Assessing electricity sharing in energy communities using real consumption and simulated generation data

Buildings account for a significant share of energy demand and CO 2 emissions, motivating microgrid solutions to support global clean energy (SDG 7) and climate action (SDG 13) goals. Energy communities with rooftop photovoltaic (PV) generation and power sharing offer a promising approach, however, their real-world performance in the context of multi-apartment buildings remains understudied. This study quantitatively evaluates the performance of a PV-based energy community in an urban microdistrict (20 different buildings, mainly 4–9-story multi-apartment residential buildings) to assess local electricity sharing, consumption dynamics and emission reductions. Real hourly electricity consumption profiles and building characteristics were analyzed as inputs. Dynamic PV generation was simulated using PolySun SPT software for each rooftop. A custom Python-based modeling framework captured hourly generation–consumption interactions and enabled energy sharing among buildings. From April to September, PV systems produce ∼78% (892.19 MWh) of annual output. During these months, local generation meets most daytime demand, with centralized grid imports needed primarily at night. Over the year, direct self-consumption meets 22% (431.28 MWh) of the community’s total electricity demand, while internal sharing meets an additional ∼10% (207.62 MWh) of the community’s total electricity demand. For the subset of the five buildings without PV, shared PV electricity supplies 26% (156.56 MWh) of their annual electricity demand. The combined PV and sharing scenario cuts CO 2 emissions by approximately 39% compared to a baseline without these measures. However, in winter the community remains a net consumer, underlining seasonal shortfalls. Internal electricity sharing significantly enhances local renewable utilization and emissions savings, illustrating the potential of community microgrids for sustainable urban energy systems. Nonetheless, long-term autonomy will require integrating energy storage and complementary renewables to address winter deficits. The presented modeling framework provides a replicable tool for future research and policy design.