电工技术学报  2015, Vol. 30 Issue (22): 181-189    DOI:
电力系统 |
基于多群组均衡协同搜索的多目标优化发电调度
周斌1, 宋艳1, 李金茗2, 余涛3, 韦化4
1. 湖南大学电气与信息工程学院 长沙 410082;
2. 国网湖南省电力公司经济技术研究院 长沙 410004;
3. 华南理工大学电力学院 广州 510640;
4. 广西大学电气工程学院 南宁 530004
Multiobjective Optimal Generation Dispatch Using Equilibria-Based Multi-Group Synergistic Searching Algorithm
Zhou Bin1, Song Yan1, Li Jinming2, Yu Tao3, Wei Hua4
1. Hunan University Changsha 410082 China;
2. State Grid Hunan Electric Company Economic and Technical Institute Changsha 410004 China;
3. South China University of Technology Guangzhou 510640 China;
4. Guangxi University Nanning 530004 China
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摘要 针对多目标、强约束及大规模电力系统发电优化调度问题,提出一种新型多群组均衡协同搜索算法(EMGSS)。该算法基于随机学习自动机的协同进化搜索以实现合作搜索群组之间的适应度分配和策略交互。此外,EMGSS提出一种分级均衡聚类方法为系统调度员提供一系列多样化的帕累托最优均衡前沿,并引入纳什均衡来抽取最终多目标解集的最优决策解。仿真算例采用标准IEEE 30节点及118节点系统,性能对比与仿真测试验证了所提算法在解决高维多目标节能减排发电调度问题中的优越性。
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周斌
宋艳
李金茗
余涛
韦化
关键词 多目标发电调度分级均衡聚类协同进化优化最优均衡解帕累托最优    
Abstract:This paper presents a novel equilibria-based multiple group synergistic searching (EMGSS) algorithm to cope with the highly constrained multi-objective generation dispatch (MOGD) with multiple contradictory objectives. As for the proposed algorithm, a synergistic evolutionary searching mechanism based on stochastic machine learning is developed to achieve the fitness assignment and strategic interaction among cooperative multi-groups. Furthermore, a novel equilibria-based hierarchical clustering is proposed to provide power dispatchers with a set of diversified optimum equilibria Pareto frontier (PF), and Nash equilibrium is used to extract the best decision solution from the resulting PF. The proposed EMGSS has been applied and tested over the IEEE 30-bus system and IEEE 118-bus system. Case studies have verified and confirmed the superiority of the algorithm to solve the multiobjective optimization problems with high-dimensional and large-scale objective functions.
Key wordsMultiobjective generation dispatch    equilibria-based hierarchical clustering    synergistic evolutionary optimization    optimum equilibria solution    Pareto optimality   
收稿日期: 2013-10-27      出版日期: 2015-11-30
PACS: TM732  
  TM761  
基金资助:国家高技术研究发展计划(863计划)(2012AA050209)和国家自然科学基金(51507056、51167001)资助项目
作者简介: 周 斌 男,1984年生,博士,助理教授,主要研究方向为智能电网调度与规划、新能源发电优化。(通信作者)宋 艳 女,1991年生,硕士研究生,主要研究方向为电力系统节能调度优化方法。
引用本文:   
周斌, 宋艳, 李金茗, 余涛, 韦化. 基于多群组均衡协同搜索的多目标优化发电调度[J]. 电工技术学报, 2015, 30(22): 181-189. Zhou Bin, Song Yan, Li Jinming, Yu Tao, Wei Hua. Multiobjective Optimal Generation Dispatch Using Equilibria-Based Multi-Group Synergistic Searching Algorithm. Transactions of China Electrotechnical Society, 2015, 30(22): 181-189.
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