Research on Economic Environment Scheduling Optimization of Power System Based on Multi-objective Particle Swarm Optimization

With the progress of time and the development of technology, human society has more and more demands on the power system. The optimization of economic emission dispatching in power systems is an important issue. Therefore, a multi-objective particle swarm optimization was proposed to achieve the optimization of economic emission dispatching in power systems. The test results confirmed that the proposed method found the lowest cost that satisfied the constraint conditions and considered the pollution emissions when the iterations reached 41, thus reaching a convergence state. The particle swarm optimization reached a convergence state after 60 iterations. The artificial bee colony algorithm reached a convergence state after 69 iterations. In sample one, the optimal compromise solution calculated by the multi-objective particle swarm optimization had a cost reduction of 50,235.156 yuan compared to the particle swarm optimization and a savings of 105,138.384 yuan compared to the artificial bee colony method. Its pollution emissions were reduced by 6831.594 lb compared to the particle swarm algorithm and 9624.269 lb compared to the artificial bee colony method. As a result, the proposed multi-objective particle swarm optimization algorithm has obvious advantages in solving the economic environment scheduling optimization of power system. Compared with the traditional particle swarm optimization algorithm and artificial bee colony algorithm, the multi-objective particle swarm optimization algorithm significantly improves the iterative efficiency. Meanwhile, the method achieves a better balance between the dual goals of economic cost and environmental protection. Specifically, the cost reduction and significant reduction of pollution emissions effectively support the sustainable development goals of the power system. This study can provide effective solutions for the economic and environmental scheduling optimization of the power system.