Optimal location for an EVPL and capacitors in grid for voltage profile and power loss: FHO-SNN approach

In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for Electric vehicle (EV) charging stations (EVCS) and capacitors in power grids to enhance voltage stability and reduce power losses, facilitating sustainable and efficient grid operation. Meeting the increasing demand for EV charging stations while simultaneously addressing grid reliability, voltage control, and power loss issues poses significant challenges. A hybrid approach for EV parking lot and optimal location of capacitors in voltage and power loss is proposed in this manuscript. The proposed hybrid method combines the Fire Hawk Optimizer and Spiking Neural Network, hence called the FHO-SNN technique. The objective of the proposed method is used to lessen the active-power-losses that improve system performance. The novelty of this proposed method is electric vehicle parking lot and optimal location of capacitors in voltage and lessens the power loss. The proposed system is done in the MATLAB, and it is validates their performance with existing methods, such as Arithmetic Opti-mization Algorithm (AOA), Seagull Optimization (SO), and Atomic Orbital Search (AOS). The proposed method shows low convergence time, which is 4 sec, number of feeders, and best function node is found compared with other existing methods. The optimization approach can enhance the stability and efficiency of electric grids by improving voltage profiles and reducing power losses. The implications of this research highlight the potential for optimizing the combination of electric vehicles and capacitors into the grid, resulting in a more sustainable, efficient, and resilient energy and transportation system.

Automated summary

In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.