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.

This work introduces a method for screening potential hotspots in monolithic interconnected thin-film silicon modules using injection-dependent electroluminescence (EL) imaging. The fraction of dark area of the cell in the low- and high-injection EL images, respectively, is used to extract the severity and localization information associated with a defect. For the first time, a factor, namely, severity-to-localization (SL), is introduced for each defect as the ratio of severity to localization. Further, defects are broadly classified as A, B, AB, and C modes. Mode A and Mode B are severe, where the former is a distributed defect across the cell, and the latter is a localized defect. In contrast, Mode C is a localized trivial defect. The severe defects that are neither entirely distributed within the cell area nor localized are classified as Mode AB. The SL factor values associated with A, B, AB, and C modes are ≈1, >4, between 1 and 4, and ≈1, respectively. Furthermore, the potential of four modes of defects for hotspot formation is tested following the IEC61215 standard. The hotspot endurance test results reveal that high SL factor defects, such as Mode B, always lead to hotspots, and low SL factor defects, such as Mode A and C, do not produce distinguishable hotspots. Similarly, Mode AB with a higher SL formed clear hotspots, and with a lower SL factor (<1.5) never formed hotspots. The proposed method applies to all thin-film technologies with monolithic interconnects and is, therefore, expected to gain significant attention.

Sunlight throughout urban areas largely impacts local climate [sustainable development goal (SDG) 13], residents’ well-being (SDG 3), and access to clean energy (SDG 7). However, sunlight availability on various urban surfaces is affected by urban geometry. Here, in this work, a probabilistic framework to evaluate the interplay between sunlight and urban geometry is presented, and its immediate applications in urban energy studies are demonstrated. A probability mass function that predicts the energy production of a group of light-collecting surfaces, such as solar photovoltaic (PV) systems, installed in rough geometries, such as urban areas, is derived. Along the way, an expression for the sky view factor (SVF) is formulated within rough geometries as well as a link between the capacity factor of the residential PV fleet and urban geometry. The predictions of the mathematical framework are validated using the digital surface model and collected PV systems data in The Netherlands. This work primarily helps understand the underlying relation between the geometrical parameters of a rough surface and the received sunlight energy on a subset of that surface. Exemplified applications are swift SVF calculations and residential PV fleet yield predictions, which, respectively, support efficient urban energy assessments and privacy-preserving electrical grid management.

Solar farm installers generally struggle with the allocation of irradiance sensors throughout the plant area, which are essential for monitoring purposes. Despite the existence of the International Electrotechnical Commission guidelines for photovoltaic (PV) plant monitoring, no specific guidance is provided when it comes to allocating sensors. This can be especially problematic for solar farms in hilly terrain. In this work, a software tool is built to allocate horizontal and in-plane irradiance sensors. Additionally, advice on the optimum number of sensors and the prevented error is provided based on the layout of the farm. The methodology consists of calculating the irradiance at every point of the solar farm area and finding the one closest to the average. This average is computed differently depending on the sensor type and monitoring purpose. A modification of the BRL irradiance decomposition model is also proposed to reduce the bias of the original model. The software has been applied to two case studies of existing solar farms in hilly areas in Greece and Germany, showing its applicability for real case scenarios in different climates and geological landscapes. The runtime of the software tool is mainly a function of solar farm size and the land morphology of its location. This methodology has been only developed for monofacial fixed-tilted PV farms.

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.

This work presents a practical approach to designing an optical filter for thermal management for photovoltaic modules. The approach emphasizes the practicality of manufacturing over optical performance. Simulation work demonstrates that, for an interdigitated back contact solar cell architecture, complete rejection of infrared radiation offers limited thermal benefits requiring highly complex optical filter designs. An alternative approach consists of reducing thermalization losses by providing reflectance at lower wavelength values. An optical filter design that fulfills this requirement is possible using simple structures based on two materials and taking advantage of the harmonics present in quarter wavelength optical thickness designs. The filter is later optimized for angular performance via second-order algorithms, resulting in a device consisting of only 15 thin-film layers. Performance simulations on two locations, Delft (the Netherlands) and Singapore, estimate a temperature reduction of 2.20°C and 2.45°C, respectively. In a single year, the optical loss produced by the filter is not compensated via temperature reduction. However, improvements in the annual degradation rate show that in Singapore, the overall effect of the filter on the lifetime DC energy yield is positive.

Here, we first visualize the achievable global efficiency for single-junction crystalline silicon cells and demonstrate how different regional markets have radically varied requirements for Si wafer thickness and injection level. Our findings showed that 219 g/kW of polysilicon can be conserved while producing slightly more electricity when c- Si cells are manufactured based on the global geographical market instead of standard test conditions. Then, we investigate the bifacial silicon cell and show that its optimal wafer thickness should be 1.67–2.89 times thicker than its monofacial counterpart, depending on the geographical region. Further, we study a double-junction two-terminal Si-based cell, reevaluate its theoretical limit as 42.8%, and illustrate that globally, tandem cells’ efficiency will only be slightly decreased when significantly reducing the bottom cell Si wafer thickness (−0.3%/mm). The outcomes of this study offer a blueprint to strategically design solar cells for target geographic markets, ensuring the conservation of substantial polysilicon volumes.

Solar photovoltaic (PV) energy is variable. The output power can change considerably in a matter of minutes, imposing challenges on the control of systems connected downstream. The power from these systems can be smoothed using electric storage, potentially increasing the system cost. An alternative is to deliberately curtail the power before it starts to change. This strategy relies on ultra-short-term forecasting to determine the curtailment point. Unfortunately, forecasting is prone to errors and high uncertainty even in the very short-term, leading to control errors. We propose an active power curtailment control strategy for a stand-alone solar photovoltaic system powering an electrolyzer. Our work's novelty relies on the controller's ability to deal with large forecasting errors and high uncertainty, combining artificial intelligence for predicting the power ramps and fuzzy logic to account for imperfect prediction. We validated our approach using a hardware emulator of the photovoltaic system, power converter and electrolyzer. Under clear sky conditions, the curtailment results in unnecessary energy loss, while under variable irradiance, the controller successfully smooths the power ramps within 10% of the PV system's nominal power. Although our approach was designed for a stand-alone system, its concept can be directly applied to grid-connected systems as well.

Silicon solar cells are the basis of the photovoltaic industry; thus, understanding their performance limits and parameter optimization under various working conditions is important. Here, we present a protocol for simulating mono-facial and bifacial silicon solar cells as well as 2-terminal double-junction X-on-silicon solar cells. We describe steps for resource gathering, input data preparation, running simulation scripts, and results visualizations. This protocol can be extended to simulate 3- and 4-terminal solar cells and more than two junctions. For complete details on the use and execution of this protocol, please refer to H. Ziar.¹

This work is a long-term, interannual, and experimental study conducted in multiple locations. It studies the effects of phase change materials (PCMs) on photovoltaic modules’ performance by reducing their operational temperature. Two PV modules were manufactured so that PCM slabs could be mechanically attached to their backside, ensuring contact with the related photovoltaic active area. Experiments were conducted in Delft, Netherlands, from 2019 until 2021 and in Catania, Italy, during the winter and start of spring of 2023. The experiment also considered two installation layouts: building integrated (Delft) and standard rack-mounted (Catania). The measurements showed that the PCM provides significant cooling under both locations, with a temperature reduction of up to 15 °C. In Delft, thermal control could be obtained for most of the sunny hours of the day, even during the summer months. In Catania, the module with PCM presented, on occasion, higher temperatures than its standard counterpart, primarily due to winter-time environmental conditions. However, the PCM provided sufficient thermal control on all conditions, ensuring increased energy yield. This increase ranged from 2.1 to 2.5 % in Delft and 1.3–1.6 % in Italy.

Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems.

Hydrogen is increasingly recognized for its role in enhancing the electrification of the built environment, particularly as a seasonal storage medium to balance the intermittent nature of renewable generation. Despite its potential, the high investment costs of hydrogen technologies make their integration challenging in current energy systems. This study addresses the gap in research concerning the impacts of hydrogen integration within energy communities, focusing on system performance and grid operations through different grid connection scenarios. We explore three grid connection capacities - unlimited, 24 kW, and 16 kW - using a case study from The Green Village. Our findings indicate that an unlimited grid connection poses a risk of grid congestion, whereas a restricted connection could result in unmet load demands. Our results suggest that aligning the grid connection capacity with the peak demand of the energy community effectively balances the need to reduce grid congestion while meeting energy requirements. This research highlights the need for strategic planning in the integration of hydrogen technologies within energy communities, advocating for a balance that supports both energy independence and grid stability.

Visible light communication (VLC) is a promising complement considering the rising radio frequency spectrum congestion. However, photodiode receivers degrade rapidly under high ambient light (>200 W/m²). Photovoltaic (PV) cells, designed for outdoor applications, offer an effective alternative. This work studies the fundamental relationship between various LEDs and seven commercial crystalline silicon (c-Si) PV cell architectures to assess simultaneous energy harvesting and communication. The results reveal that increased PV output inversely affects bandwidth. The impact of PV cell architecture on bandwidth is mainly due to bulk doping concentration and metallization design. Higher doping reduces bandwidth at short circuit but increases it at higher operating voltages. At the transmitter end, higher irradiance levels enhance communication, but this effect is minimal at the PV maximum power point (MPP). Additionally, LED color has a negligible impact on PV cell bandwidth. The highest bandwidth is 215 kHz for Al-BSF(5”) under short-circuit, while the lowest is 0.1 kHz for SHJ at MPP. Among the tested c-Si PV architectures, Al-BSF cells exhibit the best communication stability – from 100 kHz to 10 kHz, while SHJ shows the worst – from 100 kHz to 0.1 kHz. TOPCon demonstrates the optimal balance between energy harvesting and communication for Pareto optimality.

The growing global energy demand increases the need for renewable energy sources. This increase requires land to be occupied, competing with other activities such as agriculture and residency. In such a situation, renewable energy sources expand to other environments like the ocean. However, this new scene poses some challenges, such as the effect of waves on photovoltaic (PV) performance. Consequently, this study aims to evaluate the power output of an Offshore Floating PV (OFPV) system located in the North Sea considering the effect of the waves. A 3D mechanical movement model, which has been validated with data from a real system, is developed for this purpose. A sensitivity analysis is conducted to determine how the size of fluctuations depends on the dimensions of the floater. The main outcome is that a heavy and wide floater aligned with the most common wind direction reduces angle variations. Results from DC power simulations show that sea fluctuations have a negative yet small influence on PV power production. Over the course of the year, these losses amount to just 0.1% of the annual energy yield. However, a hypothetical optimally-tilted PV system placed on water would still generate 14.6% more DC power output than the floating one. On the AC side, laboratory experiments show that these oscillations negatively affect the inverter efficiency during rough sea conditions by a decrease of over 2 percentage points compared to a still system.

Due to the inherent uncertainty in photovoltaic (PV) energy generation, an accurate power forecasting is essential to ensure a reliable operation of PV systems and a safe electric grid. Machine learning (ML) techniques have gained popularity on the development of this task due to its increased accuracy. Most literature, however, focuses only on less than 5 PV systems during training process, which does not ensure generalization to unseen systems. When in presence of a large feet, regional forecasts are the norm. Nevertheless, none of these approaches are usable when it comes to monitoring residential PV systems. In this work, we propose a single ML model that is able to predict the individual power of a large fleet of 1102 PV systems. XGBoost algorithm was selected as the most suitable algorithm for the task of PV yield nowcasting due to its performance and ease of use. This algorithm obtains Mean Absolute Error (MAE) of 0.877 kWh (considering an average system size of 4.44 kWp) and Mean Absolute Percentage Error (MAPE) of 23% for hourly data aggregated to daily values. XGBoost predictions for individual PV systems are on average two times better than currently used commercial software. We discuss the lack of a suitable loss function that can combine absolute and relative errors for residential PV yield forecasting. We also point out the lack of an adequate metric to compute the error made on the predictions and provide hints on developing a suitable one.

The development of clean hydrogen and photovoltaic (PV) systems is lagging behind the goals set in the Net Zero Emissions scenario of the International Energy Agency. For this reason, efficient hydrogen production systems powered from renewable energy need to be deployed faster. This work presents an optimization procedure for a stand-alone, fully PV-powered alkaline electrolysis system. The approach is based on the Particle Swarm Optimization algorithm to obtain the best configuration of the PV plant that powers the electrolyzer and its compressor. The best configuration is determined with one of three indicators: cost, efficiency, or wasted energy. The PV plant needs to be oversized 2.63 times with respect to the electrolyzer to obtain minimum cost, while for high efficiency, this number increases by 2%. Additionally, the configuration that minimizes cost, wasted energy or maximizes efficiency does not correspond to the configuration that maximizes the annual PV yield. Optimizing for cost results also leads to the best operation of the electrolyzer at partial loads than optimizing for efficiency or wasted energy.

Integration of photovoltaics (PV) into the urban environment will play a major role in the energy transition. However, installing PV systems on building roofs can be challenging, particularly for monumental buildings with strict architectural and social value restrictions. Assessing roof surface visibility is, therefore, key to finding as much permitted roof surface area as possible that may be used for PV installation. In this study, a GIS-based large-scale visibility assessment tool is developed that can assist in evaluating roof visibility, using LiDAR, road networks, and cadastral data as inputs. The tool delivers multi-level outputs, including maps of roof binary visibility, roof visual amplitude, roof PV system layout, roof PV system AC yield, and roof PV module visibility. After optimization, an average speed of 0.12 s/m² is achieved. For each roof surface, an additional sensitivity analysis has been conducted. This step determines the optimal values for two visibility analysis parameters: assessment range and observer spacing, balancing the computational demand and result accuracy. Application of this workflow to the monumental buildings on the TU Delft campus revealed that approximately 2.68 GWh/year of electricity could be harvested from imperceptible PV modules, while an additional 0.42 GWh/year of energy is attributed to PV modules with medium visibility, and 0.37 GWh/year of energy is associated with PV modules with high visibility. This modeling workflow supports the multi-criteria decision-making process for urban roof PV planning.