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

Porter, Kevin; Rogers, J. Scott

doi:10.2172/979837

Cited by 27 publications

(38 citation statements)

References 5 publications

(5 reference statements)

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“…As discussed in Porter and Rogers (2010), several metrics are conventionally-used to quantify the performance of a wind power forecast. The most common is the Root Mean Squared Error (RMSE), which is calculated by differencing observations from forecasts and squaring the difference, and summing up over all data points.…”

Section: Resultsmentioning

confidence: 99%

“…Most operational wind energy forecasting systems use refined resolution of the lower layers of the atmosphere (Lundquist, 2008;Porter and Rogers, 2010) to capture shallow surface-based phenomena poorly represented in these simulations. Ongoing improvements in PBL schemes focused on wind energy applications (Olson et al, 2011) will further enhance model skill in predicting winds at the altitudes relevant for wind-energy applications.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, utility companies and wind farm owner/operators experience a need to forecast, with accuracy, the likelihood of a ramping event to ensure that minimal spinning reserves are maintained. The status of wind power forecasting in the North America is summarized in Porter and Rogers (2010).…”

Section: Introductionmentioning

confidence: 99%

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Mesoscale Simulations of a Wind Ramping Event for Wind Energy Prediction

Rhodes¹,

Lundquist²

2011

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Ramping events, or rapid changes of wind speed and wind direction over a short period of time, present challenges to power grid operators in regions with significant penetrations of wind energy in the power grid portfolio. Improved predictions of wind power availability require adequate predictions of the timing of ramping events. For the ramping event investigated here, the Weather Research and Forecasting (WRF) model was run at three horizontal resolutions in "mesoscale" mode: 8100m, 2700m, and 900m. Two Planetary Boundary Layer (PBL) schemes, the Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) schemes, were run at each resolution as well. Simulations were not "tuned" with nuanced choices of vertical resolution or tuning parameters so that these simulations may be considered "out-of-the-box" tests of a numerical weather prediction code. Simulations are compared with sodar observations during a wind ramping event at a "West Coast North America" wind farm. Despite differences in the boundary-layer schemes, no significant differences were observed in the abilities of the schemes to capture the timing of the ramping event. As collaborators have identified, the boundary conditions of these simulations probably dominate the physics of the simulations. We suggest that future investigations into characterization of ramping events employ ensembles of simulations, and that the ensembles include variations of boundary conditions. Furthermore, the failure of these simulations to capture not only the timing of the ramping event but the shape of the wind profile during the ramping event (regardless of its timing) indicates that the set-up and execution of such simulations for wind power forecasting requires skill and tuning of the simulations for a specific site.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Mesoscale Simulations of a Wind Ramping Event for Wind Energy Prediction

Rhodes¹,

Lundquist²

2011

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“…As wind penetration has increased, utilities are increasingly using wind forecasts to better integrate wind and ensure system reliability. Current day-ahead wind forecasts typically have errors in the range of 10%-20% mean absolute error (Grant et al 2009;Monteiro et al 2009;Porter and Rogers 2010;Lew et al 2011). Improving wind forecasts is a major focus of research and is expected to result in reduced costs to integrate variable output wind power.…”

Section: Electricity Output Characteristicsmentioning

confidence: 99%

“…These studies have shown systems to be capable of reliably and economically incorporating wind energy, but often with changes to some current operating strategies. Changes in operations include balancing area cooperation, sub-hourly scheduling (Milligan et al 2009;Milligan and Kirby 2008;Kirby et al 2010); intelligent integration of wind forecasting (Grant et al 2009;Monteiro et al 2009;Porter and Rogers 2010;Lew et al 2011); and increases in operating reserves (Doherty and O'Malley 2005;Ela et al 2011;Matos and Bessa 2011). Additional information and discussion of operational issues can be found in Volume 4. synchronous generators (Miller, Clark, and Shao 2011;Miller, Shao, and Venkataraman 2011).…”

Section: Electricity Output Characteristicsmentioning

confidence: 99%

Renewable Electricity Futures Study. Volume 2. Renewable Electricity Generation and Storage Technologies

Augustine

Bain

Chapman

et al. 2012

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Chapter 6. Biopower TechnologiesBain, R.; Denholm, P.; Heath, G.; Mai, T.; Tegen, S. (2012). "Biopower Technologies," Chapter 6. National Renewable Energy Laboratory. Renewable Electricity Futures Study, Vol. 2, Golden, CO: National Renewable Energy Laboratory; pp. 6-1 -6-58. Chapter 7. Geothermal Energy Technologies PerspectiveThe Renewable Electricity Futures Study (RE Futures) provides an analysis of the grid integration opportunities, challenges, and implications of high levels of renewable electricity generation for the U.S. electric system. The study is not a market or policy assessment. Rather, RE Futures examines renewable energy resources and many technical issues related to the operability of the U.S. electricity grid, and provides initial answers to important questions about the integration of high penetrations of renewable electricity technologies from a national perspective. RE Futures results indicate that a future U.S. electricity system that is largely powered by renewable sources is possible and that further work is warranted to investigate this clean generation pathway. The central conclusion of the analysis is that renewable electricity generation from technologies that are commercially available today, in combination with a more flexible electric system, is more than adequate to supply 80% of total U.S. electricity generation in 2050 while meeting electricity demand on an hourly basis in every region of the United States.The renewable technologies explored in this study are components of a diverse set of clean energy solutions that also includes nuclear, efficient natural gas, clean coal, and energy efficiency. Understanding all of these technology pathways and their potential contributions to the future U.S. electric power system can inform the development of integrated portfolio scenarios. RE Futures focuses on the extent to which U.S. electricity needs can be supplied by renewable energy sources, including biomass, geothermal, hydropower, solar, and wind.The study explores grid integration issues using models with unprecedented geographic and time resolution for the contiguous United States. The analysis (1) assesses a variety of scenarios with prescribed levels of renewable electricity generation in 2050, from 30% to 90%, with a focus on 80% (with nearly 50% from variable wind and solar photovoltaic generation); (2) identifies the characteristics of a U.S. electricity system that would be needed to accommodate such levels; and (3) describes some of the associated challenges and implications of realizing such a future. In addition to the central conclusion noted above, RE Futures finds that increased electric system flexibility, needed to enable electricity supply-demand balance with high levels of renewable generation, can come from a portfolio of supply-and demand-side options, including flexible conventional generation, grid storage, new transmission, more responsive loads, and changes in power system operations. The analysis also finds that the abundance and diversity of U.S. renewabl...

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Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Williams

Maxwell

Monache

2013

J Adv Model Earth Syst

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[1] Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system. Citation: Williams, J. L., III, R. M. Maxwell, and L. Delle Monache (2013), Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model, J. Adv. Model. Earth Syst., 5, 785-800,

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

Cited by 27 publications

References 5 publications

Mesoscale Simulations of a Wind Ramping Event for Wind Energy Prediction

Mesoscale Simulations of a Wind Ramping Event for Wind Energy Prediction

Renewable Electricity Futures Study. Volume 2. Renewable Electricity Generation and Storage Technologies

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

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