arXiv subjects

Mohamed Shamseldein

Publications and source records attributed to Mohamed Shamseldein.

6 recordsLinked to original sources

arXiv2026

Grid Integration of Gigawatt-Scale Hydrogen Hubs: A Multi-Timescale Stability Analysis and Connection Requirements for Weak Grid Environments

The global transition toward green hydrogen is driving the deployment of gigawatt-scale electrolysis centers, introducing a novel, converter-dominated load class to the bulk power system. Unlike conventional industrial loads, these facilities utilize extensive power electronics interfaces with fast dynamics comparable to Inverter-Based Resources (IBRs). This paper presents a comprehensive grid impact assessment of large-scale hydrogen hubs, focusing on harmonic injection, voltage stability in low Short Circuit Ratio (SCR) environments, and frequency response capabilities. Adopting a "full-spectrum" open-source modeling approach, the study utilizes PandaPower for large-scale steady-state contingency assessment; ANDES for electromechanical dynamic simulations to evaluate Fast Frequency Response (FFR); and ParaEMT for high-fidelity electromagnetic transient analysis of harmonic distortion and Low Voltage Ride-Through (LVRT). A critical finding of this study is that standard load models, including the generic PERC1 (data center) model, are insufficient for hydrogen hubs. The paper recommends specific structural modifications to the PERC1 model - specifically regarding process safety latches and restart voltage thresholds - to accurately capture the risk of prolonged plant tripping. Based on these findings, the paper proposes a set of standardized connection requirements to ensure secure integration.

eess.SY↗

arXiv2026

Generative Autonomous Grid Control: Integrating Decision Transformers with a Two-Stage Safety Stack

The displacement of synchronous generation by inverter-based resources is accelerating power system frequency dynamics beyond the response capability of conventional automatic generation control. This paper presents Autonomous Grid Generation Control with Decision Transformers, a framework coupling an offline-trained Decision Transformer with a twostage symbolic safety stack for secondary frequency control. The Decision Transformer learns a conditional dispatch policy from offline supervisory control and data acquisition records via sequence modeling, eliminating online exploration risks. A Constraint Verification Unit provides sub-ten-millisecond algebraic screening using real-time power transfer distribution factors, while an aggregate digital twin performs swing-equation-based dynamic stability certification. Validated on the Northeast Power Coordinating Council 140-bus system under low-inertia conditions, the proposed controller reduces the area control error integral by over 99% relative to tuned automatic generation control, maintains a 59.4 Hz frequency nadir, and achieves inference latency of approximately 10 ms, well within real-time constraints. Comparative evaluation against a linear quadratic regulator baseline and structural analysis against conservative Q-learning demonstrate the advantages of the sequence-modeling formulation. Small-signal eigenvalue analysis characterizes the dominant 1.87 Hz electromechanical mode and confirms that the safety stack maintains stable operation across operating points. By falling back to tuned automatic generation control whenever proposals are rejected, the safety stack bounds worst-case performance to industry-standard levels in simulation.

eess.SY↗

arXiv2026

Grid-Mind: An LLM-Orchestrated Multi-Fidelity Agent for Automated Connection Impact Assessment

Large language models (LLMs) have demonstrated remarkable tool-use capabilities, yet their application to power system operations remains largely unexplored. This paper presents Grid-Mind, a domain-specific LLM agent that interprets natural-language interconnection requests and autonomously orchestrates multi-fidelity power system simulations. The LLM-first architecture positions the language model as the central decision-making entity, employing an eleven-tool registry to execute Connection Impact Assessment (CIA) studies spanning steadystate power flow, N-1 contingency analysis, transient stability, and electromagnetic transient screening. A violation inspector grounds every decision in quantitative simulation outputs, while a three-layer anti-hallucination defence mitigates numerical fabrication risk through forced capacity-tool routing and post-response grounding validation. A prompt-level self-correction mechanism extracts distilled lessons from agent failures, yielding progressive accuracy improvements without model retraining. End-to-end evaluation on 50 IEEE 118-bus scenarios (DeepSeek-V3, 2026-02-23) achieved 84.0% tool-selection accuracy and 100% parsing accuracy. A separate 56-scenario self-correction suite passed 49 of 56 cases (87.5%) with a mean score of 89.3. These results establish a reproducible baseline for continued refinement while maintaining auditable, simulation-grounded decision support.

eess.SY↗

arXiv2025

From Liability to Asset: A Three-Mode Grid-Forming Control Framework for Centralized Data Center UPS Systems

AI workloads are turning large data centers into highly dynamic power-electronic loads; fault-time behavior and workload pulsing can stress weak-grid points of interconnection. This paper proposes a centralized medium-voltage (MV) uninterruptible power supply (UPS) control architecture implemented as three operating modes: Mode 1 regulates a DC stiff bus and shapes normal-operation grid draw, Mode 2 enforces current-limited fault-mode P--Q priority with UPS battery energy storage system (UPS-BESS) buffering and a rate-limited post-fault "soft return," and Mode 3 optionally provides droop-based fast frequency response via grid-draw modulation. Fundamental-frequency averaged dq simulations (50 MW block, short-circuit ratio (SCR) = 1.5, 0.5 p.u. three-phase dip for 150~ms) show zero unserved information-technology (IT) energy (0.00000 MWh vs.0.00208 MWh for a momentary-cessation benchmark), a 0.57 p.u. peak inverter current (vs. 1.02 p.u. for a synchronous-reference-frame phase-locked loop (SRF-PLL) low-voltage ride-through (LVRT) baseline), a nonzero mean fault-window grid draw of 0.20~p.u. (vs.approx 0 for momentary cessation), and an improved settled point-of-common-coupling (PCC) voltage minimum of 0.79 p.u. after one cycle (vs. 0.66 p.u.). A forced-oscillation case study applies a 1 Hz pulsed load (+/- 0.25 p.u.) and shows that the normal-operation shaping filters the oscillation seen by the grid while the UPS-BESS buffers the pulsing component.

eess.SY↗

arXiv2025

A Hybrid GNN-IZR Framework for Fast and Empirically Robust AC Power Flow Analysis in Radial Distribution Systems

The Alternating Current Power Flow (ACPF) problem forces a trade-off between the speed of data-driven models and the reliability of analytical solvers. This paper introduces a hybrid framework that synergizes a Graph Neural Network (GNN) with the Implicit Z-Bus Recursive (IZR) method, a robust, non-iterative solver for radial distribution networks. The framework employs a physics-informed GNN for rapid initial predictions and invokes the IZR solver as a failsafe for stressed cases identified by a two-stage trigger. A failure is defined as any solution with a maximum power mismatch exceeding 0.1 p.u., a significant operational deviation. On a challenging test set of 7,500 stressed scenarios for the IEEE 33-bus system, the GNN-only model failed on 13.11 % of cases. In contrast, the hybrid framework identified all potential failures, delegating them to the IZR solver to achieve a 0.00 % failure rate, empirically matching the 100 % success rate of the analytical solver on this specific test set. An expanded ablation study confirms that both physics-informed training and Z-bus sensitivity features are critical, collaboratively reducing the GNN's failure rate from 98.72 % (data-only) to 13.11 %. The hybrid approach demonstrates a pragmatic path to achieving the empirical reliability of an analytical solver while leveraging GNN speed, enabling a significant increase in the number of scenarios analyzable in near real-time.

eess.SY↗

arXiv2025

A Hybrid GNN-LSE Method for Fast, Robust, and Physically-Consistent AC Power Flow

Conventional AC Power Flow (ACPF) solvers like Newton-Raphson (NR) face significant computational and convergence challenges in modern, large-scale power systems. This paper proposes a novel, two-stage hybrid method that integrates a Physics-Informed Graph Neural Network (GNN) with a robust, iterative Linear State Estimation (LSE) refinement step to produce fast and physically-consistent solutions. The GNN, trained with a physics-informed loss function featuring an efficient dynamic weighting scheme, rapidly predicts a high-quality initial system state. This prediction is then refined using an iterative, direct linear solver inspired by state estimation techniques. This LSE refinement step solves a series of linear equations to enforce physical laws, effectively bypassing the non-linearities and convergence issues of traditional solvers. The proposed GNN-LSE framework is comprehensively validated on systems ranging from small radial distribution networks (IEEE 33-bus, 69-bus) to a large, meshed transmission system (IEEE 118-bus). Results show that our GNN variants are up to $8.4 \times 10^3$ times faster than NR. The LSE refinement provides a fast route to a physically-consistent solution, while heavy-loading stress tests (120%-150% of nominal) and N-1 contingencies demonstrate the method's reliability and generalization. This work presents a powerful and flexible framework for bridging fast, data-driven models with the rigorous constraints of power system physics, offering a practical tool for real-time operations and analysis.

eess.SY↗