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 ...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.展开更多
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...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/m<sup>2</sup>) 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/m<sup>2</sup> 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.展开更多
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...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
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-tempora...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.展开更多
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.Traditiona...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.展开更多
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 comprehen...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.展开更多
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 dime...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.展开更多
A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reli...A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.展开更多
Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particl...Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particle removal from photovoltaic(PV)panels by compressed air by analyzing the force exerted on the dust deposited on inclined photovoltaic panels,which also included different electrification mechanisms of dust while it is in contact with the PV panel.The results show that the effect of the particle charging mechanism in the electric field generated by the PV panel is greatly smaller than the effect of the Van der Waals force and gravity,but the effect of the particle charged by the contact electrification mechanism in the electrostatic field is very pronounced.The wind speed required for dust removal from the PV panel increases linearly with the PV panel electric field,so we suggest that the nighttime,when the PV electric field is relatively small,would be more appropriate time for dust removal.The above results are of great scientific importance for accurately grasping the dust distribution law and for achieving scientific removal of dust on PV panels.展开更多
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 v...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.展开更多
Converting solar energy into electric power or hydrogen fuel is a promising means to obtain renewable green energy.Here, we design a two-dimensional blue phosphorene(BlueP)/MoSi2N4van der Waals heterostructure(vdWH) a...Converting solar energy into electric power or hydrogen fuel is a promising means to obtain renewable green energy.Here, we design a two-dimensional blue phosphorene(BlueP)/MoSi2N4van der Waals heterostructure(vdWH) and investigate its potential application in photocatalysis and photovoltaics using first-principles calculations. We find that the BlueP/MoSi2N4vdWH possesses type-Ⅱ band structure with a large build-in electric field, thus endowing it with a potential ability to separate photogenerated electron–hole pairs. The calculated band-edge positions show that the heterostructure is a very promising water-splitting photocatalyst. Its solar-to-hydrogen efficiency(ηSTH) can reach up to 15.8%, which is quite promising for commercial applications. Furthermore, the BlueP/MoSi2N4vdWH shows remarkably light absorption capacity and distinguished maximum power conversion efficiency(ηPCE) up to 10.61%. Remarkably, its ηPCEcan be further enhanced by the external strain: the ηPCEof 21.20% can be obtained under a 4% tensile strain. Finally, we determine that adjusting the number of the BlueP sublayer is another effective method to modulate the band gaps and band alignments of the heterostructures. These theoretical findings indicate that BlueP/MoSi2N4vd WH is a promising candidate for photocatalyst and photovoltaic device.展开更多
Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large ove...Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.展开更多
Lead-based organic-inorganic hybrid perovskites have exhibited great potential in photovoltaics,achieving power conversion efficiencies(PCEs) exceeding 25%.However,the toxicity of lead and the instability of these mat...Lead-based organic-inorganic hybrid perovskites have exhibited great potential in photovoltaics,achieving power conversion efficiencies(PCEs) exceeding 25%.However,the toxicity of lead and the instability of these materials under moist conditions pose significant barriers to large-scale production.To overcome these limitations,researchers have proposed mixed-valence double perovskites,where Cs_(2)Au~ⅠAu~ⅢI_6 is a particularly effective absorber due to its suitable band gap and high absorptance efficiency.To further extend the scope of these lead-free materials,we varied the trivalent gold ion and halogen anion in Cs_(2)Au~ⅠAu~ⅢI_6,resulting in 18 new structures with unique properties.Further,using first-principles calculations and elimination criteria,we identified four materials with ideal band gaps,small effective carrier mass,and strong anisotropic optical properties.According to theoretical modeling,Cs_(2)AuSbCl_6,Cs_(2)AuInCl_6,and Cs_(2)AuBiCl_6 are potential candidates for solar cell absorbers,with a spectroscopic limited maximum efficiency(SLME) of approximately 30% in a 0.25 μm-thick film.These three compounds have not been previously reported,and therefore,our work provides new insights into potential materials for solar energy conversion.We aim for this theoretical exploration of novel perovskites to guide future experiments and accelerate the development of high-performance photovoltaic devices.展开更多
Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent developm...Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.展开更多
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are s...Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.展开更多
This paper presents a new optimization study of the placement and size of a photovoltaic source(PVS)in a distribution grid,based on annual records of meteorological parameters(irradiance,temperature).Based on the reco...This paper presents a new optimization study of the placement and size of a photovoltaic source(PVS)in a distribution grid,based on annual records of meteorological parameters(irradiance,temperature).Based on the recorded data,the production output as well as the daily average power(24-h vector)of the PVS is extracted over the year.When a power vector is available,it can be used as an input when searching for the optimal size of the PVS.This allows to take into account the constraint of the variation of the power generated by this source considering the variation of the power consumed by the electrical loads during the whole day.A multi-objective fitness function has been considered.The latter minimizes the active losses and maximizes the voltage stability index during the day,while considering the constraints of the system,that is,the security,technical,geographical,and meteorological constraints.This problem was solved using the Non-dominated Sorting Genetic Algorithm NSGA-II optimization technique under MATLAB 2021.It was applied to the distribution network of Ghardaïa of 59 nodes.展开更多
In this work,we developed the PM6:Y6-based inverted structure organic photovoltaic(i-OPV)with improved power conversion efficiency(PCE)and long-term stability by resolving the origins of the performance deterioration....In this work,we developed the PM6:Y6-based inverted structure organic photovoltaic(i-OPV)with improved power conversion efficiency(PCE)and long-term stability by resolving the origins of the performance deterioration.The deep defects between the metal oxide-based electron transport layer and bulk-heterojunction photoactive layer interface were responsible for suboptimal PCE and facilitated degradation of devices.While the density of deep traps is increased during the storage of i-OPV,the penetrative oxygen-containing defects additionally generated shallow traps below the band-edge of Y6,causing an additional loss in the open-circuit voltage.The suppression of interfacial defects by chemical modification effectively improved the PCE and long-term stability of i-OPV.The modified i-OPV(mi-OPV)achieved a PCE of 17.42%,which is the highest value among the reported PM6:Y6-based i-OPV devices.Moreover,long-term stability was significantly improved:~90%and~80%retention of its initial PCE after 1200 h of air storage and illumination,respectively.展开更多
Increasing the efficiency and proportion of photovoltaic power generation installations is one of the best ways to reduce both CO_(2) emissions and reliance on fossil-fuel-based power supplies.Solar energy is a clean ...Increasing the efficiency and proportion of photovoltaic power generation installations is one of the best ways to reduce both CO_(2) emissions and reliance on fossil-fuel-based power supplies.Solar energy is a clean and renewable power source with excellent potential for further development and utilization.In 2021,the global solar installed capacity was about 749.7 GW.Establishing correlations between solar power generation,standard coal equivalent,carbon sinks,and green sinks is crucial.However,there have been few reports about correlations between the efficiency of tracking solar photovoltaic panels and the above parameters.This paper calculates the increased power generation achievable through the use of tracking photovoltaic panels compared with traditional fixed panels and establishes relationships between power generation,standard coal equivalent,and carbon sinks,providing a basis for attempts to reduce reliance on carbon-based fuels.The calculations show that power generation efficiency can be improved by about 26.12%by enabling solar panels to track the sun's rays during the day and from season to season.Through the use of this improved technology,global CO_(2) emissions can be reduced by 183.63 Mt,and the standard coal equivalent can be reduced by 73.67 Mt yearly.Carbon capture is worth approximately EUR 15.48 billion,and carbon accounting analysis plays a vital role in carbon trading.展开更多
文摘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.
文摘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/m<sup>2</sup>) 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/m<sup>2</sup> 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.
基金This researchwas supported by the National Natural Science Foundation of China(Nos.51767017 and 51867015)the Basic Research and Innovation Group Project of Gansu(No.18JR3RA133)the Natural Science Foundation of Gansu(No.21JR7RA258).
文摘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.
文摘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.
基金supported by the Key Research and Development Projects in Shaanxi Province(Program No.2021GY-306)the Innovation Capability Support Program of Shaanxi(Program No.2022KJXX-41)the Key Scientific and Technological Projects of Xi’an(Program No.2022JH-RGZN-0005).
文摘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.
基金The Science and Technology Project of the State Grid Corporation of China(Research and Demonstration of Loss Reduction Technology Based on Reactive Power Potential Exploration and Excitation of Distributed Photovoltaic-Energy Storage Converters:5400-202333241 A-1-1-ZN).
文摘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.
基金supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200969(L.Z.,URL:http://std.jiangsu.gov.cn/)in part by Basic Science(Natural Science)Research Project of Colleges and Universities in Jiangsu Province under Grant 22KJB470025(L.R.,URL:http://jyt.jiangsu.gov.cn/)in part by Social People’s Livelihood Technology Plan General Project of Nantong under Grant MS12021015(L.Q.,URL:http://kjj.nantong.gov.cn/).
文摘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.
文摘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.
基金National Funds through FCT–Portuguese Foundation for Science and Technology under the projects PTDC/CTM-CTM/1571/2020(All-Fi BRE),LA/P/0037/2020,UIDP/50025/2020 and UIDB/50025/2020(CENIMAT/I3N)by ERC-Co G-2014,CapTherPV,647596。
文摘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.
基金King Saud University through Researchers Supporting Project number(RSP2022R426),King Saud University,Riyadh,Saudi Arabia.
文摘A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.
基金Project supported by the National Natural Science Foundation of China(Grant No.12064034)the Leading Talents Project of Science and Technology Innovation in Ningxia Hui Autonomous Region,China(Grant No.2020GKLRLX08)+1 种基金the Natural Science Foundation of Ningxia Hui Autonomous Region,China(Grant Nos.2022AAC03643 and2022AAC03117)the Major Science and Technology Project of Ningxia Hui Autonomous Region,China(Grant No.2022BDE03006)。
文摘Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particle removal from photovoltaic(PV)panels by compressed air by analyzing the force exerted on the dust deposited on inclined photovoltaic panels,which also included different electrification mechanisms of dust while it is in contact with the PV panel.The results show that the effect of the particle charging mechanism in the electric field generated by the PV panel is greatly smaller than the effect of the Van der Waals force and gravity,but the effect of the particle charged by the contact electrification mechanism in the electrostatic field is very pronounced.The wind speed required for dust removal from the PV panel increases linearly with the PV panel electric field,so we suggest that the nighttime,when the PV electric field is relatively small,would be more appropriate time for dust removal.The above results are of great scientific importance for accurately grasping the dust distribution law and for achieving scientific removal of dust on PV panels.
基金supported by the Science and Technology Support Program of Guizhou Province([2022]General 012)the Key Science and Technology Project of China Southern Power Grid Corporation(GZKJXM20220043)。
文摘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.
基金supported by the National Natural Science Foundation of China (Grant No. 11374226)the Fundamental Research Funds for the Universities of Henan Province of China (Grant No. NSFRF200331)+1 种基金the Foundation of Henan Educational Committee (Grant No. 20A140013)by the High-performance Grid Computing Platform of Henan Polytechnic University。
文摘Converting solar energy into electric power or hydrogen fuel is a promising means to obtain renewable green energy.Here, we design a two-dimensional blue phosphorene(BlueP)/MoSi2N4van der Waals heterostructure(vdWH) and investigate its potential application in photocatalysis and photovoltaics using first-principles calculations. We find that the BlueP/MoSi2N4vdWH possesses type-Ⅱ band structure with a large build-in electric field, thus endowing it with a potential ability to separate photogenerated electron–hole pairs. The calculated band-edge positions show that the heterostructure is a very promising water-splitting photocatalyst. Its solar-to-hydrogen efficiency(ηSTH) can reach up to 15.8%, which is quite promising for commercial applications. Furthermore, the BlueP/MoSi2N4vdWH shows remarkably light absorption capacity and distinguished maximum power conversion efficiency(ηPCE) up to 10.61%. Remarkably, its ηPCEcan be further enhanced by the external strain: the ηPCEof 21.20% can be obtained under a 4% tensile strain. Finally, we determine that adjusting the number of the BlueP sublayer is another effective method to modulate the band gaps and band alignments of the heterostructures. These theoretical findings indicate that BlueP/MoSi2N4vd WH is a promising candidate for photocatalyst and photovoltaic device.
基金supported by the National Natural Science Foundation of China(No.52074305)Henan Scientific and Technological Research Project(No.212102210005)Open Fund of Henan Engineering Laboratory for Photoelectric Sensing and Intelligent Measurement and Control(No.HELPSIMC-2020-00X).
文摘Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.
基金the National Natural Science Foundation of China (22175180, 21975260)。
文摘Lead-based organic-inorganic hybrid perovskites have exhibited great potential in photovoltaics,achieving power conversion efficiencies(PCEs) exceeding 25%.However,the toxicity of lead and the instability of these materials under moist conditions pose significant barriers to large-scale production.To overcome these limitations,researchers have proposed mixed-valence double perovskites,where Cs_(2)Au~ⅠAu~ⅢI_6 is a particularly effective absorber due to its suitable band gap and high absorptance efficiency.To further extend the scope of these lead-free materials,we varied the trivalent gold ion and halogen anion in Cs_(2)Au~ⅠAu~ⅢI_6,resulting in 18 new structures with unique properties.Further,using first-principles calculations and elimination criteria,we identified four materials with ideal band gaps,small effective carrier mass,and strong anisotropic optical properties.According to theoretical modeling,Cs_(2)AuSbCl_6,Cs_(2)AuInCl_6,and Cs_(2)AuBiCl_6 are potential candidates for solar cell absorbers,with a spectroscopic limited maximum efficiency(SLME) of approximately 30% in a 0.25 μm-thick film.These three compounds have not been previously reported,and therefore,our work provides new insights into potential materials for solar energy conversion.We aim for this theoretical exploration of novel perovskites to guide future experiments and accelerate the development of high-performance photovoltaic devices.
文摘Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.
基金The research is supported by the National Natural Science Foundation of China(62072469)the National Key R&D Program of China(2018AAA0101502)+2 种基金Shandong Natural Science Foundation(ZR2019MF049)West Coast artificial intelligence technology innovation center(2019-1-5,2019-1-6)the Opening Project of Shanghai Trusted Industrial Control Platform(TICPSH202003015-ZC).
文摘Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
基金the deanship of Scientific Research at Jouf University for founding this work through research grant no(DSR2020-02-387).https://www.ju.edu.sa/.
文摘This paper presents a new optimization study of the placement and size of a photovoltaic source(PVS)in a distribution grid,based on annual records of meteorological parameters(irradiance,temperature).Based on the recorded data,the production output as well as the daily average power(24-h vector)of the PVS is extracted over the year.When a power vector is available,it can be used as an input when searching for the optimal size of the PVS.This allows to take into account the constraint of the variation of the power generated by this source considering the variation of the power consumed by the electrical loads during the whole day.A multi-objective fitness function has been considered.The latter minimizes the active losses and maximizes the voltage stability index during the day,while considering the constraints of the system,that is,the security,technical,geographical,and meteorological constraints.This problem was solved using the Non-dominated Sorting Genetic Algorithm NSGA-II optimization technique under MATLAB 2021.It was applied to the distribution network of Ghardaïa of 59 nodes.
基金supported by a National Research Foundation of Korea(grant#:2020R1A2C1003929,2019R1A6A1A11053838,2020M1A2A2080746,2021M2E8A1044198,2016R1A5A1012966,2021M3H4A1A03051379).
文摘In this work,we developed the PM6:Y6-based inverted structure organic photovoltaic(i-OPV)with improved power conversion efficiency(PCE)and long-term stability by resolving the origins of the performance deterioration.The deep defects between the metal oxide-based electron transport layer and bulk-heterojunction photoactive layer interface were responsible for suboptimal PCE and facilitated degradation of devices.While the density of deep traps is increased during the storage of i-OPV,the penetrative oxygen-containing defects additionally generated shallow traps below the band-edge of Y6,causing an additional loss in the open-circuit voltage.The suppression of interfacial defects by chemical modification effectively improved the PCE and long-term stability of i-OPV.The modified i-OPV(mi-OPV)achieved a PCE of 17.42%,which is the highest value among the reported PM6:Y6-based i-OPV devices.Moreover,long-term stability was significantly improved:~90%and~80%retention of its initial PCE after 1200 h of air storage and illumination,respectively.
文摘Increasing the efficiency and proportion of photovoltaic power generation installations is one of the best ways to reduce both CO_(2) emissions and reliance on fossil-fuel-based power supplies.Solar energy is a clean and renewable power source with excellent potential for further development and utilization.In 2021,the global solar installed capacity was about 749.7 GW.Establishing correlations between solar power generation,standard coal equivalent,carbon sinks,and green sinks is crucial.However,there have been few reports about correlations between the efficiency of tracking solar photovoltaic panels and the above parameters.This paper calculates the increased power generation achievable through the use of tracking photovoltaic panels compared with traditional fixed panels and establishes relationships between power generation,standard coal equivalent,and carbon sinks,providing a basis for attempts to reduce reliance on carbon-based fuels.The calculations show that power generation efficiency can be improved by about 26.12%by enabling solar panels to track the sun's rays during the day and from season to season.Through the use of this improved technology,global CO_(2) emissions can be reduced by 183.63 Mt,and the standard coal equivalent can be reduced by 73.67 Mt yearly.Carbon capture is worth approximately EUR 15.48 billion,and carbon accounting analysis plays a vital role in carbon trading.
