电力系统自动化2024，Vol.48Issue(14)：100-109,10.DOI:10.7500/AEPS20230821005
提升光储充电站运行效率的多目标优化配置策略
Multi-objective Optimal Configuration Strategy for Improving Operation Efficiency of Photovoltaic Energy Storage Charging Stations
摘要
Abstract
The economic benefits and grid-side power quality of photovoltaic energy storage charging stations are directly affected by their operation efficiency.As insufficient consideration of operation efficiency during the capacity configuration can lead to unnecessary power losses,this paper proposes a multi-objective optimal configuration strategy for improving the operation efficiency of photovoltaic energy storage charging stations.By analyzing the impact of power losses of both the converter and the internal source line on the operation efficiency of the photovoltaic energy storage charging stations,this paper proposes evaluation indicators and calculation methods for the operation efficiency of charging stations,and discusses the impact of operation efficiency of photovoltaic energy storage charging stations on their capacity configuration.A multi-objective optimal capacity configuration strategy is established with the optimal economic benefits of charging stations,operation efficiency,and grid-side peak-valley power compensation capacity as the optimization objectives.Aiming at the characteristics of the optimization objectives,an improved non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)is proposed to obtain the solution method for the optimization strategy.The effectiveness and superiority of the optimization strategy are verified through a case study on operation scenarios of a typical photovoltaic energy storage charging station in southwest China.关键词
光储充电站/运行效率/容量优化配置/多目标优化/改进非支配排序遗传算法Key words
photovoltaic energy storage charging station/operation efficiency/optimal capacity configuration/multi-objective optimization/improved non-dominated sorting genetic algorithm引用本文复制引用
易建波,胡猛,王泽宇,胡维昊,黄琦..提升光储充电站运行效率的多目标优化配置策略[J].电力系统自动化,2024,48(14):100-109,10.基金项目
国家自然科学基金面上项目(52277083) （52277083）
四川省科技厅重点研发计划资助项目(2021YFG0098). This work is supported by National Natural Science Foundation of China(No.52277083)and Key R&D Program of Science and Technology Department of Sichuan Province(No.2021YFG0098). （2021YFG0098）
