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粒子群优化(PSO)算法在电力系统多目标优化问题中,存在易陷入局部最优,计算结果随机性较大及利用权重处理多目标问题时取值难以科学化等缺陷。为了避免这些问题,结合随机黑洞策略、惯性权重和学习因子的动态更新、NSGA-II非支配排序、拥挤距离排序、领导粒子的选择和小概率随机突变的方法对经典PSO算法进行了改进,提出了一种新的改进粒子群算法,引用Pareto前沿来处理电力系统多目标优化问题。随后,将改进粒子群算法应用于电力系统经济负荷分配问题中,综合考虑了发电机的燃料成本和有功网损成本,结果表明改进粒子群算法增强了全局搜索能力,改善了收敛速度和收敛精度,解集的分布更加均匀,并且有效地展现了Pareto前沿关系,使其在处理非线性、非凸性及高维等多目标问题时更具有优势。
Particle Swarm Optimization (PSO) algorithm has some shortcomings such as easy to fall into local optimum, large randomness of calculation results and difficult to scientifically value multi-objective problem in power system. In order to avoid these problems, the classical PSO algorithm is improved based on random black hole strategy, dynamic update of inertia weight and learning factor, NSGA-II non-dominated ordering, congestion distance ranking, leading particle selection and small probability random mutation A new improved particle swarm optimization algorithm is introduced, which refers to Pareto frontier to deal with multi-objective optimization problems of power system. Subsequently, the improved particle swarm optimization algorithm is applied to the economic load distribution in power system, and the fuel cost and active network loss cost of the generator are considered. The results show that the improved particle swarm optimization enhances the global search ability and improves the convergence speed and convergence precision , The disaggregation distribution is more uniform, and the Pareto frontier relationship is effectively demonstrated, which makes it more advantageous in dealing with multi-objective problems such as nonlinear, non-convex and high-dimensional.