arXiv subjects

Florian Dörfler

Publications and source records attributed to Florian Dörfler.

At least 19 recordsLinked to original sources

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

Decentralized Stability Certificates for Interoperability in Power-Electronics-Dominated Power Systems

The rapid integration of power electronics converters has fundamentally altered the power grid's dynamic landscape, making modularity of operation and interoperability of heterogeneous devices two critical hurdles for power system reliability. In control language, these challenges can be paraphrased as decentralized certificates on the input/output behavior of every device, assured by local control, so that stability and guaranteed performance of the interconnected system emerge. For the power system application, such certificates should eventually be part of the rule book, the so-called grid code, and thus be generic and minimally restrictive for the control architecture of each device. Classical system-theoretic angles of attack to these questions go back to dissipativity concepts, which have historically been co-developed in automatic control, circuit theory, and power systems. Since standard dissipativity (e.g., passivity or small gain) criteria fall short in terms of conservatism, a range of more flexible and advanced tools have been developed to assure less conservative decentralized certificates guaranteeing stability under interconnection as well as dynamic performance following a disturbance. In this article, we review a range of methods that have been put forward to tackle these questions, compare and validate them, and point towards practically relevant research gaps which are amenable to control-theoretic approaches. Moreover, we provide a tutorial exposition of practically relevant power systems/electronics issues accessible to control engineers.

eess.SY↗

arXiv2026

From Inference to Control: Structure-Guided Control of Hypergraph Dynamics

Controllability determines whether a system's state can be guided toward any desired configuration, making it a fundamental prerequisite for designing effective control strategies. In the context of networked systems, controllability is a well-established concept. However, many real-world systems, from biological collectives to engineered infrastructures, exhibit higher-order interactions that cannot be captured by simple graphs. Moreover, the interaction structures might be unknown and difficult to measure directly. Here, we close this gap by combining hypergraph inference with the identification of controllable nodes. Building on the inferred structure, we design a controller that, given a set of controllable nodes, steers the system toward a desired configuration. We formulate analytical controllability guarantees for polynomial systems. For non-polynomial dynamics on hypergraphs, we propose a heuristic method for identifying controllable nodes and validate the proposed approach using Kuramoto oscillators.

eess.SY↗

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

Stability Analysis in IBR-Dominated Grids: Model-Based and Data-Driven Methodologies

Stability issues in converter-dominated grids have a multifold nature, where static, small-signal and large-signal phenomena can influence each other. Capturing the interplay among these different stability aspects requires a rethinking of conventional modeling and analysis tools for power systems. As a further complication, Intellectual Property protection among manufacturers and system operators makes detailed converters and grid models unattainable. This motivates the use of data-driven approaches for stability analysis and control, nevertheless achieving an insightful representation of static, small-signal and large-signal phenomena from data still leaves many open questions. This paper discusses model-based and data-driven methodologies for stability analysis of converter-dominated grids. The instability mechanisms of static, small-signal and large-signal phenomena are conceptually explained, and the capability of different modeling and analysis tools to investigate them is examined. The effectiveness of data-driven methodologies to analyze instability phenomena in their multifold nature is debated, and the challenges on data completeness, model generalization under untrained conditions, physical interpretability, and uncertainty awareness are discussed.

eess.SY↗

arXiv2026

Quantifying Grid-Forming Behavior: Bridging Device-level Dynamics and System-Level Strength

Grid-forming (GFM) technology is widely regarded as a promising solution for future power systems dominated by power electronics. However, a universally accepted definition of GFM behavior and precise method for its quantification remain elusive. Moreover, the impact of GFM converter on system stability is not precisely quantified, creating a significant disconnect between device and system levels. To address these gaps from a small-signal perspective, at the device level, the paper introduces a novel metric, the Forming Index (FI) to quantify a converter's response to grid voltage fluctuations. Rather than enumerating various control architectures, the FI provides a metric for the converter's GFM ability by quantifying its sensitivity to grid variations. At the system level, a new quantitative measure of system strength that captures the multi-bus voltage stiffness is proposed, which quantifies the voltage and phase angle responses of multiple buses to current or power disturbances. The paper further extends and defines this concept to grid strength and bus strength to identify weak areas within the system. Finally, the device and system levels are bridged by formally proving that GFM converters enhance system strength. The proposed framework provides a unified benchmark for GFM converter design, optimal placement, and system stability assessment.

eess.SY↗

arXiv2026

Dynamic Resource Allocation with Karma: An Experimental Study

We perform a behavioral experiment of karma, a class of mechanisms for repeated resource allocation with attractive fairness and efficiency properties, in theory. Individuals in these mechanisms bid non-tradable credits that flow from resource consumers to yielders. Human subjects recruited on Amazon MTurk are repeatedly and randomly paired to bid karma according to time-varying individual urgency to acquire resources. Treatments varied in the urgency process (frequent moderate versus sporadic high urgency) and the richness of the bidding scheme (binary versus full range). Benchmarked against random allocation, karma achieves an (almost) Pareto improvement, despite subjects deviating from the theoretically optimal Nash bidding policy: maximum improvement is attained by subjects deviating by up to one karma bid unit on average, and positive improvement with deviations of up to 3-4 units. These findings hold across all treatments, among which no significant differences are found, with the exception of the sporadic high urgency process with binary bidding being (weakly) favorable. They offer behaviorally robust lower bounds for karma's expected performance in human populations and guide its future implementation.

econ.GN↗

arXiv2026

A Gray-Box Approach for Decentralized Grid-Equivalent Model Identification

We propose a decentralized, frequency-domain identification algorithm that estimates the grid-equivalent model using local measurements from the perspective of each converter. Since local electric signals in a multi-converter setup are affected by voltage inputs from the grid, estimating a direct equivalent impedance yields biased and inaccurate results. To overcome this, we design a framework that decouples the effect of the equivalent impedance (passive) from that of the equivalent voltage (active). The parameters and equivalent grid voltages are then estimated using a least-squares algorithm and a Kalman filter, respectively, applied across frequency samples, with additional pre-processing techniques to remove the influence of the grid on the locally estimated models. We then demonstrate the accuracy and performance of our algorithm on an interconnected $5-$converter system in grid-forming mode.

eess.SP↗

arXiv2026

Ensuring Stability of Non-Minimal Modes in Input-Output Data-Driven Representation

Many recent data-driven control approaches for linear time-invariant systems are based on output trajectory prediction using input-output data matrices. The system dynamics described by this predictor, which we refer to as the input-output data-driven representation, yields non-unique autoregressive with exogenous inputs (ARX) models having possibly unstable non-minimal modes. In this note, we show that the stability of these non-minimal modes is ensured by a certain choice of ARX model, which coincides with the minimum-norm least-squares predictor using the Moore-Penrose inverse of the data matrix. This stability guarantee holds regardless of the underlying system's stability. Moreover, the stability persists under sufficiently small noise in data when a suitably truncated Moore-Penrose inverse is used. Consequently, the ARX model need not be reduced to the true system order in order to avoid unstable additional modes.

eess.SY↗

arXiv2026

Forgetting While Remembering, an Invariant Online Data-Driven Predictive Control Formulation

Low signal-to-noise ratio (SNR) data is a core challenge of online Data-Driven Predictive Control (DPC) for linear, time-varying systems. This paper proposes a Bayesian, online DPC framework based on autoregressive models with exogenous inputs (ARX) that uses an externally-provided prior, which encodes inductive bias such as smooth system dynamics and stability, to safeguard performance when SNR is low. The posterior estimate of the ARX parameter is propagated forward in time using a Kalman filter with a state equation defined by an adaptation-rate hyperparameter, which is adjusted online to track the rate at which the underlying system dynamics evolve. The Kalman filter's posterior mean and covariance determine the DPC's Final Control Error cost function, which is the posterior expectation of the quadratic cost function. Critically, the Kalman filter's process equation is chosen so that, regardless of the hyperparameter adaptation, the prior distribution is invariant over time. Thus, while old data is forgotten, the prior is not. DPC tracking experiments on a time-varying second-order system demonstrate the efficacy of the proposed method.

eess.SY↗

arXiv2026

TARC: Time-Adaptive Robotic Control

Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control and the responsiveness of high-frequency feedback. As a result, systems typically default to high control rates for robustness, at the cost of wasted inference and unnecessary actuation. Addressing this, we introduce Time-Adaptive Robotic Control (TARC), a reinforcement learning framework in which the policy jointly predicts a control action and its duration of application. TARC learns temporally extended actions by optimizing task performance under soft or hard constraints on the number of control switches, enabling adaptive modulation of control rates. We evaluate TARC on two robotic hardware platforms: a high-speed RC car and the Unitree Go1 quadruped, and on a vision-language action model in simulation, where each query incurs a costly transformer forward pass. Across all settings, TARC matches the performance of high-frequency discrete-time controllers while operating at less than half their control frequency. Unlike fixed-rate controllers, TARC adapts its control frequency online, allocating high-frequency feedback only when required.

cs.RO↗

arXiv2026

Convergence and Robustness Bounds for Distributed Asynchronous Shortest-Path

This work analyzes convergence times and robustness bounds for asynchronous distributed shortest-path computation. We focus on the Adaptive Bellman--Ford algorithm, a self-stabilizing method in which each agent updates its shortest-path estimate based only on the estimates of its neighbors and forgetting its previous estimate. In the asynchronous framework considered in this paper, agents are allowed to idle or encounter race conditions during their execution of the Adaptive Bellman--Ford algorithm. We build on Lyapunov-based results that develop finite-time convergence and robustness bounds for the synchronous shortest-path setting, in order to produce finite-time convergence and robustness bounds for the asynchronous setting. We also explore robustness against interval-bounded noise processes and establish convergence and robustness guarantees for asynchronous most-probable-path algorithms.

math.OC↗

arXiv2026

Identification of $dq$-Asymmetric Impedances as Complex Transfer Functions Using a Single Arbitrary Excitation

Cross-coupling between the $dq$ coordinates makes the identification of asymmetric grid impedances a challenging problem, particularly near the fundamental frequency where the asymmetric coupling is strongest. Existing schemes usually handle it either by perturbing the two coordinates sequentially, which lengthens the measurement, or by using a time-domain method with a global parametric model whose order must be tuned. This paper develops a single-shot active non-parametric frequency-domain method that avoids both. The equivalent impedance is parameterized by a pair of single-input single-output complex transfer functions. Each spectral line is fitted with a local rational model; the leakage and transient contributions are estimated, so that neither periodic steady-state excitation nor repeated excitation cycles are required. We give the exact finite-time discrete Fourier transform relation for the conjugate-coupled complex-signal model, and analyse the distortion that a stationary-frame filter placed ahead of the Park transform imposes on the identified pair. The method is validated on a controller hardware-in-the-loop platform against an analytically derived small-signal model, for a symmetric grid and for the same grid with an added grid-following converter that renders it asymmetric. Both complex transfer functions and all four real transfer functions of the $dq$ impedance are recovered over a wide band from a single one-second record of a random excitation, at 1 Hz resolution.

eess.SP↗

arXiv2026

Optimal Functional Incentives for Control: The Linear-Quadratic Case with Bilinear Incentives

We study the design of functional incentive mechanisms for dynamical systems, in which a leader designs a fixed incentive function to motivate a self-interested follower to actuate the system beneficially over an extended horizon, without real-time revision of the incentive. This stands in contrast to the adaptive paradigm, in which the incentive is itself a continuously updated control variable. We formalize the problem as a discrete-time bi-level optimal control problem and derive analytical results for the linear-quadratic case with bilinear incentives and a myopic follower. Specifically, we establish a necessary and sufficient stability condition for the induced closed-loop system, derive a closed-form expression for the gradient of the expected leader cost with respect to the incentive parameter matrix, and obtain a fully closed-form cost expression in the scalar setting. Based on the latter, explicit characterizations of the optimal incentive parameter are provided in two asymptotic regimes: the infinite-horizon limit and the limit of high follower cost. For long horizons, the optimal incentive is shown to become independent of the follower's private cost parameter, with direct implications for robust mechanism design under private information.

eess.SY↗

arXiv2026

Stability Certificates for Receding Horizon Games

Game-theoretic MPC (or Receding Horizon Games) is an emerging control methodology for multi-agent systems that generates control actions by solving a dynamic game with coupling constraints in a receding-horizon fashion. This control paradigm has recently received increasing attention in various application fields, including robotics, autonomous driving, traffic networks, and energy grids, due to its ability to model the competitive nature of self-interested agents with shared resources while incorporating future predictions, dynamic models, and constraints into the decision-making process. In this work, we present the first formal stability analysis based on dissipativity and monotone operator theory that is valid also for non-potential games. Specifically, we derive LMI-based certificates that ensure asymptotic stability and are numerically verifiable. Moreover, we show that, if the agents have decoupled dynamics, the numerical verification can be performed in a scalable manner. Finally, we present tuning guidelines for the agents' cost function weights to fulfill the certificates and, thus, ensure stability.

eess.SY↗

arXiv2026

The Limits of "Fairness'' of the Variational Generalized Nash Equilibrium

Generalized Nash equilibrium (GNE) problems are commonly used to model strategic interactions between self-interested agents who are coupled in cost and constraints. Specifically, the variational GNE, a refinement of the GNE, is often selected as the solution concept due to its non-discriminatory treatment of agents by charging a uniform ``shadow price" for shared resources. We study the fairness concept of v-GNEs from a comparability perspective and show that it makes an implicit assumption of unit comparability of agent's cost functions, one of the strongest comparability notions. Further, we introduce a new solution concept, f-GNE in which a fairness metric is chosen a priori which is compatible with the comparability at hand. We introduce an electric vehicle charging game to demonstrate the fragility of v-GNE fairness and compare it to the f-GNE under various fairness metrics.

eess.SY↗

arXiv2026

Optimistic Online LQR via Intrinsic Rewards

Optimism in the face of uncertainty is a popular approach to balance exploration and exploitation in reinforcement learning. Here, we consider the online linear quadratic regulator (LQR) problem, i.e., to learn the LQR corresponding to an unknown linear dynamical system by adapting the control policy online based on closed-loop data collected during operation. In this work, we propose Intrinsic Rewards LQR (IR-LQR), an optimistic online LQR algorithm that applies the idea of intrinsic rewards originating from reinforcement learning and the concept of variance regularization to promote uncertainty-driven exploration. IR-LQR typically retains the structure of a standard LQR synthesis problem by only modifying the cost function, resulting in an intuitively pleasing, simple, computationally cheap, and efficient algorithm. This is in contrast to existing optimistic online LQR formulations that rely on more complicated iterative search algorithms or solve computationally demanding optimization problems. We show that IR-LQR achieves the optimal worst-case regret rate of $\sqrt{T}$, and compare it to various state-of-the-art online LQR algorithms via numerical experiments carried out on an aircraft pitch angle control and an unmanned aerial vehicle example.

eess.SY↗

arXiv2026

Coordinated Primary Frequency Regulation and Grid-Forming Control for Wind Turbine Generators

Conventional grid-forming (GFM) control strategies often treat the DC source as an unconstrained link, creating mismatches when applied to the wind turbine generators (WTGs). Focusing on primary frequency regulation, this paper systematically investigates the mismatch between the GFM-WTGs behavior and the droop-based primary frequency regulation. To address this issue, a novel coordination strategy between WTG primary frequency regulation and GFM control is proposed. By establishing well-designed relationships among the power-tracking coefficient, power set-point, and frequency deviation, the proposed strategy enables GFM-WTGs to participate consistently in primary frequency regulation within predefined frequency limits while maintaining appropriate power points and effectively utilizing the allowable power reserve. Furthermore, the proposed method preserves the control structure and dynamic performance of conventional GFM control and inherently adapts to varying wind-speed conditions. Comparative case studies under different operating scenarios demonstrate the effectiveness and superiority of the proposed strategy.

eess.SY↗