• Energy Research

spectrapepper is a Python package that aims to ease and accelerate the use of advanced tools such as machine learning and combinatorial analysis, through simple, straightforward, and intuitive code and functions. This library includes a wide range of tools for spectroscopic data analysis in every step, including data acquisition, processing, analysis, and visualization. Ultimately, spectrapepper enables the design of automated measurement systems for spectroscopy and the combinatorial analysis of big data through statistics, artificial intelligence, and machine learning. 

AbstractFlexible photovoltaic (PV) devices, such as those based on Cu (In,Ga)Se2 (CIGS) and perovskites, use polymeric front sheets for encapsulation that do not provide sufficient protection against the environment. The addition of nanometric AlxO layers by spatial atomic layer deposition (S‐ALD) to these polymeric materials can highly improve environmental protection due to their low water vapor transmission rate and is a suitable solution to be applied in roll‐to‐roll industrial production lines. A precise control of the thickness of the AlOx layers is crucial to ensure an effective water barrier performance. However, current thickness evaluation methods of such nanometric layers are costly and complex to incorporate in industrial environments. In this context, the present work describes and demonstrates a novel characterization methodology based on normal reflectance measurements and either on control parameter‐based calibration curves or machine learning algorithms that enable a precise, low‐cost, and scalable assessment of the thickness of AlOx nanometric layers. In particular, the proposed methodology is applied for precisely determining the thickness AlOx nanolayers deposited on three different substrates relevant for the PV industry: monocrystalline Si, Cu (In,Ga)Se2 multistack flexible modules, and polyethylene terephthalate (PET) flexible encapsulation foil. The proposed methodology demonstrates a sensitivity <10 nm and acquisition times ≤100 ms which makes it compatible with industrial monitoring applications. Additionally, a specific design for in‐line integration of a normal reflectance system into a roll‐to‐roll production line for thickness control of nanometric layers is defined and proposed.

Building integrated photovoltaics (PV) are promising technologies to integrate renewable energy production and achieve positive energy buildings. Transparent photovoltaics increase the integration options, especially in windows and skylights. In this context, the Tech4win project has developed a prototype tandem UV filter and organic transparent photovoltaic. The current paper presents a techno-economic analysis of the performance of this prototype. The laboratory data is introduced in a TRNSYS18 simulation for evaluating the impact in the heating, cooling, lighting, and the PV electrical production. The simulation scenarios include office and residential buildings in five different climates. Moreover, the economic analysis consists of a sensitivity study on the PV window investment cost, the electricity price, and the feed-in tariff. The results show that the PV windows increase the heating and lighting demand in all cases, but may decrease the cooling demand compared to non-solar control windows. Consequently, in the heating dominated scenarios the PV windows increase the energy demand but, in cooling dominated cases, the demand only decreases if the PV window's solar heat gain coefficient (SHGC) improves that of conventional window. Nevertheless, in all studied scenarios the PV window improved the building energy balance. Finally, the electricity pricing schedules and the feed-in tariff are key into the economic feasibility.