电力系统日发电计划的启发式遗传算法

A heuristic genetic algorithm for daily generation scheduling in power system

机组组合是电力系统日发电计划中主要的优化任务,在满足各种约束条件下求得全局最优解是一个比较困难的事情。遗传算法没有充分利用个体基因的有效信息,所以局部搜索能力较弱,而且随机遗传操作产生的大量不可行解,使得遗传算法的收敛能力降低。为了提高算法的全局搜索能力和收敛性,设计了基于局部优化算法的智能变异算子和消除约束冲突的修复算子。结果表明,运用了新算子的启发式遗传算法收敛到最优解的速度有显著提高。

The combination of generating units is an important optimization task in the daily power generation scheduling in power system. However,it also is one of the most difficult optimization problems in power system,because the problem has many constraints. Since GA does not effectively use all the available information and the operators may generate a large number of infeasible solutions, the searching process does not have satisfactory convergence. In this research work,in order to overcome these difficulties,a new intelligent mutation operator and repair operator for the problem of the combination of generating units based on local optimization method have been proposed. The simulation results show that by implementing the new operators, the heuristic genetic algorithm has satisfactory speed of convergence to the optimum solution.