Accelerating Forward-Backward Sweep Power Flow Computation on the GPU

In this study, we accelerate power flow computation used in modeling and analysis of electric power distribution systems utilizing the GPU. We use kernels and parallel computation patterns (i.e., segmented scan and reduction) running on the GPU to accelerate a common method that is used to perform power flow computation called “forward-backward sweep”. To evaluate our approach, we compare the GPU-accelerated parallel implementation of this method written in CUDA to the serial implementation that runs on the CPU. We perform our tests on binary power distribution trees that have number of nodes between 1K to 256K. Our results show that the parallel implementation brings up to 3.9x total speedup over the serial implementation. As expected, for the parts of the computation that entirely run on the GPU, larger speedups are achieved as the size of the distribution tree increases. We also provide a discussion on how the topology of the tree would affect the results.