As crystalline silicon (c-Si) solar cells approach their theoretical efficiency limit, the perovskite/silicon (PerSi) tandem technology offers a promising solution for further improving the efficiency of photovoltaic (PV) modules. However, as perovskite cells are facing stability issues, it is unclear whether PerSi modules will have a larger lifetime energy yield (LEY) than c-Si modules. In this work, we present a novel methodology to simulate the LEY of PerSi tandem devices, accounting for environmental stress factor-dependent degradation across four different climates. Our approach combines a physics-based analytical degradation model for components shared with c-Si modules and a scenario-based degradation model for the perovskite top cell. This method enables us to identify the tolerable degradation rate (k_tol) of the perovskite cell under different scenarios and climatic conditions. We find that k_tol is lowest when degradation occurs in the short-circuit current, reaching a minimum value of 1.2% per year in Delft (the Netherlands). Additionally, we demonstrate that k_tol inversely depends on the module lifetime, reaching values up 7.6% per year in Lagos (Nigeria). Moreover, we show that module efficiency (η_mod) significantly impacts k_tol. For instance, increasing η_mod from 28.0% to 32.9% raises k_tol by approximately 50%. Additionally, we propose a simplified model that can predict k_tol without the computationally intensive simulations, which has a root-mean-square error of 0.34% per year. Lastly, environmental impact assessments reveal that PerSi modules are more sustainable in all impact categories when the degradation rate is 80% of k_tol for LEY.

Light-simulation tools—exemplified by Radiance—are widely used for quantitative daylight studies and are increasingly adopted in agrivoltaics (agri-PV) to handle complex geometry via ray tracing. Yet, beyond typical workflows three practical limitations persist: spectrally resolved skies are restricted to the visible band; soft-shadow (penumbra) rendering relies on runtime-intensive solar-disk sampling; and fast, integrated canopy models remain scarce. We present a Radiance-compatible Python framework that adds: (i) atmosphere-specific sun–sky generation across the solar spectrum; (ii) efficient, equal-area sampling of the solar disk; and (iii) a simple canopy reconstruction tailored to narrow-trained orchards. To improve spectral fidelity, resolution, and range, we couple SMARTS-derived spectra to a Perez-based sky, leveraging Radiance's multispectral rendering. We deterministically sample the sun's finite extent using a Fibonacci lattice, yielding stable penumbra without prohibitive runtimes. The canopy model parameterizes porosity and seasonal development at a daily rate. Canopy representation matters: opaque–static models, common in agri-PV simulations, systematically underestimate light levels and miss spatiotemporal patterns needed to diagnose suboptimal conditions. Comparatively, a porous–dynamic model led to ≈26% higher seasonal light levels, with gains attaining ≈100% early in the season and converging to ≈16% after foliage matured. While penumbra is limited under conventional PV modules, penumbra-capable renderings enable exploration of design pathways—narrower cell layouts (half-cell and beyond) with greater module–canopy separation—that smooth lighting extremes.

Perovskite/silicon (PS) technology includes three main configurations: two-terminal (2T), three-terminal (3T), and four-terminal (4T). Previous studies have made various comparisons between these configurations, significantly advancing our understanding of these devices. While these studies mostly focus on simulations on cell level, we perform bandgap energy ((Formula presented.)) optimization at the module level for different configurations under outdoor conditions. Using opto-electrical simulations, we predict the energy yield of each module at four geographical locations, with varying values of (Formula presented.). The optimal (Formula presented.) for the 2T, 3T, and 4T modules are 1.62, 1.80, and 1.82 eV, respectively. We also perform a loss analysis to explore the differences in power losses among the configurations. These loss differences can be attributed to the configurations having different optimal (Formula presented.) values (affecting the thermalization losses) or different module designs (affecting the interconnection losses). Among all losses, mismatch losses play the most critical role in optimizing the bandgap. Overall, all optimized configurations have similar energy yields (all differences within 1.5%) across all locations. Finally, we compare the robustness of the different configurations against different scenarios of perovskite degradation. Our results show that the 4T module is the least sensitive to degradation in the perovskite subcell.

The monolithic integration of perovskite top cells on textured crystalline silicon affords efficient tandem devices with strong prospects for large-scale applications. Such integration has primarily relied on state-of-the-art recombination junctions, which typically comprise transparent conductive oxides and molecular self-assembled monolayer (SAM) contacts. However, the potential influence of bottom cell nanoroughness, which may vary based on specific processing routes and technologies, has received far less attention. Here, we systematically engineered the top surface nanoroughness of silicon heterojunction solar cells to examine its impact on monolithic perovskite–silicon tandem solar cells. We employed two approaches: (i) varying the thickness of (n)-type hydrogenated nanocrystalline silicon ((n)nc-Si:H) layers or (ii) applying a plasma treatment using a hydrogen and carbon dioxide gas mixture before the deposition of (n)nc-Si:H layers. Both methods enhanced the conductivity and crystallinity of (n)nc-Si:H layers and increased the surface nanoroughness, with plasma treatment enabling the efficient realization of distinct nanoroughness in thin (n)nc-Si:H (15-nm-thick) layers. Our results reveal that the surface nanoroughness imposed by (n)nc-Si:H layers influences the SAM anchoring, leading to increased work function shifts and improved SAM/perovskite interface quality, thereby impacting the overall tandem device performance. Notably, tandem devices incorporating higher-nanoroughness bottom cells achieved increased fill factors, dominating the observed tandem efficiency enhancements, with a peak efficiency of 32.6% enabled by a 30-second-long plasma treatment.

Offshore floating photovoltaics (OFPVs) emerge as a promising solution to overcome land constraints associated with inland renewable energy deployment. However, as OFPVs are still a developing technology, several performance-related uncertainties persist. The reduction in energy yield caused by wave-induced losses (WIL) is one such critical uncertainty that needs to be understood, quantified and minimised. To address this need, this work introduces a physics-based modelling framework that couples validated hydrodynamic simulations with opto-electrical analysis to accurately estimate WIL. An extensive sensitivity analysis is then carried out, performing over 100 simulations by systematically varying both design and environmental parameters. The results show that WIL ranges between 1%–30% on an hourly basis and exhibits a nonlinear dependence on both parameter groups. The resulting dataset is then used to develop S [Figure presented] IFT 1.0 - a surrogate model capable of predicting WIL across a wide range of design and operating conditions, achieving an average absolute RMSE of 3% relative to the physics-based model. The insights from S [Figure presented] IFT 1.0 are finally used to provide practical measures that minimise WIL at a system design level. Overall, this work provides a complete pathway to model, quantify, predict, and minimise WIL, promoting confident and scalable OFPV deployment.

Photovoltaic (PV) system performance is linked to climatic conditions in which the system operates. This leads to the Köppen-Geiger-Photovoltaic (KGPV) climate classification. KGPV is created by overlaying four irradiation levels with the commonly used Köppen-Geiger climate zones. Potential drawbacks of this approach are that the climate features are not considered in a combined manner in the sorting process and that the KGPV zones inherent a dependence on precipitation. We propose a machine-learning approach to address this deficiencies and improve PV climate classification. First, supervised learning is used to evaluate the correlation between climate features and a PV system's specific energy yield. We find that the inclusion of the darkest and brightest irradiation months as well as UV irradiation improves accuracy, while wind speed, relative humidity, precipitation and annual mean daily temperature difference have little impact on accuracy. Subsequently, k-means clustering combined with comprehensive qualitative analysis, identifies a PV classification based on seven climate features and 21 clusters. A mountainous climate characterized by moderate to low temperature and high irradiation is uncovered compared to KGPV. Moreover, this new PV climate classification reduces the sum of squared errors by 58 % compared to KGPV clearly signifying a more accurate PV climate classification approach.

The degradation of perovskite solar cells due to reverse bias (RB) is one of the remaining challenges hindering the commercialization of the technology. To overcome this challenge, a thorough understanding of and control over the breakdown (BD) voltage are crucial. A prerequisite for this is that the community “speaks the same language,” that is, that the reported BD voltages are comparable. A review of literature data shows that the impact of measurement parameters is often unknown and seems to depend strongly on sample properties. It follows that standardization is the only way to reach comparability. Here, a set of measurement parameters to fill this gap is proposed. Additionally, various definitions of a “BD voltage” are used in parallel without any way of relating them to each other; this metric and its determination need to be considered as well. After a thorough discussion of the available definitions, the use of the point of maximum curvature is introduced. Its main advantage is the possible connection to an analytical description of the BD mechanism. In this way, a starting point for scientists new to the field of RB stability is provided, and the ground for a broader discussion in the community is prepared.

Silicon is a promising alternative to the conventional graphite anodes due to its high theoretical capacity and favorable lithiation potential for lithium-ion batteries (LIBs) with liquid as well as solid-state electrolytes. However, lithiation-induced extreme volume change causes severe mechanochemical deformation and continuous formation of solid-electrolyte interphase leads to cell failure. One of the strategies to mitigate this problem is alloying silicon with a suitable element that can alter the surface electrochemistry and/or lithiation pathways, and acts as mechanical buffer. Nonetheless, these benefits come with a compromise on the specific capacity, which strongly influences the mass loading of the electrodes, highlighting the need to deconvolute the intertwined influence of composition and mass loading when designing high performance electrodes. In this work, we systematically studied the influence of composition and mass loading in monolithic amorphous silicon and non-stoichiometric silicon nitride (SiN_x) electrodes on their electrochemical performance as LIB anodes. The incorporation of nitrogen in the electrode matrix clearly improves the electrochemical stability at the expense of reduced specific capacity, while higher mass loading accelerates capacity fading, most critically in amorphous silicon electrodes. Postmortem analysis reveals that such capacity fading in the electrodes with higher mass loading can be related to delamination due to evolved tensile stress during the charge–discharge cycle. Yet, nitrogen-rich SiN_x monolithic electrodes accommodate strain more effectively. These findings demonstrate that while pristine Si delivers high specific capacity and long-term stability in thin films, thicker (>1 µm) monolithic electrodes benefit from higher nitrogen content in SiN_x, which provides more stable cycling and sustained capacity.

Advanced and emerging photovoltaic (PV) technologies play a crucial role in meeting the increasing global energy demand sustainably. Simulations are essential for predicting system behavior and improving our understanding of complex PV architectures. This work extends an existing modeling framework designed for novel PV systems, offering a modular and flexible workflow suitable for diverse research applications. The framework computes PV performance from first-principles physics, removing the need for module datasheets. It comprises two pre-processing steps and six simulation steps. The first steps determine the optical behavior of the modules, followed by irradiance modeling and temperature calculations. The final steps evaluate the electrical characteristics and the conversion to alternating current at the full-system level. The framework incorporates detailed energy loss analysis and includes advanced features such as partial shading, reverse-bias effects, and photon recycling. Two applications demonstrate its capabilities: comparing module configurations in urban settings and optimizing multi-junction PV system design. Results show that Smart modules enhance shade resilience, delivering approximately (Formula presented.) higher energy yields. Additionally, the optimal perovskite bandgap for perovskite/silicon tandem devices is found to be 1.60–1.62 eV. These outcomes highlight the framework's value for future PV system research and development. The developed software can be found at: https://github.com/YBlom1999/PVMD_Toolbox.

This article introduces a parallel differential power processing (PDPP) architecture for photovoltaic (PV)/battery applications. The PV to Virtual Bus (PV2VB) architecture enables the integration of a battery and manages its power while performing maximum power point tracking on the PV strings. In the proposed PV2VB PDPP architecture, the battery is positioned at the virtual bus, acting as the input for all string-level converters (SLCs). By selecting a lower voltage for the battery at the virtual bus compared to the PV string or the main bus voltages, component voltage ratings can be reduced. The architecture employs dual active bridge converters connected to bridgeless (BL) converters as SLCs to generate both positive and negative output voltages while providing isolation. These SLCs track the maximum power point of each PV string, while the central converter manages battery charging and discharging. Experimental results confirm the performance and effectiveness of the proposed PV2VB PDPP architecture, achieving efficiencies between 95.5% and 99%.

Photovoltaic (PV) to virtual bus parallel differential power processing (PDPP) architecture can mitigate mismatch losses among PV strings. This article presents a comprehensive dynamic analysis by deriving a small-signal model of the PDPP architecture based on its state space model. Subsequently, the corresponding transfer functions and frequency response are obtained, offering valuable insights into the dynamic behavior of the architecture. To validate the accuracy of the derived model, the frequency response has also been achieved by observed data from both PLECS simulation and experiment through system identification. Besides, this article discusses the design considerations of the discrete controllers' parameters for both virtual and intermediate bus voltages and studies the stability of the architecture. Experimental measurements confirm the ability of the central controller to stabilize the virtual bus voltage to the desired level within 0.6 seconds, while the intermediate bus voltages settle within 15 ms, enabling proper maximum power point tracking of each PV string.

This study presents a comprehensive analysis of the optical and electronic properties of thin films of molybdenum oxide and tungsten oxide to implement hole-selective contact for heterojunction solar cells. These contacts are currently viewed as an alternative for the fabrication of doping-free solar cells. However, the spreading of this technology is still limited due to the development of S-shaped J-V curves, which affect the electrical performance of the cells, and further optimization in the material deposition process is therefore crucial to overcome these challenges. To improve transition metal oxide-based heterojunction technology, this work investigates the impact of oxygen vacancies on electrical performance, particularly their role in S-shaped J-V curves. Defect density evaluation through nondestructive techniques like photothermal deflection spectroscopy together with a detailed experimental characterization is presented in this paper to highlight the structural and optical properties of the films. Prototypes of solar cells incorporating hole-selective contacts with tungsten and molybdenum oxide are prepared to show S-shaped J-V characteristics under AM 1.5 illumination. An equivalent circuit modeling was used for understanding the electrical characteristics of the prototypes. Furthermore, this approach offers insights into the optimization of the performances of devices.

Integrating photovoltaic (PV) systems in urban areas enhances local renewable electricity production but also reduces surface albedo due to the lower reflectivity of PV panels. This albedo reduction increases Earth's energy absorption, resulting in positive radiative forcing (RF), while the displacement of fossil fuels by PV electricity leads to negative RF through avoided CO₂ emissions. This study quantifies the net RF impact of urban rooftop PV deployment using a novel workflow. This proposed workflow combines: (1) a geometric spectral albedo (GSA) model, using LiDAR data and geo-referenced material maps to simulate albedo changes before and after PV integration; and (2) a simplified skyline-based PV model, using LiDAR-derived roof geometry to estimate annual PV electricity generation. The method is applied to the city of Delft, the Netherlands, and the average simulated albedo of Delft is 0.1584, differing by 6.12 % from MODIS observations (0.1493). Full PV integration on all rooftops reduces the city-wide albedo to 0.1557, corresponding to a positive RF of 3.53×10⁻⁸ W/m². This can be offset in about 40 days by negative RF from PV electricity, assuming a grid carbon intensity of 454 gCO₂-eq/kWh. However, under a low-carbon grid scenario (30 gCO₂-eq/kWh), the payback time increases to 623 days, indicating that positive RF from albedo reduction becomes more relevant in future decarbonized scenarios. This study contributes to understanding the climatic implications of urban PV deployment and offers insights into the realistic potential of PV systems in mitigating climate change.

In this study, a modeling methodology is presented for evaluating the performance of a hybrid system integrating different types of solar collectors, namely photovoltaic (PV), glazed flat plate solar thermal (ST) and unglazed photovoltaic-thermal (PVT) collectors to harvest solar energy. Further, the system is integrated with a seasonal storage that is an aquifer thermal energy storage (ATES) system, a heat exchanger and a heat pump (HP) to provide heating, including space heating (SH), domestic hot water (DHW), as well as cooling. The investigation considers various operational modes depending on the climate conditions and building characteristics. The study focuses on comparison of solar collectors in realistic scenarios, examining heating type and insulation levels. Real energy consumption data considering five residential buildings in Amsterdam is employed for the analysis. Annual simulations for the considered buildings are conducted for SH and DHW coverage, along with cooling. The results indicate that ATES combined with glazed ST collectors demonstrates superior heat storage while HP with PV/ST combination and floor heating achieves an average coefficient of performance (COP) of 6.09 for both SH and DHW. In contrast, HP combined with PVTs shows the lowest performance, with a COP of around 5 when used with radiator heating. Additionally, majority of the demand is covered using HP storage mode with seasonal storage and HP while building insulation plays a crucial role.