
An efficient opensource implementation to compute the Jacobian matrix for the NewtonRaphson power flow algorithm
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Fast Parallel NewtonRaphson Power Flow Solver for Large Number of System Calculations with CPU and GPU
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 realtime grid operation, grid planning and in further cases when computational time is critical, a novel approach on simultaneous parallelization of many NewtonRaphson power flow calculations on CPU and with GPUacceleration is proposed. The result shows a speedup of over x100 comparing to the opensource tool pandapower, when performing repetitive power flows of system with admittance matrix of the same sparsity pattern on both CPU and GPU. The speedup 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 LUrefactorization. The batched linear solver shows a large performance improvement comparing to the stateoftheart 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 reallife grid operation and innovative researches e.g. datadriven machine learning studies.
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