GPU-Accelerated Algorithm for Online Probabilistic Power Flow

This letter proposes a superior GPU-accelerated algorithm for probabilistic power flow (PPF) based on Monte-Carlo simulation with simple random sampling (MCS-SRS). By means of offloading the tremendous computational burden to GPU, the algorithm can solve PPF in an extremely fast manner, two orders o...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-01, Vol.33 (1), p.1132-1135
Main Authors: Zhou, Gan, Bo, Rui, Chien, Lungsheng, Zhang, Xu, Yang, Shengchun, Su, Dawei
Format: Article
Language:English
Subjects:
Algorithm design and analysis
GPU
Graphics processing units
Instruction sets
Load flow
Matrix decomposition
Monte-Carlo simulation
online
Parallel processing
probabilistic power flow
simple random sampling
Sparse matrices
uncertainty source
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Summary:This letter proposes a superior GPU-accelerated algorithm for probabilistic power flow (PPF) based on Monte-Carlo simulation with simple random sampling (MCS-SRS). By means of offloading the tremendous computational burden to GPU, the algorithm can solve PPF in an extremely fast manner, two orders of magnitude faster in comparison to its CPU-based counterpart. Case studies on three large-scale systems show that the proposed algorithm can solve a whole PPF analysis with 10000 SRS and ultra-high-dimensional dependent uncertainty sources in seconds and therefore presents a highly promising solution for online PPF applications.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2017.2756339