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

Abstract

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.

Reinforcement Learning Based EV Charging Management Systems–A Review

“…Some other studies focus on using renewable energy sources and their inflexibility in producing energy, monitored their energy production to achieve better managing of the distributed energy systems. Other studies maintained the battery SOC of the EVs above a certain threshold by monitoring the battery SOC to be able to produce the appropriate actions [64], [65], [66], [67], [68], [69], [70], [71]. Other parameters were used less frequently in studies as state parameters, as they were not related to the frequently desired objectives [72] and [73].…”

“…The authors in papers [61]- [72], [74], [75], [76], [77], [65], [78], [79], [80]- [81], [82], [83], [84]- [67], [85] - [86], and [87]- [69], used the charging or discharging cost as a state parameter. However, other operational costs, like waiting cost and traveling, were used mainly in two of the surveyed papers [88] and [67]. This may lead to concluding that they were not that crucial in deducing the best actions to achieve the objectives of the methods presented in these papers to control the energy distribution system costs.…”

“…Most of the studies in the literature, though, used the Qleaning as the learning algorithm for their RL-based solutions. All of the studies [96], [64], [62], [88], [90], [65], [92], [79], [102], [93], [104], [82], [94], [67], [85], [68], [70], [108] and [63] have used the Q-leaning algorithm. The Q-leaning algorithm is an advanced version of the SARSA algorithm.…”