AegisStore AI is an AI-driven Hybrid Energy Storage Management Platform designed to address the challenge of limited battery storage during extended load demand.
Instead of relying solely on batteries, AegisStore AI intelligently orchestrates multiple storage technologies using Reinforcement Learning to maximize reliability, extend backup autonomy, and minimize battery degradation.
HYBRID HACK 2026
Limited Battery Storage Capacity During Extended Load Demand
Many hybrid renewable systems experience battery depletion and degradation during prolonged periods of high demand and low renewable generation. AegisStore AI addresses this challenge through intelligent multi-tier energy storage orchestration.
- Reinforcement Learning Powered Hybrid Storage Allocator
- Multi-Tier Energy Storage Architecture
- Lithium-Ion Battery Storage
- Hydrogen Energy Storage (PEM Fuel Cell)
- Flywheel Energy Storage
- Biomass Backup Generation
- Real-Time Digital Twin Dashboard
- Performance Comparison with Conventional Systems
- Simulation Ready Architecture
- Smart Grid Ready
Solar PV
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AI Hybrid Storage Allocator (HSA)
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Flywheel Battery Hydrogen Biomass
Storage Storage Fuel Cell Generator
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Load Demand
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Solar PV powers the load and charges storage devices.
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The Hybrid Storage Allocator continuously monitors:
- Load Demand
- Battery State of Charge (SOC)
- Hydrogen Level
- Flywheel Energy
- Solar Irradiance
- Biomass Availability
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The AI predicts demand duration.
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The Reinforcement Learning agent dynamically selects the optimal storage source.
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Power is delivered with maximum efficiency and minimum degradation.
- Load Demand
- Battery SOC
- Hydrogen Level
- Flywheel Energy
- Solar Irradiance
- Biomass Availability
| Action | Description |
|---|---|
| 0 | Use Flywheel |
| 1 | Use Battery |
| 2 | Use Hydrogen |
| 3 | Use Biomass |
- Reliability
- Efficiency
- Battery Degradation
- Unmet Load
- Energy Losses
| Metric | Target |
|---|---|
| Reliability | >99% |
| Backup Duration | 18-24 Hours |
| Battery Life Improvement | +30% |
| Unmet Load | <3% |
| LCOE | ₹6-8 / kWh |
| Carbon Emission Reduction | ~45% |
| Metric | Conventional System | AegisStore AI |
|---|---|---|
| Backup Duration | Low | High |
| Battery Degradation | High | Reduced |
| Reliability | 92-95% | >99% |
| AI Optimization | Rule-Based | Reinforcement Learning |
| Energy Efficiency | Moderate | Optimized |
| Unmet Load | Higher | <3% |
- React
- TypeScript
- Tailwind CSS
- Framer Motion
- Three.js
- Recharts
- ShadCN UI
- Python
- NumPy
- Pandas
- Gymnasium
- Stable-Baselines3
- Matplotlib
- MATLAB Simulink
- HOMER Pro
- GitHub
- Vercel
Energy Simulation Environment
Rule-Based Controller
Reinforcement Learning Agent
Digital Twin Dashboard
MATLAB Simulink Integration
Economic Analysis with HOMER Pro
Smart Grid & Vehicle-to-Grid Integration
- Hospitals
- Telecom Towers
- Data Centers
- Rural Microgrids
- Smart Cities
- Critical Infrastructure
- Digital Twin Technology
- Federated Reinforcement Learning
- Predictive Maintenance
- Vehicle-to-Grid Integration
- Smart Grid Deployment
- Autonomous Energy Markets
Place screenshots inside an assets/ folder.
assets/dashboard.png
assets/digital_twin.png
assets/training_visualization.png
assets/architecture.png
assets/comparison_chart.png
B.Tech Computer Science and Engineering
SRM Institute of Science and Technology
🏆 HYBRID HACK 2026
This project is licensed under the MIT License.
"Intelligence is not about storing more energy. It is about knowing where energy should come from and when."
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