Optimal allocation of electric vehicle charging stations and distributed generation in radial distribution networks DOI Creative Commons
Ismail A. Soliman,

Vladimir N. Tulsky,

Hossam A. Abd el‐Ghany

et al.

Engineering Science and Technology an International Journal, Journal Year: 2024, Volume and Issue: 60, P. 101907 - 101907

Published: Nov. 20, 2024

Language: Английский

Techno-economic analysis of vehicle-to-grid technology: Efficient integration of electric vehicles into the grid in Portugal DOI
Diogo Melo Gomes, Rui Costa Neto

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 97, P. 112769 - 112769

Published: July 1, 2024

Language: Английский

Citations

12
Review of vehicle to grid integration to support power grid security DOI Creative Commons
Ye Yang, Wen Wang, Jian Qin

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 2786 - 2800

Published: Sept. 2, 2024

Language: Английский

Citations

9
Analyzing the impact of battery technical performance and driving conditions on the overall economic feasibility of a Vehicle-to-Grid (V2G) system implemented in the Japan electric power exchange (JEPX) market DOI Creative Commons

Muhammad Arsalan,

Jacob Etoju,

Zifei Nie

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100980 - 100980

Published: March 1, 2025

Language: Английский

Citations

0
Decision Making and Energy Storage System Management DOI
Ismail Elabbassi,

Mohamed Khala,

Naima El Yanboiy

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 69 - 80

Published: Feb. 28, 2025

This study evaluates the effectiveness of various machine learning strategies in managing energy Fuel Cell Electric Vehicles (FCEVs), focusing on fuel cell and battery inverter behaviour. The analysis compares four methods Gaussian Naive Bayes (NB), Random Forest, k-NN, AdaBoost using key metrics: Recall, f1-score, precision. NB Forest achieve identical performance for (Recall: 0.87, f1-score: 0.82, precision: 0.89) 0.66, 0.57, 0.5). In contrast, k-NN achieves a precision 0.74, while excels with 0.98 0.94. also outperforms other f1-score (0.98 cell, 0.90 battery) recall (0.95 0.84 battery), highlighting its superior behaviour control.

Language: Английский

Citations

0
Multi objective optimization of grid connected photovoltaic and V2G operation based on the influence of schedulable capacity DOI Creative Commons
Yunhan Cai,

Hongliang Hao,

Zhongkang Zhou

et al.

International Journal of Electrochemical Science, Journal Year: 2025, Volume and Issue: unknown, P. 101030 - 101030

Published: April 1, 2025

Language: Английский

Citations

0
Investment Decision Analysis Approach for Urban V2G Projects Based on Value Network DOI

Suliang Liao,

Yuping Huang, Weijia Yang

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106440 - 106440

Published: May 1, 2025

Language: Английский

Citations

0
Charge and discharge scheduling method for large-scale electric vehicles in V2G mode via MLGCSO DOI Creative Commons

Songling Pang,

Kaidi Fan,

Meimei Huo

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 9, 2025

Language: Английский

Citations

0
Optimal allocation of electric vehicle charging stations and distributed generation in radial distribution networks DOI Creative Commons
Ismail A. Soliman,

Vladimir N. Tulsky,

Hossam A. Abd el‐Ghany

et al.

Engineering Science and Technology an International Journal, Journal Year: 2024, Volume and Issue: 60, P. 101907 - 101907

Published: Nov. 20, 2024

Language: Английский

Citations

2