Artificial Intelligence Method for V2G oriented Power Distribution Network Control Strategy and Capacity Analysis

With the popularization of electric vehicle (EV), Vehicle-to-Grid (V2G) technology has become an important means to balance the supply and demand of power grid and improve the flexibility of power grid. This article aims to explore a V2G-oriented power distribution network (PDN) regulation strategy and artificial intelligence (AI) method for carrying capacity analysis, so as to realize the efficient and stable operation of the power grid. In this article, a data fusion method based on modified mind evolutionary algorithm (MMEA) is proposed to deal with complex data in PDN. Then, based on the data fusion results, a V2G control strategy is designed. Finally, MMEA algorithm is used to analyze the V2G carrying capacity of PDN. The results show that this method has achieved remarkable results in data fusion, control strategy optimization and carrying capacity analysis, and improved the operation efficiency and stability of PDN. To sum up, the V2G-oriented PDN control strategy and AI method for carrying capacity analysis proposed in this article provide strong support for the practical application of V2G technology and the intelligent development of power grid.