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tags: |
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- stable-baselines3 |
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- power-grid |
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- ppo |
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- lstm |
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- electricity |
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- reinforcement-learning |
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- forecasting |
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- tensorflow |
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- gym |
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license: mit |
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--- |
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# ⚡ Power Grid Optimization with LSTM + PPO |
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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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## 📈 Models |
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- **LSTM Demand Predictor** |
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A deep bidirectional LSTM with attention, trained on 4 years of TVA and weather data. |
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- **PPO Grid Policy** |
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Trained in a custom `PowerGridEnv` with generator output, transformer tap, and load shedding control. |
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## 🧠 Dataset Overview |
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- **Demand Data:** |
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Sourced from the U.S. EIA (TVA region, 2021–2024) |
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- Demand, Net Generation, Day-Ahead Forecasts, Interchange |
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- **Weather Data:** |
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Daily min/max temperatures + precipitation |
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- From 5 major TVA-region airports via NOAA |
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## 🧮 LSTM Model |
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- **Architecture:** |
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2-layer bidirectional LSTM + attention, followed by global pooling and dense layers. |
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- **Key Features:** |
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- Rolling temperature windows, demand lags |
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- Weekly mean demand, change rate |
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- Temp volatility, extreme flags |
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- **Metrics:** |
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| Metric | Value | |
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|---------------|--------------------| |
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| R² | 0.911 | |
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| RMSE | 19,565 MWh | |
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| Mean Error | 713 MWh (overbias) | |
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| Beats TVA Forecast | 70.08% of days | |
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## 🤖 PPO DRL Agent |
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- **Environment:** |
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PyPSA-based Lauderdale County grid |
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- 6 generators (Nuclear, Hydro, CCGT) |
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- Load centers with realistic demand shares |
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- Thermal constraints, ramp limits, marginal costs |
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- **Action Space:** |
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- Generator control |
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- Transformer tap shift |
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- Load shedding (up to 20%) |
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- **Reward Design:** |
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✅ Balance demand/supply, low thermal overload |
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❌ Penalize instability, overloads, excessive cost |
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- **Training:** |
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- Algorithm: PPO (SB3) |
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- Timesteps: 400,000 |
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- VecNormalize, 5 eval episodes per 2048 steps |
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- **Metrics:** |
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| Metric | Value | |
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|--------------------|-----------| |
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| Mean Reward | ~1480 | |
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| Explained Variance | Up to 0.85 | |
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| Blackout Risk | < 5% | |
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| Load Shedding | < 3% avg | |
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