Topology-Induced Limits to Voltage Support: The Reactive Locality Barrier in Deep Reinforcement Learning

OVERVIEW:This repository contains the source code, environment models, and datasets required to reproduce the findings presented in the paper. The framework demonstrates how a Deep Reinforcement Learning (DRL) agent identifies topological barriers ("Dark Zones") in voltage control tasks. CONTENTS:1. create_network.py : Generates the 'stressed' IEEE 118-bus network model (pandapower json).2. grid_env.py : The Gym-compatible environment representing the power system dynamics.3. ppo_agent.py : Implementation of the PPO (Proximal Policy Optimization) algorithm.4. train_agent.py : Main training script featuring the curriculum learning and limit discovery logic.5. training_history.csv : The actual training logs used to generate the figures in the manuscript.6. sensitivity_table.csv: The open-loop sensitivity data confirming the "Dark Zone". HOW TO RUN:1. Install dependencies: pip install numpy pandas pandapower gym torch 2. Generate the network model: python create_network.py (This will create 'case118_stressed.json') 3. Run the training agent: python train_agent.py NOTE:The pre-trained model weights are provided in 'pre_trained_models.zip' for direct evaluation.