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pith:2026:KL5325BYU6WOR6XVH43FTU3E5T
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Stochastic Modeling of Power-Grid Frequency Fluctuations in Low-Inertia Systems via a Gaussian-Core Potential and Superstatistics

Alessandro Lonardi, Christian Beck, Wanru Hao

A Gaussian-core potential plus superstatistics reproduces the bimodal frequency distributions observed in low-inertia power grids.

arxiv:2605.13289 v1 · 2026-05-13 · physics.soc-ph

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Claims

C1strongest claim

Fitting the model to frequency data resolved at one-second intervals from the Great Britain grid, we find that the central barrier parameter increases substantially from 2020 to 2025 as the grid inertia progressively decreases.

C2weakest assumption

The assumption that the coefficients for the grid dynamics fluctuate slowly, allowing the use of superstatistical modeling with a lognormal distribution for the drift amplitude.

C3one line summary

A Gaussian-core potential plus superstatistics reproduces the bimodal frequency distributions, central suppression, and heavy tails observed in Great Britain grid data as inertia declines.

References

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[1] P. Kundur, N. J. Balu, and M. G. Lauby,Power System Stability and Control. New York, NY , USA: McGraw-Hill, 1994, vol. 7 1994
[2] Electricity markets around the world, 2018
[3] Data-driven load profiles and the dynamics of residential electricity consumption,
[4] Available: https://doi.org/10.1038/s41467-022-31942-9 · doi:10.1038/s41467-022-31942-9
[5] Future low-inertia power systems: Requirements, issues, and solutions - A review, 2020
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First computed 2026-05-18T02:44:49.133054Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

52fbbd7438a7ace8faf53f3659d364ecfa34d842d8bd683ca4dbfa0bb7e2fd14

Aliases

arxiv: 2605.13289 · arxiv_version: 2605.13289v1 · doi: 10.48550/arxiv.2605.13289 · pith_short_12: KL5325BYU6WO · pith_short_16: KL5325BYU6WOR6XV · pith_short_8: KL5325BY
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/KL5325BYU6WOR6XVH43FTU3E5T \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 52fbbd7438a7ace8faf53f3659d364ecfa34d842d8bd683ca4dbfa0bb7e2fd14
Canonical record JSON
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    "primary_cat": "physics.soc-ph",
    "submitted_at": "2026-05-13T10:04:19Z",
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