| --- |
| tags: |
| - stable-baselines3 |
| - power-grid |
| - ppo |
| - lstm |
| - electricity |
| - reinforcement-learning |
| - forecasting |
| - tensorflow |
| - gym |
| license: mit |
| --- |
| |
| # ⚡ 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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|
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| ## 📈 Models |
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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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| ## 🧠 Dataset Overview |
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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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| --- |
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| ## 🧮 LSTM Model |
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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 | |
| |---------------|--------------------| |
| | R² | 0.911 | |
| | RMSE | 19,565 MWh | |
| | Mean Error | 713 MWh (overbias) | |
| | Beats TVA Forecast | 70.08% of days | |
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| ## 🤖 PPO DRL Agent |
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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 | |
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| --- |
