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Overview On Theory Analysis and Application of Distributionally Robust OptimizationMethod in Power System

School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China

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This work is supported by National Natural Science Foundation of China (No. 51807125) and Fundamental Research Funds for the Central Universities (No. YJ201750).

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    The uncertainty factors such as clean energy and electricity price have important influence on the planning and operation of power system. Distributionally robust optimization (DRO) method has been paid more attention since it combines the advantages of stochastic optimization and robust optimization in dealing with uncertainties. Therefore, this paper reviews the application of DRO method in power system. Firstly, the characteristics of DRO method are described and compared with other methods in coping with uncertainties. Secondly, the application status of different types of DRO methods in power system is introduced. Besides, advantages and disadvantages of various DRO methods are analyzed including two classes of probability density, moment information and distributed robust chance constrained method based on these two classes. Finally, the research direction of DRO method in the field of power system is summarized and prospected.

    表 7 Table 7
    表 6 Table 6
    表 2 基于多离散场景的DRO与其他方法对比Table 2 Comparisons between DRO based on multiple discrete scenarios and other methods
    表 1 不确定性处理方法对比Table 1 Comparisons of different uncertainty treatment methods
    表 3 基于Wasserstein距离的DRO与其他方法对比Table 3 Comparisons between DRO based Wasserstein distance and other methods
    图1 DRO方法类别Fig.1 Categories of DRO methods
    表 5 基于确定性矩的DRO与其他方法对比Table 5 Comparisons between DRO based on deterministic moment and other methods
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HE Shuaijia,RUAN Hebin,GAO Hongjun,et al.Overview On Theory Analysis and Application of Distributionally Robust OptimizationMethod in Power System[J/OL].Automation of Electric Power Systems,

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  • Received:October 22,2019
  • Revised:May 12,2020
  • Adopted:February 11,2020
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