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Gaussian Process Learning-Based Probabilistic Optimal Power Flow [PDF]
IEEE Transactions on Power Systems, 2021 In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary distribution.
Hung Nguyen, Parikshit Pareek
exaly +2 more sources
Data-Driven Probabilistic Optimal Power Flow With Nonparametric Bayesian Modeling and Inference [PDF]
IEEE Transactions on Smart Grid, 2020 In this paper, we propose a data-driven algorithm for probabilistic optimal power flow (POPF). In particular, we develop a nonparametric Bayesian framework based on the Dirichlet process mixture model (DPMM) and variational Bayesian inference (VBI) to ...
Haitao Zhang +2 more
exaly +2 more sources
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Probabilistic Optimal Power Flow for Balanced Islanded Microgrids
IEEE Latin America Transactions, 2023Wesley Peres
exaly
Scalable Probabilistic Optimal Power Flow for High Renewables Using Lite Polynomial Chaos Expansion
IEEE Systems Journal, 2023Sel Ly, Hung Nguyen, Parikshit Pareek
exaly
Toward Fast Calculation of Probabilistic Optimal Power Flow
IEEE Transactions on Power Systems, 2019Wei Dai, , Juan Yu
exaly
Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation
Neurocomputing, 2015Dajun Du
exaly