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Economic Trades in Energy Communities and Optimal Allocation

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  • Laura Wangen

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Cédric Clastres

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

Abstract

The crucial issue on how to model an optimal economic trading model for Energy Communities (ECs) reveals the need for adapted trading mechanisms and pricing strategies in emerging decentralised market forms. Yet, the extent to which these trading models influence the allocation of costs and benefits to EC members remains unexplored. This poster provides an overview of existing literature findings, lays down relevant models and derives essential principles for economic trades inside ECs. For this purpose, a comprehensive review of relevant literature across economic and engineering domains was conducted, with a specific focus on ECs and Peer-to-Peer (P2P) markets. By examining these concepts, important obstacles and enablers of local energy trading are discussed and related to the framework of ECs. Among the assessed models, the community-based P2P model emerges as highly adaptable for ECs, primarily due to its potential to foster cooperation among prosumers. Furthermore, this research delves into vital insights concerning sharing mechanisms and their integration within trading models. Finally, essential conditions and key considerations are proposed to determine the optimal energy trading structure for ECs, including the need to find a balance between efficiency, fairness and scalability in the design of allocation methods.

Suggested Citation

  • Laura Wangen & Cédric Clastres, 2024. "Economic Trades in Energy Communities and Optimal Allocation," Post-Print hal-04632076, HAL.
    • Handle: RePEc:hal:journl:hal-04632076
    Note: View the original document on HAL open archive server: https://hal.science/hal-04632076v1
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    References listed on IDEAS

    as
    1. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2022. "Cost allocation in integrated community energy systems — Performance assessment," Applied Energy, Elsevier, vol. 307(C).
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    3. Yael Parag & Benjamin K. Sovacool, 2016. "Electricity market design for the prosumer era," Nature Energy, Nature, vol. 1(4), pages 1-6, April.
    4. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
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    Keywords

    Energy Communities; Local energy trading; Peer-to-Peer Energy Trading; Optimisation models;
    All these keywords.

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