International Journal of Frontiers in Engineering Technology, 2024, 6(4); doi: 10.25236/IJFET.2024.060408.
Guangyun Li1, Wenting Ning2
1School of Mechanical and Electrical Automotive Engineering, Yantai University, Yantai, 264005, China
2School of Automation and Information Engineering, Xi’an University of Technology, Xi'an, 710048, China
To alleviate the phenomenon of increased electric grid load and expanded fluctuations caused by the surge in electric vehicles (EVs), the author of this thesis proposes a bi-directional optimal scheduling strategy based on Vehicle to Grid (V2G) technology. The plan consists of a model and a reverse model. The goal of the model is to reduce the fluctuation, in electricity grid load with a unique focus on the percentage of decentralized energy, within the entire energy framework.In contrast, the reverse model aims to maximize user benefits, intending to enhance user participation and thereby promote the sustainable development of the strategy. This study utilizes a population genetic algorithm to address the issue and compares it with a multi objective particle swarm optimization algorithm. The findings indicate that the suggested approach not successfully mitigates fluctuations, in electric grid load and minimizes peak to valley variances but also optimizes the gains, for individuals engaging in V2G services.
Electric Vehicle; Vehicle to Grid; Electric Grid Load; Bi-directional Optimization; Distributed Energy
Guangyun Li, Wenting Ning. Bi-directional Optimal Scheduling of Electric Vehicles Based on V2G Technology. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 4: 47-52. https://doi.org/10.25236/IJFET.2024.060408.
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