基于演化算法和讨价还价博弈的多目标最优潮流研究

Multi-Objective Optimal Power Flow Based on Evolutionary Algorithm and Bargaining Game

针对电力系统的多目标最优潮流问题,首先通过遗传算法取得帕累托解集,从而充分反映出不同优化目标之间相互影响、相互背离的内在关系,在此基础上利用纳什讨价还价博弈方法选取全局最优解。探讨同时考虑发电费用(或发电煤耗)最小和系统网损最小的多目标最优潮流问题,首先验证该问题满足讨价还价博弈公理,再通过强度帕累托演化算法(strong Pareto evolution algorithm 2,SPEA2)求解得到帕累托前沿,保证收敛速度较快且帕累托前沿分布均匀,最后基于纳什讨价还价博弈求得最优解,解决了不同目标函数之间可能存在的矛盾。该文通过对IEEE 14节点系统的算例计算,验证了该方法的有效性。

To solve the multi-objective optimal power flow（ OPF） problem,genetic algorithm（GA） was used to find the Pareto front,and fully reflect the interaction and deviation internal relation of different optimal objective functions. On this basis,Nash Bargaining Game was used to get the global optimal solution. This paper discussed the multi-objective OPF problem with considering the minimum generation cost（ or coal consumption） and the minimum system loss simultaneously.This paper firstly verified that the problem could satisfy the Nash Bargaining axiom,secondly obtained the Pareto front by strong Pareto evolution algorithm 2（ SPEA2）,w hich could ensure faster convergence rate and more uniform Pareto front,and then used Nash Bargaining to find the optimal solution and solve the possible contradiction between the different objective function. Case study on IEEE 14-bus system verified the effectiveness of the proposed algorithm.