Abstract:The integration of electric vehicles (EVs) in the power grid has attracted considerable attention due to its potential benefits, such as demand response and power quality improvement. However, the intermittent and unpredictable nature of EVs’ charging and discharging behavior can cause significant challenges to the grid’s stability and power quality. This research study explores the use of a droop-ANN model to improve power quality in vehicle-to-grid (V2G) systems. The proposed approach integrates an artificia… Show more
As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.
As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.
The mission of vehicle-to-grid (V2G) within the context of a smart grid involves leveraging electric vehicles not only as modes of transportation but also as integral components of the energy ecosystem. V2G aims to establish a bidirectional energy flow between EVs and the grid, enabling these vehicles to not only draw power for charging but also feed excess stored energy back into the grid when required. This facilitates demand response, aids in grid stability by balancing supply and demand, promotes the integration of renewable energy sources, and supports grid resilience during peak demand or emergencies. Ultimately, the mission of V2G within the smart grid framework is to enhance grid reliability, optimize energy usage, and reduce environmental impact by maximizing the potential of electric vehicles as both consumers and providers of electricity. This chapter acts as a reference and guide for the forthcoming technological development and commercialization of electric vehicles (EVs), providing insights and recommendations for the future of smart grid transportation.
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