Why in news?
India’s power system is set for a major transformation as AI-driven data centres expand rapidly, according to Grid India chief.
While AI depends on algorithms, it also demands vast amounts of electricity. India’s current data centre capacity of about 1.2 GW is expected to quadruple by 2030 as AI-led computing surges.
This growth will significantly reshape grid planning and operations, with data centres becoming large, complex, and dynamic loads on the power network.
What’s in Today’s Article?
- Rising Grid Risks from Data Centre Expansion
- Risk of Chaos Without Strategic Planning
- Renewables and Efficiency in Data Centre Expansion
Rising Grid Risks from Data Centre Expansion
- AI-driven data centres represent intense electricity loads, often requiring direct transmission-level connectivity rather than traditional sub-transmission networks.
- Their projected growth to 8–10 GW by 2030 will significantly strain grid infrastructure.
- Unlike conventional industrial demand, data centres exhibit sharp variability, rapid load ramps, and “silent exits” from the grid.
- As inverter-based systems, sudden withdrawal of 1–2 GW could destabilise grid operations.
- Planning and Resource Adequacy
- Experts emphasise the need for proactive infrastructure planning, including robust transmission networks, strong connectivity, and compliance mechanisms.
- Data centres must also meet resource adequacy norms, covering primary energy, reserves, and balancing requirements.
- Given AI’s unpredictable load patterns, especially during peak processing phases, forecasting becomes difficult.
- Grid authorities stress that risks cannot be fully absorbed by the grid and must be managed at the demand side.
- Evolving Standards and Storage Integration
- Globally, many jurisdictions require data centres to have dedicated generation capacity.
- India’s grid codes, standards, and energy storage integration must evolve to accommodate these emerging large-scale, dynamic loads.
Risk of Chaos Without Strategic Planning
- Rapid expansion of hyperscale data centres — each requiring nearly 1 GW — could severely strain the grid if not carefully planned.
- Such demand necessitates high-voltage substations and coordinated planning by central and state transmission utilities.
- Since hyperscale data centres have steady baseload demand, they require long-duration, stable generation sources.
- While the US increasingly relies on nuclear power, India may need a diversified supply approach.
- Mixed Energy and Storage Solutions
- Experts recommend combining grid supply with captive generation, renewable energy, and long-duration battery storage (6–9 hours) to manage reliability and reduce dependence on the grid alone.
- To avoid chaotic infrastructure expansion and rising tariffs, authorities should identify and pre-plan dedicated data centre zones.
- Without coordinated planning, ad hoc grid expansion could lead to inefficiencies and higher costs for consumers.
Renewables and Efficiency in Data Centre Expansion
- Renewable energy can power new data centres through the open access route, which allows large consumers (1 MW+) to buy electricity directly from suppliers or exchanges, bypassing DISCOMs.
- Surplus generation capacity and upcoming pump hydro projects could support affordable round-the-clock green power.
- Semiconductor-Level Efficiency Gains
- Semiconductor firms are central to the AI-driven power challenge.
- Under Intel’s 18A process, innovations such as RibbonFET, backside power delivery, and advanced packaging improve efficiency by about 15% and reduce energy use by limiting data movement.
- Intel also promotes a “heterogeneous AI” approach, arguing that not all AI tasks require energy-intensive GPUs.
- Optimised workload allocation across processors can significantly reduce total power consumption.
- Hyperscalers’ Key Requirements
- AI scaling in India is still emerging, offering time to plan.
- For large investments, hyperscalers prioritise assured renewable power, grid reliability, regulatory clarity, long-term price certainty, and quick power availability.
