Samende, C, Cao, J and Fan, Z (2022) Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints. Applied Energy, 317 (119123). ISSN 0306-2619

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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.

Item Type: Article
Additional Information: © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Uncontrolled Keywords: Multi-agent; Deep deterministic policy gradient; Peer-to-peer energy trading; Renewable generation; Markov decision process
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
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Depositing User: Symplectic
Date Deposited: 31 May 2022 15:07
Last Modified: 11 Jul 2022 14:06

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