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Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach

Author

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  • Zhang, Bin
  • Hu, Weihao
  • Cao, Di
  • Ghias, Amer M.Y.M.
  • Chen, Zhe

Abstract

Energy hub (EH) is an independent entity that benefits to the efficiency, flexibility, and reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of electric vehicles (EVs) drives the EV aggregator (EVAGG) as another independent entity to facilitate the electricity exchange with the grid. However, due to privacy consideration for different owners, it is challenging to investigate the optimal coordinated strategies for such interconnected entities only by exchanging the information of electrical energy. Besides, the existence of parameter uncertainties (load demands, EVs’ charging behaviors, wind power and photovoltaic generation), continuous decision space, dynamic energy flows, and non-convex multi-objective function is difficult to solve. To this end, this paper proposes a novel model-free multi-agent deep reinforcement learning (MADRL) -based decentralized coordination model to minimize the energy costs of EH entities and maximize profits of EVAGGs. First, a long short-term memory (LSTM) module is used to capture the future trend of uncertainties. Then, the coordination problem is formulated as Markov games and solved by the attention enabled MADRL algorithm, where the EH or EVAGG entity is modeled as an adaptive agent. An attention mechanism makes each agent only focus on state information related to the reward. The proposed MADRL adopts the forms of offline centralized training to learn the optimal coordinated control strategy, and decentralized execution to enable agents’ online decisions to only require local measurements. A safety network is employed to cope with equality constraints (demand–supply balance). Simulation results illustrate that the proposed method achieves similar results compared to the traditional model-based method with perfect knowledge of system models, and the computation performance is at least two orders of magnitudes shorter than the traditional method. The testing results of the proposed method are better than those of the Concurrent and other MADRL method, with 10.79%/3.06% lower energy cost and 17.11%/6.82% higher profits of aggregator. Besides, the electric equality constraint of the proposed method is only 0.25 MW averaged per day, which is a small and acceptable violation.

Suggested Citation

  • Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
  • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923002660
    DOI: 10.1016/j.apenergy.2023.120902
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    References listed on IDEAS

    as
    1. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    2. Najafi, Arsalan & Pourakbari-Kasmaei, Mahdi & Jasinski, Michal & Lehtonen, Matti & Leonowicz, Zbigniew, 2021. "A hybrid decentralized stochastic-robust model for optimal coordination of electric vehicle aggregator and energy hub entities," Applied Energy, Elsevier, vol. 304(C).
    3. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
    4. Xie, Shiwei & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "The optimal planning of smart multi-energy systems incorporating transportation, natural gas and active distribution networks," Applied Energy, Elsevier, vol. 269(C).
    5. Jordehi, A. Rezaee & Javadi, Mohammad Sadegh & Catalão, João P.S., 2021. "Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties," Energy, Elsevier, vol. 229(C).
    6. Ata, Mustafa & Erenoğlu, Ayşe Kübra & Şengör, İbrahim & Erdinç, Ozan & Taşcıkaraoğlu, Akın & Catalão, João P.S., 2019. "Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles," Energy, Elsevier, vol. 186(C).
    7. Rishee K. Jain & Junjie Qin & Ram Rajagopal, 2017. "Data-driven planning of distributed energy resources amidst socio-technical complexities," Nature Energy, Nature, vol. 2(8), pages 1-11, August.
    8. Ashfaq, Asad & Ianakiev, Anton, 2018. "Cost-minimised design of a highly renewable heating network for fossil-free future," Energy, Elsevier, vol. 152(C), pages 613-626.
    9. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    10. Wang, Yi & Zhang, Ning & Zhuo, Zhenyu & Kang, Chongqing & Kirschen, Daniel, 2018. "Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch," Applied Energy, Elsevier, vol. 210(C), pages 1141-1150.
    11. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    12. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    13. Xu, Xiao & Hu, Weihao & Liu, Wen & Du, Yuefang & Huang, Rui & Huang, Qi & Chen, Zhe, 2021. "Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy," Applied Energy, Elsevier, vol. 297(C).
    14. Yang, Wenjun & Guo, Jia & Vartosh, Aris, 2022. "Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization," Applied Energy, Elsevier, vol. 311(C).
    15. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    16. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    17. Liu, Haizhou & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network," Applied Energy, Elsevier, vol. 286(C).
    18. Mittelviefhaus, Moritz & Pareschi, Giacomo & Allan, James & Georges, Gil & Boulouchos, Konstantinos, 2021. "Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation," Applied Energy, Elsevier, vol. 301(C).
    19. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(C).
    20. Zhang, Suhan & Gu, Wei & Lu, Hai & Qiu, Haifeng & Lu, Shuai & Wang, Dada & Liang, Junyu & Li, Wenyun, 2021. "Superposition-principle based decoupling method for energy flow calculation in district heating networks," Applied Energy, Elsevier, vol. 295(C).
    21. Barbón, A. & Bayón-Cueli, C. & Bayón, L. & Carreira-Fontao, V., 2022. "A methodology for an optimal design of ground-mounted photovoltaic power plants," Applied Energy, Elsevier, vol. 314(C).
    22. Tian, Hang & Zhao, Haoran & Liu, Chunyang & Chen, Jian & Wu, Qiuwei & Terzija, Vladimir, 2022. "A dual-driven linear modeling approach for multiple energy flow calculation in electricity–heat system," Applied Energy, Elsevier, vol. 314(C).
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    2. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).

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