EVCS Allocation and DG Penetration with System Reconfiguration from Reliability Point of View

In this paper, a novel energy management system is proposed to optimally allocate Electrical Vehicles Charging Stations (EVCS) in conjunction with Distributed Generations (DGs) to enhance the reliability and performance of distribution networks. The innovation of this work lies in the integrated approach of using distributed system reconfiguration alongside EVCS and DG allocation, specifically targeting the reduction of system interruptions as measured by the Average Energy Not Supplied (AENS) reliability index. Unlike traditional methods, our approach considers the increasing complexity of EV integration and its impact on distribution network losses. By applying advanced optimization techniques such as the Grey Wolf Optimizer (GWO) and Artificial Gorilla Troops Optimizer (AGTO), we achieve significant improvements in system reliability and loss reduction. Additionally, a novel Plant Growth Simulation Algorithm (PGSA) is introduced to further enhance distribution system reliability through network reconfiguration. The IEEE 69-bus system is utilized as a standard testbed to validate the effectiveness of the proposed methods, showcasing their potential to address the challenges of modern power distribution networks.

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Publication Details

Published
2024-10-19
DOI
https://doi.org/10.1109/niles63360.2024.10753221
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.33
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article

EVCS Allocation and DG Penetration with System Reconfiguration from Reliability Point of View

R.A. Swief, Ibrahim Sultan, T. S. Abdel-Salam, M. Ezzat
1 citations
Fault Detection and Control Systems
0.33
article

EVCS Allocation and DG Penetration with System Reconfiguration from Reliability Point of View

R.A. Swief, Ibrahim Sultan, T. S. Abdel-Salam, M. Ezzat
article en
1 citations

Abstract

In this paper, a novel energy management system is proposed to optimally allocate Electrical Vehicles Charging Stations (EVCS) in conjunction with Distributed Generations (DGs) to enhance the reliability and performance of distribution networks. The innovation of this work lies in the integrated approach of using distributed system reconfiguration alongside EVCS and DG allocation, specifically targeting the reduction of system interruptions as measured by the Average Energy Not Supplied (AENS) reliability index. Unlike traditional methods, our approach considers the increasing complexity of EV integration and its impact on distribution network losses. By applying advanced optimization techniques such as the Grey Wolf Optimizer (GWO) and Artificial Gorilla Troops Optimizer (AGTO), we achieve significant improvements in system reliability and loss reduction. Additionally, a novel Plant Growth Simulation Algorithm (PGSA) is introduced to further enhance distribution system reliability through network reconfiguration. The IEEE 69-bus system is utilized as a standard testbed to validate the effectiveness of the proposed methods, showcasing their potential to address the challenges of modern power distribution networks.

Ain Shams University (EG)
Openalex Percentile: Top 39%
Fault Detection and Control Systems
0.33
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EVCS Allocation and DG Penetration with System Reconfiguration from Reliability Point of View — R.A. Swief, Ibrahim Sultan, et al. · (2024) | TGRS Research Map | TGRS