Abstract
In many power system applications, such as N–x static security analysis and Monte-Carlo-simulation-based probabilistic power flow (PF) analysis, it is a very time-consuming task to analyze massive number of PFs on identical or similar network topology. This letter presents a novel GPU-accelerated batch LU-factorization solver that achieves higher level of parallelism and better memory-access efficiency through packaging massive number of LU-factorization tasks to formulate a new larger-scale problem. The proposed solver can achieve up to 76 times speedup when compared to KLU library and lays a critical foundation for massive-PFs-solving applications.
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