GPU-based fast decoupled power flow with preconditioned iterative solver and inexact newton method

Power flow is the most fundamental computation in power system analysis. Traditionally, the linear solution in power flow is solved by a direct method like LU decomposition on a CPU platform. However, direct methods may suffer scalability issues in parallel computing when the scale of the system increases. In contrast, iterative solvers, as an alternative to direct solvers, are generally more scalable with better parallelism. This work presents a fast decouple power flow (FDPF) algorithm with a graphic processing unit (GPU)-based preconditioned conjugate gradient (CG) iterative solver. In addition, the inexact Newton method is integrated to further improve the GPU-based parallel computing performance for solving FDPF. The results show that the GPU-based FDPF maintains the same precision and convergence as the original CPU-based FDPF, while providing considerable performance improvement for several large-scale systems. The proposed GPU-based FDPF with inexact Newton method gives a speedup of 2.86 times for a system with over 10,000 buses if compared with traditional FDPF, both implemented based on Matlab. This demonstrates the promising potential of the proposed FDPF computation using a preconditioned iterative solver under GPU architecture.