基于局部搜索BLPSO算法的动态经济调度优化  

Optimization of Dynamic Economic Dispatch Using Biogeography-based Learning Particle Swarm Optimization with Local Search

作　　者：唐柯 TANG Ke(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《软件导刊》2021年第6期119-124,共6页Software Guide

基　　金：江苏省自然科学基金项目(BK20160540)。

摘　　要：动态经济调度可以降低发电运行成本、节省能源,在电力系统运行过程中起着重要作用。针对含电动汽车负载的动态经济调度问题,提出一种基于局部搜索策略的生物地理粒子群算法(BLPSO-OBS)。该算法在生物地理粒子群算法中引入观察蜂局部搜索策略,从而提升了求解精度。该算法用于解决含电动汽车的动态经济调度问题,并考虑了电力平衡约束、禁止操作区域约束和爬坡约束等发电约束。统计实验结果表明,BLPSO-OBS是解决动态经济调度问题的一种有效方法。Dynamic economic dispatch(DED)can reduce the operating cost of power generation,save energy resources,and it plays an important role in the power system operation.To solve the DED problem considering electric vehicle loads,this paper proposes a biogeography-based learning particle swarm optimization algorithm based on onlooker bee search strategy(BLPSO-OBS).This algorithm introduces the local search strategy of the onlooker bee into the BLPSO algorithm,thereby improving the solution accuracy.The BLPSO-OBS algorithm is applied to solve DED problemswith electrical vehicleloads,and generation constraints such as power balance,prohibited operation areas,and ramp rate limits constraints are considered.Statistical experimental results show that BLPSOOBS is an effective method to solve the DED problem.

关 键 词：动态经济调度 电动汽车负载 生物地理粒子群算法 局部搜索 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]