Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto, Canada

ABSTRACT

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.

KEYWORDS:

• Geographic information systems; photovoltaics; rooftop analysis; solar energy; solar modeling

Funding

This research was funded by an Engage Grant of the Natural Sciences and Engineering Research Council (NSERC) of Canada, and the Smart Net-Zero Energy Buildings Strategic Research Network (SNEBRN).

Acknowledgments

The authors would like to thank Ryerson University Planning Office, Shiv Tangri, Fabio Almeida, and Daniel Jakubek for their advice and help during the study. Special thanks goes towards David Forgione who started the preliminary work on this project and to John Glassmire from HOMER Energy for his technical expertise and contributions.

Additional information

Funding

This research was funded by an Engage Grant of the Natural Sciences and Engineering Research Council (NSERC) of Canada, and the Smart Net-Zero Energy Buildings Strategic Research Network (SNEBRN).