A Comprehensive Workflow for High Resolution 3D Solar Photovoltaic Potential Mapping in Dense Urban Environment: A Case Study on Campus of Delft University of Technology. Issue 5 (5th November 2021)

• Record Type: Journal Article
• Authors: Zhou, Yilong; Verkou, Maarten; Zeman, Miro; Ziar, Hesan; Isabella, Olindo
• Is Part Of: Solar RRL. Volume 6:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-05
• Subjects: building integrated PV -- complex geometry -- electrical energy yield simulation -- modeling -- photovoltaic potential -- solar mapping -- urban PV; Solar energy -- Periodicals; Photovoltaic power generation -- Periodicals; Solar energy -- Research -- Periodicals; Photovoltaic power generation -- Research -- Periodicals; Periodicals; 333.7923
• DOI: 10.1002/solr.202100478
• Languages: English
• ISSNs: 2367-198X

Abstract

Abstract : Photovoltaic (PV) technology is the most promising renewable energy source to be integrated on urban building surfaces. Modeling and simulating urban PV systems pose more challenges than the conventional ones installed in open field due to rich urban morphology. Herein, a comprehensive workflow to estimate urban solar PV potential is developed where TU Delft campus is used as a case study. This workflow only requires light detection and ranging data and building footprints as data inputs, and multiple levels of result can be delivered including accurate geo‐referenced 3D building models, annual solar irradiation map, annual DC/AC yield maps and classified roof segments according to the specific yield of mounted PV system. The study reports a total of ≈8.1 GWh year −1 of PV energy which can be collected from campus building roofs and facades. Given the total electricity demand on the entire campus being 82.6 GWh/year, this PV potential can cover roughly 10% of the current electricity demand. The results constitute an initial assessment of solar PV potential on TU Delft campus buildings that is currently being used to prioritize PV integration on buildings and accelerate the transition toward a climate‐neutral campus. Abstract : Herein, a semi‐automatized workflow to estimate residential solar photovoltaic (PV) potential is presented. This workflow only requires light detection and ranging data and building footprints as inputs, and delivers accurate geo‐referenced 3D building models, an annual solar irradiation map, annual DC/AC yield maps, and classified roof segments according to the specific yield of a mounted PV system.