An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems (Proposals Track)
Ling Zhang (University of Washington); Baosen Zhang (University of Washington)
To achieve a cleaner energy system, a diverse set of energy resources such as solar PV, battery storage and electric vehicles are entering the electric grid. Their operation is typically controlled by solving a resource allocation problem, called the AC optimal power flow (ACOPF) problem. This problem minimizes the cost of generation subject to supply/demand balance and various other engineering constraints. It is nonlinear and nonconvex, and existing solvers are generally successful in finding local solutions. As the share of renewable energy resources increases, it is becoming increasingly important to find globally optimal solutions to utilize these resources to the full extent. In this paper, we propose a simple iterative approach to find globally optimal solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem and we form a partial Lagrangian from the associated dual variables. This partial Lagrangian has a much better optimization landscape and we use its solution as a warm start for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We demonstrate the effectiveness of our algorithm on standard benchmarks. We also show how the dual variables could be found by using a neural network to further speed up the algorithm.