Smart sampling and HPC-based probabilistic look-ahead contingency analysis implementation and its evaluation with real-world data

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduce...

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Bibliographic Details
Published in2017 IEEE Power & Energy Society General Meeting pp. 1 - 5
Main Authors Yousu Chen, Etingov, Pavel, Huiying Ren, Nguyen, Tony, Zhangshuan Hou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Algorithm design and analysis
high performance computing
Load modeling
look-ahead contingency analysis
Probabilistic logic
probability analysis
Probability density function
risk assessment
Smart sampling
Uncertainty
Wind forecasting
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Summary:This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. HPC techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested with real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.
ISSN:1944-9933
DOI:10.1109/PESGM.2017.8274058