A method for design optimization of roof-integrated two-stage solar concentrators (TSSCs)

Abstract

The optimal performance of two-stage solar concentrators (TSSCs) as building-integrated energy solutions is achievable through an integrative design of TSSCs and the building geometry. Such a design process reflects a multi-criteria decision-making problem involving different stakeholders having domain-specific concerns. To support this, in this study, a method to perform a two-step, design optimization of building and TSSCs engineered concerning building geometry is presented by developing a parametric model and applying, a multi-objective optimization algorithm (NSGA-II). The method allows for the creation of different design solutions in several iterations using a population set of individuals (decision variables) related to building design (i.e., roof type and slope, building orientation, length, width, and building type), and TSSC design (type, geometric ratio, mirrors' separation distance, modules number, and receiver area). Thus in several iterations, the method enables finding optimal solutions i.e., building with maximum direct normal irradiance (DNI) and TSSCs with maximum average load match index (av.LMI), and minimum average covered roof area (av.CRA). The application of the method is shown in a case study of four different, hypothetical residential buildings under the climate settings of Berlin. These buildings are illustrative (descriptive), which depict theoretical volumes as abstract representations of future constructions. Thus, a set of simulation runs are performed according to different model settings, to enable early-stage design exploration of future/new constructions which differ in size (varying floor levels) and energy demand. Results indicate that most solutions reflect buildings of high DNI (more than 125 kW/m2/month) and TSSC of av.LMI from 0.4 to 4.2, and av.CRA from 8% to 47%. Nevertheless, the performance of TSSCs depends on the building type. The method enables a performance-driven design approach, searches for the most feasible solutions, and helps to meaningfully support the decision-making process.