AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis

Abstract

It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power loss, a degradation in voltage profile, and decreased voltage stability margin. To overcome the issues mentioned earlier, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is essential. 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, the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, to investigate the suitable nodes for EVCS and DGs in a balanced distribution system. The proposed methodology is verified on the IEEE-33 bus and IEEE-69 bus systems. 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 networks. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the electrical system’s reliability.

Keywords

• Artificial intelligence
• electric vehicle
• charging stations
• radial distribution system
• distributed generators
• reliability