A Flexible Charging Pile Scheduling Optimization Platform Based on Ant Colony Algorithm

In this paper, taking the load of electric vehicle charging piles as the cutting point, by analyzing various constraints such as the access state of electric vehicles and the characteristics of charging piles, a refined single equipment model and a participation demand response adjustment model are established, an optimal scheduling strategy and incentive compensation measures are proposed, and a charging station heuristic insertion algorithm is designed to quickly calculate the insertion position of charging stations. At the same time, an improved ant colony algorithm was proposed by designing a local search algorithm and integrating it into the iterative process of ant colony algorithm. Experimental results show that the local search embedded in the ant colony algorithm has significant advantages in improving the quality of the solution. It provides theoretical and practical support for the application of demand-side resources in the power market, and provides new ideas and methods for the balance of supply and demand in the power system and the development of new energy.