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.

The current industrial standard photovoltaic (PV) modules are dominated by crystalline-silicon (c-Si) PV technology, which cannot be easily recycled. This has generated worldwide concerns regarding the scarcity of critical raw materials and large waste streams of PV modules in the coming years. To overcome this problem, a lot of ongoing research focuses on upscaling the recycling process of present c-Si module designs by chemical, thermal and mechanical processes. Alternative research focuses on the development of new PV modules with enhanced circularity by design. In this work, we propose a new circular PV module design based on liquid-encapsulation technology in which silicon solar cells are encapsulated with a suitable liquid and an edge sealant instead of a polymer sheet. Our optical studies show that the selected liquids exhibit similar optical performance to ethylene vinyl acetate (EVA) due to their comparable refractive indices. We fabricate one-cell prototypes and observe comparable efficiency of liquid-encapsulated and EVA-encapsulated modules, signifying recyclable module design without an additional efficiency loss. To validate the module performance, we also simulate the optical performance of modules with different encapsulation materials. Furthermore, the new modules retained more than 95% of their efficiency after inducing accelerated ageing through damp heat, thermal cycling and humidity freeze tests, indicating the liquids as an excellent encapsulant material for PV. In the end, we disassemble the liquid-filled PV module and show the recovered components, which can directly be reused in new products. Therefore, this modular design bypasses the recycling process. Even more promising, the liquid encapsulation can improve the stability of perovskite/c-Si tandem solar cells due to the moisture barrier, which will be studied in the future. Adding further possibilities, in principle, with additional engineering, the liquid encapsulation could be used in a heat loop to turn the PV module into a photovoltaic-thermal (PV-T) module, thereby boosting the efficiency potential beyond that of traditional PV concept.

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.

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.

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.

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 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.

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%.

A thorough understanding of the small-signal response of solar cells can reveal intrinsic device characteristics and pave the way for innovations. This study investigates the impedance of crystalline silicon PN junction devices using TCAD simulations, focusing on the impact of frequency, bias voltage, and the presence of a low–high (LH) junction. It is shown that the PN junction exhibits the behavior of a parallel resistor–capacitor circuit (RC-loop) with fixed element values at low frequencies, but undergoes relaxation in both resistance R_j and capacitance C_j as frequency increases. Moreover, it is revealed that the addition of a LH junction impacts the impedance by altering R_j, C_j, and the series resistance R_s. Finally, while various publications on solar-cell impedance model the LH junction using an RC-loop, the findings in this study indicate that such a model does not accurately represent the underlying physics. Instead, this approach is likely compensating for the frequency-dependent behavior of R_j and C_j.

Research into the impact of innovative sustainable energy experiments and demonstrations is crucial to diversifying, scaling up, and accelerating the sustainable energy transition. Although there is vast research into sustainable energy experiments and demonstrations, research literature offers a fragmented collection of findings. A coherent overview of themes and insights regarding the transformative impact of innovative sustainable energy experiments and demonstrations on sustainable energy systems from the past, present, and near future is lacking and necessary to increase experiments and demonstrations' impact on the sustainable energy transition. The research in this study fills this knowledge gap by providing such an overview and yields novel insights into the organized function and impact of experiments and demonstrations. It spans a broad spectrum of sustainable energy technologies, the empirical domains where these are invented, developed and applied, and the stakeholders involved. The overview is the outcome of a Delphi study in which the insights of 47 international scientific research experts in sustainable energy experiments and demonstrations are bundled and explained. This study presents a thematic overview of the significant insights regarding past and current sustainable energy experiments and demonstrations and outlines a research agenda for the future. Policymakers, practitioners, and scientists can leverage this to inform their sustainable energy policies, business strategies, and research programs.

Photovoltaic modules are typically not optimized for conditions of partial shading. One proposed approach to improve their shade tolerance is to implement maximum power point tracking on different strings of cells within the modules. However, this approach increases the demand for sub-module power converters, which poses a challenge. To address this, researchers have suggested integrating power electronic components directly into the module laminate, or even within the solar cells themselves. Despite these advancements, limited research has focused on integrating the most bulky component: the inductor. This study investigates through simulations whether planar air-core inductors can yield the required properties to support sub-module power conversion. The simulated inductors have an area that is as large as an industrial crystalline silicon solar cell. First, it is shown how the interplay between the different design parameters, such as track spacing, track width, number of turns, and middle gap size, plays an important role in the inductor properties at high frequency. The coil geometries that are simulated yield inductance values between 0.3 μH and 3.2 μH. Subsequently, the feasibility of implementing these inductors into an exemplary DC–DC boost converter is evaluated. To adequately reduce the ripple current, a significant switching frequency of at least several hundred kHz is required. Additionally, at 500 kHz, an inductor thickness of around 0.5 mm is necessary to keep the ohmic losses in the inductor below 2% of the total generated power in standard test conditions. While demonstrating feasible combinations, these findings also present significant challenges.

In photovoltaic (PV) systems, unavoidable factors, such as partial shading, nonoptimal mounting angles of PV modules, and accumulation of dust result in mismatches, consequently diminishing energy yield. A promising solution to mitigate these issues is to use distributed maximum power point tracking (DMPPT) architectures. To alleviate mismatch-related losses, many DMPPT architectures, including full power processing (FPP) and differential power processing (DPP), have been documented in the literature. FPP encompasses techniques, such as microinverters, modular multilevel cascade inverters, and dc architectures, such as parallel, series, and total cross-tied. DPP variants include series DPP, parallel DPP, and series–parallel DPP architectures. Moreover, novel DMPPT architectures, such as hybrid and hierarchical architectures, along with advancements in converter topologies and control strategies, continue to emerge, aiming to improve levelized cost of energy. Each novel solution brings distinct advantages and challenges, but the extensive number of architectures, power converters topologies, and control methods have led to confusion and complexity in navigating the literature. This article systematically categorizes, reviews, and compares various DMPPT architectures, associated converters, and control strategies, providing a comprehensive overview of the evolving landscape of DMPPT development. By elucidating existing advancements and identifying gaps for further research, this review aims to offer clarity and guidance in advancing DMPPT technology for enhanced PV system performance.

The impedance of solar cells can be leveraged for a variety of innovative applications. However, for the continued advancement of such applications, it is crucial to understand how the impedance varies during practical operation. This work characterizes the impedance of modern crystalline silicon solar cells across different bias voltages and under varying illumination and temperature conditions. It is found that for a given bias voltage, variations in temperature have a notably stronger impact on PN junction impedance than changes in irradiance. However, during maximum power point (MPP) tracking, variations in irradiance have a larger influence on the PN junction impedance than temperature variations. This is related to the shifting operating voltage during operation. Furthermore, it is shown that the capacitance during practical operation can strongly vary for different solar cells. For instance, the areal MPP capacitance values of the two cells tested in this study at 0.1 sun irradiance and a temperature of 30 °C were 0.283 μF/cm2 and 20.2 μF/cm2, a 71-fold difference. Conversely, the range of the MPP diffusion resistance was found to be highly similar for different cells. The results of this study enhance the understanding of solar-cell impedance and have a broad applicability.

Recent conversion efficiency breakthroughs in double-junction (tandem) perovskite/crystalline silicon solar cells demand advanced opto-thermo-electrical simulations, that are critical for translating laboratory results into realistic photovoltaic module and system performance. A holistic framework is here developed and presented, combining cell-level simulations, spectral analysis, PV module and PV system modelling. After validating the deployed physics models against measured cells and modules, hourly spectral irradiances for Delft, the Netherlands, and Catania, Italy, are generated and clustered into representative “blue-rich” and “red-rich” spectra. The effects of spectral variations on the current-matching and energy yield of tandem modules are quantified. Realistic module architectures are simulated, integrating dynamic temperature and spectrum data. Temperature coefficients are derived as a function of both irradiance and module temperature, significantly improving upon traditional indoor-derived values. Results show that standard indoor-derived coefficients under-/overestimate values in realistic conditions, highlighting the ultimate need for location-specific power matrixes. This study offers a robust pathway to predict tandem module energy yields across seasons and climates, supporting optimized design choices for industrial production and future PV installations.

Bifacial perovskite/silicon solar cells can combine the advantages of tandem technology (high efficiencies) and bifacial modules (additional received irradiance from the rear) to increase the energy yield of photovoltaic (PV) systems further. In literature, it has already been shown that for two-terminal tandems this would require a lower bandgap energy (E_g) for the perovskite cell, as the rear irradiance increases the current in the bottom cell creating a current mismatch, if this is not considered during optimization. This work expands on bifacial two-terminal tandem optimization by considering aspects not included before. Besides the E_g, the thickness (d) of the perovskite is also optimized, as this also affects the current matching. Additionally, this work studies the trends in different energy losses of the PV module to better understand what affects the optimal perovskite cell. Our simulations show that the optimal E_g is 1.61–1.65 eV and the optimal d is 650–750 nm, which agrees with the observations in literature. The optimal E_g and d are mostly a trade-off between mismatch and thermalization losses, meaning that the mismatch losses should not be fully minimized. Additionally, the irradiance from the rear side is converted less efficiently than the front side irradiance due to larger thermalization and reflection losses. Therefore, the energy yield of bifacial tandem modules, compared to monofacial tandem ones, only increases for large ground albedo. Finally, our results show that the bifacial tandems have over a 25% gain in energy yield compared to bifacial single junction modules and up to 5% gain compared to monofacial tandem modules.

As climate change becomes more critical and renewable energy sources expand, land-based photovoltaic (PV) systems face limitations due to competition with agriculture and housing. The sea offers a promising location for offshore floating PV (OFPV) systems. Understanding fluid–structure interactions is crucial for these systems. This work explores how different parameters affect the structural load on the floating platform and related electrical power losses. We develop a multi-physics framework integrating the mechanical model of a large floating structure with the optoelectrical modeling of PV modules. This framework analyzes a hypothetical OFPV platform design with various floater configurations, from a single large floater to multiple small floaters connected with free hinges. The results reveal a trade-off in the number of floaters. Power mismatch loss is lower for platforms with fewer, longer floaters. However, structural loads vary, with high stresses in longer floaters due to the elastic response. Young’s modulus impacts longer floaters where the elastic response dominates, while cross-section fill ratio affects shorter floaters, where the rigid-body response prevails. The floater-beam thickness has the most significant impact across various floater lengths.

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.

Optimizing the deposition parameters in the fabrication of passivating contacts for crystalline silicon solar cells is critical for improving efficiency. This study explored the influence of varying RF power of Plasma-Enhanced Chemical Vapor Deposition (PECVD) on the quality of hydrogenated intrinsic amorphous silicon ( a-Si:H) films. The aim is to manufacture in-situ phosphorous-doped poly-Si/SiO_x/c-Si passivating contacts with a-Si:H as buffer layer between the tunnelling oxide and the n-type poly-Si. The microstructure factor of our intrinsic layers increases from 0.176 to 0.804, that is from higher to lower film density, as the RF power increases from 5 W to 55 W. Analysis using X-ray Photoelectron Spectroscopy and Optical Microscopy indicates that the Si content in SiO_x is correlated with the formation of pinholes. Our detailed analysis showed that varying the RF power when depositing a-Si:H contacting layer is crucial in altering both the Si⁴⁺ content in SiO_x and the pinhole density, due to the interplay between the plasma etching and the buffering effects during of the a-Si:H layer growth. Notably, the sample processed with 25 W exhibited the maximum pinhole density, the lowest Si⁴⁺ content in SiO_x and the deepest phosphorus in-diffusion, potentially yielding superior results in passivation quality and contact resistivity under optimized PECVD conditions.

This work presents a novel approach to studying, simulating, and modeling the graphene–silicon interface in Schottky diodes by integrating quantum-mechanical and device-level analyses. Such devices hold great performance potential in photodetecting, energy-harvesting, and sensing applications. Quantum-mechanical calculations determine key structural and electronic properties, such as the work function and effective mass, which are critical for understanding the interface’s behavior. These parameters are then incorporated into finite-element simulations, solving the Poisson and Continuity equations to develop a subthreshold and reverse bias model for the graphene/p-type silicon Schottky device. The model characterizes J–V curves, identifying dominant electron transport mechanisms like thermionic emission and diffusion at varying recombination velocities. It also sheds light on the image-force lowering effect, which significantly impacts current density, especially under reverse bias conditions, by modulating the Schottky barrier height.

The model is validated by comparing the model with experimental data from graphene–silicon photodetectors, demonstrating its accuracy in predicting device performance. This approach offers valuable insights into optimizing any kind of Schottky diodes. By effectively bridging quantum-mechanical theory with practical device performance, the model proves to be a powerful tool for designing advanced semiconductor devices with enhanced efficiency and functionality, ensuring consistency from the atomistic to the device level.

Polycrystalline silicon (poly-Si) carrier-selective passivating contacts (CSPCs), featuring high photoconversion efficiency (PCE) and cost-effectiveness, have emerged as a promising approach for high-efficiency crystalline silicon (c-Si) solar cells. To minimize parasitic absorption losses induced by doped poly-Si window layers, wide bandgap oxygen-alloyed poly-Si (poly-SiO_x) layers are developed. However, challenges persist in achieving excellent surface passivation for boron-doped poly-SiO_x contact stacks, likely caused by boron diffusion during annealing and the reduced doping concentration resulting from lower crystallinity as oxygen content increases. In this study, we investigate the impact on the passivating contact structure and solar cell performance of a 10-nm thick intrinsic hydrogenated amorphous silicon buffer layer with varying oxygen content (a-Si (O_x):H) deposited by plasma-enhanced chemical vapor deposition (PECVD), and placed between the tunneling silicon oxide (SiO_x) and the poly-SiO_x (p⁺). After the hydrogenation step, we obtain both high passivation quality with implied open circuit voltage (iV_oc) of 728.3 mV and low contact resistivity (ρ_c) of 59.18 mΩ cm² on polished surface for oxygen-free a-Si:H buffer layer. These improvements can be attributed to the appropriate thickness of the tunnel oxide and confirmed by transmission electron microscopy (TEM) images, to higher crystallinity of the buffer layer, which facilitates more efficient doping in the buffer layer. This is evidenced by energy dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS) results. At the device level, a front-side textured, rear-side flat, rear junction poly-SiO_x/poly-SiO_x solar cell on n-type c-Si wafer, an efficiency improvement can be observed from 3.55 % without a PECVD buffer layer to 18.9 % with an oxygen-free a-Si:H PECVD buffer layer. The impact of the buffer layer crystallinity on cell performance is further demonstrated by deploying a 10-nm thick LPCVD buffer layer, which facilitates an efficiency of 21.15 % for the same device structure.