Optimization Method of Energy Storage Configuration for Distribution Network with High Proportion of Photovoltaic Based on Source–Load Imbalance

Fangfang Zheng, Xiaofang Meng (), Tiefeng Xu, Yongchang Sun and Hui Wang
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Fangfang Zheng: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiaofang Meng: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China
Tiefeng Xu: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yongchang Sun: Economic and Technological Development Zone Heating Limited Company, Dalian 116600, China
Hui Wang: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China

Sustainability, 2023, vol. 15, issue 13, 1-17

Abstract: After a high proportion of photovoltaic is connected to the distribution network, it will bring some problems, such as an unbalanced source and load and voltage exceeding the limit. In order to solve them, this paper proposes an optimization method of energy storage configuration for a high-proportion photovoltaic distribution network considering source–load imbalance clustering. Taking the minimum total voltage deviation, the minimum total power loss and the minimum total operating cost as the objective function, and considering various constraints such as power balance constraints and energy storage operation constraints, a mathematical model for energy storage configuration optimization is established. Firstly, the source–load imbalance of the distribution network with a high proportion of photovoltaic is defined. Therefore, according to the 24 h photovoltaic and load data, the 24 h source–load imbalance can be obtained, and the optimal k value can be determined by the elbow rule, so that 24 h a day can be clustered into k periods by the k-means algorithm. Then, the fuzzy comprehensive evaluation method is used to determine the weight factors of each objective function in each period, and three scenes are determined according to the different amount of energy storage. Then, the hybrid particle swarm optimization algorithm proposed in this paper is used to solve the model, and the minimum objective function value, optimal position and optimal capacity of each energy storage grid in each scene are obtained. Finally, it is applied to an example of IEEE33. In the results, the total voltage deviation is increased by more than 10%, the total power loss is increased by more than 8% and the total operating cost is increased by more than 12%, which verifies the effectiveness of the proposed model.

Keywords: photovoltaic; source–load imbalance; k-means clustering; energy storage configuration optimization; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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