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This study presents a comprehensive approach to developing models for decomposing global solar radiation in to its diffuse and direct components, a critical step in solar energy applications. Utilizing data from multiple geographical regions and Köppen-Geiger climate zones, the research ensures a broad understanding of the environmental factors affecting solar radiation. The study meticulously selects predictors through multiple feature selection techniques, focusing on capturing essential information while minimizing redundancy. Single-predictor models are developed to provide insights into the relationships between selected predictors and the diffuse fraction, while two multi-predictor models demonstrates the potential for more accurate estimations by leveraging the collective predictive power of multiple variables. One of the developed models, called Nested, performs better than other state-of-the-art universal models, while performing slightly worse than the best climate-specific model in terms of three performance metrics. The research lays a solid foundation for future studies and practical applications in the solar energy sector, emphasizing the need for further testing of techniques and procedures for decomposition model development.

This work aims to give insight into the challenges of performing aerial daylight drone Electroluminescence (EL) inspections in PV farms. In this context, we have compared an EL inspection process using a tripod to record the images to an aerial drone EL inspection system. We focus on the challenges of the “Stop and Go” procedure for recording EL images with the drone, which consists of stopping the drone module by module during the inspection flight. The drone inspection tests resulted in an average module inspection time of 24 seconds per module and around 8 minutes per PV string. Regarding image processing challenges, drone inspection requires an added module tracking algorithm due to the drone motion which introduces some artifacts in the image but does not lower the image quality and diagnostic information of the images compared to stationary daylight EL. Images acquired with both the drone and tripod fail in identifying microcracks in the images but do well in detecting larger B and C cracks. For obtaining the best possible image quality, it is essential to have a calibrated camera, avoid shadows from the drone when recording, and avoid taking images with fast-changing irradiance.

To perform outdoor daylight electroluminescence (EL) and photoluminescence (PL) of PV modules for defect and fault detection, it is necessary to filter away any other light source apart from the solar cell's luminescence emission. Image processing is required in broad daylight to increase the signal-to-noise ratio digitally. However, it is reported in the literature that an optical (often bandpass) filter in front of the camera lens or sensor is also needed to overcome the 4-5 orders of magnitude more intense sunlight. The specs of such a "daylight filter" are often mentioned but are rarely the same between research groups and inspection companies. To our knowledge, a study has not addressed the main factors necessary to define an optimal filter for daylight luminescence imaging of PV modules. In this work, we evaluated the spectroscopic characteristics of four filter configurations. We performed daylight EL imaging to estimate their performance in signal-to-noise ratio and EL image quality.

Luminescence imaging is widely used as ground truth for defect and fault detection in many instances of research and PV inspections. Cable handling and PV shading practices for field inspections can correspond to most of the inspection time and be responsible for the highest cost and safety concerns for the procedure. Performing photoluminescence (PL) during an IV sweep that has been already programed to happen in an inverter is an excellent strategy to reach high throughput for PV luminescence-based inspections without significant additional demands in terms of cost and inspection complexity. In this work, we present a detailed image quality analysis with image acquisition performed at different sun excitation intensities, show what is the level of image quality that can be achieved compared with daylight electroluminescence, and the available possibilities to acquire PL images during an inverter IV sweep procedure.

Electroluminescence imaging is a powerful technique for PV fault diagnosis and for this reason there is a growing interest of the O&M industry to make it widely available for in site inspections instead of randomly choosing samples throughout the PV plant. Few groups in the world disclose methodologies for daylight EL (dEL) image acquisition of installed PV modules, using different equipment and image processing procedures. In this work, we aim to compare methodologies from two research groups that have been working on practical ways to perform robust dEL imaging in parallel. We present the results from acquisitions made with similar but not identical equipment, weather conditions and PV module technologies. Image processing on the same datasets from both laboratories is performed using their own methods, indicating if the image processing methods and approach provide consistent results or not. While each laboratory has developed its own signal-to-noise ratio markers, both present good consistency with signal at an exposure time analysis, but not always a direct correspondence to the image quality. This intercomparison will lead to improvements on each laboratory procedure, expand the discussion to other groups and initiate a discussion on the establishment of dEL image quality and SNR metric baselines.

The growing interest in fully decarbonizing worldwide energy systems requires abandoning traditional generation expansion planning in favour of other flexibility-enabling energy system planning tools allowing the integration of energy storage and sector coupling. Therefore, this paper proposes a mixed-integer linear programming formulation focused on enabling flexibility provision on integrated energy systems targeting independent sizing of power and energy capacities for simultaneous generation and storage expansion planning. The optimization problem minimizes investment, maintenance, operation and emissions costs over a 20 year horizon with hourly resolution. In this context, islands represent suitable study cases for the energy transition due to their exceptional renewable availability, and fast-paced development; despite being regions with extreme external dependence and isolation. In particular, the island of Santiago, Cape Verde is selected as study case given its existing targets regarding reaching 50 and 100% renewable shares in 2030 and 2040, its data availability, and the extreme seasonal variation in wind and solar resources. Four scenarios are considered. The first and second dismiss flexibility: business as usual (BAU), and another one aligned with the government goals (Green). Then, the other two exploit it: one equivalent to Green, FlexG, and a last one finding the Optimal. In addition, three energy demand growth levels are considered in order to reduce uncertainty, corresponding to 1, 3 and 5%. Last, a sensitivity analysis with three additional scenarios is performed to provide a thorough view of Cape Verde’s energy future. The results highlight the importance of flexibility exploitation which provides up to 85% savings and allows to decarbonize other sectors via electrification.

The ongoing energy transition and incoming water scarcity crisis demand coordinated research to ensure a fossil-free future for humankind. Aiming to increase energy efficiency, reduce curtailment and decarbonize water production, this paper proposes a novel energy management system (EMS) for a hybrid plant compound by a small modular nuclear reactor acting as cogeneration unit, a wind and solar farms as generators. Additionally reverse osmosis and multi-stage flash desalination plants are included as demand responsive units along with a freshwater storage. Mixed integer linear programming (MILP) is employed to formulate this stochastic optimization problem, where piecewise linear functions define operational costs and efficiencies of SMR and desalination motivating energy efficiency and safety. Renewable availability point forecasts are obtained with physics informed machine learning models whose error is characterised by fitting the predictor's residuals to different statistical distributions following an unsupervised methodology. The suitability of the EMS is addressed in two study cases, one exploring the flexibility exploitation of the algorithm and another proving its suitability for real-time implementation. The dispatcher manages to keep unaltered the SMR's core reaction while satisfying both electrical and water demand in different renewable availability regimes by fully exploiting sector coupling flexibility. Simultaneously, renewable curtailment is kept to a minimum.

This article presents image processing methods for reconstructing and calibrating contactless electroluminescence (EL) images acquired with laser line scanning for diagnostic of photovoltaic modules. The method specifically focuses on two outputs: a contactless conventional EL that is calibrated to correspond to electrically biased EL and a contactless highlighted EL that emphasizes visualization of defects. Results are evaluated first by visual inspection, showing similar resolution to electrically biased EL at 2% or 5% Isc. A quantitative analysis by peak signal to noise ratio (PSNR) measures also assesses the resemblance between the reconstructed contactless conventional EL and the reference electrically biased EL.

With the growth of photovoltaic plant installations in quantity and size, the operations & maintenance industry calls for more efficient and accurate ways to inspect the PV modules health. To expand inspection viability, Daylight Electroluminescence (dEL) plays a big role, as EL allows accurate diagnosis of PV panels deep into the cell level. Obtaining EL images in the field during direct sunlight without covers is challenging but effective and robust using a modulated electrical signal and image processing. The thresholds required by the modulation techniques and a detailed description of how the sunlight influences the capture of the EL images are not clearly described in the research or technical literature. In this case study, our goal is to report how sun irradiance in the plane of the array influences dEL image acquisition of PV panels under direct sunlight and to better specify the irradiance limit for when modulation of the forward bias is necessary instead of constant bias. All this considering reaching the minimum acceptable EL image quality for PV diagnosis using previously available and new signal-to-noise ratio (SNR) markers.

Product integrated photovoltaic (PIPV) systems, can be severely impacted by shading in the surroundings as their collector area is typically small. To ensure the proper functioning of these devices, methodologies to estimate solar irradiance and near shading accurately and efficiently in built up locations are needed. Digital Surface Models (DSM) have proven to be a good alternative to estimate solar irradiance on building surfaces and urban areas. However, high-accuracy DSM are scarce, with limited availability and tend to show the maximum elevation of the obstructions elements disregarding their real shape. We propose a methodology with onsite LiDAR measurements to improve the accuracy to which the solar irradiance can be estimated in urban environments. This methodology produces horizon matrices which can reduce the estimated irradiance’s RMSE by 25-40% compared to fisheye sky imaging methodologies, and by 45-75% to the ones obtained with low-resolution DSMs. However, at locations with low shading profiles the differences are negligible.

In this work, we present and validate a machine learning approach for estimating power loss in monocrystalline silicon PV cells due to cell cracks. The primary focus is selection and evaluation of robust and accurate prediction-features derived from electroluminescence (EL) images of solar cells. To achieve this, we have experimentally created training and validation EL and I-V measurements datasets of cells with two to up to five busbars, with different extents of cell crack degradation. We have evaluated simple features derived from the cells' luminescence histogram, which are easy to compute; these are the cell inactive area, the mean, standard deviation (STD), skewness, and kurtosis. Learning models are then trained against cell I-V measurements and validated. This cell-level approach has the advantage of being scalable so that it could be used to estimate the power loss of a full PV module based on a module EL image that is segmented in cell images. Similarly, this bottom-up approach could facilitate the development of more generalizable machine algorithms, where just one model can be used to simulate power no matter the size of the module, number, layout, or type of the cracked cells. As a baseline, a linear regression approach was attempted, which yielded a R2 score of 0.85 and a maximum error of 0.26. These results were subsequently improved by applying a 2nd degree polynomial linear regression and a custom non-linear regression to 0.869 of R2 and 0.17 of maximum error. The model selection has been chosen from a statistical exploratory analysis of the dataset, where it has been demonstrated that mono-crystalline cells of with different number of busbars behave differently in terms of histogram features correlations with the output power, and that adding a classifier variable as the busbar number as a predictor it is possible to account for the non-linearity of each cell type and model it accordingly.

This paper presents a method to increase the dynamic range of the EL signal for diagnostics of defective areas in PV modules. It applies a computational method for reconstructing a high dynamic range radiance map from a stack of standard dynamic range images at multiple exposure times designed by Debevec and Malik for visible light cameras (RGB). Increasing the precision of the EL signal has two interests: more quantitative information is then available for automatic analysis, and better visual quality is available for visual inspection in case of a small amplitude EL signal, such as acquired in an outdoor daylight setup using lock-in EL. Both are demonstrated via two experiments during which a PV module is electrically biased and imaged at 8 different exposure times with an InGaAs camera: one indoor and one outdoor in daylight. The exposure times are combined to reconstruct an EL image of higher precision. To measure the increased precision, the number of unique pixel values is used: it increases by a factor larger than 2.5 for the indoor case and a factor higher than 100 in the outdoor case. In the outdoor case the improvement towards visual inspection is also clearly apparent.

One aspect of the present disclosure relates to an inspection system, in particular a mobile inspection system, for outdoor inspection of photovoltaic modules in situ, the inspection system comprising a movable detector unit and a processing unit; wherein the detector unit is configured to obtain, when positioned at a user‐controllable position spaced apart from a photovoltaic module of a photovoltaic power system, luminescence data from a light‐receiving surface of said photovoltaic module; the detector unit comprising: a light source, in particular a laser source, configured to emit an illumination beam; means for selectively directing the illumination beam onto respective portions of the light‐receiving surface so as, when the illumination beam is directed onto a first portion of the light‐receiving surface, to only illuminate the first portion while leaving a corresponding first remaining portion of the light‐receiving surface unilluminated by the illumination beam and, when the illumination beam is directed onto a second portion of the light‐receiving surface, to only illuminate the second portion while leaving a corresponding second remaining portion of the light‐ receiving surface unilluminated by the illumination beam; an imaging system configured to capture a plurality of images of the light‐receiving surface, the plurality of images including a first image captured while only the first portion is illuminated by the illumination beam and depicting at least a part of the first illuminated portion and at least a part of the unilluminated first remaining portion, the plurality of images further including a second image captured while only the second portion is illuminated by the illumination beam and depicting at least a part of the second illuminated portion and at least a part of the unilluminated second remaining portion; wherein the processing unit is configured to process the plurality of images to provide reconstructed image data indicative of lumines

Satinated glass is an increasingly important component of PV modules, especially in the building integrated (BIPV) sector, where it can contribute to more uniform appearance as well as significantly reduce the risk of glare. Different satination methods, such as wet chemical etching or mechanical abrasion, are available, which can impact the PV-relevant optical parameters in different ways. In order to characterize the associated performance losses, in this study eight different satinated glass types are compared in terms of spectral transmittance, surface morphology, relative transmission loss and angular dependent reflection losses. Measurements show only insignificant transmission losses of < 1 % for acid-etched glass compared to reference PV glass at normal incidence, with significant gains at high incidence angles > 60°. No significant differences between different acid-etched samples can be observed, all featuring homogeneous appearance. Other satination technologies, such as sandblasting or laser-etching show significantly higher transmission and reflection losses at low incidence angles, making them less viable for use in PV applications.

The heterogenous nature and spectral distribution of rear plane-of-array irradiance RPOA presents challenges when measured by small-area sensors such as pyranometers. Bifacial reference modules serving as large-area sensors can simplify irradiance monitoring because their electrical response follows that of the power generating modules in an array. This article compares RPOA and effective irradiance GE measured by calibrated reference modules against three commonly used small-area sensors including pyranometers, reference cells, and photodiodes. A technology-matched monofacial module is mounted side-by-side with the bifacial reference to decouple effective irradiance measurements into front and backside contributions. The results show that RPOA and GE measurements made with reference panels have the best correlation to reference cells. The mean absolute errors between the two measurement approaches are 9% relative, 4 W/m2 absolute for RPOA and 4% relative, 7 W/m2 absolute for GE. When GE measurements from the four sensor types are used to predict string-level power, the reference panel measurements show a 3.4% prediction error, which is comparable to that achieved when using GE measurements from pyranometers (3.0%) and reference cells (2.9%) thereby suggesting that reference modules can be used to accurately measure RPOA and GE in bifacial systems.

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.

Potential glare through reflected sunlight can be a significant hindrance factor for PV installations, especially for building-integrated photovoltaics (BIPV) with atypical orientations and tilt angles present. Structured glass surfaces or antireflective (AR) coatings are often used as a solution, however there currently is no universally recognized method or metric for estimating their impact on glare. This work presents an evaluation of the glare potential of different surface glasses for use in PV modules based on their bi-directional reflectance distribution functions (BRDF). BRDF threshold values for retinal burn damage, flash blindness and discomfort glare are calculated based on retinal irradiance thresholds from literature. Subsequently, gonioreflectometric measurements on PV mini-modules are used to characterize the reflectance profiles of eight different glass surfaces. Results for all measured structured glasses, both satinated and macro-textured, show significant reductions in BRDF compared to smooth glass, largely eliminating the potential for flash blindness as well as discomfort glare at low incidence angles. At high incidence angles, increased potential for discomfort glare as well as forward scattering along the glass surface can be observed. AR coatings, however, are shown to be insufficient to eliminate glare causing flash blindness.

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN–LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function.

One limitation of photovoltaic energy is the intermittent and fluctuating power output, which does not necessarily follow the consumption profile. Energy storage can mitigate this issue as the generated power can be stored and used at the needed time. Integrating energy storage directly in the PV panel provides advantages in terms of simplified system design, reduced overall cost and increased system flexibility. Incorporating supercapacitors directly in the PV panel on module or cell level raises some challenges regarding the electrical integration, such as charge controlling for the capacitors, capacitor matching, as well as internal power electronics layout. Physical integration of graphene supercapacitors with solar cells, at module-or cell-level presents challenges related to physical dimensioning, thermal management and life expectation of the entire system. The main goal of this article is to review the supercapacitor technologies and perform a comparison between the available supercapacitors in the market and selecting the most suitable type for developing supercapacitor-based integrated PV-energy storage systems, to achieve optimal electrical and physical integration.

Solar energy generated from photovoltaics (PV) suffers from intermittent and fluctuating power output, as it generates in limited hours during the day, affected by irradiation fluctuations, and power generation does not necessarily follow the consumption profile. Intermittent and fluctuating power is mitigated by energy storage, as the generated power can be stored and used at the needed time. Integrating a supercapacitor as energy storage with the PV panel provides advantages such as increased system flexibility but also adds challenges. Using supercapacitors as energy storage outdoors, exposes the whole system to variable temperature, and this will affect the storage capacity. This paper is determining the optimal sizing of supercapacitor energy storage through performing laboratory testing under different temperatures in climatic chambers and different C-rates of discharging. Moreover, a system model for an off-grid PV-supercapacitor system with variable load is also presented together with an optimization method to size the supercapacitor storage unit that is capable of powering the loads 24 hours a day under all weather conditions.

An in-situ method is proposed for monitoring and estimating the power degradation of mc-Si photovoltaic (PV) modules undergoing thermo-mechanical degradation tests that primarily manifest through cell cracking, such as mechanical load tests, thermal cycling and humidity freeze tests. The method is based on in-situ measurement of the module’s dark current-voltage (I-V) characteristic curve during the stress test, as well as initial and final module flash testing on a Sun simulator. The method uses superposition of the dark I-V curve with final flash test module short-circuit current to account for shunt and junction recombination losses, as well as series resistance estimation from the in-situ measured dark I-Vs and final flash test measurements. The method is developed based on mc-Si standard modules undergoing several stages of thermo-mechanical stress testing and degradation, for which we investigate the impact of the degradation on the modules light I-V curve parameters, and equivalent solar cell model parameters. Experimental validation of the method on the modules tested shows good agreement between the in-situ estimated power degradation and the flash test measured power loss of the modules, of up to 4.31 % error (RMSE), as the modules experience primarily junction defect recombination and increased series resistance losses. However, the application of the method will be limited for modules experiencing extensive photo-current degradation or delamination, which are not well reflected in the dark I-V characteristic of the PV module.

Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in a self-sufficient and cost-effective manner. This paper presents an optimal method for designing a photovoltaic (PV)-battery system to supply base stations in cellular networks. A systematic approach is proposed for determining the power rating of the photovoltaic generator and battery capacity from a technical and economical point of view in order to minimize investment cost as well as operational expenditure, while the power autonomy of the PV-battery system is maximized in a multi-objective optimization framework. The proposed method is applied to optimally size a photovoltaic-battery system for three cases with different availability of solar power to investigate the effect of environmental conditions. Problem-solving using the proposed approach leads to a set of solutions at different costs versus different levels of power autonomy. According to the importance of each criterion and the preference of decision-makers, one of the achieved solutions can be selected for the implementation of the photovoltaic-battery system to supply base stations in cellular networks