太阳成8722(中国)有限公司-GREEN NO.1

标题: Towards feasibility of photovoltaic road for urban traffic-solar energy estimation using street view image

作者: Ziyu Liu, Anqi Yang, Mengyao Gao, Hong Jiang, Yuhao Kang, Fan Zhang, Teng Fei*

来源出版物: Journal of Cleaner Production DOI: https://doi.org/10.1016/j.jclepro.2019.04.262 出版时间: 10 August 2019

摘要：A sustainable city relies on renewable energy, which promotes the development of electric vehicles. To support electric vehicles, the concept of charging vehicles while driving has been put forward. Under such circumstances, constructing solar panels on urban roads is an innovative option with great benefits, and the accurate calculation of road photovoltaic power generation is a prerequisite. In this paper, we propose a novel framework for predicting and calculating the solar radiation and electric energy that can be collected from the roads. Google Street View images are collected to measure the sky obstruction of roads which is integrated with the solar radiation model to estimate the irradiation receiving capability. In addition to sky obstruction, we also take the impact of traffic conditions and weather situations into consideration in the calculation. Radiation maps at different times in a year are produced from our work to analyze the roads photovoltaic distribution. In order to test the feasibility of our framework, we take Boston as a case study. Results show that roads in Boston can generate abundant electricity for all future electric vehicles in the city. What’s more, main roads through Boston exhibit better power generation potential, and the effect of the traffic condition is limited. Our calculation framework confirms that utilizing solar panels as road surfaces is a great supplement of city power with the unique ability to charge moving cars.

文献类型：Article

语种：English

通讯作者地址：Teng Fei , School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

通讯作者电子邮件地址：feiteng@whu.edu.cn

作者地址：

[Ziyu Liu, Anqi Yang, Mengyao Gao, Hong Jiang, Yuhao Kang, Teng Fei] School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

[Yuhao Kang] Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison, WI, 53706, USA

[Fan Zhang] Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 0213, USA

影响因子：5.651