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Currently, most of multi-objective problems in power systems are changed into a single objective problem by weighting. This paper adopts Pareto front to solve the multi-objective problems in power systems. In order to solve this problemeffectively, we present the particle swarm optimization(PSO) algorithm. In addition, we improve PSO with the random black hole strategy, The dynamic update of inertia weight and learning factor, leading particles selection, small probability random mutation. The proposed algorithm can search the Pareto front of the multiobjective functions quickly, and the solutions have the diversity and uniformity. Finally, The algorithm is applied to solve the environmental and economic load dispatch problem of power systems. Considering the environmental impact of 2 CO emissions and the minimum fuel consumption costs of generators, we get the optimal dispatch of generators output depending on the every period load demands.

Optimal Environmental and Economic Load Dispatch of Power Systems Based on the Pareto Front of Particle Swarm Optimization