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Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants

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  • Pikkanate Angaphiwatchawal

    (Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

  • Surachai Chaitusaney

    (Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

Abstract

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method.

Suggested Citation

  • Pikkanate Angaphiwatchawal & Surachai Chaitusaney, 2024. "Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants," Energies, MDPI, vol. 17(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1423-:d:1357623
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    References listed on IDEAS

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    1. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K., 2020. "Investigating the impact of P2P trading on power losses in grid-connected networks with prosumers," Applied Energy, Elsevier, vol. 263(C).
    2. Samende, Cephas & Cao, Jun & Fan, Zhong, 2022. "Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints," Applied Energy, Elsevier, vol. 317(C).
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