IEEE Transactions on Smart Grid | Vol.10, Issue.4 | | Pages 3870-3882
A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps
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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A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps
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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starttime sharpness copula family predictive interval based evaluation management of wind ramping characteristics wpr uncertainty canonical maximum likelihood method conditional probabilistic wpr forecast cpwprf model balancing ramp gaussian mixture model wind integration costs marginal distributions multivariable bayesian information criterion publicly available wind power data coupling effects among wprs dataset wpr feature eg rate magnitude duration stochastic dependence of wind power ramp wpr features
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