4.7 Article

A Distributed Online Algorithm for Promoting Energy Sharing Between EV Charging Stations

期刊

IEEE TRANSACTIONS ON SMART GRID
卷 14, 期 2, 页码 1158-1172

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2022.3203522

关键词

Charging stations; Optimization; Electric vehicle charging; Costs; Batteries; Renewable energy sources; Convex functions; Electric vehicle; charging station; energy sharing; Lyapunov optimization; renewable energy

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This paper proposes a distributed online algorithm to address the supply-demand mismatch of electric vehicle charging stations caused by fluctuating renewable generation and unpredictable charging demands. The algorithm operates in a prediction-free manner and properly satisfies time-coupling constraints. It also provides a theoretical bound for the optimality gap between offline and online solutions. Additionally, an improved ADMM algorithm is proposed for distributed computation, ensuring privacy protection and online implementation. Case studies validate the effectiveness of the proposed method.
In recent years, electric vehicle (EV) charging stations have experienced an increasing supply-demand mismatch due to their fluctuating renewable generation and unpredictable charging demands. To reduce their operating costs, this paper proposes a distributed online algorithm to promote energy sharing between charging stations. We begin with the offline and centralized version of the EV charging stations operation problem, whose objective is to minimize the long-term time-average total cost. Then, we develop an online implementation approach based on the Lyapunov optimization framework. Although the proposed online algorithm runs in a prediction-free manner, we prove that by properly choosing the parameters, the time-coupling constraints remain satisfied. We also provide a theoretical bound for the optimality gap between the offline and online optimums. Furthermore, an improved alternating direction method of multipliers (ADMM) algorithm with an iteration truncation is proposed to enable distributed computation. The proposed algorithm can protect privacy while being suitable for online implementation. Case studies validate the effectiveness of the theoretical results. Performance comparisons are carried out to demonstrate the advantages of the proposed method.

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