Optimal Scheduling of Pumped Storage Power Station Based on Immune Ant Colony Algorithm

Abstract

At present, in the application of hydropower for multi-energy complementary, the utilization of large hydropower stations and pumped storage power stations is mainly considered. The utilization of small hydropower stations is relatively scarce. The use of small hydropower can implement complementary regulation of distributed energy nearby, which is very conducive to distributed power consumption. This paper proposes an improved genetic immune ant colony optimization algorithm to find the optimal solution. To solve the shortcomings of the ant colony algorithm in the early stage, such as the weak ability to traverse all paths, easy falling into a premature and local optimum, and slow convergence speed, a genetic algorithm is applied to initialize the genetic factor. It can dynamically adjust the step factor and introduce the genetic immune ant colony with a momentum coefficient when updating the position to search for the optimal solution. The penalty function is adopted to solve the model, and the specific steps are given. Through the comparative test of the algorithm, it is verified that the algorithm proposed has a better effect on the optimal solution, the shortest occupied time, good reliability, and strong optimization ability. It is of great practical significance to the scientific evaluation of the water resource’s eco-economic system.