Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 13, Issue 1, Pages 715-727Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3124465
Keywords
Hydrogen; Resistance heating; Cogeneration; Natural gas; Heat pumps; Reinforcement learning; Peer-to-peer computing; Multi-energy microgrids; P2P energy trading; energy conversion; multi-agent deep reinforcement learning
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Funding
- Brock University
- University of Glasgow Principal's Early Career Mobility Fund
- NSF [CNS-2128368, CNS-2107216]
- Toyota
- Amazon
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This paper investigates the problems of peer-to-peer energy trading and energy conversion in multi-energy microgrids (MEMGs) and proposes a multi-agent deep reinforcement learning approach. Simulation results demonstrate the effectiveness of the proposed method in reducing the hourly operation cost.
A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and environmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralised energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG's average hourly operation cost. The impact of carbon tax pricing is also considered.
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