基于时段耦合特性的动态环境经济调度求解方法

Solving Dynamic Economic Emission Dispatch Based on Period Coupling Characteristic

基于电力系统动态环境经济调度(dynamic economic emission dispatch,DEED)在时段间的耦合特性,提出了一种改进的教与学优化算法,用于求解DEED问题,对燃料费用和污染气体排放量同时进行优化。采用反向学习策略改善种群的多样性,单时段教与学过程来提高算法的局部寻优能力,单时段贪婪选择机制在全局范围内找到新的搜索空间,平衡局部寻优与全局寻优能力。对10机39节点系统进行仿真分析,结果表明所提策略可以显著提高算法的收敛速度和收敛效果,得到高质量的解。

This paper presents a new improved teaching-learning-based optimization algorithm（ ITLBO） to solve the dynamic economic emission dispatch（ DEED） problem based on the characteristics of period coupling. DEED is a biobjective optimization problem,which minimizes the fuel cost and emission level simultaneously. In the proposed algorithm,the opposition-based learning（ OBL） strategy is employed to improve the population diversity,the single interval teaching and learning process is used to enhance the local searching ability,and the single interval greedy selection strategy is adopted to explore a new domain in the whole searching space,aiming at balancing the local optimization and global optimization ability. Through the simulation analysis on the ten-unit 39-nodes system,the results show that the proposed strategy has a faster convergence rate and better convergence characteristic,and can obtain higher quality solutions.