“Power – Peak Shaving” Strategy
The image illustrates a 5-step process for a ‘Peak Shaving’ strategy designed to maximize power efficiency in data centers. Peak shaving is a technique used to reduce electrical load during periods of maximum demand (peak times) to save on electricity costs and ensure grid stability.
1. IT Load & ESS SoC Monitoring
This is the data collection and monitoring phase to understand the current state of the system.
- Grid Power: Monitoring the maximum power usage from the external power grid.
- ESS SoC/SoH: Checking the State of Charge (SoC) and State of Health (SoH) of the Energy Storage System (ESS).
- IT Load (PDU): Measuring the actual load through Power Distribution Units (PDUs) at the server rack level.
- LLM/GPU Workload: Monitoring the real-time workload of AI models (LLM) and GPUs.
2. ML-based Peak Prediction
Predicting future power demand based on the collected data.
- Integrated Monitoring: Consolidating data from across the entire infrastructure.
- Machine Learning Optimization: Utilizing AI algorithms to accurately predict when power peaks will occur and preparing proactive responses.
3. Peak Shaving Via PCS (Power Conversion System)
Utilizing physical energy storage hardware to distribute the power load.
- Pre-emptive Analysis & Preparation: Determining the “Time to Charge.” The system charges the batteries when electricity rates are low.
- ESS DC Power: During peak times, the stored Direct Current (DC) in the ESS is converted to Alternating Current (AC) via the PCS to supplement the power supply, thereby reducing reliance on the external grid.
4. Job Relocation (K8s/Slurm)
Adjusting the scheduling of IT tasks based on power availability.
- Scheduler Decision Engine: Activated when a peak time is detected or when ESS battery levels are low.
- Job Control: Lower priority jobs are queued or paused, and compute speeds are throttled (power suppressed) to minimize consumption.
5. Parameter & Model Optimization
The most advanced stage, where the efficiency of the AI models themselves is optimized.
- Real-time Batch Size Adjustment: Controlling throughput to prevent sudden power spikes.
- Large Model -> sLLM (Lightweight): Transitioning to smaller, lightweight Large Language Models (sLLM) to reduce GPU power consumption without service downtime.
Summary
The core message of this diagram is that High-Quality/High-Resolution Data is the foundation for effective power management. By combining hardware solutions (ESS/PCS), software scheduling (K8s/Slurm), and AI model optimization (sLLM), a data center can significantly reduce operating expenses (OPEX) and ultimately increase profitability (Make money) through intelligent peak shaving.
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Lechuck Park 