A spatial-techno-economic assessment modeling framework for optimal planning of rooftop photovoltaic systems in urban areas: the case of New Assiut City, Egypt

Rooftop photovoltaic (PV) power generation can provide an efficient solution to urban energy needs, benefiting both individual homeowners and the broader community in urban areas. However, identifying suitable rooftop spaces and optimal planning of the rooftop PV systems requires a comprehensive approach. Unlike most previous studies that have concentrated on the technical aspects of rooftop PV systems, this study emphasizes both spatial and techno-economic factors. It explores how the optimal placement of rooftop PV systems can benefit customers and contribute to a more resilient and reliable energy system in newly constructed buildings. To this aim, this study presents a detailed methodology for determining the optimal placement of rooftop PV systems in newly constructed residential areas. By employing a mixed-integer linear programming (MILP) approach, the study aims to maximize the Net Present Value (NPV) of rooftop PV owners through strategically allocating solar PV panels in conjunction with designated central service areas, while adhering to spatial, technical, and economic constraints. The developed model is validated using site-specific data from a residential area, including 12 identical residential buildings in New West Assuit City, Egypt, a newly constructed but currently unoccupied district with significant solar energy potential. The results reveal that, with a latitude-based tilt angle of 27°, each building can support between 9 and 15 PV modules. In a scenario with 15% occupancy, up to 72% of the generated electricity can be sold back to the grid, resulting in the highest NPV. Conversely, a scenario with 100% occupancy leads to the lowest NPV due to the limited surplus energy available for export. Finally, a detailed sensitivity analysis is carried out to assess the impact of the tilt angle of PV panels and feed-in tariff (FiT) on NPV outcomes.