Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 33, Issue 1, Pages 1132-1135Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2756339
Keywords
GPU; probabilistic power flow; Monte-Carlo simulation; simple random sampling; uncertainty source; online
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Funding
- Science and Technology Foundation of State Grid Corporation of China [DZ71-16-028]
- Jiangsu Province Natural Science Fund [BK20151124]
- Jiangsu Key Laboratory of Smart Grid Technology and Equipment
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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.
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