Modelling the Adoption of Renewable Energy Communities in Urban Districts: An Agent-Based Approach

Department of Architecture, Built Environment and Design, Polytechnic University of Bari, 70126 Bari, Italy

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Abstract

The transition to decentralized energy systems has introduced new configurations as Renewable Energy Communities (RECs), where individual prosumers collaborate to maximize collective energy and access to collective market energy mechanisms. Adoption dynamics within urban environments and customers' response remain poorly investigated, especially in terms of how technical, economic and social factors influence decision-making. This study develops an agent-based model to simulate the spatial and temporal enrolment of buildings in a REC at district scale over a 20-year horizon, using a mixed-use neighborhood in Bari (Italy) with rooftop photovoltaic systems as the case study. A multi-criteria utility framework is implemented for each building agent, combining financial, energy, environmental, and peer-influence components. The associated weights evolve as a function of contextual factors (i.e. tariffs, REC revenues, and awareness) and local social dynamics. Weights are calibrated against observed regional PV adoption data. At each monthly step, agents first pass a discounted payback-time filter for individual PV investment and, if feasible, compare the utilities of remaining single prosumers or joining a REC through a probabilistic decision rule. Results from 20 simulation runs indicate that REC membership grows mainly between years 1 and 5, reaching 39 buildings (about 33% of agents) and then stabilizing. Non-residential buildings with higher surplus generation systematically benefit from joining the REC, while only 14-32% of residential buildings enroll across runs, as individual prosumer configurations often remain economically more attractive under the current Italian REC remuneration scheme. Financial and energy motivations dominate in early years, whereas peer influence gradually becomes the most important weight. Despite the model assumptions, this new framework support policymakers in identifying targeted incentives, enhancing the effectiveness and inclusivity of energy transition policies.