This project develops optimization algorithms with unprecedented scalability for longstanding and computationally-hard problems faced by power system operators on a daily basis. We tackle the comprehensive problem of optimizing the schedule of power generating units, power flows, and the topology of large-scale transmission networks in the presence of uncertainties. This short-term planning problem can be computationally prohibitive due to inherent power flow nonlinearities and the presence of binary variables, representing the status of each generating unit and transmission line. While commonly used off-the-shelf solvers cannot approach this problem for power systems of significant size, our proposed framework is capable of finding solutions within small gaps from global optimality. We rely on convex algebraic geometry to solve holistic power system optimization problems with a massive number of binary variables and nonlinear constraints. An integral feature of this research is its innate parallelism to be exploited with high-performance computing platforms, particularly using graphical processing units, and achieve the desired scale, order, and speed. The PIs have award-winning, published track records in optimization and control of power and energy systems, and their respective laboratories will be well-equipped with high-performance computational capabilities for the algorithms developed in this project.
High-performance algorithms to clear the electricity market, plan operation, and assess operational security can potentially save billions of dollars, annually. Contemporary insights from optimization theory, scientific computing, and power systems will enable aggressive industry-wide adaptation of modern tools for more reliable, secure, and efficient electric power grids. The PIs distinct yet complimentary backgrounds in optimization theory and energy systems can bridge the pedagogical gap across fields to address the imminent workforce shortage in the power and energy sector. Comprehensive pedagogical activities include development of a new graduate-level course, enhancing the content of existing curriculum, boarder dissemination to K-12 students and high-school teachers, and mentorship for under-represented minority students at the University of Texas at Arlington that is a Hispanic-Serving Institution.
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.
