基于改进萤火虫算法的多目标优化潮流仿真研究

Research on multi-objective optimal power flow simulation based on improved firefly algorithm

针对电力系统多目标优化潮流(MOOPF)问题,结合基于约束优先的帕累托占优关系、非劣排序和拥挤距离计算,提出了约束优先非劣排序的多目标萤火虫算法(CNSFA),并根据模糊数学中的模糊隶属度选取最优折衷解。通过对IEEE30节点测试系统进行电力系统多目标优化潮流仿真测试以及与对比算法的比较可以看出:该算法在求解多目标优化潮流问题时,得到了分布性均匀和收敛性较强的帕累托解。

In view of the multi-objective optimal power flow（MOOPF）problem of the power system,and in combination with the Pareto dominance relation,non-inferior sorting and crowding distance calculation based on constraint priority,a constrain-prior non-dominated sorting firefly algorithm（CNSFA）proposed,and the optimal compromise solution is selected according to the fuzzy membership degree in fuzzy mathematics.Through the implementation of the simulation test of the multi-objective optimal power flow on the IEEE30 node testing system and the comparison with the contrast algorithm,it can be seen that this algorithm obtains the Pareto solution with the uniform distribution and strong convergence when the multi-objective optimal power flow problem is solved.