机组短期负荷环境／经济调度多目标混合优化

环境/经济短期负荷调度主要由调度周期内的最优机组组合和负荷环境/经济分配组成,本文将变权重多目标进化算法与混沌局部优化相结合形成混合优化算法应用到电站机组环境/经济运行多目标优化问题中,在混合多目标优化算法中采用组合结构基因,其中机组基因用于机组组合全局粗寻优,参数基因用于负荷分配局部优化,基因修正与罚函数结合解决约束问题．通过对优秀个体进行基于线性搜索的混沌局部优化,可加快收敛速度和降低计算时间．实例仿真结果说明所提出的算法能获得较好分布的Pareto优化解．

Multi-objective hybrid optimization algorithm for short term environmental/economic generation scheduling

Short term environmental/economic generation scheduling (E/EGS) is composed of optimal unit commitment (UC) and environmental/economic dispatch (ED) in the scheduling period. In this paper the multi-objective hybrid evolutionary algorithm (MHEA) which combines randomly-weighed multi-objective evolutionary algorithm (MEA) with chaotic optimal algorithm (COA) is proposed for the short term generation scheduling problem. In the MHEA, the hierarchical genes are adopted in which the commitment genes are used for global optimization in UC and the parameter genes are used for local optimization in ED, and the constrain problem can be solved by combining genes modification with punishment function method. The chaos local linear search is also applied to good solutions to accelerate the convergence speed of algorithm and reduce the computation time. Finally, the results of a case study demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multi-objective E/EGS problem.