Study on the Optimal Scheduling of a Hybrid Wind-Solar-Pumped Storage Power Generation System

In order to stabilize the randomness, fluctuation, anti-peaking and intermittency of wind power and Photovoltaic power, a hybrid wind-solar-pumped storage power generation system is built, and it is added with the prediction of sudden output changes by wind and solar. The maximized economic benefit of the hybrid system is as the objective function. According to the characteristics of premature convergence and slow convergence of particle swarm optimization (PSO), an immune PSO (immune Particle Swarm Optimization) algorithm is proposed, which dynamically adjusts the learning factors and inertia weight simultaneously. The algorithm performs asymmetric linear dynamic adjustment of the learning factors and inertial weight to enhance the global search ability in the early stage and the local search ability in the later stage, so that the global optimal solution can be obtained quickly. Finally, the validity of the model and the feasibility of the algorithm are verified by numerical examples.