Solar-Enabled Community Energy Systems: a Modular Neighborhood Approach to Net-Positive Neighborhoods
Abstract
This study investigates the integration of solar-based Community Energy Systems (CES) within a modular neighborhood framework, evaluating how urban form influences energy system performance. CES are defined as distributed, locally managed infrastructures that generate, store, and share energy at the neighborhood level to enhance self-sufficiency, resilience, and decarbonization.\n\nEleven representative neighborhood typologies, ranging from low-density detached housing to high-density mixed-use forms, were modeled in EnergyPlus to simulate annual thermal and electrical demands and assess local renewable generation potential. Rooftop, façade, and landscape-integrated photovoltaic (LPV) systems were evaluated in combination with district heating strategies (PV/T and geothermal), waste-to-energy systems, and bidirectional electric vehicle (V2G) integration. A total of 99 CES scenarios were tested across all typologies, quantifying performance using metrics such as Ratio of Performance (ROP), Self-Sufficiency Index (SSI), and net energy balance.\n\nResults indicate that typology plays a defining role in CES performance. Medium-density residential neighborhoods (e.g., 56AH) achieved up to 2.2 ROP (Ratio of Performance) with LPV integration, representing an 83% increase over baseline scenarios. In high-density mixed-use areas (e.g., MU1), LPV reduced annual net deficits by over 610,000 kWh, while geothermal scenarios delivered the highest ROP improvements, increasing performance by 25–35% compared to all-electric configurations.\n\nA dual-ranking framework, EPN (Energy-Positive Neighborhood) and NZE (Net-Zero Energy) scores, was developed to guide CES selection based on policy priorities. The EPN score favors energy surplus and grid export, while the NBE score targets balanced, self-sufficient designs with SSI ≥ 0.85. This approach allows planners to identify optimal CES setups per typology and enables strategic clustering of high-surplus and energy deficit neighborhoods for resilience and flexibility.