arXiv subjects

Gabriela Hug

Publications and source records attributed to Gabriela Hug.

At least 19 recordsLinked to original sources

arXiv2026

Automated consolidation of dispersed DSO data resources into a consistent digital twin of the distribution grid

The energy transition causes a growing number of distributed flexible energy resources in the low voltage grid. To utilize that flexibility, automation and control algorithms are needed, which typically are developed in theoretical research. We build a cyberphysical algorithm test bed to bridge the gap between theoretical research and practical application in cooperation with a local DSO in Walenstadt, Switzerland. To obtain suitable monitoring and data analysis capabilities from their existing data landscape, we propose tool-independent data harmonization measures in preparation for a digital twin of the grid. For data extraction from high-security network zones we developed a secure pipeline that implements a logical data diode ensuring one-way traffic only. Furthermore, we established a test community of voluntary prosumer households, whose flexible assets were made accessible through a common backend, giving us actuation capabilities. The presented publish-subscribe architecture is not only suitable to integrate various data sources into a central data collection, but also to manage access privileges of different stakeholders. This could support unbundling of an organization's data flows and the implementation of data space concepts.

eess.SY↗

arXiv2026

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. We present GENCO (GEometric Neural Corrective Optimizer), a unified neural solver for steady-state transmission grid analysis that handles power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared grid representation. To support advances in neural power system solvers, we introduce the open-source GridFM Development Framework, which standardizes synthetic data generation and training in a low-code environment. We also release large-scale datasets with millions of PF and OPF scenarios across diverse grid topologies to support reproducible benchmarking. We evaluate GENCO on the PFDelta and OPFData benchmarks against state-of-the-art neural solvers and classical solvers, including Newton-Raphson and IPOPT, as well as on real-world Hydro-Québec SCADA data. For large-scale PF, GENCO recovers the full AC operating state, including voltage magnitudes and reactive power that DC-PF cannot provide, while matching DC-PF-level active power-balance residuals. It achieves up to 30x speedups over Newton-Raphson at only 2x the runtime of DC-PF. For OPF, it achieves up to 85x speedups over IPOPT while improving feasibility, optimality, and runtime over DC-OPF. For SE, GENCO is more robust than classical weighted least squares to noisy measurements and grid parameter errors, and always returns a high-quality estimate even when weighted least squares fails to converge. Together, the unified architecture and development framework provide a new approach to large-scale steady-state grid analysis, lowering the barrier to entry for power system engineers and marking a step toward Grid Foundation Models.

cs.AI↗

arXiv2026

Comparison of Current-Limiting Strategies for Grid-Forming Converters Under Grid Faults

This paper investigates the performance of three state-of-the-art current-limiting strategies for grid-forming (GFM) converters under grid faults. These highly effective strategies include virtual admittance control, virtual admittance control enhanced with virtual power feedback, and the recently developed cross-forming control. The performance comparison addresses two crucial requirements for GFM converters under grid faults, namely transient stability and reactive current injection. The comparison indicates that the virtual admittance control enhanced with virtual power feedback shows the most promising transient stability performance, while the cross-forming control exhibits superior reactive current injection performance. Thus, individual benefits of each strategy can be obtained by properly combining them.

eess.SY↗

arXiv2026

Residential Peak Load Reduction via Direct Load Control under Limited Information

Thermostatically controlled loads and electric vehicles offer flexibility to reduce power peaks in low-voltage distribution networks. This flexibility can be maximized if the devices are coordinated centrally, given some level of information about the controlled devices. In this paper, we propose novel optimization-based control schemes with prediction capabilities that utilize limited information from heat pumps, electric water heaters, and electric vehicles. The objective is to flatten the total load curve seen by the distribution transformer by restricting the times at which the available flexible loads are allowed to operate, subject to the flexibility constraints of the loads to preserve customers' comfort. The original scheme was tested in a real-world setup, considering both summer and winter days. The pilot results confirmed the technical feasibility but also informed the design of an improved version of the controller. Computer simulations using the adjusted controller show that, compared to the original formulation, the improved scheme achieves greater peak reductions in summer. Additionally, comparisons were made with an ideal controller, which assumes perfect knowledge of the inflexible load profile, the models of the controlled devices, the hot water and space heating demand, and future electric vehicle charging sessions. The proposed scheme with limited information achieves 43.3 % and 27.8 % of the potential average daily peak reduction that the ideal controller with perfect knowledge would achieve in summer and winter, respectively.

eess.SY↗

arXiv2026

Forced Oscillations in Power Systems Induced by Data Centers Hosting AI Workloads

Power swings in large Data Centers (DTCs) running Artificial Intelligence (AI) workloads can excite poorly damped modes in power systems. The resulting forced oscillations can lead to flicker, equipment disconnection, or blackouts. This paper investigates the risks of such load fluctuations for different system strengths and damping conditions. Using an analytical approach based on transfer functions, we identify critical DTC locations in the power grid at which load fluctuations could induce the largest forced oscillations and further characterize the harmonic spectrum of the resulting system response. Depending on the frequency and magnitude of the DTC load fluctuations, forced oscillations can become unbounded. The underlying instabilities are classified into saddle-node bifurcations of the forced periodic response and impasse-surface encounters, using Floquet multipliers and the minimum singular value of the algebraic Jacobian. Furthermore, the impact of different duty cycles and harmonic components beyond the fundamental oscillation frequency in square-wave load profiles is analyzed. Finally, the interaction of two oscillating DTCs is investigated for different locations and forcing frequencies, considering both synchronized and unsynchronized operation. The findings can help system operators to define new regulations on the maximum load fluctuations permitted for DTC facilities at specific grid locations, without negatively affecting the stability and operation of the system.

eess.SY↗

arXiv2026

On the Concept of an Optimal Portfolio of Uncertain Flexible Loads

Flexible loads can enhance power system stability by providing reserves, but their limited energy capacity and uncertain availability distinguish them from conventional generators. To accommodate these characteristics, the Danish Transmission System Operator (TSO) recently introduced new reserve market rules that incorporate energy constraints and relax reliability requirements. In this context, the optimal reserve quantification becomes a joint chance-constrained reserve quantification problem, which is difficult to solve. In this paper, we derive two analytical reformulations of this problem: an exact one when a reserve direction dominates and an approximate one, otherwise. Furthermore, when flexible loads must collectively satisfy a reliability requirement, we introduce the concept of an optimal portfolio of flexible loads: adding loads with similar expected values but different stochastic behaviors to an existing portfolio may change the total portfolio's reserves. To support this idea, we theoretically study the marginal increase in reserves resulting from adding a load. Numerical results show that our analytical reformulations closely match the exact formulation, with a mean absolute error of 2.5%. Case studies further demonstrate the existence of optimal load groupings and our ability to predict the portfolio in which a load's marginal value is highest, leveraging our theoretical analysis.

eess.SY↗

arXiv2026

Prosumer Synchronisation Risk: Impacts of Time-Varying Tariffs on Distribution Network Expansion

The rapid deployment of distributed energy resources, including heat pumps, electric vehicles, photovoltaics, and battery storage, is reshaping the operation of low-voltage networks. While distribution system operators often aim to develop time-varying tariffs to incentivise network-friendly behaviour and defer reinforcements, they risk triggering prosumer synchronisation. As prosumers follow price signals, their behaviour may synchronise, avoiding existing load peaks while creating undesirable peaks at other times. This paper quantifies the impact on low-voltage network reinforcement needs when shifting from flat tariffs to two time-varying tariffs: Time-of-Use and Real-Time Pricing. Applying a mixed-integer linear model across 471 low-voltage reference networks in Switzerland, we evaluate expansion requirements under projections of distributed energy resource deployment for 2050. In the model, prosumers operate their distributed energy resources in accordance with their subscribed electricity tariff to minimise costs, and the distribution system operator subsequently optimises reinforcements. Our results demonstrate that while adopting time-varying tariffs can reduce reinforcement investment costs by more than 50%, high adoption rates can reverse this declining trend. Specifically, once prosumer adoption exceeds the optimal thresholds of 50% for Time-of-Use and 40% for Real-Time Pricing, reinforcement needs increase by 39% and 134%, respectively, for an additional 20% of tariff adoption.

eess.SY↗

arXiv2026

Comparative Assessment of Frequency Scans using EMT and RMS Models

Frequency scans of inverter and grid impedances are crucial for assessing small-signal stability and system strength in inverter-dominated power systems. This paper compares frequency-scan results for several generating units, including a synchronous generator, grid-following, and grid-forming inverters, using fully dq electromagnetic transient and root-mean-square (RMS) models in Simulink. The scans are performed for the same device rating and operating point. Frequency scans and time-domain simulations show that the RMS models are valid only at low frequencies. We find that the current-controlled grid-forming inverter is more difficult to represent in RMS than the voltage-controlled grid-forming inverter. However, including the inner voltage-control loops improves the accuracy of the RMS representation. The paper also investigates the influence of generator and grid impedance on the total system impedance seen from the point of common coupling and relates the oscillatory modes and zeros of the system to peaks and dips in the first and second singular values of the system impedance, respectively.

eess.SY↗

arXiv2026

Mitigating Forced Oscillations in Power Systems via Data-Enabled Predictive Control

Sustained forced oscillations in power systems, driven by large cyclic loads such as data centers, pose a challenge to conventional power system stabilizers (PSSs), which rely on fixed tuned parameters and limited adaptability. This paper investigates the use of Data-Enabled Predictive Control (DeePC) as a data-driven alternative for damping such oscillations. DeePC constructs control actions directly from measured trajectories without requiring an explicit system model, enabling adaptation to changing operating conditions. We evaluate the performance of DeePC on a multi-machine two-area system subject to forced oscillations and compare it against a conventional PSS. The study examines the impact of different input-output configurations and the role of representative historical data in the Hankel matrix construction. Results show that DeePC can achieve superior damping. However, its effectiveness depends critically on the quality and representativeness of the underlying dataset. These findings highlight the potential of data-driven predictive control to complement or outperform conventional stabilizers in modern power systems with evolving and uncertain dynamics.

eess.SY↗

arXiv2026

Loadability Limits Under Periodic Load Forcing

The static loadability limit, defined as the demand at which the equilibrium equations lose their solution, is the standard basis for interconnection screening of large new loads. This letter shows that when part of the demand varies periodically, as for data-center loads, the steady state is a forced periodic orbit whose loadability limit differs from the static one. The classical optimization argument is extended directly from equilibria to fixed points of the period map. At the limit, the monodromy matrix acquires a Floquet multiplier at +1, so the generic instability is a cyclic fold of the orbit rather than a saddle-node of equilibria. The limit is therefore a function of the forcing frequency, which no static computation can capture. Additionally, with the network Jacobian turning singular along the cycle, singularity-induced instability is extended to orbits. On a four-bus test system, a static margin of 2.5 p.u. shrinks to 0.53 p.u. near the swing frequency, the instability mechanism switches from voltage collapse to a rotor-angle fold, and cold starts fail at amplitudes where the orbit still exists. A static analysis reproduces none of these effects.

eess.SY↗

arXiv2026

Does Central Planning Fail Locally? Evaluating EV Charging Flexibility Optimization Across Grid Levels

Electric vehicles (EVs) are a key enabler of global decarbonization, and their charging flexibility is crucial for integrating high shares of renewable energy into future power systems. Research on EV charging flexibility in future power systems has largely focused on either the transmission or the distribution level, with limited integration between the two. However, these two scales cannot be separated: EV charging optimized at the national transmission level for system-wide planning is ultimately activated in local distribution grids, where it may cause voltage and loading issues. This study bridges that gap by evaluating EV charging flexibility across both grid levels, using two different control approaches. First, a top-down approach disaggregates the optimal charging demand obtained from a centralized planner at the transmission grid down to the distribution grid, explicitly accounting for grid constraints and individual driving requirements. Second, a bottom-up approach optimizes EV charging locally under distribution-grid constraints using electricity price signals from the centralized planning model. Controlled charging from the top-down and bottom-up approaches is then compared with uncontrolled charging. Results show that the top-down approach produces EV charging schedules that are compatible with individual driving behavior and reduce grid violations compared to uncontrolled charging. The bottom-up approach identifies an optimal profile similar to the centrally optimized profile, showing that electricity price signals are a suitable coordination mechanism. These findings suggest that centrally optimized EV charging remains effective when implemented at the distribution level, in systems with high DER penetration.

eess.SY↗

arXiv2026

Dataset on residential electricity load profiles in Switzerland

Residential electricity profiles are undergoing significant changes due to the increasing adoption of distributed renewable generation, heat pumps, and electric vehicles. Up-to-date real-world measurements are essential for characterizing these evolving consumption patterns and providing a timely empirical foundation for research. This paper presents a dataset of 15-minute smart meter measurements from 2,447 residential installations in Switzerland, covering the years 2023 and 2024. The dataset encompasses both active and reactive energy (withdrawal and injection) of diverse installation types, including apartments, single-family houses, heat pumps, and common building areas. A subset of the associated customers participated in a pilot project featuring novel grid tariffs and automated load control. The corresponding price profiles and metadata are also included. The dataset aims to support research focusing on distribution grid modeling, load forecasting, and the assessment of time-varying tariffs.

eess.SY↗

arXiv2026

Optimal Battery Bidding under Decision-Dependent State-of-Charge Uncertainties

Lithium Iron Phosphate (LFP) Battery Energy Storage Systems (BESSs) are a key enabler of the energy transition. However, they are known to exhibit significant inaccuracies in the estimation of their State of Charge (SOC). Such estimation errors can directly impact the participation of BESSs in electricity markets. In this work, we demonstrate that neglecting SOC uncertainty in battery bidding can lead to significant delivery failures, including the inability to meet promised frequency reserves. To address this risk, we investigate bidding strategies that account for SOC uncertainty. We propose three constraint-tightening optimization approaches of increasing complexity: (i) a fixed-margin formulation, (ii) an adaptive-margin optimizer, and (iii) an uncertainty-aware optimization model. The latter explicitly accounts for the decision-dependent nature of the uncertainty. Numerical results demonstrate that while all three approaches robustify against SOC uncertainty, the uncertainty-aware formulation outperforms the others in maximizing revenue while ensuring reliable frequency reserve provision. This highlights the significance of treating SOC uncertainty as an endogenous process within the operational strategy.

eess.SY↗

arXiv2026

On the Flexibility Potential of a Swiss Distribution Grid: Opportunities and Limitations

The growing integration of distributed renewable generation and the electrification of heating and transportation are rapidly increasing the number of flexible devices within modern distribution grids. Leveraging the aggregated flexibility of these small-scale distributed resources is essential to maintaining future grid-wide stability. This work uses the Swiss distribution grid of Walenstadt as a case study to provide insights into the aggregated flexibility potential of distribution grids. It demonstrates that incorporating devices such as heat pumps and photovoltaic systems significantly enhances distribution grid flexibility. It investigates the time-varying nature of aggregated flexibility and highlights how it can vary seasonally. Furthermore, simulations of future scenarios reveal that aggregated flexibility does not increase linearly or monotonically with higher levels of flexible device penetration. This is primarily due to the overloading of individual feeders, which underscores the impact of grid topology and network constraints on the aggregated flexibility potential.

eess.SY↗

arXiv2026

Generalized Vector Locus Transformation for Unbalanced Three-Phase Systems

Coordinate transformations significantly simplify power systems computations. Most notably, the classical Clarke and $dq0$ transformations are widely used in three-phase systems, as together they transform balanced $abc$ quantities into constant-valued signals. However, during unbalanced operation, the utility of these transformations diminishes, since a null $0$-coordinate cannot be ensured and oscillating signals emerge. While recently proposed transformations ensure a null $0$-coordinate, they either do not lead to constant-valued signals in the $dq0$ domain or fail under various unbalanced scenarios. In this paper, we propose a Generalized Vector Locus (GVL) transformation that ensures both a null $0$-coordinate and constant-valued signals. Moreover, we show that, in the balanced case, the classical amplitude-invariant Clarke transformation is an instance of the proposed GVL transformation.

eess.SY↗

arXiv2026

Dynamic Optimization of Virtual Inertia and Damping in Converter-Based Power Systems

The transition towards a sustainable power system is enabled by the replacement of conventional synchronous generators with converter-interfaced renewable energy sources. However, the resulting loss of rotational inertia and governor damping causes significant frequency deviations and can therefore cause instability. The focus of this paper is the optimal allocation of virtual inertia and damping in the power system activated by established converter control schemes. To this end, we propose a novel dynamic optimization algorithm that considers performance metrics for system stability, cost-efficiency, and resilience. In addition, our algorithm considers the magnitudes and locations of disturbances in the power system for the optimal allocation. Finally, we validate our approach on a three-area system and also compare our results with a $\mathcal{H}_2$ system-norm-based allocation approach.

eess.SY↗

arXiv2026

Grid-Forming Characterization in DC Microgrids

DC microgrids are converter-based electrical networks that are increasingly being used in various applications, including data centers and industrial distribution systems. A central challenge in their operation is maintaining the DC-bus voltage within predefined limits while ensuring overall system stability. Although a wide variety of converter control algorithms has been proposed to achieve these objectives, the literature lacks a clear and physically interpretable framework for evaluating their effectiveness and for classifying and comparing them. Moreover, the grid-forming versus grid-following distinction that exists in AC systems has largely been unexplored in DC microgrids. To address this gap, this paper introduces three novel impedance-based indices that can be used to quantify the voltage-forming and current-forming behavior of a converter. The indices also provide a basis for defining the desired converter behavior that yields superior DC-bus voltage regulation performance. Simulation results illustrate the application of the framework to several representative control strategies and highlight the strengths and limitations of these control algorithms.

eess.SY↗

arXiv2026

Exploring Converter Control Duality in Microgrids: AC Grid-Forming vs DC Droop Control

Power electronic converters are fundamental building blocks of both AC and DC microgrids, enabling the integration of renewable energy sources, energy storage systems, electronic loads, and electric vehicles. In contrast, converter control in DC microgrids has developed along the path of droop control, which is widely adopted for decentralized DC-bus voltage regulation and power sharing. Although these control strategies share certain characteristics, their similarities remain largely unexplored due to the distinct physical domains in which they operate. To bridge this gap, we introduce a novel perspective based on the concept of duality to reveal the underlying isomorphism between the two control approaches. We show that AC grid-forming and DC I--V droop control are duals of each other in several aspects, including: (i) the small-signal model of the converter; (ii) the inner current control structure; (iii) power-sharing mechanisms based on the AC swing equation and DC capacitor power balance; and (iv) disturbance signals and dynamic response. Theoretical analysis, validated through simulations on simple converter setups, illustrates these dualities and provides new insights towards a unified control design.

eess.SY↗