广东电力2025，Vol.38Issue(8)：19-31,13.DOI:10.3969/j.issn.1007-290X.2025.08.003
基于改进粒子群算法的高速公路充电站储能系统优化配置
Optimal Configuration of Energy Storage System for Highway Charging Stations Based on Improved Particle Swarm Optimization Algorithm
摘要
Abstract
Addressing the charging load fluctuation problem on highways in the context of rapid growth of new energy electric vehicles,this paper proposes a multi-objective particle swarm optimization method for energy storage configuration based on group dynamic perception and knowledge-driven approach.Through typical day extraction,vehicle charging load and renewable energy output prediction,an optimization model is constructed with voltage fluctuation,network loss,and total cost as objectives.Based on the characteristics of energy storage system configuration problems,an improved particle swarm algorithm is developed and a three-layer collaborative optimization framework is constructed including group dynamic perception mechanism,knowledge-based incremental learning strategy,and local refinement strategy.The optimal solution is selected through AHP-TOPSIS cross-scenario validation method.Algorithm performance testing demonstrates that the improved particle swarm algorithm exhibits excellent performance in addressing high-dimensional search loss of the particle,improvement of global-local search balance,and enhancement of convergence.In the test model,compared with benchmark and simple configuration schemes,voltage fluctuation is reduced by 45.6%and 14.9%respectively,while the network loss is decreased by 37.8%and 20.9%respectively,thereby enhancing both microgrid stability and economic efficiency.关键词
储能优化/多目标优化算法/动态负荷特征/TOPSIS-AHP综合评价Key words
energy storage optimization/multi-objective optimization algorithm/dynamic load characteristics/TOPSIS-AHP comprehensive evaluation分类
信息技术与安全科学引用本文复制引用
赵天翼,刘代飞,何灵奇,张曙云..基于改进粒子群算法的高速公路充电站储能系统优化配置[J].广东电力,2025,38(8):19-31,13.基金项目
湖南省重点实验室开放基金拟资助项目(2024NDL003) （2024NDL003）
