Central tower Concentrated Solar Power (CSP) technology is a renewable electricity generation method that uses heliostats to direct solar thermal radiation toward a central receiver. While it is considered a promising technology it still faces several challenges. One key issue is the implementation of a control strategy that can effectively manage the short-term variability of solar irradiance caused by cloud cover. This study presents the design and implementation of a cost-effective real-time data acquisition and processing system that uses a commercial fisheye camera and solar radiation sensors. The system allows the real-time creation of shadow maps and short-term forecasts, achieving prediction accuracies of 64.4% and 63.3%, respectively. Additionally, a simplified methodology is introduced for the dynamic identification of the thermal-optical model of the central receiver, making it suitable for real-time control applications. A control strategy is also developed, which combines a generalized predictive controller with a complementary PID. This is to maintain the fluid’s outlet temperature and heat flux at their reference values. It is done while integrating spatially distributed solar radiation forecasts to enhance responsiveness to disturbances, improve operational stability, and protect the central receiver. The system’s performance is validated using actual measured data across various operating scenarios. The implemented strategy results in a significant reduction, up to 93%, in temperature fluctuations at the receiver when compared to conventional methods, while maintaining safe levels of heat flux. These results show the practical feasibility and strong performance of the proposed hybrid predictive control system, highlighting its potential to improve operational reliability, extend equipment longevity, and improve overall efficiency in central-tower CSP plants.

This study presents a method to detect cracks in solar photovoltaic modules by analyzing their dynamic electrical response without interrupting operation. The approach evaluates indicators like settling time and damping coefficient using dynamic current and voltage measurements. Baseline assessments rely on electroluminescence imaging and I-V curve analysis. A DC/DC converter generates transient responses, and outdoor tests under stable irradiance confirm the method's reliability, achieving a correlation coefficient above 0.89. Results show that cracks affect the damping coefficient in both current and voltage. Cracked modules exhibit a damping coefficient notably different from healthy ones. A linear dynamic electrical model supports this, showing healthy modules have a more oscillatory response. This method enables real-time, non-intrusive fault detection in PV modules, offering a practical solution for continuous health monitoring in solar energy systems. Its effectiveness across varying temperatures and irradiance suggests broad applicability in real-world conditions. Future research should address nonlinear aspects of the transient response, extend testing to diverse conditions, and integrate this method with current diagnostic techniques to improve accuracy. Additionally, incorporating advanced signal processing and machine learning could further enhance its ability to identify faults.

In this study, a novel optoelectronic system for fault detection in photovoltaic (PV) cells has been developed. Three sensors, each with a photodiode, were manufactured and mathematical models developed to interpret the fault results from the sensors. The photodiodes sweep across the PV panel to identify areas of high light intensity. The goal is to produce diagnostic images of PV panels that are comparable to standard electroluminescence (EL) imaging. Each sensor was tested under two conditions: darkness and sunlight exposure. For all the sensors, the results obtained in darkness closely match the EL images. However, PV panel exposure to sunlight produces mixed results due to differences in light intensity across the PV cells. To address this issue, two enhancement techniques were developed. First, a collector was used to improve sunlight directionality, with an improved result shown in Sensor 3. Second, a voltage step was added to the PV panel, showing an improved result in all three sensors. Among the tested combinations, the combination of Sensor 3 with an alternative collector and a step-type voltage source produced the best performance. These results clearly indicate that the sensor-based approach can effectively diagnose the PV panel health condition.

The new PV technologies, such as bifacial modules, bring the challenge of analyzing the response of numerical models and their fit to actual measurements. Thus, this study explores various models available in the literature for simulating the IV curve behavior of bifacial photovoltaic modules. The analysis contains traditional models, such as single and double-diode models, and empirical or analytical methodologies. Therefore, this paper proposes and implements a model performance assessment framework. This framework aims to establish a common basis for comparison and verify the applicability of each model by contrasting it with experimental data under controlled conditions of irradiance and temperature. The study utilizes bifacial modules of PERC+, HJT, and n-PERT technologies, tracing IV curves using a high-precision A+A+A+ solar simulator and conducting two sets of laboratory illumination measurements: single-sided and double-sided. In the first case, each face of the module is illuminated separately, while in the latter, the incident frontal illuminating light is reflected on a reflective surface. Experimental data obtained from these measurements are used to evaluate three different approximations for bifacial IV curve models in the case of double-sided illumination. The employed model for single-sided illumination is a single-diode model. The evaluation of various models revealed that shadowing from frames and junction boxes contributes to an increase in the error of modeled IV curves. However, among the three evaluated bifacial electrical models, one exhibited superior performance, with current errors approaching approximately 20%. To mitigate this discrepancy, a proposed methodology highlighted the significance of accurately estimating Io, suggesting its potential to reduce errors. This research provides a foundation for comparing electrical models to identify their strengths and limitations, paving the way for the development of more accurate modeling approaches tailored to bifacial modules. The insights gained from this study are crucial for enhancing the precision of IV curve predictions under various illumination conditions, which is essential for optimizing bifacial module performance in real-world applications.

Nowadays,public policies in Chile are geared to-wards the promotion of distributed energy resources(DERs)such as distributed photovoltaic(PV)systems.However,the pre-vailing socioeconomic context and the lack of incentive to invest in DERs have posed a challenge to achieving the established goals in the coming years.This paper develops a three-entity ar-chitecture model and decision-making algorithms for peer-to-peer(P2P)PV energy trading.It seeks to conduct a sensitivity analysis of a P2P PV energy trading system in a community mi-crogrid,to assess the potential benefits for local communities and to encourage the development of new local public policies aimed at enhancing the profitability of DERs.Various scenarios are compared,both with and without P2P market,considering residential customers(RCs),encompassing both consumers and prosumers with PV systems,with or without battery energy storage systems(BESSs),an aggregator(AG),and utility grid(UG).Daily energy and economic transactions are examined with the aim of reducing the annual electricity bills for each RC,enhancing the profitability of DERs for prosumers,increas-ing incomes for the AG,and exploring potential benefits for the UG.The load profiles and meteorological data are collected from publicly available databases,and a novel electricity pric-ing scheme is proposed based on current rates offered by the lo-cal UG.The results demonstrate that the P2P market could lead to a reduction in the annual electricity bills by as much as 1.76％for consumers,an increase in annual income of up to 149％for prosumers,and a reduction in the payback period for their DERs by up to 0.4 years.This paper contributes to im-proving the investment in DER projects and provides a guide for extending the work to different regions of Chile and global emerging economies with DER potential.

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Thermal energy storage (TES) is one of the key technologies for enabling a higher deployment of renewable energy. In this context, the present study analyzes the modeling strategies of one of the most common TES systems: stratified thermal storage tanks. These systems are essential to many solar thermal installations and heat pumps, among other clean energy technologies. Three different one-dimensional tank models are compared by their computing speed and resilience to long time steps. Two of the models analyzed are numerical, one being explicit and the other one implicit, and the other is analytical. The models are validated against data from experiments carried out considering small-scale stratified tanks, showing that their performance can be improved by using the Advanced Flowrate Distribution (AFD) method. The results show that the analytical model maintains its accuracy with longer time steps and is robust against divergence. Conversely, the numerical models show equivalent performance for short time steps, while the computation time is reduced. Although the AFD method shows promising results by achieving an improvement of 43% in terms of Dynamic Time Warping, its parameter optimization must be generalized for different tank designs, flow rates, and temperatures.

This study provides a comprehensive evaluation of radiation effects on the thermal behavior of a packed bed using a discrete model for heat transfers integrating a novel method called Layer View Factor for view factors estimation, which significantly decreases computational time by less than 9000 times compared to the parallel ray tracing method. The model is validated and strongly aligned with experimental data at 823 K, with a maximum mean bias and root mean square errors of ±4.5 K and 10 K, respectively. Analyses of temperature profiles and thermocline lengths for charging, discharging, and stand-by processes at a charge temperature of 1473 K were performed, finding a thermal flattening effect with radiation, leading to lower peak temperatures and quicker thermocline length evolutions. Notably, the stand-by process exhibited the largest impact, with a maximum temperature difference of up to 100 K compared to the non-radiation case. Moreover, analysis of the temperature and energy of discharge found that the maximum discharge temperature drops below 90% of the charge temperature when radiation is included. Finally, efficiency analysis of the processes showed differences of up to 2% between having or not the radiation effect, aiming its role as a redistribution mechanism in the packed bed's temperature.

Comprehensive radiation studies on packed beds require the estimation of view factors between the surfaces, which often computationally expensive. This paper proposes a novel method based on Monte Carlo Ray Tracing for high-precision and low time computation processing of view factors in random assemblies of cylindrical packed beds. The Monte Carlo Ray Tracing method is improved by coupling it with Kowsary’s tangent sphere method under the parallel computation processing through Compute Unified Device Architecture, showing a reduction of the computational time in approximately 153 times less than a traditional Monte Carlo Ray Tracing method on the hardware used and obtaining a maximum relative error of 0.39% for configurations evaluated in the literature. Furthermore, a calculation methodology for view factors between particle–particle, particle–wall, and particle-lid under an original layer concept, is presented and applied on a set of randomly assembled monosized packed beds generated with the LIGGGHTS software, covering an average porosity range from 0.38 to 0.50 for the arrays. Finally, detailed analysis and discussion of the results are performed, allowing to find characteristic view factors for the particles according to their positions and correlations for view factors particle–wall and particle-lid which is defined as layer view factor method, considering error intervals for each interaction respectively, defining a view factor calculation methodology for packed beds within the studied porosity range.

This study presents a novel algorithm to evaluate the power losses in photovoltaic (PV) modules that are exposed to naturally accumulated soiling. The proposed algorithm uses red, green, and blue (RGB) images of the PV modules captured in the visible spectrum and measures features such as the average color intensity and standard deviation from every color channel. These features are then ranked based on the module performance by using statistical tools, and the most relevant set of features is selected. The selected features are used as an input, and the module power generation is used as the label to train an artificial neural network that outputs a continuous value that represents the instantaneous performance of the evaluated module when compared with a clean module operating under the same environmental conditions. The proposed method is effective for images captured within an irradiance ranging from 700 to 1000 (W/m 2 ), with the predictions achieving an R 2 score of 0.96 and RMSE of 0.74% for power losses ranging from 0% to 20% in natural soiling. This percentage corresponds to a clean condition, where irradiance is not used as an input, making it a cheap and reliable solution to monitor soiling conditions. This is the first work of its kind to demonstrate a correlation between the extracted color features of RGB images and module performance under outdoor conditions for the studied dataset. The proposed algorithm presents less accurate results when it is tested in modules exposed to non‐homogenous soiling, suggesting that the proposed methodology might be effective only for naturally accumulated soiling.

The bond calibration method (BCM) was designed as a calibration procedure to determine the effective Young’s modulus (E) of a compact aggregate of discrete bonded particles. By systematically varying the Young’s modulus of each particle–particle link (EB), the BCM adjusts the E of the aggregate, disregarding additional macroscopic parameters such as the Poisson’s ratio (ν) and the ultimate compressive strength (UCS). In this study, an extension of the BCM is presented in which E, ν, and UCS are the target properties in the micro-characterization of the material. This new approach relies on parametric analysis of the influence of the bond microproperties on the macroscopic mechanical indices of the aggregate obtained from discrete element simulations of uniaxial compression tests (UCTs). The proposed methodology includes three main steps: (i) determination of the influence of particle–particle bond shear modulus (GB) on ν; (ii) calibration of the particle–particle bond Young’s modulus (EB) based on UCT simulations; (iii) setting an appropriate value for the friction angle (ϕB), and performing discrete element method simulations to establish the influence of the cohesion microparameter (cB) on UCS. The reliability of the BCM was tested through DEM simulations of UCTs of Dokoohak limestone specimens with different particle radius distributions (5≤RmaxRmin≤10). The mean relative errors between the target macroproperties and the DEM simulations with microproperties obtained from BCM were 9.74%, 5.17%, 1.55%, for ν (0.25), E (26GPa), and UCS (52MPa) respectively.

Purpose Volumetric air receivers experience high thermal stress as a consequence of the intense radiation flux they are exposed to when used for heat and/or power generation. This study aims to propose a proper design that is required for the absorber and its holder to ensure efficient heat transfer between the fluid and solid phases and to avoid system failure due to thermal stress. Design/methodology/approach The design and modeling processes are applied to both the absorber and its holder. A multi-channel explicit geometry design and a discrete model is applied to the absorber to investigate the conjugate heat transfer and thermo-mechanical stress levels present in the steady-state condition. The discrete model is used to calibrate the initial state of the continuum model that is then used to investigate the transient operating states representing cloud-passing events. Findings The steady-state results constitute promising findings for operating the system at the desired airflow temperature of 700°C. In addition, we identified regions with high temperatures and high-stress values. Furthermore, the transient state model is capable of capturing the heat transfer and fluid dynamics phenomena, allowing the boundaries to be checked under normal operating conditions. Originality/value Thermal stress analysis of the absorber and the steady/transient-state thermal analysis of the absorber/holder were conducted. Steady-state heat transfer in the explicit model was used to calibrate the initial steady-state of the continuum model.

The influence of two operations modes in the solar field of a molten-salt power plant was investigated to evaluate how the receiver pipes' fatigue life changes. Temporal and spatial DNI variation from the WobaS system were integrated with STRAL to obtain the energy flux that heats the working fluid. The temperature distribution over the pipe was obtained by solving a steady-state 3D partial differential equation, validated with a literature model with a 0.1% deviation. The fatigue analysis is based on the elastic behavior assumption. So, the rainflow counting methodology was implemented to consider the irregular sets of equivalent stresses the receiver pipe faced over the transient period. An estimation of the cycles to failure was obtained, and neither operation mode caused fatigue failure before 106 cycles. Still, the controlled aiming point strategy was able to gain an average of 378 MWth over the Solar Two operation mode, where the molten salt would be redirected to the cold tank.

A discrete 1-D Eulerian and 3-D Lagrangian model was developed to analyze heat transfer in packed beds composed of spherical particles to study a in more detail the solid phase phenomenon. The proposed thermal model simulates the particles using the discrete element method, generating a particle distribution inside the packed bed. It considers direct and indirect conduction, convection, and radiation and evaluates the local heat transfer phenomena. One of its main advantages is its flexibility to deliver results at the particle level, which provides detailed information than continuous models. A comparison with experimental data from the literature indicates good agreement, with mean absolute errors lower than 8 K. Finally, through a sensitivity analysis, it was demonstrated that the selection of an appropriate Nusselt number is essential because it is related to the convective heat transfer, which corresponds to 85.8% of the total heat transfer during the charging process. For the standby process, particle fluid conduction is the predominant heat transfer mechanism, accounting for 27.5% of the total heat transfer.

Solar central receivers have inherently large time constants and time delays caused by their thermal inertia are due to the transport of the heat transfer fluid alongside the panels. This problem is even accentuated if these systems are continuously exposed to direct normal irradiance (DNI) fluctuations. Ensuring an adequate operation of this kind of process implies the adaptation of control strategies. This paper proposes a strategy that couples the temperature control of the outlet fluid while considering the heliostats' aiming points location to maintain the receiver operating below its design limit and simulates its performance under several scenarios. These tests are carried out on a non-steady-state solar plant model that considers its optical and thermal characteristics. The structure of the proposed control system is based on using intermediate flow valves along the heating flow path to reduce time delay. Hence, it renders the control system more responsive to either a DNI variation affecting the solar field or moving its operation from one state to another. The control strategy structure contains feedback and feedforward loops to determine the near-future process response to DNI measurements. It allows adding more information to the loop used to maintain the system's desired steady-state operation. The overall strategy can take the process to a steady-state operation regardless of the hour or day of operation. It produces a low variation of the outlet temperature, with a standard deviation of 3 degrees C, while the receiver is under a transient DNI and maintains the heliostats aimed toward the receiver without exceeding its heat flux limits. Results indicate that the proposed strategy achieves over 80% improvement based on the integral absolute error (IAE) performance index compared to conventional single-loop temperature control. Besides the simulated numerical results, this research also presents the advantages of considering intermediate mass-flow valves for control purposes of the receiver. This work should be considered a step towards improving multivariable control algorithms for the safe operation of solar central receivers. (c) 2021 Elsevier Ltd. All rights reserved.

The work addresses the combined effect of temperature difference, longwave atmospheric heat exchanges and solar radiation in the thermal demand produced through walls with radiative insulation. It focuses in the all-year-round performance of a prefabricated wall panel based on multi-layer reflective insulation. The study has been carried out with analytical models, which have been compared and validated by means of experimental tests. Based on the results obtained for the Santiago de Chile climate, the authors have identified the influence of the position of the reflective layers under the combined winter and summer conditions. From these results on, the study has been extended to other three representative Chilean climates, in order to identify the alternative that suits best for each case. The inclusion of up to four reflective sheets on the sides of the air chambers significantly increases the thermal resistance of the element in all cases, with specific differences depending on the position where the reflective layer is placed. All this makes it a suitable constructive solution for climates where heat exchanges are driven by temperature differences. It has also been observed a good performance against heat gains by solar irradiation incidence, irrespective of the radiative attributes of the exterior finishing. The multiple reflective layers reduce the sol-air daily cyclic variation caused by radiation, and the panel performs similarly to a highly reflective and low emissive surface. The work shows the potential of the reflective insulation solutions to cater for the combined winter and summer requests under the multiple climatic conditions typical of the Chilean complex and varied geography.

This study proposes a business model to obtain a successful off-road machinery retrofit using fuel cell technology by the means of evaluating scenarios using the net present value NPV of the project as a figure of merit. Given the uncertainty of some parameters, such as the price of diesel, cost of hydrogen, and cost of technology. It is proposed to carry out a Monte Carlo simulation to sensitize the business model. The results of the simulation declare that the possibility of achieving a positive NPV is increased from 54% considering present conditions to 99% considering projections for the year 2030. The prices of diesel and hydrogen condition the results in a more relevant manner and a price relationship is obtained between these two variables. Taxes could play a key role in the future, according to the results obtained in this study.

This article presents an artificial neural network tool able to quantify the power loss due to soiling and partial shading effects of solar photovoltaic modules in the field, which may play a key factor on an optimal operation and maintenance of PV systems. The proposed approach uses visible spectrum RGB images of multiple solar panels and environmental data to predict each module’s performance individually. The algorithm consists of three main stages. The first step is segmentation, which takes the image input and identifies every module present in the scene using Region Based Convolutional Neural Networks (RCNN) and supervised learning. In the second step, each of these regions is resized and reshaped to achieve a homogeneous format. The final step uses the processed regions and environmental data to predict the performance of each module, categorizing power loss according to a percentile classification. This step uses a convolutional neural network (CNN) designed specifically for this task. When compared to state-of-the-art computer vision architectures, the proposed approach achieved similar results with a significant reduction in computational cost. Preliminary experiments show that the classifier has an accuracy of over 73% when power loss predictions are divided into 8 percentiles ranging from 0 to 100%, where most of the errors originate from minimal differences between the actual and predicted percentiles.

The challenges encountered while concentrating solar radiation from multiple heliostats into a relatively small receiver have inspired numerous aiming methodologies to distribute such concentrated radiation. Likewise, this concentrated radiation, denominated heat flux, needs to satisfy certain constraints that primarily depend on the receiver geometry, its building materials, the operating mass flow of the heat transfer fluid, and the overall solar radiation conditions. A recent study has demonstrated the effectiveness of an aiming strategy wherein a group of heliostats use a single parameter for the entire cluster and achieve the desired heat flux profile by adjusting the tuning parameters. Along similar lines, the current study was conducted to find the optimal values and the effect of two such parameters. The first parameter limits how far the aiming point of the heliostat can move from the equator line of the receiver, while the second represents its direction (upward or downward) from this line toward the edge of the receiver. Each section of a solar field was subdivided; both parameters were estimated for each subgroup, and their effect on the heat flux profile was determined. Furthermore, a parametric study was conducted using three sets of constraints for the optimization procedure. This procedure resulted in a heat flux profile that accomplished the constraints given by the allowable flux density for the receiver during the design day. The improvement using the optimal tuning parameters for the design scenario reached around 27%. Further analysis of the set of optimal values showed an adequate performance of the system at different times of the day and different days of the year. Finally, this study demonstrates how the calculated values function as a starting point for implementing the aiming methodology in different solar field and receiver combinations.

Abstract Solar simulators have been widely used to characterize the performance of solar photovoltaics cells, which typically have a size of 156 × 156 mm2. In order to amplify the testing area, a flexible optimal design method for solar simulators is presented in this study. In this work, 20 quartz tungsten halogen lamps are used with a light filter composed of a mixture of distilled water and cyan ink. The methodology includes the measurements of the irradiance nonuniformities, spectral profile, and explores the effects of light filters on the primary light source used. During this stage, the power source of the lights should be selected, where direct current is usually assumed. As soon as the primary light source is characterized by its corresponding model, a layout is defined by optimizing the nonuniformity of the irradiance. The constructed solar simulator presents a spectral match of 1.69%, a spatial nonuniformity of irradiance of 1.66%, and a temporal instability of irradiance lower than 0.1%. In addition, the current‐voltage curves are compared under indoor and outdoor test showing a root‐mean‐squared error lower than 3%. A class CAA solar simulator is achieved according to the International Electrotechnical Commission and American Society for Testing and Materials standards over an area of 270 × 270 mm2, suitable for testing small size solar photovoltaic modules.