Samende, C, Cao, J and Fan, Z (2021) Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints. ISSN 0004-6256 (Unpublished)

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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: Any information related to this article and all relevant information related to it, including copyrights, can be found on arXiv.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
T Technology > T Technology (General)
Related URLs:
Depositing User: Symplectic
Date Deposited: 18 Mar 2022 12:57
Last Modified: 18 Mar 2022 12:57

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