Planning deep integration of energy communities in urban context: A GIS approach to optimise renewables, storage systems and demand aggregation

This paper addresses the critical need to reduce CO2 emissions and fossil fuel dependence by promoting renewable energy communities (RECs). A comprehensive methodology for planning, modelling, and evaluating RECs, with a focus on urban contexts, has been developed. The framework utilizes GIS for spatial analysis, urban building energy modelling, and a Monte Carlo method for aggregating RECs. Key steps include selecting district buildings, modelling energy consumption based on archetypal profiles, and assessing integration of renewable energy technologies and storage systems. The framework assesses different virtual building aggregations within the enabling framework of the renewable energy communities (RECs), optimising for self-consumption, self-sufficiency, and life cycle costs. Applied to a case study, the results indicate that urban areas benefit more from multiple smaller RECs rather than a few large ones, enhancing renewable energy usage and reducing life cycle costs. These findings highlight the potential for more efficient and sustainable energy solutions in urban environments through well-planned and optimized RECs.