PV module testing under standard conditions is an important and well-established procedure, which plays a vital role in module rating. However, PV modules rarely operate at standard conditions therefore their field performance should be predicted based on long term outdoor monitoring or by means of models – so called energy yield models, which combine PV module characteristics with varying environmental conditions. The present work employs a bottom-up, physics-based energy yield modelling approach, which accounts to the interacting optical, thermal and electrical mechanisms in a detailed manner. Additionally, measured data is used for the accurate calibration of the models. Such an approach permits to explore the influence of cell- and module technology details on energy yield under any specific environmental conditions. The present work employs such a method to evaluate the influence of Silicon solar cell technology on energy yield under desert and moderate climates, where the interplay of different irradiance and ambient temperature levels result in a challenging PV performance prediction problem. The purpose of this work is to identify the best-suited solar cell technologies and to understand the underlying mechanisms, which lead to superior PV performance under specific climate conditions. The study is performed by means of physics-based exploratory energy yield simulations with detailed resolution of the thermal effects. Our comparison of four different cell technologies in monofacial modules highlights that superior illumination-dependent performance can contribute to annual energy yield enhancement under both moderate and desert climates amounting to 1.75% and 0.4%, respectively; while a 0.04%/°C advantage in relative temperature coefficient increases annual energy yield (by 1.2%) only under a desert climate.@en

We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.@en

Currently, photovoltaic (PV) installations target a maximization of annual energy yield. However, as the grid penetration of PV is increasing, PV electricity generation will need to match better with local load profiles. Especially the seasonal variabilities remain challenging. While wind and PV tend to have complementary seasonal variability, wind turbine installation faces limitations especially in densely populated areas. In this paper, we discuss how this challenge may be addressed with climate- and consumption-specific PV module technology. In particular, we demonstrate how the temperature coefficient of a PV system can impact the energy yield throughout the year. In colder climates, higher temperature coefficients allow for a better energy balance, favoring production in colder seasons without a significant reduction of yearly energy yield. Simulations for locations at high latitude, and colder climates, indicate that higher temperature coefficients and improved low-light behavior not only enable a higher energy yield in cold seasons, but also negligible losses in the overall yearly energy yield compared to lower temperature coefficients and slightly better low-light behavior. Simulations show that these results can be obtained using commercial PV modules. More broadly, they indicate how PV module technology may be optimized depending on the location and climate.@en

With the growing competition of utility-scale photovoltaic (PV) plants to provide electricity to the grid, accurate energy yield simulation is becoming essential. Apart from irradiance, forced convection can have an equally significant impact on solar cell temperature, which in turn affects the power output, hence the energy yield. As a consequence, the wind must be taken into account for an accurate estimation of PV installations' energy yield. However, no earlier studies have systematically investigated the impact of wind on precise energy yield simulation. In this study, we investigate how sensitive is PV module's output to forced convection. First, we reveal a significant deviation between the simulated PV module's energy yield and cell temperature compared to experimental results in case dynamic wind effects are neglected. Secondly, for controlled incremental forced convection with a steady state scenario, we show that for higher wind speed the change of PV module's cell temperature and power output is small with different forced convection. Whereas, for the low wind speed, small changes of forced convection imply a larger variation of the PV module's cell temperature and power output. This shows that for high wind speed region energy yield estimation is less sensitive on modelling errors. Whereas for the low wind speed region it is highly sensitive.@en

Nowadays, material manufacturers are engineering module materials to optimize the energy production of the PV modules. One of the elements which can be influenced is the optical scattering of a material. In this study, we quantified the effect of a scattering front encapsulant on the energy production of PV modules. First, a wavelength-dependent scattering model was developed in the ray-tracing software PVlighthouse. This model was used to find the optimal scattering conditions, looking at the photo-generated current for a glass-glass PV module with flat front surface. It was shown that a gain of +0.63 mA/cm2 can be obtained for optimal scattering conditions. The scattering is mostly beneficial when the light strikes the module surface at a perpendicular angle. The outcome of the optimization study was implemented in IMEC’s energy yield simulation framework. This framework was used to estimate the energy gain of PV module with scattering front encapsulant when installed in Kuwait’s desert. It was shown that an energy production gain of 1.8% can be expected in case of a glass-glass module with flat front surface and ARC coating.@en