Organic photovoltaics(OPVs)have achieved remarkable progress,with laboratory-scale single-junction devices now demonstrating power conversion efficiencies(PCEs)exceeding 20%.However,these efficiencies are highly depen...Organic photovoltaics(OPVs)have achieved remarkable progress,with laboratory-scale single-junction devices now demonstrating power conversion efficiencies(PCEs)exceeding 20%.However,these efficiencies are highly dependent on the thickness of the photoactive layer,which is typically around 100 nm.This sensitivity poses a challenge for industrial-scale fabrication.Achieving high PCEs in thick-film OPVs is therefore essential.This review systematically examines recent advancements in thick-film OPVs,focusing on the fundamental mechanisms that lead to efficiency loss and strategies to enhance performance.We provide a comprehensive analysis spanning the complete photovoltaic process chain:from initial exciton generation and diffusion dynamics,through dissociation mechanisms,to subsequent charge-carrier transport,balance optimization,and final collection efficiency.Particular emphasis is placed on cutting-edge solutions in molecular engineering and device architecture optimization.By synthesizing these interdisciplinary approaches and investigating the potential contributions in stability,cost,and machine learning aspects,this work establishes comprehensive guidelines for designing high-performance OPVs devices with minimal thickness dependence,ultimately aiming to bridge the gap between laboratory achievements and industrial manufacturing requirements.展开更多
This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for ...Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for fabricating photomechanical actuators based on micro/nano-scale crystals is developed.The high versatility and generality of this method are successfully demonstrated using nine different types of photoresponsive crystalline actuators,including acylhydrazone-,anthracene-,olefin-,and azobenzene-based molecular crystals and covalent organic frameworks(COFs).The low-cost neutral silicone sealant elastomer is first chosen as the photomechanical 4D printing matrix.Notably,these actuators can be used to perform bionic motions(the first windmills spin using crystalline material,dragonflies fly,and sunflowers bloom)under the stimulation of visible light and can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane.This work provides new insights into the design and manufacturing of smart photomechanical actuators and electricity generators and expands the application scope of COFs.展开更多
Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.展开更多
Energy-saving buildings(ESBs)are an emerging green technology that can significantly reduce building-associated cooling and heating energy consumption,catering to the desire for carbon neutrality and sustainable devel...Energy-saving buildings(ESBs)are an emerging green technology that can significantly reduce building-associated cooling and heating energy consumption,catering to the desire for carbon neutrality and sustainable development of society.Smart photovoltaic windows(SPWs)offer a promising platform for designing ESBs because they present the capability to regulate and harness solar energy.With frequent outbreaks of extreme weather all over the world,the achievement of exceptional energy-saving effect under different weather conditions is an inevitable trend for the development of ESBs but is hardly achieved via existing SPWs.Here,we substantially reduce the driving voltage of polymerdispersed liquid crystals(PDLCs)by 28.1%via molecular engineering while maintaining their high solar transmittance(T_(sol)=83.8%,transparent state)and solar modulating ability(ΔT_(sol)=80.5%).By the assembly of perovskite solar cell and a broadband thermal-managing unit encompassing the electrical-responsive PDLCs,transparent high-emissivity SiO_(2) passive radiation-cooling,and Ag low-emissivity layers possesses,we present a tri-band regulation and split-type SPW possessing superb energy-saving effect in all-season.The perovskite solar cell can produce the electric power to stimulate the electrical-responsive behavior of the PDLCs,endowing the SPWs zero-energy input solar energy regulating characteristic,and compensate the daily energy consumption needed for ESBs.Moreover,the scalable manufacturing technology holds a great potential for the real-world applications.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c...As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study intro...The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study introduces a molten salt electrochemical strategy for converting photovoltaic wSi into NiSi_(2)-silicon nanorods(NiSi_(2)-SiNRs)as high-performance anode materials for lithium-ion batteries.A stable oxidized passivation layer is formed on the wSi surface via controlled oxidation,and further in situ generated highly active NiSi_(2) droplets.The molten salt electric field modulates the surface energy of silicon,while particle integration drives localized directional growth,enabling the self-assembly of NiSi_(2)-SiNRs composites.These NiSi_(2)-SiNRs anodes exhibit rapid ion transport and effective strain buffering.The high aspect ratio of SiNRs and the presence of retained NiSi_(2) facilitate both longitudinal and transverse Li^(+) diffusion.Owing to their robust structural design,the NiSi_(2)-SiNRs anode achieves an excellent initial Coulombic efficiency of 91.61%and retains 72.99%of its capacity after 800 cycles at 2 A·g^(−1).This study establishes a model system for investigating silicide/silicon interfaces in molten salt electrochemical synthesis and provides an effective strategy for upcycling photovoltaic wSi into high-performance lithium-ion battery anodes.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this pape...To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network(CNN)-iTransformer-long short-term memory(LSTM)model.First,the rime-optimized random forest regression algorithm(RIME-RF)is employed to perform spatial downscaling on numerical weather prediction(NWP)data,thereby improving its local applicability.Second,a CNN-iTransformer-LSTM hybrid prediction model is constructed.This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data,employs an iTransformer to model the global dependencies among multiple variables,and leverages an LSTM to enhance the learning of short-term temporal dynamic features,thereby achieving efficient collaborative mining of multi-scale features.Finally,experiments are conducted using actual data from a photovoltaic power station in Hebei,China,during various seasons and weather conditions.The results show that the proposed model outperforms the comparison models in terms of the root mean square error(RMSE),mean absolute error(MAE),and R2,maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days.The downscaling process further enhances the prediction performance,verifying the effectiveness and practicality of this method.展开更多
Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those co...Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.展开更多
Achieving simultaneous enhancement of crystallinity and optimal domain size remains a fundamental challenge in organic photovoltaics(OPVs),where conventional crystallization strategies often trigger excessive aggregat...Achieving simultaneous enhancement of crystallinity and optimal domain size remains a fundamental challenge in organic photovoltaics(OPVs),where conventional crystallization strategies often trigger excessive aggregation of small-molecule acceptors.This work pioneers a kinetic paradigm for resolving the crystallinity-domain size trade-off in organic photovoltaics through dual-additive-guided stepwise crystallization.By strategically pairing 1,2-dichlorobenzene(o-DCB,low binding energy to Y6)and 1-fluoronaphthalene(FN,high binding energy),we achieve temporally decoupled crystallization control:o-DCB first mediates donor-acceptor co-crystallization during film formation,constructing a metastable network,whereupon FN induces confined Y6 crystallization within this framework during thermal annealing,refining nanostructure without over-aggregation.Morphology studies reveal that this synergy enhances crystallinity of(100)diffraction peaks by 21%–10%versus single-additive controls(o-DCB/FN alone),while maintaining optimal domain size.These morphological advantages yield balanced carrier transport(μh/μe=1.23),near-unity exciton dissociation(98.53%),and a champion power conversion efficiency(PCE)of 18.08%for PM6:Y6,significantly surpassing single-additive devices(o-DCB:17.20%;FN:17.53%).Crucially,the dual-additive strategy demonstrates universal applicability across diverse active layer systems,achieving an outstanding PCE of 19.27%in PM6:L8-BO-based devices,thereby establishing a general framework for morphology control in high-efficiency OPVs.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.展开更多
Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.Th...Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.The published version showed“Hongzhen Chen”,whereas the correct spelling should be“Hongzheng Chen”.The correct author name has been provided in this Correction,and the original article[1]has been corrected.展开更多
High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective he...High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.展开更多
With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused o...With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused on static analysis.However,as the scale of photovoltaic power generation devices grows and the methods of integration diversify,a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks.Therefore,this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity,aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in distribution networks.First,the BASS diffusion model,combined with existing photovoltaic capacity data and roof area information,is used to predict the trends in photovoltaic installed capacity for each substation area,providing a scientific basis for consumption evaluation.Secondly,an improved random scenario simulation method is proposed for assessing the photovoltaic consumption capacity in distribution networks.This method generates photovoltaic integration schemes based on the diffusion probabilities of different regions and evaluates the consumption capacity of each scheme.Finally,the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is used to comprehensively evaluate the generated schemes,ensuring that the selected scheme not only meets the consumption requirements but also offers high economic benefits and reliability.The effectiveness and feasibility of the proposedmethod are validated through simulations of the IEEE 33-node system,providing strong support for optimizing photovoltaic consumption schemes in distribution networks.展开更多
The reliability and efficiency of photovoltaic(PV)systems are essential for sustainable energy produc-tion,requiring accurate fault detection to minimize energy losses.This study proposes a hybrid model integrating Ne...The reliability and efficiency of photovoltaic(PV)systems are essential for sustainable energy produc-tion,requiring accurate fault detection to minimize energy losses.This study proposes a hybrid model integrating Neighborhood Components Analysis(NCA)with a Convolutional Neural Network(CNN)to improve fault detection and diagnosis.Unlike Principal Component Analysis(PCA),which may compromise class relationships during feature extraction,NCA preserves these relationships,enhancing classification performance.The hybrid model combines NCA with CNN,a fundamental deep learning architecture,to enhance fault detection and diagnosis capabilities.The performance of the proposed NCA-CNN model was evaluated against other models.The experimental evaluation demonstrates that the NCA-CNN model outperforms existing methods,achieving 100%fault detection accuracy and 99%fault diagnosis accuracy.These findings underscore the model’s potential in improving PV system reliability and efficiency.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still rel...In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still relies mainly on experience and is incapable of quantifying the balance between the photovoltaic(PV)generation and the light requirements for agricultural production.As a result,actual PVGs are primarily PV-based,without carefully considering the needs of agricultural daylighting.To quantify the influence of the design parameters of PVGs and the layout of PV panels on the internal daylighting of serrated PVGs,and to optimize the daylighting design of the roof,this paper utilizes the Design Builder software to establish gradient models for a multi-span serrated-type PVG in tropical regions.Gradient models were established in terms of aspects,namely span,width of longitudinal/transverse daylighting strip,height,roof angle,and photovoltaic panel coverage rate(PCR).Daylighting in the greenhouse of each gradient model was simulated,and with the annual average daily light integral(A_(DLI))and distribution uniformity(DU)as evaluation indicators,the influence of various design parameters on the daylighting inside the greenhouse was quantified.The result reveals that:(1)PCR is the decisive indicator for daylighting in the PVG,and a function between PCR and the A_(DLI) is derived as A_(DLI)=-15.5 PCR+16.841;(2)Increasing the width of longitudinal daylighting strip significantly improves the A_(DLI) and enhances DU while increasing the span has a noticeable effect on improving A_(DLI) but does not significantly enhance DU;(3)Increasing the eave height without changing PCR does not enhance A_(DLI) but effectively improves DU;increasing the transverse daylighting strip and adjusting the roof angle hardly improves A_(DLI).In summary,it is recommended that the optimal span for PVGs in tropical regions be set within the range of 6.5-8.0m,and the eave height be set within the range of 2.5-3.5m.Preferably,the longitudinal daylighting strip with a width ranging from 0.5-0.8m should be installed.Based on the above relationship function,the PCR can be calculated according to the appropriate light demand for the cultivated crops.The daylighting design theory proposed in this paper can provide a theoretical basis and reference for the healthy development of the PV industry in tropical regions.展开更多
基金supported by Natural Science Foundation of Zhejiang Province(Nos.LQ23E030002,LZ23B040001)the National Natural Science Foundation of China(Nos.52303226,21971049)L.Zhan acknowledges the research start-up fund from Hangzhou Normal University(4095C50222204002).
文摘Organic photovoltaics(OPVs)have achieved remarkable progress,with laboratory-scale single-junction devices now demonstrating power conversion efficiencies(PCEs)exceeding 20%.However,these efficiencies are highly dependent on the thickness of the photoactive layer,which is typically around 100 nm.This sensitivity poses a challenge for industrial-scale fabrication.Achieving high PCEs in thick-film OPVs is therefore essential.This review systematically examines recent advancements in thick-film OPVs,focusing on the fundamental mechanisms that lead to efficiency loss and strategies to enhance performance.We provide a comprehensive analysis spanning the complete photovoltaic process chain:from initial exciton generation and diffusion dynamics,through dissociation mechanisms,to subsequent charge-carrier transport,balance optimization,and final collection efficiency.Particular emphasis is placed on cutting-edge solutions in molecular engineering and device architecture optimization.By synthesizing these interdisciplinary approaches and investigating the potential contributions in stability,cost,and machine learning aspects,this work establishes comprehensive guidelines for designing high-performance OPVs devices with minimal thickness dependence,ultimately aiming to bridge the gap between laboratory achievements and industrial manufacturing requirements.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
基金the National Natural Science Foundation of China(22175099,22205116,22301147)National Key Research and Development Program of China(2021YFC2102100)+1 种基金Frontiers Science Center for New Organic Matter of Nankai University(63181206)111 Project(B12015).
文摘Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for fabricating photomechanical actuators based on micro/nano-scale crystals is developed.The high versatility and generality of this method are successfully demonstrated using nine different types of photoresponsive crystalline actuators,including acylhydrazone-,anthracene-,olefin-,and azobenzene-based molecular crystals and covalent organic frameworks(COFs).The low-cost neutral silicone sealant elastomer is first chosen as the photomechanical 4D printing matrix.Notably,these actuators can be used to perform bionic motions(the first windmills spin using crystalline material,dragonflies fly,and sunflowers bloom)under the stimulation of visible light and can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane.This work provides new insights into the design and manufacturing of smart photomechanical actuators and electricity generators and expands the application scope of COFs.
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
基金supported by Natural Science Foundation of China(Grant No.52372076,52073081,52203322,5252200843)Ministry of Science and Technology of the People’s Republic of China(2023YFB3812800)Fundamental Research Funds for the Central Universities(FRF-TP-25-073)。
文摘Energy-saving buildings(ESBs)are an emerging green technology that can significantly reduce building-associated cooling and heating energy consumption,catering to the desire for carbon neutrality and sustainable development of society.Smart photovoltaic windows(SPWs)offer a promising platform for designing ESBs because they present the capability to regulate and harness solar energy.With frequent outbreaks of extreme weather all over the world,the achievement of exceptional energy-saving effect under different weather conditions is an inevitable trend for the development of ESBs but is hardly achieved via existing SPWs.Here,we substantially reduce the driving voltage of polymerdispersed liquid crystals(PDLCs)by 28.1%via molecular engineering while maintaining their high solar transmittance(T_(sol)=83.8%,transparent state)and solar modulating ability(ΔT_(sol)=80.5%).By the assembly of perovskite solar cell and a broadband thermal-managing unit encompassing the electrical-responsive PDLCs,transparent high-emissivity SiO_(2) passive radiation-cooling,and Ag low-emissivity layers possesses,we present a tri-band regulation and split-type SPW possessing superb energy-saving effect in all-season.The perovskite solar cell can produce the electric power to stimulate the electrical-responsive behavior of the PDLCs,endowing the SPWs zero-energy input solar energy regulating characteristic,and compensate the daily energy consumption needed for ESBs.Moreover,the scalable manufacturing technology holds a great potential for the real-world applications.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金supported by Science and Technology Project of China Southern Power Grid Company(036000KK52222007(GDKJXM20222121)).
文摘As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the Yunnan Province Basic Research General Program,China(No.202201BE070001-002)the Major Science and Technology Projects in Yunnan Province,China(No.202402AF 080005).
文摘The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study introduces a molten salt electrochemical strategy for converting photovoltaic wSi into NiSi_(2)-silicon nanorods(NiSi_(2)-SiNRs)as high-performance anode materials for lithium-ion batteries.A stable oxidized passivation layer is formed on the wSi surface via controlled oxidation,and further in situ generated highly active NiSi_(2) droplets.The molten salt electric field modulates the surface energy of silicon,while particle integration drives localized directional growth,enabling the self-assembly of NiSi_(2)-SiNRs composites.These NiSi_(2)-SiNRs anodes exhibit rapid ion transport and effective strain buffering.The high aspect ratio of SiNRs and the presence of retained NiSi_(2) facilitate both longitudinal and transverse Li^(+) diffusion.Owing to their robust structural design,the NiSi_(2)-SiNRs anode achieves an excellent initial Coulombic efficiency of 91.61%and retains 72.99%of its capacity after 800 cycles at 2 A·g^(−1).This study establishes a model system for investigating silicide/silicon interfaces in molten salt electrochemical synthesis and provides an effective strategy for upcycling photovoltaic wSi into high-performance lithium-ion battery anodes.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
文摘To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network(CNN)-iTransformer-long short-term memory(LSTM)model.First,the rime-optimized random forest regression algorithm(RIME-RF)is employed to perform spatial downscaling on numerical weather prediction(NWP)data,thereby improving its local applicability.Second,a CNN-iTransformer-LSTM hybrid prediction model is constructed.This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data,employs an iTransformer to model the global dependencies among multiple variables,and leverages an LSTM to enhance the learning of short-term temporal dynamic features,thereby achieving efficient collaborative mining of multi-scale features.Finally,experiments are conducted using actual data from a photovoltaic power station in Hebei,China,during various seasons and weather conditions.The results show that the proposed model outperforms the comparison models in terms of the root mean square error(RMSE),mean absolute error(MAE),and R2,maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days.The downscaling process further enhances the prediction performance,verifying the effectiveness and practicality of this method.
文摘Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.
基金supported by the Shaanxi Provincial High level Talent Introduction Project(5113220044)the Shaanxi Outstanding Youth Project(2023-JC-JQ-33)+8 种基金the Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology(TJ-2022-088)the Project funded by China Postdoctoral Science Foundation(2023TQ0273,2023TQ0274,2023M742833)the NationalNatural Science Foundation of China(62304181)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0726,2025JC-YBQN-469)the GuangdongBasic and Applied Basic Research Foundation(2022A1515110286,2024A1515012538)the Basic Research Programs of Taicang(TC2024JC04)the Suzhou Science and Technology Development Plan Innovation Leading Talent Project(ZXL2023183)the Fundamental Research Funds for the Central Universities(G2022KY05108,G2024KY0605,G2023KY0601)and the Aeronautical Science Foundation of China(2018ZD53047).
文摘Achieving simultaneous enhancement of crystallinity and optimal domain size remains a fundamental challenge in organic photovoltaics(OPVs),where conventional crystallization strategies often trigger excessive aggregation of small-molecule acceptors.This work pioneers a kinetic paradigm for resolving the crystallinity-domain size trade-off in organic photovoltaics through dual-additive-guided stepwise crystallization.By strategically pairing 1,2-dichlorobenzene(o-DCB,low binding energy to Y6)and 1-fluoronaphthalene(FN,high binding energy),we achieve temporally decoupled crystallization control:o-DCB first mediates donor-acceptor co-crystallization during film formation,constructing a metastable network,whereupon FN induces confined Y6 crystallization within this framework during thermal annealing,refining nanostructure without over-aggregation.Morphology studies reveal that this synergy enhances crystallinity of(100)diffraction peaks by 21%–10%versus single-additive controls(o-DCB/FN alone),while maintaining optimal domain size.These morphological advantages yield balanced carrier transport(μh/μe=1.23),near-unity exciton dissociation(98.53%),and a champion power conversion efficiency(PCE)of 18.08%for PM6:Y6,significantly surpassing single-additive devices(o-DCB:17.20%;FN:17.53%).Crucially,the dual-additive strategy demonstrates universal applicability across diverse active layer systems,achieving an outstanding PCE of 19.27%in PM6:L8-BO-based devices,thereby establishing a general framework for morphology control in high-efficiency OPVs.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
文摘Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.The published version showed“Hongzhen Chen”,whereas the correct spelling should be“Hongzheng Chen”.The correct author name has been provided in this Correction,and the original article[1]has been corrected.
基金funded by King Abdullah City for Atomic and Renewable Energy(KACARE),grant number“PC-2020-1”.
文摘High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.
基金supported in part by theThe Planning Subject Project of Guangdong Power Grid Co.,Ltd.(62273104).
文摘With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused on static analysis.However,as the scale of photovoltaic power generation devices grows and the methods of integration diversify,a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks.Therefore,this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity,aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in distribution networks.First,the BASS diffusion model,combined with existing photovoltaic capacity data and roof area information,is used to predict the trends in photovoltaic installed capacity for each substation area,providing a scientific basis for consumption evaluation.Secondly,an improved random scenario simulation method is proposed for assessing the photovoltaic consumption capacity in distribution networks.This method generates photovoltaic integration schemes based on the diffusion probabilities of different regions and evaluates the consumption capacity of each scheme.Finally,the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is used to comprehensively evaluate the generated schemes,ensuring that the selected scheme not only meets the consumption requirements but also offers high economic benefits and reliability.The effectiveness and feasibility of the proposedmethod are validated through simulations of the IEEE 33-node system,providing strong support for optimizing photovoltaic consumption schemes in distribution networks.
文摘The reliability and efficiency of photovoltaic(PV)systems are essential for sustainable energy produc-tion,requiring accurate fault detection to minimize energy losses.This study proposes a hybrid model integrating Neighborhood Components Analysis(NCA)with a Convolutional Neural Network(CNN)to improve fault detection and diagnosis.Unlike Principal Component Analysis(PCA),which may compromise class relationships during feature extraction,NCA preserves these relationships,enhancing classification performance.The hybrid model combines NCA with CNN,a fundamental deep learning architecture,to enhance fault detection and diagnosis capabilities.The performance of the proposed NCA-CNN model was evaluated against other models.The experimental evaluation demonstrates that the NCA-CNN model outperforms existing methods,achieving 100%fault detection accuracy and 99%fault diagnosis accuracy.These findings underscore the model’s potential in improving PV system reliability and efficiency.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金2024 Science and Technology Commissioner Service Group's Emergency Science and Technology Research Project for Wind Disaster Relief in Hainan Province(ZDYF2024YJGG002-8)China Huaneng Group Co.,Ltd.Headquarters Technology Project,Optimization of Photovoltaic Vegetable Greenhouse Structure and Research on Planting Agronomy in Tropical Regions(HNKJ22-HF77)。
文摘In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still relies mainly on experience and is incapable of quantifying the balance between the photovoltaic(PV)generation and the light requirements for agricultural production.As a result,actual PVGs are primarily PV-based,without carefully considering the needs of agricultural daylighting.To quantify the influence of the design parameters of PVGs and the layout of PV panels on the internal daylighting of serrated PVGs,and to optimize the daylighting design of the roof,this paper utilizes the Design Builder software to establish gradient models for a multi-span serrated-type PVG in tropical regions.Gradient models were established in terms of aspects,namely span,width of longitudinal/transverse daylighting strip,height,roof angle,and photovoltaic panel coverage rate(PCR).Daylighting in the greenhouse of each gradient model was simulated,and with the annual average daily light integral(A_(DLI))and distribution uniformity(DU)as evaluation indicators,the influence of various design parameters on the daylighting inside the greenhouse was quantified.The result reveals that:(1)PCR is the decisive indicator for daylighting in the PVG,and a function between PCR and the A_(DLI) is derived as A_(DLI)=-15.5 PCR+16.841;(2)Increasing the width of longitudinal daylighting strip significantly improves the A_(DLI) and enhances DU while increasing the span has a noticeable effect on improving A_(DLI) but does not significantly enhance DU;(3)Increasing the eave height without changing PCR does not enhance A_(DLI) but effectively improves DU;increasing the transverse daylighting strip and adjusting the roof angle hardly improves A_(DLI).In summary,it is recommended that the optimal span for PVGs in tropical regions be set within the range of 6.5-8.0m,and the eave height be set within the range of 2.5-3.5m.Preferably,the longitudinal daylighting strip with a width ranging from 0.5-0.8m should be installed.Based on the above relationship function,the PCR can be calculated according to the appropriate light demand for the cultivated crops.The daylighting design theory proposed in this paper can provide a theoretical basis and reference for the healthy development of the PV industry in tropical regions.
