Multi-Method Simulation and Optimisation for Maximising Benefits in Renewable Energy Communities: A Real-World Case Study from Italy

Abstract

This paper introduces a novel multi-method modelling framework for Renewable Energy Communities (RECs), integrating agent-based modelling, discrete-event simulation, and system dynamics. This hybrid approach enables a comprehensive assessment of RECs, capturing both their technical and economic dynamics. The work’s key contributions are twofold: (i) a flexible technical modelling framework adaptable to diverse geographical and regulatory contexts, and (ii) an advanced optimisation model aimed at minimising costs and maximising benefits for decision support. The optimisation model has been built upon the modelling framework and can be adjusted to various REC configurations, allowing for variations in photovoltaic capacity, demand patterns, energy price structures, and regulatory schemes. This flexibility enables a policy-aware and context-sensitive simulation and optimisation of REC operations. The model enables the evaluation of a wide range of scenarios, helping stakeholders assess both short-term and long-term technical and economic performance, making it a robust tool for forecasting and strategic planning. A real-world case study in Val d’Aosta, Italy, demonstrates the model’s applicability and effectiveness. The study highlights the framework’s ability to incorporate country-specific REC regulations while optimizing REC configurations. Results show a reduction in external energy reliance and an increase in shared energy, leading to enhanced energy autonomy and economic benefits. These findings validate the model’s robustness and scalability, establishing it as a pioneering framework for REC planning and policy innovation.