AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis
Mohd Bilal, M. Rizwan, Ibrahim Alsaidan, Fahad M. Almasoudi
Abstract
It is expected that the future transport will rely on electric vehicles (EVs) due to its sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes a number of other concerns, including a generation-demand mismatch, an increase in network active power loss, a degradation in voltage profile, and a decrease in voltage stability margin. To overcome the aforesaid issues, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is utmost important. The connection of an EVCS to the electricity grid will bring new challenges. Distributed generation (DG) sources are incorporated with EVCS to lessen the impact of EV charging load. In this study, charging stations are combined with DG units, which increases the motivation to use EVs. This study proposes an artificial intelligence (AI) approach, which is the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, for investigating the suitable nodes of EVCS and DGs in a balanced distribution system. The proposed methodology is verified on IEEE-33 bus and IEEE-69 bus system. According to the findings, the obtained results are consistent as compared to other existing techniques. These findings are taken into consideration to analyze the reliability of electrical distribution network. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the reliability of the electrical system.