A new simplified vector-based model to support solar energy planning at urban scale (2019)

Evaluation of solar potential is a necessary step for integrating solar technologies in buildings in order to properly assess the benefits of harvesting solar energy and draw well-informed decisions in various design phases. Solar energy planning at urban scale requires large-scale solar analysis to support various decision-making contexts, such as making urban solar targets, prioritizing urban zones or buildings for solar integration, and optimizing solar technologies tailored for targeting buildings. Existing tools have the following major limitations to support such decision-making situations. (1) Current advanced simulation models based on ray trace and ray interception techniques are not effectively scalable to evaluate solar potential at urban scale due to the expensive modelling process and computational cost. (2) Simple and statistical models developed for large-scale analysis are not suitable to accurately predict solar irradiance on individual surfaces with proper consideration of urban shading and reflection. This dissertation addresses the need for developing scalable, efficient analysis methods to support the solar energy planning process. This dissertation has developed a simplified vector-based model that effectively predicts the solar potential of urban areas on the basis of consideration of the urban context. The proposed model is based on vector-based methods without the use of ray trace and ray interception techniques, and consists of new methods that suitably account for the non-uniform solar radiation of the sky, obstruction by urban surfaces, and reflection by urban surfaces in urban areas. The proposed model establishes three new methods to simplify the calculation in the context of urban applications: (1) a two-segment discretisation model, (2) an edge-angle detection obstruction model, and (3) a unified view-angle-based reflection model. This dissertation demonstrates the usability of the new model in supporting decision-making in the solar energy planning process. It addresses the following two hypotheses to examine the usability of the new model: (1) Simplified, vector-based model, tailored to urban applications, predict accurate solar radiation on urban surfaces to effectively support urban-scale analysis and (2) solar analysis with full representation of urban surroundings is necessary in the calculation of urban shading and solar reflection to correctly support distributed PV planning. For the first hypothesis, the performance of the method is compared against the advanced daylight simulation program RADIANCE and measurements obtained from controlled experiments. The first comparison demonstrates the new method provides flexible setting options for different resolution and prediction accuracy requirements and generates reasonably accurate predictions. The second comparison further confirms the prediction accuracy against the measurements for the horizontal and vertical surfaces under different shading and reflection conditions. The comparison with the ray interception approach demonstrates the computational efficiency of the proposed obstruction model for solar analysis that substantially reduces calculation iterations for detecting sky and building obstructions. For the second hypothesis, predictions and decisions derived by the developed method are compared against those by a lower fidelity models to investigate the importance of modelling urban shading and reflection with full representation of urban surroundings in three decision making contexts of urban-scale distributed PV planning process. Additionally, the second hypothesis is furthered examined and highlighted by investigating the effect of an additional dynamic PV model on decision-makings in comparison with the effect of the proposed high-fidelity solar radiation model for urban shading and solar reflection. The new model is demonstrated to enable cost-efficient solar potential analysis based on urban contexts for supporting solar energy planning at urban scale.