  With the growth of residential rooftop PV adoption in recent decades, the
problem of effective layout design has become increasingly important in recent
years. Although a number of automated methods have been introduced, these tend
to rely on simplifying assumptions and heuristics to improve computational
tractability. We demonstrate a fully automated layout design pipeline that
attempts to solve a more general formulation with greater geometric flexibility
that accounts for shading losses. Our approach generates rooftop areas from
satellite imagery and uses MINLP optimization to select panel positions,
azimuth angles and tilt angles on an individual basis rather than imposing any
predefined layouts. Our results demonstrate that shading plays a critical role
in automated rooftop PV optimization and significantly changes the resulting
layouts. Additionally, they suggest that, although several common heuristics
are often effective, they may not be universally suitable due to complications
resulting from geometric restrictions and shading losses. Finally, we evaluate
a few specific heuristics from the literature and propose a potential new rule
of thumb that may help improve rooftop solar energy potential when shading
effects are considered.
