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.

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.

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.

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.

Adaptive Predefined-Time Backstepping Control for Grid Connected Photovoltaic Inverter

The system performance of grid-connected photovoltaic(PV)has a serious impact on the grid stability.To improve the control performance and shorten the convergence time,a predefined-time controller based on backstepping technology and dynamic surface control is formulated for the inverter in the grid-connected photovoltaic.The time-varying tuning functions are introduced into state-tracking errors to realize the predefined-time control effect.To address the“computational explosion problem”in the design process of backstepping control,dynamic surface control is adopted to avoid the analytical calculations of virtual control.The disturbances of the PV system are estimated and compensated by adaptive laws.The control parameters are chosen and the global stability of the closed-loop is ensured by Lyapunov conditions.Simulation results confirm the effectiveness of the proposed controller and ensure the predefined time control in the photovoltaic inverter.

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.

Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.

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.

Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show multi-peaks.Traditional maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output power.In order to solve the above problems,intelligent algorithms are used in MPPT.However,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search oscillation.Therefore,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this paper.Firstly,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that interval.Compared with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking GMPP.Finally,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Experimental Investigation of the Stability of the Performance Characteristics of a Photovoltaic Module in the Face of Environmental and Meteorological Factors

The explosive technological improvement of photovoltaic systems as well as the necessity of populations to come to less expensive energy sources, that have led to an implosion at the level of solar panel manufacturers. This causes a large flow of these equipments to developing countries where the need is high, without any quality control. That conducted an experimental investigation on the performance characteristics of a 250 wp monocrystalline silicon photovoltaic module in other to check the verification and quality control. Most of these PV panels which often have missing informations are manufactured and tested in places that are inadequate for our environmental and meteorological conditions. Also, their influences on the stability of internal parameters were evaluated in order to optimize their performance. The results obtained at maximum illumination (1000 w/m2) confirmed those produced by the manufacturer. The analysis of these characteristics showed that the illumination and the temperature (meteorological factors) influenced at most the stability of the internal characteristics of the module in the sense that the maximum power increased very rapidly beyond 750 w/m2 but a degradation of performance was accentuated for a temperature of the solar cells exceeding 50°C. The degradation coefficients were evaluated at -0.0864 V/°C for the voltage and at -1.6248 w/°C for the power. The 10° inclination angle of the solar panel proved to be ideal for optimizing overall efficiency in practical situations.

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.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

Cost Management and Cost Control of Photovoltaic Projects

With the rapid development of the photovoltaic(PV)industry and policy support,photovoltaic engineering has attracted much attention as a clean energy project.However,the complexity and huge investment scale of photovoltaic projects make cost management and cost control the key to project success.The purpose of this paper is to discuss the cost management and cost control strategies of photovoltaic projects,analyze their importance and challenges in the process of project implementation,and discuss the common cost control methods and techniques in photovoltaic projects,to improve cost management and cost control in photovoltaic projects,and to provide a reference for the sustainable development of the industry.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the“dual carbon goals,”there has been a robust development of distributed photovoltaic power generation projects in the promotion of their construction.As part of this initiative,a comprehensive and systematic analysis has been conducted to study the overall benefits of photovoltaic power generation projects.The evaluation process encompasses economic,technical,environmental,and social aspects,providing corresponding analysis methods and data references.Furthermore,targeted countermeasures and suggestions are proposed,signifying the research’s importance for the construction and development of subsequent distributed photovoltaic power generation projects.

Recent Advances and Challenges Toward Application of Fibers and Textiles in Integrated Photovoltaic Energy Storage Devices

Flexible microelectronic devices have seen an increasing trend toward development of miniaturized,portable,and integrated devices as wearable electronics which have the requirement for being light weight,small in dimension,and suppleness.Traditional three-dimensional(3D)and two-dimensional(2D)electronics gadgets fail to effectively comply with these necessities owing to their stiffness and large weights.Investigations have come up with a new family of one-dimensional(1D)flexible and fiber-based electronic devices(FBEDs)comprising power storage,energy-scavenging,implantable sensing,and flexible displays gadgets.However,development and manufacturing are still a challenge owing to their small radius,flexibility,low weight,weave ability and integration in textile electronics.This paper will provide a detailed review on the importance of substrates in electronic devices,intrinsic property requirements,fabrication classification and applications in energy harvesting,energy storage and other flexible electronic devices.Fiber-and textile-based electronic devices for bulk/scalable fabrications,encapsulation,and testing are reviewed and presented future research ideas to enhance the commercialization of these fiber-based electronics devices.

Coordinated planning for flexible interconnection and energy storage system in low-voltage distribution networks to improve the accommodation capacity of photovoltaic

The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.