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

摘要

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.

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