4 citations · 7 across the 7 of their papers we have counts for
8 papers
CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics
Vsevolod Morozov, Artem Galliamov, Aleksandr Lukashevich +2
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disast…
A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids with Renewable Energy
Aleksander Lukashevich, Aleksander Bulkin, Yury Maximov
Renewable energy sources (RES) are increasingly integrated into power systems to support the United Nations' Sustainable Development Goals of decarbonization and energy security. H…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich +4
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power gr…
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev +3
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncert…
Data-Driven Stochastic AC-OPF using Gaussian Processes
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev +4
In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into…
Power Grid Reliability Estimation via Adaptive Importance Sampling
Aleksandr Lukashevich, Yury Maximov
Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutral…
An Accelerated Second-Order Method for Distributed Stochastic Optimization
Artem Agafonov, Pavel Dvurechensky, Gesualdo Scutari +4
We consider distributed stochastic optimization problems that are solved with master/workers computation architecture. Statistical arguments allow to exploit statistical similarity…
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich +4
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in para…