TY - JOUR
T1 - Full Parallel Power Flow Solution
T2 - A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton-Raphson Implementation
AU - Su, Xueneng
AU - He, Chuan
AU - Liu, Tianqi
AU - Wu, Lei
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Graphic processing unit
KW - Newton-Raphson
KW - power flow
KW - sparse technique
KW - vectorization parallelization
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U2 - 10.1109/TSG.2019.2943746
DO - 10.1109/TSG.2019.2943746
M3 - Article
AN - SCOPUS:85083916055
SN - 1949-3053
VL - 11
SP - 1833
EP - 1844
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 3
M1 - 8848455
ER -