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Peer-to-Peer Electrical Energy Trading Considering Matching Distance and Available Capacity of Distribution Line

Author

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  • Natnaree Tubteang

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
    Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Paramet Wirasanti

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

The concept of peer-to-peer (P2P) energy trading leads to the flexible energy transaction of prosumers and consumers, for which the P2P business model is normally the main attention. It still requires system operators to address the challenges in trading and constraint problems. In this context, this work regards the congestion constraint in conjunction with energy trading. Firstly, a matching approach based on the cost path is proposed. It is consistent with the cost for the dispatch along each route, making a suitable matching in both distance and bids. In combination with the matching process, the available capacity has to be considered to avoid line congestion. Secondly, the bus transfer factor (BTF) and the partitioning zone approach are proposed to overcome the issue. BTF refers to a response of bus power to the congested line power. The partitioning zone, separated into the source and the load area, enables a simple management strategy. Thereby, the power adjustment in each area follows BTF. Moreover, compensation and opportunity cost are discussed. In comparison with the demand-side reprofiling approach, this work creates more trading chances for buyers and sellers by 24.70% and 30%, respectively. The reason is traders do not have to curtail their power unnecessarily for congestion management.

Suggested Citation

  • Natnaree Tubteang & Paramet Wirasanti, 2023. "Peer-to-Peer Electrical Energy Trading Considering Matching Distance and Available Capacity of Distribution Line," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2520-:d:1089930
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    References listed on IDEAS

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    1. Guerrero, Jaysson & Sok, Bunyim & Chapman, Archie C. & Verbič, Gregor, 2021. "Electrical-distance driven peer-to-peer energy trading in a low-voltage network," Applied Energy, Elsevier, vol. 287(C).
    2. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
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    Cited by:

    1. Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.

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