PV Optimization with PyGAD and pandapower

This project optimizes photovoltaic (PV) placement and sizing on a power distribution network using a genetic algorithm (PyGAD) and simulates grid behavior with pandapower.

Stucture

• main.py: entry point
• optimizer/
  • ga_runner.py – GA configuration, baseline and WTGA implementations
  • crossover.py – Custom crossover operator for PV siting and sizing
  • fitness.py – Cost-based fitness function with stochastic scenarios
  • power_model.py – Simplified PV generation and demand model
• requirements.txt: reproducible environment

To run

pip install -r requirements.txt
python main.py