jacksonferrigno/Grid_AI

⚡ 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.

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📈 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.

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🧠 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

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🧮 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

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🤖 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

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