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A constrained DRL-based bi-level coordinated method for large-scale EVs charging

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  • Ming, Fangzhu
  • Gao, Feng
  • Liu, Kun
  • Li, Xingqi

Abstract

With the vigorous development of battery electric vehicles (BEVs), BEVs’ charging scheduling is essential for better economy and safety. In this paper, we aim to minimize the electricity purchasing cost considering a large number of BEVs and distributed energy. This problem is challenging to get the optimal charging policy due to a large number of uncertainties and dimension disasters caused by a large scale of BEVs and renewable energy. To meet these challenges, we propose an improved bi-level schedule framework, which decomposes the primal problem into two sub-problems to reduce the computational complexity and designs a communication mechanism to ensure the consistency of optimality between different levels. Then the problem is modeled as constrained multi-level Markov decision processes (CMMDP). In the upper level, a constrained deep reinforcement learning method (CDRL) is proposed to get the total charging or discharging energy of BEV groups. An action constraint module is constructed to ensure the feasibility of chosen actions and a novel reward shaping function is designed to optimize action selection. In the lower level, an optimal descending order charging policy (DOCP) is taken to fast decide the charging or discharging behavior for each BEV based on the upper level’s decision. Numerical experiments show that our method has obvious superiority in training efficiency and solution accuracy compared with state of art DRL methods, and reduces the cost by 12% to 28% compared with an experience charging policy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016385
    DOI: 10.1016/j.apenergy.2022.120381
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    1. Paolo Scarabaggio & Raffaele Carli & Graziana Cavone & Mariagrazia Dotoli, 2020. "Smart Control Strategies for Primary Frequency Regulation through Electric Vehicles: A Battery Degradation Perspective," Energies, MDPI, vol. 13(17), pages 1-19, September.
    2. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    3. Zeynali, Saeed & Nasiri, Nima & Marzband, Mousa & Ravadanegh, Sajad Najafi, 2021. "A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets," Applied Energy, Elsevier, vol. 300(C).
    4. Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
    5. Zhang, Xingping & Liang, Yanni & Yu, Enhai & Rao, Rao & Xie, Jian, 2017. "Review of electric vehicle policies in China: Content summary and effect analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 698-714.
    6. Su, Jun & Lie, T.T. & Zamora, Ramon, 2020. "A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market," Applied Energy, Elsevier, vol. 275(C).
    7. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    8. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    9. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    10. Graber, Giuseppe & Calderaro, Vito & Mancarella, Pierluigi & Galdi, Vincenzo, 2020. "Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints," Applied Energy, Elsevier, vol. 260(C).
    11. 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.
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    2. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Zhou, Xinlei & Xue, Shan & Du, Han & Ma, Zhenjun, 2023. "Optimization of building demand flexibility using reinforcement learning and rule-based expert systems," Applied Energy, Elsevier, vol. 350(C).

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