Why in News
- A surge in AI adoption globally and in India is creating significant new energy demands, particularly from data centres.
- Reports from IEA (2024) and McKinsey project data centre power demand could more than double by 2030, with AI as the primary driver.
- Raises the dilemma: Will AI improve energy efficiency or exacerbate energy stress?
Relevance
- GS III (Energy): Data centre electricity demand, renewable integration, energy efficiency.
- GS III (Science & Technology): AI applications, smart grids, green infrastructure.
- GS III (Environment & Sustainability): Carbon footprint, resource use, sustainable development.
Context
- AI systems and data centres require massive computational power → high electricity consumption.
- Global context:
- Data centres currently use ~1–2% of global electricity, expected to rise to 3–4% by 2030.
- Annual data centre capacity demand may increase 19–22% from 2023 to 2030 (from 60 GW to 171–219 GW).
- India context:
- Data centre demand: 1.2 GW in 2024 → 4.5 GW by 2030 (driven by AI and digital adoption).
- Additional projected electricity consumption: 40–50 TWh annually by 2030.
- Major hubs: Mumbai (41%), Chennai (23%), NCR (14%).
- Cooling requirements: Increased demand for freshwater for server cooling.
Potential Benefits of AI for Energy
- Smarter energy management: AI optimises grid operations, renewable integration, and load forecasting.
- Renewable energy utilisation: AI predicts and manages solar, wind, and hybrid plants, ensuring 24/7 access.
- Energy efficiency in real estate: AI-driven solutions (smart lighting, predictive HVAC, automated controls) can reduce energy consumption up to 25%.
- Green infrastructure:
- Nearly 25% of India’s data centre capacity is green-certified.
- ~67% of Grade A office stock in top cities is green-certified.
- Policy alignment: AI aids Energy Conservation Building Code, Roadmap of Sustainable and Holistic Approach to National Energy Efficiency, and smart grid missions.
Challenges & Risks
- Rising energy demand: Data centres’ increasing electricity consumption may strain India’s energy systems, adding to demand from coal, oil, and gas.
- Carbon emissions: AI expansion could increase emissions despite efficiency gains.
- Resource intensity: High freshwater use for cooling; reliance on imported critical minerals for AI infrastructure.
- Cybersecurity risks: AI could intensify energy security strains, e.g., sophisticated cyberattacks on utilities.
AI Mitigation Potential
- Optimisation: AI can forecast load, detect faults, and “heal” grid sections (e.g., BESCOM in Karnataka).
- Renewables integration: AI enables solar-wind-battery hybrid systems, predictive energy management.
- Sustainable AI development: Using recycled water, improving power efficiency in AI operations.
- Digital energy grid approach: Unified, interoperable power infrastructure can amplify AI benefits.
Quantitative Insights
- Global TWh demand (IEA): 945 TWh for data centres by 2030; AI-optimised centres → 4× increase.
- India’s electricity impact: Additional 40–50 TWh/year for AI-driven data centres.
- Capacity growth: India must expand data centre capacity ~3.75× by 2030 to meet AI and digital demands.
Policy & Strategic Implications
- Energy transition: AI can accelerate adoption of renewables and energy-efficient technologies.
- Investment planning: Critical for energy infrastructure, green data centres, and smart grids.
- Sustainability focus: Balancing AI growth with emissions reduction and water use efficiency.
- Regulatory support: Governments may need to “nudge” AI adoption toward sustainable practices.
