Solar Harvesting Swarms in Dynamic and 3D Urban Environments: Modeling and Simulation

 Modern cities concentrate most of the world's population, energy demand, and human-induced greenhouse emissions. It is crucial to support a paradigm shift in which cities move beyond being energy consumers and strive to become sustainable producers of energy resources. 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, playing a key role in this energy transition. Swarms of interconnected solar harvesting devices may one day coordinate to provide urban services. These technologies may harvest solar energy as a sustainable open-ended source (e.g., powering households with building-integrated photovoltaics), or as a means to extend range and functionality (e.g., managing heat and daylight, or powering public lights, pollution sensors, traffic signs, and drones for urban deliveries). Most cities have only begun to tap into their solar energy potential. But the densely-populated urban environment is particularly complex and involves various phenomena that are difficult to capture, including many fixed and moving shadow-casting objects, highly constrained 3D spaces that are shared for multiple purposes, and dynamic conditions in frequent and sometimes unpredictable change. Therefore, it can be challenging to make the most of the available resources to improve the cost-effectiveness and efficiency of these urban technologies. To support the design, optimization, and evaluation of such systems, this project focuses on developing and characterizing new modeling and simulation capabilities for solar harvesting in urban environments. The goal is for these capabilities to balance and navigate the trade-offs between modeling accuracy, precision, efficiency, flexibility, and practicality, while maintaining theoretical consistency with anisotropic conceptualizations of solar irradiance. This is important because many common assumptions that are reasonable in more traditional open spaces may lead to insufficient geometrical flexibility, theoretical inconsistencies, or non-negligible inaccuracies in complex environments, especially concerning diffuse irradiance phenomena. The methodology involves systematically building a modeling framework with modules related to view factors, transmittance, beam and diffuse shadows, heat transfer equilibrium, and a virtual environment that emulates urban infrastructure and receives weather and radiation data as inputs. Novel mathematical and statistical models, algorithms, and programs are developed. Each module and the framework as a whole are evaluated and fine-tuned based on tens of thousands of simulations. These capabilities are integrated to build simulation tools, which enable the exploration of urban solar harvesting applications across a wide range of virtual scenarios from cities around the world, with different latitudes, weather patterns, and infrastructure complexity. The results indicate that anisotropic solar radiation phenomena have complex interactions that are crucial for maximizing solar harvesting in urban environments, that modeling these phenomena involves decisive trade-offs, and that these trade-offs can be leveraged to capture most of the complex dynamics with lower computational cost. The contributions of this project seek to support a more diverse and widespread integration of solar harvesting technologies into urban environments, which will hopefully help cities to become more resilient, efficient, and sustainable.