共享储能电站优化选址定容研究
摘要:为了有效平抑分布式电源带来的不利影响并能获得超额利润，提出一种改进的多目标粒子群算法研究配电网中共享储能电站优化选址定容问题。首先，建立了共享储能系统数学模型，得出优化条件及目标函数，并计算得到共享储能经济成本。其次，利用改进的多目标粒子群算法对IEEE 33节点进行算例研究。算例中，通过经济总成本最小择优确定共享储能系统接入个数、选址位置与容量配置，并着重分析接入最优储能个数的充放电功率及储能荷电状态。最后，对电池储能系统的优化选址定容方法及算例特点进行了归纳与总结。结论表明，共享储能系统确实可以有效平抑分布式电源带来的不利影响，且经济性更好，在配电网中接入2个储能系统且分别配置0.5 MW储能为最优选址个数与最优容量配置。
Abstract：In order to effectively suppress the adverse effects of distributed generation and obtain excess profits, an improved multi-objective particle swarm optimization algorithm is proposed to study the optimal location and capacity of shared energy storage power stations in distribution networks. Firstly, this paper establishes the mathematical model of shared energy storage system, lists the optimization conditions and objective functions, and lists the economic cost calculation of shared energy storage. Secondly, the IEEE 33 bus is studied by using the improved multi-objective particle swarm optimization algorithm. In the example, the access number, location and capacity configuration of the shared energy storage system are determined by minimizing the total economic cost, and the charging and discharging power and energy storage SOC (state of charge) of the optimal access number are emphatically analyzed. Finally, the method of optimal location and capacity of battery energy storage system and the characteristics of calculation examples are summarized. The conclusion shows that the shared energy storage system can effectively suppress the adverse effects of distributed power generation, and the economy is better. When two energy storage systems are connected in the distribution network and 0.5 MW is configured respectively, it is the optimal number of location and capacity configuration.
标题：共享储能电站优化选址定容研究
title:Optimal Location and Capacity of Shared Energy Storage Power Station
作者：李建林,康靖悦,董子旭,崔宜琳,张国强
authors:LI Jianlin,KANG Jingyue,DONG Zixu,CUI Yilin,ZHANG Guoqiang
关键词：共享储能选址,Pareto前沿,粒子群,熵权法,
keywords:shared energy storage site selection,Pareto frontier,particle swarm,entropy weight method,
发表日期：2022-08-11
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