Owing to the uncertainty and volatility of wind energy,forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems.In this study,fore...Owing to the uncertainty and volatility of wind energy,forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems.In this study,forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms.According to the theory of copulas,marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine(R-vine)copula,respectively.Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas,the R-vine copula provides more accurate results in describing the complicated dependence of wind power.In the case of 26 wind farms located in East China,highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method,and the results are evaluated using a comprehensive verification framework.展开更多
基金supported by National Key R&D Program of China(No.2018YFB0904200)eponymous Complement S&T Program of State Grid Corporation of China(No.SGLNDKOOKJJS1800266).
文摘Owing to the uncertainty and volatility of wind energy,forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems.In this study,forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms.According to the theory of copulas,marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine(R-vine)copula,respectively.Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas,the R-vine copula provides more accurate results in describing the complicated dependence of wind power.In the case of 26 wind farms located in East China,highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method,and the results are evaluated using a comprehensive verification framework.
