Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities

Rooftop photovoltaics (PVs) are considered a promising solution to alleviating current cities' escalating energy usage and carbon emissions. In high-density cities, complex shading effects and rooftop availabilities (caused by diversified rooftop obstacles and irregular rooftop outlines) jointly make planning of large-scale distributed rooftop PV systems critically challenging. This study proposed an optimal packing and planning method for large-scale distributed rooftop PV systems under complex shading and rooftop availabilities, tackling the chal-lenges by decoupling optimal packing and planning into two-step optimization. Utilizing horizontal-level genetic algorithm, the method first optimized PV-panels packing on irregular-shaped rooftops to maximize area utili-zation. Second, adopting sequential integer linear programming, the method optimized the planning of indi-vidual rooftop packing levels to minimize levelized cost of electricity (LCOE). Based on a 139-rooftop Hong Kong case study, the method was verified against 1 billion Monte-Carlo solutions, which reduced LCOE by 48.0% at most and achieved the lowest LCOE of 0.365 HKD/kWh. Further analysis showed that the proposed method outperformed a rule-based planning method because of its better utilization of high solar-energy-intensity areas, reducing the LCOE by 15.4%. In practice, the method can be used to facilitate deployment of large-scale distributed rooftop PV, enhancing overall system cost-effectiveness and city decarbonization.

Automated summary

This study proposes an optimal packing and planning method for large-scale distributed rooftop photovoltaic systems in high-density cities. It decouples optimal packing and planning into two-step optimization, utilizing a genetic algorithm for packing and integer linear programming for planning. The method was verified using a Hong Kong case study and achieved a significant reduction in the levelized cost of electricity. The proposed method outperformed a rule-based planning method and can enhance cost-effectiveness and decarbonization in cities.