苏黎世联邦理工学院

Renewable generators must commit to day-ahead market bids despite uncertainty in both production and real-time prices. While forecasts provide valuable guidance, rare and unpredictable extreme events (so-called black swans) can cause substantial financial losses. This paper models the nomination problem as an instance of optimal transport-based distributionally robust optimization (OT-DRO), a principled framework that balances risk and performance by accounting not only for the severity of deviations but also for their likelihood. The resulting formulation yields a tractable, data-driven strategy that remains competitive under normal conditions while providing effective protection against extreme price spikes. Using four years of Finnish wind farm and market data, we demonstrate that OT-DRO consistently outperforms forecast-based nominations and significantly mitigates losses during black swan events.

The identification of dq-asymmetric impedances presents a challenging estimation problem due to the strong cross-coupling between axes, which is especially prominent at low frequencies. Conventional identification schemes are often hampered by the requirement for sequential perturbations or the difficulty of selecting appropriate global parametric models. This paper introduces an active non-parametric frequency-domain approach that utilizes complex transfer functions and local parametric approximations. This formulation eliminates the requirement for periodic steady-state measurements while providing precision comparable to that of correctly specified parametric models. By solving localized linear least-squares problems, the approach maintains high computational efficiency while remaining agnostic to the specific grid structure. Electromagnetic transient (EMT) simulations demonstrate that the proposed method achieves high-fidelity identification with a 1 Hz frequency resolution using only one second of noise-corrupted measurements. The results confirm a significant reduction in measurement time compared to available techniques. Additionally, the approach shows superior robustness against model-order selection errors, ensuring accurate wideband modeling of grid dynamics.

This paper presents a decentralized frequency-domain framework to characterize the influence of the operating point on the small-signal stability of converter-dominated power systems. The approach builds on Scaled Relative Graph (SRG) analysis, extended here to address Linear Parameter-Varying (LPV) systems. By exploiting the affine dependence of converter admittances on their steady-state operating points, the centralized small-signal stability assessment of the grid is decomposed into decentralized, frequency-wise geometric tests. Each converter can independently evaluate its feasible stability region, expressed as a set of linear inequalities in its parameter space. The framework provides closed-form geometric characterizations applicable to both grid-following (GFL) and grid-forming (GFM) converters, and validation results confirm its effectiveness.

The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.

The recent availability of a consistent electron collision cross-section set for the ultra-low GWP HFO-1234ze(E) (HFO) enables performance simulations of resistive plate chambers (RPCs) operated with HFO-based gas mixtures. We present a simulation framework that reproduces key detector observables measured in trigger-purpose RPCs, such as detection efficiency and cluster size. The simulation additionally provides new insights into streamer formation through a novel streamer inception criterion that captures the observed trends in streamer probability. For mixtures containing CO2, HFO, SF6, and isobutane, the Pareto front between avalanche-streamer separation and relative CO2 equivalent emissions is evaluated using a multi-objective Bayesian optimization approach under a low working point constraint, so the mixture can be used with already installed infrastructure. The optimization confirms that the ECO2 gas mixture (60% CO2, 35% HFO, 4% isobutane, 1% SF6), previously identified through experimental studies, is a Pareto optimal choice when the operation is constrained to a low working point. If higher working points are acceptable, mixtures with a higher HFO percentage provide larger avalanche-streamer separation while enabling a reduction of the SF6 percentage, thereby reducing the environmental impact even further.