Economic impacts of advanced weather forecasting on energy system operations

We analyze the impacts of adopting advanced weather forecasting systems at different levels of the decision-making hierarchy of the power grid. Using case studies, we show that state-of-the-art numerical weather prediction (NWP) models can provide high-precision forecasts and uncertainty information that can significantly enhance the performance of planning, scheduling, energy management, and feedback control systems. In addition, we assess the forecasting capabilities of the Weather Research and Forecast (WRF) model in several application domains.

[1]  Victor M. Zavala,et al.  Unit Commitment With Wind Power Generation: Integrating Wind Forecast Uncertainty and Stochastic Programming. , 2009 .

[2]  James E. Braun,et al.  Evaluating the Performance of Building Thermal Mass Control Strategies , 2001 .

[3]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[4]  Victor M. Zavala,et al.  Weather forecast-based optimization of integrated energy systems. , 2009 .

[5]  S. Sahu,et al.  Improved space–time forecasting of next day ozone concentrations in the eastern US , 2009 .

[6]  Herbert K. H. Lee,et al.  Efficient models for correlated data via convolutions of intrinsic processes , 2005 .

[7]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[8]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[9]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[10]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[11]  Victor M. Zavala,et al.  On the Optimal On-Line Management of Photovoltaic-Hydrogen Hybrid Energy Systems , 2009 .

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[13]  Renate Hagedorn,et al.  Representing model uncertainty in weather and climate prediction , 2005 .

[14]  I. Erlich,et al.  A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System , 2009, IEEE Transactions on Power Systems.

[15]  Ø. Ulleberg.  The importance of control strategies in PV–hydrogen systems , 2004 .

[16]  Georges Kariniotakis,et al.  Uncertainty estimation of wind power forecasts: Comparison of Probabilistic Modelling Approaches , 2008 .

[17]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[18]  Victor M. Zavala,et al.  On-line economic optimization of energy systems using weather forecast information. , 2009 .

[19]  Mark J. Schervish,et al.  Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.