Parallel Power Flow Computation Trends and Applications: A Review Focusing on GPU

A power flow study aims to analyze a power system by obtaining the voltage and phase angle of buses inside the power system. Power flow computation basically uses a numerical method to solve a nonlinear system, which takes a certain amount of time because it may take many iterations to find the final solution. In addition, as the size and complexity of power systems increase, further computational power is required for power system study. Therefore, there have been many attempts to conduct power flow computation with large amounts of data using parallel computing to reduce the computation time. Furthermore, with recent system developments, attempts have been made to increase the speed of parallel computing using graphics processing units (GPU). In this review paper, we summarize issues related to parallel processing in power flow studies and analyze research into the performance of fast power flow computations using parallel computing methods with GPU.

[1]  Fernando L. Alvarado,et al.  Sparse matrix inverse factors (power systems) , 1990 .

[2]  James Demmel,et al.  A Supernodal Approach to Sparse Partial Pivoting , 1999, SIAM J. Matrix Anal. Appl..

[3]  Fangxing Li,et al.  Distributed algorithms with theoretic scalability analysis of radial and looped load flows for power distribution systems , 2003 .

[4]  Yu Wang,et al.  GPU-Accelerated Sparse LU Factorization for Circuit Simulation with Performance Modeling , 2015, IEEE Transactions on Parallel and Distributed Systems.

[5]  Mitsuo Gen,et al.  A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling , 2019, Comput. Ind. Eng..

[6]  Machi Zawidzki,et al.  Effective multi-objective discrete optimization of Truss-Z layouts using a GPU , 2018, Appl. Soft Comput..

[7]  Goran Strbac,et al.  Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids , 2017, IEEE Transactions on Smart Grid.

[8]  V. C. Ramesh,et al.  On distributed computing for on-line power system applications , 1996 .

[9]  Emmett Kilgariff,et al.  Fermi GF100 GPU Architecture , 2011, IEEE Micro.

[10]  Daniel Tylavsky,et al.  Coarse grain scheduling in parallel triangular factorization and solution of power system matrices , 1991 .

[11]  Weerakorn Ongsakul,et al.  Managing the bottlenecks in parallel Gauss-Seidel type algorithms for power flow analysis , 1993 .

[12]  Fangxing Li,et al.  GPU-based power flow analysis with Chebyshev preconditioner and conjugate gradient method , 2014 .

[13]  D. Maratukulam,et al.  A parallel load flow algorithm , 1994 .

[14]  Tao Zhang,et al.  GPU-accelerated sparse matrices parallel inversion algorithm for large-scale power systems , 2019, International Journal of Electrical Power & Energy Systems.

[15]  Balho H. Kim,et al.  A fast distributed implementation of optimal power flow , 1999 .

[16]  Dragoslav D. Šiljak,et al.  An improved block-parallel Newton method via epsilon decompositions for load-flow calculations , 1996 .

[17]  K. S. Swarup,et al.  Parallel contingency analysis in a high performance computing environment , 2009, 2009 International Conference on Power Systems.

[18]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .

[19]  Venkata Dinavahi,et al.  Fast Batched Solution for Real-Time Optimal Power Flow With Penetration of Renewable Energy , 2018, IEEE Access.

[20]  Olaf Schenk,et al.  Solving unsymmetric sparse systems of linear equations with PARDISO , 2004, Future Gener. Comput. Syst..

[21]  Yoshikazu Fukuyama,et al.  Simultaneous parallel power flow calculations using hybrid CPU-GPU approach , 2019 .

[22]  M. R. Irving,et al.  Decomposed load-flow algorithm suitable for parallel processor implementation , 1985 .

[23]  Gan Zhou,et al.  GPU-Accelerated Algorithm for Online Probabilistic Power Flow , 2018, IEEE Transactions on Power Systems.

[24]  Anjan Bose,et al.  Parallel solution of large sparse matrix equations and parallel power flow , 1995 .

[25]  N. Xiang,et al.  Parallel reduced gradient optimal power flow solution , 1989 .

[26]  Gan Zhou,et al.  GPU-Based Batch LU-Factorization Solver for Concurrent Analysis of Massive Power Flows , 2017, IEEE Transactions on Power Systems.

[27]  John Matthew Burgess.  RTX on—The NVIDIA Turing GPU , 2020, IEEE Micro.

[28]  Rui Bo,et al.  A novel GPU-accelerated strategy for contingency screening of static security analysis , 2016 .

[29]  Chuan He,et al.  Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation , 2020, IEEE Transactions on Smart Grid.

[30]  Gan Zhou,et al.  GPU-Accelerated Batch-ACPF Solution for N-1 Static Security Analysis , 2017, IEEE Transactions on Smart Grid.

[31]  Youngmin Yi,et al.  CPU-GPU2Trigeneous Computing for Iterative Reconstruction in Computed Tomography , 2016 .

[32]  Maksym Spiryagin,et al.  Parallel computing in railway research , 2018, International Journal of Rail Transportation.

[33]  Fangxing Li,et al.  GPU-Based Fast Decoupled Power Flow With Preconditioned Iterative Solver and Inexact Newton Method , 2017, IEEE Transactions on Power Systems.

[34]  Vincent Roberge,et al.  Parallel Power Flow on Graphics Processing Units for Concurrent Evaluation of Many Networks , 2017, IEEE Transactions on Smart Grid.

[35]  Mehmet Karakose,et al.  An adaptive fault diagnosis approach using pipeline implementation for railway inspection , 2018, Turkish J. Electr. Eng. Comput. Sci..

[36]  Minseong Kim,et al.  Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation , 2018, Energies.

[37]  Seon Wook Kim,et al.  Study of Cache Performance on GPGPU , 2015 .