  To analyze large sets of grid states, e.g. when evaluating the impact from
the uncertainties of the renewable generation with probabilistic Monte Carlo
simulation or in stationary time series simulation, large number of power flow
calculations have to be performed. For the application in real-time grid
operation, grid planning and in further cases when computational time is
critical, a novel approach on simultaneous parallelization of many
Newton-Raphson power flow 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 algorithm improvement and highly optimized
parallelization strategy, which can reduce the repetitive work and saturate the
high hardware computational capability 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-the-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.
