使用放射性基础深度学习的快速充电站集成的孤立微电网的最佳技术经济评估
Abdelmonem Draz1, Ahmed M Othman2, Attia A El-Fergany2
1Electrical Power and Machines Department, Zagazig University, Zagazig, 44519, Egypt. aaderaz@zu.edu.eg.
Scientific reports
|September 4, 2024
概括
这项研究优化了使用可再生能源 (RES) 和储能设备 (ESD) 的快速充电站 (FCS). 深度学习方法有效地确定电动汽车基础设施的最佳组件选择和容量.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 交通 电气化 交通 电气化
背景情况:
- 交通运输部门正在快速电气化,对公共收费基础设施的需求不断增加.
- 快速充电站 (FCS) 对电动汽车 (EV) 的采用至关重要,需要与可再生能源 (RES) 进行整合.
- 优化各种储能装置 (ESD) 的FCS技术经济性能对于可持续的运输至关重要.
研究的目的:
- 提出一种优化模型,用于对由RES和ESD驱动的FCS进行技术经济评估.
- 评估飞轮和超级电容器作为FCS中的ESD的性能.
- 调查RES不确定性对FCS设计和容量的影响.
主要方法:
- 包括光伏 (PV) 和风力轮机在内的FCS的技术经济评估模型.
- 飞轮和超级电容器的性能评估,用于高功率密度和充/放电循环.
- 考虑技术和经济关键绩效指标的分布式发电机的最佳尺寸.
- 深度学习方法使用辐射基础网络 (RBN) 进行在线能源管理和优化.
主要成果:
- 在考虑技术和经济因素的情况下,FCS组件的最佳尺寸得到了实现.
- 该研究分析了可再生能源不确定性对FCS设计的影响.
- 与传统方法相比,辐射基网络 (RBN) 在快速找到最佳解决方案方面表现出卓越的性能.
结论:
- 拟议的优化模型为技术经济评估FCS提供了一个框架.
- 飞轮和超级电容器由于其特点而适合用于FCS应用的ESD.
- 深度学习,特别是RBN,提供了一种高效有效的方法来管理和优化不确定性下的FCS操作.
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