Challenges and Solutions for Power Grid Stability with the Expansion of AI Data Centers

 

Challenges and Solutions for Power Grid Stability with the Expansion of AI Data Centers

Eng. Armando Cavero Miranda (UPS Specialist)

▶AI data centers experience extreme power fluctuations on the scale of milliseconds to minutes.Due to the synchronization characteristics of hundreds of thousands of GPUs during checkpoints, synchronization delays, and training completion, resulting in a greater amplitude of variation.10 times larger compared to the traditional cloud.In peak and trough situations, the total load can drop drastically, for example, from 100 (normalized base) to 42, representing a direct risk to system stability.

▶These Sudden load fluctuations are difficult to match with the response speed (MW/min) of existing generators.When combined with the decrease in system inertia due to the expansion of renewable energies, they can lead to the risk ofchain of blackoutsAccording to an analysis by ERCOT, there is a possibility of widespread voltage instability in the event of a simultaneous power outage exceeding 2.5 GW.

▶As countermeasures, hardware-based solutions are being implemented in conjunction, such as BESS (Battery Energy Storage System), grid-connected UPS (Uninterruptible Power Supply) and synchronous capacitors, software controls such as workload-aware smoothing and institutional measures such asmandatory LVRT (Voltage Sag Support) and conditional connection regulations.

1. Prospects for Accelerated Growth in Energy Demand from Global Data Centers

 The expansion of AI technology and the prospect of accelerated growth in energy demand from global data centers.

In 2024, data center energy consumption represented 1.5% (415 TWh) of global electricity consumption. It is expected to exceed 945 TWh by 2030, more than doubling. [1)]

The main reason for the increased energy demand in data centers is the growing demand for AI and digital services.

The US currently accounts for approximately 35-40% of the global data center market (based on GW).

 

2. Load Fluctuation Patterns of AI Data Centers

❏Load Fluctuation Characteristics of AI Data Centers

During GPU batch processing, power consumption spikes during array operations, and drops dramatically during data transfer and synchronization

• Checkpoint EventDuring the checkpoint process to save progress, the charge drops to near 'zero' for milliseconds, followed by a sharp increase as it instantly recovers.
• Synchronization DelayDuring parallel summation (AllReduce) operations on clusters of hundreds of thousands of GPUs, network transmission delay causes some devices to remain idle for a few seconds.
• End of TrainingFollowing a large-scale operation, if there is no immediate subsequent workload, gigawatt-scale loads can be disconnected simultaneously in a single event.

※Checkpoint: The process of saving intermediate AI learning results, allowing the execution of the same point to be resumed later.

※Parallel Sum Operation (AllReduce): A communication operation in distributed learning where the results calculated by each GPU (e.g., gradients from matrix operations) are summed collectively, and then the result is distributed equally to all GPUs. Because all devices must wait/synchronize simultaneously during this process, patterns of instantaneous load drops or peaks may occur.

※According to some data, based on Google Cloud data, it is reported that under specific conditions, AI workloads showed a load fluctuation approximately 10 times greater (1.5MW → 15MW) compared to the traditional cloud, but these are values ​​from individual cases and the proportion relative to total equipment was not disclosed.

3. Measures to Respond to Sudden Load Fluctuations

☐Hardware Solutions

❍Battery Energy Storage System (BESS)

• It acts as a physical "shock absorber" that absorbs abrupt fluctuations in AI load.
• It actively manages power quality with Fast Frequency Response (FFR) on a millisecond scale and contributes to improving LVRT capability.
• It goes beyond mere cost, transforming into a revenue asset through peak shaving, energy arbitrage, and participation in the ancillary services market.

❍Grid-Interactive Uninterruptible Power Supply (GIPS)

※An Uninterruptible Power Supply (UPS) functions to provide stable power for a certain period immediately when there is a momentary interruption in the

 power supply or voltage fluctuation. The power is drawn from the electrical grid and used.

• It evolves from a passive emergency power source to a Distributed Energy Resource (DER) that actively contributes to grid stabilization.
• It monitors the network frequency in real time, discharging when the frequency drops and charging when it rises, contributing to stabilization.
  ※It was marketed at Microsoft's Dublin data center, also serving as a backup power source.

❍Synchronous Capacitors and Other Equipment

• They provide the physical inertia of the electrical grid, which has been reduced due to the increased participation of renewable energies, ensuring frequency stability.
• They provide reactive power to dynamically support the voltage and increase the robustness of the system. [6)]
• STATCOM/SVCThey provide fast voltage support, and Grid-Forming Inverters provide virtual inertia, being used in a complementary way with BESS.

※STATCOM (Static Synchronous Compensator): A device that uses power electronics equipment to supply/absorb reactive power in real time, maintaining a stable voltage.

※SVC (Static Reactive Power Compensator): A device that controls the reactive power of the network to reduce voltage fluctuations. It has a slower response than STATCOM, but is cheaper and widely used.

※Grid-Forming Inverter: A device where distributed sources such as solar power and batteries create their own voltage/frequency reference, acting as a "mini power plant" to stabilize the grid.

New Challenges Presented by AI Data Centers

The AsExtreme load fluctuations on a millisecond-second-minute scale in AI data centers.They can fundamentally threaten the stability of the existing electrical grid.

·         They originate fromintrinsic characteristics of AI learning workloadswhere hundreds of thousands of GPUs operate in a synchronized manner, unlike the asynchronous workloads of the traditional cloud.

·         Due to unpredictable events such as checkpoints, synchronization delays, and training terminations, loads on the GW scale change abruptly in milliseconds.

·         THEThe response speed in minutes (MW/min) of existing generators is not capable of handlingwith this, and in addition, thereduction in system inertia due to increased renewable energyThis can further amplify the vulnerability.

OOsimultaneous tripping of large-scale loadscould emerge as a real risk ofchain blackout.

This is not merely a theoretical scenario, but follows a path similar to large-scale energy collapses that have already occurred.

·         The April 2025 event in the Iberian Peninsula (Spain, Portugal), where 2.2 GW of generation was lost and the entire grid collapsed in 27 seconds, is a representative example.

·         The isolated structure of the Texas power grid, lacking external interconnections, is similar to that of the Iberian Peninsula in Europe, exposing it to the same risk of chain reaction collapse.

References

-Donnellan, D., Lawrence, A., Bizo, D., and Judge, P., “Uptime Institute

Global Data Center Survey 2024”, Uptime Institute, 2024.

-Park Chan-guk, Assistant Professor, Faculty of Climate Change Convergence, Hankuk University

-Energytrackerasia, “AI Data Center Development in Japan and Clean Energy

Transition”, 2025.

-Entsoe, “Synchronous Condensers,” 2025a.

-[Paper Review] Power Stabilization for AI Training Datacenters