Draz Abdelmonem, Othman Ahmed M, El-Fergany Attia A
Electrical Power and Machines Department, Zagazig University, Zagazig, 44519, Egypt.
Sci Rep. 2024 Sep 4;14(1):20571. doi: 10.1038/s41598-024-70063-9.
The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES's uncertainties on the selection criterion of the FCS's components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time.
全球交通运输电气化商业领域目前正蓬勃发展。利益相关者越来越关注电动汽车和电动巴士融入交通网络的情况。因此，迫切需要投资公共充电基础设施，特别是快速充电设施。因此，为了完善绿色环境组合，这些快速充电站（FCS）采用100%可再生能源（RES）进行设计。因此，本文提出了一种用于FCS技术经济评估的优化模型，该模型包括带有各种储能装置（ESD）的光伏和风力涡轮机。在这方面，由于飞轮和超级电容器具有高功率密度以及充放电循环和速率，因此使用它们来评估FCS的性能。然后，考虑各种技术和经济关键性能指标，实现这些分布式发电机的最优规模确定。之后，通过研究RES的不确定性对FCS组件选择标准、设计和容量的影响，使问题变得更加复杂。最后，作为致力于在线能源管理方法的一项工作，实施、验证并开展了一种基于径向基网络（RBN）的深度学习方法。与传统优化方法形成鲜明对比的是，RBN通过在相对较短的时间内获得最优解证明了其优越性。
