Research on multi-objective scheduling optimization of power system based on hybrid model of genetic algorithm and particle swarm optimization

This paper presents a hybrid model combining Genetic Algorithm and Particle Swarm Optimization for multi-objective scheduling optimization in power systems. The model aims to minimize power generation costs, reduce emissions, and enhance system stability. A real-time scheduling approach is incorporated, enabling dynamic adjustments to fluctuating loads and optimizing the power system through rolling-horizon rescheduling. Experimental results based on actual data from a regional power system show that the hybrid GA-PSO model reduces generation costs by 12.5%, emissions by 9.8%, and improves system stability by 15.3%. These results demonstrate the model’s effectiveness in solving multi-objective scheduling problems and its potential application in sustainable power system management.