arXiv subjects

Gabriela Hug

Publications and source records attributed to Gabriela Hug.

At least 37 records · Page 2Linked to original sources

arXiv2026

Next-Generation Grid Codes: Towards a New Paradigm for Dynamic Ancillary Services

This paper introduces a conceptual foundation for Next Generation Grid Codes (NGGCs) based on stability and performance certificates, enabling the provision of dynamic ancillary services such as fast frequency and voltage regulation through decentralized frequency-domain criteria. The NGGC framework offers two key benefits: (i) rigorous closed-loop stability guarantees, and (ii) explicit performance guarantees for frequency and voltage dynamics in power systems. Regarding (i) stability, we employ loop-shifting and passivity-based techniques to derive local frequency-domain stability certificates for individual device dynamics. These certificates ensure the closed-loop stability of the entire interconnected power system through fully decentralized verification. Concerning (ii) performance, we establish quantitative bounds on critical time-domain indicators of system dynamics, including the average-mode frequency and voltage nadirs, the rate-of-change-of-frequency (RoCoF), steady-state deviations, and oscillation damping capabilities. The bounds are obtained by expressing the performance metrics as frequency-domain conditions on local device behavior. The NGGC framework is non-parametric, model-agnostic, and accommodates arbitrary device dynamics under mild assumptions. It thus provides a unified, decentralized approach to certifying both stability and performance without requiring explicit device-model parameterizations. Moreover, the NGGC framework can be directly used as a set of specifications for control design, offering a principled foundation for future stability- and performance-oriented grid codes in power systems.

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arXiv2026

Fast frequency response with heterogeneous communication delay management under the SCION Internet architecture

System operators can increasingly exploit distributed energy resources (DERs) and controllable loads (CLs) to provide frequency response services. In conventional practice, communication between the system operator and flexible devices relies on the Border Gateway Protocol (BGP)-based Internet. However, existing BGP-based architectures face challenges in providing latency-guaranteed control, while direct private and proprietary communication networks lead to additional deployment and maintenance costs. In contrast, the SCION-based Internet architecture supports latency-minimum path selection, which makes it suitable for latency-sensitive frequency contingency services such as fast frequency response (FFR). Hence, this paper proposes a real-time reserve dispatch framework to optimally select a portfolio of flexible devices to deliver FFR services using the SCION-based Internet. First, an analytical expression of the system frequency dynamics with respect to heterogeneous communication latencies is derived. Next, a cyber-physical co-optimization model is formulated to jointly schedule communication paths and physical flexibility resources for real-time FFR provision. To improve the computation efficiency, we propose a heuristic FFR allocation algorithm to approximate the optimal response portfolio, integrating contributions from both DERs and CLs. Numerical case studies demonstrate the benefits of the proposed algorithm and its capability to approximate the optimality of the reserves allocation while significantly reducing the computation time.

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arXiv2026

Effect of Dispatch Decisions on Small-Signal Stability of Converter-Dominated Power Systems

Small-signal stability of modern converter-dominated power systems has been the subject of extensive research, particularly from the perspective of device-level control design for grid-forming (GFM) and grid-following (GFL) converters. However, the influence of power flow variables on system stability has received limited attention. Conventional small-signal stability analyses are typically conducted at a specific operating point, emphasizing the selection of control or system design parameters while neglecting the sensitivity of stability characteristics to operating conditions. This paper seeks to bridge this gap by systematically investigating the impact of dispatch decisions on the small-signal stability of converter-based power systems. Our findings are first illustrated on a three-bus system and then validated on the standard IEEE 39-bus test system to demonstrate scalability. Across the test systems, we find that high-voltage capacitive operation of GFL converters limits its active power injection, whereas inductive operation permits higher injections, and it is generally preferable for the GFM converter to supply more active power.

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arXiv2025

Bidding Aggregated Flexibility in European Electricity Auctions

Bidding flexibility in day-ahead and intraday auctions would enable decentralized flexible resources, such as electric vehicles and heat pumps, to efficiently align their consumption with the intermittent generation of renewable energy. However, because these resources are individually too small to participate in those auctions directly, an aggregator (e.g., a utility) must act on their behalf. This requires aggregating many decentralized resources, which is a computationally challenging task. In this paper, we propose a computationally efficient and highly accurate method that is readily applicable to European day-ahead and intraday auctions. Distinct from existing methods, we aggregate only economically relevant power profiles, identified through price forecasts. The resulting flexibility is then conveyed to the market operator via exclusive groups of block bids. We evaluate our method for a utility serving the Swiss town of Losone, where flexibility from multiple heat pumps distributed across the grid must be aggregated and bid in the Swiss day-ahead auction. Results show that our method aggregates accurately, achieving 98% of the theoretically possible cost savings. This aggregation accuracy remains stable even as the number of heat pumps increases, while computation time grows only linearly, demonstrating strong scalability.

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arXiv2025

Uncertainty-Aware Flexibility of Buildings: From Quantification to Provision

Buildings represent a promising flexibility source to support the integration of renewable energy sources, as they may shift their heating energy consumption over time without impacting users' comfort. However, a building's predicted flexibility potential is based on uncertain ambient weather forecasts and a typically inaccurate building thermal model. Hence, this paper presents an uncertainty-aware flexibility quantifier using a chance-constrained formulation. Because such a quantifier may be conservative, we additionally model real-time feedback in the quantification, in the form of affine feedback policies. Such adaptation can take the form of intra-day trades or rebound around the flexibility provision period. To assess the flexibility quantification formulations, we further assume that flexible buildings participate in secondary frequency control markets. The results show some increase in flexibility and revenues when introducing affine feedback policies. Additionally, it is demonstrated that accounting for uncertainties in the flexibility quantification is necessary, especially when intra-day trades are not available. Even though an uncertainty-ignorant potential may seem financially profitable in secondary frequency control markets, it comes at the cost of significant thermal discomfort for inhabitants. Hence, we suggest a comfort-preserving approach, aiming to truly reflect thermal discomfort on the economic flexibility revenue, to obtain a fairer comparison.

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arXiv2025

Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland

The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.

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arXiv2025

Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives

A key challenge in combinatorial auctions is designing bid formats that accurately capture agents' preferences while remaining computationally feasible. This is especially true for electricity auctions, where complex preferences complicate straightforward solutions. In this context, we examine the XOR package bid, the default choice in combinatorial auctions and adopted in European day-ahead and intraday auctions under the name "exclusive group of block bids". Unlike parametric bid formats often employed in US power auctions, XOR package bids are technology-agnostic, making them particularly suitable for emerging demand-side participants. However, the challenge with package bids is that auctioneers must limit their number to maintain computational feasibility. As a result, agents are constrained in expressing their preferences, potentially lowering their surplus and reducing overall welfare. To address this issue, we propose decision support algorithms that optimize package bid selection, evaluate welfare losses resulting from bid limits, and explore alternative bid formats. In our analysis, we leverage the fact that electricity prices are often fairly predictable and, at least in European auctions, tend to approximate equilibrium prices reasonably well. Our findings offer actionable insights for both auctioneers and bidders.

econ.GN↗

arXiv2025

Network-Independent Incremental Passivity Conditions for Grid-Forming Inverter Control

Grid-forming inverters control the power transfer between the AC and DC sides of an electrical grid while maintaining the frequency and voltage of the AC side. This paper focuses on ensuring large-signal stability of an electrical grid with inverter-interfaced renewable sources. We prove that the Hybrid-Angle Control (HAC) scheme for grid-forming inverters can exhibit incremental passivity properties between current and voltage at both the AC and DC ports. This incremental passivity can be certified through decentralized conditions. Inverters operating under HAC can, therefore, be connected to other passive elements (e.g. transmission lines) with an immediate guarantee of global transient stability regardless of the network topology or parameters. Passivity of Hybrid Angle Control is also preserved under small-signal (linearized) analyses, in contrast to conventional proportional droop laws that are passivity-short at low frequencies. Passivity and interconnected-stability properties are demonstrated through an example case study.

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arXiv2025

Scalable and Efficient Aggregation of Energy-Constrained Flexible Loads

Loads represent a promising flexibility source to support the integration of renewable energy sources, as they may shift their energy consumption over time. By computing the aggregated flexibility of power and energy-constrained loads, aggregators can communicate the group's flexibility without sharing individual private information. However, this computation is, in practice, challenging. Some studies suggest different inner approximations of aggregated flexibility polytopes, but all suffer from large computational costs for realistic load numbers and horizon lengths. In this paper, we develop a novel approximation of the aggregated flexibility of loads based on the concept of worst-case energy dispatch, i.e., if aggregated energy consumptions are assumed to be dispatched in the worst manner possible. This leads to conservative piecewise linear bounds that restrict the aggregated energy consumption only based on the previous aggregated energy consumed. A comparative case study reveals that our method can compute an approximation of the aggregation of thousands of loads efficiently, while displaying an accuracy comparable to other approximation techniques.

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arXiv2025

Trajectory-Independent Flexibility Envelopes of Energy-Constrained Systems with State-Dependent Losses

As non-dispatchable renewable power units become prominent in electric power grids, demand-side flexibility appears as a key element of future power systems' operation. Power and energy bounds are intuitive metrics to describe the flexibility of energy-constrained loads. However, to be used in operation, any power consumption trajectory fulfilling the power and energy bounds must necessarily fulfill the load's constraints. In this paper, we demonstrate that energy bounds defined as the minimum and maximum energy consumption potential of a load with state-dependent losses are Trajectory-Dependent (TD), i.e., for any energy value in the bounds a feasible power trajectory exists, but not all power trajectories enclosed in the energy envelopes satisfy the load's constraints. To guarantee the satisfaction of load constraints for all trajectories, we define Trajectory-Independent (TI) energy bounds. We present TI envelope formulations for individual loads, as well as physically coupled loads and assess the proposed formulations in a building heating system, a system with state-dependent losses. We find that using a TD envelope as energy bounds in operation may yield room temperature up to 3.8°C higher and 3.4°C lower than admissible. Overall, poorly insulated buildings observe a TI energy envelope that differs significantly from their TD envelope.

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arXiv2025

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

The integration of experimental technologies with large language models (LLMs) is transforming scientific research. It positions AI as a versatile research assistant rather than a mere problem-solving tool. In the field of power systems, however, managing simulations -- one of the essential experimental technologies -- remains a challenge for LLMs due to their limited domain-specific knowledge, restricted reasoning capabilities, and imprecise handling of simulation parameters. To address these limitations, this paper proposes a feedback-driven, multi-agent framework. It incorporates three proposed modules: an enhanced retrieval-augmented generation (RAG) module, an improved reasoning module, and a dynamic environmental acting module with an error-feedback mechanism. Validated on 69 diverse tasks from Daline and MATPOWER, this framework achieves success rates of 93.13% and 96.85%, respectively. It significantly outperforms ChatGPT 4o, o1-preview, and the fine-tuned GPT-4o, which all achieved a success rate lower than 30% on complex tasks. Additionally, the proposed framework also supports rapid, cost-effective task execution, completing each simulation in approximately 30 seconds at an average cost of 0.014 USD for tokens. Overall, this adaptable framework lays a foundation for developing intelligent LLM-based assistants for human researchers, facilitating power system research and beyond.

cs.CL↗

arXiv2025

Recursive Dynamic State Estimation for Power Systems with an Incomplete Nonlinear DAE Model

Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This paper proposes a novel, centralized dynamic state estimator for power systems that lack models of some components. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, we apply the least squares criterion to estimate all dynamic and algebraic states recursively. The approach results in an algorithm that generalizes the iterated extended Kalman filter and does not require static network observability. We further derive a graph theoretic condition for placing phasor measurement units that guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature within computational times that are feasible for real-time application.

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arXiv2025

Modeling Charging Demand and Quantifying Flexibility Bounds for Large-Scale BEV Fleets

This paper presents a bottom-up method to model baseline charging power demand and quantify available flexibility for large-scale BEV fleets. The method utilizes geographic and sociodemographic information to represent the fleet's mobility and driving energy needs. It models the charging decisions of drivers based on their driving energy needs and range comfort level using real-world data. The flexibility quantification provides an hourly maximum and minimum bound for the charging power and limits the amount of daily flexible charging energy. We apply the methodology to the future fully electrified fleet of Switzerland as a case study and compare the spatio-temporal characteristics of the charging demand and flexibility of different geographic areas and urbanization levels.

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arXiv2025

Optimal PMU Placement for Kalman Filtering of DAE Power System Models

Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power systems modeled by differential-algebraic equations. The method identifies optimal sensor locations by minimizing the steady-state covariance matrix of the Kalman filter, thus minimizing the error of joint differential and algebraic state estimation. The problem is reformulated as a mixed-integer semidefinite program and effectively solved using off-the-shelf numerical solvers. Numerical results demonstrate the merits of the proposed approach by benchmarking its performance in phasor measurement unit placement in comparison to greedy algorithms.

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arXiv2024

A Proportional-Integral Model for Fractional Voltage Tripping of Distributed Energy Resources

In regions with high shares of distributed energy resources (DERs), massive disconnection of small-scale DERs in low-voltage distribution grids during disturbances poses a serious threat to power system security. However, modeling this effect in a computationally efficient way remains challenging. This paper proposes a novel proportional-integral aggregate model for predicting the fraction of tripped DERs based on the voltage at the substation connection point. The model effectively captures the cumulative behavior of the system, is simple to implement, and includes seven parameters for undervoltage tripping and seven for overvoltage tripping behavior, each with a distinct physical meaning. We further propose an optimization-based approach to tune the model parameters. Simulation results show significantly more accurate predictions compared to the DER\_A model -- a standard dynamic model for aggregate DER behavior -- even when the latter is optimized, with only a minor increase in model complexity.

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arXiv2024

Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline

The integration of experiment technologies with large language models (LLMs) is transforming scientific research, offering AI capabilities beyond specialized problem-solving to becoming research assistants for human scientists. In power systems, simulations are essential for research. However, LLMs face significant challenges in power system simulations due to limited pre-existing knowledge and the complexity of power grids. To address this issue, this work proposes a modular framework that integrates expertise from both the power system and LLM domains. This framework enhances LLMs' ability to perform power system simulations on previously unseen tools. Validated using 34 simulation tasks in Daline, a (optimal) power flow simulation and linearization toolbox not yet exposed to LLMs, the proposed framework improved GPT-4o's simulation coding accuracy from 0% to 96.07%, also outperforming the ChatGPT-4o web interface's 33.8% accuracy (with the entire knowledge base uploaded). These results highlight the potential of LLMs as research assistants in power systems.

eess.SY↗

arXiv2024

Quantifying and Optimizing the Time-Coupled Flexibilities at the Distribution-Level for TSO-DSO Coordination

The flexibilities provided by the distributed energy resources (DERs) in distribution systems enable the coordination of transmission system operator (TSO) and distribution system operators (DSOs). At the distribution level, the flexibilities should be optimized for participation in the transmission system operation. This paper first proposes a flexibility quantification method that quantifies the costs of providing flexibilities and their values to the DSO in the TSO-DSO coordination. Compared with traditional power-range-based quantification approaches that are mainly suitable for generators, the proposed method can directly capture the time-coupling characteristics of DERs' individual and aggregated flexibility regions. Based on the quantification method, we further propose a DSO optimization model to activate the flexibilities from DER aggregators in the distribution system for energy arbitrage and ancillary services provision in the transmission system, along with a revenue allocation strategy that ensures a non-profit DSO. Numerical tests on the IEEE test system verify the proposed methods.

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arXiv2024

A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems

This paper presents a novel model order reduction technique tailored for power systems with a large share of inverter-based energy resources. Such systems exhibit an increased level of dynamic stiffness compared to traditional power systems, posing challenges for time-domain simulations and control design. Our approach involves rotation of the coordinate system of a linearized system using a transformation matrix derived from the real Jordan canonical form, leading to mode decoupling. The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness. Applying the same transformation to the original nonlinear system results in an approximate separation of slow and fast states, which can be truncated to reduce the stiffness. The resulting reduced-order model demonstrates an accurate time-domain performance, the slow eigenvalues of the linearized system are correctly preserved, and a reduction in the model stiffness is achieved, allowing for accurate integration with increased step size. Our methodology is assessed in detail for a 3-bus system with generation units involving grid-forming/following converters and synchronous machines, where it allows for a computational speed-up of up to 100x compared to the original system. Several standard larger test systems are also considered.

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