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

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Title: Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants.
Authors: Angaphiwatchawal, Pikkanate1 (AUTHOR) pikkanate.a@gmail.com, Chaitusaney, Surachai1 (AUTHOR) surachai.c@chula.ac.th
Source: Energies (19961073). Mar2024, Vol. 17 Issue 6, p1423. 23p.
Subjects: Energy industries, Marginal pricing, Marginal distributions, Graphical modeling (Statistics), Radial distribution function
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. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2024, Vol. 17 Issue 6, p1423. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Energy+industries%22">Energy industries</searchLink><br /><searchLink fieldCode="DE" term="%22Marginal+pricing%22">Marginal pricing</searchLink><br /><searchLink fieldCode="DE" term="%22Marginal+distributions%22">Marginal distributions</searchLink><br /><searchLink fieldCode="DE" term="%22Graphical+modeling+%28Statistics%29%22">Graphical modeling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Radial+distribution+function%22">Radial distribution function</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        Type: general
      – SubjectFull: Marginal pricing
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      – SubjectFull: Marginal distributions
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      – SubjectFull: Graphical modeling (Statistics)
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      – SubjectFull: Radial distribution function
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              Text: Mar2024
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