Optimal Locating and Sizing of BESSs in Distribution Network Based on Multi-Objective Memetic Salp Swarm Algorithm

• Published in: Frontiers in energy research Vol. 9
• Main Authors: Peng, Sui, Gong, Xianfu, Liu, Xinmiao, Lu, Xun, Ai, Xiaomeng
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 27.07.2021
• Subjects: battery energy storage systems; distribution networks; ideal-point decision method; multi-objective memetic salp swarm algorithm; optimal locating and sizing
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2021.707718

Battery energy storage systems (BESSs) are a key technology to accommodate the uncertainties of RESs and load demand. However, BESSs at an improper location and size may result in no-reasonable investment costs and even unsafe system operation. To realize the economic and reliable operation of BESSs in the distribution network (DN), this paper establishes a multi-objective optimization model for the optimal locating and sizing of BESSs, which aims at minimizing the total investment cost of BESSs, the power loss cost of DN and the power fluctuation of the grid connection point. Firstly, a multi-objective memetic salp swarm algorithm (MMSSA) was designed to derive a set of uniformly distributed non-dominated Pareto solutions of the BESSs allocation scheme, and accumulate them in a retention called a repository. Next, the best compromised Pareto solution was objectively selected from the repository via the ideal-point decision method (IPDM), where the best trade-off among different objectives was achieved. Finally, the effectiveness of the proposed algorithm was verified based on the extended IEEE 33-bus test system. Simulation results demonstrate that the proposed method not only effectively improves the economy of BESSs investment but also significantly reduces power loss and power fluctuation.