Optimal operation of hybrid PV-BESS in collective self-consumption installations. Case study validated in real environment

The integration of renewable energy sources into modern power systems introduces new operational challenges for collective self-consumption schemes, especially when managing shared assets in energy communities. This study proposes a cohesive framework for forecasting and optimizing. This framework combines day-ahead load and PV generation forecasts with a Mixed-Integer Linear Programming model to schedule battery energy storage system (BESS) operation under real regulatory and technical constraints. The approach is implemented and validated in a real energy community in Catalonia, where 26 members share a hybrid PV-BESS installation. Three operational scenarios are evaluated: charging only from PV surplus, charging from total PV production, and a third scenario where the battery can charge from the grid — tested solely for comparison, since grid charging is not allowed in the actual installation. Results from several week-long windows across different seasons show that optimized operation can reduce annual electricity costs for the energy community by 15.3%–31.2%, depending on the scenario and seasonal conditions. The findings emphasize that, despite regulatory constraints limiting grid-charging capabilities, data-driven BESS scheduling can significantly enhance economic performance and local renewable consumption. Overall, this study demonstrates the practical value of combining forecasting infrastructure with optimization tools for real-world energy communities and outlines pathways for improving future profitability through enhanced flexibility services.