基于差分进化算法的动态环境经济电力系统调度优化

Dynamic Environment Economic Dispatch Based on Differential Evolution Algorithm

电力系统动态环境经济调度优化隶属于非线性优化问题范畴,并具有多目标、高维、多约束条件等特点。经典的数学规划方法无法处理此类复杂问题。为此,提出了新的方法来解决这个问题。首先,通过代价惩罚因子将双目标优化问题转化为单目标优化问题。然后,设计启发式搜索策略来解决调度问题中的爬坡约束、动态电力平衡约束。采用启发式策略修正解决方案,能够提高群体的多样性,拓展搜索空间。基于优先列表的启发式策略能够使能耗低的火力发电机拥有更高的优先级进行更多的电力输出,以得到更优的调度解决方案。最后,改进差分进化算法,以加快搜索的速度并提高解决方案的质量。

Dynamic environment economic dispatch is of non-linear optimization problems. It represents the characteris- tics of multi-oblective, high dimensions and constraints. So the traditional methods are no longer fit to solving these op- timization problems. A price penalty factor approach was utilized here to convert the hi-objective problems into single objective ones. In order to handle constraints effectively, heuristic rules were proposed to handle ramp rate constraints, and heuristic strategies based on priority list are employed to handle active power balance constraints. The heuristic strategies also can increase the variety of the individual and extend the search scope. The thermal unit with the lower average full-load cost will have the higher priority to dispatch more generation power in the heuristic strategies based on priority list, so that the even better scheduling solutions can be obtained. At last, the differential evolution algorithm was improved to enhance the search ability and improve the solution quality.