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Robust Data-Driven Linear Power Flow Model With Probability Constrained Worst-Case Errors.
- Source :
-
IEEE Transactions on Power Systems . Sep2022, Vol. 37 Issue 5, p4113-4116. 4p. - Publication Year :
- 2022
-
Abstract
- To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model. It applies to both transmission and distribution systems and can achieve better robustness than the recent data-driven models. The key idea is to probabilistically constrain the worst-case errors through distributionally robust chance-constrained programming. It also allows guaranteeing the linearization accuracy for a chosen operating point. Comparison results with three recent LPF models demonstrate that the worst-case error of the RD-LPF model is significantly reduced over 2- to 70-fold while reducing the average error. A compromise between computational efficiency and accuracy can be achieved through different ambiguity sets and conversion methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 37
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 158649840
- Full Text :
- https://doi.org/10.1109/TPWRS.2022.3189543