AI for Power – Accelerating the Clean Energy Transition

Abstract

The panel examined how artificial intelligence can speed India’s transition to a low‑carbon, resilient electricity system. Speakers highlighted concrete pilots—ranging from AI‑enabled demand‑flexibility for EV charging and water‑pumping, to minute‑level solar/wind forecasting, predictive asset health, digital twins, and data‑center load‑shifting. They explored how the ElectronVibe platform, the India Energy Stack, and emerging regulatory tools (sandboxes, performance‑based incentives, updated grid codes) can turn these pilots into scalable solutions for the Global South.

Detailed Summary

Moderator welcomed participants, thanked the Ministry of Electronics & Information Technology and the India AI Impact Summit, and introduced Dr. Mahesh Patankar to outline MP Ensystems’ activities.
Mahesh Patankar gave a brief overview of MP Ensystems’ work across transmission, distribution, generation, and resource adequacy, emphasising demand‑flexibility as a central theme.

2. AI Applications Delivering Immediate System‑Level Benefits

2.1 Forecasting Variable Renewable Generation

Siddharth Singh (IEA) identified three macro‑trends driving AI adoption: (1) accelerating electrification, (2) surge in variable renewables (solar & wind now ~30 % of global mix, projected 30 % by 2030), and (3) the need for affordable electricity.
He argued that AI is uniquely suited to handle the resulting complexity, especially minute‑by‑minute solar‑irradiance and wind‑speed predictions that go beyond traditional weather models.
Nalin Agarwal corroborated, noting India’s 15‑minute market intervals and the deviation settlement mechanism, which rewards accurate renewable forecasts.

2.2 Demand Response → Demand Flexibility

Mahesh Patankar reframed “Demand Response” as Demand Flexibility, stressing two‑way control: not only curtailing load but also strategically ramping up certain loads (e.g., electric arc furnaces, water‑pumping in Uttarakhand).
He cited a World Bank‑sponsored pilot in Uttarakhand where 40 % of state consumption comes from water‑pumping; AI‑driven flexibility could mobilise ~1 GW of load with up to 20 % usable for grid balancing.

2.3 Data Centres as Grid‑Friendly Assets

Siddharth Singh presented research estimating global data‑centre electricity use at 1.5 % of generation (rising to 3 % by 2030), with the AI portion expected to grow five‑fold.
He highlighted that data‑centres can shift 20 % of peak demand across geographic locations, but regulatory frameworks to signal flexibility are currently weak.
Namrata Mukherjee (ISA) added that many large operators already purchase 100 % renewable electricity on an annual basis, yet hour‑by‑hour matching remains a challenge; emerging “hourly renewable” targets are driving new flexibility markets.

2.4 Predictive Asset Health & Maintenance

Abhishek Ranjan (BRPL) described BRPL’s nine‑year journey from AI‑driven demand forecasting (day‑ahead & intraday) to real‑time spot‑market price forecasting, achieving 70‑80 % cost reduction in power‑purchase costs.
He detailed a predictive‑prescriptive maintenance platform that uses drone/satellite imagery, smart‑meter data, and machine‑learning models to anticipate feeder failures, now deployed on 23 kV and 11 kV feeders.

2.5 Digital Twins & Smart‑Meter Rollout

Namrata Mukherjee explained ISA’s digital utility‑twin pilot in Jaipur, funded through a multi‑donor trust fund (G‑App, Sequoia Capital, etc.). The twin integrates smart‑meter data, loss‑reduction analytics, and distribution‑level optimisation.
Nalin Agarwal highlighted India’s 250 million smart‑meter programme, signalling the foundational data layer for AI applications.

3. Innovation Platforms & Scaling Mechanisms

3.1 ElectronVibe & AI‑for‑Power Innovation Platform

Rishi Nair introduced ElectronVibe, a Climate Collective‑run open‑innovation platform that matches utilities with startups. Since 2020 it has worked with 22 utilities and 63 startups, resulting in 20 pilots.
The platform comprises three pillars:
1. ElectronVibe Programme – continuous pilots.
2. Knowledge Hub – shared lessons‑learned (what works, what fails).
3. Solutions Marketplace – utilities can instantly access vetted AI tools and share anonymised aggregated data with innovators.

3.2 India Energy Stack (IES) – A Coordinated Digital Infrastructure

Sujith Nair framed the India Energy Stack as a set of five core digital building blocks (interoperability standards, data governance, performance‑based incentives, regulatory sandboxes, and open‑source AI tools).
He argued that without a common “coordination fabric”, utilities would be forced to create bespoke solutions for each use case, dramatically raising costs.

4. Policy, Regulation, and Enabling the AI‑Powered Grid

Policy Lever	Speaker	Key Points
Regulatory Sandboxes & Concessional Finance	Nalin Agarwal	Sandboxes allow rapid testing of P2P trading, blockchain‑based markets; concessional funding de‑risches pilots.
Mandatory Renewable Forecasting & Grid‑Code Updates	Nalin Agarwal	Calls for regulatory mandates on forecasting accuracy; grid codes must evolve to accommodate 5‑minute/1‑minute markets.
Performance‑Based Incentives	Nalin Agarwal	Tie utility rebates to measurable outcomes (e.g., reduced RE curtailment, forecasting accuracy, outage reduction).
Data & AI Governance	Sujith Nair	Need privacy‑secure, interoperable data standards; promote open‑AI tools as public infrastructure.
Consumer‑Centric Markets	Nalin Agarwal	Design flexible market mechanisms enabling prosumers (e.g., rooftop solar, EVs, data‑centres) to provide peak‑shaving, voltage support.
Capacity‑Building for Regulators	Nalin Agarwal	State‑level regulators lack AI expertise; systematic training programmes are essential.

5. From Pilots to Large‑Scale Deployments

Mahesh Patankar emphasized the importance of scalable pilot design (e.g., water‑pumping flexibility in Uttarakhand) that can be replicated across states.
Rishi Nair noted that commercialising pilots requires bridging data gaps, legacy‑system integration, and procurement simplification—hence the Solutions Marketplace.
Namrata Mukherjee stressed that South‑South learning (e.g., ISA work in 126 countries) can accelerate deployment, but each country must first build a digital data layer.

6. Bottlenecks & Risks

Barrier	Speaker(s)	Insight
Skills Gap	Siddharth Singh (survey), Nalin Agarwal	Energy‑sector salaries are roughly ½ of tech‑sector salaries, leading to talent drain.
Data Access & Quality	Sujith Nair, Siddharth Singh	Smart‑meter roll‑out is crucial, yet data silos and privacy concerns hinder AI model training.
Compute & Cybersecurity	Siddharth Singh, Sujith Nair	Large AI models demand compute; security of grid‑critical AI systems needs robust safeguards.
Regulatory Alignment	Nalin Agarwal	Without clear mandates (e.g., forecasting requirements) utilities lack incentives to adopt AI.
Coordination Costs	Sujith Nair	Without common building blocks, each utility would develop its own solution, inflating costs.

7. Vision for the Next Five Years

Abhishek Ranjan envisages a self‑organising, consumer‑centric energy system where the marginal cost of delivering affordable, reliable power approaches zero, enabled by AI‑driven coordination.
Sujith Nair sees the India Energy Stack as the “digital public infrastructure” that will allow inter‑utility data exchange, standardised AI services, and performance‑based funding, all underpinned by interoperability principles.
Rishi Nair and Namrata Mukherjee highlighted the potential for AI to foster new livelihoods (e.g., rooftop solar entrepreneurs, flexible‑load aggregators) reminiscent of the telecom and banking revolutions.

8. Closing Remarks

The moderator thanked participants, noting the convergence of AI, renewable integration, and policy innovation as the cornerstone of India’s clean‑energy transition.
He underscored that while the session had to end early, the dialogue reflects an urgent, collaborative effort across utilities, startups, regulators, and international partners.

Key Takeaways

AI is already delivering tangible value in India through high‑resolution renewable forecasting, demand‑flexibility programs, and predictive asset health monitoring.
Demand Flexibility (not just curtailment) is emerging as a critical tool; pilots such as Uttarakhand’s water‑pumping flexibility demonstrate gigawatt‑scale potential.
Data centres can act as flexible, high‑value loads, but require regulatory signals and proper siting to become grid assets rather than burdens.
The ElectronVibe platform and India Energy Stack provide structured pathways to scale pilots, share knowledge, and offer utilities ready‑made AI tools.
Policy levers—mandatory forecasting, updated grid codes, performance‑based incentives, and regulatory sandboxes—are essential to move from isolated pilots to nationwide roll‑outs.
Smart‑meter rollout (250 M devices) and digital utility twins form the data backbone needed for AI‑driven decision‑making.
Skills shortage is the most frequently cited bottleneck; aligning compensation and training with the tech sector is critical.
South‑South collaboration (ISA’s multi‑donor trust fund, Climate Collective’s accelerator network) can accelerate learning, but each market must first establish a digital data layer.
A common set of interoperable building blocks (as defined in the India Energy Stack) can dramatically reduce coordination costs and enable population‑scale AI solutions.
In the next five years, AI‑enabled grids could become self‑organising, consumer‑centric ecosystems, lowering the marginal cost of clean electricity and unlocking new economic opportunities.

See Also:

India AI Impact Summit 2026

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