电力系统保护与控制2017，Vol.45Issue(10)：1-10,10.DOI:10.7667/PSPC160752
基于多目标粒子群算法的电力系统环境经济调度研究
Environmental/economic power dispatch based on multi-objective particle swarm constraint optimization algorithm
摘要
Abstract
A new multi-objective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) is proposed. Infeasible solutions can be revised to feasible ones by designing specific constraints correction factor. And on the basis of that, a new fitness function model for multi-objective particle swarm is built based on the penalty function method. The historical set and global set for non-dominated solutions are formed, according to the Pareto dominant conditions. A crowding distance-based approach is introduced to assign the global leader. Moreover, a new technique called slope method is proposed to further filter the non-dominated solutions based on the slope characteristics of the Pareto optimal front (POF). Then, fuzzy mathematical method for satisfaction evaluation is employed to extract the best compromise solution over the POF. Finally, several optimization runs of the proposed algorithm are carried out on the standard IEEE 30-bus test system, the results validate that the proposed method is feasible and effective.关键词
环境经济调度/多目标粒子群/约束处理/帕累托最优解/斜率法/折衷最优解Key words
environmental economic dispatch/multi-objective particle swarm optimization/constraint handling/Pareto optimal solution/slope method/best compromise solution引用本文复制引用
张子泳,仉梦林,李莎..基于多目标粒子群算法的电力系统环境经济调度研究[J].电力系统保护与控制,2017,45(10):1-10,10.基金项目
国家自然科学基金项目(51207113) （51207113）
高等学校博士学科点专项科研基金项目(20110141110032) （20110141110032）
西安交通大学电力设备电气绝缘国家重点实验室资助(EIPE13205) This work is supported by National Natural Science Foundation of China (No. 51207113), Research Fund for the Doctoral Program of Higher Education of China (No. 20110141110032), and State Key Laboratory of Electrical Insulation and Power Equipment of Xi'an Jiaotong University (No. EIPE13205). （EIPE13205）
