Hybrid Genetic Algorithm-Modified Salp Swarm Algorithm to Determine the Optimal Location of Electric Vehicle Charging Station in Distribution Networks

Abstract

This study utilized a newly developed hybrid algorithm to ascertain the most favorable site for the installation of electric vehicle charging stations (EVCs). Random installation of EVCs may cause voltage disturbances, power quality disturbances and power losses in the distribution network. The hybrid genetic algorithm-modified salp swarm algorithm (HGAMSSA) was used to determine the optimal EVCs placement in the distribution network by considering power losses, network capacity and voltage regulation. HGAMSSA is a combination of GA and MSSA. In the first stage, the GA method distributes the population randomly and the second stage crosses the membership between dimensions. In the third stage, MSSA processes the best population from the GA to determine the best objective function. The HGAMSSA incorporates Optimal Power Flow (OPF) calculations based on Newton-Raphson. The OPF simulation uses a modified 20kV IEEE 33 bus distribution network. For validation, HGAMSSA was compared with four different algorithms: GA, PSO, SSA, and MPSO. There are three test scenarios with populations of 100, 50, and 20. As a result, the power loss generated as the best objective function wase 0.15715 MW. When the population is 100, HGAMSSA has the fastest iteration (5th) in achieving the best objective function compared with MPSO (13th) and SSA (17th). Furthermore, the best objective function can only be achieved by HGAMSSA when the populations are 50 and 20. This shows that HGAMSSA can explore and exploit populations performance to identify the optimal objective function.

Keywords

• Hybrid genetic algorithm-modified salp swarm algorithm (HGAMSSA)
• charging station
• electric vehicle
• OPF
• hybrid algorithm