EV Charging Station Placement Optimization in Montreal

This project optimizes the placement of EV charging stations in Montreal using bio-inspired Genetic Algorithms (GAs). The objective is to minimize deployment costs while meeting traffic demand and station capacity constraints.

Features

• Optimization Methods: Implements Standard GA, Modified GA (with elitism), and Adaptive GA (dynamic parameter adjustment).
• Cost Minimization: Balances deployment, service, and penalty costs.
• Interactive Visualizations: Provides maps and plots to analyze optimized station placements.

Setup

Prerequisites

• Python 3.8+
• Required Libraries:
  • pandas
  • numpy
  • folium
  • geopandas
  • osmnx
  • networkx
  • matplotlib
  • geopy

Installation

Install the required libraries using pip:

pip install pandas numpy folium geopandas osmnx networkx matplotlib geopy