As renewables surge into the grid, their variability introduces new operational and economic challenges. Solar and wind don’t follow demand curves – and traditional grids weren’t designed for bidirectional, distributed energy flows.
AI makes smart grids truly intelligent – balancing volatility, unlocking flexibility, and enabling a new era of clean, reliable, real-time energy coordination.
🎯 How AI Can Make This Product or Solution Much Better
🌤️ Forecasting Renewable Energy Generation
AI uses weather, irradiance, and wind speed data to forecast solar and wind generation down to the minute.
This enhances day-ahead and intra-day planning, reducing reserve requirements and improving scheduling accuracy.
⚖️ Real-Time Grid Balancing and Voltage Stability
AI continuously manages voltage, frequency, and reactive power using DERs, inverters, and storage systems.
This prevents blackouts and maintains power quality in low-inertia, high-renewable grids.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist⛔ Dynamic Curtailment Minimization
AI predicts oversupply situations and shifts flexible loads or stores excess energy, helping reduce renewable curtailment.
It increases clean energy utilization and prevents grid saturation.
🌐 Automated DER Orchestration and VPP Control
AI aggregates and controls solar, wind, batteries, EVs, and loads into virtual power plants (VPPs).
These VPPs provide real-time balancing, peak shaving, and frequency response services.
🔍 Grid Congestion and Bottleneck Prediction
AI models grid topology to identify potential congestion or reverse power flows, rerouting energy before problems arise.
💹 Market Participation & Renewable Dispatch Optimization
AI helps clean energy assets decide how much to dispatch, when, and at what price, maximizing profits while maintaining grid stability.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| Intermittent solar and wind supply | AI improves forecast precision and adaptive control for real-time balancing |
| No inertia from inverter-based resources | AI coordinates synthetic inertia from batteries and responsive loads |
| Overloads in distribution networks | AI dynamically routes power and adjusts loads to prevent congestion |
| Small DERs can’t access energy markets | AI enables automated bidding through VPPs and DERMS platforms |
🤖 Main AI Tools and Concepts Used
- Machine learning for renewable forecasting
- Reinforcement learning for dispatch and control
- Graph neural networks for grid topology and congestion modeling
- Deep learning for inverter behavior and response
- AI-integrated Distributed Energy Resource Management Systems (DERMS)
📊 Case Studies
- Next Kraftwerke (Germany): Operates a 10,000+ unit AI-controlled VPP providing balancing services across Europe.
- Tata Power-DDL (India): Uses AI to forecast and balance rooftop solar in Delhi’s urban distribution network.
- National Grid ESO (UK): Applies AI to minimize curtailment and manage renewable variability across the UK grid.
🚀 Relevant Startups & Providers (TRL 8–9)
| Company | Highlights |
|---|---|
| AutoGrid (USA) | AI platform for DERs, VPPs, and real-time grid integration of renewables |
| Next Kraftwerke | Runs large-scale AI-driven VPPs with dynamic market bidding |
| Grid Edge (UK) | Predicts and aligns building load with grid signals and renewable supply |
| eSmart Systems | Monitors grid asset health and renewable performance for smarter maintenance |
💡 Want more?
Follow us to explore how AI is powering the next generation of smart, flexible, and renewable-friendly grids. From virtual power plants to curtailment control, we break down the future of clean energy – one insight at a time.
