no code implementations • 6 Dec 2023 • Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L., Audun Botterud
Furthermore, we find that when transmission capacity increases, the proposed bilevel model will still reduce the system cost, whereas the myopic strategy may incur a much higher cost due to over-scheduling of VRES in the day-ahead market and the lack of flexible conventional generators in real time.
1 code implementation • 28 Sep 2023 • Vladimir Dvorkin
A network of spatially distributed data centers can provide operational flexibility to power systems by shifting computing tasks among electrically remote locations.
no code implementations • 2 Aug 2023 • Vladimir Dvorkin, Ferdinando Fioretto
While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices.
1 code implementation • 20 Mar 2023 • Vladimir Dvorkin, Audun Botterud
While power systems research relies on the availability of real-world network datasets, data owners (e. g., system operators) are hesitant to share data due to security and privacy risks.
no code implementations • 25 Nov 2022 • Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L., Audun Botterud
Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency.
no code implementations • 18 Sep 2022 • Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity.
1 code implementation • 7 Oct 2020 • Vladimir Dvorkin, Anubhav Ratha, Pierre Pinson, Jalal Kazempour
Furthermore, the chance-constrained optimization provides the foundation of a stochastic pricing scheme for natural gas networks, which improves on a deterministic market settlement by offering the compensations to network assets for their contribution to uncertainty and variance control.
Optimization and Control
1 code implementation • 22 Jun 2020 • Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, Pierre Pinson
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.