Power Flow Optimization Strategy of Distribution Network with Source and Load Storage Considering Period Clustering

Fangfang Zheng, Xiaofang Meng (), Lidi Wang and Nannan Zhang
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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
Lidi Wang: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China
Nannan Zhang: College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China

Sustainability, 2023, vol. 15, issue 5, 1-14

Abstract: The large-scale grid connection of new energy will affect the optimization of power flow. In order to solve this problem, this paper proposes a power flow optimization strategy model of a distribution network with non-fixed weighting factors of source, load and storage. The objective function is the lowest cost, the smallest voltage deviation and the smallest power loss, and many constraints, such as power flow constraint, climbing constraint and energy storage operation constraint, are also considered. Firstly, the equivalent load curve is obtained by superimposing the output of wind and solar turbines with the initial load, and the best k value is obtained by the elbow rule. The k-means algorithm is used to cluster the equivalent load curve in different periods, and then the fuzzy comprehensive evaluation method is used to determine the weighting factor of the optimization model in each period. Then, the particle swarm optimization algorithm is used to solve the multi-objective power flow optimization model, and the optimal strategy and objective function values of each unit output in the operation period are obtained. Finally, IEEE33 is used as an example to verify the effectiveness of the proposed model through two cases: a fixed proportion method to determine the weighting factor, and this method to determine the weighting factor. The proposed method can improve the economy and reliability of distribution networks.

Keywords: distribution network; power flow optimization; k-means period clustering; energy storage system; 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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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