Increasing penetrations of stochastic renewable generation motivate the solution of robust optimal power flow (OPF) problems, which seek minimum-cost operating points for which no realization of the power injections will result in constraint violations. We consider the robust AC OPF problem, where particularly challenging aspect is to guarantee existence of a solution to the non-linear, implicit AC power flow equations for all possible realizations of the uncertain power injections. We use so-called convex restrictions, which defines a convex inner approximation to the AC feasible region in power injection space (without limiting assumptions on nodal power balance), and devise a sequential solution algorithm which provide rigorous robustness guarantees. Empirical results using a variety of test cases demonstrate the algorithm's fast convergence, scalability, and limited conservativeness.

Bio: Line Roald is an Assistant Professor and Grainger Institute Fellow in the Department of Electrical and Computer Engineering in University of Wisconsin—Madison. She received her Ph.D. degree in Electrical Engineering (2016) from ETH Zurich, Switzerland. Prior to joining UW Madison, she was a postdoctoral research fellow with the Center of Non-Linear Studies at Los Alamos National Laboratory. She is the recipient of an NSF CAREER award and the UW Madison ECE Outstanding Graduate Mentor award. Her research interests center around modeling and optimization of energy systems, with a particular focus on managing uncertainty and risk from extreme weather and  renewable energy variability.

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