CAREER: Learning-Assisted Optimal Power Flow with Confidence

This NSF CAREER project aims to revolutionize the way electric power grids operate by integrating data-driven techniques into grid operations in order to reliably operate assets on faster time-scales. As more renewable energy is introduced into the grid and as grid operations grow increasingly complex, grid components such as power plants and energy storage must be operated on faster time-scales to balance fluctuations in power and keep the grid operating reliably.

This project transforms the traditional optimization problems that grid operators solve by expediting their solution time using historical data and machine learning. The intellectual merits of this project focus on embedding the learning-based solutions into existing grid operations using a hybrid approach, by combining conventional optimization with machine learning.

Rather than relying on purely data-driven solutions, grid operators can have increased confidence that the grid can continue to operate reliably under highly renewable energy futures. The broader impacts of the project include a "learning-for-energy" program with scholarship prizes, and publicly released datasets and benchmarks for algorithms emerging in this area of research.

In contrast to traditional approximations of these difficult problems that are typically used to address convergence speed and success, this project proposes to leverage data to preserve more complex relationships while maintaining extremely fast computational speeds. Multiple "learning-assisted" algorithms will be developed to enhance various facets of power system operations including stochastic, distributed, and AC optimal power flow.

Neural networks in particular can approximate complex functions well and perform inference in milliseconds, but alone, are black-box solutions that may not give grid operators the confidence to use them in real system operation. Here, hybrid approaches that combine optimization and machine learning are taken to determine better warm-starting points for nonconvex solvers, assist the convergence time of distributed optimization techniques, improve convergence success in difficult-to-solve optimal power flow scenarios, and reduce problem complexity in stochastic grid optimization.

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.