Collaborative Research: Physics Informed Real-time Optimal Power Flow

This NSF project aims to develop a physics-informed real-time optimal power flow model using machine learning techniques to address the gap in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The intellectual merits of the project include developing techniques to integrate physical and dynamical principles in machine learning pipelines and methods to ensure scalable and reliable solutions to optimal power flow problems. The broader impacts of the project include significant long-term impacts on power grids, reducing carbon emissions and increasing grid reliability, especially under extreme weather, increased demand, and uncertainty from intermittent generation. The PIs will also engage with national laboratories and non-profit organizations to ensure that the developed model is accessible and usable by the broader community, including utilities, policymakers, and researchers. Furthermore, the project will provide opportunities for training and education in the intersection of physics, engineering, and machine learning, thereby contributing to the development of a skilled workforce in the field of energy and sustainability. The project makes four key scientific and engineering contributions: (1) Advancements in combining physics-informed neural networks with conventional feed-forward neural networks to predict solutions to optimal power flow problems in real-time, pursuing dynamic stability while also optimality. (2) Novel approaches of ensuring constraint satisfaction in the learned embedding. (3) Investigation of techniques to ensure scalability of training to large, realistically-sized networks. (4) Pursuit of model robustness by assessing model performance under measurement noise and analyzing model reliability to develop insights into high-quality approximations of the optimal power flow problem. The proposed model holds the promise to expedite the adoption of increased renewable energy into the power grid, reducing curtailment resulting from stability concerns and suboptimalities resulting from conventional heuristic droop control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.