基于改进多目标粒子群算法的微电网储能优化配置  

Optimal configuration of energy storage in a microgrid based on improved multi-objective particle swarm optimization

作　　者：陆立民 褚国伟 张涛[2] 杨志超[2] LU Limin;CHU Guowei;ZHANG Tao;YANG Zhichao(Changzhou Power Supply Company,State Grid Jiangsu Electric Power Co.,Ltd.,Changzhou 213003,China;Nanjing Institute of Technology,Nanjing 211167,China)

机构地区：[1]国网江苏省电力有限公司常州供电分公司,江苏常州213003 [2]南京工程学院,江苏南京211167

出　　处：《电力系统保护与控制》2020年第15期116-124,共9页Power System Protection and Control

基　　金：国家自然科学基金项目(51707089);国家电网有限公司科技项目“以电为核心多能互补集成功率变换装备关键技术及实证研究”。

摘　　要：储能参与微电网的优化运行能有效解决可再生能源大规模并网所引起的系统安全稳定问题。基于双层规划理论建立以负荷波动、系统成本以及储能SOC偏差为目标的储能优化配置模型,提出一种改进的多目标粒子群算法求解该模型。依据最优相似度来指导惯性权重的取值,适时引入交叉变异操作,在保证算法收敛性的同时,提高其跳出局部最优解的能力。为保证pareto解集的全局性和均匀性,提出了一种多迭代方向pareto解集动态更新策略。最终基于信息熵确立权重,采用TOPSIS法选取最优方案。通过修改的IEEE-33节点系统进行算例分析,验证了该算法在求解微电网储能优化配置问题上的有效性和优越性。The energy storage participation in the optimal operation of the microgrid can effectively solve the problem of system security and stability caused by large-scale grid connection of renewable energy. Based on bi-level programming theory, an energy storage optimization configuration model with load fluctuation, system cost and energy storage SOC deviation is established. An improved multi-objective particle swarm optimization algorithm is proposed to solve the model. According to the optimal similarity, the value of inertia weight is guided, and the cross-mutation operation is introduced in time to improve the convergence of the algorithm and improve the ability to jump out of the local optimal solution. To ensure the globality and uniformity of the Pareto solution set, a strategy for dynamically updating the set based on multiple iteration directions is proposed. Finally, the weight is established based on information entropy, and the optimal scheme is selected by the TOPSIS method. The modified IEEE-33 node system is used to analyze the effectiveness and superiority of the proposed algorithm in solving the optimal configuration of microgrid energy storage.

关 键 词：微电网 储能 双层规划 多目标粒子群算法 多迭代方向 

分 类 号：TM727[电气工程—电力系统及自动化]