基于改进秃鹰算法的微电网群经济优化调度研究  

RESEARCH ON ECONOMIC OPTIMAL DISPATCHING OF MICROGRID CLUSTERS BASED ON IMPROVED BALD EAGLE SEARCH ALGORITHM

作　　者：周辉 张玉[1,2] 肖烈禧 赵冠皓 Zhou Hui;Zhang Yu;Xiao Liexi;Zhao Guanhao(College of Mechanical and Control Engineering,Guilin University of Technology,Guilin 541006,China;Guangxi Key Laboratory of Building New Energy and Energy Saving,Guilin 541006,China)

机构地区：[1]桂林理工大学机械与控制工程学院,桂林541006 [2]广西建筑新能源与节能重点实验室,桂林541006

出　　处：《太阳能学报》2024年第2期328-335,共8页Acta Energiae Solaris Sinica

基　　金：广西建筑新能源与节能重点实验室开放研究基金(桂科能17-J-21-4)。

摘　　要：传统优化算法相较于智能优化算法在求解微电网群优化调度问题中较难寻出可行解或最优解,对此提出一种基于融合反向学习和柯西变异改进的秃鹰算法(IBES),在秃鹰搜索空间猎物阶段采用融合反向学习和柯西变异策略,使得秃鹰算法有效跳出局部最优,解决算法求解精度低等问题。通过与粒子群算法(PSO)、麻雀算法(SSA)、鲸鱼算法(WOA)进行对比,仿真结果表明IBES寻优精度更高,可有效减少微电网群系统的经济成本。Compared with the intelligent optimization algorithm,the traditional optimization algorithm is more difficult to find a feasible or optimal solution in solving the optimal dispatch problem of microgrid clusters.This paper presents an improved bald eagle search algorithm(IBES)based on fusion reverse learning and Cauchy mutation,which uses the fusion reverse learning and Cauchy mutation strategy in the bald eagle search space prey stage to make BES jump out of local optimization effectively and solve the problem of low accuracy in solving the algorithm.By comparing with particle swarm optimization(PSO),sparrow search algorithm(SSA),and whale optimization algorithm(WOA),the simulation results show that IBES is more accurate and can effectively reduce the economic cost of microgrid cluster system.

关 键 词：微电网 优化 调度 改进秃鹰算法 电动汽车 

分 类 号：TM732[电气工程—电力系统及自动化]