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Hierarchical distributed framework for EV charging scheduling using exchange problem

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  • Khaki, Behnam
  • Chu, Chicheng
  • Gadh, Rajit

Abstract

In this paper, a distributed trilayer multi-agent framework is proposed for optimal electric vehicle charging scheduling (EVCS). The framework reduces the negative effects of electric vehicle charging demand on the electrical grids. To solve the scheduling problem, a novel hierarchical distributed EV charging scheduling (HDEVCS) is developed as the exchange problem, where the agents are clustered based on their coupling constraints. According to the separability of the agents’ objectives and the clusters’ coupled constraints, HDEVCS is solved efficiently in a distributed manner by the alternating direction method of multipliers (ADMM). Comparing to the exiting trilayer methods, HDEVCS reduces the convergence time and the iteration numbers since its structure allows the agents to update their primal optimization variable simultaneously. The performance of HDEVCS is evaluated by numerical simulation of two small- and large- scale case studies consisting of 306 and 9051 agents, respectively. The results verify the scalability and efficiency of the proposed method, as it reduces the convergence time and iteration numbers by 60% compared to the state-of-the-art methods, flattens the load profile and decreases the charging cost considerably without violating the grid feeders’ capacity. The significant outcome of our method is the accommodation of a large EV population without investment in grid expansion.

Suggested Citation

  • Khaki, Behnam & Chu, Chicheng & Gadh, Rajit, 2019. "Hierarchical distributed framework for EV charging scheduling using exchange problem," Applied Energy, Elsevier, vol. 241(C), pages 461-471.
    • Handle: RePEc:eee:appene:v:241:y:2019:i:c:p:461-471
    DOI: 10.1016/j.apenergy.2019.03.008
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    References listed on IDEAS

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    Cited by:

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    2. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    3. Xu, Jie & Huang, Yuping, 2022. "The short-term optimal resource allocation approach for electric vehicles and V2G service stations," Applied Energy, Elsevier, vol. 319(C).
    4. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    5. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    6. Shang, Yitong & Yu, Hang & Niu, Songyan & Shao, Ziyun & Jian, Linni, 2021. "Cyber-physical co-modeling and optimal energy dispatching within internet of smart charging points for vehicle-to-grid operation," Applied Energy, Elsevier, vol. 303(C).
    7. Liu, Rong-Peng & Sun, Wei & Yin, Wenqian & Zhou, Dali & Hou, Yunhe, 2021. "Extended convex hull-based distributed optimal energy flow of integrated electricity-gas systems," Applied Energy, Elsevier, vol. 287(C).
    8. Maneesha, Ampolu & Swarup, K. Shanti, 2021. "A survey on applications of Alternating Direction Method of Multipliers in smart power grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).

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