Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

The significant contribution of buildings to global energy-related CO 2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO 2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

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Publication Details

Journal
Energy and Buildings
Published
2024-10-03
DOI
https://doi.org/10.1016/j.enbuild.2024.114863
Citations
21
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
5.57

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article

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

Ali Katal, Saeed Rayegan, Ursula Eicker, Liangzhu Wang et al.
21 citations
Energy and Buildings
Building Energy and Comfort Optimization
5.57
article

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

Ali Katal, Saeed Rayegan, Ursula Eicker, Liangzhu Wang, Radu Zmeureanu, Mohammad Mortezazadeh, Sepehrdad Tahmasebi
article en
21 citations

Abstract

The significant contribution of buildings to global energy-related CO 2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO 2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

Energy and BuildingsVol. 324
Concordia University (CA)
Canada First Research Excellence Fund, Natural Sciences and Engineering Research Council of Canada
Sustainable cities and communities
Openalex Percentile: Top 3%
Building Energy and Comfort Optimization
5.57
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