This research develops a co-design optimization framework for microgrids that simultaneously designs physical infrastructure and control systems. By improving both reliability and cost-effectiveness, it enables more resilient renewable energy networks, supports upgrades to existing microgrids, and helps communities maintain electricity during extreme weather events and grid failures.

This research uses computational photography and machine learning to monitor electricity quality through the flickering patterns of everyday lights. By analyzing images captured in cities such as Kampala and Nairobi, the work offers a low-cost method for measuring voltage instability and improving power-grid planning in underserved communities lacking reliable electricity infrastructure.