Trading models for Energy Communities: Optimisation of collective benefits under various scenarios for P2P trades

This paper investigates the impacts of Peer-to-Peer (P2P) energy trading within an Energy Community (EC) and analyses the results of linear programming models for optimal energy allocation and maximised collective benefits. The P2P model is evaluated under three configurations: a baseline model, a model incorporating the willingness-to-pay (WTP), and a model integrating energy storage systems (BESS). To validate the outcomes, a case study of a 250-member community is included, where members are categorised into quartile clusters based on load and production profiles to examine sharing and trading patterns. Results show that while overall self-consumption and community trades remain stable, prioritising self-consumption reduces community trades without significantly affecting electricity bills. The Energy Community remains highly dependent on grid imports, as the collective load exceeds total production. Negative social welfare values highlight trading conditions where grid costs outweigh the collective opportunity benefits of internal trades. These results emphasise that the investigated P2P models foster collaboration, prioritising community-wide benefits over individual gains. The insights from this study are essential for energy planning and policy-making, highlighting the potential of P2P trading in strengthening energy resilience, supporting decentralised energy management, and informing tariff structures that incentivise local energy exchange, ultimately fostering more sustainable and economically viable Energy Communities.