Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150725
Title: Gaussian Process Learning-based Probabilistic Optimal Power Flow
Authors: Pareek, Parikshit
Nguyen, Hung D.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Pareek, P. & Nguyen, H. D. (2021). Gaussian Process Learning-based Probabilistic Optimal Power Flow. IEEE Transactions On Power Systems, 36(1), 541-544. https://dx.doi.org/10.1109/TPWRS.2020.3031765
Journal: IEEE Transactions on Power Systems
Abstract: 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. The proposed method relies on a non-parametric Bayesian inference-based uncertainty propagation approach, called Gaussian Process (GP). We also suggest a new type of sensitivity called Subspace-wise Sensitivity, using observations on the interpretability of GP-POPF hyperparameters. The simulation results on 14-bus and 30-bus systems show that the proposed method provides reasonably accurate solutions when compared with Monte-Carlo Simulations (MCS) solutions at different levels of uncertain renewable penetration and load uncertainties. The proposed method requires a lesser number of samples and elapsed time. The non-parametric nature of the proposed method is manifested by testing uncertain injections that follow various distributions in the 118-bus system. The low error value results verify the proposed method's capability of working with different types of input uncertainty distributions.
URI: https://hdl.handle.net/10356/150725
ISSN: 1558-0679
DOI: 10.1109/TPWRS.2020.3031765
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPWRS.2020.3031765
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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