arXiv subjects

Jochen Cremer

Publications and source records attributed to Jochen Cremer.

7 recordsLinked to original sources

arXiv2026

Network Topology Reconfiguration: Optimal Transition Planning

Network topology reconfiguration (NTR) can reduce power system operating costs by co-optimizing generation dispatch and substation switching, but the reported savings describe a target operating point rather than a way to reach it. Reaching this operating point requires a sequence of intermediate operating points, each satisfying the AC power flow equations and thermal limits; because each topology admits its own feasible dispatch region, a naive transition that switches or redispatches first can drive intermediate flows past their thermal limits. Existing methods leave this gap open: snapshot NTR identifies a target but not a route. We formulate the Optimal Transition Planning (OTP) problem, co-optimizing the switching sequence and dispatch trajectory subject to AC feasibility at every intermediate point. We solve this problem with a receding-horizon framework: a DC planner proposes a trajectory that is certified against an AC feasibility filter, and infeasible topologies are excluded using reusable combinatorial cuts. Case studies on congested PGLib-OPF systems up to 1354-bus show that the method produces AC-feasible transitions that reduce operating cost by up to 18.4% compared to the no-switching ACOPF solution on commodity hardware.

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arXiv2026

Revisiting data-driven dynamic security assessment with a tabular foundation model

Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches face two limitations. First, they require a large labelled database for training, with a separate model trained, tuned, and maintained for each contingency in a potentially long list of credible contingencies. Second, the trained models generalize poorly to unseen contingencies. This work addresses the limitations by using a tabular foundation model (TFM) that assesses stability through in-context learning, requiring no retraining or hyperparameter optimization. A single TFM can assess many contingencies at once, removing the need for one model per classifier. We also characterize when the use of electrical distance coordinates (EDC) as continuous features enables generalization of TFM to unseen contingencies and when they do not, demonstrating how a few labelled samples can reliably improve generalization. Through comprehensive case studies on the IEEE 68-bus system, we show that a single TFM attains an average Macro F1 score of about 90% with only 120 labelled samples per contingency, roughly two orders of magnitude fewer than conventionally assumed, without any model retraining or hyperparameter tuning. For new/unseen contingencies, we show that using just 10 labelled samples of the new contingency with EDC encoding matches the best achievable transfer learning oracle model, which requires fully labelled data and is not deployable in practice. Overall, this initial study paves the way towards developing and deploying foundation models for power system operations, with possible applications across multiple operational tasks.

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arXiv2026

MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model

Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance. To expose and address this failure mode, we introduce MxGPS (Multiplex GPS), a multiplex graph transformer that runs K task-specialised GPS branches over a shared node encoder, jointly trained on Static State Estimation (SSE) and AC Power Flow (PF) via a self-supervised pre-training and multi-task fine-tuning protocol, with a cross-branch attention module evaluated in ablation. The joint SSE+PF objective forces the shared encoder to simultaneously satisfy complementary gradient signals, preventing it from overfitting to topology-specific relational structure. Under a 3-fold sliding-window cross-validation spanning four unseen topologies (14-, 24-, 162-, and 300-bus), MxGPS attains 0% boundary violation rate (BVR) on all four zero-shot Power Flow topologies. Critically, models with substantially lower in-distribution PF error degrade by 190% to 1400% under topology shift, whereas MxGPS degrades by only 39%, an inversion that directly implicates topology overfitting as the failure mechanism rather than insufficient model capacity. With only 1.6M parameters (12x fewer than the GridFM reference baseline), MxGPS demonstrates that multi-task joint training is a principled and parameter-efficient mechanism for topology-agnostic generalisation in power grid foundation models.

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arXiv2026

Sensitivity Quantification for Distribution System State Estimation

Pseudo-measurements are the dominant source of uncertainty in distribution system state estimation (DSSE), yet their distributional assumptions are treated as fixed inputs by existing uncertainty quantification methods. This paper investigates whether the uncertainty bounds assumed by weighted least squares (WLS)-based DSSE are sensitive to these distributional assumptions, and whether this sensitivity is quantifiable using the Fisher Information Matrix (FIM). We propose a diagnostic framework that compares the true Cram\'er-Rao Bound (CRB) against the WLS-assumed CRB via a per-bus, per-scenario ratio, computed directly from the converged WLS solution. Pseudo-measurement distributions are varied across five types in 22 variants matched at equal spread to isolate shape effects from variance. Experiments on the CIGRE MV network across 100 operating scenarios yield three findings. First, heavy-tailed and skewed distributions show consistently that WLS systematically overstates its uncertainty bounds. Second, the degree of miscalibration varies across buses and operating scenarios, confirming that distributional sensitivity is not uniform. Third, the CRB ratio is structurally blind to mean-shift bias, exposing a fundamental limitation of variance-based uncertainty diagnostics. Together, these results confirm the hypothesis and show that the choice of pseudo-measurement distribution directly distorts the confidence limits under WLS-based assumptions, which must be explicitly accounted for in any uncertainty-aware DSSE method.

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arXiv2026

Residual Power Flow for Neural Solvers

The energy transition challenges operational tasks based on simulations and optimisation. These computations need to be fast and flexible as the grid is ever-expanding, and renewables' uncertainty requires a flexible operational environment. Learned approximations, proxies or surrogates -- we refer to them as Neural Solvers -- excel in terms of evaluation speed, but are inflexible with respect to adjusting to changing tasks. Hence, neural solvers are usually applicable to highly specific tasks, which limits their usefulness in practice; a widely reusable, foundational neural solver is required. Therefore, this work proposes the Residual Power Flow (RPF) formulation. RPF formulates residual functions based on Kirchhoff's laws to quantify the infeasibility of an operating condition. The minimisation of the residuals determines the voltage solution; an additional slack variable is needed to achieve AC-feasibility. RPF forms a natural, foundational subtask of tasks subject to power flow constraints. We propose to learn RPF with neural solvers to exploit their speed. Furthermore, RPF improves learning performance compared to common power flow formulations. To solve operational tasks, we integrate the neural solver in a Predict-then-Optimise (PO) approach to combine speed and flexibility. The case study investigates the IEEE 9-bus system and three tasks (AC Optimal Power Flow (OPF), power-flow and quasi-steady state power flow) solved by PO. The results demonstrate the accuracy and flexibility of learning with RPF.

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arXiv2025

Addressing Model Inaccuracies in Transmission Network Reconfiguration via Diverse Alternatives

The ongoing energy transition places significant pressure on the transmission network due to increasing shares of renewables and electrification. To mitigate grid congestion, transmission system operators need decision support tools to suggest remedial actions, such as transmission network reconfigurations or redispatch. However, these tools are prone to model inaccuracies and may not provide relevant suggestions with regard to important unmodeled constraints or operator preferences. We propose a human-in-the-loop modeling-to-generate alternatives (HITL-MGA) approach to address these shortcomings by generating diverse topology reconfiguration alternatives. Case studies on the IEEE 57-bus and IEEE 118-bus systems show the method can leverage expert feedback and improve the quality of the suggested topology reconfigurations.

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arXiv2023

Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems

Ground fault detection in inverter-based microgrid (IBM) systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when there are partially decoupled disturbances and modeling uncertainties. The conventional solution of installing more relays to obtain additional measurements is costly and also increases the complexity of the system. In this paper, we propose a data-assisted diagnosis scheme based on an optimization-based fault detection filter with the output current as the only measurement. Modeling the microgrid dynamics and the diagnosis filter, we formulate the filter design as a quadratic programming (QP) problem that accounts for decoupling partial disturbances, robustness to non-decoupled disturbances and modeling uncertainties by training with data, and ensuring fault sensitivity simultaneously. To ease the computational effort, we also provide an approximate but analytical solution to this QP. Additionally, we use classical statistical results to provide a thresholding mechanism that enjoys probabilistic false-alarm guarantees. Finally, we implement the IBM system with Simulink and Real Time Digital Simulator (RTDS) to verify the effectiveness of the proposed method through simulations.

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