Distributionally Robust Optimal Dispatch Method Considering Mining of Wind Power Statistical Characteristics

被引：0

作者：

Xu, Chaoran ^{[1
]}

Xu, Xiaoyuan ^{[1
]}

Yan, Zheng ^{[1
]}

Li, Hengjie ^{[1
,2
]}

机构：

[1] Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai,200240, China

[2] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou,730050, China

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 02期

关键词：

(1. Key Laboratory of Control of Power Transmission and Conversion; Ministry of Education (Shanghai Jiao Tong University); Shanghai; 200240; China; 2. College of Electrical and Information Engineering; Lanzhou University of Technology; Lanzhou; 730050; China) Abstract: A power system dispatch method based on data-driven distributionally robust optimization (DRO) is proposed to deal with the operation problem of power system considering the uncertainty of wind power. Firstly; the statistical information of wind power data is mined; and a construction method of probability distribution ambiguity set for wind power based on principal component analysis and kernel density estimation is proposed to describe the randomness of wind power and the spatial correlation between the outputs of different wind turbines. Secondly; aiming at the dispatch problem with wind power; a two-stage DRO problem considering probability distribution ambiguity set is established. Thirdly; the DRO problem is transformed into its equivalent solvable form; and the affine strategy and duality principle are used to transform it into a linear programming problem for solving. The range parameter selection strategy of probability distribution ambiguity set based on out-of-sample test is proposed to ensure the reliability and economy of the dispatch scheme. Finally; the 6-bus and IEEE 118-bus systems are used for simulation analysis; and the proposed DRO method is compared with the DRO method without consideration of the correlation of random variables and the traditional stochastic and robust optimization methods to verify the effectiveness of proposed method. This work is supported by National Natural Science Foundation of China (No. 52077136). Key words: data-driven; distributionally robust optimization (DRO); economical dispatch; uncertainty; wind power output;

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页码：33 / 42