Data Center Model for Transient Stability Analysis of Power Systems
arXiv preprint (University College Dublin) · Alberto Jimenez-Ruiz, Federico Milano
A study proposing a dynamic load model for data centres that covers UPS behaviour, cooling and AI training loads, applied to the Irish grid.
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Overview
This arXiv preprint by Alberto Jimenez-Ruiz and Federico Milano (University College Dublin, 2025, arXiv:2505.16575) proposes a dynamic data-center load model for transient stability studies. The model has three main parts: a UPS between servers and grid that disconnects on voltage or frequency excursions and later reconnects, cooling represented as an induction motor (chiller compressors and pumps), and a pulsing load representing AI training transients.
The model is applied to the all-island Irish transmission system with data on connected data centers. As context, a fault in the Irish system caused a 204 MW drop in data-center demand, giving a RoCoF of 0.12 Hz/s and a frequency peak of 50.22 Hz. Irish data-center penetration is about 800 MW over a 7 GW system peak and is expected to reach 1.6 GW, or 30% of demand, by 2030. Cooling accounts for about 30% of total power of standalone data centers. Typical UPS switching margins are 0.2-0.3 Hz in frequency and 10% of nominal voltage.
In the case study, 300 MW of servers cooled by a 60 MW chiller, with UPS tripping at +/-0.1 pu or +/-0.3 Hz. With a 10 s disconnection time, the sudden reconnection pushed frequency below 49.7 Hz and re-disconnected the site, a cycle that can repeat. A data center made of UPSs with different reconnection times allows seamless reconnection with only moderate transients. Ramp-rate settings of compute load can soften AI-driven swings. Results are limited to the Irish system and the assumed model parameters.
Key points
- A fault in the Irish system caused a 204 MW drop in data-center demand, giving a RoCoF of 0.12 Hz/s and a frequency peak of 50.22 Hz.
- Irish data-center penetration is about 800 MW over a 7 GW system peak and is expected to reach 1.6 GW, or 30% of demand, by 2030.
- Cooling accounts for about 30% of total power of standalone data centers and is modelled as an induction motor.
- Typical UPS switching margins are 0.2-0.3 Hz in frequency and 10% of nominal voltage; outside either the UPS disconnects the loads.
- Case: 300 MW of servers cooled by a 60 MW chiller, UPS tripping at +/-0.1 pu or +/-0.3 Hz; with a 10 s disconnection time, sudden reconnection pushed frequency below 49.7 Hz and re-disconnected the site, a cycle that can repeat.
- A data center made of UPSs with different reconnection times allows seamless reconnection with only moderate transients.
- The model has three parts: UPS, cooling as an induction motor, and a pulsing AI load.
- Ramp-rate settings of compute load can soften AI-driven swings.
Using it in design
- Expose UPS transfer thresholds (voltage %, frequency Hz), return-to-mains delay and staggering across UPS modules as design parameters, and warn when all modules share identical thresholds and delays, since synchronised reconnection can re-trip the grid.
- Model chiller and pump motors as dynamic load in grid-study exports, and give the AI load a ramp-rate setting.
- For sites in small or islanded grids (data-center share of demand above about 10-30%), raise the priority of ride-through and staged-reconnection checks.
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Who should read it
Electrical engineers studying the grid impact of large data centre loads, grid connection study specialists and those designing UPS parameters.
Publication details
- Authors
- Alberto Jimenez-Ruiz; Federico Milano
- Publisher
- arXiv preprint (University College Dublin)
- Year
- 2025
- Document number
- arXiv:2505.16575
- Document language
- English
- Source type
- Scientific
- Access
- Free to read
- Licence
- CC BY-NC-ND 4.0 — share unaltered with attribution for noncommercial purposes
Original licence note
arXiv CC BY-NC-ND 4.0 - citation only
Citation
Alberto Jimenez-Ruiz; Federico Milano (2025). Data Center Model for Transient Stability Analysis of Power Systems. arXiv preprint (University College Dublin). arXiv:2505.16575. https://doi.org/10.48550/arXiv.2505.16575
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