This paper proposes a parallel Newton-Raphson Power Flow (PNPF) method which is suitable for GPU (Graphics Processing Unit). Aiming at the most time-consuming part of power flow–solving the sparse linear equations, an efficient hierarchy parallel solver is presented, in which LU decomposition and forward/back substitution were implemented in parallel level by level under the direction of path tree. To improve the efficiency of the method, a short-type path tree was formed, an optimization on data transfer and a self-adapted task-allocation algorithm was designed. In addition, the sparse Jacobian matrix and the right-hand side of the linear equations were also generated in parallel in a fine-grained pattern. At last, the developed GPU-based PNPF program has been tested on large-scale power systems of up to 23215 buses. The method provides a speedup of 3.91 times compared to mainstream commercial software, which proves the effectiveness and practicality of the algorithm proposed in this paper.
['Han Jiang', 'Deyang Chen', 'Yalou Li', 'Ran Zheng']
A Fine-Grained Parallel Power Flow Method for Large Scale Grid Based on Lightweight GPU Threads