Restoring Microgrids After Power Loss Requires Smarts

Researchers at the University of California, Santa Cruz have tested the use of machine learning to optimize power restoration on microgrids after power outages. Microgrids are small-scale power grids that often include renewable energy sources and can be as small as a group of residential buildings with solar panels. Deep reinforcement learning was used to manage the load restoration process in bringing a microgrid back online after a power loss. Practical constraints such as the cost of running fuel-powered generators and the fluctuating availability of renewable energy make it a complex optimization problem.