8 citations · 8 across the 9 of their papers we have counts for
9 papers
Fast Risk Certification of Candidate Trajectories under Uncertain Time-Varying Constraints
Srimanta Santra, Oleksii Molodchyk, Matti Noack +1
This paper studies the certification of a fixed candidate trajectory on a finite certification grid under parametric uncertainty. For each constraint-time pair, we define a scalar…
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, partic…
Data-Driven Power Flow for Radial Distribution Networks with Sparse Real-Time Data
Oleksii Molodchyk, Omid Mokhtari, Samuel Chevalier +2
Real-time control of distribution networks requires accurate information about the system state. In practice, however, such information is difficult to obtain because real-time mea…
Towards Stochastic (N-1)-Secure Redispatch
Oleksii Molodchyk, Hendrik Drögehorn, Martin Lindner +2
The intermittent nature of renewable power availability is one of the major sources of uncertainty in power systems. While markets can guarantee that the demand is covered by the a…
Towards Data-Driven Multi-Stage OPF
Oleksii Molodchyk, Philipp Schmitz, Alexander Engelmann +2
The operation of large-scale power systems is usually scheduled ahead via numerical optimization. However, this requires models of grid topology, line parameters, and bus specifica…
Towards safe Bayesian optimization with Wiener kernel regression
Oleksii Molodchyk, Johannes Teutsch, Timm Faulwasser
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, t…
Exploring the Links between the Fundamental Lemma and Kernel Regression
Oleksii Molodchyk, Timm Faulwasser
Generalizations and variations of the fundamental lemma by Willems et al. are an active topic of recent research. In this note, we explore and formalize the links between kernel re…
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty
T. Faulwasser, O. Molodchyk
Gaussian Processes (GPs) are a versatile method that enables different approaches towards learning for dynamics and control. Gaussianity assumptions appear in two dimensions in GPs…
System-level Testing of the Congestion Management Capability of a Hardware-Independent Optimal Power Flow Algorithm
Thomas Schwierz, Rajkumar Palaniappan, Oleksii Molodchyk +1
The integration of distributed energy resources (DERs) into the electrical grid causes various challenges in the distribution grids. The complexity of smart grids as multi-domain e…