6 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 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…
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…