| 引用本文: | 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.[点击复制] |
| ZHANG Ziyong,ZHANG Menglin,LI Sha.Environmental/economic power dispatch based on multi-objective particle swarm constraint optimization algorithm[J].Power System Protection and Control,2017,45(10):1-10[点击复制] |
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| 摘要: |
| 提出了一种新的多目标粒子群优化(Multi-Objective Particle Swarm Optimization, MOPSO )算法，用于求解电力系统的环境/经济调度问题。通过设计特定的约束修正因子，将不可行解修正成可行解，并在此基础上用惩罚函数法构建了新的适用于多目标粒子群的适应度函数模型。根据帕累托占优条件形成历史帕累托最优解集和全局帕累托最优解集，引入稀疏度排序法选择全局最优解，基于帕累托最优前沿的斜率特性，提出用斜率法筛选非劣解，采用基于模糊数学的满意度评价模型选择POF的折衷最优解。最后，用IEEE-30节点标准测试系统对所提算法进行了仿真测试，并与其他算法进行了对比。仿真结果表明所提算法可行、有效。 |
| 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 |
| DOI：10.7667/PSPC160752 |
| 投稿时间：2016-05-25修订日期：2016-10-18 |
| 基金项目:国家自然科学基金项目(51207113);高等学校博士学科点专项科研基金项目(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) |
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| Environmental/economic power dispatch based on multi-objective particle swarm constraint optimization algorithm |
| ZHANG Ziyong,ZHANG Menglin,LI Sha |
| (Guangdong Power Dispatch and Control Center, Guangzhou 510000, China;School of Electrical Engineering, Wuhan University, Wuhan 430072, China;Guangdong Power Science Academy, Guangzhou 510000, China) |
| 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. 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). |
| Key words: environmental economic dispatch multi-objective particle swarm optimization constraint handling Pareto optimal solution slope method best compromise solution |
