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• Electrical-thermal modeling of power converter as part of a BIPV library in Modelica. • Power converter integration in BIPV module frame in a 'plug and play' configuration. • Experimental validation using a converter prototype tested in a climate chamber. • Improved accuracy compared to TRNSYS power conditioner across all KPIs. • Converter placement inside the frame strongly a?ects the components temperature. European legislation on building performance and energy efficiency pushes the shift towards minimizing the environmental footprint of buildings. Building-integrated photovoltaics (BIPV) is a promising technology that can accelerate the transition to energy-neutral buildings. Quantifying the potential of BIPV is crucial and one means of obtaining those results is through simulation. The state-of-the-art tools offer either thermal or electrical specialization; in particular, balance of system components (BOS) such as power converters have not been studied in detail within the building simulations BIPV domain. In this paper, a multi-physics model of a BIPV integrated DC/DC converter is developed in the Modelica language, taking into account the thermal and electrical couplings inherent to power electronic systems. The model has been validated using representative outdoor BIPV measurements and a DC/DC converter prototype. It has been found that the proposed model provides reasonable accuracy and outperforms an equivalent power conditioning model in TRNSYS. To demonstrate the model's functionality, two case studies are performed. First, the temperature-dependence of the converter's efficiency and losses is quantified and analyzed and, second, the prominent contributors to the converter losses are identified and discussed. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Rh/v is defined to estimate the potential of solar energy on the vertical area. • Sun-tracking PV shading elements integrated with windows are modeled and analyzed. • Novel optimum sun-tracking methods and cell layout are first proposed. • Annual energy generation and average efficiency are improved by 27.40% and 19.17%. • Optimum sun-tracking methods reveal great ability to protect glare from the sun. Abstract Vertical space bears great potential of solar energy especially for congested urban areas, where photovoltaic (PV) windows in high-rise buildings can contribute to both power generation and daylight harvest. Previous studies on sun-tracking PV windows strayed into the trade-off between tracking performance and mutual shading, failing to achieve the maximum energy generation. Here we first build integrated models which couple the performance of sun-tracking PV windows to the rotation angles. Secondly, one-degree-of-freedom (DOF) and two-DOF sun tracking are mathematically proven to be not able to gain either maximum power generation or non-glare daylighting under reasonable assumptions. Then we derive the optimum rotation angles of the variable-pivot-three-degree-of-freedom (VP-3-DOF) sun-tracking elements and demonstrate that the optimum VP-3-DOF sun tracking can achieve the aforementioned goals. When the restriction of the proposed model is relaxed, the same performance can be achieved by the optimum one-DOF sun tracking with extended PV slats and particular design of cell layout, requiring less complicated mechanical structures. Simulation results of nine global cities show that the annual energy generation and average module efficiency are improved respectively by 27.40% and 19.17% via the optimum VP-3-DOF sun tracking over the conventional perpendicular sun tracking. The proposed optimum sun-tracking methods also reveal better protection against sun glare. The optimum VP-3-DOF sun tracking is also demonstrated to be applicable to horizontal PV windows, as those applied in the sun roof of a glass greenhouse. [ABSTRACT FROM AUTHOR]

Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure's skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days). • An AI-based approach is proposed for predicting PV module temperature for BIPV. • The obtained models show relative errors of less than 4% in all cases. • In comparison to Sandia model, this method reduces the error by up to 11%. • GE+DE is a reliable tool easily applicable to non-conventional PV applications. [ABSTRACT FROM AUTHOR]

Abstract: The shading-type BIPV claddings can act as power generators as well as external shading devices and insulation panels. There is little available information about the impacts of orientations and inclinations on the combined energy effects of the shading-type BIPV claddings, including the power output of PV modules, the cooling load reduction of windows and concrete walls. It is worth studying the effects of different shading-type BIPV cladding designs on the total energy saving in order to maximize the system’s energy performance. By considering the meteorological conditions in Hong Kong, the energy effect of the shading-type BIPV claddings with different surface azimuth angles, in terms of electricity generation and cooling energy consumption reduction is analyzed in this paper. Based on the investigations, the optimum design of the shading-type BIPV claddings for different orientations can be achieved. [Copyright &y& Elsevier]

• Rooftop PV key driver in the PV market, thus modelled in IMAGE IAM. • Global estimated potential 8.3 PWh y−1: 1.5 times residential electricity demand. • Scenarios show key role for rooftop PV but regional characteristics crucial. • Income levels and grid electricity prices dominate regional deployment. • Low-irradiation western Europe better than high-irradiation Middle East. The use of solar photovoltaic has strongly increased in the last decade. A significant part of this growth comes from home owners installing rooftop photovoltaic. Despite this key role, most long-term model-based scenarios do not consider decentralized supply of rooftop photovoltaic but concentrate on utility-scale photovoltaic instead. In this paper, we implement rooftop photovoltaic in the Integrated Assessment Model IMAGE to study its possible role in energy and climate scenarios. We first calculated the global technical and economic potential to derive regional cost-supply curves for rooftop photovoltaic. Next, we have added a new decision in the IMAGE model allowing household investment in rooftop photovoltaic based on the comparison of the whole-sale electricity price with the price of rooftop photovoltaic. The global suitable roof surface area was assessed at 36 billion m2, or 4.7 m2 capita−1, leading to a potential for rooftop photovoltaic of 8.3 PWh y−1, roughly 1.5 times the 2015 global residential electricity demand. In the baseline scenario, adding rooftop photovoltaic could lead to a 80–280% increased share of photovoltaic electricity production in 2050 (i.e. from 6% to 17% in total power production). This increase depends on regional characteristics that are essential to the deployment of rooftop photovoltaic: differences in social-economic and policy factors (capital costs, household income, and electricity prices) are considerably more important than physical factors, such as solar irradiance. [ABSTRACT FROM AUTHOR]