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.

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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.

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East/West (E/W) vertical bifacial photovoltaic (PV) modules can achieve higher profits than the conventional North/South (N/S) tilted configuration depending on the design choices and external conditions. In this study a model based on 2D view factor concept is developed to estimate the power generated by a large-scale bifacial PV farm, considering the non-uniformity of the incident irradiance and the spectral impact. A validation using measured data is performed, focusing on the non-uniformity of the rear irradiance. This model is used to compare the profitability between E/W vertical and N/S tilted PV farm configurations, considering higher prices during noon with respect to morning/evening periods. The results identify the ratio between these two price values as the key variable that influences the comparison between the PV farm configurations. Specifically, a sufficiently high price ratio ensures the higher profitability of E/W vertical modules, however, the exact value is dependent on the location and the design variables. In general, higher row-to-row distance and lower diffuse fraction enhance the profitability of the E/W vertical over the N/S tilted configuration. On the other hand, elevation of the modules, curtailment strategies and hybrid solutions have a minor influence.@en

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.

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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.

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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

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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.

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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.

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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.

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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.

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This paper presents the fundamental study on the relationship between LED light sources and the performance of solar cell as receivers in visible light communication (VLC) links. Here, different colors of LEDs are modulated with a sinusoidal signal, and the bandwidth of the VLC system based on various c-Si PV architectures is characterized at different bias voltages. The preliminary results show that the bandwidth of such VLC system is highly influenced by the bias voltage, where higher bias voltage leads to lower bandwidth. This means that there is a trade-off between energy harvesting and communication performance when solar cells are used as VLC receivers. Meanwhile, we also observed that the bandwidth decreases as the LED irradiance level increases, and the color of LED (currently red and blue LEDs are characterized) does not pose a significant impact on the bandwidth. The highest bandwidth is found at 50 kHz for a VLC system using a 6-in TOPCON solar cell under 100 W/m2 with 100 mV bias voltage.@en

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.

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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.

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This review provides a comprehensive overview of the dynamics of low-temperature water electrolyzers and their influence on coupling the three major technologies, alkaline, Proton Exchange Membrane (PEM) and, Anion Exchange Membrane (AEM) with photovoltaic (PV) systems. Hydrogen technology is experiencing considerable interest as a way to accelerate the energy transition. With no associated CO2 emissions and fast response, water electrolyzers are an attractive option for producing green hydrogen on an industrial scale. This can be seen by the ambitious goals and large-scale projects being announced for hydrogen, especially with solar energy dedicated entirely to drive the process. The electrical response of water electrolyzers is extremely fast, making the slower variables, such as temperature and pressure, the limiting factors for variable operation typically associated with PV-powered electrolysis systems. The practical solar-to-hydrogen efficiency of these systems is in the range of 10% even with a very high coupling factor exceeding 99% for directly coupled systems. The solar-to-hydrogen efficiency can be boosted with a battery, potentially sacrificing the cost. The intermittency of solar irradiance, rather than its variability is the biggest challenge for PV-hydrogen systems regarding operation and degradation.@en

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.

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Machine learning is arising as a major solution for the photovoltaic (PV) power prediction. Despite the abundant literature, the effect of climate on yield predictions using machine learning is unknown. This work aims to find climatic trends by predicting the power of 48 PV systems around the world, equally divided into four climates. An extensive data gathering process is performed and open-data sources are prioritized. A website www.tudelft.nl/open-source-pv-power-databases has been created with all found open data sources for future research. Five machine learning algorithms and a baseline one have been trained for each PV system. Results show that the performance ranking of the algorithms is independent of climate. Systems in dry climates depict on average the lowest Normalized Root Mean Squared Error (NRMSE) of 47.6 %, while those in tropical present the highest of 60.2 %. In mild and continental climates the NRMSE is 51.6 % and 54.5 %, respectively. When using a model trained in one climate to predict the power of a system located in another climate, on average systems located in cold climates show a lower generalization error, with an additional NRMSE as low as 5.6 % depending on the climate of the test set. Robustness evaluations were also conducted that increase the validity of the results.

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Monitoring residential scale photovoltaic (PV) systems is important for maximizing the energy yield and detecting malfunctions. Analytical-based approaches are not reliable in these systems because of the lack of on-site measurements and detailed PV system specifications. In this paper, a collaborative approach is proposed which does not depend on weather data but on similar PV systems. Based on the so-called performance-to-peer approach, the aim of this work is to improve this baseline model by adding PV systems characteristics and by optimizing with machine learning techniques. The methodology has been tested in a fleet of more than 12,000 PV systems located in the Netherlands with up to 7 years of data per system. The proposed model achieves an average (Formula presented.) of 94.1% and a NRMSE of 0.05, outperforming in terms of (Formula presented.) the baseline model by 1.4 points, and the analytical approach by 3.8. The data requirements of this model are not high: With 1,700 years of PV system data with daily resolution, the maximum performance can be achieved as long as a minimum of 6 months of data per system and 100 PV systems are considered. The application of this model for fault detection and categorization has also been shown. The proposed approach has shown its strengths with respect to other methods through its ability of distinguishing between system mismatch and actual fault and of adapting to new situations via retraining.

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We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems.

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The surface of the vessels, as moving tiny islands, can be utilized to implement vehicle-integrated photovoltaics (VIPV). Herein, a methodology is reported to calculate the power generated by a fleet of urban vessels as a function of time. Then, the result is shown for the largest European shipping fleet, using sailing data of 2746 Dutch general cargo vessels. Results show that the studied fleet can produce ≈ 226 GWh of energy per year, which corresponds to ≈ 6.5% of the whole fleet's energy demand. Next, this research validates the model with three week experimental data gathered by a test vessel sailing through the Netherlands. The validation phase reveals that the model can predict within a 4% error range. Finally, as an interesting finding, it is experimentally shown that the energy production profile of a fleet of urban vessels follows a Weibull distribution, quantified by scale (λ) and shape (k) parameters: λ = 880 Wh Wp⁻¹ and k = 27 for the Dutch fleet. A sensitivity analysis shows that the parameters of the Weibull distribution are a function of urban fabric roughness and the climate. Such probability distribution can be extended to other urban fleets, such as solar cars, and help estimate the financial feasibility of integrating PV into vehicles.

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After developing the methodology, we applied it to the case of the Netherlands highways. We show that the average irradiation on the Dutch highway network is around 880 kWh/m2/y, 35% less than the potential of an optimally tilted conventional PV system in the south of the Netherlands. Covering the entire 1600 km of the Dutch highways network with solar road modules of poly c-Si, mono c-Si and CIGS would respectively generate 5.2 TWh/y, 6.6 TWh/y, and 3.4 TWh/y of DC electricity. This could be used to fully power the Dutch national public lighting demand. Moreover, to include the effect of traffic on these values, a model was developed to account for the energy potential reduction due to vehicles shading. Using real traffic data from two of the top-four busiest roads in the Netherlands, the A12 and A16, it was found that traffic accounts for an average of 3% reduction of solar road irradiation and DC yield potential. The maximum reduction of 9% was observed in particular locations, such as bridges and nearby ramp roads. The result of such mapping methodology could serve as a useful tool for research advisory, private industry, and governmental projects.@en