“We don’t need to build our way out of the energy crisis. We need to outthink it.”
The International Energy Agency’s recent Energy and AI report carried a powerful insight—perhaps its most radical yet understated:
“Up to 175 GW of transmission capacity could be unlocked globally through AI-enabled optimisation—without building a single new line.”
This is more than a statistic. It’s a paradigm shift.
If AI can free up the equivalent of Japan’s entire power system, then surely India, with its infrastructure constraints and rising demand, stands to gain more than most.
The Indian Grid’s Hidden Power: Real Projects, Real Results
We are already seeing early glimpses of this AI-led revolution in India. Let’s look at five powerful case studies from across the country:
🛰️ 1. POSOCO + IIT Delhi – AI for Grid Forecasting
POSOCO’s collaboration with IIT Delhi has integrated AI to improve grid stability through better wind and solar forecasting.
- Impact: 10–12% improvement in load balancing
- Outcome: Fewer ramping errors, reduced thermal peaking
AI made variable renewables more predictable—and cheaper to integrate.
⚡ 2. MSETCL – Dynamic Line Rating in Maharashtra
MSETCL deployed AI and IoT-based sensors to trial Dynamic Line Rating (DLR), adjusting real-time line capacity based on weather and load.
- Result: 25% higher line throughput
- Savings: ₹400 crore in deferred transmission upgrades
DLR doesn’t just improve capacity—it buys time and saves capex.
🏙️ 3. BSES Delhi – AI + Smart Meters for Load Control
Delhi’s BSES discom uses AI on smart meter data to manage transformer-level loads.
- Outcome: Prevented overloads, optimised EV charging
- Savings: ₹150 crore in avoided asset upgrades
AI is not only scaling urban grids—it’s extending their lifespan.
🌬️ 4. Tamil Nadu – Forecasting RE to Cut Curtailment
With 9.5 GW of wind power, Tamil Nadu’s grid often faces curtailment. With AI-enhanced forecasting (via TANTRANSCO + IITM), this is changing.
- Result: Curtailment reduced by ~200 MWh/day
- Accuracy: Forecast improvements of 15%+
The key to RE integration isn’t more coal—it’s more code.
🧪 5. Greenko GridOS – Dispatchable RE Using AI
Greenko is using AI to manage dispatch across its hybrid solar, wind, and pumped hydro systems.
- Capability: Round-the-clock green power
- Clients: SECI, C&I buyers
- Impact: Displaces thermal balancing capacity
AI is making clean energy baseload-capable—something we once thought impossible.
National Potential: How Much Can India Unlock?
According to studies by ISGF, CEA, and Firstgreen estimates:
| Application | Capacity/Impact | National Equivalent |
|---|---|---|
| AI-enabled grid optimisation | ~30 GW by 2030 | 30 large coal plants |
| DSM + Smart Grid tools | ₹70,000 crore saved | CapEx avoided |
| Forecasting-driven RE use | 8–10% more RE absorption | 15–20 TWh/year |
The Policy Imperative: Think Digital First
India cannot afford to treat AI as a future nice-to-have. It is an immediate infrastructure multiplier. Here’s how we can act:
- Mandate AI forecasting in RE-rich states
- Incentivise DLR pilots under the revamped RDSS
- Standardise digital twins and congestion mapping tools
- Create a national program for AI skills in the power sector
The Big Picture: Intelligence, Not Infrastructure
AI will not replace physical infrastructure, but it will determine how much of it we truly need.
As India strives to meet its 500 GW non-fossil capacity target by 2030, we must complement solar panels and transmission towers with algorithms and sensors.
The future power grid of India won’t just be smart. It will be self-learning, self-correcting, and self-optimising.
We at Firstgreen Consulting believe the time to embrace AI is not later—it’s now.