Fabian Leimgruber

Marcel Otte, Fabian Leimgruber, Roland Bründlinger et al.

Renewables are key enablers for the realization of a sustainable energy supply but grid operators and energy utilities have to mange their intermittent behavior and limited storage capabilities by ensuring the security of supply and power quality. Advanced control approaches, automation concepts, and communication technologies have the potential to address these challenges by providing new intelligent solutions and products. However, the validation of certain aspects of such smart grid systems, especially advanced control and automation concepts is still a challenge. The main aim of this work therefore is to introduce a hardware-in-the-loop co-simulation-based validation framework which allows the simulation of large-scale power networks and control solutions together with real-world components. The application of this concept to a selected voltage control example shows its applicability.

Felix Lehfuss, Georg Lauss, Christian Seitl et al.

This paper addresses the validation of electric vehicle supply equipment by means of a real-time capable co-simulation approach. This setup implies both pure software and real-time simulation tasks with different sampling rates dependent on the type of the performed experiment. In contrast, controller and power hardware-in-the-loop simulations are methodologies which ask for real-time execution of simulation models with well-defined simulation sampling rates. Software and real-time methods are connected one to each other using an embedded software interface. It is able to process signals with different time step sizes and is called "LabLink". Its design implies both common and specific input and output layers (middle layer), as well as a data bus (core). The LabLink enables the application of the co-simulation methodology on the proposed experimental platform targeting the testing of electric vehicle supply equipment. The test setup architecture and representative examples for the implemented co-simulation are presented in this paper. As such, a validation of the usability of this testing platform can be highlighted aiming to support a higher penetration of electric vehicles.

Georg Lauss, Filip Pröstl Andrén, Fabian Leimgruber et al.

Real-time simulation methods for investigations on electric networks and integration of grid connected generation units are increasingly in the focus of ongoing research areas. While laboratory testing methods are the predominant method for the verification of safety and quality related features of grid-connected generation units in the past, load flow modeling verification methods have been integrated in state-of-the-art standardization frameworks recently. The next step is comprised in real-time simulation methodologies applied for compliance testing of entire power electronic systems integrated in power distribution networks. The Controller Hardware-in-the-Loop (CHIL) approach is an appropriate methodology that combines numerical simulations with software modeling approaches and classical hardware testing in labs. Control boards represent the hardware device directly connected to the power electronic periphery, which is entirely simulated in a real-time simulation environment. Hereby, input signals from voltage and current measurements and output signals for power system control are exchanged in real-time. Thanks to this setup the testing of the true behavior of entire generation units within the electric network can be emulated precisely. With the application of CHIL a shorter time to market and a lower risk in the development phase can be achieved. However, an analysis from realized CHIL experiments shows the need for more harmonized procedures. This paper addresses this topic and provides an outlook about necessary future CHIL standardization needs.

Runyao Yu, Yuchen Tao, Fabian Leimgruber et al.

Probabilistic forecasting of intraday electricity prices is essential to manage market uncertainties. However, current methods rely heavily on domain feature extraction, which breaks the end-to-end training pipeline and limits the model's ability to learn expressive representations from the raw orderbook. Moreover, these methods often require training separate models for different quantiles, further violating the end-to-end principle and introducing the quantile crossing issue. Recent advances in time-series models have demonstrated promising performance in general forecasting tasks. However, these models lack inductive biases arising from buy-sell interactions and are thus overparameterized. To address these challenges, we propose an end-to-end probabilistic model called OrderFusion, which produces interaction-aware representations of buy-sell dynamics, hierarchically estimates multiple quantiles, and remains parameter-efficient with only 4,872 parameters. We conduct extensive experiments and ablation studies on price indices (ID1, ID2, and ID3) using three years of orderbook in high-liquidity (German) and low-liquidity (Austrian) markets. The experimental results demonstrate that OrderFusion consistently outperforms multiple competitive baselines across markets, and ablation studies highlight the contribution of its individual components. The project page is at: https://runyao-yu.github.io/OrderFusion/.