Brief Context
Context India’s power system is headed towards a “paradigm shift” as artificial intelligence (AI)-driven data centres are emerging as large, complex, and electricity-intensive infrastructure. Rising Power Demand from Data Centres India has an installed data centre capacity of 1.2 GW, which will grow to about 10 GW by 2030, with investments of over $200 billion. Power Demand by Data Centres: AI workloads use large numbers of Graphic Processing Units (GPUs) with individual racks consuming 80-150 K
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Syllabus: GS3/ Science and Technology
Context
- India’s power system is headed towards a “paradigm shift” as artificial intelligence (AI)-driven data centres are emerging as large, complex, and electricity-intensive infrastructure.
Rising Power Demand from Data Centres
- India has an installed data centre capacity of 1.2 GW, which will grow to about 10 GW by 2030, with investments of over $200 billion.
- Power Demand by Data Centres: AI workloads use large numbers of Graphic Processing Units (GPUs) with individual racks consuming 80-150 KW compared to 15-20 KW for traditional enterprise servers.
- This computational intensity drives an insatiable demand for electricity, making AI the most significant driver of increased energy consumption within the data centre sector.
- Continuous yet Highly Variable Demand: Data centres operate round the clock with a stable base load due to uninterrupted computing and cooling needs.
- However AI-driven workloads can cause sudden spikes in electricity consumption during peak processing periods, leading to rapid load fluctuations that challenge grid balancing and frequency stability.
Implications for Grid Infrastructure
- Pressure on Transmission Systems: Existing sub-transmission infrastructure may not be capable of meeting the massive power requirements of hyperscale facilities.
- Therefore, new high-capacity transmission corridors, ultra-high-voltage substations, and dedicated connectivity will be required.
- Resource Adequacy Challenges: Meeting data centre demand involves more than installing additional generation capacity. The system must also maintain adequate reserves, balancing power, and ancillary services to ensure reliability during sudden fluctuations.
- Difficulty in Demand Forecasting: AI-driven computing demand is inherently unpredictable. This makes load forecasting and scheduling significantly more complex for system operators, thereby increasing the risk of supply-demand mismatches.
Measures to Address Data Centre Power Demand
- Demand-Side Measures:
- Energy-efficient computing infrastructure: Adoption of advanced chips, efficient cooling systems, and optimized hardware reduces electricity consumption per unit of computation.
- Heterogeneous computing: Using a mix of CPUs, GPUs, and specialized accelerators ensures that energy-intensive processors are used only when necessary.
- On-site energy storage: Battery systems can supply short-term power during spikes, reducing sudden draw from the grid.
- Supply-Side Measures:
- Expansion of reliable baseload generation: Stable sources such as coal, hydro, gas, and nuclear power are required to ensure uninterrupted electricity supply.
- Hybrid energy systems: Combining grid supply with captive generation and renewable installations enhances reliability and reduces dependence on a single source.
- Development of high-voltage substations and transmission corridors is essential to deliver large quantities of power.
Way Ahead
- AI-driven data centres represent both a major opportunity for economic growth and a significant challenge for India’s power system.
- India must adopt a forward-looking strategy to integrate digital infrastructure expansion with energy planning.
- This strategy should include a dedicated policy framework for data centre power supply, updated grid codes for large dynamic loads, and accelerated development of low-carbon power sources such as nuclear and hydro energy.
Source: IE