Multi-objective hybrid optimization algorithm for short term environmental/economic generation scheduling
机组短期负荷环境／经济调度多目标混合优化

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.