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With the growth of residential rooftop PV adoption in recent decades, theproblem of effective layout design has become increasingly important in recentyears. Although a number of automated methods have been introduced, these tendto rely on simplifying assumptions and heuristics to improve computationaltractability. We demonstrate a fully automated layout design pipeline thatattempts to solve a more general formulation with greater geometric flexibilitythat accounts for shading losses. Our approach generates rooftop areas fromsatellite imagery and uses MINLP optimization to select panel positions,azimuth angles and tilt angles on an individual basis rather than imposing anypredefined layouts. Our results demonstrate that shading plays a critical rolein automated rooftop PV optimization and significantly changes the resultinglayouts. Additionally, they suggest that, although several common heuristicsare often effective, they may not be universally suitable due to complicationsresulting from geometric restrictions and shading losses. Finally, we evaluatea few specific heuristics from the literature and propose a potential new ruleof thumb that may help improve rooftop solar energy potential when shadingeffects are considered.

Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery