Optimization Scheduling of Power System Based on Improved Particle Swarm Optimization

To address the issue of short-term scheduling of power system that contains clean energy, in this paper, an adaptive weighted immune particle swarm optimization algorithm is proposed to overcome the shortcomings of the traditional particle swarm optimization algorithm including easily falling into the local optimum value and slow convergence speed. The superiority of the improved algorithm is verified on the 10-unit test system. On the basis of the existing scheduling model, we propose the spinning reserve model to improve the ability of power system to cope with risks. In view of the complex inequalities and equality constraints in the scheduling model, this paper proposes an extra penalty point hybrid constraint plan based on elite retention. By introducing the elite retention mechanism, the quality of reservation and information transferring in the iterative process is improved, the quality of the particle swarm optimization algorithm is optimized and the optimization speed of the original hybrid constraint plan is effectively accelerated. Finally, the rationality and feasibility of the improved particle swarm algorithm and scheduling model are verified on the improved IEEE-RTS test system, which provides tactics and feasible suggestions for the economic dispatching operation of power systems with clean energy.