Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 10, Issue 4, Pages 3870-3882Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2018.2841932
Keywords
Conditional probabilistic forecast; copula theory; Gaussian mixture model; wind power ramps
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Funding
- U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technology Office
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Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic WPR forecast (cp-WPRF) model based on copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model is adopted to characterize the WPR uncertainty and features. The Canonical maximum likelihood method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.
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