Impact of Plug-in Electric Vehicle Charging Stations Load on Distribution Network /

Abstract

To reduce greenhouse gas emissions and other hazardous substances produced by traditional engine-driven vehicles, the deployment of electric vehicles as a future method of transportation is crucial. One of the key factors in the widespread usage of electric vehicles is the installation of charging stations. Distribution system operators face a significant issue when trying to plan EVCSs optimally. Power losses and generation-demand mismatch rise with EV load adoption in the electrical grid. This has the effect of lowering the voltage level and decreasing the voltage stability margin. Integrating EVCSs at the proper locations is important for reducing the negative effects of growing EV load penetration on Radial Distribution Systems (RDS). The distribution system is negatively affected by the improper planning of EVCSs, which results in voltage variation and an increase in power losses. DG units are integrated with EVCSs to lessen this. A vital task for effective operation is reducing power losses in a distribution system to save energy. One of the most effective methods to reduce losses is distributed generation (DG). The DGs help maintain a voltage profile within accepted limits, which reduces power flows and losses and improves power quality and reliability. To prevent issues like protection, voltage rise, and increased power loss, the DGs should be optimally allocated and sized along with the EVCS. his work presents a strategy to reduce losses using numerous DGs and EVCS at the best possible placement and size. This analysis suggests the size and location of two DG units as well as EV charging stations. In this work, voltage magnitudes and power, losses are determined using the Backward and Forward Sweep (BFS) method. The optimal placement of the electric vehicle charging stations and distributed generators is determined by using the optimization technique known as the Particle Swarm Optimization (PSO) technique. The proposed work is tested on IEEE- 33 bus distribution network. The findings show that the suggested optimization technique enhances the system's performance and efficiency by ensuring the best possible DG and EV planning and operation. The outcome based on simulation findings is compared with other optimization techniques currently in use. The proposed Particle Swarm Optimization (PSO) algorithm shows 1.2x to 2.5x times better performance in reducing Power losses as compared to other existing techniques like simulated annealing, Genetic Algorithm and Ant Bee Colony.