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Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning

Author

Listed:
  • Liangyi Pu

    (Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
    These authors contributed equally to this work.)

  • Song Wang

    (Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
    These authors contributed equally to this work.)

  • Xiaodong Huang

    (Chongqing Huizhi Energy Corporation Ltd., State Power Investment Corporation (SPIC), Chongqing 401127, China
    These authors contributed equally to this work.)

  • Xing Liu

    (College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
    These authors contributed equally to this work.
    Current address: Shaanxi Branch, Industrial and Commercial Bank of China (ICBC), Xi’an 710004, China.)

  • Yawei Shi

    (College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China)

  • Huiwei Wang

    (Key Laboratory of Intelligent Information Processing, Chongqing Three Gorges University, Chongqing 404100, China
    Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401120, China)

Abstract

This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community’s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community.

Suggested Citation

  • Liangyi Pu & Song Wang & Xiaodong Huang & Xing Liu & Yawei Shi & Huiwei Wang, 2022. "Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning," Energies, MDPI, vol. 15(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9633-:d:1008005
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    References listed on IDEAS

    as
    1. Esmat, Ayman & de Vos, Martijn & Ghiassi-Farrokhfal, Yashar & Palensky, Peter & Epema, Dick, 2021. "A novel decentralized platform for peer-to-peer energy trading market with blockchain technology," Applied Energy, Elsevier, vol. 282(PA).
    2. Qiu, Dawei & Ye, Yujian & Papadaskalopoulos, Dimitrios & Strbac, Goran, 2021. "Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach," Applied Energy, Elsevier, vol. 292(C).
    3. Daniel S. Bernstein & Robert Givan & Neil Immerman & Shlomo Zilberstein, 2002. "The Complexity of Decentralized Control of Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 27(4), pages 819-840, November.
    4. Hannie Zang & JongWon Kim, 2021. "Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market," Energies, MDPI, vol. 14(14), pages 1-18, July.
    5. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    6. Jin-Gyeom Kim & Bowon Lee, 2020. "Automatic P2P Energy Trading Model Based on Reinforcement Learning Using Long Short-Term Delayed Reward," Energies, MDPI, vol. 13(20), pages 1-27, October.
    7. Soto, Esteban A. & Bosman, Lisa B. & Wollega, Ebisa & Leon-Salas, Walter D., 2021. "Peer-to-peer energy trading: A review of the literature," Applied Energy, Elsevier, vol. 283(C).
    8. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.
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