Accelerated Computation and Tracking of AC Optimal Power Flow Solutions Using GPUs

Abstract

We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of alternating current optimal power flow (ACOPF) problem. Such a fast computation is particularly useful for mitigating the negative impact of frequent load and generation fluctuations on the optimal operation of a large electrical grid. To this end, we decompose a given ACOPF problem by grid components, resulting in a large number of small independent nonlinear nonconvex optimization subproblems. The computation time of these subproblems is significantly accelerated by employing the massive parallel computing capability of GPUs. In addition, the warm-start ability of our method leads to faster convergence, making the method particularly suitable for fast tracking of optimal solutions. We demonstrate the performance of our method on a 70,000 bus system by solving associated optimal power flow problems with both cold start and warm start.