Electric Taxi Charging Guidance Strategy Based on Electric Vehicle Agent-based Simulation

Abstract

Electric vehicles connected to the electricity grid will have a great impact on the power system, especially with the high penetration of electric vehicles, the safety and stability of the power system could be threatened. In order to accurately simulate the electric taxi operation behavior and load characteristic, this paper proposes a mothed based on multi-agent technique and reinforcement learning algorithm to study the operation behavior of electric taxis. Different charging strategies on the operation of electric taxi are studied. Based on the charging strategy of shortest path, a charging guidance strategy model is proposed, which considers the charging distance, the charging queue time and the charging equilibrium degree of the charging equipment. The simulation results show that the electric taxi adopting the charging guidance strategy will not only decrease the charging queue time, improve the operation time and income of the electric taxi, but also help improve the equilibrium degree and utilization rate of the charging equipment, reduce the power grid loss and voltage offset.