1 code implementation • 17 Feb 2024 • Mile Mitrovic
To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model.
no code implementations • 1 Mar 2023 • Ognjen Kundacina, Gorana Gojic, Mile Mitrovic, Dragisa Miskovic, Dejan Vukobratovic
Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation.
no code implementations • 16 Feb 2023 • Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
1 code implementation • 26 Sep 2022 • Sajjad Asefi, Mile Mitrovic, Dragan Ćetenović, Victor Levi, Elena Gryazina, Vladimir Terzija
This paper presents a new algorithm for detecting anomaly presence, classifying the anomaly type and identifying the origin of the anomaly, i. e., measurements that contain gross errors in case of bad data, or buses associated with loads experiencing a sudden change, or state variables targeted by false data injection attack.
1 code implementation • 30 Aug 2022 • Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty.
1 code implementation • 21 Jul 2022 • Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Semen Budenny, Yury Maximov, Deepjoyti Deka
Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies.