Microgrid Energy Trading Platform

A real-time energy trading simulation system that analyzes market conditions and generates intelligent BUY / SELL / WAIT recommendations using a decision engine.

This project demonstrates full-stack development with real-time data streaming, decision-making logic, and interactive visualization.

Overview

The system simulates a microgrid energy market where households can trade energy based on pricing conditions.

• Backend continuously generates market data and trading decisions
• WebSocket streams real-time updates to the frontend
• Frontend displays insights like recommendations, confidence, and risk levels

Key Features

• Real-time decision engine (BUY / SELL / WAIT)
• WebSocket-based live data streaming
• Confidence score and risk analysis
• Interactive dashboard visualization
• Notification system for decision updates
• Energy trading simulation between households

Tech Stack

Frontend

• React.js
• Recharts
• Axios

Backend

• FastAPI (Python)
• WebSockets
• Pydantic

Database

• SQLite

System Architecture

Backend (FastAPI) → Market Simulation + Decision Engine → WebSocket Stream → Frontend (React Dashboard)

Project Structure

app/ ├── api/ ├── core/ ├── models/ ├── schemas/ ├── services/ └── main.py

frontend/ ├── src/ ├── public/ └── vite.config.js

scripts/ └── simulation / utilities

requirements.txt microgrid.db

How It Works

1. Backend generates simulated energy market data
2. Decision engine analyzes:
   • Market price trends
   • Energy availability
   • Risk conditions
3. System produces recommendation:
   • BUY → purchase energy
   • SELL → sell energy
   • WAIT → hold position
4. Frontend displays:
   • Recommendation
   • Confidence level
   • Risk level
   • Expected outcome

Getting Started

1. Clone Repository

git clone https://github.com/your-username/microgrid-energy-trading-platform.git cd microgrid-energy-trading-platform

2. Backend Setup

cd app
pip install -r ../requirements.txt
uvicorn main:app --reload

Backend runs at: http://127.0.0.1:8000

3. Frontend Setup

cd frontend
npm install
npm run dev

Frontend runs at: http://localhost:5173

Screenshots

(Add your dashboard screenshots here)

Example Output

• Action: SELL
• Confidence: 96%
• Risk Level: LOW
• Expected Profit: +$0.61

Challenges Solved

• Real-time synchronization using WebSockets
• React hook state management issues
• Decision consistency across updates
• Preventing notification spam
• Handling asynchronous data flow

Future Improvements

• Machine Learning-based prediction model
• Cloud deployment (AWS / Render / Vercel)
• Authentication system
• Multi-user simulation
• Advanced trading strategies

Author

-Mehul Batham

-Yathartha Jain

Engineering Students (Information Technology)

License

This project is open-source and available for learning and experimentation.