National Library of Energy BETA

Sample records for base forecast natural

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

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

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

    On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

    On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

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

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e

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

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

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

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

    SciTech Connect (OSTI)

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

    2008-01-07

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

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

  11. How regulators should use natural gas price forecasts

    SciTech Connect (OSTI)

    Costello, Ken

    2010-08-15

    Natural gas prices are critical to a range of regulatory decisions covering both electric and gas utilities. Natural gas prices are often a crucial variable in electric generation capacity planning and in the benefit-cost relationship for energy-efficiency programs. High natural gas prices can make coal generation the most economical new source, while low prices can make natural gas generation the most economical. (author)

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

    SciTech Connect (OSTI)

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

    2009-03-01

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

  13. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect (OSTI)

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

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

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

    SciTech Connect (OSTI)

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

    2015-01-01

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

  15. Base Oil Market Segment Forecasts up to 2020,Research Reports...

    Open Energy Info (EERE)

    Market Research Home > Groups > Future of Condition Monitoring for Wind Turbines Wayne31jan's picture Submitted by Wayne31jan(150) Contributor 11 June, 2015 - 03:19 Base Oil...

  16. Assessment and Forecasting Natural Gas Reserve Appreciation in the Gulf Coast Basin

    SciTech Connect (OSTI)

    Kim, E.M.; Fisher, W.L.

    1997-10-01

    Reserve appreciation, also called reserve growth, is the increase in the estimated ultimate recovery (the sum of year end reserves and cumulative production) from fields subsequent to discovery from extensions, infield drilling, improved recovery of in-place resources, new pools, and intrapool completions. In recent years, reserve appreciation has become a major component of total U.S. annual natural gas reserve additions. Over the past 15 years, reserve appreciation has accounted for more than 80 percent of all annual natural gas reserve additions in the U.S. lower 48 states (Figure 1). The rise of natural gas reserve appreciation basically came with the judgment that reservoirs were much more geologically complex than generally thought, and they hold substantial quantities of natural gas in conventionally movable states that are not recovered by typical well spacing and vertical completion practices. Considerable evidence indicates that many reservoirs show significant geological variations and compartmentalization, and that uniform spacing, unless very dense, does not efficiently tap and drain a sizable volume of the reservoir (Figure 2). Further, by adding reserves within existing infrastructure and commonly by inexpensive recompletion technology in existing wells, reserve appreciation has become the dominant factor in ample, low-cost natural gas supply. Although there is a wide range in natural gas reserve appreciation potential by play and that potential is a function of drilling and technology applied, current natural gas reserve appreciation studies are gross, averaging wide ranges, disaggregated by broad natural gas provinces, and calculated mainly as a function of time. A much more detailed analysis of natural gas reserve appreciation aimed at assessing long-term sustainability, technological amenability, and economic factors, however, is necessary. The key to such analysis is a disaggregation to the play level. Plays are the geologically homogeneous

  17. New Mexico Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) New Mexico Natural Gas in Underground Storage (Base Gas) ... Underground Base Natural Gas in Storage - All Operators New Mexico Underground Natural Gas ...

  18. New York Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) New York Natural Gas in Underground Storage (Base Gas) ... Underground Base Natural Gas in Storage - All Operators New York Underground Natural Gas ...

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

    SciTech Connect (OSTI)

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

    2014-05-01

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

  20. Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

    SciTech Connect (OSTI)

    Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar; Marianno, Fernando J.; Shao, Xiaoyan; Zhang, Jie; Hodge, Bri-Mathias; Hamann, Hendrik F.

    2015-07-15

    With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

  1. Community-Based Forest (Natural) Resource Management: A Path...

    Open Energy Info (EERE)

    Based Forest (Natural) Resource Management: A Path to Sustainable Environment and Development Jump to: navigation, search Name Community-Based Forest (Natural) Resource Management:...

  2. Solar Forecasting

    Broader source: Energy.gov [DOE]

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

  3. Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  4. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect (OSTI)

    Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  5. New Mexico Natural Gas Liquids Lease Condensate, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) New Mexico Natural Gas Liquids Lease ... Referring Pages: Lease Condensate Estimated Production New Mexico Lease Condensate Proved ...

  6. Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  7. New Mexico Natural Gas Plant Liquids, Reserves Based Production...

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

    Reserves Based Production (Million Barrels) New Mexico Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  8. An economic feasibility analysis of distributed electric power generation based upon the Natural Gas-Fired Fuel Cell: a model of the operations cost.

    SciTech Connect (OSTI)

    Not Available

    1993-06-30

    This model description establishes the revenues, expenses incentives and avoided costs of Operation of a Natural Gas-Fired Fuel Cell-Based. Fuel is the major element of the cost of operation of a natural gas-fired fuel cell. Forecasts of the change in the price of this commodity a re an important consideration in the ownership of an energy conversion system. Differences between forecasts, the interests of the forecaster or geographical areas can all have significant effects on imputed fuel costs. There is less effect on judgments made on the feasibility of an energy conversion system since changes in fuel price can affect the cost of operation of the alternatives to the fuel cell in a similar fashion. The forecasts used in this model are only intended to provide the potential owner or operator with the means to examine alternate future scenarios. The operations model computes operating costs of a system suitable for a large condominium complex or a residential institution such as a hotel, boarding school or prison. The user may also select large office buildings that are characterized by 12 to 16 hours per day of operation or industrial users with a steady demand for thermal and electrical energy around the clock.

  9. Science on the Hill: The forecast calls for flu

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

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

  10. Forecast Change

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

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (cents/kWh) 12.06 12.09 12.58 13.04 12.95 12.84 -0.9% Expenditures $415 $405 $393 $396 $408 $424 3.9% New England Usage (kWh) 2,122 2,188 2,173 1,930 1,992 2,082 4.5% Price (cents/kWh) 15.85 15.50 16.04 17.63 18.64 18.37 -1.5% Expenditures $336 $339 $348 $340 $371 $382 3.0% Mid-Atlantic Usage (kWh) 2,531 2,548 2,447 2,234 2,371 2,497 5.3% Price (cents/kWh) 16.39 15.63

  11. 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, ...

  12. ASSESSING AND FORECASTING, BY PLAY, NATURAL GAS ULTIMATE RECOVERY GROWTH AND QUANTIFYING THE ROLE OF TECHNOLOGY ADVANCEMENTS IN THE TEXAS GULF COAST BASIN AND EAST TEXAS

    SciTech Connect (OSTI)

    William L. Fisher; Eugene M. Kim

    2000-12-01

    A detailed natural gas ultimate recovery growth (URG) analysis of the Texas Gulf Coast Basin and East Texas has been undertaken. The key to such analysis was determined to be the disaggregation of the resource base to the play level. A play is defined as a conceptual geologic unit having one or more reservoirs that can be genetically related on the basis of depositional origin of the reservoir, structural or trap style, source rocks and hydrocarbon generation, migration mechanism, seals for entrapment, and type of hydrocarbon produced. Plays are the geologically homogeneous subdivision of the universe of petroleum pools within a basin. Therefore, individual plays have unique geological features that can be used as a conceptual model that incorporates geologic processes and depositional environments to explain the distribution of petroleum. Play disaggregation revealed important URG trends for the major natural gas fields in the Texas Gulf Coast Basin and East Texas. Although significant growth and future potential were observed for the major fields, important URG trends were masked by total, aggregated analysis based on a broad geological province. When disaggregated by plays, significant growth and future potential were displayed for plays that were associated with relatively recently discovered fields, deeper reservoir depths, high structural complexities due to fault compartmentalization, reservoirs designated as tight gas/low-permeability, and high initial reservoir pressures. Continued technology applications and advancements are crucial in achieving URG potential in these plays.

  13. Colorado Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Colorado Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 11 10 9 8 9 8 8 9 10 1990's 10 12 13 14 15 18 17 21 18 19 2000's 21 22 23 24 26 26 26 27 38 48 2010's 58 63 57 52 61 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015

  14. Kansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 29 1980's 26 24 14 17 20 20 19 19 18 18 1990's 17 26 27 27 29 29 31 24 28 30 2000's 28 26 25 22 22 19 18 18 18 16 2010's 16 16 15 11 12 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  15. Lower 48 States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Lower 48 States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 579 1980's 572 580 564 568 597 580 566 569 572 549 1990's 556 577 599 608 608 616 655 655 631 649 2000's 688 655 657 593 627 597 615 637 654 701 2010's 734 773 854 920 1,107 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  16. Michigan Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Michigan Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 11 1980's 12 12 11 10 10 8 9 8 8 8 1990's 6 6 6 5 5 5 5 4 4 4 2000's 4 4 3 3 3 3 2 3 3 2 2010's 3 2 2 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  17. Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 8 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 1 1 1 1 0 2010's 0 0 0 1 24 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  18. North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 3 4 4 5 6 6 5 6 5 5 1990's 5 5 5 5 4 4 4 4 4 4 2000's 5 5 5 4 5 5 6 6 6 8 2010's 9 11 19 26 36 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  19. Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 59 1980's 62 65 67 70 75 77 76 76 79 73 1990's 75 76 77 77 76 70 74 71 69 70 2000's 69 66 61 59 64 65 67 69 74 77 2010's 82 88 96 99 117 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  20. Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Liquids Lease Condensate, Reserves Based Production (Million Barrels) Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 4 1980's 5 11 8 20 26 31 31 28 25 23 1990's 16 17 15 14 14 9 8 8 8 14 2000's 7 11 11 10 10 12 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  1. West Virginia Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) West Virginia Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 6 6 5 5 6 7 6 6 7 7 1990's 7 7 7 7 6 4 4 4 4 4 2000's 6 6 6 4 4 4 5 5 5 5 2010's 5 5 8 10 41 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

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

    SciTech Connect (OSTI)

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  3. California Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) California Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 1991 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 248,389 248,389 248,389 248,389 1992 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 250,206 1993 250,206 250,206

  4. Tennessee Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Tennessee Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 340 340 340 340 340 340 340 340 340 340 340 340 1999 340 340 340 340 340 340 340 340 340 340 340 340 2000 340 340 340 340 340 340 340 340 340 340 340 340 2001 340 340 340 340 340 340 340 340 340 340 340 340 2002 340 340 340 340 340 340 340 340 340 340 340 340 2003 340 340 340 340 340 340 340

  5. Mountain Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) Mountain Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 421,075 420,615 419,767 420,250 420,606 420,353 422,402 422,811 423,525 423,507 423,501 421,314 2015 421,311 421,304 423,663 423,684 423,689 423,689 423,690 423,699 423,698 423,690 425,847 426,205 2016 426,151 426,075 426,050 426,104 426,133 426,165 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  6. Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 0 0 0 0 0 0 0 0 0 2000's 0 1 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  7. Florida Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Florida Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 5 4 3 2 2 1 1 1 1990's 1 1 1 1 1 1 1 1 1 1 2000's 1 1 1 1 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  8. Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 3 1980's 3 2 3 2 2 2 2 1 2 1 1990's 1 2 2 2 3 3 3 3 3 3 2000's 2 3 3 3 3 3 3 3 3 4 2010's 5 4 5 5 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  9. Montana Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Montana Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 1 1 1 1 0 0 0 0 0 2000's 0 0 1 1 1 1 1 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  10. Louisiana Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Louisiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 1991 264,324 264,324 264,304 264,497 265,121 265,448 265,816 266,390 262,350 266,030 267,245 267,245 1992 267,245 267,245 265,296 262,230 262,454 263,788 266,852 260,660 257,627 258,575 259,879 262,144 1993 261,841 255,035 251,684

  11. Formulation of cracking catalyst based on zeolite and natural clays

    SciTech Connect (OSTI)

    Aliev, R.R.; Lupina, M.I.

    1995-11-01

    Domestically manufactured cracking catalysts are based on a synthetic amorphous aluminosilicate matrix and Y zeolite. A multistage {open_quotes}gel{close_quotes} technology is used in manufacturing the catalysts. The process includes mixing solutions of sodium silicate and acidic aluminum sulfate, forming, syneresis, and activation of the beaded gel. In the manufacture of bead catalysts, the next steps in the process are washing, drying, and calcining; in the manufacture of microbead catalysts, the next steps are dispersion and formation of a hydrogel slurry, spray-drying, and calcining. The Y zeolite is either introduced into the alumina-silica sol in the stage of forming the beads, or introduced in the dispersion stage. With the aim of developing an active and selective cracking catalyst based on Y zeolite and natural clays, with improved physicomechanical properties, the authors carried out a series of studies, obtaining results that are set forth in the present article.

  12. Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 352,686 352,686 352,686 351,920 352,686 352,686 353,407 353,407 353,407 353,407 359,236 358,860 1991 349,459 348,204 334,029 335,229 353,405 349,188 350,902 352,314 353,617 354,010 353,179 355,754 1992 358,198 353,313 347,361 341,498 344,318 347,751 357,498 358,432 359,300 359,504 359,321 362,275 1993 362,222 358,438

  13. probabilistic energy production forecasts

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

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

  14. Wind Power Forecasting Data

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

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

  15. Forecasting Water Quality & Biodiversity

    Broader source: Energy.gov (indexed) [DOE]

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

  16. Wind Power Forecasting

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

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

  17. Intermediate future forecasting system

    SciTech Connect (OSTI)

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

    1983-12-01

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

  18. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

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

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

  19. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

    SciTech Connect (OSTI)

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

  20. NREL: Transmission Grid Integration - Forecasting

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

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

  1. Washington Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Washington Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,300 21,300 21,300 21,300 0 21,300 21,300 21,300 21,300 21,300 21,300 1991 21,300 21,300 21,300 21,300 21,300 21,300 21,300 21,300 21,300 18,800 18,800 18,800 1992 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 1993 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800

  2. Minnesota Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Minnesota Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1991 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1992 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1993 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1994 4,655 4,655

  3. Mississippi Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Mississippi Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 1991 47,530 47,483 47,483 47,483 47,483 47,868 48,150 48,150 48,150 48,150 48,150 48,150 1992 48,150 48,150 48,149 48,149 48,149 48,149 48,149 48,149 48,149 48,149 47,851 48,049 1993 48,039 48,049 48,049 48,049 47,792 48,049 48,049 48,049

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

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

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

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

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

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

  6. 2016 Solar Forecasting Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

    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 integration cost of high levels of solar energy into the electricity grid. This will help SunShot to assess current technology and practices in this field and identify the gaps and needs for further research.

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

    SciTech Connect (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 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.

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

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

    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

  9. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers.

    SciTech Connect (OSTI)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments. Biotechnol. Bioeng.

  10. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

    SciTech Connect (OSTI)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.

  11. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

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

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are onemore » of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.« less

  12. Today's Forecast: Improved Wind Predictions

    Broader source: Energy.gov [DOE]

    Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable.

  13. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more...

  14. 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

  15. Control method for mixed refrigerant based natural gas liquefier

    DOE Patents [OSTI]

    Kountz, Kenneth J.; Bishop, Patrick M.

    2003-01-01

    In a natural gas liquefaction system having a refrigerant storage circuit, a refrigerant circulation circuit in fluid communication with the refrigerant storage circuit, and a natural gas liquefaction circuit in thermal communication with the refrigerant circulation circuit, a method for liquefaction of natural gas in which pressure in the refrigerant circulation circuit is adjusted to below about 175 psig by exchange of refrigerant with the refrigerant storage circuit. A variable speed motor is started whereby operation of a compressor is initiated. The compressor is operated at full discharge capacity. Operation of an expansion valve is initiated whereby suction pressure at the suction pressure port of the compressor is maintained below about 30 psig and discharge pressure at the discharge pressure port of the compressor is maintained below about 350 psig. Refrigerant vapor is introduced from the refrigerant holding tank into the refrigerant circulation circuit until the suction pressure is reduced to below about 15 psig, after which flow of the refrigerant vapor from the refrigerant holding tank is terminated. Natural gas is then introduced into a natural gas liquefier, resulting in liquefaction of the natural gas.

  16. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

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

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  19. Alabama (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Alabama (with State Offshore) Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 182 1980's 193 167 158 166 152 143 139 132 130 130 1990's 122 110 118 103 91 72 67 59 50 50 2000's 46 32 29 27 21 30 15 21 14 16 2010's 18 19 18 14 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  20. Alaska (with Total Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Alaska (with Total Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 13 1980's 11 10 9 8 0 382 381 418 401 380 1990's 340 360 347 321 301 306 337 631 320 299 2000's 277 405 405 387 369 352 338 325 312 299 2010's 288 288 288 288 241 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  1. California (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) California (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 107 1980's 109 73 146 139 128 124 118 109 1990's 101 87 94 98 86 88 89 92 71 97 2000's 100 75 95 101 121 135 130 126 113 129 2010's 114 94 99 102 112 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  2. Louisiana (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Louisiana (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 400 287 301 294 294 1990's 324 321 317 260 281 430 381 261 234 281 2000's 241 204 186 183 167 191 176 191 201 231 2010's 216 192 189 212 243 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  3. Louisiana--North Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Louisiana--North Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 54 1980's 59 63 59 50 38 47 39 33 39 40 1990's 38 38 41 38 48 55 61 50 34 36 2000's 35 35 30 48 53 57 60 69 68 98 2010's 79 54 35 52 83 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  4. Louisiana--South Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Louisiana--South Onshore Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 413 1980's 273 291 258 289 225 222 220 235 228 215 1990's 249 242 229 201 214 359 284 199 187 222 2000's 178 128 119 100 87 103 94 97 78 90 2010's 113 94 134 144 145 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  5. Lower 48 Federal Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Lower 48 Federal Offshore Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 363 382 350 331 337 1990's 295 329 295 309 309 239 245 389 370 427 2000's 515 486 511 364 423 416 399 369 321 302 2010's 341 355 405 335 399 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  6. Mississippi (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Future Production (Million Barrels) Expected Future Production (Million Barrels) Mississippi (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 5 1980's 5 5 6 6 5 4 3 3 3 3 1990's 3 3 3 3 3 3 2 2 3 3 2000's 2 2 2 2 1 2 2 3 3 4 2010's 4 6 4 3 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  7. Utah and Wyoming Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Utah and Wyoming Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 280 1980's 294 363 381 483 577 681 700 701 932 704 1990's 641 580 497 458 440 503 639 680 600 531 2000's 858 782 806 756 765 710 686 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  8. New Mexico--East Natural Gas Plant Liquids, Reserves Based Production...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) New Mexico--East Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  9. New Mexico--West Natural Gas Plant Liquids, Reserves Based Production...

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

    Reserves Based Production (Million Barrels) New Mexico--West Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  10. California--State Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Feet) Marketed Production (Million Cubic Feet) California--State Offshore Natural Gas Marketed Production (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 7,211 6,467 7,204 5,664 5,975 6,947 6,763 6,500 2000's 6,885 6,823 6,909 6,087 6,803 6,617 6,652 7,200 6,975 5,832 2010's 5,120 4,760 5,051 5,470 5,961 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  11. Texas (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2,125 1980's 2,081 2,285 2,393 2,650 2,660 2,610 2,671 2,509 2,339 2,270 1990's 2,305 2,237 2,162 2,211 2,151 2,269 2,337 2,376 2,262 2,257 2000's 2,479 2,318 2,368 2,192 2,466 2,723 2,913 3,158 3,148 3,432 2010's 3,983 4,541 4,727 5,653

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

    SciTech Connect (OSTI)

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and

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

    SciTech Connect (OSTI)

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

    2015-10-30

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

  14. Study forecasts disappearance of conifers due to climate change

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

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

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

    SciTech Connect (OSTI)

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

    2015-08-05

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

  16. Natural Gas Weekly Update

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

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  17. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  19. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect (OSTI)

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

    2014-12-30

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

  20. Coal supply/demand, 1980 to 2000. Task 3. Resource applications industrialization system data base. Final review draft. [USA; forecasting 1980 to 2000; sector and regional analysis

    SciTech Connect (OSTI)

    Fournier, W.M.; Hasson, V.

    1980-10-10

    This report is a compilation of data and forecasts resulting from an analysis of the coal market and the factors influencing supply and demand. The analyses performed for the forecasts were made on an end-use-sector basis. The sectors analyzed are electric utility, industry demand for steam coal, industry demand for metallurgical coal, residential/commercial, coal demand for synfuel production, and exports. The purpose is to provide coal production and consumption forecasts that can be used to perform detailed, railroad company-specific coal transportation analyses. To make the data applicable for the subsequent transportation analyses, the forecasts have been made for each end-use sector on a regional basis. The supply regions are: Appalachia, East Interior, West Interior and Gulf, Northern Great Plains, and Mountain. The demand regions are the same as the nine Census Bureau regions. Coal production and consumption in the United States are projected to increase dramatically in the next 20 years due to increasing requirements for energy and the unavailability of other sources of energy to supply a substantial portion of this increase. Coal comprises 85 percent of the US recoverable fossil energy reserves and could be mined to supply the increasing energy demands of the US. The NTPSC study found that the additional traffic demands by 1985 may be met by the railways by the way of improved signalization, shorter block sections, centralized traffic control, and other modernization methods without providing for heavy line capacity works. But by 2000 the incremental traffic on some of the major corridors was projected to increase very significantly and is likely to call for special line capacity works involving heavy investment.

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

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

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

  2. Using Wikipedia to forecast diseases

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

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

  3. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

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

  4. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

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

  5. The forecast calls for flu

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

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

    Open Energy Info (EERE)

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

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

    SciTech Connect (OSTI)

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

    2010-04-20

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

  8. 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 ...

  9. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

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

  10. Project Profile: Forecasting and Influencing Technological Progress...

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

    Soft Costs Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar ...

  11. National Oceanic and Atmospheric Administration Provides Forecasting...

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

    ... will share their expertise with CLASIC and CHAPS forecasters and project leaders as they consult on the forecast that will determine the day's operations plan. -- Storm Prediction ...

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

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  13. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

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

    1986-01-01

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

  14. Science on Tap - Forecasting illness

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

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

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

    SciTech Connect (OSTI)

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

    2011-12-06

    (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  16. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

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

    2014-11-13

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

  17. Global disease monitoring and forecasting with Wikipedia

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

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

    2014-11-13

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

  18. 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-2016-07-20.xlsx (72.85 KB) More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Assessment Report: OAS-V-15-01

  19. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  20. Holographic imaging based on time-domain data of natural-fiber-containing materials

    DOE Patents [OSTI]

    Bunch, Kyle J.; McMakin, Douglas L.

    2012-09-04

    Methods and apparatuses for imaging material properties in natural-fiber-containing materials can utilize time-domain data. In particular, images can be constructed that provide quantified measures of localized moisture content. For example, one or more antennas and at least one transceiver can be configured to collect time-domain data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz, according to a time-domain impulse function. A computing device is configured to transform the time-domain data to frequency-domain data, to apply a synthetic imaging algorithm for constructing a three-dimensional image of the natural-fiber-containing materials, and to provide a quantified measure of localized moisture content based on a pre-determined correlation of moisture content to frequency-domain data.

  1. Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western United States based on Weather Research and Forecasting chemistry and regional climate simulations

    SciTech Connect (OSTI)

    Qian, Yun; Gustafson, William I.; Leung, Lai-Yung R.; Ghan, Steven J.

    2009-02-14

    Radiative forcing induced by soot on snow is a major anthropogenic forcing affecting the global climate. However, it is uncertain how the soot-induced snow albedo perturbation affects regional snowpack and the hydrological cycle. In this study we simulated the deposition of soot aerosol on snow and investigated the resulting impact on snowpack and the surface water budget in the western United States. A yearlong simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine an annual budget of soot deposition, followed by two regional climate simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the net solar radiation flux at the surface during late winter to early spring, increase the surface air temperature, reduce snow water equivalent amount, and lead to reduced snow accumulation and less spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow covered regions. Our simulations indicate that the change of maximum snow albedo induced by soot on snow contributes to 60% of the net albedo reduction over the central Rockies. Snowpack reduction accounts for the additional 40%.

  2. 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 ...

  3. Natural language processing-based COTS software and related technologies survey.

    SciTech Connect (OSTI)

    Stickland, Michael G.; Conrad, Gregory N.; Eaton, Shelley M.

    2003-09-01

    Natural language processing-based knowledge management software, traditionally developed for security organizations, is now becoming commercially available. An informal survey was conducted to discover and examine current NLP and related technologies and potential applications for information retrieval, information extraction, summarization, categorization, terminology management, link analysis, and visualization for possible implementation at Sandia National Laboratories. This report documents our current understanding of the technologies, lists software vendors and their products, and identifies potential applications of these technologies.

  4. Picture of the Week: Forecasting Flu

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

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

  5. ,"U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)"

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

    - Base Gas (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)",1,"Monthly","6/2016" ,"Release Date:","08/31/2016" ,"Next Release Date:","09/30/2016" ,"Excel File

  6. Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 0 1990's 0 0 1 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  7. Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 4 4 9 9 9 10 10 10 9 8 1990's 8 7 8 8 7 8 8 7 6 7 2000's 7 7 9 9 9 10 10 10 10 10 2010's 9 9 9 10 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  8. U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels)

    Gasoline and Diesel Fuel Update (EIA)

    Based Production (Million Barrels) U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 580 1980's 572 580 564 568 597 585 569 585 592 566 1990's 574 601 626 635 634 646 688 690 655 697 2000's 710 675 677 611 645 614 629 650 667 714 2010's 745 784 865 931 1,124 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  9. U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production

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

    (Million Barrels) Based Production (Million Barrels) U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 147 1980's 159 161 157 157 179 168 169 162 162 165 1990's 158 153 147 153 157 145 162 174 178 199 2000's 208 215 207 191 182 174 182 181 173 178 2010's 224 231 274 311 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  10. The Value of Wind Power Forecasting

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

    ... day-ahead wind generation forecasts yields an average of 195M savings in annual operating costs. Figure 6 shows how operating cost savings vary with improvements in forecasting. ...

  11. EIA lowers forecast for summer gasoline prices

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

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

  12. Natural gas resource data base for the United States (1987). Final report, June-December 1987

    SciTech Connect (OSTI)

    Kent, H.C.; Finney, J.J.

    1988-02-01

    This data base gives a detailed summary of the estimated potential resources of natural gas in the United States, including postulated depth distributions, field sizes, well recoveries and success rates. The study (an expansion on the 1986 resource estimates of the Potential Gas Committee) analyzed the distribution and characteristics of the resource potential estimated to occur in the onshore geologic provinces of the lower 48 states, as well as the resources beneath the continental shelf and slope offshore from Louisiana and Texas. The areas that hold the greatest potential for future natural gas exploration and development include the Atlantic, Gulf Coast, Mid-Continent and Rocky Mountain areas, which contain approximately 92% of the estimated undiscovered resources. The results of the study are intended to be used to assist in making cost determinations which can be utilized in the development of supply models and in planning.

  13. Wind Forecasting Improvement Project | Department of Energy

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

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

  14. UPF Forecast | Y-12 National Security Complex

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

    Subcontracting / Subcontracting Forecasts / UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be revised or cancelled, depending on program budget funding or departmental needs. If you have questions or would like to express an interest in any of the opportunities listed below, contact UPF Procurement. Descriptiona Methodb NAICS Est. Dollar Range RFP release/ Award datec Buyer/ Phone Commodities

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

    SciTech Connect (OSTI)

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

    2015-06-23

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

  16. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

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

    2015-06-23

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

  17. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

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

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

    2015-06-23

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

  18. U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million Cubic

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

    Feet) - Base Gas (Million Cubic Feet) U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 4,310,511 4,299,144 4,304,605 4,305,250 4,311,328 4,310,801 4,313,863 4,313,462 4,311,826 4,311,686 4,309,746 4,316,503 1995 4,311,142 4,313,967 4,307,833 4,306,142 4,338,851 4,351,366 4,285,411 4,285,137 4,286,773 4,282,697 4,286,509 4,289,504 1996 4,291,262 4,285,701 4,227,609 4,249,339 4,268,329 4,277,305 4,275,962

  19. Pacific Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Pacific Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 258,736 258,541 258,456 258,619 258,736 258,736 258,736 258,736 258,736 259,036 259,036 259,036 2015 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,331 259,331 259,331 2016 259,331 259,331 259,331 259,331 259,331 259,331 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  20. AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 2,700,245 2,697,308 2,696,823 2,698,489 2,699,802 2,699,840 2,700,331 2,701,227 2,701,285 2,702,703 2,702,571 2,703,149 1995 2,699,674 2,699,575 2,696,880 2,695,400 2,726,268 2,726,255 2,668,312 2,671,818 2,672,399 2,672,258 2,671,362 2,672,808 1996 2,670,906 2,670,070 2,646,056 2,654,836

  1. AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 1,039,864 1,032,160 1,033,297 1,032,517 1,037,294 1,037,338 1,038,940 1,036,193 1,037,422 1,035,931 1,035,050 1,043,103 1995 1,051,669 1,054,584 1,051,120 1,051,697 1,052,949 1,062,613 1,058,260 1,054,218 1,054,870 1,051,687 1,056,704 1,060,588 1996 1,067,220 1,062,343 1,027,692 1,040,511 1,055,164

  2. AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 607,596 607,629 612,749 613,849 614,562 614,534 615,937 617,412 614,732 615,667 615,712 613,840 1995 613,874 613,874 613,898 613,357 613,699 616,811 613,151 613,413 613,504 613,752 613,514 615,837 1996 616,124 616,330 616,610 617,033 616,902 617,159 616,822 615,039 616,632 616,849 617,148

  3. Alaska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Alaska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 7,622 14,197 14,197 14,196 14,196 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2014 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2015 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2016 14,197 14,197 14,197 14,197 14,197 14,197 - = No Data Reported; -- =

  4. Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 571,959 571,959 571,959 571,959 572,425 572,423 572,421 572,421 572,419 572,419 573,776 577,424 1991 577,418 577,418 577,418 568,227 568,178 568,160 568,158 568,157 568,157 568,158 568,158 568,158 1992 576,257 576,227 576,227 576,227 576,227 576,227 576,227 576,234 576,234 577,202 577,202 579,715 1993 620,575 620,856 620,777 621,051

  5. Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 153,933 153,933 153,933 153,933 153,920 153,919 153,917 153,917 153,916 153,916 153,916 153,916 1991 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 1992 154,574 154,574 154,574 154,161 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 1993 200,700 199,929 199,482 200,679

  6. Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 191,402 190,669 1991 188,597 191,203 191,198 191,198 191,126 192,733 192,736 192,798 192,798 192,805 192,563 192,563 1992 190,943 190,963 190,914 190,591 190,765 190,714 190,611 190,578 190,606 190,643 189,320 186,399 1993 184,254 180,510 181,152 186,315

  7. Kentucky Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Kentucky Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 1991 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 1992 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 1993 105,430 105,394 105,392 105,446

  8. Texas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Texas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 134,707 134,707 134,707 160,665 160,663 160,663 160,663 160,697 160,697 160,697 160,697 160,697 1991 165,309 165,039 165,039 164,407 164,407 164,407 164,407 168,776 169,114 169,114 170,183 170,183 1992 170,483 170,633 170,631 170,630 170,630 170,631 170,630 170,630 170,630 171,139 171,359 171,360 1993 248,991 239,554 235,259 239,554

  9. Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,505,645 1,504,979 1,497,798 1,502,556 1,498,128 1,498,610 1,498,610 1,498,610 1,498,887 1,496,791 1,496,848 1,497,021 2015 1,497,256 1,496,957 1,496,400 1,495,858 1,495,743 1,496,917 1,496,915 1,489,324 1,490,195 1,488,404 1,488,432 1,488,593 2016 1,488,560 1,488,552 1,487,836 1,487,397 1,488,033 1,489,057 - = No

  10. West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 310,640 310,640 310,640 310,640 310,640 310,640 311,765 311,765 311,765 311,765 312,670 309,331 1991 331,618 332,229 331,898 332,278 332,288 332,288 331,275 332,283 332,269 332,264 332,259 332,070 1992 336,854 336,689 335,303 335,602 335,965 336,044 336,309 336,528 336,527 336,526 336,525 305,441 1993 305,478 304,578

  11. Michigan Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Michigan Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 395,529 395,529 395,529 395,529 395,529 395,180 396,744 396,491 396,293 396,099 395,934 395,790 1991 394,527 393,885 392,506 394,146 413,930 413,764 413,617 413,530 413,468 413,390 413,242 413,275 1992 413,430 413,426 413,356 413,302 413,258 413,224 413,182 413,226 413,225 413,194 413,136 413,069 1993 413,736 413,707 410,316 411,038

  12. Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 109,573 109,573 109,573 109,573 112,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1991 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1992 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 1993 169,892 169,892 169,892 169,892

  13. East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,113,096 1,112,811 1,110,723 1,111,592 1,111,730 1,113,003 1,113,262 1,113,458 1,113,383 1,113,607 1,113,589 1,113,356 2015 1,111,081 1,110,574 1,112,593 1,112,719 1,113,055 1,114,216 1,119,070 1,118,884 1,119,057 1,119,175 1,119,046 1,119,011 2016 1,118,751 1,118,483 1,111,752 1,111,114 1,111,399 1,112,116 - = No Data

  14. Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 336,243 331,979 1991 357,743 357,743 357,743 357,674 351,476 357,598 357,566 357,743 357,743 357,743 357,743 357,743 1992 357,689 357,689 356,333 355,927 356,779 356,747 356,880 357,810 357,808 357,856 357,856 358,966 1993 358,966 357,823 354,044 354,688

  15. Oklahoma Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Oklahoma Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 167,385 163,458 167,385 163,458 167,385 167,385 167,385 167,385 167,385 167,385 173,097 172,762 1991 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 1992 172,757 172,757 172,368 172,573 172,757 172,757 172,757 172,757 172,757 172,757 176,765 176,765 1993 228,593 227,252 227,560 226,942

  16. South Central Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) South Central Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,050,691 1,049,083 1,049,047 1,049,443 1,049,496 1,053,249 1,054,073 1,058,479 1,060,363 1,060,181 1,060,298 1,059,866 2015 1,057,760 1,057,807 1,054,816 1,054,786 1,057,044 1,058,973 1,059,103 1,058,987 1,058,721 1,060,652 1,061,199 1,055,894 2016 1,054,232 1,054,693 1,053,049 1,057,433 1,058,680

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

    SciTech Connect (OSTI)

    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.

  18. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

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

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

    SciTech Connect (OSTI)

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

    2010-09-01

    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.

  20. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

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

    SciTech Connect (OSTI)

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

    2014-09-01

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

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

    SciTech Connect (OSTI)

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

    2013-05-01

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

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

    SciTech Connect (OSTI)

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

    2014-11-01

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

  4. Supply Forecast and Analysis (SFA)

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

    Matthew Langholtz Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies Office Goal Statement * Provide timely and credible estimates of feedstock supplies and prices to support - the development of a bioeconomy; feedstock demand analysis of EISA, RFS2, and RPS mandates - the data and analysis of other projects in Analysis and Sustainability, Feedstock Supply and Logistics,

  5. World Natural Gas Model

    Energy Science and Technology Software Center (OSTI)

    1994-12-01

    RAMSGAS, the Research and Development Analysis Modeling System World Natural Gas Model, was developed to support planning of unconventional gaseoues fuels research and development. The model is a scenario analysis tool that can simulate the penetration of unconventional gas into world markets for oil and gas. Given a set of parameter values, the model estimates the natural gas supply and demand for the world for the period from 1980 to 2030. RAMSGAS is based onmore » a supply/demand framwork and also accounts for the non-renewable nature of gas resources. The model has three fundamental components: a demand module, a wellhead production cost module, and a supply/demand interface module. The demand for gas is a product of total demand for oil and gas in each of 9 demand regions and the gas share. Demand for oil and gas is forecast from the base year of 1980 through 2030 for each demand region, based on energy growth rates and price-induced conservation. For each of 11 conventional and 19 unconventional gas supply regions, wellhead production costs are calculated. To these are added transportation and distribution costs estimates associated with moving gas from the supply region to each of the demand regions and any economic rents. Based on a weighted average of these costs and the world price of oil, fuel shares for gas and oil are computed for each demand region. The gas demand is the gas fuel share multiplied by the total demand for oil plus gas. This demand is then met from the available supply regions in inverse proportion to the cost of gas from each region. The user has almost complete control over the cost estimates for each unconventional gas source in each year and thus can compare contributions from unconventional resources under different cost/price/demand scenarios.« less

  6. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect (OSTI)

    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)

  7. Stress state and nature of failure of detonation coatings based on alumina

    SciTech Connect (OSTI)

    Anisimov, M.I.; Galeev, I.M.; Gol`dfain, V.N.

    1995-03-01

    Detonation coatings based on alumina are used on an increasing scale in industry for increasing the corrosion and wear resistance of materials. The physicomechanical and service characteristics of coatings are determined by the stress state . In this work, investigations were carried out into the distribution of residual phase stresses in the layer and the nature of failure of coatings in combined deformation with the substrate at T = 600{degrees}C. Coatings of electrocorundum powder of 24A grade with a grain size of M40 were deposited on a substrate of KhN78 alloy. In certain cases, an intermediate layer of PN85Yu15 powder was deposited on the substrate prior to spraying. Spraying was carried out in ADK Prometei equipment using an oxygen-acetylene mixture. The thickness of the coatings was 0.4-0.5 mm.

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

    SciTech Connect (OSTI)

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

    2010-01-01

    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.

  9. ARM - CARES - Tracer Forecast for CARES

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

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

  10. LED Lighting Forecast | Department of Energy

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

    Publications » Market Studies » LED Lighting Forecast LED Lighting Forecast 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. With declining costs and improving performance, LED products have been seeing increased adoption for general illumination applications. This is a positive development in terms of energy consumption, as LEDs use significantly

  11. Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1

    SciTech Connect (OSTI)

    Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

    1994-05-01

    This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

  12. Alabama Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Alabama Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 880 880 880 880 880 880 880 880 880 880 880 880 1996 880 650 650 650 880 1,071 1,083 1,088 1,190 1,190 1,190 1,190 1997 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1998 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1999 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190

  13. Arkansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Arkansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 1991 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 1992 19,202 19,202 19,112 19,021 19,007 19,007 19,007 19,007 19,007 18,887 18,748 18,615 1993 18,607 18,523 18,484 18,472 18,156 17,897 17,888 17,888 17,888

  14. Colorado Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Colorado Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 45,393 1991 45,258 45,263 45,263 45,252 45,252 45,252 45,252 45,252 45,252 45,252 45,252 45,252 1992 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 1993 45,210 45,210 45,210 45,210 45,210 45,210 45,210 45,210 45,210

  15. Indiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Indiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 74,572 74,572 74,558 74,558 74,558 74,565 74,572 74,572 74,572 74,572 74,572 74,729 1991 74,588 70,962 70,956 70,856 70,892 70,956 70,957 70,962 70,962 81,536 71,050 71,050 1992 71,050 71,050 71,005 70,920 71,043 71,050 71,050 71,050 71,050 71,139 71,139 71,139 1993 71,407 71,390 71,377 71,255 71,338 71,407 71,407 71,407 71,407 71,453

  16. Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,944 46,944 46,944 46,944 48,144 46,944 49,350 50,457 51,244 51,397 42,464 42,464 1991 42,454 42,454 44,628 44,342 45,120 49,179 51,258 49,908 48,558 47,678 47,118 47,118 1992 47,118 47,739 48,770 49,900 50,972 52,189 53,369 54,688 55,934 57,208 49,578 49,736 1993 49,736 49,742 49,749 50,238 51,803 51,028 52,377 53,704 54,973 54,847

  17. Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 2,345 2,371 2,369 2,366 2,361 2,356 2,353 2,347 2,289 2,382 2,436 2,433 1999 2,485 2,478 2,470 2,467 2,464 2,459 2,437 2,450 2,443 2,434 2,424 2,410 2000 2,400 2,441 2,475 2,394 2,094 2,094 2,094 2,152 2,134 2,192 2,192 2,192 2001 2,192 2,312 2,312 2,312 2,312 2,312 2,312 2,312 2,312 2,362 2,362 2,372

  18. Wyoming Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Wyoming Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 31,205 31,205 31,205 31,205 31,353 31,205 31,501 31,638 31,735 31,754 30,652 30,652 1991 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 1992 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,127 59,382 1993 59,382 59,382 59,382 59,382 59,382 59,382 59,382 59,427 59,427 59,427

  19. Maryland Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Maryland Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1991 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1992 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1993 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677

  20. Missouri Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Missouri Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1991 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1992 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1993 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600

  1. Nebraska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Nebraska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1991 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1992 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1993 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312

  2. Oregon Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Oregon Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1991 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1992 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1993 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1994 3,291 3,291 3,291 4,896 4,896

  3. Study on systems based on coal and natural gas for producing dimethyl ether

    SciTech Connect (OSTI)

    Zhou, L.; Hu, S.Y.; Chen, D.J.; Li, Y.R.; Zhu, B.; Jin, Y.

    2009-04-15

    China is a coal-dependent country and will remain so for a long time. Dimethyl ether (DME), a potential substitute for liquid fuel, is a kind of clean diesel motor fuel. The production of DME from coal is meaningful and is studied in this article. Considering the C/H ratios of coal and natural gas (NG), the cofeed (coal and NG) system (CFS), which does not contain the water gas shift process, is studied. It can reduce CO{sub 2} emission and increase the conversion rate of carbon, producing more DME. The CFS is simulated and compared with the coal-based and NG-based systems with different recycling ratios. The part of the exhaust gas that is not recycled is burned, producing electricity. On the basis of the simulation results, the thermal efficiency, economic index, and CO{sub 2} emission ratio are calculated separately. The CFS with a 100% recycling ratio has the best comprehensive evaluation index, while the energy, economy, and environment were considered at the same time.

  4. NREL: Resource Assessment and Forecasting Home Page

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

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

    Broader source: Energy.gov (indexed) [DOE]

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

  6. Forecast and Funding Arrangements - Hanford Site

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

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

  7. NREL: Resource Assessment and Forecasting - Webmaster

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

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

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

    Broader source: Energy.gov (indexed) [DOE]

    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 ...

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

    Broader source: Energy.gov (indexed) [DOE]

    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 ...

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

    SciTech Connect (OSTI)

    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.

  11. Natural Gas Transmission and Distribution Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the archived version of the Natural Gas Transmission and Distribution Model that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 2014.

  12. Characterization and testing of amidoxime-based adsorbent materials to extract uranium from natural seawater

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

    Kuo, Li-Jung; Janke, Christopher James; Wood, Jordana; Strivens, Jonathan E.; Gill, Gary

    2015-11-19

    Extraction of uranium (U) from seawater for use as a nuclear fuel is a significant challenge due to the low concentration of U in seawater (~3.3 ppb) and difficulties to selectively extract U from the background of major and trace elements in seawater. The Pacific Northwest National Laboratory (PNNL) s Marine Sciences Laboratory (MSL) has been serving as a marine test site for determining performance characteristics (adsorption capacity, adsorption kinetics, and selectivity) of novel amidoxime-based polymeric adsorbents developed at Oak Ridge National Laboratory (ORNL) under natural seawater exposure conditions. This report describes the performance of three formulations (38H, AF1, AI8)more » of amidoxime-based polymeric adsorbent produced at ORNL in MSL s ambient seawater testing facility. The adsorbents were produced in two forms, fibrous material (40-100 mg samples) and braided material (5-10 g samples), exposed to natural seawater using flow-through columns and recirculating flumes. All three formulations demonstrated high 56 day uranium adsorption capacity (>3 gU/kg adsorbent). The AF1 formulation had the best uranium adsorption performance, with 56-day capacity of 3.9 g U/kg adsorbent, saturation capacity of 5.4 g U/kg adsorbent, and ~25 days half-saturation time. The two exposure methods, flow-through columns and flumes were demonstrated to produce similar performance results, providing confidence that the test methods were reliable, that scaling up from 10 s of mg quantities of exposure in flow-through columns to gram quantities in flumes produced similar results, and that the manufacturing process produces a homogenous adsorbent. Adsorption kinetics appear to be element specific, with half-saturation times ranging from minutes for the major cations in seawater to 8-10weeks for V and Fe. Reducing the exposure time provides a potential pathway to improve the adsorption capacity of U by reducing the V/U ratio on the adsorbent.« less

  13. Pollutant Exposures from Natural Gas Cooking Burners: A Simulation-Based Assessment for Southern California

    SciTech Connect (OSTI)

    Logue, Jennifer M.; Klepeis, Neil E.; Lobscheid, Agnes B.; Singer, Brett C.

    2014-06-01

    Residential natural gas cooking burners (NGCBs) can emit substantial quantities of pollutants and they are typically used without venting. The objective of this study is to quantify pollutant concentrations and occupant exposures resulting from NGCB use in California homes. A mass balance model was applied to estimate time-dependent pollutant concentrations throughout homes and the "exposure concentrations" experienced by individual occupants. The model was applied to estimate nitrogen dioxide (NO{sub 2}), carbon monoxide (CO), and formaldehyde (HCHO) concentrations for one week each in summer and winter for a representative sample of Southern California homes. The model simulated pollutant emissions from NGCBs, NO{sub 2} and CO entry from outdoors, dilution throughout the home, and removal by ventilation and deposition. Residence characteristics and outdoor concentrations of CO and NO{sub 2} were obtained from available databases. Ventilation rates, occupancy patterns, and burner use were inferred from household characteristics. Proximity to the burner(s) and the benefits of using venting range hoods were also explored. Replicate model executions using independently generated sets of stochastic variable values yielded estimated pollutant concentration distributions with geometric means varying less than 10%. The simulation model estimates that in homes using NGCBs without coincident use of venting range hoods, 62%, 9%, and 53% of occupants are routinely exposed to NO{sub 2}, CO, and HCHO levels that exceed acute health-based standards and guidelines. NGCB use increased the sample median of the highest simulated 1-hr indoor concentrations by 100, 3000, and 20 ppb for NO{sub 2}, CO, and HCHO, respectively. Reducing pollutant exposures from NGCBs should be a public health priority. Simulation results suggest that regular use of even moderately effective venting range hoods would dramatically reduce the percentage of homes in which concentrations exceed health-based

  14. Efficient Use of Natural Gas Based Fuels in Heavy-Duty Engines

    Office of Energy Efficiency and Renewable Energy (EERE)

    Natural gas and other liquid feedstocks for transportation fuels are compared for use in a dual-fuel engine. Benefits include economic stability, national security, environment, and cost.

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

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

  16. Computerized map-based information management system for natural resource management

    SciTech Connect (OSTI)

    Miller, K.

    1995-12-01

    Federal agencies, states and resource managers have control and stewardship responsibility over a significant inventory of natural resources. A number of federal regulations require the review, protection and preservation of natural resource protection. Examples of such actions include the reauthorization of the Clean Water Act and the modification of the National Contingency Plan to incorporate the requirements of the Oil Pollution Act of 1990. To successfully preserve conserve and restore natural resources on federal reservations, and state and private lands, and to comply with Federal regulations designed to protect natural resources located on their sites, and the type of information on these resources required by environmental regulations. This paper presents an approach using a computerized, graphical information management system to catalogue and track data for the management of natural resources under Federal and state regulations, and for promoting resource conservation, preservation and restoration. The system is designed for use by Federal facility resource managers both for the day-to-day management of resources under their control, and for the longer-term management of larger initiatives, including restoration of significant or endangered resources, participation in regional stewardship efforts, and general ecosystem management. The system will be valuable for conducting natural resource baseline inventories an implementing resource management plans on lands other than those controlled by the Federal government as well. The system can provide a method for coordinating the type of natural resource information required by major federal environmental regulations--thereby providing a cost-effective means for managing natural resource information.

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

    Broader source: All U.S. Department of Energy (DOE) Office 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. Study forecasts disappearance of conifers due to climate change

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

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

  19. 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 ...

  20. Technology-Based Oil and Natural Gas Plays: Shale Shock! Could There Be Billions in the Bakken?

    Reports and Publications (EIA)

    2006-01-01

    This report presents information about the Bakken Formation of the Williston Basin: its location, production, geology, resources, proved reserves, and the technology being used for development. This is the first in a series intending to share information about technology-based oil and natural gas plays.

  1. STEO December 2012 - natural gas production

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

    2012 natural gas production seen at record 69 billion cubic feet per day U.S. natural gas production is expected to increase 4.5 percent this year to a record 69 billion cubic feet per day, according to the new monthly energy forecast from the U.S. Energy Information Administration. A big portion of that natural gas is going to the U.S. electric power sector, which is generating more electricity from gas in place of coal. Consumption of natural gas for power generation this year is forecast to

  2. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

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

  3. Coal Fired Power Generation Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  4. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  7. Flood Forecasting in River System Using ANFIS

    SciTech Connect (OSTI)

    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.

  8. energy data + forecasting | OpenEI Community

    Open Energy Info (EERE)

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

  9. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  10. Accounting for fuel price risk when comparing renewable togas-fired generation: the role of forward natural gas prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2004-07-17

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.

  11. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01

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

  12. Calif--Coastal Region Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Liquids Lease Condensate, Proved Reserves (Million Barrels) Calif--Coastal Region Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 1 1 0 0 0 0 1990's 0 1 1 2 2 1 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 3 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  13. Texas--RRC District 1 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 1 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 16 1980's 18 20 24 35 33 33 30 22 23 15 1990's 20 23 24 23 23 23 44 46 32 161 2000's 49 35 34 24 31 31 32 43 44 87 2010's 163 158 197 233 343 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  14. Texas--RRC District 10 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 10 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 356 1980's 350 349 376 397 425 416 411 402 351 331 1990's 318 346 327 316 305 343 323 372 342 191 2000's 191 311 326 315 373 367 396 458 473 494 2010's 566 578 522 481 598 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  15. Texas--RRC District 2 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 2 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 19 1980's 16 20 23 26 22 24 20 32 25 16 1990's 17 14 14 14 12 11 8 12 10 12 2000's 13 14 11 13 15 19 16 17 17 15 2010's 47 229 506 594 706 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  16. Texas--RRC District 3 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 3 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 54 1980's 52 51 53 57 53 49 53 75 58 73 1990's 49 48 39 57 54 68 79 116 77 74 2000's 69 82 71 72 72 78 75 128 65 74 2010's 75 76 81 63 67 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Texas--RRC District 4 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 4 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 76 1980's 75 77 85 80 87 86 84 80 74 72 1990's 71 69 65 65 70 70 82 86 96 122 2000's 90 97 91 85 73 71 87 77 79 74 2010's 96 202 181 228 223 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  18. Texas--RRC District 5 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 5 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 24 1980's 32 42 44 61 61 62 73 76 72 65 1990's 61 53 55 50 50 47 48 31 31 24 2000's 24 43 39 40 44 40 42 50 126 192 2010's 225 237 214 183 193 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  19. Texas--RRC District 6 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 6 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 228 1980's 268 259 232 280 253 247 224 213 210 212 1990's 195 195 205 202 218 223 242 221 235 182 2000's 182 215 213 195 233 264 279 324 318 330 2010's 369 360 269 376 387 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  20. Texas--RRC District 7B Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 7B Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 62 1980's 82 99 99 129 103 101 106 90 95 71 1990's 74 81 67 73 61 69 64 57 48 34 2000's 34 28 24 31 42 89 131 200 269 326 2010's 359 416 295 332 312 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  1. Texas--RRC District 7C Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 7C Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 168 1980's 120 172 184 204 219 242 232 231 226 225 1990's 234 218 266 250 241 255 285 309 266 291 2000's 291 271 326 319 365 391 404 464 402 412 2010's 465 549 524 438 473 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  2. Texas--RRC District 8 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 8 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 452 1980's 452 498 554 650 662 646 697 623 530 542 1990's 545 466 426 430 398 432 417 447 479 479 2000's 479 504 488 484 487 559 547 525 524 536 2010's 618 689 802 830 1,240 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  3. Texas--RRC District 8A Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 8A Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 350 1980's 289 335 296 262 282 282 331 307 325 332 1990's 353 333 257 297 267 284 262 290 226 222 2000's 222 250 180 163 197 248 231 260 194 201 2010's 230 239 242 239 245 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  4. Texas--RRC District 9 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 9 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 75 1980's 81 81 111 115 113 106 112 107 102 90 1990's 100 96 89 88 94 90 116 96 91 156 2000's 156 182 229 228 228 276 372 347 348 419 2010's 488 552 542 578 662 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. Texas--State Offshore Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Marketed Production (Million Cubic Feet) Texas--State Offshore Natural Gas Marketed Production (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 78,263 79,234 84,573 63,181 63,340 64,528 60,298 48,918 2000's 41,195 53,649 57,063 53,569 44,946 36,932 24,785 29,229 46,786 37,811 2010's 28,574 23,791 16,506 14,036 11,222 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  6. Natural Gas Weekly Update, Printer-Friendly Version

    Gasoline and Diesel Fuel Update (EIA)

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  7. Natural Gas Weekly Update, Printer-Friendly Version

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

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

  8. Natural gas transmission and distribution model of the National Energy Modeling System

    SciTech Connect (OSTI)

    1997-02-01

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. From 1982 through 1993, the Intermediate Future Forecasting System (IFFS) was used by the EIA for its analyses, and the Gas Analysis Modeling System (GAMS) was used within IFFS to represent natural gas markets. Prior to 1982, the Midterm Energy Forecasting System (MEFS), also referred to as the Project Independence Evaluation System (PIES), was employed. NEMS was developed to enhance and update EIA`s modeling capability by internally incorporating models of energy markets that had previously been analyzed off-line. In addition, greater structural detail in NEMS permits the analysis of a broader range of energy issues. The time horizon of NEMS is the midterm period (i.e., through 2015). In order to represent the regional differences in energy markets, the component models of NEMS function at regional levels appropriate for the markets represented, with subsequent aggregation/disaggregation to the Census Division level for reporting purposes.

  9. Natural Abundance 17O Nuclear Magnetic Resonance and Computational Modeling Studies of Lithium Based Liquid Electrolytes

    SciTech Connect (OSTI)

    Deng, Xuchu; Hu, Mary Y.; Wei, Xiaoliang; Wang, Wei; Chen, Zhong; Liu, Jun; Hu, Jian Z.

    2015-07-01

    Natural abundance 17O NMR measurements were conducted on electrolyte solutions consisting of Li[CF3SO2NSO2CF3] (LiTFSI) dissolved in the solvents of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and their mixtures at various concentrations. It was observed that 17O chemical shifts of solvent molecules change with the concentration of LiTFSI. The chemical shift displacements of carbonyl oxygen are evidently greater than those of ethereal oxygen, strongly indicating that Li+ ion is coordinated with carbonyl oxygen rather than ethereal oxygen. To understand the detailed molecular interaction, computational modeling of 17O chemical shifts was carried out on proposed solvation structures. By comparing the predicted chemical shifts with the experimental values, it is found that a Li+ ion is coordinated with four double bond oxygen atoms from EC, PC, EMC and TFSI- anion. In the case of excessive amount of solvents of EC, PC and EMC the Li+ coordinated solvent molecules are undergoing quick exchange with bulk solvent molecules, resulting in average 17O chemical shifts. Several kinds of solvation structures are identified, where the proportion of each structure in the liquid electrolytes investigated depends on the concentration of LiTFSI.

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

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

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

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

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

  12. 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 ...

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

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01

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

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

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

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

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

    Open Energy Info (EERE)

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

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

    SciTech Connect (OSTI)

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

    2010-04-01

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

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

    SciTech Connect (OSTI)

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

    2010-04-15

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

  18. LCO Nature | Open Energy Information

    Open Energy Info (EERE)

    LCO Nature Jump to: navigation, search Name: LCO Nature Place: Germany Sector: Renewable Energy Product: Based in Hamburg, LCO Nature is a consultancy and renewable energy project...

  19. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

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

  20. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

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

  1. 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 QER - Comment of Edison Electric Institute (EEI) 2

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

    SciTech Connect (OSTI)

    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.

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

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

  4. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation’s low-income households by primary heating fuel type, nationally and by Census Region. The statistics are intended for the use of policymakers in the Department of Energy’s Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2006 fiscal year.

  5. Weekly Natural Gas Storage Report

    Weekly Natural Gas Storage Report (EIA)

    Weekly Natural Gas Storage Report PERFORMANCE EVALUATION for 2011 through 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 September 2014 U.S. Energy Information Administration | PERFORMANCE EVALUATION for 2011 through 2013 i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

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

    SciTech Connect (OSTI)

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

    2014-08-15

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

  7. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

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

    2011-02-23

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

  8. EIA - Natural Gas Pipeline Network - Natural Gas Supply Basins...

    Gasoline and Diesel Fuel Update (EIA)

    Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates U.S. Natural Gas Supply Basins Relative to Major Natural ...

  9. EIA - Natural Gas Pipeline Network - Natural Gas Transmission Path Diagram

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

    Transmission Path Diagram About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Transmission Path Natural Gas Transmission Path

  10. High natural gas output and inventories contribute to lower prices

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

    High natural gas output and inventories contribute to lower prices High natural gas production and ample gas inventories are expected to keep natural gas prices relatively low for the rest of 2015. In its new monthly forecast, the U.S. Energy Information Administration says that while expected production growth is slowing from last year's torrid pace, domestic natural gas production in 2015 is still expected to be almost 6 percent above the 2014 level. Higher production has pushed U.S. natural

  11. Developing an industrial end-use forecast: A case study at the Los Angeles department of water and power

    SciTech Connect (OSTI)

    Mureau, T.H.; Francis, D.M.

    1995-05-01

    The Los Angeles Department of Water and Power (LADWP) uses INFORM 1.0 to forecast industrial sector energy. INFORM 1.0 provides an end-use framework that can be used to forecast electricity, natural gas or other fuels consumption. Included with INFORM 1.0 is a default date set including the input data and equations necessary to solve each model. LADWP has substituted service area specific data for the default data wherever possible. This paper briefly describes the steps LADWP follows in developing those inputs and application in INFORM 1.0.

  12. EIA - Natural Gas Pipeline Network - Combined Natural Gas Transportation

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

    Maps Combined Natural Gas Transportation Maps About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Pipeline Network Map of U.S. Natural Gas Pipeline Network Major Natural Gas Supply Basins Relative to Natural Gas Pipeline Transportation Corridors Map of Major Natural Gas Supply Basins Relative to Natural Gas Pipeline Transportation Corridors see related text enlarge see related text enlarge U.S. Regional Breakdown

  13. 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

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

    Energy Savers [EERE]

    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 ...

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

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

    Data and Resources National Solar Radiation Database NREL resource assessment and forecasting research information is available from the following sources. Renewable Resource Data ...

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

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

    transition from shallow to deep convection using a dual mass flux boundary layer scheme Roel Neggers European Centre for Medium-range Weather Forecasts Introduction " " % % &...

  17. 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. ...

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

    SciTech Connect (OSTI)

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

    2015-12-08

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

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

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

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

    ... RIT forecasting is saving costs and improving operational practices for IPC and helping integrate wind power more efficiently and cost effectively. Figure 3 shows how the ...

  1. A Review of Variable Generation Forecasting in the West: July...

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

    ... Cost Assignment - Only a few respondents partly or fully recover forecasting costs from variable generators. Many simply absorb the costs, possibly viewing them as relatively ...

  2. ANL Software Improves Wind Power Forecasting | Department of...

    Broader source: Energy.gov (indexed) [DOE]

    ... The licensing arrangement helps to facilitate transfer of the statistical learning algorithms developed in the project to industry use. A leading forecast provider in the United ...

  3. Selected papers on fuel forecasting and analysis

    SciTech Connect (OSTI)

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

    1983-05-01

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

  4. Waste-to-wheel analysis of anaerobic-digestion-based renewable natural gas pathways with the GREET model.

    SciTech Connect (OSTI)

    Han, J.; Mintz, M.; Wang, M.

    2011-12-14

    In 2009, manure management accounted for 2,356 Gg or 107 billion standard cubic ft of methane (CH{sub 4}) emissions in the United States, equivalent to 0.5% of U.S. natural gas (NG) consumption. Owing to the high global warming potential of methane, capturing and utilizing this methane source could reduce greenhouse gas (GHG) emissions. The extent of that reduction depends on several factors - most notably, how much of this manure-based methane can be captured, how much GHG is produced in the course of converting it to vehicular fuel, and how much GHG was produced by the fossil fuel it might displace. A life-cycle analysis was conducted to quantify these factors and, in so doing, assess the impact of converting methane from animal manure into renewable NG (RNG) and utilizing the gas in vehicles. Several manure-based RNG pathways were characterized in the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model, and their fuel-cycle energy use and GHG emissions were compared to petroleum-based pathways as well as to conventional fossil NG pathways. Results show that despite increased total energy use, both fossil fuel use and GHG emissions decline for most RNG pathways as compared with fossil NG and petroleum. However, GHG emissions for RNG pathways are highly dependent on the specifics of the reference case, as well as on the process energy emissions and methane conversion factors assumed for the RNG pathways. The most critical factors are the share of flared controllable CH{sub 4} and the quantity of CH{sub 4} lost during NG extraction in the reference case, the magnitude of N{sub 2}O lost in the anaerobic digestion (AD) process and in AD residue, and the amount of carbon sequestered in AD residue. In many cases, data for these parameters are limited and uncertain. Therefore, more research is needed to gain a better understanding of the range and magnitude of environmental benefits from converting animal manure to RNG via AD.

  5. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

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

  6. Technical analysis in short-term uranium price forecasting

    SciTech Connect (OSTI)

    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.

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

    SciTech Connect (OSTI)

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

    2012-07-01

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

  8. Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

  9. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    Open Energy Info (EERE)

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

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

    Reports and Publications (EIA)

    2010-01-01

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

  11. Natural Gas Value-Chain and Network Assessments

    SciTech Connect (OSTI)

    Kobos, Peter H.; Outkin, Alexander V.; Beyeler, Walter E.; Walker, LaTonya Nicole; Malczynski, Leonard A.; Myerly, Melissa M.; Vargas, Vanessa N.; Tenney, Craig M.; Borns, David J.

    2015-09-01

    The current expansion of natural gas (NG) development in the United States requires an understanding of how this change will affect the natural gas industry, downstream consumers, and economic growth in order to promote effective planning and policy development. The impact of this expansion may propagate through the NG system and US economy via changes in manufacturing, electric power generation, transportation, commerce, and increased exports of liquefied natural gas. We conceptualize this problem as supply shock propagation that pushes the NG system and the economy away from its current state of infrastructure development and level of natural gas use. To illustrate this, the project developed two core modeling approaches. The first is an Agent-Based Modeling (ABM) approach which addresses shock propagation throughout the existing natural gas distribution system. The second approach uses a System Dynamics-based model to illustrate the feedback mechanisms related to finding new supplies of natural gas - notably shale gas - and how those mechanisms affect exploration investments in the natural gas market with respect to proven reserves. The ABM illustrates several stylized scenarios of large liquefied natural gas (LNG) exports from the U.S. The ABM preliminary results demonstrate that such scenario is likely to have substantial effects on NG prices and on pipeline capacity utilization. Our preliminary results indicate that the price of natural gas in the U.S. may rise by about 50% when the LNG exports represent 15% of the system-wide demand. The main findings of the System Dynamics model indicate that proven reserves for coalbed methane, conventional gas and now shale gas can be adequately modeled based on a combination of geologic, economic and technology-based variables. A base case scenario matches historical proven reserves data for these three types of natural gas. An environmental scenario, based on implementing a $50/tonne CO 2 tax results in less proven

  12. Comparison of natural and forced amplification regimes in plasma-based soft-x-ray lasers seeded by high-order harmonics

    SciTech Connect (OSTI)

    Oliva, Eduardo; Zeitoun, Philippe; Lambert, Guillaume; Sebban, Stephane; Fajardo, Marta; Ros, David; Velarde, Pedro

    2011-07-15

    The amplification of high-order harmonics (HOH) in a plasma-based amplifier is a multiscale, temporal phenomenon that couples plasma hydrodynamics, atomic processes, and HOH electromagnetic fields. We use a one-dimensional, time-dependent Maxwell-Bloch code to compare the natural amplification regime and another regime where plasma polarization is constantly forced by the HOH. In this regime, a 10-MW (i.e., 100 times higher than current seeded soft x-ray laser power), 1.5-{mu}J, 140-fs pulse free from the parasitic temporal structures appearing on the natural amplification regime can be obtained.

  13. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect (OSTI)

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

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

    SciTech Connect (OSTI)

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

    2005-07-01

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

  15. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

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

  16. Crude oil and alternate energy production forecasts for the twenty-first century: The end of the hydrocarbon era

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

    Predictions of production rates and ultimate recovery of crude oil are needed for intelligent planning and timely action to ensure the continuous flow of energy required by the world`s increasing population and expanding economies. Crude oil will be able to supply increasing demand until peak world production is reached. The energy gap caused by declining conventional oil production must then be filled by expanding production of coal, heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources (solar, wind, and geothermal). Declining oil production forecasts are based on current estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for the United States and close to 3 trillion barrels for the world. Peak world crude oil production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil production in the United States is forecast to terminate by about 2090, and world production will be close to exhaustion by 2100.

  17. Natural gas inventories at record levels

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

    Natural gas inventories at record levels U.S. natural gas inventories at the end of October tied the all-time record high and inventories could climb to 4 trillion cubic feet in November for the first time. In its new monthly forecast, the U.S. Energy Information Administration said weekly injections of natural gas into storage may continue into November, after inventories at the close of October matched the record high of just over 3.9 trillion cubic feet. High inventories, along with rising

  18. Natural gas production on the rise

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

    Natural gas production on the rise After a brief slowdown in early 2016, U.S. natural gas production is expected to increase during the second half of this year and continue rising through 2017. In its new monthly forecast, the U.S. Energy Information Administration said domestic natural gas output during the fourth quarter of this year is expected to top 80 billion cubic feet per day and then climb to a new record of 82 billion cubic feet per day by end of 2017. With record production and

  19. 2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    2006-07-01

    This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

  20. Preliminary evaluation of a concept using microwave energy to improve an adsorption-based, natural gas clean-up process

    SciTech Connect (OSTI)

    Grimes, R.W.

    1992-12-01

    This report describes the results of a preliminary evaluation performed to: (1) determine if microwave energy could be used to regenerate a zeolite adsorbent and (2) to evaluate the feasibility of using microwave energy to improve the desorption phase of a pressure swing adsorption process applied to upgrading natural gas (methane) contaminated with nitrogen. Microwave regeneration was evaluated by comparing the adsorption characteristics of a zeolite preconditioned by heating under vacuum to the characteristics of the same zeolite after various lengths of exposure to microwave energy. The applicability of microwave regeneration to natural gas cleanup was evaluated by measuring the rise in adsorbent temperature resulting from the microwave exposure. Microwave energy consumed by heating the adsorbent is not productive and must therefore be minimal for a process to be economically viable. Exposure of the methane-saturated chabazite for 2 minutes to microwave energy effectively regenerated the adsorbent, but resulted in a 75{degrees}F (42{degrees}C) rise in adsorbent temperature. This temperature rise indicates that the concept is unacceptable for natural gas processing due to excessive energy consumption.

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

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

    SciTech Connect (OSTI)

    Lantz, E.; Hand, M.

    2010-05-01

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

  3. Model documentation: Natural gas transmission and distribution model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1995-02-17

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of integrated Analysis and Forecasting of the Energy information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. The NGTDM is the model within the NEMS that represents the transmission, distribution, and pricing of natural gas. The model also includes representations of the end-use demand for natural gas, the production of domestic natural gas, and the availability of natural gas traded on the international market based on information received from other NEMS models. The NGTDM determines the flow of natural gas in an aggregate, domestic pipeline network, connecting domestic and foreign supply regions with 12 demand regions. The methodology employed allows the analysis of impacts of regional capacity constraints in the interstate natural gas pipeline network and the identification of pipeline capacity expansion requirements. There is an explicit representation of core and noncore markets for natural gas transmission and distribution services, and the key components of pipeline tariffs are represented in a pricing algorithm. Natural gas pricing and flow patterns are derived by obtaining a market equilibrium across the three main elements of the natural gas market: the supply element, the demand element, and the transmission and distribution network that links them. The NGTDM consists of four modules: the Annual Flow Module, the Capacity F-expansion Module, the Pipeline Tariff Module, and the Distributor Tariff Module. A model abstract is provided in Appendix A.

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

    SciTech Connect (OSTI)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are

  5. World oil inventories forecast to grow significantly in 2016...

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

    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 ...

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

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

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

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

    Reports and Publications (EIA)

    2003-01-01

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

  8. 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 ...

  9. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

    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...

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

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy has just published the latest edition of its biannual report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, which models the...

  11. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2015-02-01

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

  12. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

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

    Broader source: Energy.gov (indexed) [DOE]

    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 ...

  14. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  15. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    1995-05-01

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

  16. New Climate Research Centers Forecast Changes and Challenges | Department

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

    of Energy Climate Research Centers Forecast Changes and Challenges New Climate Research Centers Forecast Changes and Challenges October 25, 2013 - 12:24pm Addthis This artist's rendering illustrates the full site installation, including a new aerosol observing system (far left) and a precipitation radar (far right, with 20-ft tower). The site is located near the Graciosa Island aiport terminal, hidden by the image inset. | Image courtesy of ARM Climate Research Facility. This artist's

  17. 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,

  18. Minnesota Supplemental Supplies of Natural Gas

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

    situation update March 12, 2014 | Washington, DC By Energy Information Administration NOAA forecast shows below normal temperatures across most of the Midwest for March 10 through March 18 U.S. Energy Information Administration 2 Source: National Oceanic and Atmospheric Administration Climate Prediction Center, made March 11 6-10 day outlook 8-14 day outlook Natural gas and electricity are the major heating fuels for most of the United States U.S. Energy Information Administration 3 Source: U.S.

  19. Model documentation natural gas transmission and distribution model (NGTDM) of the national energy modeling system. Volume II: Model developer`s report

    SciTech Connect (OSTI)

    Not Available

    1995-01-03

    To partially fulfill the requirements for {open_quotes}Model Acceptance{close_quotes} as stipulated in EIA Standard 91-01-01 (effective February 3, 1991), the Office of Integrated Analysis and Forecasting has conducted tests of the Natural Gas Transmission and Distribution Model (NGTDM) for the specific purpose of validating the forecasting model. This volume of the model documentation presents the results of {open_quotes}one-at-a-time{close_quotes} sensitivity tests conducted in support of this validation effort. The test results are presented in the following forms: (1) Tables of important model outputs for the years 2000 and 2010 are presented with respect to change in each input from the reference case; (2) Tables of percent changes from base case results for the years 2000 and 2010 are presented for important model outputs; (3) Tables of conditional sensitivities (percent change in output/percent change in input) for the years 2000 and 2010 are presented for important model outputs; (4) Finally, graphs presenting the percent change from base case results for each year of the forecast period are presented for selected key outputs. To conduct the sensitivity tests, two main assumptions are made in order to test the performance characteristics of the model itself and facilitate the understanding of the effects of the changes in the key input variables to the model on the selected key output variables: (1) responses to the amount demanded do not occur since there are no feedbacks of inputs from other NEMS models in the stand-alone NGTDM run. (2) All the export and import quantities from and to Canada and Mexico, and liquefied natural gas (LNG) imports and exports are held fixed (i.e., there are no changes in imports and exports between the reference case and the sensitivity cases) throughout the forecast period.

  20. Reducing Onshore Natural Gas and Oil Exploration and Production Impacts Using a Broad-Based Stakeholder Approach

    SciTech Connect (OSTI)

    Amy Childers

    2011-03-30

    Never before has the reduction of oil and gas exploration and production impacts been as important as it is today for operators, regulators, non-governmental organizations and individual landowners. Collectively, these stakeholders are keenly interested in the potential benefits from implementing effective environmental impact reducing technologies and practices. This research project strived to gain input and insight from such a broad array of stakeholders in order to identify approaches with the potential to satisfy their diverse objectives. The research team examined three of the most vital issue categories facing onshore domestic production today: (1) surface damages including development in urbanized areas, (2) impacts to wildlife (specifically greater sage grouse), and (3) air pollution, including its potential contribution to global climate change. The result of the research project is a LINGO (Low Impact Natural Gas and Oil) handbook outlining approaches aimed at avoiding, minimizing, or mitigating environmental impacts. The handbook identifies technical solutions and approaches which can be implemented in a practical and feasible manner to simultaneously achieve a legitimate balance between environmental protection and fluid mineral development. It is anticipated that the results of this research will facilitate informed planning and decision making by management agencies as well as producers of oil and natural gas. In 2008, a supplemental task was added for the researchers to undertake a 'Basin Initiative Study' that examines undeveloped and/or underdeveloped oil and natural gas resources on a regional or geologic basin scope to stimulate more widespread awareness and development of domestic resources. Researchers assessed multi-state basins (or plays), exploring state initiatives, state-industry partnerships and developing strategies to increase U.S. oil and gas supplies while accomplishing regional economic and environmental goals.

  1. EIA - Natural Gas Pipeline Network - Underground Natural Gas Storage

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

    Facilities Map U.S. Underground Natural Gas Storage Facilities Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Underground Natural Gas Storage Facilities, Close of 2007 more recent map U.S. Underground Natural Gas Storage Facilities, 2008 The EIA has determined that the informational map displays here do not raise security concerns, based on the application of the Federal Geographic Data Committee's Guidelines for

  2. EIA - Natural Gas Pipeline Network - Natural Gas Pipeline Compressor

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

    Stations Compressor Stations Illustration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Pipeline Compressor Stations Illustration, 2008 Map of U.S. Natural Gas Pipeline Compressor Stations Source: Energy Information Administration, Office of Oil & Gas, Natural Gas Division, Natural Gas Transportation Information System. The EIA has determined that the informational map displays here do not raise security

  3. Natural Gas Weekly Update

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

    to last week's relatively moderate temperatures and a forecast for a continuation of this weather pattern have contributed to lower prices at most markets. After remaining...

  4. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

    SciTech Connect (OSTI)

    Intharathirat, Rotchana; Abdul Salam, P.; Kumar, S.; Untong, Akarapong

    2015-05-15

    Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.

  5. Forecasting the 2013–2014 influenza season using Wikipedia

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

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

    2015-05-14

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

  6. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

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

    2015-05-14

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

  7. EIA - Natural Gas Pipeline Network - Natural Gas Pipeline Mileage by

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

    Region/State Mileage by State About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Estimated Natural Gas Pipeline Mileage in the Lower 48 States, Close of 2008 Estimated Natural Gas Pipeline Mileage in the Lower 48 States, Close of 2008

  8. EIA - Natural Gas Pipeline Network - Natural Gas Transportation Corridors

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

    Map Corridors > Major U.S. Natural Gas Transportation Corridors Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Major U.S. Natural Gas Transportation Corridors, 2008

  9. EIA - Natural Gas Pipeline Network - Generalized Natural Gas Pipeline

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

    Capacity Design Schematic Generalized Design Schematic About U.S. Natural Gas Pipelines- Transporting Natural Gas based on data through 2007/2008 with selected updates Generalized Natural Gas Pipeline Capacity Design Schematic Generalized Natural Gas Pipeline Capcity Design Schematic

  10. FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

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

  12. Natural Gas

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

    Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, ... Grid Integration & Advanced Inverters Materials & Fabrication Microsystems Enabled ...

  13. Natural Gas Weekly Update

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

    of 1 Tcf from the 1994 estimate of 51 Tcf. Ultimate potential for natural gas is a science-based estimate of the total amount of conventional gas in the province and is an...

  14. EIA - Natural Gas Pipeline Network - Major Natural Gas Transportation

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

    Corridors Natural Gas Transportation Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Major Natural Gas Transportation Corridors Corridors from the Southwest | From Canada | From Rocky Mountain Area | Details about Transportation Corridors The national natural gas delivery network is intricate and expansive, but most of the major transportation routes can be broadly categorized into 11 distinct corridors or flow

  15. EIA - Natural Gas Pipeline Network - Underground Natural Gas Storage

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

    Storage About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Underground Natural Gas Storage Overview | Regional Breakdowns Overview Underground natural gas storage provides pipelines, local distribution companies, producers, and pipeline shippers with an inventory management tool, seasonal supply backup, and access to natural gas needed to avoid imbalances between receipts and deliveries on a pipeline network. There are three

  16. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

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

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

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

    Department of Energy Making 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.

  18. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

    2008-01-15

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

  19. Economic feasibility analysis of distributed electric power generation based upon the natural gas-fired fuel cell. Final report

    SciTech Connect (OSTI)

    Not Available

    1994-03-01

    The final report provides a summary of results of the Cost of Ownership Model and the circumstances under which a distributed fuel cell is economically viable. The analysis is based on a series of micro computer models estimate the capital and operations cost of a fuel cell central utility plant configuration. Using a survey of thermal and electrical demand profiles, the study defines a series of energy user classes. The energy user class demand requirements are entered into the central utility plant model to define the required size the fuel cell capacity and all supporting equipment. The central plant model includes provisions that enables the analyst to select optional plant features that are most appropriate to a fuel cell application, and that are cost effective. The model permits the choice of system features that would be suitable for a large condominium complex or a residential institution such as a hotel, boarding school or prison. Other applications are also practical; however, such applications have a higher relative demand for thermal energy, a characteristic that is well-suited to a fuel cell application with its free source of hot water or steam. The analysis combines the capital and operation from the preceding models into a Cost of Ownership Model to compute the plant capital and operating costs as a function of capacity and principal features and compares these estimates to the estimated operating cost of the same central plant configuration without a fuel cell.

  20. Changes in Natural Gas Monthly Consumption Data Collection and the Short-Term Energy Outlook

    Reports and Publications (EIA)

    2010-01-01

    Beginning with the December 2010 issue of the Short-Term Energy Outlook (STEO), the Energy Information Administration (EIA) will present natural gas consumption forecasts for the residential and commercial sectors that are consistent with recent changes to the Form EIA-857 monthly natural gas survey.

  1. 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 ...

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

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

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

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

    Broader source: Energy.gov [DOE]

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

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

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

  5. EIA revises up forecast for U.S. 2013 crude oil production by...

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

    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 ...

  6. Natural gas inventories heading to record levels at start of winter heating season

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

    Natural gas inventories heading to record levels at start of winter heating season U.S. natural gas inventories are expected to be at record levels to start the winter heating season. In its new forecast, the U.S. Energy Information Administration said the amount of natural gas stored underground should total almost 4 trillion cubic feet by the beginning of November, reflecting record high natural gas production. Inventories could go even higher if heating demand is not strong during October

  7. Natural gas inventories end the winter at a record high

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

    Natural gas inventories end the winter at a record high U.S. natural gas inventories finished the winter heating season at their highest level ever. In its new monthly forecast, the U.S. Energy Information Administration said natural gas stocks at the end of March stood at 2.478 trillion cubic feet. That's slightly above the previous end-of- winter record set in 2012. This year's natural gas inventories are 67% above their 2015 levels and 53% higher than the five- year average for this time of

  8. 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

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01

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

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

    SciTech Connect (OSTI)

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

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

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

    SciTech Connect (OSTI)

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

    2011-03-28

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

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

    Energy Science and Technology Software Center (OSTI)

    2012-05-01

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

  13. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

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

    2011-11-29

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

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

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

  16. ,"Minnesota Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:41 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Minnesota Natural Gas in ...

  17. ,"Michigan Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:40 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Michigan Natural Gas in ...

  18. ,"Louisiana Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:38 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Louisiana Natural Gas in ...

  19. ,"Oklahoma Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:50 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Oklahoma Natural Gas in ...

  20. ,"Tennessee Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:54 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Tennessee Natural Gas in ...

  1. ,"Alaska Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:26 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Alaska Natural Gas in ...

  2. ,"Missouri Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:43 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Missouri Natural Gas in ...

  3. ,"Arkansas Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:28 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Arkansas Natural Gas in ...

  4. ,"Maryland Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:40 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Maryland Natural Gas in ...

  5. ,"Ohio Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:49 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Ohio Natural Gas in ...

  6. ,"Illinois Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:34 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Illinois Natural Gas in ...

  7. ,"Nebraska Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:46 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Nebraska Natural Gas in ...

  8. ,"Wyoming Underground Natural Gas Storage - All Operators"

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

    ...282016 11:30:00 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Wyoming Natural Gas in ...

  9. ,"Utah Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:56 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Utah Natural Gas in ...

  10. ,"Kentucky Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:37 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Kentucky Natural Gas in ...

  11. ,"Virginia Underground Natural Gas Storage - All Operators"

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

    ...282016 11:29:57 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Virginia Natural Gas in ...

  12. ,"California Underground Natural Gas Storage - All Operators...

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

    ...282016 11:29:29 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","California Natural Gas in ...

  13. ,"Mississippi Underground Natural Gas Storage - All Operators...

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

    ...282016 11:29:44 AM" "Back to Contents","Data 1: Total Underground Storage" ... Natural Gas in Underground Storage (Base Gas) (MMcf)","Mississippi Natural Gas in ...

  14. FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

  15. Thermoacoustic natural gas liquefier

    SciTech Connect (OSTI)

    Swift, G.; Gardner, D.; Hayden, M.; Radebaugh, R.; Wollan, J.

    1996-07-01

    This is the final report of a two-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). This project sought to develop a natural-gas-powered natural-gas liquefier that has absolutely no moving parts and requires no electrical power. It should have high efficiency, remarkable reliability, and low cost. The thermoacoustic natural-gas liquefier (TANGL) is based on our recent invention of the first no-moving-parts cryogenic refrigerator. In short, our invention uses acoustic phenomena to produce refrigeration from heat, with no moving parts. The required apparatus comprises nothing more than heat exchangers and pipes, made of common materials, without exacting tolerances. Its initial experimental success in a small size lead us to propose a more ambitious application: large-energy liquefaction of natural gas, using combustion of natural gas as the energy source. TANGL was designed to be maintenance-free, inexpensive, portable, and environmentally benign.

  16. Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

    SciTech Connect (OSTI)

    B. Faybishenko

    2006-09-11

    At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing them with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.

  17. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect (OSTI)

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  18. Natural Gas Weekly Update

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

    cooler temperatures all the way down into Texas, Tuesday's release of the Salomon Smith Barney (SSB) forecast for a colder-than-normal winter, and rumors that the federal...

  19. EIA - Natural Gas Pipeline Network - Natural Gas Pipeline Development &

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

    Expansion Pipelinesk > Development & Expansion About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipeline Development and Expansion Timing | Determining Market Interest | Expansion Options | Obtaining Approval | Prefiling Process | Approval | Construction | Commissioning Timing and Steps for a New Project An interstate natural gas pipeline construction or expansion project takes an average of about three years

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

    SciTech Connect (OSTI)

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

    2011-09-13

    wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.

  1. Forecast of transportation energy demand through the year 2010

    SciTech Connect (OSTI)

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

    1991-04-01

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

  2. Microsoft Word - Documentation - Price Forecast Uncertainty.doc

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

    October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy- related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the market- clearing process

  3. EIA - Natural Gas Pipeline Network - Natural Gas Supply Basins Relative to

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

    Major Natural Gas Pipeline Transportation Corridors Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Supply Basins Relative to Major Natural Gas Pipeline Transportation Corridors, 2008 U.S. Natural Gas Transporation Corridors out of Major Supply Basins

  4. Towards a Science of Tumor Forecast for Clinical Oncology

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

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

    2015-01-01

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

  5. Toward a science of tumor forecasting for clinical oncology

    SciTech Connect (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 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.

  6. Toward a science of tumor forecasting for clinical oncology

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

    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

  7. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect (OSTI)

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

    2015-01-01

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

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

    SciTech Connect (OSTI)

    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.

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

    Broader source: Energy.gov [DOE]

    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 energy forecasts, and to demonstrate the economic value of these improvements.

  10. Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results

    SciTech Connect (OSTI)

    Koomey, J.G.; Brown, R.E.; Richey, R.

    1995-12-01

    This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  12. Natural Gas Processing Plants in the United States: 2010 Update...

    Gasoline and Diesel Fuel Update (EIA)

    3. Natural Gas Processing Plants Utilization Rates Based on 2008 Flows Figure 3. Natural Gas Processing Plants Utilization Rates Based on 2008 Flows Note: Average utilization rates...

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

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

    Technology | Department of Energy Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology IBM logo.png As part of this project, new solar forecasting technology will be developed that leverages big data processing, deep machine learning, and cloud modeling integrated in a universal platform with an open architecture. Similar to the Watson computer system, this proposed technology

  14. 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

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

    SciTech Connect (OSTI)

    Rogers, J.; Porter, K.

    2011-03-01

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

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

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

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

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

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

    SciTech Connect (OSTI)

    None, None

    2006-12-20

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

  19. 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 ...

  20. Energy Department Announces $2.5 Million to Improve Wind Forecasting...

    Broader source: Energy.gov (indexed) [DOE]

    turbines operate closer to maximum capacity, leading to lower energy costs for consumers. ... for the Weather Research and Forecasting model, a widely used weather prediction system. ...

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

    SciTech Connect (OSTI)

    Hodge, B.

    2013-12-01

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

  2. EERE Success Story-Solar Forecasting Gets a Boost from Watson...

    Broader source: Energy.gov (indexed) [DOE]

    electric system operators, and solar project owners better predict when and how much ... production varies, an accurate solar forecast is needed in order to maintain an ...

  3. EIA - Natural Gas Pipeline Network - Natural Gas Market Centers and Hubs

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

    Market Centers and Hubs About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Market Centers and Hubs in Relation to Major Natural Gas Transportation Corridors, 2009 Natural Gas Market Centers and Hubs in Relation to Major Natural Gas Transportation Corridors, 2009 DCP = DCP Midstream Partners LP; EPGT = Enterprise Products Texas Pipeline Company. Note: The relative widths of the various transportation corridors are based

  4. EIA - Natural Gas Pipeline Network - Largest Natural Gas Pipeline Systems

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

    Interstate Pipelines Table About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Thirty Largest U.S. Interstate Natural Gas Pipeline Systems, 2008 (Ranked by system capacity) Pipeline Name Market Regions Served Primary Supply Regions States in Which Pipeline Operates Transported in 2007 (million dekatherm)1 System Capacity (MMcf/d) 2 System Mileage Columbia Gas Transmission Co. Northeast Southwest, Appalachia DE, PA, MD, KY, NC, NJ, NY,

  5. EIA - Natural Gas Pipeline Network - Aquifer Storage Reservoir

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

    Configuration Aquifer Storage Reservoir Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Aquifer Underground Natural Gas Storage Reservoir Configuration Aquifer Underground Natural Gas Well

  6. Quantifying the value that energy efficiency and renewable energy provide as a hedge against volatile natural gas prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Bachrach, Devra; Golove, William

    2002-05-15

    Advocates of energy efficiency and renewable energy have long argued that such technologies can mitigate fuel price risk within a resource portfolio. Such arguments--made with renewed vigor in the wake of unprecedented natural gas price volatility during the winter of 2000/2001--have mostly been qualitative in nature, however, with few attempts to actually quantify the price stability benefit that these sources provide. In evaluating this benefit, it is important to recognize that alternative price hedging instruments are available--in particular, gas-based financial derivatives (futures and swaps) and physical, fixed-price gas contracts. Whether energy efficiency and renewable energy can provide price stability at lower cost than these alternative means is therefore a key question for resource acquisition planners. In this paper we evaluate the cost of hedging gas price risk through financial hedging instruments. To do this, we compare the price of a 10-year natural gas swap (i.e., what it costs to lock in prices over the next 10 years) to a 10-year natural gas price forecast (i.e., what the market is expecting spot natural gas prices to be over the next 10 years). We find that over the past two years natural gas users have had to pay a premium as high as $0.76/mmBtu (0.53/242/kWh at an aggressive 7,000 Btu/kWh heat rate) over expected spot prices to lock in natural gas prices for the next 10 years. This incremental cost to hedge gas price risk exposure is potentially large enough - particularly if incorporated by policymakers and regulators into decision-making practices - to tip the scales away from new investments in variable-price, natural gas-fired generation and in favor of fixed-price investments in energy efficiency and renewable energy.

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-01-20

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

  8. Average Depth of Crude Oil and Natural Gas Wells

    Gasoline and Diesel Fuel Update (EIA)

    Active hurricane season expected to shut-in higher amount of oil and natural gas production An above-normal 2013 hurricane season is expected to cause a median production loss of about 19 million barrels of U.S. crude oil and 46 billion cubic feet of natural gas production in the Gulf of Mexico, according to the new forecast from the U.S. Energy Information Administration. That's about one-third more than the amount of oil and gas production knocked offline during last year's hurricane season.

  9. LANL Natural Resource Damage Assessment

    Broader source: Energy.gov [DOE]

    This Performance Work Statement (PWS) sets forth the tasks to be performed to complete a Natural Resource Damage Assessment (NRDA) and Restoration Plan based on injuries to natural resources from the release of hazardous substances from the Los Alamos National Laboratory (LANL).

  10. Natural Gas Industry and Markets

    Reports and Publications (EIA)

    2006-01-01

    This special report provides an overview of the supply and disposition of natural gas in 2004 and is intended as a supplement to the Energy Information Administration's (EIA) Natural Gas Annual 2004 (NGA). Unless otherwise stated, all data and figures in this report are based on summary statistics published in the NGA 2004.

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

    SciTech Connect (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.; 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.

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

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

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

    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

  14. EIA - Natural Gas Pipeline Network - Underground Natural Gas Storage

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

    Facilities Map LNG Peak Shaving and Import Facilities Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. LNG Peaking Shaving and Import Facilities, 2008 U.S. LNG Peak Shaving and Import Facilities, 2008 The EIA has determined that the informational map displays here do not raise security concerns, based on the application of the Federal Geographic Data Committee's Guidelines for Providing Appropriate Access to Geospatial

  15. ,"Natural Gas Consumption",,,"Natural Gas Expenditures"

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

    Census Division, 1999" ,"Natural Gas Consumption",,,"Natural Gas Expenditures" ,"per Building (thousand cubic feet)","per Square Foot (cubic feet)","per Worker (thousand cubic...

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

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

    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

  17. Short-Term Energy Outlook Supplement: Weather Sensitivity in Natural Gas Markets

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

    Short-Term Energy Outlook Supplement: Weather Sensitivity in Natural Gas Markets October 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Weather Sensitivity in Natural Gas Markets i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are

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

    SciTech Connect (OSTI)

    Thomas, L.C.

    1994-10-01

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

  19. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

    SciTech Connect (OSTI)

    Mellit, Adel; Pavan, Alessandro Massi

    2010-05-15

    Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

  20. Natural Gas Weekly Update

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

    natural gas demand, thereby contributing to larger net injections of natural gas into storage. Other Market Trends: EIA Releases The Natural Gas Annual 2006: The Energy...

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

    Broader source: Energy.gov [DOE]

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

  2. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center (OSTI)

    2014-08-01

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

  3. Heat distribution by natural convection

    SciTech Connect (OSTI)

    Balcomb, J.D.

    1985-01-01

    Natural convection can provide adequate heat distribution in many situations that arise in buildings. This is appropriate, for example, in passive solar buildings where some rooms tend to be more strongly solar heated than others. Natural convection can also be used to reduce the number of auxiliary heating units required in a building. Natural airflow and heat transport through doorways and other internal building apertures are predictable and can be accounted for in the design. The nature of natural convection is described, and a design chart is presented appropriate to a simple, single-doorway situation. Experimental results are summarized based on the monitoring of 15 passive solar buildings which employ a wide variety of geometrical configurations including natural convective loops.

  4. ,"Texas Underground Natural Gas Storage - All Operators"

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

    ...010TX2","N5020TX2","N5070TX2","N5050TX2","N5060TX2" "Date","Texas Natural Gas Underground Storage Volume (MMcf)","Texas Natural Gas in Underground Storage (Base Gas) (MMcf)","Texas ...

  5. EIA - Natural Gas Pipeline System - Midwest Region

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

    Midwest Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Midwest Region Overview | Domestic Gas | Canadian Imports | Regional Pipeline Companies & Links Overview Twenty-six interstate and at least eight intrastate natural gas pipeline companies operate within the Midwest Region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin). The principal sources of natural gas supply for the

  6. Natural Gas Basics

    SciTech Connect (OSTI)

    NREL Clean Cities

    2010-04-01

    Fact sheet answers questions about natural gas production and use in transportation. Natural gas vehicles are also described.

  7. U.S. natural gas exports to exceed imports for first time in 60 years

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

    U.S. natural gas exports to exceed imports for first time in 60 years The United States is on track during the second half of 2017 to export more natural gas than it imports for the first time since 1957. In its new monthly forecast, the U.S. Energy Information Administration said the United States will become a net exporter after years of steadily increasing its natural gas production and building the terminals necessary to ship super-cooled liquefied natural gas to overseas markets. Just last

  8. Natural gas inventories to remain high at end of winter heating season

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

    Natural gas inventories to remain high at end of winter heating season Despite the jump in natural gas use to meet heating demand during the recent winter storm that walloped the East Coast, total U.S. natural gas inventories remain near 3 trillion cubic feet. That's about 20 percent higher than at this time last year. In its new monthly forecast, the U.S. Energy Information Administration said that by the end of the winter heating season at the close of March, it expects natural gas inventories

  9. Short and Long-Term Perspectives: The Impact on Low-Income Consumers of Forecasted Energy Price Increases in 2008 and A Cap & Trade Carbon Policy in 2030

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred

    2008-01-01

    The Department of Energy's Energy Information Administration (EIA) recently released its short-term forecast for residential energy prices for the winter of 2007-2008. The forecast indicates increases in costs for low-income consumers in the year ahead, particularly for those using fuel oil to heat their homes. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation's low-income households by primary heating fuel type, nationally and by Census Region. The report provides an update of bill estimates provided in a previous study, "The Impact Of Forecasted Energy Price Increases On Low-Income Consumers" (Eisenberg, 2005). The statistics are intended for use by policymakers in the Department of Energy's Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2008 fiscal year. In addition to providing expenditure forecasts for the year immediately ahead, this analysis uses a similar methodology to give policy makers some insight into one of the major policy debates that will impact low-income energy expenditures well into the middle decades of this century and beyond. There is now considerable discussion of employing a cap-and-trade mechanism to first limit and then reduce U.S. emissions of carbon into the atmosphere in order to combat the long-range threat of human-induced climate change. The Energy Information Administration has provided an analysis of projected energy prices in the years 2020 and 2030 for one such cap-and-trade carbon reduction proposal that, when integrated with the RECS 2001 database, provides estimates of how low-income households will be impacted over the long term by such a carbon reduction policy.

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

    SciTech Connect (OSTI)

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

    2011-01-17

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

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

    SciTech Connect (OSTI)

    Widiss, R.; Porter, K.

    2014-03-01

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

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

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

    ... renewable energy systems Nexus of natural gas and renewable energy Modeling of electric sector regulation and policy in capacity expansion and dispatch models, e.g. the ...

  13. SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY...

    Office of Environmental Management (EM)

    SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY Based on the finding of a growing potential ...

  14. EIA - Natural Gas Pipeline Network - Salt Cavern Storage Reservoir...

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

    Salt Cavern Storage Reservoir Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Salt Cavern ...

  15. EIA - Natural Gas Pipeline Network - Expansion Process Flow Diagram

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

    Development & Expansion > Development and Expansion Process Figure About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates ...

  16. EIA - Natural Gas Pipeline Network - Aquifer Storage Reservoir...

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

    Aquifer Storage Reservoir Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Aquifer Underground ...

  17. EIA - Natural Gas Pipeline Network - Depleted Reservoir Storage...

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

    Depleted Reservoir Storage Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Depleted Production ...

  18. Atena Tecnologias em Energia Natural | Open Energy Information

    Open Energy Info (EERE)

    Atena Tecnologias em Energia Natural Jump to: navigation, search Name: Atena Tecnologias em Energia Natural Place: Martinopolis, Sao Paulo, Brazil Product: Brazil based ethanol...

  19. North American Natural Gas Markets

    SciTech Connect (OSTI)

    Not Available

    1989-02-01

    This report summarizes die research by an Energy Modeling Forum working group on the evolution of the North American natural gas markets between now and 2010. The group's findings are based partly on the results of a set of economic models of the natural gas industry that were run for four scenarios representing significantly different conditions: two oil price scenarios (upper and lower), a smaller total US resource base (low US resource case), and increased potential gas demand for electric generation (high US demand case). Several issues, such as the direction of regulatory policy and the size of the gas resource base, were analyzed separately without the use of models.

  20. North American Natural Gas Markets

    SciTech Connect (OSTI)

    Not Available

    1988-12-01

    This report sunnnarizes the research by an Energy Modeling Forum working group on the evolution of the North American natural gas markets between now and 2010. The group's findings are based partly on the results of a set of economic models of the natural gas industry that were run for four scenarios representing significantly different conditions: two oil price scenarios (upper and lower), a smaller total US resource base (low US resource case), and increased potential gas demand for electric generation (high US demand case). Several issues, such as the direction of regulatory policy and the size of the gas resource base, were analyzed separately without the use of models.

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

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

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

  2. Natural Solutions Pvt Ltd | Open Energy Information

    Open Energy Info (EERE)

    Jump to: navigation, search Name: Natural Solutions Pvt. Ltd. Place: Mumbai, Maharashtra, India Sector: Renewable Energy Product: Mumbai-based IT consultant. The firm plans to set...

  3. Nature Elements Capital | Open Energy Information

    Open Energy Info (EERE)

    Capital Jump to: navigation, search Name: Nature Elements Capital Place: Beijing, Beijing Municipality, China Zip: 100125 Product: Beijing-based private equity firm investing in...

  4. Natural Fuel Energy Inc | Open Energy Information

    Open Energy Info (EERE)

    Fuel Energy Inc Jump to: navigation, search Name: Natural Fuel & Energy Inc Place: Boston, Massachusetts Zip: 2100 Product: Boston - based biodiesel producer that operates a...

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

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

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

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

    SciTech Connect (OSTI)

    Not Available

    2009-11-01

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

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

    Broader source: Energy.gov [DOE]

    The University Corporation for Atmospheric  Research (UCAR) will develop a solar power forecasting system that advances the state of the science through cutting-edge research.

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

    SciTech Connect (OSTI)

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

    2013-01-01

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

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

    SciTech Connect (OSTI)

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

    2015-06-23

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

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

    SciTech Connect (OSTI)

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

    2012-06-01

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

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    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...

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

    SciTech Connect (OSTI)

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

    2013-11-01

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

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

    Broader source: Energy.gov [DOE]

    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...

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

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

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

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

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

    Gasoline price forecast to stay below 3 a gallon in 2015 The national average pump price of gasoline is expected to stay below 3 per gallon during 2015. In its new monthly ...

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

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01

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

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

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

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

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

    SciTech Connect (OSTI)

    1995-01-01

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

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

    SciTech Connect (OSTI)

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

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

  20. Effects of Alaska Oil and Natural Gas Provisions of H. R. 4 and S. 1766 on U.S. Energy Markets

    Reports and Publications (EIA)

    2002-01-01

    On December 20, 2001, Sen. Frank Murkowski, the Ranking Minority Member of the Senate Committee on Energy and Natural Resources requested an analysis of selected portions of Senate Bill 1766 (S. 1766, the Energy Policy Act of 2002) and House Bill H.R. 4 (the Securing America's Future Energy Act of 2001). In response, the Energy Information Administration (EIA) has prepared a series of analyses showing the impacts of each of the selected provisions of the bills on energy supply, demand, and prices, macroeconomic variables where relevant, import dependence, and emissions. The analysis provided is based on the Annual Energy Outlook 2002 (AEO2002) midterm forecasts of energy supply, demand, and prices through 2020.

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

    SciTech Connect (OSTI)

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

    2012-08-01

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

  2. EIA - Natural Gas Pipeline Network - Depleted Reservoir Storage

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

    Configuration Depleted Reservoir Storage Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Depleted Production Reservoir Underground Natural Gas Storage Well Configuration Depleted Production Reservoir Storage

  3. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    of natural gas vehicles. The Department of Energys Office of Energy Efficiency and Renewable Energy reports that there were 841 compressed natural gas (CNG) fuel stations and 41...

  4. Natural Gas Applications

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

    Gas Applications. If you need assistance viewing this page, please call (202) 586-8800. Energy Information Administration Home Page Home > Natural Gas > Natural Gas Applications...

  5. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Weekly Underground Natural Gas Storage Report. The sample change occurred over a transition period that began with the release of the Weekly Natural Gas Storage Report (WNGSR)...

  6. Historical Natural Gas Annual

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

    6 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  7. Historical Natural Gas Annual

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

    7 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  8. Historical Natural Gas Annual

    Gasoline and Diesel Fuel Update (EIA)

    8 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  9. Natural Gas Weekly Update

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

    wide array of affiliates in all sectors of the energy market. Along with the changing nature of the energy affiliates, the changing nature of the transmission providers themselves...

  10. Natural Gas Weekly Update

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

    5, 2009 Next Release: July 2, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, June 24, 2009) Natural gas...

  11. Natural Gas Weekly Update

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

    Next Release: Thursday, May 19, 2011 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, May 11, 2011) Natural...

  12. Natural Gas Weekly Update

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

    6, 2009 Next Release: August 13, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 5, 2009) Natural...

  13. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    , 2008 Next Release: July 10, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview Since Wednesday, June 25, natural gas spot prices...

  14. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Sources & Uses Petroleum & Other Liquids Crude oil, gasoline, heating oil, diesel, propane, and other liquids including biofuels and natural gas liquids. Natural Gas...

  15. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Release: Thursday, April 28, 2011 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, April 20, 2011) Natural...

  16. Natural Gas Weekly Update

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

    Release: Thursday, August 26, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 18, 2010) Natural...

  17. Natural Gas Weekly Update

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

    2008 Next Release: November 6, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the week ending Wednesday, October 29) Natural gas...

  18. Natural Gas Weekly Update

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

    9, 2008 Next Release: June 26, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview Since Wednesday, June 11, natural gas spot prices...

  19. Natural Gas Weekly Update

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

    cooling demand for natural gas. Meanwhile, it became increasingly clear that Hurricane Frances likely would not pose a significant threat to natural gas production in the Gulf of...

  20. Unconventional Natural Gas

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

    ... lb Pound LCA Life cycle analysis LNG Liquefied natural gas M Magnitude (Richter ... reversed plans to import liquefied natural gas (LNG), and many are now proposing exports. ...

  1. Short-Term Energy Carbon Dioxide Emissions Forecasts August 2009

    Reports and Publications (EIA)

    2009-01-01

    Supplement to the Short-Term Energy Outlook. Short-term projections for U.S. carbon dioxide emissions of the three fossil fuels: coal, natural gas, and petroleum.

  2. EIA - Natural Gas Pipeline System - Central Region

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

    Central Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Central Region Overview | Domestic Gas | Exports | Regional Pipeline Companies & Links Overview Twenty-two interstate and at least thirteen intrastate natural gas pipeline companies (see Table below) operate in the Central Region (Colorado, Iowa, Kansas, Missouri, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). Twelve

  3. EIA - Natural Gas Pipeline System - Northeast Region

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

    Northeast Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Northeast Region Overview | Domestic Gas | Canadian Imports | Regional Pipeline Companies & Links Overview Twenty interstate natural gas pipeline systems operate within the Northeast Region (Connecticut, Delaware, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Virginia, and West Virginia). These

  4. EIA - Natural Gas Pipeline System - Southwest Region

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

    Southwest Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Southwest Region Overview | Export Transportation | Intrastate | Connection to Gulf of Mexico | Regional Pipeline Companies & Links Overview Most of the major onshore interstate natural gas pipeline companies (see Table below) operating in the Southwest Region (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) are primarily

  5. EIA - Natural Gas Pipeline System - Western Region

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

    Western Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Western Region Overview | Transportation South | Transportation North | Regional Pipeline Companies & Links Overview Ten interstate and nine intrastate natural gas pipeline companies provide transportation services to and within the Western Region (Arizona, California, Idaho, Nevada, Oregon, and Washington), the fewest number serving

  6. EIA - Natural Gas Pipeline Network - Regulatory Authorities

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

    Regulatory Authorities About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Regulatory Authorities Beginning | Regulations Today | Coordinating Agencies | Regulation of Mergers and Acquisitions Beginning of Industry Restructuring In April 1992, the Federal Energy Regulatory Commission (FERC) issued its Order 636 and transformed the interstate natural gas transportation segment of the industry forever. Under it,

  7. EIA - Natural Gas Pipeline Network - U.S. Natural Gas Pipeline Network Map

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

    Network Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Pipeline Network, 2009 U.S. Natural Gas Pipeline Network Map The EIA has determined that the informational map displays here do not raise security concerns, based on the application of the Federal Geographic Data Committee's Guidelines for Providing Appropriate Access to Geospatial Data in Response to Security Concerns

  8. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  9. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  10. Builds in U.S. natural gas storage running above five-year average

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

    Builds in U.S. natural gas storage running above five-year average The amount of natural gas put into underground storage since the beginning of the so-called "injection season" in April has been above the five-year average by a wide margin. In its new forecast, the U.S. Energy Information Administration said natural gas inventories, which are running more than 50% above year ago levels, are on track to reach almost 4 trillion cubic feet by the end of October which marks the start of

  11. NATURAL RESOURCES ASSESSMENT

    SciTech Connect (OSTI)

    D.F. Fenster

    2000-12-11

    The purpose of this report is to summarize the scientific work that was performed to evaluate and assess the occurrence and economic potential of natural resources within the geologic setting of the Yucca Mountain area. The extent of the regional areas of investigation for each commodity differs and those areas are described in more detail in the major subsections of this report. Natural resource assessments have focused on an area defined as the ''conceptual controlled area'' because of the requirements contained in the U.S. Nuclear Regulatory Commission Regulation, 10 CFR Part 60, to define long-term boundaries for potential radionuclide releases. New requirements (proposed 10 CFR Part 63 [Dyer 1999]) have obviated the need for defining such an area. However, for the purposes of this report, the area being discussed, in most cases, is the previously defined ''conceptual controlled area'', now renamed the ''natural resources site study area'' for this report (shown on Figure 1). Resource potential can be difficult to assess because it is dependent upon many factors, including economics (demand, supply, cost), the potential discovery of new uses for resources, or the potential discovery of synthetics to replace natural resource use. The evaluations summarized are based on present-day use and economic potential of the resources. The objective of this report is to summarize the existing reports and information for the Yucca Mountain area on: (1) Metallic mineral and mined energy resources (such as gold, silver, etc., including uranium); (2) Industrial rocks and minerals (such as sand, gravel, building stone, etc.); (3) Hydrocarbons (including oil, natural gas, tar sands, oil shales, and coal); and (4) Geothermal resources. Groundwater is present at the Yucca Mountain site at depths ranging from 500 to 750 m (about 1,600 to 2,500 ft) below the ground surface. Groundwater resources are not discussed in this report, but are planned to be included in the hydrology

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

    SciTech Connect (OSTI)

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

    2014-04-30

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

  13. Market-Based Indian Grid Integration Study Options: Preprint

    SciTech Connect (OSTI)

    Stoltenberg, B.; Clark, K.; Negi, S. K.

    2012-03-01

    The Indian state of Gujarat is forecasting solar and wind generation expansion from 16% to 32% of installed generation capacity by 2015. Some states in India are already experiencing heavy wind power curtailment. Understanding how to integrate variable generation (VG) into the grid is of great interest to local transmission companies and India's Ministry of New and Renewable Energy. This paper describes the nature of a market-based integration study and how this approach, while new to Indian grid operation and planning, is necessary to understand how to operate and expand the grid to best accommodate the expansion of VG. Second, it discusses options in defining a study's scope, such as data granularity, generation modeling, and geographic scope. The paper also explores how Gujarat's method of grid operation and current system reliability will affect how an integration study can be performed.

  14. An economic feasibility analysis of distributed electric power generation based upon the natural gas-fired fuel cell: a model of a central utility plant.

    SciTech Connect (OSTI)

    Not Available

    1993-06-30

    This central utilities plant model details the major elements of a central utilities plant for several classes of users. The model enables the analyst to select optional, cost effective, plant features that are appropriate to a fuel cell application. These features permit the future plant owner to exploit all of the energy produced by the fuel cell, thereby reducing the total cost of ownership. The model further affords the analyst an opportunity to identify avoided costs of the fuel cell-based power plant. This definition establishes the performance and capacity information, appropriate to the class of user, to support the capital cost model and the feasibility analysis. It is detailed only to the depth required to identify the major elements of a fuel cell-based system. The model permits the choice of system features that would be suitable for a large condominium complex or a residential institution such as a hotel, boarding school or prison. The user may also select large office buildings that are characterized by 12 to 16 hours per day of operation or industrial users with a steady demand for thermal and electrical energy around the clock.

  15. Model documentation Natural Gas Transmission and Distribution Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1996-02-26

    The Natural Gas Transmission and Distribution Model (NGTDM) of the National Energy Modeling System is developed and maintained by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting. This report documents the archived version of the NGTDM that was used to produce the natural gas forecasts presented in the Annual Energy Outlook 1996, (DOE/EIA-0383(96)). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic approach, and provides detail on the methodology employed. Previously this report represented Volume I of a two-volume set. Volume II reported on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.

  16. Model documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System; Volume 1

    SciTech Connect (OSTI)

    1994-02-24

    The Natural Gas Transmission and Distribution Model (NGTDM) is a component of the National Energy Modeling System (NEMS) used to represent the domestic natural gas transmission and distribution system. NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the Energy Information Administration (EIA) and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. This report documents the archived version of NGTDM that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 1994, DOE/EIA-0383(94). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic design, provides detail on the methodology employed, and describes the model inputs, outputs, and key assumptions. It is intended to fulfill the legal obligation of the EIA to provide adequate documentation in support of its models (Public Law 94-385, Section 57.b.2). This report represents Volume 1 of a two-volume set. (Volume 2 will report on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.) Subsequent chapters of this report provide: (1) an overview of the NGTDM (Chapter 2); (2) a description of the interface between the National Energy Modeling System (NEMS) and the NGTDM (Chapter 3); (3) an overview of the solution methodology of the NGTDM (Chapter 4); (4) the solution methodology for the Annual Flow Module (Chapter 5); (5) the solution methodology for the Distributor Tariff Module (Chapter 6); (6) the solution methodology for the Capacity Expansion Module (Chapter 7); (7) the solution methodology for the Pipeline Tariff Module (Chapter 8); and (8) a description of model assumptions, inputs, and outputs (Chapter 9).

  17. EIA-914 Monthly Crude Oil, Lease Condensate, and Natural Gas Production Report Revision Policy

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

    Revision Policy December 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | EIA-94 Monthly Crude Oil, Lease Condensate, and Natural Gas Production Report Methodology i This revision policy was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by

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

    SciTech Connect (OSTI)

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

    2002-02-25

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

  19. Electrospinning of PVC with natural rubber

    SciTech Connect (OSTI)

    Othman, Muhammad Hariz; Abdullah, Ibrahim; Mohamed, Mahathir

    2013-11-27

    Polyvinyl chloride (PVC) was mixed with natural rubbers which are liquid natural rubber (LNR), liquid epoxidised natural rubber (LENR) and liquid epoxidised natural rubber acrylate (LENRA) for a preparation of a fine non-woven fiber’s mat. PVC and each natural rubbers(PVC:LENR, PVC:LNR and PVC:LENRA) were mixed based on ratio of 70:30. Electrospinning method was used to prepare the fiber. The results show that the spinnable concentration of PVC/ natural rubber/THF solution is 16 wt%. The morphology, diameter, structure and degradation temperature of electrospun fibers were investigated by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). SEM photos showed that the morphology and diameter of the fibers were mainly affected by the addition of natural rubber. TGA results suggested that PVC electrospun fiber has higher degradation temperature than those electrospun fibers that contain natural rubber.

  20. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

    SciTech Connect (OSTI)

    Dyson, Brian; Chang, N.-B. . E-mail: nchang@even.tamuk.edu

    2005-07-01

    Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.

  1. Asian natural gas

    SciTech Connect (OSTI)

    Klass, D.L. ); Ohashi, T. )

    1989-01-01

    This book presents an overview of the present status and future development in Asia of domestic and export markets for natural gas and to describes gas utilization technologies that will help these markets grow. A perspective of natural gas transmission, transport, distribution, and utilization is presented. The papers in this book are organized under several topics. The topics are : Asian natural gas markets, Technology of natural gas export projects, Technology of domestic natural gas projects, and Natural gas utilization in power generation, air conditioning, and other applications.

  2. Natural gas monthly, July 1995

    SciTech Connect (OSTI)

    1995-07-21

    The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. The data in this publication are collected on surveys conducted by the EIA to fulfill its responsibilities for gathering and reporting energy data. Some of the data are collected under the authority of the Federal Energy Regulatory Commission (FERC), an independent commission within the DOE, which has jurisdiction primarily in the regulation of electric utilities and the interstate natural gas industry. Geographic coverage is the 50 States and the District of Columbia. Explanatory Notes supplement the information found in tables of the report. A description of the data collection surveys that support the NGM is provided in the Data Sources section. A glossary of the terms used in this report is also provided to assist readers in understanding the data presented in this publication. All natural gas volumes are reported at a pressure base of 14.73 pounds per square inch absolute (psia) and at 60 degrees Fahrenheit. Cubic feet are converted to cubic meters by applying a factor of 0.02831685.

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

    SciTech Connect (OSTI)

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

    1994-05-01

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

  4. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    The report provides an overview of U.S. international trade in 2008 as well as historical data on natural gas imports and exports. Net natural gas imports accounted for only 13...

  5. Natural Gas Weekly Update

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

    heating-related demand for natural gas that limited the size of the net addition to storage. The economic incentives for storing natural gas for next winter are considerably...

  6. EIA - Natural Gas Publications

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

    these data from 2005 to 2009 are presented for each State. (12282010) U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Proved Reserves: 2009 National and State...

  7. Natural gas annual 1996

    SciTech Connect (OSTI)

    1997-09-01

    This document provides information on the supply and disposition of natural gas to a wide audience. The 1996 data are presented in a sequence that follows natural gas from it`s production to it`s end use.

  8. Natural Gas Weekly Update

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

    a high degree of price volatility seems inherent in natural gas markets because of the nature of the commodity. However, the annual volatility during 1994 and 2006 does not exhibit...

  9. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    of natural gas into storage. However, shut-in natural gas production in the Gulf of Mexico reduced available current supplies, and so limited net injections during the report...

  10. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    York Mercantile Exchange (NYMEX), the August 2011 natural gas contract price also lost ground over the week, closing at 4.217 per MMBtu on Wednesday. The natural gas rotary rig...

  11. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    York Mercantile Exchange (NYMEX), the August 2011 natural gas contract price also lost ground over the week, closing at 4.315 per MMBtu on Wednesday. The natural gas rotary rig...

  12. Natural Gas Weekly Update

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

    next heating season. Net injections reported in today's release of EIA's Weekly Natural Gas Storage Report brought natural gas storage supplies to 2,163 Bcf as of Friday, June 1,...

  13. ,"Texas Natural Gas Prices"

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

    Data for" ,"Data 1","Texas Natural Gas Prices",8,"Monthly","2... 6:46:23 AM" "Back to Contents","Data 1: Texas Natural Gas Prices" "Sourcekey","N3050TX3"...

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

    SciTech Connect (OSTI)

    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.

  15. EIA - Natural Gas Pipeline Network - Natural Gas Import/Export Locations

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

    Map Export Pipelines > Import/Export Locations Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Import/Export Locations, as of the end of 2008 Natural Gas Import and Export Locations Source: Energy Information Administration, Office of Oil and Gas, Natural Gas Division, Imports/Export Points Database. The EIA has determined that the informational map displays here do not raise security concerns, based

  16. EIA - Natural Gas Pipeline Network - Natural Gas Imports/Exports Pipelines

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

    Pipelines About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Import/Export Pipelines As of the close of 2008 the United States has 58 locations where natural gas can be exported or imported. 24 locations are for imports only 18 locations are for exports only 13 locations are for both imports and exports 8 locations are liquefied natural gas (LNG) import facilities Imported natural gas in 2007 represented almost 16 percent

  17. Federal Offshore--California Natural Gas Plant Liquids, Reserves...

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

    Reserves Based Production (Million Barrels) Federal Offshore--California Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3...

  18. Louisiana--State Offshore Natural Gas Plant Liquids, Reserves...

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

    Reserves Based Production (Million Barrels) Louisiana--State Offshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3...

  19. Federal Offshore--Louisiana and Alabama Natural Gas Plant Liquids...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Federal Offshore--Louisiana and Alabama Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1...

  20. New Mexico--East Natural Gas Liquids Lease Condensate, Reserves...

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

    Reserves Based Production (Million Barrels) New Mexico--East Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 ...