Efficient polynomial chaos expansion for uncertainty quantification in power systems

Growing uncertainty from renewable energy integration and distributed energy resources motivate the need for advanced tools to quantify the effect of uncertainty and assess the risks it poses to secure system operation. Polynomial chaos expansion (PCE) has been recently proposed as a tool for uncert...

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Bibliographic Details
Published in:Electric power systems research 2020-12, Vol.189 (C), p.106791, Article 106791
Main Authors: Métivier, David, Vuffray, Marc, Misra, Sidhant
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy
Distributed generation
Electricity generation
Energy resources
Energy sources
Flow equations
Mathematics
Optimal power flow
Optimization and Control
Polynomial chaos expansion
Polynomials
Power flow
Quality assessment
Sparsity
Uncertainty
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Summary:Growing uncertainty from renewable energy integration and distributed energy resources motivate the need for advanced tools to quantify the effect of uncertainty and assess the risks it poses to secure system operation. Polynomial chaos expansion (PCE) has been recently proposed as a tool for uncertainty quantification in power systems. The method produces highly accurate results, but has proved to be computationally challenging to scale to large systems. We propose a modified algorithm based on PCE with significantly improved computational efficiency that retains the desired high level of accuracy of the standard PCE. Our method uses computational enhancements by exploiting the sparsity structure and algebraic properties of the power flow equations. We show the scalability of the method on the 1354 pegase test system, assess the quality of the uncertainty quantification in terms of accuracy and robustness, and demonstrate an example application to solving the chance constrained optimal power flow problem.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106791