Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic succ...

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Hauptverfasser: Mitrovic, Mile, Lukashevich, Aleksandr, Vorobev, Petr, Terzija, Vladimir, Maximov, Yury, Deka, Deepjyoti
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Computer Science - Learning
Computer Science - Systems and Control
Statistics - Machine Learning
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creator Mitrovic, Mile
Lukashevich, Aleksandr
Vorobev, Petr
Terzija, Vladimir
Maximov, Yury
Deka, Deepjyoti
description The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.
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Statistics - Machine Learning
title Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
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