Data-Driven Intelligent EV Charging Operating With Limited Chargers Considering the Charging Demand Forecasting
Publication Year: 2022
Author(s): Liu J, Lin G, Rehtanz C, Huang S, Zhou Y, Li Y
Coordinated charging scheduling can improve the operating economics of charging stations and reduce the required amount of charging facilities. However, existing optimal scheduling schemes either simplify the charging station capacity modeling when considering the traffic uncertainty or ignore the future charging demands when considering charging capacity limitations. To tackle this issue, a data-driven intelligent EV charging scheduling algorithm is proposed in this paper by scheduling in response to the time-of-use (TOU) electricity price, the limitation of charging facilities, and a detailed charger operating process is also considered. First, based on the neural network algorithm, a charging demand forecasting method is introduced to establish the charging task of the charging station. Then, according to the established task, an optimization model comprehensively considers the charging costs, battery degradation, and users’ dissatisfaction is proposed. The proposed model is formulated as a mixed-integer nonlinear programming problem, and a corresponding approach for solving the model is also proposed. Finally, the real-time operation process of the proposed scheduling method in the actual charging station is presented. Simulation results verify the better effectiveness and performance of the proposed scheduling method by comparing it with the existing methods.
Source of Publication: International Journal of Electrical Power and Energy Systems
Vol/Issue: 141, 108218
DOI No.: 10.1016/j.ijepes.2022.108218
Publisher/Organisation: Elsevier Ltd.
Rights: Elsevier Ltd.
Theme: Vehicle Technology | Subtheme: Electric vehicles
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