Effective Electric Vehicles Navigation Strategy Considering the Uncertainty of the Charging Load

Xun Li1; Mengge Shi2; Jiashuo Hu2; Shan He3; Dazhong Zou1; Youwei Jia2

10.1109/ACFPE56003.2022.9952305

2022

978-1-6654-7085-8

2022 Asian Conference on Frontiers of Power and Energy (ACFPE)

394-398

21-23 Oct. 2022

Chengdu, China

With the increased penetration level of electric vehicles (EV), there is a virtual issue to address the uncertainty of EVs. Under the premise of uncertain information, how to maximize the revenue of charging stations (CSs) by guiding the EVs' charging behavior is the concern of this paper. In this paper, long short-term memory neural network has been used to forecast the charging load of the CSs, and the model predictive control (MPC) method is combined to formulate an effective two-stage energy scheduling strategy for CSs. n order to better motivate electric vehicles to achieve the corresponding CS, this paper constructs an incentive subsidy model, which effectively guides the EVs' charging behavior by changing the service price of the station. In the day-ahead phase, CSs trade with the grid based on forecast information. In the day-ahead market, energy is purchased at a lower price through the trading pool; in the real-time stage, by changing the price of charging services, price incentives are given to EVs, guiding electric vehicles to transfer between stations, and changing EVs at each station. load, thereby further compensating for the deviation of the charging load to reduce the cost of CSs. The effectiveness and economy of the two-stage EV navigation strategy based on incentive price is verified by experimental results.

electric vehicle uncertainty energy scheduling long short-term memory model predictive control

其他

IEEE

1.CSG EV Service Co., Ltd, Shenzhen, China
2.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
3.Shenzhen Power Supply Bureau Co., Ltd, Shenzhen, China

Xun Li,Mengge Shi,Jiashuo Hu,et al. Effective Electric Vehicles Navigation Strategy Considering the Uncertainty of the Charging Load[C],2022:394-398.