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Optimization of Vehicle-to-Grid, Grid-to-Vehicle, and Vehicle-to-Everything Systems Using Artificial Bee Colony Optimization

Authors: Gebresenbet, Girma;

Optimization of Vehicle-to-Grid, Grid-to-Vehicle, and Vehicle-to-Everything Systems Using Artificial Bee Colony Optimization

Abstract

The integration of vehicle-to-grid (V2G), grid-to-vehicle (G2V), and vehicle-to-everything (V2X) systems into an energy ecosystem represents a transformative approach. These systems enable bidirectional energy flow between electric vehicles (EVs), power grids, and other entities. In this study, the energy sources for the V2G, G2V, and V2X systems were derived from green and blue energies, emphasizing sustainability. The primary objective of this research is to optimize V2G, G2V, and V2X systems, focusing on enhancing their performance. The novel contribution of this work lies in the application of advanced optimization techniques, specifically Artificial Bee Colony Optimization (ABCO), to improve system efficiency and stability. The system was simulated in MATLAB, where ABCO achieved a 64.5% improvement in reactive power optimization over Brain Emotional Intelligent Control (BEIC). This result underscores the effectiveness of ABCO in optimizing energy exchange within the V2G, G2V, and V2X systems, confirming its suitability for these applications. These findings highlight the potential of ABCO to enhance the performance of V2G, G2V, and V2X systems, contributing to a more sustainable, resilient, and efficient energy ecosystem.

Country
Sweden
Keywords

Energy Systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green