Berlin EV Charger Placement Simulation

This project provides tools to simulate and visualize the optimal placement of Electric Vehicle (EV) chargers in Berlin using NSGA-II optimization.

Project Structure

• download_data.py: Downloads the latest charger location data from Berlin's WFS service.
• generate_results.py: Runs the NSGA-II optimization to determine optimal charger allocations.
• visualize.py: Generates heatmaps and plots based on the simulation results.
• requirements.txt: List of Python dependencies.

Setup

1. Install Dependencies: Ensure you have Python installed. Then run:

   pip install -r requirements.txt

Usage

1. Download Data

Download the latest charger locations:

python download_data.py

This will create chargers_locations.geojson.

2. Run Simulation (Optional)

Note: This step requires the raw BNetzA CSV file (e.g., Ladesaeulenregister_BNetzA_*.csv) to be present in the directory.

Run the optimization algorithm:

python generate_results.py

This will generate nsga2_all_k_results_GENERATED.csv.

3. Visualize Results

Generate charts and heatmaps using the existing results:

python visualize.py

This will create a simulation_outputs_final_geojson directory containing:

• Heatmaps (.html)
• Summary metrics (.csv)
• Plots (.png)

Data Sources

• Charger Locations: Berlin Open Data (WFS)
• District Boundaries: Berlin Geodaten