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arxiv: 2605.01173 · v1 · submitted 2026-05-02 · 📡 eess.SY · cs.SY

Limiting the Impact of AI Data Centers on Fatigue Life of Thermal Turbine Generators in the Grid: A Frequency-Domain Approach

Fiaz Hossain , Nilanjan Ray Chaudhuri , Alok Sinha , Sai Gopal Vennelaganti , Mohammed E. Nassar This is my paper

Pith reviewed 2026-05-09 18:37 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords AI data centerstorsional fatiguesynchronous generatorsfrequency-domain analysisload flowpower system stabilityfatigue lifegrid integration
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The pith

A three-step frequency-domain method sets safe limits on AI data center load fluctuations to avoid excess torsional fatigue in grid turbine generators.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a framework to evaluate how rapid changes in artificial intelligence data center electricity demand create torsional oscillations that shorten the fatigue life of steam and gas turbines attached to synchronous generators. It presents a three-step procedure: first calculate the largest safe power swing at each generator using only its mechanical multi-mass model, then compute an algebraic interaction factor from ordinary load-flow studies that converts a load change at any data center bus into the resulting power change at every generator, and finally screen possible siting locations and solve an optimization to find the largest allowable fluctuations that keep every generator inside its safe mechanical limits. The approach relies on frequency-domain analysis rather than repeated full time-domain simulations, and the authors show it works on standard test systems up to the 2000-bus Texas model. A reader would care because data centers are growing quickly and their load swings could otherwise reduce the operating life of expensive grid assets without operators noticing until damage accumulates.

Core claim

The impact of AI data center load variations on synchronous-generator torsional fatigue can be quantified and limited by first deriving per-generator mechanical power limits from first principles, then using load-flow-derived algebraic interaction factors to translate bus-level load changes into generator-level power changes, and finally applying frequency-domain screening plus optimization to set safe fluctuation bounds at candidate data-center sites.

What carries the argument

The algebraic interaction factor derived from load-flow analysis, which directly maps a change in AI data center load at a given bus to the corresponding change in electrical power output at each synchronous generator for use in torsional fatigue assessment.

If this is right

  • Each synchronous generator receives an explicit upper bound on allowable electrical power variation that keeps its turbine within safe torsional fatigue life.
  • Buses can be ranked by their interaction factors so that data centers are preferentially sited where their load swings affect the fewest or least vulnerable generators.
  • An optimization problem yields the largest permissible fluctuation amplitude for every data center bus while satisfying all generator-level mechanical limits simultaneously.
  • The entire procedure scales to systems the size of the synthetic 2000-bus Texas grid without requiring exhaustive dynamic simulation of every candidate site.
  • Frequency-domain analysis supplies the necessary screening information once the mechanical limits and interaction factors are known.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the interaction factor remains stable across operating conditions, the same mapping could support real-time curtailment signals to data centers when grid stress is high.
  • The method could be extended to other fast-varying loads such as large-scale battery storage or electric-vehicle fast-charging hubs by substituting their power profiles into the same three steps.
  • Operators might combine the ranking step with economic incentives to encourage data centers to locate near generators that have greater mechanical damping or spare torsional margin.

Load-bearing premise

The steady-state algebraic interaction factor obtained from load flow accurately predicts the dynamic mapping from AI data center load changes to each generator's electrical power output under the conditions relevant to torsional fatigue.

What would settle it

Run time-domain simulations on the IEEE 68-bus system with the optimized AI data center fluctuation limits from the three-step method and observe whether the resulting torsional stresses at any synchronous generator exceed the fatigue thresholds calculated from the first step.

Figures

Figures reproduced from arXiv: 2605.01173 by Alok Sinha, Fiaz Hossain, Mohammed E. Nassar, Nilanjan Ray Chaudhuri, Sai Gopal Vennelaganti.

Figure 1
Figure 1. Figure 1: (a) The rth shaft section; (b) A single-frequency stress variation with time. electromagnetic torque; δr is the angular displacement of mass r in a synchronously rotating frame in elect. rad.; ωr is the angular speed of mass r in elect. rad/s; and ωs is the synchronous speed in elect. rad/s. Note that Te is present only in the generator mass, and Tm is not present in both the generator and the exciter mass… view at source ↗
Figure 3
Figure 3. Figure 3: “Augmented” modified Goodman diagram [27]. Step 1.a: Find maximum allowable single-frequency power variation at SG terminal considering torsional oscillations and blade vibrations in subsynchronous zone Step 1.b: Determine maximum allowable multi-frequency power variation at SG terminal using the minimum of the values in step 1.a Step 2: Calculate algebraic interaction factors Step 3: Find maximum allowabl… view at source ↗
Figure 4
Figure 4. Figure 4: Flowchart of the proposed approach for determining power variation limits of AI DC loads. of no SG in the system is negatively affected. The overall approach is summarized in view at source ↗
Figure 5
Figure 5. Figure 5: Generalized DP-based modeling framework for multi-mass SGs, transmission system, AI DC loads, and GFL IBRs. Exc Gen LPB LPA IP HP Turbine Infinite bus A B C view at source ↗
Figure 6
Figure 6. Figure 6: IEEE First benchmark model for SSR [29]. properties of DPs: (1) view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of responses from DP model and EMT model of IEEE FBM. Inst. DP: x(τ ) ≈ P k∈U ⟨x⟩k (t)e jkωsτ . G3 G4 GFL IBR1 AI DC GFL IBR2 view at source ↗
Figure 8
Figure 8. Figure 8: Modified IEEE 4-machine system [32]. the eigenvalues presented in [30]. Moreover, a three phase￾to-ground (LLL-G) fault is applied at bus C in both the DP and a publicly available EMT model of the system in Matlab/Simscape [31]. The simulation results are compared in view at source ↗
Figure 9
Figure 9. Figure 9: Step 1.a for modified IEEE 4-machine system (p.u. on SG base). Notches correspond to torsional mode frequencies. time, s 0 0.5 1 1.5 2 Pd c;t o t al, M W 1740 1760 1780 1800 AI DC load power (a) Pd c , M W 0 10 20 30 40 FFT of AI DC load power variation frequency, Hz 0 10 20 30 40 50 60 X 20.2276 Y 32.3915 (b) time, s 0 20 40 60 80 100 < G E, p.u. -0.5 0 0.5 1 stress at shaft section G ! E of G4 -0.5 0 0.5… view at source ↗
Figure 10
Figure 10. Figure 10: (a) AI DC load power in MW in bus 9; (b) FFT of (a); (c) p.u. stress (cyan) in generator-exciter (G-E) shaft section of G4 with the maximum allowable value (orange) obtained from view at source ↗
Figure 13
Figure 13. Figure 13: AI DC load variation equaling P max(j) dc with twelve NETS candidate buses: (a) AI DC load variation in three representative buses; (b) P12 j=1 IF6jP (j) dc overlapped with P max(6) e ; (c) p.u. stress (cyan) in shaft section IP-LPA of G7 with maximum allowable value (orange) as base; and (d) frequency deviation (cyan) overlapped with maximum allowed (orange). heatmap of normalized variables generator G2 … view at source ↗
Figure 12
Figure 12. Figure 12: Algebraic IFs of modified IEEE-68 bus system. and not the whole system; therefore, we consider only the buses in NETS as candidates, which are ranked based on p.u. P max(j) dc in Table III. The values of P max(j) dc obtained from algebraic IFs are conservative and their ratios with P max(j) dc,actual range between 0.75 and 1. Suppose that we consider all of the twelve buses with AI DC loads in NETS (since… view at source ↗
Figure 14
Figure 14. Figure 14: Fluctuations in AI DC loads respect limits obtained from LP: Heatmap of the resulting transient peak stresses and deviations in frequency normalized with respect to corresponding limits in eight SGs in modified IEEE-68 bus system. 1123 loads is considered. We solve the LP focusing on the 39 generators in Zone 1 assumed as thermal units (P max(i) e assumed as 1% of generator rating) and take all load buses… view at source ↗
Figure 15
Figure 15. Figure 15: Algebraic IFs of 2000-bus Texas system. no. of AI DC loads 14 13 limit o n s u m o f F F T c o m p o n e n t s P (j) d c , M W 10 15 20 25 30 35 box plot of solution of (11) view at source ↗
Figure 16
Figure 16. Figure 16: Box plots of LP solutions for 2000-bus Texas system. scalable and easily accessible approach that helps to determine reasonably conservative optimal variations in AI DC loads in the frequency domain without impacting turbine fatigue life. The optimum limit is on the sum of the amplitudes of the subsynchronous frequency components of each AI DC load. An FFT can be performed on the AI DC power consumption t… view at source ↗
read the original abstract

A framework is established that assesses the impact of variations in artificial intelligence (AI) data center (DC) loads on the fatigue damage of steam/gas turbines of the synchronous generators (SGs) from torsional oscillations. Next, a simple three-step process that is supported by frequency-domain analysis is laid out to quantify the limits on fluctuations in AI DC loads. In the first step, the maximum allowable variation in electrical power output at each SG terminal is independently determined from the first principles. This step needs only a lumped multi-mass model of the mechanical side of the SG. In the second step, we propose a new approach that relies on load flow to determine the so-called algebraic `interaction factor' that maps the change in AI DC load at a given bus to the corresponding change in each of the SG power outputs. In the third step, we propose a screening method to rank the candidate buses to site AI DCs and solve an optimization problem to determine the optimal allowable fluctuations in the AI DCs. We demonstrate the applicability of the proposed approach through frequency-domain and time-domain analyses in the modified IEEE 4-machine and IEEE-68 bus systems using a dynamic phasor framework. Finally, we demonstrate the scalability of the proposed approach on the synthetic 2000-bus Texas system.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript establishes a framework to assess how variations in AI data center loads affect torsional fatigue life of steam/gas turbines in synchronous generators. It outlines a three-step process: (1) independently compute the maximum allowable electrical power variation at each SG terminal from a lumped multi-mass mechanical model using first principles; (2) derive an algebraic 'interaction factor' from load-flow analysis to map AI DC load changes at a bus to changes in each SG's power output; (3) screen candidate buses and solve an optimization problem to set optimal allowable fluctuations. Applicability is shown via frequency- and time-domain analyses with a dynamic phasor framework on the modified IEEE 4-machine and IEEE-68 bus systems, plus scalability on the synthetic 2000-bus Texas system.

Significance. If the central mapping holds, the work supplies a practical, scalable method to quantify and constrain AI DC load fluctuations to protect generator fatigue life, which is relevant given rising AI infrastructure. Credit is given for the parameter-free first-principles mechanical step, the dynamic-phasor validation approach, and the large-system demonstration showing computational feasibility without full electromagnetic transient simulations.

major comments (2)
  1. [§3] §3 (step 2): The algebraic interaction factor is obtained from steady-state load-flow analysis, but torsional fatigue is driven by oscillations at 5–50 Hz. The manuscript does not show that this factor accurately translates DC load changes into SG electrical power variations at those frequencies, where network dynamics and controls alter effective transfer. This mapping is load-bearing for the fatigue limits derived in step 1 and the optimization in step 3; the dynamic-phasor results in §5 do not include a direct frequency-dependent sensitivity comparison.
  2. [§5.2–5.3] §5.2–5.3: The time-domain validation on the IEEE systems and Texas case reports fatigue metrics but does not quantify the discrepancy between the load-flow interaction factor and the actual dynamic mapping at the dominant torsional modes identified in the mechanical model. Without this, it is unclear whether the proposed limits are conservative or optimistic.
minor comments (3)
  1. [Abstract] The abstract states the process is 'supported by frequency-domain analysis,' yet step 2 relies on algebraic load flow; a brief clarification of how frequency-domain tools enter each step would improve readability.
  2. [Table 1] Table 1 (or equivalent summary of interaction factors) would benefit from an additional column showing the frequency range over which the factor was verified.
  3. [§2.1] A few instances of undefined notation (e.g., the exact form of the fatigue damage index) appear in §2.1; adding an equation number and reference to the S-N curve used would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough and constructive review. We address each major comment point by point below and have revised the manuscript to incorporate the requested comparisons.

read point-by-point responses

  1. Referee: [§3] §3 (step 2): The algebraic interaction factor is obtained from steady-state load-flow analysis, but torsional fatigue is driven by oscillations at 5–50 Hz. The manuscript does not show that this factor accurately translates DC load changes into SG electrical power variations at those frequencies, where network dynamics and controls alter effective transfer. This mapping is load-bearing for the fatigue limits derived in step 1 and the optimization in step 3; the dynamic-phasor results in §5 do not include a direct frequency-dependent sensitivity comparison.

    Authors: We agree that the interaction factor is derived from steady-state load-flow analysis while torsional fatigue concerns arise from oscillations in the 5–50 Hz range, where network dynamics and controls can modify the effective transfer. The algebraic factor is used to enable scalable screening and optimization on large networks (as demonstrated on the 2000-bus Texas system) without requiring full dynamic models for every candidate. The dynamic-phasor framework in §5 validates the overall approach by showing that the derived limits keep fatigue metrics within safe bounds under time-domain operation. However, we acknowledge that an explicit frequency-dependent sensitivity comparison is not provided. In the revised manuscript we have added a small-signal frequency-response analysis using the dynamic-phasor model to compute the transfer at the dominant torsional frequencies and compare it directly with the algebraic interaction factor. revision: yes

  2. Referee: [§5.2–5.3] §5.2–5.3: The time-domain validation on the IEEE systems and Texas case reports fatigue metrics but does not quantify the discrepancy between the load-flow interaction factor and the actual dynamic mapping at the dominant torsional modes identified in the mechanical model. Without this, it is unclear whether the proposed limits are conservative or optimistic.

    Authors: The time-domain results report the fatigue metrics obtained when AI data-center fluctuations are constrained by the proposed limits. We agree that the manuscript does not explicitly quantify the discrepancy between the algebraic factor and the dynamic mapping at the dominant torsional modes. To clarify whether the limits are conservative or optimistic, we have revised §§5.2–5.3 to include this quantification: the amplitude of electrical power oscillations at the identified torsional frequencies is extracted from the dynamic-phasor simulations and compared with the value predicted by the interaction factor. This addition has been incorporated into the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity in the three-step derivation chain

full rationale

The paper's central three-step process begins with an independent first-principles calculation of maximum allowable electrical power variations at each SG using only a lumped multi-mass mechanical model. The algebraic interaction factor is then computed via standard load-flow analysis, an external algebraic procedure that does not reference or depend on the mechanical fatigue limits or the subsequent optimization. The screening and optimization steps combine these precomputed quantities without any self-referential definitions, fitted parameters renamed as predictions, or load-bearing self-citations. Dynamic-phasor demonstrations serve as validation of the overall framework rather than re-deriving or redefining the core mapping or limits. No ansatzes, uniqueness theorems, or renamings of known results are introduced that reduce the claimed results to their inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review prevents identification of specific free parameters, axioms, or invented entities. The proposed interaction factor appears to be a derived mapping but its exact formulation and any underlying assumptions are not detailed.

pith-pipeline@v0.9.0 · 5554 in / 1300 out tokens · 55560 ms · 2026-05-09T18:37:23.301951+00:00 · methodology

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Reference graph

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