Physics-informed machine learning for Power Systems

Environmental and sustainability concerns are transforming the US electricity sector with aggressive targets to achieve 100% carbon pollution-free electricity by 2035. Achieving this objective while maintaining a safe and reliable power grid in the presence of intermittent renewable generation requires new operating paradigms of computationally fast and accurate decision making in dynamic and service-critical environments. Optimization and machine learning emerge as key approaches, but neither alone are sufficient for future power system control and decision making. Classical approaches for optimization (particularly for large-scale problems and in the presence of mixed integer variables) remain prohibitively slow for dynamic applications where fast and frequent decision making is required. Data driven methods offer a significant speed-up, but out-of-the-box implementations typically cannot enforce hard constraints or address mixed integer variables. To close this gap of accuracy from model-based approaches and speed from data-driven methods, we propose the domain of physics-informed machine learning