Shadow modeling in urban environments for solar harvesting devices with freely defined positions and orientations

As a sustainable alternative regarding environmental impact, cost-effectiveness, and social integration, solar energy is expected to become an ever more ubiquitous part of our intricate human world. Dropping prices and growing demand are making it more viable for a variety of solar devices to be implemented in urban and other complex environments. From devices helping people meet their energy needs to solar-powered drones fulfilling urban services like maintenance, security, carrying goods, or even transporting people. These environments involve constrained and dynamic conditions, encouraging the use of solar harvesting devices that can freely adopt tailor-made positioning and tracking strategies to make the most of available resources. A crucial challenge is improving the geometrical flexibility and efficiency of modeling capabilities. In particular, developing practical approaches that account for detailed shadow effects in complex scenarios can be computationally challenging, and it is not clear how different approaches compare face-to-face in urban contexts and with freely defined harvesting surfaces. In this work, four shadow modeling approaches are developed and demonstrated in urban scenes of varying complexity; accuracy and precision are characterized versus computational cost; run-time trends are analyzed as functions of scene complexity, and energy estimation implications are examined. The approaches converge within 1% deviations, and the highest performing approach is three orders of magnitude faster than the most computationally costly. This work supports the selection and development of accurate, efficient, and flexible modeling frameworks that will play a role in enabling a diverse range of solar harvesting devices in challenging urban environments.