Recent research led by Syechu Dwitya Nugraha from the Department of Electrical Engineering at Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia, has unveiled a novel approach to optimizing the placement of electric vehicle charging stations (EVCs) in distribution networks. Published in ‘IEEE Access’, this study introduces a hybrid algorithm known as the Hybrid Genetic Algorithm-Modified Salp Swarm Algorithm (HGAMSSA), which aims to enhance the efficiency and reliability of EVC installations.
As electric vehicles become increasingly popular, the demand for strategically located charging stations has surged. However, randomly placed EVCs can lead to voltage disturbances, power quality issues, and increased power losses within the electrical distribution network. By employing HGAMSSA, the research team was able to determine the most effective locations for EVCs while considering crucial factors such as power losses, network capacity, and voltage regulation.
The hybrid algorithm combines elements of the Genetic Algorithm (GA) and the Modified Salp Swarm Algorithm (MSSA). In the initial phase, GA randomly distributes a population of potential solutions. This is followed by a crossover process that enhances the solutions between different dimensions. Finally, MSSA refines the best solutions identified by GA to arrive at the optimal placement for the charging stations. The study also incorporates Optimal Power Flow (OPF) calculations using the Newton-Raphson method, specifically testing its application on a modified 20kV IEEE 33 bus distribution network.
The results are promising. The HGAMSSA achieved a power loss objective function of just 0.15715 MW, demonstrating its effectiveness in optimizing charging station placement. Notably, when using a population of 100, HGAMSSA reached its best objective function in just five iterations, significantly outperforming other algorithms such as the Modified Particle Swarm Optimization (MPSO) and the Salp Swarm Algorithm (SSA), which took 13 and 17 iterations, respectively.
This research not only highlights the technical advancements in EVC infrastructure planning but also presents significant commercial opportunities for the energy sector. As cities and companies invest in electric vehicle infrastructure, the ability to minimize power losses and improve voltage regulation can lead to more reliable and efficient charging networks. This could result in lower operational costs and enhanced customer satisfaction, ultimately fostering greater adoption of electric vehicles.
Syechu Dwitya Nugraha stated, “HGAMSSA can explore and exploit populations’ performance to identify the optimal objective function.” This capability suggests that the algorithm could be a valuable tool for energy companies and urban planners looking to enhance their electric vehicle charging infrastructure strategically.
As the demand for electric vehicles continues to rise, the implications of this research extend beyond academic interest. The development and implementation of optimized charging station networks could play a crucial role in shaping the future of urban transportation and energy distribution. The findings from this study underscore the importance of innovative algorithms in addressing the challenges posed by the growing electric vehicle market, paving the way for a more efficient and sustainable energy landscape.
