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

TL;DR

This paper introduces a highly efficient parallel Newton-Raphson power flow solver leveraging CPU and GPU acceleration, significantly speeding up large-scale grid state analyses for real-time and planning applications.

Contribution

It presents a novel parallelization approach with batched sparse matrix operations and a custom linear solver, achieving over 100x speed-up compared to existing tools.

Findings

Over 100x speed-up on large power flow calculations

Superior performance of the batched linear solver over KLU

Effective GPU saturation with small problem sizes

Abstract

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.