Big Data Driven Optimal Sizing of Stand-alone Photovoltaic Energy Systems

被引:0
|
作者
Yuan, Qiheng [1 ]
Zhou, Keliang [1 ]
Lu, Wenzhou [2 ]
Yao, Jing [3 ]
机构
[1] Univ Glasgow, Sch Engn, Glasgow, Lanark, Scotland
[2] Jiangnan Univ, Sch Internet Things, Wuxi, Jiangsu, Peoples R China
[3] Univ Glasgow, Urban Big Data Ctr, Glasgow, Lanark, Scotland
来源
PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018) | 2018年
关键词
Stand-alone photovoltaic system; Energy storage; Big data simulation; Optimal sizing; WIND;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The issues of global climate change and energy security stimulate significant boost of renewable energy (RE) integration in the past decades. However, the mismatch between the intermittent renewable energy and time-varying load demand may cause power system operation instability and power supply unreliability. Energy storage devices (e.g. battery bank) is widely used to mitigate such supply-demand mismatch. It is a challenging topic to optimize the size of energy storage devices for efficient, reliable and cost-effective power supply in the presence of the intermittent renewable energy. Instead of complex intelligent algorithms, a big data driven approach is proposed to optimize the size of the battery bank in the standalone photovoltaic (SAPV) energy systems in this paper. The big data simulation based case studies which employ a mess of worldwide solar irradiation data indicates that, there exists a cut-off value for the battery bank capacity in the SAPV energy systems below the cut-off capacity, both reliability and efficiency of RE systems will grow rapidly with the increase of the battery bank capacity; above the cut-off value, both reliability and efficiency will intend to saturate with the increase of the battery bank capacity. Such big data driven approach provides a key to the optimal size of SAPV energy systems.
引用
收藏
页码:679 / 684
页数:6
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