Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints - Samende, Cephas; Cao, Jun; Fan, Zhong (Applied energy, 2022)
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. •Deep reinforcement learning-based algorithm for P2P energy trading considering network constraints is proposed.•The resulting trading strategy minimizes the total energy cost of prosumers.•Distribution Network Tariffs (DNT) are proposed to manage the network constraints.•Results show the effectiveness of the proposed algorithm and that DNTs improves voltage regulation, reduces network losses and peak congestion.