基于混沌遗传混合优化算法的短期负荷环境和经济调度

Short Term Environmental/Economic Generation Scheduling Based on Chaos Genetic Hybrid Optimization Algorithm

环境和经济短期负荷调度主要由在调度周期内的最优机组组合和负荷分配组成,该文将优先次序法、遗传算法与混沌优化相结合,以应用到电站机组环境／经济运行优化问题中,在混沌遗传算法中采用递阶基因结构,将控制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, 基因修正与罚函数相结合解决约束问题,采用混沌扰动避免遗传算法早熟,运用基于线性搜索的混沌局部优化方法,加快算法的收敛速度和降低计算时间,优化计算结果可以同时得到最优机组组合及负荷最优分配,为实际调度系统提供了一个良好的方法。

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 a hybrid optimal algorithm （HCGA） combining genetic algorithm （GA） with chaotic optimal algorithm （COA） and priority list （PL） is applied in the short term generation scheduling. The hierarchical genes are utilized in the HCGA in which the control genes are used for global optimization in UC, the parameter genes are used for local optimization in ED, and genes modification and punishment are combined to resolve the constrain problem. Chaotic disturbing is used in GA to avoid trapping local extrema. A chaos local linear search is used to accelerate the speed of convergence and reduce the computation time. The optimal unit commitment and dispatch can be obtained simultaneously. A more effective approach for real generation scheduling is proposed.