We investigate the performance of a novel flat photovoltaic-thermal (PV-T) module under high-vacuum through a 1D numerical model based on steady-state energy balance, with the aims of optimizing the simultaneous production of thermal and electrical energy. In the proposed design, the photovoltaic (PV) cell is positioned directly above the selective solar absorber (SSA), in a multilayer or fully integrated PV-SSA structure, which allows full exploitation of spectral solar radiation. In fact, in this configuration the losses related to non-absorption of low-energy photons and thermalization, typical of a classical single-junction PV cell, are reduced. The present study is conducted as the emittance and energy bandgap of the PV layer varied, thus admitting a wide variety of materials into the analysis. The dependence of the temperature coefficient, β(%/K), on the energy bandgap of the PV cell is also included. In the last part of the work, we discuss the performance of the proposed evacuated PV-T equipped with a SSA layer and thin film solar cells, namely those made of CdTe, CdS and GaAs. Overall, the paper highlights the great advantage of using high vacuum insulation, which suppresses conductive losses, and the versatility of the proposed system, which could be adapted to the user's needs simply by choosing the appropriate material for the photovoltaic layer.

This work deals with the performance evaluation of novel flat photovoltaic-thermal (PV-T) modules under vacuum. Through a 1D (dimensional) steady-state-energy-balance numerical model developed in MATLAB, two different layouts are studied: the first consisting of a photovoltaic (PV) cell installed just below the glass encapsulating the flat panel, and the second where the PV cell is placed on the selective solar absorber (SSA). In both cases the thermal and electrical efficiencies have been evaluated at different SSA operating temperatures, in the range of 323 K to 423 K. The analysis has been conducted at different energy bandgap (E_bg) of the PV cell and assuming a variable transmittance or emittance of the PV cell, depending on the design. The two systems efficiency comparison has been carried out at the same operating temperature. Overall, this work highlights the importance of high vacuum insulation, which guarantees the reduction of convective thermal losses, and shows that the maximum energy is produced for PV cells with E_bg ≈1.5-1.7 eV, depending on layout and operating temperature, by including the thermal output in the PV-T optimization. The energy and exergy efficiencies obtainable using the proposed PV-T systems are considerably improved compared to the results previously reported in the literature.

In this paper, a study of modeling PV cell (solar cell), PV module (solar modules) and PV array solar array) using Matlab/Simulink software is presented. This study is focused on the study to design photovoltaic cell, photovoltaic modules, photovoltaic panels and PV array (solar array) and to analyze the change in PV performance according to changes in different parameters such temperature, solar irradiation, reverse saturation current, series resistance and shunt resistance. This study shows the PV and IV characteristics curve for each model from which MPP (maximum power point) is determined. In this study, different PV and IV characteristic curves are gained for varying temperatures with specified solar irradiation conditions,s and then the MPP is tracked. The highest power output is also compared between the designed PV module and the selected PV module (MX Solar USA MX60-245)

Recently, solar energy is growing as a power source for potential alternative to meet the global demand. Unlike other energy sources such as coal, nuclear, gas and oil, their prices are not only stable, they prevent the harmful side-effects on the environment, being one of the best sources of clean energy (solar energy). This article presents an analysis of the transformation of the static system for the treatment of solar energy using photovoltaic modules. It is designed to generate energy for future generations to be more useful from different parts of the photovoltaic energy conversion system, such as a DC-DC converter, current inverter, maximum power tracking algorithm (MPPT), filter, the stability of a system, etc. The above result will be useful in the improvement of efficiency in photovoltaics structures.

Integration of battery energy storage in photovoltaic (PV) systems can reduce the electricity costs and provide desirable flexibility and reliability to these systems decreasing renewable energy fluctuations. This paper presents a review of the PV-battery application in Brazil, highlighting the challenges and prospects based on the state-of-art. A PV-battery systems description is presented in this work, as well as the most applied battery technology and its comparison. The paper also describes the set of applications such as voltage and frequency regulation, renewable energy integration, power quality, etc. In the Brazilian scenario, there are applications of PV-battery systems, most of them part of research and development projects (R&D’s), and some real cases are shown, including its goals, applied equipment, operation modes, strategies, and perspectives. Additionally, this work evaluates the Brazilian scenario regarding the energy storage systems implementation challenges, such as regulatory barriers, business models, and opportunities for R&D in the energy market. In conclusion, it is need develop proper regulatory models to expand PV-battery systems and make them visible to the agents in the electricity sector.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms generation is important both for the producers/traders to minimize the imbalance costs and for the Transmission System Operators to insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale PV plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome these undesired effects of the current dispatching regulations.

This paper focuses on designing and assessing Pumped Hydroelectric Energy Storage Systems (PHES), connected to the grid and PV system for self-consumption structured at Mutah university in an area of high solar potential. In focusing on PHES and PV literature was made to have data on the field, based on the grid code needed in Jordan. Next, a prospection to find the proper location for the installation was done. Afterward, a load profile was inserted to know the energy demand of the university. Then The productivity of the solar power plant of Mutah University was included. Finally, MATLAB software was used to realize the amount of energy to be stored, this data was used to implement the system which was chosen and sized. PHES layout was created to find the most accurate values for parameters to optimize the system performance, and to investigate the loss analysis. The system attains 9230.89 MWh/year. An annual load yields 4430 MWh/year, which covers the Mutah university demand with an estimated saving of 2039773 JD.

In this paper, the seven traditional models of PV modules are reviewed comprehensively to find out the appropriate model to be reliable. All the models are validated using the Matlab code and make a graphical comparison. The accuracy and convergence of each model are evaluated using data of manufactured PV panels. Then, a novel model is proposed showing its consistent performance. The most three key parameters of single-diode model are self revised to adapt to various type of PV modules. This new method is verified in three types of PV panels' data measured by National Renewable Energy Laboratory (NREL), US. The validated data show promising results when the error RMSEs' range of the proposed model is under 0.36.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

High-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling. Volt-VAr is one of the common control functions for DER power converters used to enhance the stability and the reliability of the voltage in the distribution system and, thus, fulfilling the network operator requirements. In this study, a centralized algorithm will provide local Volt-VAr control parameters to each PV inverter, based on the electrical grid characteristics where each equipment is installed. Since accurate information of grid characteristics is typically not available, the parametrization of the electrical grid is done using power meter data in DER location and a voltage sensitivity matrix. The algorithm has different optimization modes to both minimize voltage deviation and line current. In order to validate the effectiveness of the algorithm and its deployment in real infrastructure, it has been tested in an experimental setup with PV emulators in a set of 5-day tests. Volt-VAr control algorithm successfully adapted its parameters based on grid topology and PV inverter characteristics, achieving a voltage reduction up to 25% of the allowed voltage deviation.

The outdoor field test of the 4-terminal on Si tandem photovoltaic module (specifically, InGaP/GaAs on Si) was investigated and performance model, considering spectrum change affected by fluctuation of atmospheric parameters, was developed and validated. The 4-terminal on Si tandem photovoltaic module had about 40 % advantage in seasonal performance loss compared with standard InGaP/GaAs/InGaAs 2-terminal tandem photovoltaic module. This advantage is expanded in (subarctic zone) < (temperate zone) < (subtropical zone). The developed and validated model used all-climate spectrum model and considered fluctuation of atmospheric parameters, and can be applied every type of on-Si tandem solar cells.

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

Because of the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade circumstances (PSC). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPT) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time is reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, optimal initialization of search agents, and optimal design of the dc-dc converter is introduced for optimal MPPT performance.

The rapid development of the photovoltaic (PV) industry will result in an increase in the amount of electrical and electronic waste from used PV panels. Only in Poland, the total capacity installed in photovoltaic sources in Poland in May 2020 exceeded 1,950 MW, and the installation weight was approximately 120,000. tone. The problem arises in the recycling or management of this waste. This work presents methods used to recycle waste into photovoltaic modules. The authors investigated the possibility of mechanical and chemical processing of crystalline silicon. A method of thermal treatment of the panel was also proposed. As a result of the research, it was found that the stage of separating materials (crystalline cream, EVO foil, aluminum frame) plays an important role in the recycling process, which is not easy. Chemical treatment allows the silicon to become plastic and the temperature melts the back layer of the panel.

Nowadays, in low voltage (LV) electric distribution networks (EDNs), the Distribution Network Operators (DNOs) are encountering a high number of connected small-scale distributed generation units, mainly photovoltaic (PV) prosumers. The irregular behavior of the prosumers, together with the uncertainty degree of the requested and injected powers associated with all end-users from the LV EDN, can cause voltage variations with violations of the allowable limits. In this context, the paper developed an efficient and resilient expert system integrating the rule-based reasoning applied to the On-load Tap Changer (OLTC)-fitted transformer to improve the efficiency of the voltage control in the PV-rich LV EDNs. An in-depth analysis based on 75 scenarios, resulting from the combinations of three indicators: the penetration degree of the PV prosumers, consumption evolution associated with the consumers, and energy production of the PV systems, has been performed to demonstrate the efficiency of the proposed expert system in an LV EDN from a rural area belonging to a Romanian DNO. The success rate of the expert system has been 86.7% (65 out of 75 scenarios didn't have voltage issues, all values being between the allowable limits in 100% of the time slots associated with the analyzed period). For the other scenarios (representing 13.3%), the values have been inside the range [-10%, +10%] at least 95% of the time slots, according to the European Power Quality Standard, and for the rest of the time slots up to 100% being very close to the upper and lower limits of the range, with highly mitigating of the voltage variations.

A programmable DC power supply with Real-time Digital Simulator (RTDS)-based photovoltaic (PV) Power Hardware-In-the-Loop (PHIL) simulators have been used to improve the control algorithm and reliability of PV Inverter. This paper proposes a supervisory control algorithm for PV PHIL simulator with non-RTDS device that is an alternative solution of high cost PHIL simulator. However, when such a simulator with conventional algorithm which is used in RTDS is connected to a PV inverter, the output is in the transient state and it makes it impossible to evaluate the performance of the PV Inverter. Therefore proposed algorithm controls the voltage and current target values according to the constant voltage (CV) and constant current (CC) modes to overcome the limitation of the Computing Unit, DC power supply and also uses a multi-rate system to account for the characteristics of each component of simulator. A mathematical models of a PV system, programmable DC power supply, isolated DC measurement device and Computing Unit are integrated to form a real-time processing simulator. Performance tests using a PV PHIL simulator which is applied proposed algorithm connected a PV inverter are carried out and proved superiority and utility of this method against conventional methods.

The research work explores the impact of temperature on Silicon photovoltaic (PV) panels considering Nigeria as case study. It is found that high solar radiation in Nigeria increases surface temperature of PV panel above 25oC of optimal operating temperature of PV panels. Redundant energy gain from incident solar energy is dissipated at the rear of PV panels as heat, which reduces the efficiency of PV panel. Cooling mechanism is needed to cool the PV panels to increase the efficiency. In this study we demonstrated a unique design of a bifunctional photovoltaic-thermal (PVT) system by employing heat exchanger at the rear of PV panel and water is used as a working fluid that is circulated through the heat exchanger to remove the excess heat. It is observed from the simulation results that Maiduguri peak annual electrical power output gives 1907kWh/kWp which is the highest, because of high solar radiation average of 727W/m2 in a year. And for Makurdi the peak annual electrical power output of 1542kWh/kWp while for Port Harcourt the peak power output of 1355kWh/kWp respectively. Interestingly, Polycrystalline Si-PV surface temperature decrease from 49.25oC to 38.38oC. Electrical power increased from 1526.83W to 1566.82W in a day and efficiency increase from 13.99% to 15.01%.

This contribution considers an improved control scheme for three-phase two-stage grid-tied photovoltaic (PV) power system based on integral sliding mode control (ISMC) theory. The proposed control scheme consists of maximum power point tracking (MPPT), DC-Link voltage regulation and grid currents synchronization. A modified voltage-oriented maximum power point tracking (VO-MPPT) method based on ISMC theory is proposed for design of an enhanced MPPT under irradiation changes. Moreover, a novel DC-Link voltage control based on ISMC theory is proposed in order to achieve good regulation of DC-Link voltage over its reference. To inject the generated PV power into the grid with high quality, a voltage oriented control based on space vector modulation (SVM) and ISMC (VOC-ISMC-SVM) has been developed to control the grid currents synchronization. Numerical simulations are performed in Matlab/SimulinkTM environment in order to evaluate the proposed control strategy. In comparison with conventional control scheme, the developed control strategy provides an accurate MPP tracking with less power oscillation as well as a fast and an accurate DC-Link regulation under climatic conditions variations. Moreover, the transfer of the extracted power into the grid is achieved with high quality.

This paper aims to present a smart high speed battery charger, powered by a photovoltaic module array, for a LiFePO4 battery as a solar energy storage device. With a battery charging strategy, the presented battery charger involves a Buck converter as the core equipped with a simple maximum power point (MPP) tracker. Considering complexity reduction and easy hardware implementation, a constant voltage MPP tracking approach is adopted such that the maximum amount of output power can be delivered to the load in response to an arbitrary change in the solar radiation. A smart two-stage charging strategy, with a constant current mode followed by a constant voltage mode, is employed in such a way that the battery charge process can be accelerated largely, while the damage caused by overcharging can be prevented. In the end, the performance of this proposal is validated experimentally.

All solar thermal systems, due to their thermal nature, can be hybridised or operated with both fossil fuel and solar energy. By enhancing availability and dispatch ability, hybridization has the potential to boost the value of concentrating solar thermal technology. Hybrid photovoltaic thermal systems (PV/T) offer a solution to improve on the limited capacity growth seen with standard solar panels. The analysis focuses on the creation of alternative structural and design so-lutions that may be used to boost lacking efficiency that was previously hampered by limited spectrum absorption of sun light. As a result, the spectrum is separated into two spectrum ranges, one that directly matches the bandgap of PV materials and the other that is absorbed for thermal absorption outputs. Most two channel systems are used to capture or reflect sunlight, with the layer specifically located right above the PV cell. Another layer of nanofluid is employed to evacuate heat from the PV cell, acting as the second channel, to specifically eliminate overheating difficulties. To address concerns that have arisen as a result of years of observation, the most recent study involves nano-particle-based fluids employed in PV/T, which uses a cooling medium to provide an alternate heat transfer option. Aside from nanofluids, there are phase change materials, channel alterations that result in a higher flow rate and hence improve overall performance.

Almost all single-family detached house in Canada consume huge electricity for space heating and domestic hot water (DHW) purposes. There are many possibilities to design an energy-efficient house. A solar water heating system can be used for domestic water and space heating. Water temperature can be kept constant always by connecting a heat pump or oil burner because solar energy is intermittent. Proper and optimized solar photovoltaic and collector design, tank design, heat pump selection, house insulation, total demand calculation in each section are essential. Energy-Efficient house design has been proposed with water heating and space heating system and compared with the existing system, solar PV based systems, and solar collector based system. The tracking and non-tracking based solar thermal collector based and the solar photovoltaic based system has been compared in this paper and investigate the suitable one for practically applicable and acceptable by the people. Simulation has been done by using the PolySun software. It found that by implementing the proposed PV based system with tracking is highly suitable considering lower cost, high output power, flexibility, easy installation.

This paper presents a microgrid distributed energy resources (DERs) for a rural standalone system. It is made up of solar photovoltaic (solar PV) system, battery energy storage system (BESS), and wind turbine coupled to permanent magnet synchronous generator (WT-PMSG). The DERs are controlled by maximum power point tracking (MPPT) based proportional intergral (PI) controllers for both maximum power tracking and error feedback compensation. The MPPT uses the perturb and observe (P&O) algorithm for tracking the maximum power point of the DERs. The PI gains are tuned using the Ziegler-Nichol’s method. The developed system was built and simulated in MATLAB/Simulink under two conditions - constant load, and step load changes. The controllers enabled the BESS to charge even during conditions of varying load and other environmental factors such as change of irradiance and wind speed. The reference was tracked very well by the output voltage of the DC grid. This is a useful research for electrifying the rural islanded areas, too far from the grid.

Despite the immense application of combustion in reactive materials, one of the challenges people are facing globally is the auto-ignition of such materials, resulting in fire and explosion hazards if proper measures are not considered. To avoid this unfortunate occurrence, a mathematical model describing the thermal decomposition of combustible polymer material in a rectangular stockpile is formulated. A nonlinear momentum equation is provided with the assumption that the combustible polymer follows Carreau constitutive relation. The chemical reaction of the polymer material is assumed exothermic; therefore, Arrhenius’s kinetic theory is considered in the energy balance equation. The bivariate spectral local linearization Scheme (BSLLS) is utilized to provide a numerical solution for the dimensionless equations governing the problem. The obtained results are validated by the collocation weighted residual method (CWRM) and a good agreement is achieved. Dimensionless velocity, temperature, and thermal stability results are presented and explained comprehensively with suitable applications.

Buckypaper (BP), a flexible and porous material, exhibits photovoltaic properties when exposed to light. In this study, we have employed Radio Frequency (RF) Sputtering of Zinc Oxide (ZnO) followed by rapid thermal annealing to enhance the photovoltaic response of BP. We investigated the impact of various sputtering parameters, such as the gas flow ratio of argon to oxygen and deposition time, on the morphology, composition, resistivity, and photovoltaic characteristics of ZnO-modified BP. Additionally, the photovoltaic performance of the samples under different illumination modes and wavelengths was compared. It was found that optimal sputtering conditions—argon to oxygen flow ratio of 1:2, deposition time of 20 minutes, and power of 100 watts—resulted in a ZnO film thickness of approximately 45 nanometers. After annealing at 400°C for 10 minutes, the ZnO-modified BP demonstrated a significant increase in photocurrent and photovoltage, along with a reduction in resistivity, compared to unmodified BP. Moreover, under gradient illumination, the ZnO-modified BP exhibited a photovoltage enhancement of 14.70 times and a photocurrent increase of 13.86 times, compared to uniform illumination. Under blue light, it showed a higher photovoltaic response than under other colors. The enhancement in photovoltaic response is attributed to the formation of a Schottky junction between ZnO and BP, an increased carrier concentration gradient, and an expanded light absorption spectrum. Our results validate that ZnO sputtering followed by annealing is an effective method for modifying BP for photovoltaic applications such as solar cells and photodetectors.

The path towards decarbonization requires a progressive adaptation of all refrigeration systems, but only stationary ones have been intensely studied to improve their environmental performance. However, refrigerated transport is a vital piece of the cold chain, and it must be considered in the green transition. In this paper, we propose a model for a hybrid refrigerated van that includes photovoltaic panels and electric batteries to decrease total greenhouse gas emissions from the engine. Thermal, electrical, and battery sub-models are considered and integrated into the comprehensive hybrid solar-powered refrigerated van model. Different technologies are compared in economic terms, including Lithium and Lead-acid batteries and three different types of photovoltaic panels. The model was validated regarding van fuel consumption, showing a 4% deviation. Single and multiple delivery scenarios are considered to assess the energetic, economic, and environmental benefits. Monthly CO2,e emissions could be reduced to 20% compared to a standard refrigerated van. Despite the environmental benefits provided by this sustainable solution, the payback period is still too long (above 20 years) because of the necessary investment to adapt the vehicle and considering fuel and electricity prices currently.

Dye sensitized solar cell technology is having an important role in renewable energy research due to its features and low cost manufacturing processes. Devices based on this technology appear very well suited for integration into glazing systems due to their characteristics of transparency, color tuning and manufacturing directly on glass substrates. Field data of thermal and electrical characteristics of dye sensitized solar modules (DSM) are important since they can be used as input of building simulation models for the evaluation of their energy saving potential when integrated into buildings. However still few works in the literature provide this information. The study here presented wants to contribute to fill this gap providing a thermal and electrical characterization of a DSM in real operating conditions using a method developed in house. This method uses experimental data coming from test boxes exposed outdoor and dynamic simulation to provide thermal transmittance and solar heat gain coefficient (SHGC) of a DSM prototype. The device exhibits an U-value of 3.6 W/m²K, confirmed by an additional measurement carried on in the lab using a heat flux meter, and a SHGC of 0.2, value compliant with literature results. Electrical characterization evidences an increase of module power with respect to temperature causing DSM suitable for integration in building facades.

The objective of the prototype is to eliminate the polluting contamination of water sources, due to the leak of industrial waste without any kind of treatment, mainly generated by the industries and home sector. In this project, a prototype of water purification by plasma technology has been designed. The prototype will convert contaminated water into the plasma stream and eliminate the pathogens from the water by exposing it to ultraviolet radiation and plasma sterilisation. The polluted water will be accelerated at high speed using a water pump in order to convert it into a liquid-gas mixture for ease plasma generation. This process will be achieved when the electric supply from a source of alternating current (AC) is applied to the water by means of high voltage electrodes. After which, the mixture slows down to return into liquid form and the clean water is obtained. The whole process takes place without significantly raising the temperature also knows as non-thermal plasma. The device also has an automatic flow and pressure control system. Finally, a short feasibility study has been conducted on the water samples collected and report obtained from Chennai Metropolitan Water Supply and Sewage boards are reported. It has been concluded that this new plasma-based water treatment system will be more efficient and cheaper than the current wastewater treatment techniques and can be used in the future as the replacement of current secondary and tertiary treatments of industrial wastewater.

A novel hybrid CSP-PV power plant is presented. Instead of the integration used in current hybrid power plants, where part of the PV production is charged into the thermal energy storage system through electrical resistors, the proposed system integrates both PV and thermal solar fields using a high-temperature heat pump. Both the heat pump and the heat engine are based on Brayton supercritical CO2 thermodynamic cycles. Such integration allows for charging the molten salts storage as if a central tower receiver field supplied the thermal energy, whereas parabolic trough collectors are employed. Unlike conventional hybrid plants, where the storage of PV production leads to a decrease in power injected into the grid throughout the day, the power injected by the proposed system remains constant. The heat engine efficiency is 44.4%, and the COP is 2.32. The LCOE for a 50 MWe plant with up to 12 hours of storage capacity is 171 $/MWh, which is lower than that of existing CSP power plants with comparable performance. Although the cost is higher compared to a PV plant with batteries, this hybrid system offers two significant advantages: it eliminates the consumption of critical raw materials in batteries, and all the electricity produced comes from a synchronous machine.

Polyanilines are a class of conductive polymers that have attracted significant interest in the field of organic electronics due to their unique properties. In this article, the fluorescence spectroscopy and photovoltaic properties of polyaniline-based thin films and copolymers are investigated. The study focuses on understanding the relationship between the composition and structure of polyanilines and their optical and electrical properties. The article discusses the results of experiments on the photoluminescent properties of S1-S9 copolymers in a DMSO solution and of thin-film photoresistors based on polyanilines. The findings reveal that the photovoltaic properties of the thin films depend on the conditions of their fabrication and their surface morphology. The solubility of polyanilines is excellent, making the production of electronic components compatible with modern printing technologies in organic electronics. The work also points out that the photoconductivity of polyanilines can be improved by incorporation of additional molecular groups into the polymer matrix. Overall, this study sheds light on the potential of polyanilines as promising materials for organic electronics and provides insights into how their properties can be optimized for specific applications.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid bat-tery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is of particular importance because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system perfor-mance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, and cost-effective energy storage op-tion. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. In this work, battery-supercapacitor storage system (HBSS) is established by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that ef-fectively controls the different power sources. The algorithm's purpose is to regulate the charge and discharge cycles of the HBSS, ensuring that the state of charge (SOC) of both batteries and supercapacitors remains within the desired range. The proposed management algorithm is de-signed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. By maintaining the SOC of these energy storage components within the specified limits, the proposed method ensures their longevity and max-imizes their performance. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Energy demand and consumption in recent times have witnessed a rapid proliferation influenced by technological developments, increased population and economic growth. This has fuelled research trends in the domain of energy management employing tri-generation systems such as the combined cooling, heating and power (CCHP) systems. Furthermore, the incorporation of renewable energy, especially solar energy, to complement the thermal input by fossil fuels has facilitated the effectiveness and sustainability of CCHP systems. This study proposes a new approach to improve the overall efficiency of CCHP systems and compute the optimal design parameters in order to assist decision makers to identify the best geometrical configuration. A multi-objective optimization formulation of a solar-assisted CCHP system was adopted to maximize the net power, the exergy efficiency and minimize the CO2 emission using the greywolf optimization technique. In addition, the effects of the decision variables on the objective functions were analysed. The proposed optimization approach yielded 100 set of Pareto optimal solutions which would serve as options to the decision maker to make a selection in order to improve the performance of a solar-assisted CCHP system. This study demonstrates that the proposed approach is potentially suitable for the optimization of a solar-assisted CCHP system.

This paper introduces a structured design for an effective power-sharing technique among converter-interfaced distributed generation (DG) units within a microgrid that operates without a synchronous generator. The proposed power-sharing technique leverages the battery energy storage system (BESS) to promptly respond to network changes. The real power output of each commercial distributed energy resources (CDER) unit is governed by a frequency-droop characteristic and a complementary frequency restoration strategy. Deloading techniques are employed by solar and wind power generators to enhance response during power supply or demand disturbances. To ensure microgrid stability, small-signal analysis is conducted for controller design, including the determination of stability margins. Initial tuning of the power-sharing technique parameters is achieved using the Ziegler-Nichols Method, followed by further optimization through a meta-heuristical algorithm to enhance the response time of energy sources. The proposed power-sharing technique's performance is evaluated on a benchmark medium voltage network using industry-standard commercial software. The test results demonstrate the precise and rapid power-sharing capabilities among DGs facilitated by the proposed technique, highlighting its effectiveness in dynamic microgrid environments.

A widely used material for the photovoltaic (PV) arrays is crystalline silicon. The PV conversion losses of a power plant as a yearly average, include: light reflection losses (3,1%), low radiation and shadowing losses (3,2%), DC board losses (1,2%), DC/AC conversion losses (4%), mismatching losses (5,7%), temperature losses (7,6%), it can be stated that the major fraction of losses is related to the temperature increase of the sili-con solar cells. In real operating conditions, solar cells and modules operate at different temperatures, either due to changes in ambient temperature (atmospheric conditions changing with the seasons) and cooling rate, depending on wind speed and insolation, rain, snow, etc., or due to changes in the amount of heat (electrical power lost on the internal resistance of the cell), emitted during their operation. The possibility of operation of these devices in ground applications in the temperature range from -20 to +70C should be taken into account. The given range, of course, does not apply to work in the tropics. The detailed studies of the temperature on the electrical parameters of crystalline silicon solar cells have been presented. The theoretical justification of the temperature influence mechanism on the exploitation parameters of the silicon solar cells was submitted. Photovoltaic cells and modules are of better quality, the lower the value of temperature coefficients, in particular, attention should be paid to the decrease in maximum power with increasing temperature.The experimental results were compared with the theoretical predictions and the results, obtained by other authors and producers.

Castor oil may be differentiated from other non-edible vegetable oils because of its main composition of hydroxylated fatty acids. Ricinoleic acid comprises 80–90% wt. of fatty acids in castor oil (Ricinus communis). In this study, the thermo-oxidative stability and tribological behavior of bio-based lubricant samples synthesized from castor oil using isoamyl alcohol were evaluated. Initially, the compositional and physicochemical properties of the obtained samples were assessed using 1H NMR, FTIR, and ASTM methods. Oxidative stability of the samples was evaluated using Rancimat method at 110 °C under air flow. The final biolubricant sample (BL2), obtained after esterification, epoxidation, and oxirane rings opening reactions, presented an oxidation stability time (OST) of 14.3 h. The thermal stability was also evaluated by thermogravimetry (TG) from the mass variations under inert and oxidative atmosphere. BL2 showed higher thermal stability compared to the other samples, demonstrating higher decomposition temperatures in both inert (339.04 °C) and oxidative (338.47 °C) atmospheres, for a mass loss of 50%. The tribological properties of the samples were evaluated using a four-ball tribometer configuration. The BL1 and BL2 samples exhibited lower friction coefficients than the mineral oil sample (MOS) by 21.5% and 43.1%, respectively. Regarding wear, the observed wear scar diameter (WSD) was also lower in BL1 and BL2 compared to MOS by 5.2% and 40.4%, respectively. The results of the tribological evaluation suggest that both samples obtained in this study have promising potential for applications in lubricating machines and mechanical systems.

A ducted photovoltaic façade (DPV) unit Studied using experimental Prototype and simulated in a full scale Computational Fluid Dynamics CFD Model. The Study comes in two parts; This is Part I with the title detailed above and Part II titled ‘A Ducted Photovoltaic Façade Unit with Buoyancy Cooling: Part II CFD Simulation’.. The process adopted in the experimental study is replicated in the simulation Part. The aim was to optimize the duct width behind the solar cells to allow for maximum buoyancy-driven cooling for the cells during operation. Duct widths from 5 to 50 cm were tested in a Proto-type. A duct width of 45 cm had the maximum calculated heat removed from the duct; however, the lowest cell-operating temperature was reported for duct width of 50 cm. It was found that the DT between ducts' inlets and outlets range from 5.47 °C to 12.32 °C for duct widths of 5–50 cm, respectively. The ducted system enhanced module efficiency by 12.69% by reducing PV temperature by 27 °C from 100°C to 73 °C. The maximum calculated heat recovered from the ducted PV system is 422 W. This is 47.98% from the incident radiation in the test. Total summation of heat recovered and power enhanced by the ducted system is 61.67%.

The growing interest in the use of energy storage systems to improve the performance of tramways has prompted the development of control techniques and optimal storage devices, displacement, and sizing to obtain the maximum profit and reduce the total installation cost. Recently, the rapid diffusion of renewable energy generation from photovoltaic panels has also created a large interest in coupling renewable energy and storage units. This study analyzed the integration of a photovoltaic power plant, supercapacitor energy storage system, and railway power system. Random optimization was used to verify the feasibility of this integration in a real tramway electric system operating in the city of Naples, and the benefits and total cost of this integration were evaluated.

Energy resources are important sources of social and economic development, human survival and security. However, energy resources are objective and scarce, especially the fossil energy, such as coal, oil, natural gas, etc. Human survival environment is increasingly damaged, and the condition of energy is also gradually becoming deficient, so it is necessary to develop new energy resources to meet the increasing demand of material civilization. Solar energy can meet the needs of human development in the future as a kind of clean resource and it has been concerned by all countries in the world. We analyzed the effectiveness of subsidies to the PV industry of both the price and R&D by the spatial econometric model. In some region, the production capacity of photovoltaic industry has shown a significant spatial agglomeration situation in China since 2012. At the same time, benchmarking price and R&D subsidies played a positive incentive to the photovoltaic industry production capacity. It is a certain degree of inverse correlation between the scale of policy subsidies and the resource endowment of solar resources. Therefore, the countermeasures should be considered from the aspects of institutions, industry, technology and public financial policies.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

This research is a preliminary phase of a general effort to develop a generic-data transferring capability via human haptic thermal sensation (generic-data refers to a coded language like Morse or Braille). For the capability to be effective, it must include a large variety of short recognizable cues. Hence, we propose the concept of cues based on sequences of thermal pulses, i.e., combinations of warm and cool pulses with several levels of intensity. The objective of this study was to determine the feasibility of basing a generic-data-transfer capability on thermal cues composed of sequences of short pulses. The research included defining the basic characteristics of the stimuli parameters and developing practical methods for generating and measuring them. Several sequences were designed in light of the relevant data known to date, and tests were conducted. The thermal cues presented to the participants were sensed and recognized by touch. The results indicate high feasibility for a capability that is applicable in various scenarios. In addition, the low impact on human skin temperature due to short stimuli duration represents an inherent advantage for later implementation. This report presents promising findings and offers insights for further investigation.

Photovoltaic (PV) technologies are developing rapidly as a result of their ability to reduce energy consumption from conventional sources as well as the growth of global energy expenditures. Nevertheless, several impact categories during the PV panel life cycle occur and some principal sectors are affected. These impact categories are assessed by applying life cycle analysis (LCA) techniques through various software programs, databases and methods. Such analyses of energy technologies are crucial, as they can reveal the occurrence of hazardous emissions at distinct life cycle stages. In the present paper, a PV panel impact assessment through life cycle analysis is carried out. More precisely, a combination of PV technologies—monocrystalline, multicrystalline, copper indium gallium selenide (CIGS), amorphous, and ribbon—is studied. The impact categories of ozone depletion, human toxicity, particulate matter, ionising radiation, photochemical ozone formation, acidification, eutrophication, and ecotoxicity, as well as the sectors of human health, ecosystem quality, and environment, are assessed using the openLCA software, the ecoinvent database, the eco-indicator 99, IMPACT 2002+, ReCiPe, and TRACI methods.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

This research investigated high-temperature corrosion (500 °C) of Cr-Mo steel processed using water jet peening or multifunction cavitation (MFC), and the suitability of such steel for high-temperature boilers and reaction vessels. High-temperature corrosion was induced using an embedment test and a coating test using sulfide-type K2SO4-Na2SO4 powder. To measure the relaxation of the residual stress due to the decrease in work hardening caused by an increase in specimen temperature and the difference in thermal shrinkage between the surface and interior of the specimen, a thermal cycling test was conducted. For the MFC-processed specimen, the oxide film that formed on the surface suppressed mass loss, prevented crack formation, and reduced the compressive residual stress caused by high-temperature corrosion. MFC-processed Cr-Mo steel is thus suitable for a high-temperature corrosion environment.

Zinc oxide (ZnO) has been widely studied over the last decade for its remarkable properties in optoelectronic and photovoltaic devices because of its high electron mobility and excitonic properties, probably the broadest range of nanostructured forms, and their ease and low cost of synthesis by a wide variety of methods. The volume of recent work on ZnO nanostructures and their devices can potentially overshadow significant developments in the field. Therefore, there is a need for a concise description of the most recent advances in the field. In this review, we focus on the effect of ZnO nanostructure morphologies on the performance of ZnO-based solar cells sensitized using methylammonium lead iodide perovskite. We present an exhaustive discussion of the synthesis routes for different ZnO nanostructure morphologies, ways to control the morphology, and the impact of morphology on the photo-conversion efficiency of a given PSC.

The paper presents the author’ original modification of the residual method in order to construct an efficient tool for assessing the condition of photovoltaic cells, in particular – panels made of amorphous cells which are characterized by significant random operation parameter changes. The solution constructed has been verified through experiments, which show the usefulness of the proposed methodology as it allows to minimize the risk of obtaining false diagnostic information in the process of assessing the condition of photovoltaic panels.

Although solar photovoltaic (PV) system costs have declined, capital cost remains a barrier to widespread adoption. Do-it-yourself (DIY) system designs can decrease costs by about 50% by reducing labor costs, but if not attached to a building structure demand ground penetration for conventional footings. This is not technically and economically feasible at all sites. To overcome these challenges, this study details systems designed to eliminate drilling holes and pouring concrete. Two different designs for both fixed tilt and variable tilt PV racking are investigated to assess their structural integrity, constructability, and economic cost when fabricated with a ballast-supported foundation. The ballast-supported foundations are analyzed for eight such systems by proposing two separate ballast designs: one for single line of post systems, and one for double line of post systems built on a 4-kW basis. The results of the analysis found that both designs are slightly more expensive than typical in-ground concrete systems by 25%, but the overall DIY systems costs remain economically advantageous. Sensitivity analyses are conducted to show how modifications to the dimensions including clearances and distances between gabions influence the weight of the system and thus change the economic value of the design, so users can trade dimensional freedom for cost-savings, and vice versa. Overall, all the wood-based PV racking system designs provide users with cost-effective and easy DIY alternatives to conventional metal racking, and the novel ballast systems presented provide more versatility for PV systems installations.

The growing interest in heat-conducting nanofluids requires highly sensitive methods for analyzing thermal properties. Thermal-lens spectrometry, despite its advantages over classical methods, does not have a general approach to measuring and interpreting results for dispersed systems. In this paper, by the example of nanofluids of silicon oxide in water in a wide range of concentrations and sizes, the selection of measurement parameters for transient and steady-state thermal lensing is justified, and the interpretation of the results of thermal diffusivity measurements is substantiated. The features of measurements of thermal diffusivity by thermal lens spectrometry under stationary state for dispersed systems are considered. Using this approach, it is possible to detect and distinguish thermal effects with high accuracy. For dispersions of silicon oxide, with increasing concentration, the thermal diffusivity passes through a minimum. Silicon oxide dispersions can be used both as a coolant or as a heat-removing liquid by selecting the particle size and concentration.

The assessment of photovoltaic devices, which convert light energy into electricity, becomes significantly relevant due to the aspiration to reduce pollution on a global scale. In this context, the pursuit of optimizing the efficiency in converting light energy into electrical energy involves exhaustive studies and structural analyses of solar cells, all directed toward achieving this goal. This study introduces a research proposal aimed at analyzing the losses associated with series resistance (Rs). The analysis takes into account each component comprising this resistance, pro-posing a network of resistances that precisely models each of these elements. The aforementioned research focused on simple-structured crystalline silicon cells. During this investigation, the junction depth (xj) of n-p materials was varied with the aim of achieving efficiencies in the range of 12%. However, in the pursuit of this desired efficiency, a significant impact on series resistance was observed when analyzing the I-V curves of the cells obtained in each manufacturing process.

This study is focused on an optimal sizing method based on genetic algorithms (GA) in order to design a high-performance photovoltaic system that would be installed in the Alioune Diop University of Bambey, Senegal. The multi-objective optimization allowed us to find a better trade- off between the Total Life Cycle cost (TLCC) and the Loss of Power Supply Probability (LPSP). The goal is to minimize the TLCC while maintaining a good satisfactory of the system with a desired LPSP, under certain constraints. The expressions of the TLCC and the LPSP are established according to the total photovoltaic energy and the battery capacity. The toolbox of Matlab software is used to implement the optimization problem with twelve blocks of the site and its different corresponding loads. The obtained results have led to a several combinations of the photovoltaic and battery capacities of which the best one is selected based on the lowest LPSP, that guaranteed a better load coverage during the night. The results of this proposed method are compared with intuitive method that has been firstly applied. For the block with the lowest load demand, the proposed optimization model leads to a reduction a reduction of 70% and 48% in the storage capacity and the TLCC respectively for a LPSP of 0.007 %.

Recent researches indicated that metal nanoparticles which have the unique optical properties can be used to enhance the spectral response of the photovoltaic modules. Since most of the nanoparticles have enhancement effects in a specific wavelength range, improving the spectral response of the photovoltaic modules in a broadband range is crucial for their applications in imaging, energy harvesting, and optical communication. In this study, flower-like silver particles are applied to achieve the enhancement effects in a broadband range. The optical absorption of photovoltaic modules is improved in a broad wavelength range of 400~2000 nm by immobilizing flower-like silver particles onto an amorphous Si p-i-n structure, and the peak responsivity of the spectral response is enhanced by about 10 times. Theoretical investigation further elaborates that the enhancement originates from the near-field effects of silver particles due to the interaction of different parts of the flower-like silver particles. Through these studies, we demonstrate that, utilizing the subwavelength silver particles with roughness surface can achieve the spectral response of the photovoltaic modules enhanced in broadband range, which can improve the utilization efficiency of optical energy for the applications of sensing, imaging, optical communication, and energy harvesting.

Concentrator photovoltaic (CPV) technology offers an alternative to conventional photovoltaic systems, focusing on the concentration of solar radiation through the optics of the system onto smaller and more efficient solar cells. However, the surface on which the solar beam is incident must remain oriented towards the position of the sun, which requires the use of large and heavy external elements known as solar trackers. In addition to their size, these elements must be as accurate as possible to avoid misalignment. To avoid the need of these solar trackers, the concept of tracking-integrated concentrating systems has been developed. This technology has been researched to make CPV systems accessible in places where conventional systems are not viable, such as building roofs or agrivoltaic fields. This review presents a detailed classification of the existing designs in the literature and providing an overview of this type of system. By eliminating the need for external solar trackers, tracking-integrated CPV systems offer a promising solution to increase the adoption of this technology. Therefore, it is expected that these systems will be a viable and effective option for solar power generation.

The current work investigates how adding a battery of optimal capacity to a grid-connected photovoltaic (PV) system can improve its economic feasibility. Also, the effect of different parameters on the feasibility of the PV system was evaluated. The OBC was determined for different saving targets of the annual electricity consumption of the chosen building. For this aim, real electricity consumption data of a residential building in Landskrona, Sweden, was used as energy consumption profile. Solar World SW325XL, which is a monocrystalline solar panel, was selected as PV panels. The calculations were performed under the metrological and economic conditions of southern Sweden. Different working parameters (WP)were considered (prices of the battery, feed-in tariffs, and saving targets). The performed calculations show that the optimal battery capacity (OBC), in which the payback time (PBT) of the system is maximized, strongly depends on the WP. The proper selection of the battery can considerably increase the economic feasibility of the PV system in southern Sweden. However, in some cases, using battery can have a negative impact on the PBT of the system. The results show that the electricity price, the module price, the inverter price, and the inverter lifetime have the highest effect on the PBT.

We demonstrated an ITO-free, highly transparent organic solar cell with the potential to be integrated into window panes for energy harvesting purposes. A transparent, conductive ZnO/Ag/ZnO multilayer electrode and a Ag:Ca thin film electrode were used in this transparent device as the bottom and top electrode, respectively. To further improve the transmittance of the solar cell, the thickness of the top ZnO layer was investigated by both experiment and simulation. An average visible transmittance of > 60% was reached, with a maximum transmittance of 73% at 556 nm. Both top and bottom illumination of the solar cell generated comparable power conversion efficiencies, which indicates the wide application of this solar cell structure. In addition, we fabricated distributed Bragg reflector mirrors with sputtered SiO2 and TiO2, which efficiently increased the power conversion efficiency over 20% for the solar cells on glass and PET substrates,

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

In the presented paper, a comprehensive study will be done on shape factor analysis of MoS2-GO in H2O-C2H6O2 based hybrid nanoliquids associated with effect and influence of transverse magnetic field and thermal radiation. The effect of variation in different parameters and nanoliquids shapes under temperature and velocity distribution is explored and also non-linear thermal radiation will be analyzed. Algorithms are introduced in proportion to mathematical modeling based on their numerical results and comparative curves for further explanation. In addition, it will be done research for influence and effect of new significant parameters emerged to the model to do sensitivity analysis and also their output results are demonstrated, examined and compared together by presenting graphs and tables. Based on detailed discussions, authentication of attained results designates the high accuracy of applied methods deployed to solve presented model in the paper. Our results satisfy that our used approach is accurate, highly reliable and also effective. All mentioned steps will be described throughout the literature.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

This work presents the design, construction and performance evaluation of a photovoltaic greenhouse as an energy hub (EH) in modern agriculture that integrates battery energy storage (BES) and an electric vehicle charging station (EV). The greenhouse is composed of 48 semi-transparent PV panels with nominal transparency of 20% and 110 W capacity. The control of the PV greenhouse as EH was approached as an optimization problem with the aim of minimizing the cost of the energy used from the grid. Simulation results indicate that the integrated BES is capable of balancing power transactions within the microgrid. In addition, it was found that the case of slow charging of the EV at night was less demanding on the BES than fast charging during the day in terms of abrupt power transitions and average depletion of BES SOC (61% vs 53%). Empirical results also demonstrated the negative impact of dust generated by agricultural activity on the performance of solar panels. For a period analyzed of three years, an average yearly production loss of 6.8% was calculated.

The Internet of Things (IoT) is experiencing exponential growth, revolutionizing various industries with its ability to connect devices and enable advanced applications. In the realm of energy production, IoT offers significant potential for optimizing processes and improving efficiency. This study presents a comprehensive monitoring and energy management strategy for the University of Cuenca's microgrid photovoltaic (PV) system, utilizing IoT and ThingSpeak as key technologies. The proposed strategy leverages intelligent environments and utilizes ThingSpeak as a robust platform for data presentation and analysis. By integrating IoT devices and sensors, real-time monitoring of PV system parameters such as solar radiation and temperature is achieved. The collected data is then analyzed using advanced algorithms and visualized through ThingSpeak, enabling effective energy management and decision-making. The developed monitoring system was rigorously tested in a laboratory microgrid setting, where the PV system is interconnected with other generation and storage systems, as well as the electrical grid. Through the seamless integration of these components, the monitoring system provides enhanced visibility and control over the microgrid's energy production. The results demonstrate the successful implementation of the monitoring system, showcasing its ability to improve the supervision, automation, and analysis of daily energy production. By leveraging IoT technologies and ThingSpeak, stakeholders can access real-time data, analyze performance trends, and optimize energy resources.

The aim of this study is to analyse problems related to thermal mapping obtained from thermal data acquired from Unmanned Aerial Systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software used to obtain the surface temperature maps of thermal images acquired by the UAS. To achieve this aim, we used artificial thermal reference during the surveys, as well as natural hot targets, i.e. thermal anomalies in the Pisciarelli hydrothermal system in Campi Flegrei caldera (CFc). Artificial thermal targets, expressly created and designed for this goal, are a prototype here called “developed thermal target” (DTT) made by the drone Laboratory at Istituto Nazionale di Geofisica e Vulcanologia - Osservatorio Vesuviano (INGV-OV). We show the results obtained by three surveys during which thermal targets were positioned on land at different flight heights of the UAS. Different heights were also necessary to test spatial resolution of the DTT with the used thermal camera as well as possible temperature differences between the raw images acquired by UAS with the thermal mapping obtained from the PIX4D Mapper software. In this work we have estimated the uncertainty that may be introduced by the mosaic procedure and furthermore we find an attenuation of the measured temperatures introduced by the different distances between the thermal anomaly and sensor. These results appear to be of great importance for the subsequent calibration phase of the thermal maps especially in cases where these methodologies are applied for monitoring purposes of volcanic/geothermal areas.

In recent years, several alternatives for improving the thermal comfort conditions inside buildings have been proposed. Among these alternatives, Thermally Activated Building Systems (TABS) have become of interest due to the benefits this technology brings to the building sector. The TABS are embedded in different building components and exchange heat with building envelope to improve the indoor air temperature. This review presents relevant results presented in the literature on the thermal behavior of TABS, the different types of TABS configurations, and the main parameters of TABS studied such as pipe separation, fluid inlet temperature, fluid velocity, and volumetric flow rate. The potential of TABS to improve thermal comfort conditions and provide energy savings is also discussed. Further, this study presents the different modes of application.

Carbohydrate and menthol mouth-swilling have been used to enhance exercise performance in the heat. However, these strategies differ in mechanism and subjective experience. Participants (n=12) sat for 60 min in hot conditions (35°C; 15±2%), following a 15 min control period, participants undertook three 15 min testing blocks. A randomised swill (Carbohydrate; Menthol; Water) was administered per testing block (one swill every three minutes within each block). Heart rate, tympanic temperature, thermal comfort, thermal sensation and thirst were recorded every three minutes. Data were analysed by ANOVA, with carbohydrate intake controlled for via ANCOVA. Small elevations in heart rate were observed after carbohydrate (ES: 0.22 ± 90% CI: -0.09 to 0.52) and water swilling (0.26; -0.04 to 0.54). Menthol showed small improvements in thermal comfort relative to carbohydrate (-0.33; -0.63 to 0.03) and water (-0.40; -0.70 to -0.10), and induced moderate reductions in thermal sensation (-0.71; -1.01 to -0.40 and -0.66; -0.97 to -0.35, respectively). Menthol reduced thirst by a small to moderate extent. These effects persisted when controlling for dietary carbohydrate intake. Carbohydrate and water may elevate heart rate, whereas menthol elicits small improvements in thermal comfort, moderately improves thermal sensation and may mitigate thirst; these effects persist when dietary carbohydrate intake is controlled for.

In this paper, the effect of microstructure on the thermal conductivity of plasma-sprayed Y₂O₃ stabilized ZrO₂ (YSZ) thermal barrier coatings (TBCs) is investigated. Nine freestanding samples deposited on aluminum-base superalloy are studied. Cross-section morphology such as pores, cracks, m-phase content, grain boundary density of the coated samples are examined by scanning electron microscopy (SEM) and electron back-scattered diffraction (EBSD). Multiple linear regressions are used to develop quantitative models which describe the relationship between the particle parameters, m-phase content and the microstructure such as porosity, crack-porosity, the length density of small-angle-crack and the length density of big-angle-crack. Moreover, the relationship between microstructure and thermal conductivity is investigated. Results reveal that the thermal conductivity of the coating is mainly determined by the microstructure and grain boundary density at room temperature (25 ℃) and by the length density of big-angle-crack, monoclinic phase content and grain boundary density at high temperature (1200 ℃).

Graphene and borophene are highly attractive two-dimensional materials with outstanding physical properties. In this study we employed a combined atomistic continuum multiscale modeling to explore the effective thermal conductivity of polymers nanocomposites made of PDMS polymer as the matrix and graphene and borophene as nanofillers. We first conduct classical molecular dynamics simulations to investigate the interfacial thermal conductance between graphene/PDMS and borophene/PDMS interfaces. Acquired results confirm that the interfacial thermal conductance between nanosheets and polymer increases from the single-layer to multilayered nanosheets and finally converges. The data provided by the atomistic simulations were then used in the finite element method simulations to evaluate the effective thermal conductivity of polymer nanocomposites at continuum level. We explore the effects of nanofillers type, their volume content, geometry aspect ratio and thickness on the nanocomposites effective thermal conductivity. As a very interesting finding, we show that borophene nanosheets, despite almost two orders of magnitude lower thermal conductivity than graphene, can yield very close enhancement in the effective thermal conductivity in comparison with graphene, particularly for low volume content and small aspect ratios and thicknesses. We conclude that for the polymer-based nanocomposites, significant improvement in the thermal conductivity can be reached by improving the bonding between the fillers and polymer or in another word enhancing the thermal conductance at the interface. By taking into account the high electrical conductivity of borophene, our results suggest borophene nanosheets as promising nanofillers to simultaneously enhance the polymers thermal and electrical conductivity.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

Photovoltaic shading devices (PVSDs) have the dual function of providing shade and generating electricity, which can reduce building energy consumption and improve indoor daylighting levels. This study adopts a parametric performance design method and establishes a one-click simulation process by using the Grasshopper platform and Ladybugtools. The research focuses on the effect of dynamic PVSDs on daylighting and energy consumption in an office building in Qingdao. Through simulation, the study calculates the monthly energy consumption and useful daylight illuminance (UDI) under different inclination angles and installation heights of PVSDs. And the optimal configuration of PVSDs for each month under three dynamic strategies (rotation, sliding, and hybrid) is determined. Additionally, different control strategies and fixed PVSDs are compared to clarify the impact of various control strategies on daylighting and energy consumption. The findings reveal that, compared to no shading, dynamic PVSDs in rotation strategy (with an installation height of 0 m), sliding strategy (with an inclination angle of 20°), and hybrid strategy can achieve energy savings of 32.13%, 47.22%, and 50.38%, respectively. They can increase the annual average UDI by 1.39%, 2.8%, and 3.1%, respectively.

As one of the main projects of facility agriculture promotion, the photovoltaic greenhouse has the problems of photovoltaic power generation competing for light with crop production, strong indoor chiaroscuro, and uneven light distribution. The internal light uniformity is tested by a zigzag greenhouse model to compare the light transmission effects of different light-transmitting materials applied to photovoltaic greenhouses. 20 line\inch 3mm and 30 line\inch 3mm, 40 line\inch 2mm, 25 line\inch 4mm grating plates, 2mm, and 3mm thick ordinary glass were used as light transmitting components, and the light intensity and light uniformity in the greenhouse were the measurement indicators. The results show that the use of grating plates as covering material can improve the light intensity at the intersection of light and dark, but the overall light transmittance is not as good as glass, because it is plastic which is easy to age with low light transmittance. It can also improve the use of land under the shade of photovoltaic modules to provide a better growth environment for crops. Test results show that the use of a 2mm thick 40-line grating plate can maximize the light intensity of the greenhouse, and the peak value can reach 69336 Lx. In sunny weather, the light intensity from point G to point I in the greenhouse is greater than 20,000 Lx, and the light environment in other areas is between 5,000 Lx and 20,000 Lx, which is suitable for planting shade-loving crops.

In the natural environment, sand accumulation and desertification can influence the efficiency of photovoltaic arrays. In this study, PV module output characteristics were examined under different sand particle size, varying sand densities and inclination angles, and at wind speeds of 5m/s, 10m/s, and 15m/s. Both theoretical and experimental results showed that the output power of the module declines with increasing sand density. As the sand particle size increases, the module maximum output power rises and then stabilizes. The two control variables, namely sand density and sand particle size, influence the module temperature and light transmittance distinctly. Contrary to the impact of sand density on the module filling factor curve, the module filling factor exhibits varying trends under different sand particle sizes. Additionally, the pattern of the module filling factor as a function of sand density contrasts with that of the module temperature under analogous conditions. Specifically, when the sand density on the PV module surface increases from 0 to 40g/m², there is a 32.2% reduction in maximum output power. At a wind speed of 15m/s, this reduction was found consistent across all wind speed categories. Furthermore, the trend of the module filling factor rel-ative to sand accumulation density contrasts with the module temperature trend under analogous conditions. Notably, the filling factor reached its apex at a sand accumulation density of 35g/m², which corresponded to the module temperature's nadir during the experiment.

In recent years, intensive work has been carried out to achieve maximum energy production in photovoltaic power plants. Today, this challenge has to be solved for arrays of solar panels that move with the object on which they are installed (vehicles, trucks, ships, etc.). The present work tries to find the best solution for both constant voltage systems and for cases of maximum intensity. Once found, these solutions will be applied in real time for the dynamic recharging of battery packs, trying to achieve energetically autonomous vehicles.To solve these situations, a new computational method of voltage and amperage calculation has been developed in this paper, based on Dijkstra’s minimum path finding algorithm on graph theory, adapted to electrical circuits. For this purpose, panel performance analysis sensors, developed at the University of Malaga, have been combined with different electronic solutions (wifi relay devices using esp8266 or similar chips) achieving an accurate and sufficiently fast solution at very low cost. With this, series or parallel configurations can be carried out depending on the type of energy generation sought. The theoretical solutions using Minkowski paths, achieved in the past, have been simulated and subsequently constructed in this article, indicating the diagrams necessary for their realisation.

Due to the fact that irrigation networks are water and energy-hungry and that both resources are scarce, many strategies have been developed to reduce this consumption. Otherwise, solar energy sources have become a green alternative with lower energy costs and, as a consequence, lower environmental impacts. In this work, it is proposed a new methodology to select the scheduled program for irrigation which minimizes the number of photovoltaic solar panels to be installed and which better fits energy consumption (calculated for discrete potential combinations; using a programming software to assist) to available energy obtained by panels without any power conditioning unit. So, the irrigation hours available to satisfy the water demands are limited by sunlight, the schedule type of irrigation has to be rigid (rotation predetermined) and the pressure at any node has to be above the minimum pressure required by standards. A real case study has been performed.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

Polyvinyl Alcohol (PVA)/ Graphene Quantum Dots (GQDs) polymer nanocomposite films have been prepared, and the relationship between their structural, thermal and nanoscale morphologi-cal properties, and their photoluminescent response, has been investigated. Although according to X-ray Diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FT-IR) and Differential Thermal Analysis (DTA) the incorporation of GQDs does not significantly affect the percentage crystallinity of the PVA matrix, for a range of added GQD concentrations, Atomic Force Micros-copy (AFM) showed the formation of islands with apparent crystalline morphology on the surface of the PVA/GQD films, with GQD presumably acting as a nucleating agent for island growth. The incorporation of GQD also led to the formation of characteristic surface pores with increased stiffness and frictional contrast, according to Ultrasonic Force Microscopy (UFM) and Frictional Force Microscopy (FFM) data. The photoluminescence (PL) spectra of the films were found to de-pend both on the amount of GQD incorporated and on the film morphology. For GQD loads > 1,2wt%, a GQD-related band was observed at ~ 1650 cm-1 in FT-IR, along with an increase in the PL band at lower energy. For a load of ~2wt% GQD, the surface morphology was characterized by extended cluster aggregates with lower stiffness and friction than the surrounding matrix, and the PL signal decreased.

What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. The internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.

Traditional graphene-based films normally possess high thermal conductivity (TC) only along single direction, which is not suitable for thermal interface materials (TIMs). Here, a graphene film with excellent bidirectional TC and mechanical properties was prepared by hot-pressing super-elastic graphene aerogel (SEGA). Thermal annealing at 1800℃ improves the further restacking of graphene sheets, bring the SEGA high structure stability for enduring the hot-pressing process. The junctions and nodes between the graphene layers in the hot-pressed SEGA (HPSEGA) film provide bidirectional heat transport paths. The in-plane TC and through-plane TC of HPSEGA film with thickness of 101μm reach 740 Wm-1K-1 and 42.5 Wm-1K-1, respectively. In addition, HPSEGA film with higher thickness still maintains excellent thermal transport properties due to the interconnected structure reducing the effect of the defects. The infrared thermal images visually manifest the excellent thermal-transfer capability and thermal dissipation efficiency of the HPSEGA films, indicating the great potential as advanced bidirectional TIMs.

A variety of factors ranging from ethnicity and occupants’ lifestyles to local climatic characteristics of any studied location and people’s age factors can affect thermal comfort assessment globally. Due to the detrimental impact of climate change, the building industry has started to implement energy efficiency schemes while considering the thermal comfort of vulnerable population both for the society and any sphere of economy. Although, considering human-based approaches have been neglected by other scholars in thermal comfort studies. This paper reviews energy effectiveness of state-of-the-art passive systems in providing neutral adaptive thermal comfort for elderly people, through exploring passive design strategies in four distinct climates namely, Canada, India, Abu Dhabi and the South-eastern Mediterranean basin. The aim of the study is to analyze the available data provided by the ASHRAE Global Thermal Comfort Database II version record 2.1. The main objective of the study is to develop an effective methodological framework for the on-going development of adaptive thermal comfort theory. To this extend, this study presents a comprehensive review on the assessment of energy effectiveness of passive design systems. To accomplish this, the impact of climate change factor in passive design systems was investigated. The meta-analysis method was adopted to determine the input variables for the statistical analysis. Cramer’s V and Fisher’s Exact tests were used to assess occupants’ thermal sensation votes (TSVs). The findings revealed that there are discrepancies detected between the in-situ field experiments and the data recorded in the ASHRAE Global Thermal Comfort Database II. According to the Köppen-Geiger climate classification, it was found from the study that the slightly high temperature fluctuations were identified in India where the weather is dry and savanna climate. The study findings contribute to the development of adaptive thermal comfort theory by reviewing the existing methodologies globally. Furthermore, a critical review on the significance of occupants’ age differences should be conducted in the identification of neutral adaptive thermal comfort.

A thermal-oxidative aging test at 120°C was condutcted on Ethylene Propylene Diene Monomer (EPDM) vulcanizates of the semi-efficient vulcanization system. The effect of thermal-oxidative aging on EPDM vulcanizates was systematically studied by curing kinetics, aging coefficient, cross-linking density, macroscopic physical properties, contact angle, Fourier Transform Infrared Spectrometer (FTIR), Thermogravimetric analysis (TGA) and thermal decomposition kinetics. The results show that the content of hydroxyl and carbonyl groups as well as the carbonyl index increased with increasing aging time, indicating that EPDM vulcanizates are gradually oxidized and degraded. As a result, the EPDM vulcanized rubber chains were cross-linked, and its conformational transformation was limited and its flexibility was weakened. The thermogravimetric analysis demonstrates the thermal degradation of EPDM vulcanizates had competitive reactions of cross-linking and degradation, and the thermal decomposition curve can be divided into three stages, meanwhile the thermal stability of EPDM vulcanizates gradually decreases with the increase of aging time. The introduction of antioxidant in the system can promote the cross-linking speed and reduce the cross-linking density of EPDM vulcanizates, while inhibiting the surface thermal and oxygen aging reaction. This is attributed to the fact that the antioxidant can reduce the thermal degradation reaction level, but it is not conducive to the formation of a perfect crosslinking network structure and reduce the activation energy of thermal degradation of the main chain.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

Modelling water vapour flow, heat transfer and porosity in porous adsorbent is somewhat challenging simulation problem. Primary macroscopic water vapour flow models, such as Darcy's law, fail to predict the pressure drop entirely correctly for the reason that many of flow parameters not considered because of the simplifications that remain made for the multi-scale structure of the porous adsorbents. For one to develop a good physical understanding of such water vapour flows and the accuracy of existing 3D simulation models, there is a need for some accurate 3D geometry to be studied. This present work describes two-phase water vapour flow and adsorption/ desorption performed on porous adsorbent by a Dynamic vapour sorption (DVS). The CFD simulation results are associated with experiments results. It is decided that for such complex porous adsorbent CFD simulation problems the use of COMSOL Multiphysics and SolidWorks flow simulation will be utilised.

The performance of ground heat exchangers systems depends on the knowledge of the thermal parameters of the ground like thermal conductivity, thermal capacity and diffusivity. The knowledge of these parameters often requires quite accurate experimental analysis, known under the name of Thermal Response Test (TRT). In this paper, after a general analysis of the various available types of TRT and the study of the theoretical basics of the method, the perspective of the definition of a simplified routine method of analysis based on the combination of a particular version of TRT and the routine geotechnical tests for the characterization of soil stratigraphy and of the ground characteristics, mandatory before the construction of a new buildings, even if limited to quite short drilling depth (lower than 30 m). The idea of developing TRT in connection with geotechnical test activity has the objective of promoting a widespread use of in-situ experimental analysis and of reducing TRT costs and time duration of the experimental analysis. The considerations exposed in the present paper lead to reconsider a particular variety of the TRT in particular the version known as Thermal Response Test while Drilling (TRTWD).

The internet of things and growing demand for smaller and advanced devices has created the problem of high heat production in the electronic equipment which greatly reduces the work performance and life of the electronic instruments. Thermal interface materials (TIMs) are placed in between heat generating micro-chip and the heat dissipater to conduct all the produced heat to the heat sink. Development of suitable TIMs with excellent thermal conductivity (TC) in both in-plane and through-plane directions is a very important need at present. For efficient thermal management, polymer composites are potential candidates. But in general their thermal conductivity is low compared to that of metals. The filler integration into the polymer matrix is one of the two approaches used to increase the thermal conductivity of polymer composites and is also easy to scale up for industrial production. Another way to achieve this is to change the structure of polymer chains, which fall out of the scope of this work. In this review, considering the first approach, the authors have summarized recent development in many types of fillers with different scenarios by providing multiple cases with successful strategies to improve through-plane thermal conductivity (TPTC) (K⊥). For better understanding of TC the comprehensive background is also presented. In the end, it is given a detailed conclusion which provides drawbacks of some filler, multiple significant routes recommended by other researchers to build Thermally Conductive Polymer Composites, future aspects along with direction so that the researchers can get a guideline to design an effective polymer based Thermal interface materials. A number of methods to improve the effective (out-plane) thermal conductivity of polymer composites are also discussed.

The study sought to determine solar irradiation in Homa Bay County which can be tapped and utilized in improving lives of residents of the region by converting the solar thermal energy in Home Bay to other forms of energy such as electric form, mechanical form and light. The study was done by assessing the local atmospheric conditions which included sunshine duration data and air temperature records for the period of two years and the data obtained subjected to statistical analysis to determine the localized characteristics of the resource. The characteristics that were examined include; seasonal and annual power expectations as well as resource reliability. The solar irradiance of Home County was found to be 768.0 W/m².

Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the concept of Digital Twin (DT) and the interest of its application in photovoltaic installations. A comparison is made on how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, the energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It is reviewed how, by providing real-time data and analysis, DTs enable more informed decisions in the solar energy sector. The objectives of the review are to study the digital twin technology and to analyze its application and implementation in photovoltaic systems.

Sustainable agriculture has become increasingly important in Tunisia due to the drought that has been affecting the country's climate in recent years. This has become a major threat to the economy, making it necessary to explore alternative methods of agriculture that are more sustainable. Moreover, smart hydroponic cultivation based on renewable energies has become increasingly widespread, promoting the quality and efficiency of agricultural production despite weather parameters variations and global warming. This work aims to create a sustainable agriculture system in Tunisia through the design and implementation of a smart photovoltaic (PV) hydroponic greenhouse. The greenhouse will utilize advanced technology to optimize plant growth and reduce water usage, while also incorporating solar panels to generate renewable energy. The end goal is to create a self-sufficient and eco-friendly agricultural system that can provide fresh produce to local communities year-round. In this work, we are interested to the automation of the hydroponic greenhouse (HG)’s thermal conditioning system based on an asynchronous motor pump and powered by a PV generator. In the first stage, we analyzed the mathematical modeling of a stand-alone PV-HG system that consists of a PV generator, a three-phase inverter, an asynchronous motor pump, and a hydroponic system. After that, we developed a Field Oriented Control based on proportional integral (PI) regulators to adjust the rotation speed of the asynchronous motor pump, which aimed to provide the cooling and heating needs of the plant. The simulation results demonstrated the effectiveness of the proposed system . Additionally, we designed and implemented a smart system based on the internet of things (IoT) to enable remote control of internal and external parameters of the HG. This intelligent solution utilizes sensors, microcontrollers, and other devices and ensures the monitoring and the maintaining of the ideal growing conditions for our plants, guaranteeing that they receive the proper amount of temperature, humidity, and lighting. This type of system can be an invaluable tool for maximizing yields and achieving optimal results. With the right approach and attention to detail, we can create a sustainable and efficient growing environment that will help our plants flourish year-round.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

As Alberta increases solar power generation, land use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases increased) agricultural output is the co-locating of solar photovoltaics and agriculture: agrivoltaics. This study reviews policies that impact the growth of agri-voltaics in Alberta. Solar PV-based electricity generation is governed by three regula-tions based on system capacity. In addition, agrivoltaics falls under various legisla-tions, frameworks, and guidelines for land utilization. These include Land Use Frame-work, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Dis-position, Bill 22 and other laws/policies all of which are reviewed in the context of agrivoltaics. Several policies are recommended to support rapid diffusion of agrivolta-ics. First, open access research into agrivoltaics, which not only will help optimize agrivoltaic systems for the region, but also coupled with public education is expected to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaic technology, developing agrivoltaic standards, making agri-voltaic technology-friendly regulations/frameworks and developing programs and pol-icies to incentivize agrivoltaic deployment over conventional PV will all accelerate dif-fusion. Through these measures, Alberta can achieve conservation and sustainability in food and energy sector while simultaneously addressing the renewable energy and climate-related goals.

A Life Cycle Analysis was performed considering three existing power plants of comparable size operating with different sources of renewable energy: geothermal, solar and wind. Primary data were used for building the life cycle inventories. The geothermal power plant includes emissions treatment for removal of hydrogen sulfide and mercury. The scenario about the substitution of natural emissions from geothermal energy, with specific reference to the greenhouse effect, is also investigated performing a sensitivity analysis. The results are characterized employing a wide portfolio of environmental indicators employing the Recipe 2016 and the ILCD 2011 Midpoint+ methods; normalization and weighting are also applied using the Recipe 2016 method at endpoint level. The results demonstrate a good eco-profile of geothermal power plant with respect to other renewable energy systems and allow for a critical analysis to support potential improvements of the environmental performances.

The main development direction of energy efficiency technologies in construction is the creation of various materials with a complex structure and unique strength, thermal and other properties. The aerogel is a material with high porosity and excellent thermal insulation properties. This paper provides the state-of-the-art of aerogel applications for the additive manufacturing of energy efficient buildings. This work provides the experimental and numerical assessment results of thermal conductivity of aerogel-enhanced blanket, the experimental assessment results of thermal performance of aerogel-enhanced building structure, the experimental assessment results of the aerogel application as mixture powder component of the concrete mixture to printing building and the experimental assessment results of the aerogel application as parget powder component. Experimental results show the effect of aerogel powder component application: thermal conductivity decreased by 25%.

The paper presents theoretical, experimental and numerical studies on the thermal behavior of mineral wool used in sandwich panels. The aim of this study is to investigate the thermal properties of mineral wool at elevated temperatures and provide a simple model that would allow to determine the heat propagation in sandwich panels during fire. The paper proposes a new method to experimentally evaluate thermal diffusivity, derived from theoretical premises. Experiments are conducted in a laboratory furnace, where specimens are placed and temperatures inside specimens are measured. Different methods are used to process the test results and calculate the thermal diffusivity of mineral wool. Finally, a numerical analysis of heat transfer using finite element method (FEM) is performed to validate the obtained thermal properties.

In this investigation, polymethyl methacrylate (PMMA) was mixed with cyclic olefin copolymer (COC) because of its hardness, strength, and transparency properties. The results of thermal analysis through TGA and DTG showed that the thermal properties of the alloy are improved using 40% cyclic olefin copolymer. Kinetics of thermal degradation (pyrolysis) of polymers have been studied and analyzed and thermal pyrolysis of polymethyl methacrylate and cyclic olefin copolymer thermoplastic polymer was conducted. The computation of kinetic analysis is measured along with the different methods used to study the kinetics. The activation energy (E) of degradation of studied materials was estimated using Ozawa Flynn and Wall (OFW), Starink and Kissinger’s methods, and evaluation of three kinetic parameters taken appropriate kinetic model in terms of percent change for both types of polymers have been proposed, and finally, simulated curves were compared with the experimental curves. Both mechanisms of degradation for COC and PMMA under nitrogen atmosphere will reflect intramolecular transfer and random scission of the main chain.

Polymer composites with high thermal conductivity have a great potential for applications in modern electronics due to their low cost, easy process, and stable physical and chemical properties. Nevertheless, most polymer composites commonly possess unsatisfactory thermal conductivity, primarily because of the high interfacial thermal resistance between inorganic fillers. Herein, we developed a novel method through silver functionalized graphene nanosheets (GNS) and multiwalled carbon nanotube (MWCNT) composites with excellent thermal properties to meet the requirements of thermal management. The effects of composites on interfacial structure and properties of the composites were identified, and the microstructures and properties of the composites were studied as a function of the volume fraction of fillers. An ultrahigh thermal conductivity of 12.3 W/mK for polymer matrix composites was obtained, which is an approximate enhancement of 69.1 times compared to the polyvinyl alcohol (PVA) matrix. Moreover, these composites showed more competitive thermal conductivities compared to untreated fillers/PVA composites applied to the desktop central processing unit, making these composites a high-performance alternative to be used for thermal management.

The study aims to analyze the characteristic parameters of rooftop photovoltaic (PV) power generation on industrial plant buildings in the Ningxia region of China, in order to evaluate the impact of passive design characteristic parameters on the benefits of PV power generation and determine the degree of impact of different passive design characteristic parameters. The methodology involves analyzing the characteristics of existing industrial plant buildings in Ningxia, China, and conducting a series of regional studies on the parameters of plant PV power generation influencing factors. The simulation results revealed that five features, including roof form, PV panel laying pattern, PV panel laying area, azimuth angle, and PV module material, have a significant impact on PV power generation benefits of industrial plant buildings.The study further uses MATLAB 2022b to build a backpropagation neural network model with 5 neurons in the implicit layer to predict the PV power generation benefits. The model calculates the whole-life cycle power generation benefits and the whole-life cycle reduction of carbon emissions as output data. Finally, the Mean Impact Value (MIV) method is employed to select the feature parameter values one by one within their range, and the passive design feature parameters that have the greatest influence on the prediction results are identified as the PV panel laying method, the PV panel tilt angle, and the PV material parameters, respectively.

Manufacturers of photovoltaic modules do not provide all the information necessary to predict their performance under different climatic conditions. This forces the development of mathematical models of behavior and the application of mathematical methods for the calculation of their associated parameters. A toolbox called PVMSim implemented in MATLAB/GUI is proposed in this paper. PVMSIm provides models allowing the simulation of photovoltaic modules. A two-diode model is used to represent the effect of irradiance and temperature on the V-I and V-P characteristic curves. The accuracy of PVMSim has been vaildated by applying the models to two mono-crystalline and multi-crystalline solar modules of different power ratings and manufacturers. The model PVMSim model accuracy was verified against the manufacturer’s datasheet. This paper shows that this tool is useful for designers and professionals who require an intuitive graphical interface for fast and accurate modeling, simulation and sizing of photovoltaic systems.

The aim of the study was to find the effect of polyethylene (PE) coatings on the short-circuit current of silicon photovoltaic cells covered with glass, in order to improve the short-circuit current of the cells. Various combinations of PE films (thicknesses ranging from 9 to 23 µm, number of layers ranging from 2 to 6) with glasses (greenhouse, float, optiwhite and acrylic glass) were investigated. The best current gain of 4.05% was achieved for the coating combining a 1.5 mm-thick acrylic glass with 2 × 12 µm-thick PE films. This effect can be related to the formation of an array of micro-wrinkles and micrometer-sized air bubbles with a diameter of 50 to 600 µm in the films, which serve as micro-lenses and enhance light trapping.

This article has been developed to assess the economic feasibility of a roof-top photovoltaic installation of industrial self-consumption. Numerical models that enable an interested person to obtain the main expected parameters will be generated. To do this, a calculation methodology will be generated through which the reader, knowing the location of the facility and dimensions of the roof, will be able to calculate the maximum installable power, the main parameters related to production, the cost of the installation, and the LCOE of the plant. The use of actual costs will be facilitated in case they are known, but it will remain possible to apply the costs of the major equipment (modules, inverter, and structure) considered throughout the article. This developed calculation methodology will also allow a quick comparison of the forecasts of production, CAPEX, and LCOE of plants designed with different inclinations and different types of modules. Consequently, it will be especially useful for decision-making before developing the plant's basic engineering. Moreover, the calculations used for modeling the LCOE will be analyzed in depth. This analysis will allow evaluating how the different technical variables affect the profitability of a photovoltaic installation, such as the selected tilt, the location, the module's technology, or the available area.

This paper presents a detailed description of three methodologies to electrify the rural households of small loads with reliable and economical method using solar energy as input source of power. It is observed that high DC voltage is better and economical solution rather than AC based solar systems and low voltage DC systems. In this paper, three methodologies were analysed to select the right combination of portable converters with low prices, which may further enhance the energy access and affordability to common people. The proposed three methodologies were tested through action research in collaboration with a team of scientists and technical experts of National Institute of Solar Energy. The comparative analysis is carried out on three different converters, various kind of house loads and types of power supply i.e. AC/DC. The performance, quality and economic evaluation has been carried out with the help of MATLAB SIMULINK and other measuring instruments specific for solar photovoltaic systems. The derived results were analysed and inferences made for use in village household loads and their income groups. Further, a three-step analytical framework on village-level power energy efficient power at affordable power preposition was presented. This study will help in the designing as well as in the performance and economic evaluation of converters based on solar photovoltaic systems of different technologies.

The energy yield of the Vehicle-integrated photovoltaic (VIPV) differs from that of the standard photovoltaics (PV). It is mainly by the difference of the solar irradiance onto the car-roof and car-bodies as well as its curved-shape. Both meaningful and practical modeling and measurement of the solar irradiance for VIPV are needed to be newly established, not the extension of the current technologies. The solar irradiance was modeled by a random distribution of the shading objects and car-orientation with the correction of the curved surface of the PV modules. The measurement of the solar irradiance onto the car-roof and car-body was done using five pyranometers in five local axes on the car for one year. The measured dynamic solar irradiance onto the car-body and car-roof was used for validation of the solar irradiance model in the car.

A car-roof photovoltaic has an enormous potential to change our society. With this technology, 70% of the personal car can run by the solar energy collected by the solar panel on its car-roof. Unfortunately, it is not a simple extension of the conventional photovoltaic technology. This paper list what we need to do for realizing the future that majority of the personal cars run by renewable solar energy, after clarification of the difference from the conventional photovoltaic technology. In addition to the technological development, the standardization of this innovative technology will be important, and the list was made highlighting the standardization.