基于模糊聚类分析与模型识别的微电网多目标优化方法  

Multi-Objective Optimization Method of Microgrid Based on Fuzzy Clustering Analysis and Model Recognition

作　　者：赵劲帅 邱晓燕[1] 马菁曼 陈科彬 

机构地区：[1]智能电网四川省重点实验室(四川大学),四川省成都市610065

出　　处：《电网技术》2016年第8期2316-2323,共8页Power System Technology

基　　金：四川省科技支撑项目(2014JY0191)~~

摘　　要：在微电网调度过程中综合考虑经济、环境、蓄电池的循环电量,建立多目标优化数学模型。针对传统多目标粒子群算法(multi-objective particle swarm optimization,MOPSO)的不足,提出引入模糊聚类分析的多目标粒子群算法(multi-objective particle swarm optimization algorithm based on fuzzy clustering,FCMOPSO),在迭代过程中引入模糊聚类分析来寻找每代的集群最优解。与MOPSO相比,FCMOPSO增强了算法的稳定性与全局搜索能力,同时使优化结果中Pareto前沿分布更均匀。在求得Pareto最优解集后,再根据各目标的重要程度,用模糊模型识别从最优解集中找出不同情况下的最优方案。最后以一欧洲典型微电网为例,验证算法的有效性和可行性。In order to improve traditional multi-objective particle swarm optimization algorithm（MOPSO）, multi-objective particle swarm optimization algorithm based on Fuzzy Clustering（FCMOPSO） is proposed considering economy, environment and battery storage capacity in microgrid scheduling process. Fuzzy clustering analysis is used to find optimal solution of each generation. Compared with MOPSO, FCMOPSO enhances stability and global search ability of the algorithm and makes Pareto distribution in optimization results more uniform. After Pareto optimal solution set is obtained, according to importance of each target in different conditions, optimal solution is found out from Pareto optimal solution set with fuzzy model. Finally, effectiveness and feasibility of the proposed algorithm are verified with a typical microgrid in Europe.

关 键 词：微电网 多目标优化 模糊聚类 模糊模型识别 

分 类 号：TM72[电气工程—电力系统及自动化]