Article
GPU-Accelerated Algorithm for On-Line Probabilistic Power Flow
IEEE Transactions on Power Systems
Abstract
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.
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
- Graphics Processing Unit,
- Intelligent Systems,
- Large Scale Systems,
- Monte Carlo Methods,
- Online Systems,
- Uncertainty Analysis,
- Case-Studies,
- Computational Burden,
- GPU-Accelerated,
- Online,
- Orders of Magnitude,
- Probabilistic Power Flow,
- Simple Random Sampling,
- Uncertainty Sources,
- Electric Load Flow,
- GPU,
- Monte-Carlo Simulation,
- Uncertainty Source
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
1-1-2018
Publication Date
01 Jan 2018
Disciplines
Citation Information
Gan Zhou, Rui Bo, Lungsheng Chien, Xu Zhang, et al.. "GPU-Accelerated Algorithm for On-Line Probabilistic Power Flow" IEEE Transactions on Power Systems Vol. 33 Iss. 1 (2018) p. 1132 - 1135 ISSN: 0885-8950 Available at: http://works.bepress.com/rui-bo/29/