A framework for the digital energy management and decarbonization of sustainable and renewable energy communities

The increasing complexity of energy systems at the community scale, both in urban and rural contexts, requires advanced digital tools capable of supporting data integration, simulation, and optimization. This contribution proposes a sequential framework that evolves from a digital model, dedicated to the collection, interoperability, and normalization of energy data, to a full Digital Twin (DT), which integrates predictive models, optimization algorithms, and advanced control for the coordinated management of sustainable urban and rural communities. The proposed framework is conceived as a methodological structure applicable to different types of sustainable urban and rural communities. Within this broad scope, Renewable Energy Communities (RECs) are adopted as a paradigmatic application case. RECs represent a particularly suitable example due to their clear regulatory definition, structured incentive mechanisms, and explicit focus on collective renewable energy management, which make them an ideal testbed for advanced digital solutions. The framework addresses multiple planning and operational functions, including energy optimization, scenario-based simulation, predictive maintenance, and the management of demand- and supply-side flexibility. It enables increased integration of renewable sources, reduction of peak loads, improvement of local grid performance, and support for decarbonization strategies in both urban and rural environments. The proposed approach provides a unified methodological structure to guide the transition from simple digital tools to full-fledged urban DTs capable of supporting operational and planning decisions in smart cities.