A visual-inertial system to determine accurate solar insolation and optimal PV panel orientation at a point and over an area. (July 2020)

• Record Type: Journal Article
• Authors: Singh, Sarvesh Kumar; Lohani, Bharat; Arora, Lavish; Choudhary, Devendra; Nagarajan, Balasubramanian
• Is Part Of: Renewable energy. Volume 154(2020)
• Page Start: 223
• Page End: 238
• Publication Date: 2020-07
• Subjects: Solar insolation -- Viewshed -- Shading loss -- Atmospheric loss; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.02.107
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: To provide the best return on investments it is often required to assess the suitability of a site for installation of solar photovoltaic panel and quantify shading and atmospheric losses. The shading analysis is generally done using light detection and ranging or 3D geographic information system-based approaches which are cost-effective only for large-scale analysis. In several cases, particularly in developing countries, LiDAR data or 3D GIS are not available. In this study, a terrestrial image-based system is developed to accurately estimate solar insolation at a place. The positions of obstructions obtained using captured images are integrated with sun position model to provide solar insolation and optimal solar panel orientation. To further refine the result, the effect of sky conditions on the obtained solar insolation is also considered at monthly and yearly scale. About 40% reduction in solar insolation is observed due to shading which rose to 51% when the atmospheric conditions were included in the analysis of the selected sites. Further, an approach to estimate solar insolation over an area using some discrete point location is also presented and demonstrated. Results from 30 sites show that the obtained error in insolation estimate over an area is within 4%. Highlights: About 51% reduction in solar insolation was observed at the selected sites due to shading and atmospheric losses. The error in solar insolation estimate over an area using the given approach is within 4%. Optimal PV panel orientation based on shading analysis improves efficiency. Cloud affects the solar insolation at a place which can be quantified using cloud factor. Terrestrial images aid in locating obstructions with respect to sun position model.