• Energy Research

Abstract 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.

Building integrated photovoltaic (BIPV) is a key concept for the realisation of sustainable buildings. Despite the progress in BIPV modelling, the use of sensitivity analysis (SA) is still scarce in the BIPV literature. SA can help the modeller to identify which model inputs influence the model outputs the most. This paper presents a simulation framework that combines global SA methods with a multi-physics BIPV model. The analysis focuses on the performance of naturally ventilated BIPV facade elements (cell temperature and power). Building performance indicators, such as the total heat flux to the building interior and the building wall temperature, are also analysed. Inputs to the SA include convective heat transfer coefficients, cavity airflow rate, and weather conditions. As expected, the SA results were found to be highly dependent on the range selected for the inputs. For a narrow variation in weather conditions, the exterior convective heat transfer coefficient was identified as the input with the strongest influence on the BIPV performance. Results also showed that cavity ventilation becomes more important as the exterior convective heat transfer decreases. These findings indicate the need for accurate models to represent exterior convective heat transfer in BIPV facades and corroborate the importance of cavity ventilation.

Building integrated photovoltaic (BIPV) applications are key to increase the share of renewable energy in the built environment. A large potential for BIPV deployment is related to building facades. The assessment of BIPV facades depends on the accurate modelling of exterior convective heat transfer coefficients (eCHTC). However, eCHTC models commonly used in BIPV modelling tend to be simplified. This paper proposes a simulation framework that combines detailed computational fluid dynamics with a multi-physics BIPV model to investigate the influence of eCHTC on the performance of BIPV facades (cell temperature and power). The evaluation is performed for different building geometries, wind speeds, wind directions, solar irradiations, and ambient temperatures. The results show that local variations in eCHTC can cause variations in cell temperature up to 42 °C between the BIPV modules across the facade. These temperature differences are associated with differences in power up to 17% across the BIPV facade. In general, lower temperatures (and higher power output) occur near the edges of the facade while higher temperatures (and lower power output) occur at the middle and at the bottom of the facade. Therefore, a detailed assessment of wind effects is recommended in order to verify the local operating conditions of the BIPV modules. The framework and findings presented in this work are relevant for applications in which the knowledge of the local behaviour is important, such as degradation studies.

Building integrated photovoltaic (BIPV) systems provide an opportunity for renewable energy generation in the built environment. In order to quantify the BIPV potential, numerical models of varying levels of complexity have been developed. This paper investigates how the complexity of BIPV models affects their predictions. The study starts with a detailed multi-physics BIPV model that combines a high-resolution one-diode model with physics-based thermal and airflow models. Next, simplifications are introduced into the model. The model predictions are compared to experimental data from a BIPV curtain wall installed in a test building in Leuven, Belgium. The results show that the detailed BIPV model is capable of estimating the BIPV daily energy yield with an average difference of 6.2 % (2.0 % for clear sky days) and the back-of-module temperature with an average difference of 1.74 °C. The use of a linear power model instead of a high-resolution one-diode model affects the average differences, but not significantly: 8.7 % for daily energy yield predictions (4.5 % for clear sky days) and 1.71 °C for temperature predictions. The use of two different empirical temperature correlations instead of a physics-based approach increases the average temperature difference to 3.5 and 4.4 °C. The average difference in daily energy yield increases to 10.2 and 10.4 %, respectively (5.9 and 5.5 % for clear sky days). These findings indicate that the detailed version of multi-physics BIPV model provides the best agreement with experimental data, but it is still possible to reduce the model complexity with acceptable accuracy.

As the future of urban living appears increasingly daunting, many people and communities are already experiencing climate impacts. This paper highlights the disproportionate nature of climate change, from unequal responsibilities for environmental degradation to unequal climate impacts that fall on the most vulnerable and unequal prospects that hinder people and countries from adapting to a changing climate now and in the future. Through a comprehensive literature review, the paper demonstrates that both the effects of climate change and the responses to them often reinforce existing inequalities, systematically pushing people, communities and countries into further vulnerability. Acknowledging that spatial processes play a critical role in creating, shaping, and perpetuating inequalities and oppression, we advocate for spatial justice in climate action and offer eight principles to support spatial scholars and practitioners in adopting a critical perspective on climate change in urban contexts.

Abstract Building-integrated photovoltaics (BIPV) is seen as a key technology to reduce the environmental impact and net power consumption of buildings. The integration of PV into building components, such as facade, window, roof or shading elements, leads to a distributed generation over the building envelope with a profound impact on the electrical installation. Designing the electrical system with string inverters and a possible wide variety of module sizes and technologies is a challenging task. To overcome this issue, Module-Level Converters (MLCs) can be used. A supplementary benefit is that the consequences of partial shading can strongly be reduced. This paper investigates whether the current generation of MLCs is suited for embedment in facade BIPV modules. The PV output is categorized and compared to the input parameters of the converters. Besides the discrepancy between the physical dimensions of the converters and the desired installation location, thermal and electrical measurements on a prototype BIPV curtain wall element reveal that daily energy losses can be as high as 50% due to thermal overload when used in a moderate climate such as Belgium. The paper concludes by discussing further standardization of BIPV module-level converters.

Governments often use price-based policies such as tax-subsidies and rebates to encourage households to shift to renewable energy sources like rooftop solar photovoltaics (PV). These policies, however, have primarily benefited high-income homeowners, leaving others behind. This paper proposes leveraging social networks’ influence on attitudes and perceptions to design more equitable solar PV adoption programs. Using data from Albany county (New York State, USA) we develop an Agent-based model, integrating a novel implementation of circles of influence into the theory of planned behavior. We test two policy categories (generic and targeted) under two network scenarios (integrated and segregated). Resulting solar PV adoption rates are evaluated using egalitarian, utilitarian and cost metrics to analyze policy impact on different income groups. Our findings indicate that network structure significantly influences adoption rates within income groups. Low-income groups in segregated networks can experience higher adoption driven by positive attitudes towards solar PV, while high-income groups in segregated networks may face poor policy performance despite higher affordability. Seeding policies and information dissemination through influential network members may not necessarily improve adoption rates, as trust can a more important role. The study underscores the importance of trusted information sources in influencing adoption decisions. The insights gained from this research can guide policy design for tailored interventions to improve access to renewable energy for all income groups. ; System Engineering ; Spatial Planning and Strategy ; Policy Analysis

Presentation given on the "Spatial Planning & Strategy" seminars at TU Delft.