Update README.md

README.md

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