中国电机工程学报2017，Vol.37Issue(7)：1927-1936,10.DOI:10.13334/j.0258-8013.pcsee.152851
基于Markov-ELM的独立混合微网分布式电源多目标容量优化配置
Multi-objective Optimal Sizing for Distributed Generation of Isolated Hybrid Microgrid Using Markov-based Electromagnetism-like Mechanism
摘要
Abstract
Taking the comprehensive optimum of economy, environmental protection and energy efficiency as the objective, and the system reliability as the constraint, an optimal allocation model of distributed generation in the hybrid microgrid was built. The common optimization process needed to be iterated through the annual data for time series analysis, which would result in extensive computations and low operation efficiency. To solve this problem, the fuzzy c-means clustering algorithm (FCM) was used to extract typical operational scenarios from the annual 8760h wind speed, light and load data. Then the corresponding Markov models were built by using stochastic process method. To get rid of the subjectivity of artificial weight, the entropy theory was introduced to determine the weight coefficient of multi- objective function. Moreover, the population mobile strategy, self-adaptive variable steps, genetic variations and reduction of search space according to the proportion coefficient were adopted to improve the convergence of electromagnetism-like mechanism (ELM). Finally, the aforementioned Markov models were embedded in the improved ELM for optimization solution. Taking Guangxi Weizhou Island as a case study, the effectiveness of the models and algorithms were verified. The results show that, compared to chronology-based ELM, the proposed Markov-ELM has obvious advantages in rapidity, accuracy and global convergence.关键词
独立微电网/多目标优化/熵理论/模糊C-均值/Markov模型/仿电磁学算法Key words
isolated microgrid/multi-objective optimization/entropy theory/fuzzy C-means/Markov model/electromagnetism-like mechanism分类
信息技术与安全科学引用本文复制引用
吕智林,谭颖,李捷,王琥,王先齐..基于Markov-ELM的独立混合微网分布式电源多目标容量优化配置[J].中国电机工程学报,2017,37(7):1927-1936,10.基金项目
国家自然科学基金项目(61364027),广西自然科学基金项目(2014GXNSFAA118384). Project Supported by National Natural Science Foundation of China (61364027), Project Supported by Natural Science Foundation of Guangxi (2014GXNSFAA118384). （61364027）
