Why in News?

• At the India AI Impact Summit held at Bharat Mandapam, senior policymakers and global experts deliberated on the theme ‘Global Mission on AI for Energy Scaling through citizen-centric India Energy Stack’.
• Reflecting the global interest in India’s AI-energy convergence model, the Indian government highlighted how Artificial Intelligence (AI) can become a game changer for India’s rapidly expanding Distributed Renewable Energy (DRE) sector.

What’s in Today’s Article?

• Understanding Distributed Renewable Energy (DRE)
• India’s Renewable Energy Landscape
• Why AI is Crucial for the Next Phase of Energy Transition
• AI as Development Infrastructure
• Governance and Regulation
• Defining Success - What Will AI-RE Convergence Achieve in the Next 2-3 Years?
• Key Challenges and Way Forward
• Conclusion

Understanding Distributed Renewable Energy (DRE):

• DRE refers to small-scale, decentralised renewable power systems (few kW to MW scale) located near the point of consumption — such as rooftop solar systems, small wind turbines, biomass-based units, and solar pumps.
• Unlike conventional centralised grids, DRE promotes energy decentralisation, local generation, and consumer participation.

India’s Renewable Energy Landscape:

• Key data points:
  • 52% (about 272 GW) of India’s total installed power capacity is now from non-fossil fuel sources.
  • Solar capacity: ~140 GW.
  • DRE: 38 GW. Nearly 18 GW was added in the DRE segment in the last 15 months.
• Public expenditure: Approximately $9 billion on rooftop solarisation, and $4 billion on PM-KUSUM.
• Major schemes driving DRE expansion: Pradhan Mantri Surya Ghar Muft Bijli Yojana, and Pradhan Mantri KUSUM Yojana.
• Enabling factors: This rapid scale-up was enabled through technology integration benefiting consumers, vendors, banks, field workers, and DISCOMs.

Why AI is Crucial for the Next Phase of Energy Transition?

• Structural challenges in the grid:
  • Transformers designed for unidirectional power flow. Emergence of ‘prosumers’ (consumers who also generate electricity). Increased stress on distribution networks.
  • Need for demand response management and predictive maintenance.
• AI applications in DRE:
  • AI can enable -
    • Weather forecasting and predictive analytics for solar generation.
    • Asset performance monitoring across geographies.
    • Peer benchmarking for rooftop systems.
    • B2B electricity trading enablement.
    • Predictive load management.
    • Grid stability management.
  • Government’s emphasis: AI will move the system from reactive governance to predictive governance — enabling India to “act, not react”.

AI as Development Infrastructure:

• AI should be viewed as core development infrastructure, similar to power grids, DISCOMs, and smart meters.
• This aligns with India’s digital public infrastructure (DPI) approach — suggesting the creation of an India Energy Stack, analogous to India Stack in fintech.
• Strategic vision:
  • Scale AI deployment — not treat it as pilot projects.
  • Position India as the “Google of AI for Energy” globally.
  • Build interoperable digital architecture for energy markets.

Governance and Regulation:

• Concerns:
  • Energy transition increases system complexity.
  • AI innovation does not automatically equal progress.
  • Poor digital regulation (e.g., social media concentration) led to Big Tech dominance.
• Key governance principles:
  • Open standards (like TCP/IP model).
  • Open-source AI systems.
  • Prevent monopolisation by global AI giants.
  • Promote local solutions tailored to farms, grids, and decentralised energy systems.
• This raises critical questions about data sovereignty, digital regulation, energy security, and technological self-reliance (Atmanirbhar Bharat).

Defining Success - What Will AI-RE Convergence Achieve in the Next 2-3 Years?

• Reduction in overall cost of power to consumers.
• Increased industrial competitiveness.
• Transition from consumer empowerment to prosumer empowerment.
• Grid readiness for high renewable penetration.
• Improved energy access and reliability.

Key Challenges and Way Forward:

• Legacy grid infrastructure constraints: Build an India Energy Stack - interoperable digital layers for generation, distribution, trading.
• DISCOM financial stress: Promote open-source AI ecosystem - encourage startups, enable local innovation, avoid concentration risks.
• Data governance, cybersecurity risks and risk of AI monopolisation: Strengthen regulatory frameworks - open standards, anti-monopoly safeguards, data privacy protections.
• AI-energy integration:
  • Invest in AI-driven grid modernisation - smart transformers, real-time load balancing, AI-based forecasting.
  • Integrate AI with climate goals - support India’s Net Zero 2070 target, align with Nationally Determined Contributions (NDCs).

Conclusion:

• India stands at the intersection of energy transition and digital transformation.
• With over half its installed capacity already non-fossil, and rapid growth in distributed renewable energy, the next phase will depend not just on adding capacity but on intelligently managing
• The convergence of AI and DRE may well determine whether India becomes a passive technology adopter — or a global leader shaping the future of sustainable, citizen-centric energy systems.