This repository showcases a hybrid deep learning + reinforcement learning system for power grid optimization in Lauderdale County, AL. The system forecasts demand using a weather-informed LSTM model and trains a PPO-based agent to maintain stability and minimize blackout risk under stress.
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LSTM Demand Predictor
A deep bidirectional LSTM with attention, trained on 4 years of TVA and weather data.
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PPO Grid Policy
Trained in a custom PowerGridEnv with generator output, transformer tap, and load shedding control.
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Demand Data:
Sourced from the U.S. EIA (TVA region, 2021–2024)
- Demand, Net Generation, Day-Ahead Forecasts, Interchange
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Weather Data:
Daily min/max temperatures + precipitation
- From 5 major TVA-region airports via NOAA
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Architecture:
2-layer bidirectional LSTM + attention, followed by global pooling and dense layers.
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Key Features:
- Rolling temperature windows, demand lags
- Weekly mean demand, change rate
- Temp volatility, extreme flags
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Metrics:
| Metric | Value |
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| R² | 0.911 |
| RMSE | 19,565 MWh |
| Mean Error | 713 MWh (overbias) |
| Beats TVA Forecast | 70.08% of days |
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Environment:
PyPSA-based Lauderdale County grid
- 6 generators (Nuclear, Hydro, CCGT)
- Load centers with realistic demand shares
- Thermal constraints, ramp limits, marginal costs
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Action Space:
- Generator control
- Transformer tap shift
- Load shedding (up to 20%)
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Reward Design:
✅ Balance demand/supply, low thermal overload
❌ Penalize instability, overloads, excessive cost
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Training:
- Algorithm: PPO (SB3)
- Timesteps: 400,000
- VecNormalize, 5 eval episodes per 2048 steps
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Metrics:
| Metric | Value |
|---|
| Mean Reward | ~1480 |
| Explained Variance | Up to 0.85 |
| Blackout Risk | < 5% |
| Load Shedding | < 3% avg |
