Research on intelligent energy management method of multifunctional fusion electric vehicle charging station based on machine learning
Tao Shi, Fang Zhao, Hangyu Zhou, Caijuan Qi
Abstract
The machine-learning based approach to energy management of multifunctional charging stations that meets the needs in the context of "carbon neutrality". The method takes multifunctional charging station as the research object, considers the uncertainty elements such as market price signal, charging load, and the change of photovoltaic power generation, and aims at maximizing the comprehensive operation efficiency of charging station in the decision cycle, realizes the prediction of power generation and load power by using deep learning method, and obtains the optimal energy management strategy by reinforcement learning, and realizes intelligent management and operation of multifunctional charging station. A typical case study is presented and simulation analysis is carried out for typical cases to verify the correctness and effectiveness of the method. It is shown that the method can effectively cope with the uncertain elements in the operation of multifunctional charging stations, reduce the hardware configuration and manual operation and maintenance costs of charging stations while ensuring safety constraints, and effectively improve the energy utilization efficiency and economy of the whole charging station.