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Challenges estimating distributed solar potential with utilization factors: California universities case study

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  • Thai, Clinton
  • Brouwer, Jack

Abstract

Utilization factors are a popular method to identify distributed solar photovoltaic potential in many scopes. Though not the most accurate method, it is computationally inexpensive. In this work, Google’s Project Sunroof is reverse engineered to produce an image analysis model capable of predicting photovoltaic capacity for user-defined volumes. The developed model is tuned with a set of reference zip codes and applied to an adjacent set of zip codes, resulting in an error range from as small as 1% to as great as 30%. This analysis guides an investigation of how rooftop utilization factors vary across adjacent zip codes and building types. The findings provide the basis to question the accuracy of similar current and future analyses that extrapolate using fixed utilization factors to identify distributed solar photovoltaic potential between similar settings (e.g. one residential area to another). Sensitivity of these results are established by referring to actual and modeled installations’ utilization factors ranging from 0.28 to 0.89. Such variance suggests upmost caution or necessitates strong justification when assuming a utilization factor for distributed solar potential quantification. The range of utilization factors can be effectively reduced when considering multiple similar samples (e.g. several multi-functional higher education campuses). The results from the model are used to guide the justification of a utilization factor to determine the combined distributed photovoltaic capacities of University of California campuses. A total of 471 MW when liberally installing solar parking canopies and 345 MW when conservatively anticipating building development on parking lots is found.

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  • Thai, Clinton & Brouwer, Jack, 2021. "Challenges estimating distributed solar potential with utilization factors: California universities case study," Applied Energy, Elsevier, vol. 282(PB).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pb:s0306261920316056
    DOI: 10.1016/j.apenergy.2020.116209
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    2. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    3. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    4. Thebault, Martin & Desthieux, Gilles & Castello, Roberto & Berrah, Lamia, 2022. "Large-scale evaluation of the suitability of buildings for photovoltaic integration: Case study in Greater Geneva," Applied Energy, Elsevier, vol. 316(C).
    5. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    6. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    7. Xingyu Zhu & Yuexia Lv & Jinpeng Bi & Mingkun Jiang & Yancai Su & Tingting Du, 2023. "Techno-Economic Analysis of Rooftop Photovoltaic System under Different Scenarios in China University Campuses," Energies, MDPI, vol. 16(7), pages 1-18, March.

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