Large industrial facilities such as aluminum smelting plants demand sustained, high-current power that can fluctuate rapidly and threaten grid stability. Managing the millisecond-scale power oscillations of large industrial loads and AI workloads requires a comprehensive strategy combining monitoring, grid-side damping, load-side conditioning, and operational coordination. Lessons from Alcoa’s smelting operations provide a blueprint. Adapting these principles to the evolving demands of AI data centers, with fast-acting inverters and predictive analytics, will safeguard grid stability and enable future growth.
There are a number of papers examining the nature of these millisecond-scale load oscillations, their impacts on transmission systems, and the layered mitigation strategies employed by utilities. The emphasis for AI workloads should be on real-time monitoring and grid-side damping devices along with possible load-side conditioning equipment and coordinated operational practices. From the many large load oscillations for industrial applications, there are detailed case studies, including Alcoa’s Warrick facility which illustrates practical implementations. Modern AI data centers introduce similarly fast and large oscillations, requiring tailored strategies that build on lessons from traditional heavy industry.
Aluminum smelting via the Hall–Héroult process consumes hundreds of megawatts per plant, with rectifier stacks drawing nonlinear currents that can oscillate in milliseconds. Such rapid changes have challenged grid operators, causing voltage sags, frequency deviations, and resonant interactions with protective relays. As industrial electrification expands into data centers, hydrogen production, and other energy-intensive sectors, understanding and mitigating these oscillations remains critical to regional reliability and resilience.
Impacts of Smelting Plant Oscillations
Smelting plants feature several unique load dynamics:
Sudden current surges and drops during potline startups, shutdowns, or fault clearances
Harmonic distortion from high-voltage direct current (HVDC) rectifiers
Feedback-driven fluctuations when potline controllers engage to maintain cell voltage
These effects manifest as forced oscillations, with periods ranging from a few milliseconds to several seconds, depending on control loop tuning and network impedance.
Rapid load oscillations impose multiple risks on a transmission system:
Voltage sags and swells that exceed equipment ride-through capabilities
Frequency excursions when generation does not instantly match demand changes
Resonant amplification of harmonics, potentially tripping protective devices
Cumulative stress on transformers and switchgear, reducing asset lifetime
Such disturbances can propagate through interconnected grids, threatening widespread outages if left unchecked.
Utility Mitigation Strategies
Utilities have developed a multilayered approach to manage industrial load oscillations:
Real-Time Monitoring Phasor Measurement Units (PMUs) and Wide-Area Monitoring Systems continuously track voltage and current phasors. Advanced algorithms detect forced oscillation signatures and pinpoint their sources.
Grid-Side Damping
Power System Stabilizers modulate generator excitation to dampen power swings.
Flexible AC Transmission Systems (FACTS), including Static VAR Compensators (SVCs) and STATCOMs, inject or absorb reactive power within one cycle to stabilize voltage.
HVDC interties decouple large rotating generators from AC disturbances by converting power through DC links.
Load-Side Conditioning (as needed)
Sag Ride-Through units measure voltage dips and instantly supply synthesized power to maintain process stability.
Tap-changing transformers equipped with silicon-controlled rectifiers adjust secondary voltage within a single cycle.
Local uninterruptible power supplies protect sensitive control electronics, with full UPS and generator support similar to typical data center designs.
Operational Coordination Utilities coordinate with plant operators on staggered potline ramp rates and scheduled maintenance to avoid simultaneous load transients. Interconnection agreements define permitted start/stop profiles and contingency protocols.
Engineering Devices for Millisecond-Scale Shifts
Device
Function
Response Time
Application
Sag Ride-Through (SRT) Units
Synthesizes voltage to correct sags
<2 ms
Industrial substations
Static VAR Compensators (SVC)
Dynamically injects/absorbs reactive power
<1 cycle
Transmission and distribution level
SCR-Equipped Transformers
Electronically adjusts transformer taps
~1 cycle
Medium-voltage networks
Phasor Measurement Units
Detects oscillations and measures phase
Real-time
Wide-area monitoring
HVDC Links
Isolates oscillatory loads from AC network
Continuous
Large industrial clusters
Case Study: Alcoa Warrick Facility
At the Warrick Operations, four 800 MW generating units supplied power to multiple potlines. Collaboration with the local utility led to:
Installation of damping controllers on turbine generators
Deployment of STATCOMs to buffer reactive swings during potline transitions
Implementation of staggered potline startups, limiting concurrent current surges
This integrated solution reduced voltage deviations by over 75% during peak load changes and eliminated several nuisance trippings.
Oscillations from AI Workloads: Challenges and Mitigation Strategies
The rapid rise of AI-driven data centers now poses a new frontier of fast, large power oscillations; although new, the rapid snaps of power use are not unlike those studied elsewhere. Training large language models demands sustained high power along with sudden and repeated oscillations of 60% of the load, while inference workloads trigger repetitive spikes lasting seconds to minutes. These dynamic profiles introduce:
Dramatic ramp-rates from hundreds of kilowatts per second to hundreds of megawatts per second
High harmonic content from GPU/TPU nonlinear switching
Persistent multi-timescale variability driven by workload orchestration and scheduling changes
Localized grid distortions affecting surrounding communities within varying distances of the facility sites, depending on the routing, substations, and mitigation practices.
To address these challenges, operators and utilities are extending traditional mitigation frameworks:
Enhanced Monitoring and Analytics
Site-level power quality meters capture sub-cycle voltage, frequency, and current waveforms.
AI/ML-based models predict load spikes in advance using historical telemetry.
Advanced Grid-Side Controls
Grid-forming inverters provide synthetic inertia and fast reactive support during transients.
SiC-based FACTS devices achieve sub-millisecond response to compensate harmonics and flicker (perceived or real from the AI loads).
On-Site Energy Buffering
Data center energy storage (UPS) can absorb training-related surges and release stored power during inference spikes; however, there is a high cost and performance for cycling UPS and batteries to mitigate oscillations.
Software may be utilized to operate IT and GPUs at a steady operating state. This is through creating 'false' loads to mitigate or prevent the large swings but is certainly wasteful and costly to the owner/operator.
Workload and Infrastructure Coordination
Utilities and data center operators agree on ramp-rate limits and flexible scheduling windows. Many utilities already have this in place; however, the ramp-rate limits to be applied are much slower than the rapid oscillations that can be created by AI workloads. From the past, Alcoa and others have developed and operated their own power supplies around direct hydroelectric supply to avoid such grid and utility limitations.
Demand response integration allows shifting non-critical training tasks to off-peak hours, smoothing aggregate demand while potentially avoiding some ramp-rate limits.
By integrating these measures, AI data centers can achieve grid compliance and protect the facility operations and utility networks from fast, large-amplitude oscillations.
Outlook
Emerging technologies promise further resilience:
Grid-forming inverters with synthetic inertia to support frequency and voltage during transients
AI-based oscillation prediction using historical PMU and site-level data
Hybrid energy storage systems colocated with industrial plants to absorb and inject power on millisecond scales
As the electrification of heavy industry and AI infrastructure accelerates, these innovations will be essential to maintaining reliable, low-carbon power systems.
References
Apt4Power, “How AI Compute Loads Are Transforming Data Center Infrastructure.”
Nicolás Louzán Pérez and Manuel Pérez Donsión, "Technical Methods for the Prevention and Correction of Voltage Sags and Short Interruptions inside the Industrial Plants and in the Distribution Networks," INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY AND POWER QUALITY (ICREPQ 2003)
Visintini, Roberto, "Mitigation of voltage sag effects on sensitive plants: an exemplary case study", Electric Power Systems Research, 2002.
