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Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints

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  • Samende, Cephas
  • Cao, Jun
  • Fan, Zhong

Abstract

In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable energy generation and consumption, (ii) difficulties in developing an accurate and efficient energy trading model, and (iii) the need to satisfy distribution network constraints, it is challenging for prosumers to obtain optimal energy trading decisions that minimize their individual energy costs. To address the challenge, we first formulate the above problem as a Markov decision process and propose a multi-agent deep deterministic policy gradient algorithm to learn optimal energy trading decisions. To satisfy the distribution network constraints, we propose distribution network tariffs which we incorporate in the algorithm as incentives to incentivize energy trading decisions that help to satisfy the constraints and penalize the decisions that violate them. The proposed algorithm is model-free and allows the agents to learn the optimal energy trading decisions without having prior information about other agents in the network. Simulation results based on real-world datasets show the effectiveness and robustness of the proposed algorithm.

Suggested Citation

  • Samende, Cephas & Cao, Jun & Fan, Zhong, 2022. "Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints," Applied Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005025
    DOI: 10.1016/j.apenergy.2022.119123
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Pikkanate Angaphiwatchawal & Surachai Chaitusaney, 2024. "Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants," Energies, MDPI, vol. 17(6), pages 1-23, March.
    2. Meng, Yuan & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints," Applied Energy, Elsevier, vol. 356(C).
    3. Hosseini Dolatabadi, Sayed Hamid & Bhuiyan, Tanveer Hossain & Chen, Yang & Morales, Jose Luis, 2024. "A stochastic game-theoretic optimization approach for managing local electricity markets with electric vehicles and renewable sources," Applied Energy, Elsevier, vol. 368(C).
    4. Cephas Samende & Zhong Fan & Jun Cao & Renzo Fabián & Gregory N. Baltas & Pedro Rodriguez, 2023. "Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(19), pages 1-20, September.
    5. Chang, Weiguang & Dong, Wei & Yang, Qiang, 2023. "Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market," Applied Energy, Elsevier, vol. 336(C).

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