Forestvoltaics,Floatovoltaics and Building Applied Photovoltaics(BAPV)Potential for a University Campus

The United Nations’Sustainable Development Goals(SDGs)highlight the importance of affordable and clean energy sources.Solar energy is a perfect example,being both renewable and abundant.Its popularity shows no signs of slowing down,with solar photovoltaic(PV)panels being the primary technology for converting sunlight into electricity.Advancements are continuously being made to ensure cost-effectiveness,high-performing cells,extended lifespans,and minimal maintenance requirements.This study focuses on identifying suitable locations for implementing solar PVsystems at theUniversityMalaysia PahangAl SultanAbdullah(UMPSA),Pekan campus including buildings,water bodies,and forest areas.A combined technical and economic analysis is conducted using Helioscope for simulations and the Photovoltaic Geographic Information System(PVGIS)for economic considerations.Helioscope simulation examine case studies for PV installations in forested areas,lakes,and buildings.This approach provides comprehensive estimations of solar photovoltaic potential,annual cost savings,electricity costs,and greenhouse gas emission reductions.Based on land coverage percentages,Floatovoltaics have a large solar PV capacity of 32.3 Megawatts(MW);forest-based photovoltaics(Forestvoltaics)achieve maximum yearly savings of RM 37,268,550;and Building Applied Photovoltaics(BAPV)have the lowest CO2 emissions and net carbon dioxide reduction compared to other plant sizes.It also clarifies the purpose of using both software tools to achieve a comprehensive understanding of both technical and economic aspects.

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Reducing Condensation Inside the Photovoltaic(PV)Inverter according to the Effect of Diffusion as a Process of Vapor Transport

A photovoltaic(PV)inverter is a vital component of a photovoltaic(PV)solar system.Photovoltaic(PV)inverter failure can mean a solar system that is no longer functioning.When electronic devices such as photovoltaic(PV)inverter devices are subjected to vapor condensation,a risk could occur.Given the amount of moisture in the air,saturation occurswhen the temperature drops to the dewpoint,and condensationmay formon surfaces.Numerical simulation with“COMSOL Software”is important for obtaining knowledge relevant to preventing condensation by using two steps.At first,the assumption was that the device’s water vapor concentration was homogeneous to evaluate the amount of liquid water accumulated on the internal walls of the photovoltaic(PV)inverter box.Second,by considering the effect of external wind velocity onmoisture transport at the air interface to evaluate water vapor transport outdoors and reduce condensation.General factorial designs are utilized for analyzing the nature of the relationship between the vapor condensation response and the variables.Reducing vapor condensation inside the solar inverter by the effect of external wind speed on diffusion as a process of transporting moister air outside the inverter box is the main solution for this problem.During the movement and assessment of the flow of water vapor,the impact of vapor condensation is reduced.The saturation period was determined by using a Boolean saturation indicator.The saturation indicator was set to 1 when saturation was detected(relative humidity greater than or equal to 1)and 0 otherwise.Calculating the flow and dispersion of moist air as a function of wind speed helped solve the problem.

Numerical Assessment of the Thermal Efficiency of a Concentrated Photovoltaic/Thermal (CPV/T) Hybrid System Using Air as Heat Transfer Fluid

In this paper, we propose a thermal model of a hybrid photovoltaic/thermal concentration system. Starting from the thermal balance of the model, the equation is solved and simulated with a MATLAB code, considering air as the cooling fluid. This enabled us to evaluate some of the parameters influencing the electrical and thermal performance of this device. The results showed that the temperature, thermal efficiency and electrical efficiency delivered depend on the air mass flow rate. The electrical and thermal efficiencies for different values of air mass flow are encouraging, and demonstrate the benefits of cooling photovoltaic cells. The results show that thermal efficiency decreases air flow rate greater than 0.7 kg/s, whatever the value of the light concentration used. The thermal efficiency of the solar cell increases as the light concentration increases, whatever the air flow rate used. For a concentration equal to 30 sun, the thermal efficiency is 0.16 with an air flow rate equal to 0.005 kg/s;the thermal efficiency increases to 0.19 with an air flow rate equal to 0.1 kg/s at the same concentration. An interesting and useful finding was that the proposed numerical model allows the determination of the electrical as well as thermal efficiency of the hybrid CPV/T with air flow as cooling fluid.

Electro-Optical Model of Soiling Effects on Photovoltaic Panels and Performance Implications

In this paper,a detailed model of a photovoltaic(PV)panel is used to study the accumulation of dust on solar panels.The presence of dust diminishes the incident light intensity penetrating the panel’s cover glass,as it increases the reflection of light by particles.This phenomenon,commonly known as the“soiling effect”,presents a significant challenge to PV systems on a global scale.Two basic models of the equivalent circuits of a solar cell can be found,namely the single-diode model and the two-diode models.The limitation of efficiency data in manufacturers’datasheets has encouraged us to develop an equivalent electrical model that is efficient under dust conditions,integrated with optical transmittance considerations to investigate the soiling effect.The proposed approach is based on the use of experimental current-voltage(I-V)characteristics with simulated data using MATLAB/Simulink.Our research outcomes underscores the feasibility of accurately quantifying the reduction in energy production resulting from soiling by assessing the optical transmittance of accumulated dust on the surface of PV glass.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

A Distributed Photovoltaics Ordering Grid-Connected Method for Analyzing Voltage Impact in Radial Distribution Networks

In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.

Progress of semitransparent emerging photovoltaics for building integrated applications

With the rapid development of emerging photovoltaics technology in recent years,the application of building-integrated photovoltaics(BIPVs)has attracted the research interest of photovoltaic communities.To meet the practical application requirements of BIPVs,in addition to the evaluation indicator of power conversion efficiency(PCE),other key performance indicators such as heat-insulating ability,average visible light transmittance(AVT),color properties,and integrability are equally important.The traditional Si-based photovoltaic technology is typically limited by its opaque properties for application scenarios where transparency is required.The emerging PV technologies,such as organic and perovskite photovoltaics are promising candidates for BIPV applications,owing to their advantages such as high PCE,high AVT,and tunable properties.At present,the PCE of semitransparent perovskite solar cells(ST-PSCs)has attained 14%with AVT of 22–25%;for semitransparent organic solar cells(ST-OSCs),the PCE reached 13%with AVT of almost 40%.In this review article,we summarize recent advances in material selection,optical engineering,and device architecture design for high-performance semitransparent emerging PV devices,and discuss the application of optical modeling,as well as the challenges of commercializing these semitransparent solar cells for building-integrated applications.

A New Dynamic and Vertical Photovoltaic Integrated Building Envelope for High-Rise Glaze-Facade Buildings

Substantially glazed facades are extensively used in contemporary high-rise buildings to achieve attractive architectural aesthetics.Inherent conflicts exist among architectural aesthetics,building energy consumption,and solar energy harvesting for glazed facades.In this study,we addressed these conflicts by introducing a new dynamic and vertical photovoltaic integrated building envelope(dvPVBE)that offers extraordinary flexibility with weather-responsive slat angles and blind positions,superior architectural aesthetics,and notable energy-saving potential.Three hierarchical control strategies were proposed for different scenarios of the dvPVBE:power generation priority(PGP),natural daylight priority(NDP),and energy-saving priority(ESP).Moreover,the PGP and ESP strategies were further analyzed in the simulation of a dvPVBE.An office room integrated with a dvPVBE was modeled using EnergyPlus.The influence of the dvPVBE in improving the building energy efficiency and corresponding optimal slat angles was investigated under the PGP and ESP control strategies.The results indicate that the application of dvPVBEs in Beijing can provide up to 131%of the annual energy demand of office rooms and significantly increase the annual net energy output by at least 226%compared with static photovoltaic(PV)blinds.The concept of this novel dvPVBE offers a viable approach by which the thermal load,daylight penetration,and energy generation can be effectively regulated.

Flexibility potential of Cs_(2)BX_(6)(B=Hf,Sn,Pt,Zr,Ti;X=I,Br,Cl)with application in photovoltaic devices and radiation detectors

As interest in double perovskites is growing,especially in applications like photovoltaic devices,understanding their mechanical properties is vital for device durability.Despite extensive exploration of structure and optical properties,research on mechanical aspects is limited.This article builds a vacancyordered double perovskite model,employing first-principles calculations to analyze mechanical,bonding,electronic,and optical properties.Results show Cs_(2)Hfl_(6),Cs_(2)SnBr_(6),Cs_(2)SnI_(6),and Cs_(2)PtBr_(6)have Young's moduli below 13 GPa,indicating flexibility.Geometric parameters explain flexibility variations with the changes of B and X site composition.Bonding characteristic exploration reveals the influence of B and X site electronegativity on mechanical strength.Cs_(2)SnBr_(6)and Cs_(2)PtBr_(6)are suitable for solar cells,while Cs_(2)HfI_(6)and Cs_(2)TiCl_(6)show potential for semi-transparent solar cells.Optical property calculations highlight the high light absorption coefficients of up to 3.5×10^(5) cm^(-1)for Cs_(2)HfI_(6)and Cs_(2)TiCl_(6).Solar cell simulation shows Cs_(2)PtBr_(6)achieves 22.4%of conversion effciency.Cs_(2)ZrCl_(6)holds promise for ionizing radiation detection with its 3.68 eV bandgap and high absorption coefficient.Vacancy-ordered double perovskites offer superior flexibility,providing valuable insights for designing stable and flexible devices.This understanding enhances the development of functional devices based on these perovskites,especially for applications requiring high stability and flexibility.

Recent advances in two-dimensional photovoltaic devices

Two-dimensional(2D)materials have attracted tremendous interest in view of the outstanding optoelectronic properties,showing new possibilities for future photovoltaic devices toward high performance,high specific power and flexibility.In recent years,substantial works have focused on 2D photovoltaic devices,and great progress has been achieved.Here,we present the review of recent advances in 2D photovoltaic devices,focusing on 2D-material-based Schottky junctions,homojunctions,2D−2D heterojunctions,2D−3D heterojunctions,and bulk photovoltaic effect devices.Furthermore,advanced strategies for improving the photovoltaic performances are demonstrated in detail.Finally,conclusions and outlooks are delivered,providing a guideline for the further development of 2D photovoltaic devices.

Analysis of the Effect of Temperature and Relative Humidity on the Reliability of a Photovoltaic Module

Photovoltaic energy occupies a significant place in the renewable energy market, with photovoltaic (PV) modules playing a vital role in converting solar energy into electricity. However, their effectiveness is likely to be affected by variations in environmental conditions, including temperature and relative humidity. The study examines the impact of these major climatic factors on the reliability of PV modules, aiming to provide crucial information for optimizing and managing these systems under varying conditions. Inspired by Weibull’s law to model the lifespan of components, we proposed a mathematical model integrating a correction factor linked to temperature and relative humidity. Using this approach, simulations in Matlab Simulink reveal that increasing temperature and relative humidity have an adverse impact on the reliability and lifespan of PV modules, with a more pronounced impact on temperature. The results highlight the importance of considering these environmental parameters in the management and optimization of photovoltaic systems to ensure their long-term efficiency.

Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization

Improving the accuracy of solar power forecasting is crucial to ensure grid stability,optimize solar power plant operations,and enhance grid dispatch efficiency.Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties,challenges such as labor-intensive parameter adjustments and complex optimization processes persist.Thus,this study proposed a novel approach for solar power prediction using a hybrid model(CNN-LSTM-attention)that combines a convolutional neural network(CNN),long short-term memory(LSTM),and attention mechanisms.The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy.To prepare high-quality training data,the solar power data were first preprocessed,including feature selection,data cleaning,imputation,and smoothing.The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture,followed by hyperparameter optimization employing Bayesian methods.The experimental results indicated that within acceptable model training times,the CNN-LSTM-attention model outperformed the LSTM,GRU,CNN-LSTM,CNN-LSTM with autoencoders,and parallel CNN-LSTM attention models.Furthermore,following Bayesian optimization,the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model,as evidenced by MRE evaluations.This highlights the clear advantage of the optimized model in forecasting fluctuating data.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Systematic Review on Ground-Based Cloud Tracking Methods for Photovoltaics Nowcasting

Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.

Enhancement of vertical phase separation in sequentially deposited organic photovoltaics through the independent processing of additives

Herein,the impact of the independent control of processing additives on vertical phase separation in sequentially deposited (SD) organic photovoltaics (OPVs) and its subsequent effects on charge carrier kinetics at the electron donor-acceptor interface are investigated.The film morphology exhibits notable variations,significantly depending on the layer to which 1,8-diiodooctane (DIO) was applied.Grazing incidence wide-angle X-ray scattering analysis reveals distinctly separated donor/acceptor phases and vertical crystallinity details in SD films.Time-of-flight secondary ion mass spectrometry analysis is employed to obtain component distributions in diverse vertical phase structures of SD films depending on additive control.In addition,nanosecond transient absorption spectroscopy shows that DIO control significantly affects the dynamics of separated charges in SD films.In SD OPVs,DIO appears to act through distinct mechanisms with minimal restriction,depending on the applied layer.This study emphasizes the significance of morphological optimization in improving device performance and underscores the importance of independent additive control in the advancement of OPV technology.

Polymer Fiber Rigid Network with High Glass Transition Temperature Reinforces Stability of Organic Photovoltaics

Organic photovoltaics(OPVs)need to overcome limitations such as insufficient thermal stability to be commercialized.The reported approaches to improve stability either rely on the development of new materials or on tailoring the donor/acceptor morphology,however,exhibiting limited applicability.Therefore,it is timely to develop an easy method to enhance thermal stability without having to develop new donor/acceptor materials or donor–acceptor compatibilizers,or by introducing another third component.Herein,a unique approach is presented,based on constructing a polymer fiber rigid network with a high glass transition temperature(T_(g))to impede the movement of acceptor and donor molecules,to immobilize the active layer morphology,and thereby to improve thermal stability.A high-T_(g) one-dimensional aramid nanofiber(ANF)is utilized for network construction.Inverted OPVs with ANF network yield superior thermal stability compared to the ANF-free counterpart.The ANF network-incorporated active layer demonstrates significantly more stable morphology than the ANF-free counterpart,thereby leaving fundamental processes such as charge separation,transport,and collection,determining the device efficiency,largely unaltered.This strategy is also successfully applied to other photovoltaic systems.The strategy of incorporating a polymer fiber rigid network with high T_(g) offers a distinct perspective addressing the challenge of thermal instability with simplicity and universality.

High Efficiency Formamidinium-Cesium Perovskite-Based Radio-Photovoltaic Cells

Radio-photovoltaic cell is a micro nuclear battery for devices operating in extreme environments,which converts the decay energy of a radioisotope into electric energy by using a phosphor and a photovoltaic converter.Many phosphors with high light yield and good environmental stability have been developed,but the performance of radio-photovoltaic cells remains far behind expectations in terms of power density and power conversion efficiency,because of the poor photoelectric conversion efficiency of traditional photovoltaic converters under low-light conditions.This paper reports an radio-photovoltaic cell based on an intrinsically stable formamidinium-cesium perovskite photovoltaic converter exhibiting a wide light wavelength response from 300 to 800 nm,high open-circuit voltage(V_(oc)),and remarkable efficiency at low-light intensity.When a He ions accelerator is adopted as a mimickedαradioisotope source with an equivalent activity of 0.83 mCi cm^(-2),the formamidinium-cesium perovskite radio-photovoltaic cell achieves a V_(oc)of 0.498 V,a short-circuit current(J_(sc))of 423.94 nA cm^(-2),and a remarkable power conversion efficiency of 0.886%,which is 6.6 times that of the Si reference radio-photovoltaic cell,as well as the highest among all radio-photovoltaic cells reported so far.This work provides a theoretical basis for enhancing the performance of radio-photovoltaic cells.

Stability Study of Low Voltage Electrical Distribution Network: Audit and Improvement of DJEGBE Mini Solar Photovoltaic Power Plant in the Commune of OUESSE (Benin)

The supply of quality energy is a major concern for distribution network managers. This is the case for the company ASEMI, whose subscribers on the DJEGBE mini-power station network are faced with problems of current instability, voltage drops, and repetitive outages. This work is part of the search for the stability of the electrical distribution network by focusing on the audit of the DJEGBE mini photovoltaic solar power plant electrical network in the commune of OUESSE (Benin). This aims to highlight malfunctions on the low-voltage network to propose solutions for improving current stability among subscribers. Irregularities were noted, notably the overloading of certain lines of the PV network, implying poor distribution of loads by phase, which is the main cause of voltage drops;repetitive outages linked to overvoltage caused by lightning and overcurrent due to overload;faulty meters, absence of earth connection at subscribers. Peaks in consumption were obtained at night, which shows that consumption is greater in the evening. We examined the existing situation and processed the data collected, then simulated the energy consumption profiles with the network analyzer “LANGLOIS 6830” and “Excel”. The power factor value recorded is an average of 1, and the minimum value is 0.85. The daily output is 131.08 kWh, for a daily demand of 120 kWh and the average daily consumption is 109.92 kWh, or 83.86% of the energy produced per day. These results showed that the dysfunctions are linked to the distribution and the use of produced energy. Finally, we proposed possible solutions for improving the electrical distribution network. Thus, measures without investment and those requiring investment have been proposed.