Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation

The rapid expansion in scale of power systems and the emergence of new smart grid technologies continuously increase computational complexity of power system simulations. Graphic processing unit (GPU), which features massive concurrent threads and excellent floating-point performance, brings new opportunities into power system simulations. This paper introduces an advanced GPU-CPU based parallel power flow (PF) approach by adopting vectorization parallelization and sparse techniques. Specifically, the root cause behind sparsity property of PF and its impacts on the first two steps of Newton-Raphson (NR) based PF calculation, i.e., forming nodal power mismatch vector and updating Jacobian matrix, are quantitatively analyzed. Moreover, a novel GPU-CPU based parallel PF approach is presented, which effectively integrates advanced GPU-based vectorization parallelization and sparse techniques to accelerate performance of PF calculations. Numerical studies validate the effectiveness of various customized parallel schemes for individual key steps of the proposed NR-based parallel PF approach.