基于改进多目标蜉蝣算法的配网电池储能系统最优选址定容  

Optimal location and sizing of battery energy storage systems in a distribution network based on a modified multi-objective mayfly algorithm

作　　者：安东 杨德宇[1] 武文丽[1] 蔡文超 李赫 杨博[2] 韩一鸣 AN Dong;YANG Deyu;WU Wenli;CAI Wenchao;LI He;YANG Bo;HAN Yiming(Inner Mongolia Power Research Institute,Huhhot 010020,China;Faculty of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]内蒙古电力科学研究院,内蒙古呼和浩特010020 [2]昆明理工大学电力工程学院,云南昆明650500

出　　处：《电力系统保护与控制》2022年第10期31-39,共9页Power System Protection and Control

基　　金：国家自然科学基金项目资助(61963020);内蒙古电力(集团)有限责任公司科技研究项目资助(内电科技〔2021〕3号)。

摘　　要：电池储能系统(BESSs)在配电网的选址定容是保证BESSs和配电网经济可靠运行的关键。基于此,提出了一种配电网BESSs最优选址定容方法。首先,采用C-均值聚类算法对全年的负荷曲线和风、光出力曲线进行典型日聚类。进而,以BESSs日均综合成本、电压波动和负荷波动最小为目标,建立了配电网BESSs最优选址定容的多目标优化模型。为获得BESSs等决策变量的Pareto最优解集,设计了改进的多目标蜉蝣算法(MMOMA)进行求解。为实现三个目标的最佳权衡,采用改进理想点决策(IIPBD)方法对Pareto最优解集进行折中决策。最后,利用扩展的IEEE33节点配电系统进行仿真测试,以验证所提方法的有效性。仿真结果表明,与另外两种传统多目标优化算法相比:所提MMOMA获得的Pareto前沿分布更广、更均匀;IIPBD方法获得的折中决策方案有效实现了BESSs投资成本的最小化,同时能显著降低配电网的电压波动和负荷波动。Optimal location and sizing of battery energy storage systems(BESSs)in a distribution network(DN)is essential to guarantee their economic and reliable operation.Given this,this paper proposes an optimal siting and sizing method for BESSs.First,the C-mean clustering algorithm is used for typical scene clustering of load curves,and wind and photovoltaic(PV)output curves over a year.Secondly,a multi-objective optimization model for optimal location and sizing is established to minimize the daily cost of BESSs,and voltage and load fluctuation.To obtain the Pareto optimal solution set of BESS location,this paper designs a modified multi-objective mayfly algorithm(MMOMA)to analyze the model.An improved ideal-point-based decision(IIPBD)method is employed to select a compromise solution among the Pareto optimal solution set.Thus the best trade-off of three objectives is achieved.Finally,this paper uses the expanded IEEE33 bus system to validate the proposed method.Simulation results show that MMOMA can obtain a widely spread and well-distributed Pareto front compared with two traditional multi-objective optimization algorithms.The compromise decision scheme obtained by the IIPBD method effectively minimizes the comprehensive cost of BESSs,and significantly reduces voltage and load fluctuation of the DN.

关 键 词：电池储能系统 最优选址定容 Pareto多目标优化 改进多目标蜉蝣算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM91[自动化与计算机技术—控制科学与工程]