Dynamic urban solar harvesting: Anisotropic shadows in energy estimation

As one of the most sustainable alternatives 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 in photovoltaics that can harvest clean energy in a decentralized, safe, and modular manner are making it more viable for solar devices to be implemented in complex environments, such as urban settings. These scenarios involve constrained and dynamic conditions, encouraging the use of solar devices that can adopt arbitrary positions and personalized tracking behaviors to make the most of available resources. In modeling the available solar radiation for such conditions, some common simplifying assumptions may be limiting, in particular, not considering the anisotropic nature of diffuse shadows. We develop a variety of shadow modeling approaches for all anisotropic components of the radiation; four approaches for Beam radiation and three for Diffuse components. Through thousands of simulations in urban scenes of varying complexity, these approaches are tested, characterized, and compared in terms of accuracy, precision, and run-time efficiency. The Beam and Diffuse approaches converge within 1% and 5% deviations, respectively. Critical trade-offs are revealed between accuracy and run-time as a function of the type of approach and resolution. The highest performing approaches compared to the most computationally costly are approximately three orders of magnitude faster for Beam and 7 times faster for Diffuse. This work supports the development and selection of modeling frameworks that can be useful toward more widely adopted solar harvesting in the challenging human environment.

Automated summary

Solar energy is expected to play an increasingly important role in urban environments due to its sustainability and cost-effectiveness. This study develops shadow modeling approaches to accurately simulate solar radiation in complex urban scenes, taking into account the anisotropic nature of diffuse shadows. The approaches are tested and compared in terms of accuracy, precision, and run-time efficiency, revealing trade-offs between these factors. The highest performing approaches are significantly faster while maintaining acceptable accuracy levels. This research contributes to the development of modeling frameworks for widespread solar harvesting in challenging human environments.