摘要
针对电力系统动态环境经济调度高纬度、强耦合、非线性、非凸等特点,提出一种双群体伪并行GA-DE(genetic algorithm-differential evolution)多目标算法.该算法基于外部精英存档和Pareto占优概念,利用差分进化算法和遗传算法构成双种群协同进化模式;采用平均熵及立方混沌映射初始化策略,增加种群多样性;根据相邻解的分布情况,改进Pareto解集的裁剪方式.与传统模型不同,将线损作为优化目标加入模型,采用动态松弛约束机制处理模型的复杂约束.经典10机组系统的验证结果表明:该算法在解决电力系统调度问题上具有可行性.
Aiming at the characteristics of high latitude,strong coupling,non-linearity and non-convexity in power system dynamic environmental economic dispatch,a double swarm peudo pallelism multi-objective genetic and differential evolution(GA-DE)algorithm was proposed.Based on the concepts of external elite reservation and Pareto dominance,the differential evolution algorithm and genetic algorithm were used,and a model of dual-population evolution was built in this paper.To increase the diversity of the population,the mean entropy and cubic chaotic map initialization strategy were adopted in proposed algorithm.According to the distribution of adjacent solutions,the pruning method of Pareto solution set was improved.Different from the traditional model,the line loss was added to the model as the optimization objective,then a dynamic relaxation constraint processing mechanism was adopted,and the complex constraints in the model could be dealt with well.The simulation results of the 10-machine test system showed that the algorithm was feasible in solving the power system dispatch problem.
作者
李笑竹
王维庆
徐其丹
LI Xiaozhu;WANG Weiqing;XU Qidan(Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology, Xinjiang University, Urumqi 830047, China;State Grid Xinjiang Electric Power Co., Ltd., Economic and Technological Research Institute, Urumqi 830000, China)
出处
《安徽大学学报（自然科学版）》
CAS
北大核心
2021年第3期42-49,共8页
Journal of Anhui University(Natural Science Edition)
基金
国家自然科学基金资助项目(51667020)。
关键词
动态环境经济调度
多目标优化
多种群协同
并行搜索
动态松弛
dynamic environmental economic dispatch
multi-objective optimization
multi group collaboration
parallel search
dynamic relaxation
