Optimising renewable energy community aggregation for urban districts decarbonisation

This study investigates the integration of renewable energy communities (RECs) in large urban districts, using a GIS-based approach to evaluate various aggregation strategies and their effects on energy and economic performance. To assess the reliability of the developed method of RECs planning in urban areas, a case study of a large district located in Naples (Italy) is analysed. This district is characterised by 730 buildings comprising residential buildings, offices, and shops. Two aggregation approaches are compared: uncontrolled and controlled aggregation. In the uncontrolled approach, where buildings are randomly grouped in RECs, significant variability in energy self-consumption and self-sufficiency is observed, with individual RECs showing considerable performance discrepancies. In contrast, controlled aggregation methods – such as balancing and clustering based on energy needs, peak demand, and roof area – reduce variability of energy and economic outcomes. The case study results reveal that while uncontrolled aggregation leads to higher economic uncertainty, the controlled approach minimises financial risk by stabilising payback times. A sensitivity analysis indicates that the number of RECs significantly impacts both energy and economic performance, with a higher number of RECs generally decreasing self-consumption and increasing payback time. The case study reveals that controlled aggregation scenarios achieve a self-consumption rate of 100 %, with no variability in the performance of individual RECs. Self-sufficiency reaches 50 %, with a variability of ±10 %, while the payback period averages 8.5 years, varying by ±2 years. These findings highlight the importance of strategic aggregation methods in optimising REC integration within urban districts and provide valuable insights for urban planners and policymakers. • A new GIS-based approach plans REC integration in large urban areas. • Energy-economic impacts of uncontrolled and controlled REC aggregations. • A Monte Carlo-based approach assesses RECs performance under random aggregations. • Clustering with k-means and optimisation methods simulate controlled aggregations. • Controlled aggregation enhances performance and eliminates REC variability.