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NSGA-II Algorithm for Optimal EV Charging Station Placement

This project utilizes the NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm II) for optimizing the placement of electric vehicle (EV) charging stations on a road network. The optimization is performed using multiple parameters, such as station locations, battery capacities, and other factors, with the goal of achieving the most efficient charging network design.

Stage 1: Finding the Optimal Charging Station Locations

  • stage1_NSGA-II.py: The first stage of the NSGA-II algorithm is implemented here, aiming to find the best locations for the EV charging stations. The results are stored in a table in tables_output.txt.

  • probabilities.py and weights_for_station.py: These files are used to design the tables used in Stage 1, based on relevant research papers and literature.

Stage 1 Results:

After multiple runs of the algorithm, the best solutions (best_solutions) were found, which include the station locations and their evaluation based on several factors.

Stage 2: BESS Capacity Calculation

  • stage2_BESS_from_PV.py: In the second stage, the appropriate BESS (Battery Energy Storage Systems) capacity for each best solution is calculated. This calculation depends on the photovoltaic voltage at each charging station.

Solution Classification and Analysis

  • solutions_mif_sorted.py: In this file, the best solutions, along with BESS characteristics and other parameterization factors, are sorted using the μ factor. This allows extracting the most reliable and efficient solutions for the electric vehicle charging network.