BLACKOUT by hack ops

An intelligent web-based system for transmission planners to calculate and visualize real-time power line ratings, perform N-1 contingency analysis, predict daily load stress patterns, and leverage AI-powered recommendations. This system prevents grid blackouts by dynamically tracking line capacity, simulating outage scenarios, and identifying stress points before failures occur.

Project Overview

This prototype answers three critical questions:

1. Threshold Detection: At what ambient temperature or wind conditions do lines exceed their safe limits?
2. Stress Progression: Which lines or areas show stress first as ambient temperature increases?
3. Impact Assessment: How significant is the stress on the system?

Key Features

1. Interactive Geospatial Visualization

• Real-time interactive map displaying all transmission lines
• Color-coded lines by stress level (green=normal, yellow=caution, orange=high, red=critical)
• Hover tooltips showing line details
• Click for comprehensive line information including:
  • Current rating and loading percentage
  • Safe operating limits
  • Margin to overload
  • Weather conditions affecting the line

2. Weather-Based Dynamic Ratings

• Adjust ambient temperature, wind speed, and solar conditions
• Instant recalculation of line ratings using IEEE 738 standard
• Compare dynamic ratings vs static ratings
• Quick scenario buttons (Cool & Windy, Hot & Calm, Extreme Heat)

3. Threshold Analysis

• Find exact temperature where lines begin to overload
• Visualize loading progression with temperature changes
• Identify early warning indicators
• Graph showing stress development over temperature range

4. Alert & Insights Dashboard

• Prioritized list of top 10 lines approaching limits
• Real-time grid health status indicator
• Summary statistics (total lines, overloaded, high stress, caution)
• Actionable recommendations for operators
• System health classification

5. Transmission Line Outage Simulation (N-1 Contingency Analysis)

• Simulate single or multiple line outages
• AC/DC power flow analysis using PyPSA
• Identify cascading effects on remaining lines:
  • Newly overloaded lines
  • Loading changes for all active lines
  • Islanded buses (stranded substations)
  • Network stability metrics
• Visual outage maps showing before/after comparison
• Comprehensive impact reports with metrics

6. Daily Load Scaling Analysis

• 24-hour transmission system stress profiling
• Hourly power flow analysis with load variations
• Peak stress hour identification
• Load curve simulation (minimum at 6 AM, maximum at 6 PM)
• Convergence tracking for reliability assessment
• Most stressed lines by hour

7. AI-Powered Chatbot Assistant

• Natural language interface powered by Claude AI
• Context-aware responses using live grid data
• Capabilities:
  • Data explanation and technical guidance
  • Line status queries
  • Weather impact predictions
  • Variable impact analysis
  • Autonomous agent insights integration
• Suggested questions for quick analysis

8. Autonomous Grid Monitoring Agent

• Continuous grid state monitoring for anomalies
• Predictive alerts using weather forecasts
• Prioritized operator recommendations
• Learning from operator feedback
• Persistent state management across sessions
• Decision audit logging for compliance

Technical Architecture

Backend (Python + Flask)

• Dynamic Rating Engine: IEEE 738 standard implementation
• Data Processing: Pandas & NumPy for CSV/GeoJSON handling
• RESTful API: Flask with CORS support
• Power Flow Analysis: PyPSA integration for contingency analysis
• Load Profiling: Daily load scaling analyzer with hourly resolution
• Visualization: Plotly for interactive network maps
• AI Integration: Anthropic Claude API for chatbot and autonomous agent
• State Management: Persistent JSON storage for agent learning

Frontend (React + TypeScript)

• Interactive Maps: Plotly-based geospatial visualization with real-time updates
• Modern UI: React 18 with TypeScript and Hooks
• Component Library: Custom React components with Lucide icons
• Build Tool: Vite for fast development and optimized production builds
• Dark Theme: Professional dark mode interface
• Responsive Design: Mobile-friendly layout

Calculations

• IEEE 738 Standard: Thermal rating calculations
• Weather Parameters: Temperature, wind speed, solar radiation
• Conductor Properties: Resistance, diameter, MOT (Maximum Operating Temperature)
• MVA Conversion: √3 × I × V × 10⁻⁶

Project Structure

AEPhacks/
├── backend/
│   ├── app.py                      # Flask API server (port 5001)
│   ├── data_loader.py              # CSV/GeoJSON data loader
│   ├── rating_calculator.py        # IEEE 738 rating engine
│   ├── ieee738.py                  # IEEE 738 thermal calculations
│   ├── map_generator.py            # Plotly interactive map generation
│   ├── chatbot_service.py          # Claude AI chatbot integration
│   ├── agent.py                    # Autonomous monitoring agent
│   ├── outage_simulator.py         # N-1 contingency analysis
│   ├── load_scaling_analyzer.py    # Daily load profiling
│   ├── requirements.txt            # Python dependencies
│   └── data/
│       ├── lines.csv               # Transmission line data
│       ├── buses.csv               # Substation data
│       ├── generators.csv          # Generation units
│       ├── loads.csv               # Load points
│       ├── conductor_library.csv   # Conductor specs (IEEE 738)
│       ├── oneline_lines.geojson   # Line geographic coordinates
│       └── oneline_busses.geojson  # Bus locations
│
└── frontend/
    ├── src/
    │   ├── components/
    │   │   ├── AlertDashboard.tsx          # Real-time alerts
    │   │   ├── NetworkMap.tsx              # Plotly network visualization
    │   │   ├── WeatherAnalysis.tsx         # Weather controls & comparison
    │   │   ├── WeatherControlsAdvanced.tsx # Advanced weather inputs
    │   │   ├── OutageAnalysis.tsx          # Outage simulation UI
    │   │   ├── LoadScalingAnalysis.tsx     # Daily load profiling UI
    │   │   ├── Chatbot.tsx                 # AI assistant interface
    │   │   └── ThresholdAnalysis.tsx       # Temperature threshold charts
    │   ├── services/
    │   │   └── api.ts                      # Backend API client
    │   ├── App.tsx                         # Main application
    │   └── main.tsx
    ├── package.json
    └── vite.config.ts

Installation & Setup

Prerequisites

• Python 3.8 or higher
• Node.js 18 or higher
• npm or yarn

Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the server
python app.py

Backend API will be available at http://localhost:5001

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend will be available at http://localhost:5173

Usage

Live Grid Map

1. Open the application in your browser
2. The map displays all transmission lines color-coded by stress level
3. Adjust Weather: Use the controls in the right sidebar to modify:
   • Ambient Temperature (-10°C to 50°C)
   • Wind Speed (0 to 10 ft/s)
   • Time of Day (0-23 hours)
4. Quick Scenarios: Try preset weather scenarios
5. Explore Lines:
   • Hover over lines to see quick info
   • Click lines for detailed information panel

Threshold Analysis

1. Click the "Threshold Analysis" tab
2. Set temperature range to analyze (e.g., 20°C to 50°C)
3. Set wind speed conditions
4. Click "Run Analysis"
5. View:
   • First overload temperature
   • Progressive stress charts
   • Operational recommendations

API Endpoints

Core Endpoints

Method	Endpoint	Description
GET	/api/health	Health check
GET	/api/grid/topology	Get grid topology (lines & buses)
POST	/api/lines/ratings	Calculate line ratings for weather conditions
POST	/api/lines/threshold	Find temperature threshold for overloads
GET	/api/lines/<id>	Get specific line details

Outage Simulation Endpoints

Method	Endpoint	Description
GET	/api/outage/available-lines	List all lines for simulation
POST	/api/outage/simulate	Simulate line outage(s) with N-1 analysis
POST	/api/contingency/n1	Legacy N-1 contingency analysis

Load Scaling Endpoints

Method	Endpoint	Description
GET	/api/load-scaling/daily?hours=24	24-hour load profile analysis
GET	/api/load-scaling/hour/<hour>	Analyze specific hour (0-23)
GET	/api/load-scaling/profile	Get load profile without analysis

AI & Agent Endpoints

Method	Endpoint	Description
POST	/api/chatbot	AI chatbot with grid context
POST	/api/chatbot/analyze-impact	Variable impact analysis
GET	/api/agent/status	Autonomous agent status
POST	/api/agent/predict	Predict future grid states
POST	/api/agent/recommendations	Get AI recommendations
POST	/api/agent/feedback	Submit operator feedback

Map Generation Endpoints

Method	Endpoint	Description
POST	/api/map/generate	Generate interactive network map
POST	/api/map/line/<id>	Get line details for map
POST	/api/map/outage	Generate outage visualization map

Example: Calculate Line Ratings

Request:

curl -X POST http://localhost:5001/api/lines/ratings \
  -H "Content-Type: application/json" \
  -d '{
    "ambient_temp": 35,
    "wind_speed": 2.0,
    "wind_angle": 90,
    "sun_time": 14,
    "date": "21 Jun"
  }'

Response:

{
  "weather": {
    "Ta": 35,
    "WindVelocity": 2.0,
    "WindAngleDeg": 90,
    "SunTime": 14,
    "Date": "21 Jun"
  },
  "lines": [
    {
      "name": "L0",
      "branch_name": "ALOHA138 TO HONOLULU138 CKT 1",
      "rating_mva": 228.5,
      "flow_mva": 79.2,
      "loading_pct": 34.7,
      "stress_level": "normal",
      "margin_mva": 149.3,
      "voltage_kv": 138,
      "conductor": "ACSR 795"
    }
  ],
  "summary": {
    "total_lines": 186,
    "overloaded_lines": 3,
    "high_stress_lines": 12,
    "avg_loading": 45.3,
    "max_loading": 112.5
  }
}

Example: Simulate Outage

Request:

curl -X POST http://localhost:5001/api/outage/simulate \
  -H "Content-Type: application/json" \
  -d '{
    "outage_lines": ["L48", "L49"],
    "use_lpf": false
  }'

Response:

{
  "success": true,
  "outage_lines": ["L48", "L49"],
  "overloaded_lines": [...],
  "affected_lines": [...],
  "islanded_buses": [],
  "metrics": {
    "total_lines": 186,
    "outaged_lines_count": 2,
    "overloaded_count": 5,
    "max_loading_pct": 125.3,
    "max_loading_increase": 45.2
  }
}

Data Sources

The system uses the Hawaii40 synthetic grid test case from Texas A&M Electric Grid Test Cases database.

Data Files:

• osu_hackathon/hawaii40_osu/csv/lines.csv - Line properties
• osu_hackathon/hawaii40_osu/gis/oneline_lines.geojson - Geographic data
• osu_hackathon/ieee738/conductor_library.csv - Conductor properties
• osu_hackathon/hawaii40_osu/line_flows_nominal.csv - Nominal power flows

IEEE 738 Standard

The system implements the IEEE Standard 738-2006 for calculating the current-temperature relationship of bare overhead conductors.

Key Parameters:

• Ambient Temperature (Ta): Air temperature in °C
• Wind Speed (Vw): Wind velocity in ft/s
• Solar Radiation: Based on time of day and date
• Conductor Properties: Resistance, diameter, emissivity
• Maximum Operating Temperature (MOT): Line-specific thermal limit

Formula (simplified):

Rating (Amps) = f(Ta, Vw, Solar, Conductor, MOT)
Rating (MVA) = √3 × I × V × 10⁻⁶
Loading (%) = (Flow / Rating) × 100

Success Criteria

✅ Accuracy: Correct IEEE 738 calculations and overload detection ✅ Usability: Clear, intuitive visualization requiring minimal training ✅ Actionability: Specific insights and recommendations for operators ✅ Innovation: Real-time dynamic rating analysis with geographic context ✅ Performance: Sub-second response for rating recalculations ✅ Practical Value: Direct applicability to AEP operations

Demo Highlights

1. Normal Conditions (25°C, 2 ft/s wind): All lines green, ~35-40% average loading
2. Hot Day (35°C, 1 ft/s wind): Some lines turn yellow/orange, approaching limits
3. Extreme Heat (40°C, 0.5 ft/s wind): Critical lines turn red, overloads detected
4. Threshold Analysis: Identify exact temperature (e.g., 37°C) where first overload occurs

Configuration

Environment Variables

Create a backend/.env file for optional features:

# AI Chatbot (Optional - Claude API integration)
ANTHROPIC_API_KEY=your_api_key_here

# Autonomous Agent Configuration
AGENT_ENABLED=true
AGENT_STATE_PATH=backend/data/agent_state.json
AGENT_LOG_PATH=backend/data/agent_decisions.log
AGENT_PERSISTENCE=true

# Flask Configuration
FLASK_ENV=development
FLASK_DEBUG=true

Note: The application works without the API key, but AI chatbot features will be disabled.

Port Configuration

Default ports:

• Backend: http://localhost:5001
• Frontend: http://localhost:5173

To change ports:

• Backend: Edit backend/app.py line 1025
• Frontend API: Edit frontend/src/services/api.ts line 1

Troubleshooting

Port 5000 Conflict (macOS AirPlay)

Issue: macOS AirPlay Receiver uses port 5000 by default.

Solution: This app is configured to use port 5001 to avoid conflicts. If you still see port issues, verify:

• backend/app.py:1025 uses port=5001
• frontend/src/services/api.ts:1 uses http://localhost:5001/api

Backend Won't Start

Common solutions:

• Install dependencies: pip install -r requirements.txt
• Verify data files exist in backend/data/
• Check Python version: python --version (requires 3.8+)
• Check port availability: lsof -i :5001

Frontend API Errors

Common solutions:

• Ensure backend is running on port 5001
• Check browser console for CORS errors (Flask-CORS is enabled)
• Verify API endpoint URLs in frontend/src/services/api.ts

Missing Data Files

Ensure these files exist in backend/data/:

• lines.csv, buses.csv, generators.csv, loads.csv
• conductor_library.csv (required for IEEE 738 calculations)
• oneline_lines.geojson, oneline_busses.geojson (required for maps)

Future Enhancements

Phase 2 (Potential Additions)

• Real Weather API: Integration with NOAA/weather services for live data
• Historical Analysis: Time-series tracking of grid performance
• Alerts & Notifications: Email/SMS for critical conditions
• Export Reports: PDF generation for compliance and documentation
• Multi-Scenario Comparison: Side-by-side analysis of multiple configurations

Phase 3 (Advanced Features)

• Load Forecasting: ML models for demand prediction
• Renewable Integration: Solar/wind generation impact analysis
• Multi-Utility Support: Scale to multiple service territories
• Mobile App: iOS/Android native applications
• Real-Time Streaming: WebSocket integration for live grid updates

Development Team

BLACKOUT by hack ops - Built for the AEP Transmission Planning Hackathon

Technologies: React 18, TypeScript, Python, Flask, Plotly, PyPSA, IEEE 738, Anthropic Claude API, Pandas, NumPy, Vite

Key Capabilities:

• ✅ Real-time dynamic line ratings (IEEE 738-2006)
• ✅ Interactive geospatial network visualization
• ✅ N-1 contingency analysis with cascading failure detection
• ✅ Daily load scaling analysis (24-hour profiling)
• ✅ AI-powered chatbot with Claude integration
• ✅ Autonomous monitoring agent with learning capabilities
• ✅ Weather impact analysis and threshold detection
• ✅ Comprehensive API with 20+ endpoints

License

This is a prototype system developed for demonstration purposes.

Support

For questions or issues, please refer to:

• IEEE Standard 738-2006 documentation
• OSU Hackathon resources in osu_hackathon/ directory
• Backend API documentation in backend/README.md

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Remember: The goal is not just to display data, but to provide transmission planners with clear, actionable intelligence that helps them maintain grid reliability and prevent overloads before they occur.