基于BIM的光伏电站自动化布置及发电量评估

丁俊杰, 白建波, 李建, 王跃, 姜猛, 牛勇

太阳能学报 ›› 2022, Vol. 43 ›› Issue (5) : 305-311.

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (5) : 305-311. DOI: 10.19912/j.0254-0096.tynxb.2020-0913

基于BIM的光伏电站自动化布置及发电量评估

  • 丁俊杰1, 白建波1, 李建1, 王跃1, 姜猛2, 牛勇2
作者信息 +

BIM-BASED PHOTOVOLTAIC POWER STATION AUTOMATION LAYOUT AND POWER GENERATION EVALUATION

  • Ding Junjie1, Bai Jianbo1, Li Jian1, Wang Yue1, Jiang Meng2, Niu Yong2
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摘要

对光伏电站自动化布置的区域填充算法以及进行阴影分析的射线检测算法进行研究,进而以BIM技术为基础在Sketchup平台上实现光伏组件自动参数化布置及逐时阴影遮挡分析功能,并和实际电站的性能进行比较。研究结果表明:自动化布置算法和阴影分析算法符合电站实际应用情况,相对于传统的光伏电站性能模拟方法可视化性能有明显提升,此外基于实测气象数据模拟电站全年发电量和实测发电量的差异约为1.5%,具有一定精度。

Abstract

This paper presented an area filling algorithm for automatic layout of photovoltaic power stations and a ray detection algorithm for shadow analysis. The functions of PV modules automatic layout and hourly shadow analysis were implemented on Sketchup platform based on BIM technology, and then the results can be compared with actual PV power stations. The results showed that the automatic layout and the shadow analysis algorithms were in line with actual application of PV power stations. Compared with traditional PV station performance simulation methods, the visual degree of the presented work has a significant improvement. In addition, based on the measured meteorological data the difference between the simulated annual power generation and the actual power generation is about 1.5%, which indicates a certain accuracy of the proposed method.

关键词

光伏电站 / BIM / 发电量模拟 / 组件自动化布置 / 逐时阴影分析

Key words

PV power stations / building information modeling / power generation simulation / automated assembly of components / hourly shadow analysis

引用本文

导出引用
丁俊杰, 白建波, 李建, 王跃, 姜猛, 牛勇. 基于BIM的光伏电站自动化布置及发电量评估[J]. 太阳能学报. 2022, 43(5): 305-311 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0913
Ding Junjie, Bai Jianbo, Li Jian, Wang Yue, Jiang Meng, Niu Yong. BIM-BASED PHOTOVOLTAIC POWER STATION AUTOMATION LAYOUT AND POWER GENERATION EVALUATION[J]. Acta Energiae Solaris Sinica. 2022, 43(5): 305-311 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0913
中图分类号: TU18    TK51   

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基金

国家自然科学基金面上项目(51676063); 江苏省研究生创新基金(KYCX20_0526)

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