自适应差分进化算法及对动态环境经济调度问题应用

为了应对动态环境经济调度(DEED)问题的高维性和大规模约束性,提出了一种自适应多目标差分进化算法(ADEA)。设计自适应差分交叉模块,提出改进的current to best/1交叉策略提高种群的多样性,有效地提高传统进化算法的探索与开采能力,提出一种修补策略处理功率平衡约束和爬坡率约束。为了验证该方法的有效性,数值仿真将ADEA应用于10机系统进行测试,并与同类算法展开比较,仿真结果表明ADEA具有较好的收敛能力,获得的Pareto前沿具有较好的均匀性和延展性,通过模糊决策获得的最好折中解能为电力系统调度人员提供较为合理的调度方案。

Adaptive different evolutionary algorithm and its application of dynamic emission economic dispatch problem

In order to solve the dynamic emission economic dispatch(DEED)problem with high-dimensional and large scale constraints,this paper proposed an adaptive multiobjective differential evolutionary algorithm(ADEA).The ADEA designed an adaptive differential crossover module,developed an improving current to best/1 crossover scheme to enhance the diversity of population.These strategies improved the exploration and exploitation ability of the ADEA,it also applied a repair strategy to deal with the equality and inequality constrains in DEED problem.Numerical experiments tested the effectiveness of ADEA on 10-unit system,and compared with several peer algorithms.Simulation results indicate that ADEA has good convergence.The uniformity and ductility of the Pareto front obtained by ADEA is better than that of the compared algorithms.It provides a more efficient scheduling decision-making method for power system dispatcher by a fuzzy decision method.