Trading models for Energy Communities

Laura Wangen ()
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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

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Abstract: With the integration of Energy Communities (ECs) into the energy system, the development of customised trading models and pricing strategies is essential. This underlines the need for new trading mechanisms and presents the challenge of optimising economic frameworks for ECs. However, the extent to which various trading models affect the outcomes for EC members has not yet been sufficiently explored. This paper analyses the impacts of peer-to-peer (P2P) energy trades within an EC and models a linear program for optimal energy allocation among its members. To this end, the optimisation model for a Full P2P scheme by Perger et al. (2021) is generalised and evaluated through three configurations: (1) a baseline model reflecting standard trading mechanisms; (2) a model considering the willingness-to-pay as a key factor; and (3) a model integrating energy storage systems into the community. To validate the methodology, a case study of an EC with 250 members is included (Faia et al., 2023). To support this analysis, members are categorised into quartile clusters based on a number of selected criteria in order to examine load and sharing patterns across diverse prosumer profiles. The results reveal significant disparities in energy production, consumption, and trading potential, both within the community as well as between the community and the grid. The overall impact of these model configurations for the EC suggests that WTP integration promotes self-consumption practices within the community, while batteries will decrease energy sharing volumes, thus reliance on both the EC and the grid. Furthermore, production capacity alone does not predict energy consumption or community reliance. By simulating different P2P configurations, the findings provide new insights into optimal trading models that balance fairness, cost efficiency, and sustainability for large ECs within decentralised energy markets.

Keywords: Local energy trading; Energy Communities; Peer-to-Peer trading; Optimisation models; Willingness-to-Pay. (search for similar items in EconPapers)
Date: 2024-11-05
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Published in FAEE PhD Student Workshop 2024, FAEE, Nov 2024, Grenoble, France

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04771848

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