DOI:10.35833/MPCE.2018.000570 |
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Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas |
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Page view: 123
Net amount: 732 |
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Author:
Zhao Wang1,2,Weisheng Wang1,Chun Liu1,Bo Wang1
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Author Affiliation:
1.State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing, China;2.Department of Electrical Engineering, Tsinghua University, Beijing, China
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Foundation: |
This work was supported by National Key R&D Program of China (No. 2018YFB0904200) and eponymous Complement S&T Program of State Grid Corporation of China (No. SGLNDKOOKJJS1800266). |
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Abstract: |
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, high-quality 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. |
Keywords: |
Forecasted scenarios ; wind power ; distance-weighted kernel density estimation (KDE) ; regular vine (R-vine) copula ; spatio-temporal correlation |
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Received:December 23, 2017
Online Time:2020/03/02 |
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