloulou (EIA) Dana

  6. Summary of AEO2015 Renewable Electricity Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE February 29, 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: Renewable Electricity Analysis Team SUBJECT: Summary of AEO2015 Renewable Electricity Working Group Meeting held on February 9, 2016 Presenter: Chris

  7. Summary of AEO2016 Electricity Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    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 PRESENTERS: Thad Huetteman and Chris Namovicz ATTENDEES: 14 EIA, 27 external (list provided following

  8. Summary of AEO2017 Electricity Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE. MEMORANDUM SUBJECT: Summary of AEO2017 Electricity Working Group Meeting held on September 1, 2016 DATE: September 29, 2016 TO: Ian Mead Assistant Administrator for Energy Analysis Jim Diefenderfer Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader, Analysis Integration Team FROM: Thad Huetteman Team Leader for Electricity Analysis Team PRESENTERS: Thad Huetteman, Chris Namovicz, Nancy

  9. Summary of AEO2017 Renewables Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    September 20, 2016 MEMORANDUM FOR: Ian Mead Assistant Administrator for Energy Analysis Jim Diefenderfer Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg Team Leader Analysis Integration Team FROM: Chris Namovicz Team Leader for Renewable Electricity Analysis Team SUBJECT: Summary of AEO2017 Renewables Working Group Meeting held on September 1, 2016 PRESENTERS: Chris Namovicz ATTENDEES: 12 EIA, 30 external (list provided following meeting summary) Presentation

  10. Summary of First AEO2014 Electricity Working Group Meeting

    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

  11. Summary of First AEO2015 Electricity Working Group Meeting

    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

  12. Summary of Second AEO 2014 Electricity Working Group Meeting

    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)

  13. AEO 2014 Renewable Electricity Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    DATE: September 30, 2013 MEMORANDUM FOR: John Conti Assistant Administrator for Energy Analysis Office of Energy Analysis Alan Beamon Office Director Office of Electricity, Coal, Nuclear, and Renewables Analysis FROM: Renewable Electricity Analysis Team SUBJECT: AEO 2014 Renewable Electricity Working Group Meeting Summary ATTENDEES: In person John Conti Alan Beamon Bob Eynon Chris Namovicz Danielle Lowenthal-Savy Erin Boedecker Gwen Bredehoeft Jim Diefenderfer Marie Rinkoski Spangler Michael

  14. AEO2014 Coal Working Group Meeting I Summary

    Energy Information Administration (EIA) (indexed site)

    July 22, 2013 MEMORANDUM TO: John Conti Assistant Administrator for Energy Analysis Alan Beamon Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis FROM: Coal and Uranium Analysis Team SUBJECT: AEO2014 Coal Working Group Meeting I Summary Attendees (41) Name Affiliation Greg Adams (Moderator) US DOE: EIA Vlad Dorjets Bob Eynon Karen Freedman Tyler Hodge Paul Holtberg Elias Johnson Ayaka Jones Diane Kearney Mike Leff Mike Mellish Carrie Milton Nick Paduano Margaret Cook US

  15. AEO2014 Liquid Fuels Markets Working Group Meeting 1

    Energy Information Administration (EIA) (indexed site)

    2 AEO2014 Liquid Fuels Markets Working Group Meeting 1 July 24, 2013 Attendance (In Person) (EIA) John Powell, Mindi Farber-DeAnda, Mike Cole, Beth May, Adrian Geagla, Vish Mantri, Tony Radich, Irene Olson, Julie Harris (non-EIA) Jeff Meyer (HIS CERA, Oil Market Analyst), Adam Christensen (Johns Hopkin) Attendance (WebEx) Dave Schmalzer, Seth Snyder (Argonne National Laboratory), Donald Hanson (Argonne National Laboratory), Wyatt Thompson (FAPRI, University of Missouri), Jarrett Whistance

  16. AEO2014 Oil and Gas Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    9 August 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: First AEO2014 Oil and Gas Working Group Meeting Summary (presented on July 25, 2013) Attendees: Anas Alhajji (NGP)* Samuel Andrus (IHS)* Emil Attanasi (USGS)* Andre Barbe (Rice University) David J. Barden (self) Joseph

  17. AEO2015 Liquid Fuels Markets Working Group Presentation

    Energy Information Administration (EIA) (indexed site)

    Independent Statistics & Analysis Assumptions for Annual Energy Outlook 2015: Liquid Fuels Markets Working Group AEO2015 Liquid Fuels Markets Working Group Meeting Office of Petroleum, Natural Gas & Biofuels Analysis July 17, 2014 | Washington, DC WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Discussion topics Office of Petroleum, Natural Gas, & Biofuels Analysis Working Group Presentation for Discussion Purposes Washington

  18. AEO2017 Industrial Working Group Meeting Preliminary Results

    Energy Information Administration (EIA) (indexed site)

    2: Preliminary results Industrial Working Group Industrial Team: Kelly Perl, Team Leader; Chris Dickerson, Peter Gross, Susan Hicks, Paul Otis, & Matt Skelton September 22, 2016| Washington, DC Preliminary Results. Do not Disseminate. AEO2017 What we did * Extend model to 2050 (now complete) * Individual industry benchmark improvements * Regulation: Kept Boiler MACT as is * Lowered DRI and relaxed constraints on EAF usage * Running Limited side cases: macro, price, and resource Industrial

  19. First AEO2015 Liquid Fuels Markets Working Group Meeting

    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

  20. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    1 July 2012 Short-Term Energy Outlook Highlights * EIA projects the West Texas Intermediate (WTI) crude oil spot price to average about $88 per barrel over the second half of 2012 and the U.S. refiner acquisition cost (RAC) of crude oil to average $93 per barrel, both about $7 per barrel lower than last month's Outlook. EIA expects WTI and RAC crude oil prices to remain roughly at these second half levels in 2013. Beginning in this month's Outlook, EIA is also providing a forecast of Brent crude

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

    Reports and Publications

    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.

  2. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

  3. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

  4. CONTINATION HEETIREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

  5. CONTINUATION S EFIIERENCE NO OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

  6. CONTINUATON SHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

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

    U.S. Department of Energy (DOE) - all 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

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

    SciTech Connect

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

    Annual Energy Outlook

    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...

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    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).

  13. Second AEO2016 Macro-Induistrial Working Group Meeting summary

    Energy Information Administration (EIA) (indexed site)

    ... recovers later in the forecast, thereby allowing for ... moderate growth in the long-term spurred on by the relative ... 7. Why is there petroleum fuel growth in the steel ...

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

    SciTech Connect

    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.

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

    SciTech Connect

    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

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

    Reports and Publications

    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.

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

    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

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

    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

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

    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,

  20. Solar Forecasting

    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....

  1. AEO2016 - Issues in Focus articles - U.S. Energy Information Administration

    Gasoline and Diesel Fuel Update

    Issues in Focus Effects of the Clean Power Plan Release Date: 6/20/2016 The Clean Power Plan (CPP) [1] rule, issued under Section 111(d) of the Clean Air Act, is the U.S. Environmental Protection Agency (EPA) program to regulate carbon dioxide (CO2) emissions at existing fossil-fired electric power plants. EPA estimates that the CPP will reduce CO2 emissions from the power sector by 32% from 2005 levels by 2030. As described in the Annual Energy Outlook 2016 (AEO2016) Legislation and Regulations

  2. Forecast Change

    Annual Energy Outlook

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (centskWh) 12.06 12.09 12.58 13.04 12.95 12.84 ...

  3. Value of Wind Power Forecasting

    SciTech Connect

    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.

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

    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

  5. Microsoft Word - macro industrial AEO2013 working group 08-02...

    Gasoline and Diesel Fuel Update

    GI's (Global Insights') long-term forecast of GDP. There was no ... of a dynamic price driver (comprised of the ... incentivize boiler fuel switching to natural gas ...

  6. Flood Forecasting in River System Using ANFIS

    SciTech Connect

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  7. probabilistic energy production forecasts

    U.S. Department of Energy (DOE) - all 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 ...

  8. Wind Power Forecasting Data

    U.S. Department of Energy (DOE) - all 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...

  9. Solar Forecasting Technical Workshop

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting Technical Workshop August 3, 2016 901 D St SW, Suite #930, Washington, DC Agenda 8:00-8:30 Check-in 8:30-8:45 Welcome & Opening remarks Guohui Yuan, DOE 8:45-9:15 Overview of Motivation and Techniques for Solar Forecasting Jan Kleissl, UCSD 9:15-9:45 Collaborative Research on Solar Power Forecasting: Challenges, Methods, and Assessment Tara Jensen, NCAR 9:45-10:00 Break 10:00-10:30 Machine-learning Based Enhancements for Renewable Energy Forecasting: From Research to Applications

  10. Wind Power Forecasting

    U.S. Department of Energy (DOE) - all 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...

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

    SciTech Connect

    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.

  12. AEO2014 - Legislation and Regulations articles - U.S. Energy Information

    Gasoline and Diesel Fuel Update

    Chart Gallery for November 2016 Short-Term Energy Outlook 0 20 40 60 80 100 120 140 Jan 2015 Jul 2015 Jan 2016 Jul 2016 Jan 2017 Jul 2017 West Texas intermediate (WTI) crude oil price dollars per barrel Historical spot price STEO price forecast NYMEX futures price 95% NYMEX futures upper confidence interval 95% NYMEX futures lower confidence interval Source: Short-Term Energy Outlook, November 2016. Note: Confidence interval derived from options market information for the 5 trading days ending

  13. NREL: Transmission Grid Integration - Forecasting

    U.S. Department of Energy (DOE) - all 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 ...

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

    SciTech Connect

    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.

  15. 2016 SSL Forecast Report

    Energy.gov [DOE]

    The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is a biannual report which models the adoption of LEDs in the U.S. general-lighting market,...

  16. SSL Forecast Report

    Energy.gov [DOE]

    The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is the latest edition of a biannual report which models the adoption of LEDs in the U.S....

  17. 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

  18. Issues in midterm analysis and forecasting, 1996

    SciTech Connect

    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.

  19. Evaluation Framework and Tools for Distributed Energy Resources

    SciTech Connect

    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

  20. Baseline and Target Values for PV Forecasts: Toward Improved...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

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

  1. Using Wikipedia to forecast diseases

    U.S. Department of Energy (DOE) - all 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)

  2. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect

    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.

  3. Forecast Energy | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

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

  4. The forecast calls for flu

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Science on the Hill: The forecast calls for flu Using mathematics, computer programs, ... We're getting close. Using mathematics, computer programs, statistics and information ...

  5. Solar Energy Market Forecast | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

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

  6. Upcoming Funding Opportunity for Wind Forecasting Improvement...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am ...

  7. Intermediate future forecasting system

    SciTech Connect

    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.

  8. Science on Tap - Forecasting illness

    U.S. Department of Energy (DOE) - all 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

  9. 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. Acquisition-Forecast-2016-11-10.xlsx (70.03 KB) More Documents & Publications National Nuclear Security Administration - Juliana Heynes Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment

  10. AEO2015 BWG

    Energy Information Administration (EIA) (indexed site)

    heaters, refrigerators, freezers, dishwashers, clothes washers, clothes dryers * Contract report from LeidosNavigant will be available online Discussion purposes only - do ...

  11. 2016 Solar Forecasting Workshop | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Solar Forecasting Workshop 2016 Solar Forecasting Workshop On August 3, 2016, the SunShot Initiative's systems integration subprogram hosted the Solar Forecasting Workshop to convene experts in the areas of bulk power system operations, distribution system operations, weather and solar irradiance forecasting, and photovoltaic system operation and modeling. The goal was to identify the technical challenges and opportunities in solar forecasting as a capability that can significantly reduce the

  12. Selected papers on fuel forecasting and analysis

    SciTech Connect

    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.

  13. 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

    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

  14. 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 Wind Forecast Improvement Project Southern ...

  15. Picture of the Week: Forecasting Flu

    U.S. Department of Energy (DOE) - all 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

  16. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE PAGES [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 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

  17. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect

    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.

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

    Reports and Publications

    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.

  19. EIA lowers forecast for summer gasoline prices

    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. ...

  20. Wind Forecasting Improvement Project | Department of Energy

    Energy Saver

    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 ...

  1. July 2016 Systems Integration Solar Forecasting:

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    2016 Systems Integration Solar Forecasting: Maximizing its value for grid integration Introduction The forecasting of power generated by variable energy resources such as wind and solar has been the focus of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting

  2. Forecasting Water Quality & Biodiversity

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National Laboratory This presentation does not contain any proprietary, confidential, or otherwise restricted information 2015 DOE Bioenergy Technologies Office (BETO) Project Peer Review Goal Statement Addresses the following MYPP BETO goals:  Advance scientific methods and models for measuring and understanding

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

    SciTech Connect

    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.

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

    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/

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

    SciTech Connect

    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.

  6. The Value of Wind Power Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Wind Power Forecasting Preprint Debra Lew and Michael Milligan National Renewable Energy Laboratory Gary Jordan and Richard Piwko GE Energy Presented at the 91 st American ...

  7. July 2016 Systems Integration Solar Forecasting:

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    ... Those costs comprise fuel costs from expensive generators ... an improved-accuracy forecast of the solar power generation. ... analog ensemble for short-term probabilistic solar power ...

  8. NREL: Resource Assessment and Forecasting Home Page

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research:...

  9. Funding Opportunity Announcement for Wind Forecasting Improvement...

    Energy.gov [DOE] (indexed site)

    There is no cost to participate and all applicants are encouraged to attend. To join the ... Related Articles Upcoming Funding Opportunity for Wind Forecasting Improvement Project in ...

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

    Energy.gov [DOE] (indexed site)

    This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as ...

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

    Energy.gov [DOE] (indexed site)

    Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices 63wateruseoptimizationprojectanlgasper.ppt (7.72 MB) More ...

  12. NREL: Resource Assessment and Forecasting - Webmaster

    U.S. Department of Energy (DOE) - all 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...

  13. Forecast and Funding Arrangements - Hanford Site

    U.S. Department of Energy (DOE) - all 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...

  14. Sensing, Measurement, and Forecasting | Grid Modernization | NREL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Sensing, Measurement, and Forecasting NREL measures weather resources and power systems, forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support a modern grid. Photo of solar resource monitoring equipment Modernizing the grid involves assessing its health in real time, predicting its behavior and potential disruptions, and quickly responding to events-which requires understanding vital parameters throughout the electric

  15. Study forecasts disappearance of conifers due to climate change

    U.S. Department of Energy (DOE) - all 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 ...

  16. 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 ...

  17. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ ... Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen, ...

  18. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect

    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.

  19. Assumptions and Expectations for Annual Energy Outlook 2017: Oil and Gas Working Group

    Energy Information Administration (EIA) (indexed site)

    Oil and Gas Working Group AEO2017 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis August 25, 2016| 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 Overview * "Short" AEO2017 with extension of model projection period to 2050 * World oil prices * Upstream - Offshore Gulf of Mexico and Alaska - Feedback on AEO2016 results *

  20. Toward a science of tumor forecasting for clinical oncology

    DOE PAGES [OSTI]

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for 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 therapiesmore » 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. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.« less

  1. Toward a science of tumor forecasting for clinical oncology

    SciTech Connect

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for 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. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

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

    SciTech Connect

    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.

  3. Offshore Lubricants Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

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

  4. Coal Fired Power Generation Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

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

  5. Forecast of transportation energy demand through the year 2010

    SciTech Connect

    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.

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

    Reports and Publications

    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.

  7. ARM - CARES - Tracer Forecast for CARES

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    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 contains 72-hr...

  8. Text-Alternative Version LED Lighting Forecast

    Energy.gov [DOE]

    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....

  9. energy data + forecasting | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

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

    Gasoline and Diesel Fuel Update

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

  11. Slide 1

    Energy Information Administration (EIA) (indexed site)

    ... * Multi-team effort to forecast NGL prices - ... Price in 2011 million BTU NEMS run 9412 AEO2012 ... BOM most affected - Update fuel costselection factors to ...

  12. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect

    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.

  13. Funding Opportunity Announcement: Solar Forecasting 2 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy Solar Forecasting 2 Funding Opportunity Announcement: Solar Forecasting 2 Subprogram: Systems Integration Funding Number: DE-FOA-0001649 Funding Amount: $10 million Description The Solar Forecasting 2 funding program will support projects that enable grid operators to better forecast how much solar energy will be added to the grid and accelerate the integration of these forecasts into energy management systems used by grid operators and utility companies. These tools will enable grid

  14. AEO2014: Preliminary Industrial Output

    Energy Information Administration (EIA) (indexed site)

    and demand computed from Input-Output basis * Major drivers: capacity utilization, interest rates, relative prices, ... For the energy industries (coal mining, oil & gas ...

  15. AEO2017 Preliminary Macroeconomic Results

    Energy Information Administration (EIA) (indexed site)

    ... equations in the macro model. * Test the incorporation of price movements from the industrial output model into the macro model. * Test integration between the IHS macro ...

  16. AEO2017 Preliminary Macroeconomic Results

    Energy Information Administration (EIA) (indexed site)

    Preliminary Macroeconomic Results For Macroeconomic Working Group July 28, 2016 | Washington, DC By Vipin Arora, Elizabeth Sendich, and Russ Tarver Macroeconomic Analysis Team Economic growth in major trading partners slows over the projection period while the dollar gradually depreciates Macroeconomic Working Group, Washington DC, July 28, 2016 2 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039 real trade-weighted GDP of major trading partners

  17. AEO2012 Early Release Overview

    Annual Energy Outlook

    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. ...

  18. AEO Early Release 2013 - oil

    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 ...

  19. AEO2012 Early Release Overview

    Gasoline and Diesel Fuel Update

    9 U.S. Energy Information Administration | International Energy Outlook 2016 Chapter 2 Petroleum and other liquid fuels Overview In the International Energy Outlook 2016 (IEO2016) Reference case, worldwide consumption of petroleum and other liquid fuels increases from 90 million barrels per day (b/d) in 2012 to 100 million b/d in 2020 and 121 million b/d in 2040. Much of the growth in world liquid fuels consumption is projected for the emerging, non-Organization for Economic Cooperation and

  20. Industrial Plans for AEO2014

    Energy Information Administration (EIA) (indexed site)

    ... * Quadrennial MECS update to 2010 * New nonmanufacturing data approach - Uses Census and USDA data to derive usage data from expenditures - Improves estimation of nonmanufacturing ...

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

    U.S. Department of Energy (DOE) - all 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

  2. 1994 Solid waste forecast container volume summary

    SciTech Connect

    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.

  3. Contact Us - U.S. Energy Information Administration (EIA) - U.S. Energy

    Gasoline and Diesel Fuel Update

    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 Long-Term (AEO) Analysis and Forecasting Experts Fax: (202) 586-3045 Annual Energy Outlook General questions/Executive summary Angelina LaRose 202-586-6135 angelina.larose@eia.gov Carbon dioxide emissions Perry Lindstrom 202-586-0934 perry.lindstrom@eia.gov Coal supply and

  4. Evaluation

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

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

  5. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    SciTech Connect

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

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was 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 in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

  6. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    DOE PAGES [OSTI]

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

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

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

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... up-ramp reserves c down cost in MWh of down-ramp reserves R down MW range for ... power forecasting and the increased gas usage that comes with less-accurate forecasting. ...

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

    Energy Saver

    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 ...

  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...

    U.S. Department of Energy (DOE) - all 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. Wind power forecasting in U.S. electricity markets.

    SciTech Connect

    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.

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

    SciTech Connect

    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)

  13. Improving the Accuracy of Solar Forecasting Funding Opportunity |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Improving the Accuracy of Solar Forecasting Funding Opportunity Improving the Accuracy of Solar Forecasting Funding Opportunity Through the Improving the Accuracy of Solar Forecasting Funding Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other stakeholders better forecast when, where, and how much solar power will be produced at the desired locations in the United States. More accurate solar

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

    OpenEI (Open Energy Information) [EERE & EIA]

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

  15. ANL Software Improves Wind Power Forecasting | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    ANL Software Improves Wind Power Forecasting ANL Software Improves Wind Power Forecasting May 1, 2012 - 3:19pm Addthis This is an excerpt from the Second Quarter 2012 edition of the Wind Program R&D Newsletter. Since 2008, Argonne National Laboratory and INESC TEC (formerly INESC Porto) have conducted a research project to improve wind power forecasting and better use of forecasting in electricity markets. One of the main results from the project is ARGUS PRIMA (PRediction Intelligent

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

    SciTech Connect

    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.

  17. Today's Forecast: Improved Wind Predictions | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical

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

    SciTech Connect

    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

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

    SciTech Connect

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

    2015-10-05

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

  2. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect

    Chin, H S

    2005-07-26

    direction, using its wind tunnel facility at the windmill farm at the Altamont Pass. The main objective of LLNL's involvement is to provide UC-Davis with improved wind forecasts to drive the parameterization scheme of turbine power curves developed from the wind tunnel facility. Another objective of LLNL's effort is to support the windmill farm operation with real-time wind forecasts for the effective energy management. The forecast skill in capturing the situation to meet the cut-in and cutout speed of given turbines would help reduce the operation cost in low and strong wind scenarios, respectively. The main focus of this report is to evaluate the wind forecast errors of LLNL's three-dimensional real-time weather forecast model at the location with the complex terrain. The assessment of weather forecast accuracy would help quantify the source of wind energy forecast errors from the atmospheric forecast model and/or wind-tunnel module for further improvement in the wind energy forecasting system.

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

    SciTech Connect

    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.

  4. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect

    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.

  5. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect

    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

    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. Global disease monitoring and forecasting with Wikipedia

    DOE PAGES [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: 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

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

    SciTech Connect

    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.

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

    SciTech Connect

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

    SciTech Connect

    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.

  11. Wind speed forecasting in the central California wind resource area

    SciTech Connect

    McCarthy, E.F.

    1997-12-31

    A wind speed forecasting program was implemented in the summer seasons of 1985 - 87 in the Central California Wind Resource Area (WRA). The forecasting program is designed to use either meteorological observations from the WRA and local upper air observations or upper air observations alone to predict the daily average windspeed at two locations. Forecasts are made each morning at 6 AM and are valid for a 24 hour period. Ease of use is a hallmark of the program as the daily forecast can be made using data entered into a programmable HP calculator. The forecasting program was the first step in a process to examine whether the electrical energy output of an entire wind power generation facility or defined subsections of the same facility could be predicted up to 24 hours in advance. Analysis of the results of the summer season program using standard forecast verification techniques show the program has skill over persistence and climatology.

  12. 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 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] campaign was scheduled to take place from 15 July

  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 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] campaign was scheduled to take place from 15 July

  14. Funding Opportunity Announcement for Wind Forecasting Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    in Complex Terrain | Department of Energy Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain April 4, 2014 - 9:47am Addthis 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 that take place in complex

  15. Wind Forecast Improvement Project Southern Study Area Final Report |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf (15.76 MB) More Documents & Publications QER - Comment of Edison Electric Institute (EEI) 1 QER - Comment of Canadian Hydropower Association Team roster: Dan Paikowsky, Management; Christian Bain, Entrepreneurship; Noah Meunier, Mechanical Engineering &

  16. Module 6 - Metrics, Performance Measurements and Forecasting | Department

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    of Energy 6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement tools used in Earned Value. This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as estimate to complete (ETC) and estimate at completion (EAC). Begin Module >> (471

  17. 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. Report of the External Peer Review Panel (777.84 KB) More Documents & Publications Industrial Technologies Funding Profile by Subprogram Survey of Emissions Models for Distributed Combined Heat and Power

  18. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Complex Terrain | Department of Energy 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 improve

  19. Validation of Global Weather Forecast and Climate Models Over...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Validation of Global Weather Forecast and Climate Models Over the North Slope of Alaska Xie, Shaocheng Lawrence Livermore National Laboratory Klein, Stephen Lawrence Livermore ...

  20. DOE Publishes New Forecast of Energy Savings from LED Lighting...

    Office of Environmental Management (EM)

    Addthis Related Articles DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting DOE Publishes Pricing and Efficacy Trend Analysis for Utility Program ...

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

    SciTech Connect

    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.

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

    U.S. Department of Energy (DOE) - all 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 ...

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

    SciTech Connect

    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.

  4. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

    SciTech Connect

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

    2015-10-02

    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.

  5. 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, ...

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

    Office of Scientific and Technical Information (OSTI)

    forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. ...

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

    U.S. Department of Energy (DOE) - all 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...

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

    U.S. Department of Energy (DOE) - all 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...

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

    Energy Saver

    Through the Improving the Accuracy of Solar Forecasting Funding Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and ...

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

    SciTech Connect

    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.

  11. Voluntary Green Power Market Forecast through 2015

    SciTech Connect

    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.

  12. Technical analysis in short-term uranium price forecasting

    SciTech Connect

    Schramm, D.S.

    1990-03-01

    As market participants anticipate the end of the current uranium price decline and its subsequent reversal, increased attention will be focused upon forecasting future price movements. Although uranium is economically similar to other mineral commodities, it is questionable whether methodologies used to forecast price movements of such commodities may be successfully applied to uranium.

  13. 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

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

    SciTech Connect

    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.

  15. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

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

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

    SciTech Connect

    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.

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

    Reports and Publications

    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.

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

    SciTech Connect

    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.

  19. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

    2014-07-09

    Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

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

    SciTech Connect

    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

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

    SciTech Connect

    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.

  2. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema

    Gonzalez, Frank

    2016-07-12

    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.

  3. Wind Energy Technology Trends: Comparing and Contrasting Recent Cost and Performance Forecasts (Poster)

    SciTech Connect

    Lantz, E.; Hand, M.

    2010-05-01

    Poster depicts wind energy technology trends, comparing and contrasting recent cost and performance forecasts.

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

    Energy.gov [DOE]

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

  5. Solar Trackers Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    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,...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    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....

  7. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect

    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.

  8. Recently released EIA report presents international forecasting data

    SciTech Connect

    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.

  9. DOE Releases Latest Report on Energy Savings Forecast of Solid...

    Energy.gov [DOE] (indexed site)

    The sixth iteration of the Energy Savings Forecast of Solid-State Lighting in General Illumination Applications compares the annual lighting energy consumption in the U.S. with and ...

  10. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect

    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.

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

    Energy.gov [DOE] (indexed site)

    The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored research project whose overarching goals are to improve the accuracy of short-term wind ...

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

    Office of Scientific and Technical Information (OSTI)

    U.S. DEPARTMENT OF HP IENERGY Office of Science DOESC-ARM-15-024 915-MHz Wind Profiler ... M Jensen et al., March 2016, DOESC-ARM-15-024 915-MHz Wind Profiler for Cloud Forecasting ...

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

    U.S. Department of Energy (DOE) - all 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...

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

    Reports and Publications

    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.

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    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

  16. Energy Department Forecasts Geothermal Achievements in 2015 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector, including Jay Nathwani, Acting Director of the Energy Department's Geothermal Technologies Office. Nathwani shared achievements and challenges in the program's technical portfolio. The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector,

  17. Study forecasts disappearance of conifers due to climate change

    U.S. Department of Energy (DOE) - all 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

  18. 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

  19. Project Profile: Forecasting and Influencing Technological Progress in

    Energy Saver

    Solar Energy | Department of Energy Soft Costs » Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of North Carolina at Charlotte, Arizona State University, and the University of Oxford. -- This project is inactive -- The University of North Carolina at Charlotte, along with their partners at Arizona State University and the University of Oxford,

  20. Weather-based forecasts of California crop yields

    SciTech Connect

    Lobell, D B; Cahill, K N; Field, C B

    2005-09-26

    Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.

  1. Notice of Intent to Issue Solar Forecasting 2 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Solar Forecasting 2 Notice of Intent to Issue Solar Forecasting 2 Subprogram: Systems Integration Funding Number: DE-FOA-0001658 Funding Amount: $10,000,000 The SunShot Initiative intends to release a funding opportunity announcement (FOA) to support advancements in solar forecasting to enable higher penetration of solar power in the electric grid. The Solar Forecasting 2 FOA will focus on improving solar forecasting skills, especially during challenging conditions, such as partly cloudy weather

  2. Interpolating Low Time-Resolution Forecast Data

    Energy Science and Technology Software Center

    2015-11-03

    Methodology that interpolates low time-resolution data (e.g., hourly) to high time-resolution (e.g., minutely) with variability patterns extracted from historical records. Magnitude of the variability inserted into the low timeresolution data can be adjusted according to the installed capacity represented by the low time-resolution data compared to that by historical records. This approach enables detailed analysis of the impacts from wind and solar on power system intra-hour operations and balancing reserve requirements even with only hourlymore » data. It also allows convenient creation of high resolution wind or solar generation data with various degree of variability to investigate their operational impacts. The methodology comprises of the following steps: 1. Smooth the historical data (set A) with an appropriate window length l to get its trend (set B); l can be a fraction of an hour (e.g., 15 minutes) or longer than an hour, of which the length of the variability patterns will be; 2. Extract the variable component (set C) of historical data by subtracting the smooth trend from it, i.e. set C = set A – set B 3. For each window length l of the variable component data set, find the average value x (will call it base component) of the corresponding window of the historical data set; 4. Define a series of segments (set D) that the values of data will be grouped into, e.g. (0, 0.1), (0.1, 0.2), …, (0.9, 1.0) after normalization; Link each variability pattern to a data segment based on its corresponding base component x; after this step, each data segment should be linked to multiple variability patterns after this step; 5. Use spline function to interpolate the low time-resolution forecast data (set E) to become a high time-resolution smooth curve (set F); 6. Based on the window length l , calculate the average value y in each window length of set F; find the data segment that y belongs to; then randomly select one of the variability patterns linked to this

  3. AVLIS: a technical and economic forecast

    SciTech Connect

    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.

  4. Waste Treatment Plant Liquid Effluent Treatability Evaluation

    SciTech Connect

    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.

  5. AEO Early Release 2013 - LNG exports

    Energy Information Administration (EIA) (indexed site)

    U.S. expected to become net exporter of natural gas by end of decade The United States is on track to become a net exporter of natural gas by 2020 as domestic gas production continues to increase faster than consumption through this decade. Growing production and low prices will help spur exports, according to the new long-term outlook from the U.S. Energy Information Administration. Some of that gas will be sent overseas in huge ocean-going tankers carrying super-cooled liquefied natural gas,

  6. AEO Early Release 2013 - renewable generation

    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

  7. 2017 Levelized Costs AEO 2012 Early Release

    Energy Information Administration (EIA) (indexed site)

    ... of Congress, the General Accounting Office, or other Federal agencies authorized by law to receive such information. A court of competent jurisdiction may obtain this ...

  8. AEO2014 Renewables Working Group Meeting

    Gasoline and Diesel Fuel Update

    ... Owen noted that there were no big projects in the works, but that they would be updating their core data sets for interconnection limits and baseline capacity and generation. A ...

  9. AEO2016 Preliminary Industrial Output Results

    Gasoline and Diesel Fuel Update

    through network analysis will inform future work on analyzing supply matrices - We plan on incorporating detailed industrial price movements which will come from the ...

  10. 2017 Levelized Costs AEO 2012 Early Release

    Annual Energy Outlook

    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 ...

  11. State Appliance Standards (released in AEO2009)

    Reports and Publications

    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.

  12. Energy Demand (released in AEO2010)

    Reports and Publications

    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.

  13. 2017 Levelized Costs AEO 2012 Early Release

    Annual Energy Outlook

    Market Prices and Uncertainty Report Crude Oil Prices: After reaching a four-month low in the beginning of August, crude oil prices rebounded close to the highest levels of the ...

  14. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Addendum to Potential Impacts of Reductions in Refinery Activity on Northeast Petroleum Product Markets 1 May 11, 2012 ADDENDUM Potential Impacts of Reductions in Refinery Activity on Northeast Petroleum Product Markets Additional Information on Jones Act Vessels' Potential Role in Northeast Refinery Closures The U.S. Energy Information Administration's (EIA) recent report exploring the potential impacts of reductions in refinery activity in the Northeast on petroleum product markets in that

  15. 2017 Levelized Costs AEO 2012 Early Release

    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)

  16. CAFE Standards (released in AEO2010)

    Reports and Publications

    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.

  17. Microsoft Word - macroeconomic_aeo2012.docx

    Gasoline and Diesel Fuel Update

    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...

  18. Coal Transportation Issues (released in AEO2007)

    Reports and Publications

    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%).

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    ... 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 Working Group - AEO2017 Summary Notes

    Energy Information Administration (EIA) (indexed site)

    Clark Leidos *Bill Meroney Environmental Protection Agency *Leslie Coleman National Mining Association *Greg Moxness Department of Labor John Dean JD Energy, Inc. *Chris Nichols ...

  1. Comparing Efficiency Projections (released in AEO2010)

    Reports and Publications

    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.

  2. 2017 Levelized Costs AEO 2012 Early Release

    Annual Energy Outlook

    ... and other advanced technology vehicles (e.g., gasoline or diesel- electric hybrid vehicles) that vehicle suppliers made available in a calendar year and plan to make available ...

  3. Efficiency and Intensity in the AEO 2010

    Gasoline and Diesel Fuel Update

    Average annual rate of decrease Steve Wade, 2010 Energy Conference, April 7, 2010 6 * Conservation - Changes in energy use that reduce consumption by reducing services provided * ...

  4. AEO 2013 Liquid Fuels Markets Working Group

    Energy Information Administration (EIA) (indexed site)

    year Under the LCFS Brazilian sugarcane Ethanol is favorable due to its carbon intensity ... as it is more expensive than corn based ethanol Q: Is the California LCFS an attempt to ...

  5. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    6 11.7 44.2 83.7 12.9 46.8 83.8 13.8 46.1 83.4 14.4 46.2 85.1 14.3 46.6 84.9 0 10 20 30 40 50 60 70 80 90 100 Residential Commercial Industrial 2011 2012 2013 2014 2015 Note: These deliveries included quantities covered by long-term contracts and gas involved in short-term or spot market sales. Source: Energy Information Administration (EIA), Form EIA -176, "Annual Report of Natural and Supplemental Gas Supply and Disposition." Figure 18. Percent of natural gas deliveries in the United

  6. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Outlook Market Prices and Uncertainty Report Crude Oil Prices: International crude oil futures prices declined in March and are near the bottom of their recent trading range. The North Sea Brent front month futures price settled at $106.15 per barrel (bbl) on April 3, a decrease of $5.05/bbl from March 3 (Figure 1). The West Texas Intermediate (WTI) front month futures price declined by $4.63/bbl over the same period, settling at $100.29/bbl on April 3. An apparent decline in risks associated

  7. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  8. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  9. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  10. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  11. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  12. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices broke below $45/b and out of of a two-month-long trading range in July. The front-month Brent crude oil price decreased $6.06 per barrel (b) since July 1, settling at $44.29/b on August 4 (Figure 1). The West Texas Intermediate (WTI) crude oil price settled at $41.93/b, declining $7.06/b over the same time. Crude oil production outages, which removed more than 3.7 million barrels per day (b/d) of production in May, declined

  13. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Market Prices and Uncertainty Report Crude Oil Prices: International crude oil benchmarks moved higher in November, showing their first month-over-month increase since August, while U.S. crude oil prices moved higher during the first week of December. The North Sea Brent front month futures price settled at $110.98 per barrel on December 5, an increase of $5.07 per barrel since its close on November 1 (Figure 1). The West Texas Intermediate (WTI) front month futures contract rose $2.77 per

  14. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    1 December 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: Crude oil prices continued to move lower in November and recorded their fifth consecutive month of declines. The North Sea Brent front month futures price settled at $69.64/bbl on December 4, a decline of $15.14/bbl from November 3 (Figure 1). The front month West Texas Intermediate (WTI) contract price settled at $66.81/bbl on December 4, decreasing by $11.97/bbl since the start of November. The

  15. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  16. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  17. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  18. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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%,

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  20. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  2. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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.

  3. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  4. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Market Prices and Uncertainty Report Crude Oil Prices: Despite some price volatility in June and early July, crude oil prices remained in a narrow trading range near recent highs. The front-month Brent crude oil price decreased $3.32 per barrel (b) since June 1, settling at $46.40/b on July 7 (Figure 1). The West Texas Intermediate (WTI) crude oil price settled at $45.14/b, declining $3.87/b over the same time. Drilling activity in the United States, as measured by the Baker Hughes oil rig

  5. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    4 1 June 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: International crude oil futures prices increased slightly over the previous month but remained rangebound. The North Sea Brent front month futures price settled at $108.79 per barrel (bbl) on June 5, an increase of $1.03/bbl from May 1 (Figure 1). The front month West Texas Intermediate (WTI) contract also rose, settling at $102.48/bbl on June 5, $3.06/bbl higher than on May 1. Lower-than-previously

  6. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  7. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Market Prices and Uncertainty Report Crude Oil Prices: Global crude oil prices increased for the fourth consecutive month in May, closing at more than $50 per barrel (b) for the first time since November 2015. The front- month Brent crude oil price increased $4.21/b since May 2, settling at $50.04/b on June 2. The West Texas Intermediate (WTI) crude oil price increased $4.39 over the same period to settle at $49.17/b (Figure 1). Global unplanned supply outages were estimated at 3.7 million

  8. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  9. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  10. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  11. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  12. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  13. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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.

  14. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  15. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    4 1 November 2014 Short-Term Energy Outlook Market Prices and Uncertainty Report Crude Oil Prices: Both international and domestic crude oil prices moved sharply lower over the previous five weeks. The North Sea Brent front month futures price settled at $82.86/bbl on November 6, a decline of $11.30/bbl from October 1 (Figure 1). The front month West Texas Intermediate (WTI) contract price settled at $77.91/bbl on November 6, decreasing by $12.82/bbl since the start of October. November marked

  16. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  17. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  18. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  19. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  20. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  1. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    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

  2. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    Market Prices and Uncertainty Report Crude Oil Prices: After reaching a four-month low in the beginning of August, crude oil prices rebounded close to the highest levels of the year. The front-month Brent crude oil price increased $3.31 per barrel (b) since August 1, settling at $45.45/b on September 1 (Figure 1). The West Texas Intermediate (WTI) front-month crude oil price settled at $43.16/b, an increase of $3.10/b over the same period. Price volatility in global equity markets declined in

  3. 2017 Levelized Costs AEO 2012 Early Release

    Gasoline and Diesel Fuel Update

    end uses such as space heating, air conditioning, water heating, refrigeration, and so on. ... Total Btu Space Heating AC Water Heating Etc. (known) total (unknown) end-use components ...

  4. 2017 Levelized Costs AEO 2012 Early Release

    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. ...

  5. First AEO2017 Industrial Working Group Meeting

    Gasoline and Diesel Fuel Update

    ... Among other differences, it also models heat while NEMS does not. 2. Do you have access to the U.S. Department of Census Manufacturing consumption data? Answer: We do not have ...

  6. Nonconventional Liquid Fuels (released in AEO2006)

    Reports and Publications

    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.

  7. CONTINATIONSHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

  8. CONTINATIONSHEETREFERENCE NO. OF DOCUMENT BEING CONTINUED AEO

    U.S. Department of Energy (DOE) - all 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

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

    SciTech Connect

    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.

  10. A Scenario Generation Method for Wind Power Ramp Events Forecasting

    SciTech Connect

    Cui, Ming-Jian; Ke, De-Ping; Sun, Yuan-Zhang; Gan, Di; Zhang, Jie; Hodge, Bri-Mathias

    2015-07-03

    Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

  11. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect

    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.

  12. Supplement to the Annual Energy Outlook 1993

    SciTech Connect

    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.

  13. Incorporating Uncertainty of Wind Power Generation Forecast into Power System Operation, Dispatch, and Unit Commitment Procedures

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian; Huang, Zhenyu; Subbarao, Krishnappa

    2011-06-23

    An approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. An assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty - both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures). A new method called the 'flying-brick' technique is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through EMS integration illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems in control rooms.

  14. Incorporating Wind Generation Forecast Uncertainty into Power System Operation, Dispatch, and Unit Commitment Procedures

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Subbarao, Krishnappa

    2010-10-19

    In this paper, an approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty 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 is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through integration with an EMS system illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems from other vendors.

  15. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer

    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).

  16. Forecast of contracting and subcontracting opportunities. Fiscal year 1996

    SciTech Connect

    1996-02-01

    This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

  17. Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Wind Energy Predictable: New Profilers Provide Hourly Forecasts Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts May 11, 2016 - 6:48pm Addthis Balancing the power grid is an art-or at least a scientific study in chaos-and the Energy Department is hoping wind energy can take a greater role in the act. Yet, the intermittency of wind-sometimes it's blowing, sometimes it's not-makes adding it smoothly to the nation's electrical grid a challenge. If wind

  18. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer

    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. Initial Comments of the National Rural Electric Cooperative Associatio...

    Energy Saver

    ... 4 DOE NOPR spreadsheet dt-nopr-lcc-dl01-50kva.xlsb, Forecast Cells tab. ... The AEO price, which includes the recovery of fuel, generation capacity, transmission capacity, and a rate ...

  20. Are there Gains from Pooling Real-Time Oil Price Forecasts?

    Energy Information Administration (EIA) (indexed site)

    ... Gamma and that of Q is inverse Wishart. 5 Our forecasts take into account that the model parameters continue to drift over the forecast horizon according to their law of motion. ...

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

    SciTech Connect

    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

    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. Final Report - Integration of Behind-the-Meter PV Fleet Forecasts...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

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

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

    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...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    "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 periods

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

    SciTech Connect

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

    SciTech Connect

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

    2010-01-01

    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.

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

    SciTech Connect

    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.

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

    Energy Science and Technology Software Center

    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.

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

    SciTech Connect

    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.

  11. Use of wind power forecasting in operational decisions.

    SciTech Connect

    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

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

    SciTech Connect

    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.

  13. LED Lighting Forecast | Department of Energy

    Energy Saver

    Research & Development » Technology Application R&D » LED Lighting Facts LED Lighting Facts LED lighting facts - A Program of the U.S. DOE DOE's LED Lighting FactsŸ program showcases LED products for general illumination from manufacturers who commit to testing products and reporting performance results according to industry standards. For lighting buyers, designers, and energy efficiency programs, the program provides information essential to evaluating SSL products. Central to the

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

    SciTech Connect

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

    2010-09-01

    features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. 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 (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

  15. Forecasting photovoltaic array power production subject to mismatch losses

    SciTech Connect

    Picault, D.; Raison, B.; Bacha, S.; de la Casa, J.; Aguilera, J.

    2010-07-15

    The development of photovoltaic (PV) energy throughout the world this last decade has brought to light the presence of module mismatch losses in most PV applications. Such power losses, mainly occasioned by partial shading of arrays and differences in PV modules, can be reduced by changing module interconnections of a solar array. This paper presents a novel method to forecast existing PV array production in diverse environmental conditions. In this approach, field measurement data is used to identify module parameters once and for all. The proposed method simulates PV arrays with adaptable module interconnection schemes in order to reduce mismatch losses. The model has been validated by experimental results taken on a 2.2 kW{sub p} plant, with three different interconnection schemes, which show reliable power production forecast precision in both partially shaded and normal operating conditions. Field measurements show interest in using alternative plant configurations in PV systems for decreasing module mismatch losses. (author)

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

    U.S. Department of Energy (DOE) - all 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. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect

    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.

  18. Towards a Science of Tumor Forecast for Clinical Oncology

    DOE PAGES [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 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

  19. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect

    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.

  20. Supplement to the annual energy outlook 1994

    SciTech Connect

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

  3. Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

    SciTech Connect

    Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

    2012-08-15

    We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

  4. Forecasting the northern African dust outbreak towards Europe in April 2011: A model intercomparison

    DOE PAGES [OSTI]

    Huneeus, N.; Basart, S.; Fiedler, S.; Morcrette, J. -J.; Benedetti, A.; Mulcahy, J.; Terradellas, E.; Garcia-Pando, C. Perez; Pejanovic, G.; Nickovic, S.; et al

    2016-04-21

    In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distributionmore » was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. In this paper, our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.« less

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg 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 the project are designed to integrate novel PV power

  6. Final Report - Integration of Behind-the-Meter PV Fleet Forecasts into

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Utility Grid System Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Final Report - Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Awardee: Clean Power Research Location: Napa, CA Subprogram: Systems Integration Funding Program: Solar Utility Networks: Replicable Innovations in Solar Energy (SUNRISE) Project: Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid

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

    SciTech Connect

    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.

  8. 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

    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

  9. ARM - PI Product - CCPP-ARM Parameterization Testbed Model Forecast Data

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ProductsCCPP-ARM Parameterization Testbed Model Forecast Data ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : CCPP-ARM Parameterization Testbed Model Forecast Data 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

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

  12. Energy Savings Forecast of Solid-State Lighting in General Illuminatio...

    Energy Saver

    (1.54 MB) More Documents & Publications Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report 2016 SSL Forecast Report LED ADOPTION REPORT

  13. The Value of Improved Wind Power Forecasting in the Western Interconne...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    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 ...

  14. Integration of Behind-the-Meter PV Fleet Forecasts into Utility...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting behind-the-meter distributed PV generation power production within a region ... This project is expected to reduce the costs of integrating higher penetrations of PV into ...

  15. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    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.

  16. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

    DOE PAGES [OSTI]

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; et al

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

  17. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

    SciTech Connect

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; Hodge, Bri-Mathias; Finley, Catherine; Nakafuji, Dora; Peterson, Jack L.; Maggio, David; Marquis, Melinda

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.

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

    SciTech Connect

    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.

  19. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    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.

  20. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE PAGES [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 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

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

    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

  2. Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

    Energy.gov [DOE]

    Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

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

    SciTech Connect

    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.

  4. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center

    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

  5. Annual Energy Outlook 2016: Electricity Sector Preliminary Results

    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

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

    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

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

    Energy Information Administration (EIA) (indexed site)

    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 February 29, 2016| 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 Overview * Natural gas markets - Natural gas supply and delivered prices - Natural gas consumption - Pipeline imports/exports - LNG exports *

  8. U.S. Energy Information Administration (EIA) - Ap

    Gasoline and Diesel Fuel Update

    Browse by Tag (alphabetical) Sort by: Alphabetical | Frequency | Tag Cloud AEO2012 (Annual Energy Outlook 2012) (3) AEO2013 (Annual Energy Outlook 2013) (1) annual (5) baseload capacity (1) California (5) Canada (1) capacity and generation (12) capacity factor (1) China (1) Congressional & other requests (1) electricity (28) electricity generating fuel mix (3) Florida (1) forecast (1) generation (21) generation capacity (13) generators (2) historical (2) imports (4) international (9)

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

    SciTech Connect

    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

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

    SciTech Connect

    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.

  11. U.S. oil production forecast revised up for 2016 and 2017

    Energy Information Administration (EIA) (indexed site)

    oil production forecast revised up for 2016 and 2017 U.S. crude oil production is expected to be higher this year and in 2017 than previously forecast, because of a slower decline in onshore production. In its new monthly forecast, the U.S. Energy Information Administration revised up its estimate for domestic oil production by about 110,000 barrels per day for 2016 and by 150,000 barrels per day next year. EIA said increased drilling activity in the Permian Basin area located in West Texas and

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

    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

  13. Energy Department Announces $2.5 Million to Improve Wind Forecasting |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Improve Wind Forecasting Energy Department Announces $2.5 Million to Improve Wind Forecasting January 8, 2015 - 12:00pm Addthis The Energy Department today announced $2.5 million for a new project to research the atmospheric processes that generate wind in mountain-valley regions. This in-depth research, conducted by Vaisala of Louisville, Colorado, will be used to improve the wind industry's weather models for short-term wind forecasts, especially for those issued less

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    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 ...

  17. Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

    Office of Energy Efficiency and Renewable Energy (EERE)

    Report forecasting the U.S. energy savings of LED white-light sources compared to conventional white-light sources (i.e., incandescent, halogen, fluorescent, and high-intensity discharge) over the...

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    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....

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

    SciTech Connect

    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.

  4. Gasoline price forecast to stay below 3 dollar a gallon in 2015

    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 ...

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

    SciTech Connect

    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.)

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

    SciTech Connect

    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.)

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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%.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

  14. Analysis & Projections - Pub - U.S. Energy Information Administration...

    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 ...

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

    SciTech Connect

    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

  16. 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

    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.

  17. Annual energy outlook 1995, with projections to 2010

    SciTech Connect

    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.

  18. Evaluation of the St. Lucia geothermal resource: macroeconomic models

    SciTech Connect

    Burris, A.E.; Trocki, L.K.; Yeamans, M.K.; Kolstad, C.D.

    1984-08-01

    A macroeconometric model describing the St. Lucian economy was developed using 1970 to 1982 economic data. Results of macroeconometric forecasts for the period 1983 through 1985 show an increase in gross domestic product (GDP) for 1983 and 1984 with a decline in 1985. The rate of population growth is expected to exceed GDP growth so that a small decline in per capita GDP will occur. We forecast that garment exports will increase, providing needed employment and foreign exchange. To obtain a longer-term but more general outlook on St. Lucia's economy, and to evaluate the benefit of geothermal energy development, we applied a nonlinear programming model. The model maximizes discounted cumulative consumption.

  19. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    SciTech Connect

    Zulkepli, Jafri Abidin, Norhaslinda Zainal; Fong, Chan Hwa

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

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

    SciTech Connect

    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.

  4. Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment

    SciTech Connect

    Zupanski, M. )

    1993-08-01

    Four-dimensional variational data assimilation is applied to a regional forecast model as part of the development of a new data assimilation system at the National Meteorological Center (NMC). The assimilation employs an operational version of the NMC's new regional forecast model defined in eta vertical coordinates, and data used are operationally produced optimal interpolation (OI) analyses (using the first guess from the NMC's global spectral model), available every 3 h. Humidity and parameterized processes are not included in the adjoint model integration. The calculation of gradients by the adjoint model is approximate since the forecast model is used in its full-physics operational form. All experiments are over a 12-h assimilation period with subsequent 48-h forecast. Three different types of assimilation experiments are performed: (a) adjustment of initial conditions only (standard [open quotes]adjoint[close quotes] approach), (b) adjustment of a correction to the model equations only (variational continuous assimilation), and (c) simultaneous or sequential adjustment of both initial conditions and the correction term. Results indicate significantly better results when the correction term is included in the assimilation. It is shown, for a single case, that the new technique [experiment (c)] is able to produce a forecast better than the current conventional OI assimilation. It is very important to note that these results are obtained with an approximate gradient, calculated from a simplified adjoint model. Thus, it may be possible to perform an operational four-dimensional variational data assimilation of realistic forecast models, even before more complex adjoint models are developed. Also, the results suggest that it may be possible to reduce the large computational cost of assimilation by using only a few iterations of the minimization algorithm. This fast convergence is encouraging from the prospective of operational use. 37 refs., 10 figs., 1 tab.

  5. Microsoft Word - Final AEO2007 Commercial Doc.doc

    Gasoline and Diesel Fuel Update

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

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

    Gasoline and Diesel Fuel Update

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

  7. Annual Energy Outlook (AEO) 2006 - Supplemental Tables - All Tables

    SciTech Connect

    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.

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

    Reports and Publications

    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.

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

    U.S. Department of Energy (DOE) - all 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...

  10. Second AEO2014 Transportation Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    Maples (EIAOECEA) Steve Plotkin (ANL) Attending by Phone: Kevin Bollon (EPA) Dallas Burkholder (EPA) John Davies (DOT) Kaoru Horie (Honda) Ken Howden (DOEEERE) Ryan Keefe ...

  11. First AEO2014 Macro-Industrial Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    Frances Wood (OnLocation) Bhima Sastri (DOE-EERE) Don Hanson (ANL) Betsy Dutrow (EPA) Gale Boyd (Duke University) 2 WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT ...

  12. Buildings Working Group Meeting AEO2016 Preliminary Results

    Energy Information Administration (EIA) (indexed site)

    Clean Power Plan * EIA issued analysis of proposed Clean Power Plan (CPP) in 2015; CPP was finalized by EPA and is currently under judicial review. The Supreme Court has issued a ...

  13. First AEO2014 Buildings Sector Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    0, 2013 MEMORANDUM FOR: JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY ...

  14. First AEO2015 Macro-Industrial Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    ... period for the NAICS revisions is just ending now, so we don't expect the revisions to happen before 2017. 2. Do the macro or industrial models explicitly model climate change? a. ...

  15. AEO 2013 Liquid Fuels Markets Working Group 2

    Gasoline and Diesel Fuel Update

    Slide 12 - Corn Oil and Tallow are needed for biodiesel to meet the LCFS Slide 13 - Energy crops are needed to meet the LCFS There is not enough cellulosic production to meet the ...

  16. First AEO2017 Liquid Fuels Markets Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    ... The following assumptions related to biofuelsnon-petroleum production were also maintained: ethanol and biodiesel tax credits are not extended beyond current expiration dates; ...

  17. Second AEO2014 Liquids Fuels Markets Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    ... biofuels to California - corn ethanol, biodiesel, Brazilian sugarcane ethanol, etc. ... A- 10 billion gallons per year - Biodiesel is included in Northeast heating oil ...

  18. Workshop on Biofuels Projections in AEO Presenters Biographies

    Annual Energy Outlook

    Prior to his service at DOE, Howard was Senior Staff Economist at the Council of Economic ... of biofuels, the national Renewable Fuel Standard (RFS), the California Low Carbon Fuel ...

  19. AEO2013 Early Release Base Overnight Project Technological Total...

    Energy Information Administration (EIA) (indexed site)

    4.39 30.64 8,800 8,740 Integrated Coal-Gasification Comb Cycle (IGCC) 7 2016 1200 4 3,475 ... 1.05 1.00 1,759 7.62 17.14 10,042 9,880 Biomass 2016 50 4 3,685 1.07 1.02 4,041 5.17 ...

  20. Summary of AEO2015 Renewable Electricity Working Group Meeting

    Annual Energy Outlook

    ... tracking systems in the future. A participant wanted to know if the wind capacity is hitting any growth bounds, and if the wind growth bounds are specified at the EMM level. ...

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    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.

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

    Reports and Publications

    2010-01-01

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

  7. EPACT2005 Loan Guarantee Program (released in AEO2008)

    Reports and Publications

    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.

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

    Reports and Publications

    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.

  9. New NHTSA CAFE Standards (released in AEO2009)

    Reports and Publications

    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.

  10. Second AEO2016 Buildings Sector Workingb Group Meeting Summary

    Gasoline and Diesel Fuel Update

    equipment standards and ENERGY STAR specifications; an ... The presentation materials are provided as a separate ... generation (DG) and solar photovoltaics (PV): The ...

  11. AEO2012 Preliminary Assumptions: Oil and Gas Supply

    Energy Information Administration (EIA) (indexed site)

    3 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis July 31, 2012 | Washington, DC WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE Assumptions and Expectations for Annual Energy Outlook 2013: Oil and Gas Working Group Overview 2 Office of Petroleum, Gas, and Biofuels Analysis Working Group Presentation for Discussion Purposes Washington, DC, July 31, 2012 DO NOT QUOTE OR CITE as results are subject to

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

    Reports and Publications

    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.

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

    Gasoline and Diesel Fuel Update

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

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

    Reports and Publications

    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.

  15. AEO2017 Modeling updates in the transportation sector

    Gasoline and Diesel Fuel Update

    Massachusetts, Rhode Island, Vermont * CD2: New ... * Update total freight ton-mile and vehicle miles traveled ... - Stock is made up of three types of aircraft: ...

  16. Workshop on Biofuels Projections in AEO Attendance List

    Annual Energy Outlook

    ... Schremp California Energy Commission Anthony Shen Stifel Daniel Sinks Phillips 66 Russ Smith U.S. EPA Wyatt Thompson FAPRI-MU Paul Trupo USDA Lisa Twedt USDA - Foreign Agricultural ...

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

    Annual Energy Outlook

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

  18. Summary of AEO2017 Electricity Working Group Meeting

    Annual Energy Outlook

    Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg ... issues raised, including prospects for nuclear re-licensing, 2) continued inclusion of ...

  19. Summary of AEO2016 Electricity Working Group Meeting

    Gasoline and Diesel Fuel Update

    Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg ... The results shown include electricity sales, coal and nuclear retirements, electricity and ...

  20. Summary of Second AEO 2014 Electricity Working Group Meeting

    Gasoline and Diesel Fuel Update

    Director Office of Electricity, Coal, Nuclear, and Renewables Analysis Paul Holtberg ... Crozat, Matthew P. (US DOE: Office of Nuclear Energy) Diefenderfer, Jim (EIA OEA) ...

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

    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 ...

  2. Electricity Plant Cost Uncertainties (released in AEO2009)

    Reports and Publications

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

    Reports and Publications

    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.

  4. Federal Air Emissions Regulations (released in AEO2006)

    Reports and Publications

    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.

  5. Clean Air Mercury Rule (released in AEO2009)

    Reports and Publications

    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.

  6. Distributed Generation in Buildings (released in AEO2005)

    Reports and Publications

    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.

  7. Mercury Emissions Control Technologies (released in AEO2006)

    Reports and Publications

    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.

  8. Second AEO2015 Macro-Industrial Workiing Group Meeting Summary

    Annual Energy Outlook

    TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ...

  9. First AEO2014 Macro-Industrial Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ...

  10. First AEO2015 Macro-Industrial Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    TEAM LEADER ANALYSIS INTEGRATION TEAM JAMES TURNURE DIRECTOR OFFICE OF ENERGY CONSUMPTION & EFFICIENCY ANALYSIS LYNN WESTFALL DIRECTOR OFFICE OF ENERGY MARKETS & FINANCIAL ...

  11. Second AEO2014 Macro-Industrial Working Group Meeting Summary

    Gasoline and Diesel Fuel Update

    net migration, a lower starting population size, and increased life expectancies, which ... Is it primarily aircraft or motor vehicles? a. Transportation equipment shipments are ...

  12. Handling Key AEO2017 Electric Sector Policy Assumptions and Key...

    Annual Energy Outlook

    time * PV load shapes * Renewables Integration - Energy Storage - Address Solar curtailments * Solar resource data update * Regional solar costs * State RPS policy updates ...

  13. First AEO2014 Transportation Working Group Meeting Summary

    Energy Information Administration (EIA) (indexed site)

    inform HDV model year, vehicle type and VMT aspects using the Polk data. 4. What does the measure "E85 market share" represent on the Consumer choice for E85 graph on slide 11? a. ...

  14. Clean Air Interstate Rule (released in AEO2009)

    Reports and Publications

    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.

  15. Clean Air Nonroad Diesel Rule (released in AEO2005)

    Reports and Publications

    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.

  16. Electricity Prices in Transition (released in AEO2007)

    Reports and Publications

    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.

  17. Tax Credits and Renewable Generation (released in AEO2009)

    Reports and Publications

    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.

  18. Second AEO2014 Buildings Sector Working Group Meeting

    Energy Information Administration (EIA) (indexed site)

    JOHN CONTI ASSISTANT ADMINISTRATOR FOR ENERGY ANALYSIS PAUL HOLTBERG TEAM LEADER ... Ackerly (Alliance for Green Heat) Xiaojing Sun (Georgia Tech) Matt Cox (Georgia Tech) ...

  19. Overview of Levelized Cost of Energy in the AEO

    Gasoline and Diesel Fuel Update

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 798,236 818,233 807,040 793,261 774,978 815,754 747,154 784,209 815,512 2000's 876,145 786,947 830,504 895,100 917,014 918,593 4,290,187 4,430,466 4,839,942 5,225,005 2010's 5,864,402 6,958,125 8,225,321 689,082 631,536 600,656

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 826,576 1990's 810,100 784,362 800,913 788,472 774,724 759,728 805,491 736,679 775,235 800,579 2000's

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

    Annual Energy Outlook

    Analysis & Projections Annual Energy Outlook 2016 Full Release Date: September 15, ... Summary Case Tables Table 1. Total Energy Supply, Disposition, and Price Summary Table 2. ...

  1. Alaskan Natural Gas Pipeline Developments (released in AEO2007)

    Reports and Publications

    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.

  2. Expectations for Oil Shale Production (released in AEO2009)

    Reports and Publications

    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.

  3. Changing Trends in the Refining Industry (released in AEO2006)

    Reports and Publications

    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.

  4. World Oil Price Cases (released in AEO2005)

    Reports and Publications

    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.

  5. Economic Effects of High Oil Prices (released in AEO2006)

    Reports and Publications

    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.

  6. Summer gasoline price forecast slightly higher, but drivers still pay less than last year

    Energy Information Administration (EIA) (indexed site)

    Summer gasoline price forecast slightly higher, but drivers still pay less than last year Rising crude oil prices are likely to be passed on to consumers at the pump, but U.S. drivers are still expected to pay the lowest summer gasoline prices since 2004, and for all of 2016 the average household will spend $900 less on gasoline than it did two years ago." In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average

  7. ARM - Field Campaign - 915 MHz Wind Profiler for Cloud Forecasting at BNL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    govCampaigns915 MHz Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : 915 MHz Wind Profiler for Cloud Forecasting at BNL 2011.05.31 - 2012.05.31 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of the installation of a 37 MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study

  8. 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

  9. ARM - Field Campaign - Radar Wind Profiler for Cloud Forecasting at BNL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    govCampaignsRadar Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : Radar Wind Profiler for Cloud Forecasting at BNL 2013.07.15 - 2015.08.06 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of recent activities funded by the DOE Energy Efficiency and Renewable Energy (EERE) to produce short-term

  10. Annual energy outlook 1994: With projections to 2010

    SciTech Connect

    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.

  11. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2003 THRU FY2046 VERSION 2003.1 VOLUME 2 [SEC 1 & 2

    SciTech Connect

    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.

  12. Annual energy outlook 2005 with projections to 2025

    SciTech Connect

    2005-02-01

    The Annual Energy Outlook 2005 (AEO2005) presents midterm forecasts of energy supply, demand, and prices through 2025 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modelling System (NEMS). The report begins with an 'Overview' summarizing the AEO2005 reference case. The next section, 'Legislation and Regulations', discusses evolving legislative and regulatory issues in the USA. Issues in Focus includes discussions on key energy market issues and examines their potential impacts. In particular, it includes a discussion of the world oil price assumptions used in the reference case and four alternative world oil price cases examined in AEO2005. 'Issues in Focus' is followed by 'Market Trends', which provides a summary of energy market trends in the AEO2005 forecast. The analysis in AEO2005 focuses primarily on a reference case, lower and higher economic growth cases, and four alternative oil price cases, a low world oil price case, an October oil futures case, and two high world oil price cases. Forecast tables for those cases are provided in Appendixes A through D. The major results for the alterative cases, which explore the impacts of varying key assumption in NEMS (such as rates of technology penetration), are summarized in Appendix E. Appendix F briefly describes NEMS and the alternative cases. 115 figs., 38 tabs., 8 apps.

  13. Expectations models of electric utilities' forecasts: a case study of econometric estimation with influential data points

    SciTech Connect

    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.

  14. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect

    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.

  15. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect

    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.

  16. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    SciTech Connect

    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

  17. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting

    SciTech Connect

    Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

    2010-02-21

    The operators of electrical grids, sometimes referred to as Balancing Authorities (BA), typically make critical decisions on how to most reliably and economically balance electrical load and generation in time frames ranging from a few minutes to six hours ahead. At higher levels of wind power generation, there is an increasing need to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts on this time scale have typically been strongly dependent on short-term trends indicated by the time series of power production and meteorological data from a wind farm. Additional input information is often available from the output of Numerical Weather Prediction (NWP) models and occasionally from off-site meteorological towers in the region surrounding the wind generation facility. A widely proposed approach to improve short-term forecasts is the deployment of off-site meteorological towers at locations upstream from the wind generation facility in order to sense approaching wind perturbations. While conceptually appealing, it turns out that, in practice, it is often very difficult to derive significant benefit in forecast performance from this approach. The difficulty is rooted in the fact that the type, scale, and amplitude of the processes controlling wind variability at a site change from day to day if not from hour to hour. Thus, a location that provides some useful forecast information for one time may not be a useful predictor a few hours later. Indeed, some processes that cause significant changes in wind power production operate predominantly in the vertical direction and thus cannot be monitored by employing a network of sensors at off-site locations. Hence, it is very challenging to determine the type of sensors and deployment locations to get the most benefit for a specific short-term forecast application. Two tools recently developed in the meteorological research community have the potential to help determine the locations and parameters to

  18. EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day

    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.

  19. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications

    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.

  20. Analysis methods for fast impurity ion dynamics data

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Residential/Commercial Buildings AEO2016 Meetings First AEO2016 Meeting (December 8, 2015) Summary of meeting Presentation Second AEO2016 Meeting (February 18, 2016) 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