GPU-Accelerated Solutions to Optimal Power Flow Problems

The optimal power flow problem (OPF) has been of importance to power system operators for many decades. Being able to quickly determine optimal operating points and analyzing larger networks can lead to advantages for operators from reliability, stability, cost and market fairness perspectives. This work aims at achieving those ends by solving OPF problems by utilizing hardware acceleration capabilities of graphical processing units (GPUs). At present, nearly all desktop and laptop computers ship with general-purpose GPUs that can be harnessed to accelerate analysis. This work will present important concepts regarding effective use of GPUs as it pertains to OPF problems and illustrate the types of problems that stand to benefit most from their use. The benefits of GPU acceleration are demonstrated by implementing a predictor-corrector interior-point method with the majority of the computation offloaded onto a GPU. Experiments are used to validate the developments by analyzing well-known power systems.