Optimal aggregation of users to form Energy Communities

Energy Communities are a key strategy for decarbonization. Nevertheless, their diffusion remains limited, partly due to the difficulty of engaging local participants. In particular, stakeholders investing in renewable energy systems lack practical tools to aggregate local consumers and enhance the economic attractiveness of their projects. This paper introduces a novel aggregation approach to determine the optimal number and type of participants in an Energy Community. A two-level evolutionary algorithm is developed to select the most suitable participants from a pool of end-users belonging to the same local area. The methodology is applied to a case study involving a producer investing in a 1 MW photovoltaic system, with the goal of finding consumers with whom to form the Energy Community. The perspectives of the various stakeholders are reflected through dedicated objective functions. In the configuration identified as the most favourable, the Energy Community includes the producer, four commercial end-users, and a small single-shift factory, while residential end-users are excluded. The producer achieves payback in seven years, while consumers benefit from an 8 % reduction in electricity bills. Additionally, 94 % of the energy made available by the photovoltaic system is utilized locally, thereby limiting reverse power flows to the primary transformer.