Herein, the application of a comprehensive modeling framework that can help optimize the design of multilayered optical filters for coloring photovoltaic (PV) modules is presented based on crystalline silicon solar cells. To overcome technical issues related to the implementation of color filters (CFs) on PV modules, like glare and color instability, colorimetry metrics, such as the hue, chroma, luminance color space, and the quantitative concept of difference between two colors are extensively deployed. It is showcased in this work that designing colored modules with high hue and chroma stability is possible by using a front-side texturing with edged geometry, like V-shaped grooves and inverted pyramids, while obtaining colors with relatively high luminance values, indicating good brightness. Furthermore, it is argued that adapting the rear surface of the front glass with a random textured layout where the CF is applied can improve color and luminance stability without significant loss of chroma while eliminating glare. Finally, the models can be used to optimize the number of layers for a given CF, reducing unnecessary optical losses. Compared to a standard PV module, performance simulation of optimized, bright-colored PV modules predicts relative energy yield losses ranging from 7% to 25%.

Integration of photovoltaics (PV) into the urban environment will play a major role in the energy transition. However, installing PV systems on building roofs can be challenging, particularly for monumental buildings with strict architectural and social value restrictions. Assessing roof surface visibility is, therefore, key to finding as much permitted roof surface area as possible that may be used for PV installation. In this study, a GIS-based large-scale visibility assessment tool is developed that can assist in evaluating roof visibility, using LiDAR, road networks, and cadastral data as inputs. The tool delivers multi-level outputs, including maps of roof binary visibility, roof visual amplitude, roof PV system layout, roof PV system AC yield, and roof PV module visibility. After optimization, an average speed of 0.12 s/m² is achieved. For each roof surface, an additional sensitivity analysis has been conducted. This step determines the optimal values for two visibility analysis parameters: assessment range and observer spacing, balancing the computational demand and result accuracy. Application of this workflow to the monumental buildings on the TU Delft campus revealed that approximately 2.68 GWh/year of electricity could be harvested from imperceptible PV modules, while an additional 0.42 GWh/year of energy is attributed to PV modules with medium visibility, and 0.37 GWh/year of energy is associated with PV modules with high visibility. This modeling workflow supports the multi-criteria decision-making process for urban roof PV planning.

Photovoltaic (PV) technology is the most promising renewable energy source to be integrated on urban building surfaces. Modeling and simulating urban PV systems pose more challenges than the conventional ones installed in open field due to rich urban morphology. Herein, a comprehensive workflow to estimate urban solar PV potential is developed where TU Delft campus is used as a case study. This workflow only requires light detection and ranging data and building footprints as data inputs, and multiple levels of result can be delivered including accurate geo-referenced 3D building models, annual solar irradiation map, annual DC/AC yield maps and classified roof segments according to the specific yield of mounted PV system. The study reports a total of ≈8.1 GWh year⁻¹ of PV energy which can be collected from campus building roofs and facades. Given the total electricity demand on the entire campus being 82.6 GWh/year, this PV potential can cover roughly 10% of the current electricity demand. The results constitute an initial assessment of solar PV potential on TU Delft campus buildings that is currently being used to prioritize PV integration on buildings and accelerate the transition toward a climate-neutral campus.