An Integrated Approach for 3D Solar Potential Assessment at the City Scale

Abstract

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions.

Keywords

• LiDAR point cloud
• 3D city modeling
• urban environment
• smart cities