Fast Parallel Newton-Raphson Power Flow Solver for Large Number of System Calculations with CPU and GPU

Abstract

To analyze large sets of grid states, e.g. in time series calculations or parameter studies, large number of power flow calculations have to be performed, as well as evaluating the impact from uncertainties of the renewable energy e.g. wind and PV of power systems with Monte-Carlo simulation. For the application in real-time grid operation and in cases when computational time is critical, a novel approach on parallelization of Newton-Raphson power flow for many calculations on CPU and with GPU-acceleration is proposed. The result shows a speed-up of over x100 comparing to the open-source tool pandapower, when performing repetitive power flows of system with admittance matrix of the same sparsity pattern on both CPU and GPU. The speed-up relies on the optimized algorithm and parallelization strategy, which can reduce the repetitive work and saturate the high hardware performance of modern CPUs and GPUs well. This is achieved with the proposed batched sparse matrix operation and batched linear solver based on LU-refactorization. The batched linear solver shows a large performance improvement comparing to the state-of-art linear system solver KLU library and a better saturation of the GPU performance with small problem scale. Finally, the method of integrating the proposed solver into pandapower is presented, thus the parallel power flow solver with outstanding performance can be easily applied in challenging real-life grid operation and innovative researches e.g. data-driven machine learning studies.