Gaussian Process Learning-based Probabilistic Optimal Power Flow

Parikshit Pareek; Hung D. Nguyen

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arXiv · 2004.07757

Gaussian Process Learning-based Probabilistic Optimal Power Flow

Parikshit Pareek·Hung D. Nguyen

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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 as well as load uncertainties, while requiring much less number of samples and elapsed time.

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BibTeX RIS

Parikshit Pareek, Hung D. Nguyen. 2020-04-16. Gaussian Process Learning-based Probabilistic Optimal Power Flow. https://arxiv.org/abs/2004.07757

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