With the development of new energy technology, there are increasing applications of grid-connected photovoltaic power generation system. However, there is little research on development of electromechanical model of l...With the development of new energy technology, there are increasing applications of grid-connected photovoltaic power generation system. However, there is little research on development of electromechanical model of large scale photovoltaic power station. The computational speed will be very slow if electromagnetic transient model is used for stability study because of its complexity. Therefore, study on electromechanical transient model of grid-connected photovoltaic power generation system is of great meaning. In this paper, electromagnetic transient model of photovoltaic power generation system is introduced first, and then a general electromechanical transient model is proposed. These two kinds of simulation model are set up in PSCAD. By comparing the simulation results of two models, the correctness and validity of the electromechanical transient model is verified. It provides reference model for efficient simulation and modeling of grid-connected photovoltaic power station in large-scale power systems.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
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
Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorolog...Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.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
Analysis of photovoltaic power plant(PVPP)operations is important to both investors and users and to producers of equipment,researchers and designers as well.This paper presents the analysis of the year-long operation...Analysis of photovoltaic power plant(PVPP)operations is important to both investors and users and to producers of equipment,researchers and designers as well.This paper presents the analysis of the year-long operations of the group PVPPs in Serbia connected to the distribution network which was carried out through specific indicators such as electric power generation,power,capacity factors,specific yield,losses,period of cost effectiveness of the investment,etc.Special attention has been paid to the influence of the azimuth and tilt of the photovoltaic modules on the performances of PVPPs.Results obtained through the year-long extensive measurements were compared to expected yields of PVPPs on the basis of the Climate SAF PVGIS application.The results of the analysis indicate that available software applications cannot always provide a good enough forecast of expected electric power production since they cannot always assess well enough the effects of the surrounding facilities on operations of a PVPP.In addition,it has been found that the correlation between losses of annual achieved specific yield of a PVPP and the azimuth is represented by a polynomial of the second degree,whereas the correlation between losses of the annual achieved specific yield of a PVPP and the tilt is represented by an exponential function.展开更多
Integration of Solar Photovoltaic (PV) generation into an existing distribution system has many impacts on the system, with the power flow being one of the major issues. This impact is not generic for any network, but...Integration of Solar Photovoltaic (PV) generation into an existing distribution system has many impacts on the system, with the power flow being one of the major issues. This impact is not generic for any network, but it may manifest itself either positively or negatively, depending on the grid configuration, interface control modes, operation mode, and load profile. Grid-connected PV systems have three control options of the local voltage controller of the interface DC-AC converter. These control modes are Power Factor control, voltage control, and Droop Voltage control. This paper aims at evaluating and comparing the impacts of those control modes on the grid power flow. A set of evaluation criteria and indices is defined and mathematically formulated. Based on the requirements of the used program (Power Factory Dig Silent V14.1.3), a computation plan (algorithm) has been proposed. The algorithm has been applied to a typical weak network and a wide range of simulations has been carried out. Simulation results have been thoroughly discussed and important findings have been concluded.展开更多
This paper presents a comparison of the results obtained from the integration of the photovoltaic system to the electrical grid through the Active Power Filter.The innovative aspect of this article consists of the ela...This paper presents a comparison of the results obtained from the integration of the photovoltaic system to the electrical grid through the Active Power Filter.The innovative aspect of this article consists of the elaboration of 4 models in Matlab/Simulink for 4 methods of control of the Active Power Filter introduced between the photovoltaic system and the alternating current grid and the highlighting of the performances obtained regarding the quality and quantity of electricity transferred for each control method in part.Following the analysis of the results,it was found that by using the Indirect Control(CI)method of the Active Power Filter,the best results are obtained THDu=3.24%,THDi=2.46%if the photovoltaic system produces a larger amount of energy than the nonlinear load,and by using the methods of control of the Active Power Filter Synchronous Reference Frame(DQ)and Instantaneous Power(PQ)the best results are obtained:THDu=3.22%,THDi=5.26%for the method(DQ)and THDu=3.41%,THDi=5.17%for the method(PQ)if the photovoltaic system produces a smaller amount of energy than the nonlinear load.展开更多
Radio-photovoltaic cell is a micro nuclear battery for devices operating in extreme environments,which converts the decay energy of a radioisotope into electric energy by using a phosphor and a photovoltaic converter....Radio-photovoltaic cell is a micro nuclear battery for devices operating in extreme environments,which converts the decay energy of a radioisotope into electric energy by using a phosphor and a photovoltaic converter.Many phosphors with high light yield and good environmental stability have been developed,but the performance of radio-photovoltaic cells remains far behind expectations in terms of power density and power conversion efficiency,because of the poor photoelectric conversion efficiency of traditional photovoltaic converters under low-light conditions.This paper reports an radio-photovoltaic cell based on an intrinsically stable formamidinium-cesium perovskite photovoltaic converter exhibiting a wide light wavelength response from 300 to 800 nm,high open-circuit voltage(V_(oc)),and remarkable efficiency at low-light intensity.When a He ions accelerator is adopted as a mimickedαradioisotope source with an equivalent activity of 0.83 mCi cm^(-2),the formamidinium-cesium perovskite radio-photovoltaic cell achieves a V_(oc)of 0.498 V,a short-circuit current(J_(sc))of 423.94 nA cm^(-2),and a remarkable power conversion efficiency of 0.886%,which is 6.6 times that of the Si reference radio-photovoltaic cell,as well as the highest among all radio-photovoltaic cells reported so far.This work provides a theoretical basis for enhancing the performance of radio-photovoltaic cells.展开更多
Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,ru...Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.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
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
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
To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based o...To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.展开更多
Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy....Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.展开更多
Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a rel...Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a reliable amortization schedule. In the context of photovoltaic systems connected to the interconnected electricity grid, the quintessence of damping is the amount of energy injected into the grid. Thus it is fundamental to know the parameters of this network and their variation. This paper presents an evaluation of the impact of power grid disturbances on the performance of a solar PV plant under real conditions. The CICAD photovoltaic solar plant, connected to the Senelec distribution network, with an installed capacity of 2 MWp is the study setting. An energy audit of the plant is carried out. Then the percentage of each loss is determined: voltage drops, module degradation, inverter efficiency. The duration of each disconnection is measured and recorded daily. The corresponding quantity of lost energy is thus calculated from meteorological data (irradiation, temperature, wind speed, illumination) recorded by the measurement unit in one-minute steps. The observation period is three months. The total duration of disconnections related to the instability of the electrical network during the study period is 46.7 hours. The amount of energy lost is estimated at 22.6 MWh. This represents 2.4% of the actual calculated production.展开更多
The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete ra...The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.展开更多
The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,th...The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.展开更多
The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although ...The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.展开更多
文摘With the development of new energy technology, there are increasing applications of grid-connected photovoltaic power generation system. However, there is little research on development of electromechanical model of large scale photovoltaic power station. The computational speed will be very slow if electromagnetic transient model is used for stability study because of its complexity. Therefore, study on electromechanical transient model of grid-connected photovoltaic power generation system is of great meaning. In this paper, electromagnetic transient model of photovoltaic power generation system is introduced first, and then a general electromechanical transient model is proposed. These two kinds of simulation model are set up in PSCAD. By comparing the simulation results of two models, the correctness and validity of the electromechanical transient model is verified. It provides reference model for efficient simulation and modeling of grid-connected photovoltaic power station in large-scale power systems.
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.
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.
基金supported by National Natural Science Foundation of China(No.516667017).
文摘Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.
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.
文摘Analysis of photovoltaic power plant(PVPP)operations is important to both investors and users and to producers of equipment,researchers and designers as well.This paper presents the analysis of the year-long operations of the group PVPPs in Serbia connected to the distribution network which was carried out through specific indicators such as electric power generation,power,capacity factors,specific yield,losses,period of cost effectiveness of the investment,etc.Special attention has been paid to the influence of the azimuth and tilt of the photovoltaic modules on the performances of PVPPs.Results obtained through the year-long extensive measurements were compared to expected yields of PVPPs on the basis of the Climate SAF PVGIS application.The results of the analysis indicate that available software applications cannot always provide a good enough forecast of expected electric power production since they cannot always assess well enough the effects of the surrounding facilities on operations of a PVPP.In addition,it has been found that the correlation between losses of annual achieved specific yield of a PVPP and the azimuth is represented by a polynomial of the second degree,whereas the correlation between losses of the annual achieved specific yield of a PVPP and the tilt is represented by an exponential function.
文摘Integration of Solar Photovoltaic (PV) generation into an existing distribution system has many impacts on the system, with the power flow being one of the major issues. This impact is not generic for any network, but it may manifest itself either positively or negatively, depending on the grid configuration, interface control modes, operation mode, and load profile. Grid-connected PV systems have three control options of the local voltage controller of the interface DC-AC converter. These control modes are Power Factor control, voltage control, and Droop Voltage control. This paper aims at evaluating and comparing the impacts of those control modes on the grid power flow. A set of evaluation criteria and indices is defined and mathematically formulated. Based on the requirements of the used program (Power Factory Dig Silent V14.1.3), a computation plan (algorithm) has been proposed. The algorithm has been applied to a typical weak network and a wide range of simulations has been carried out. Simulation results have been thoroughly discussed and important findings have been concluded.
基金supported by the CRESC INTEL project“Knowledge Transfer Regarding the Energy Efficiency Increase and Intelligent Power Systems”,ID/Cod My SMIS:P_40_340/105803。
文摘This paper presents a comparison of the results obtained from the integration of the photovoltaic system to the electrical grid through the Active Power Filter.The innovative aspect of this article consists of the elaboration of 4 models in Matlab/Simulink for 4 methods of control of the Active Power Filter introduced between the photovoltaic system and the alternating current grid and the highlighting of the performances obtained regarding the quality and quantity of electricity transferred for each control method in part.Following the analysis of the results,it was found that by using the Indirect Control(CI)method of the Active Power Filter,the best results are obtained THDu=3.24%,THDi=2.46%if the photovoltaic system produces a larger amount of energy than the nonlinear load,and by using the methods of control of the Active Power Filter Synchronous Reference Frame(DQ)and Instantaneous Power(PQ)the best results are obtained:THDu=3.22%,THDi=5.26%for the method(DQ)and THDu=3.41%,THDi=5.17%for the method(PQ)if the photovoltaic system produces a smaller amount of energy than the nonlinear load.
基金the financial support from the National Natural Science Foundation of China(grant numbers 11922507,12050005,52002140)Fundamental Research Funds for the Central Universities(2020kfyXJJS008)+1 种基金Major State Basic Research Development Program of China(2021YFB3201000)Young Elite Scientists Sponsorship Program by CAST
文摘Radio-photovoltaic cell is a micro nuclear battery for devices operating in extreme environments,which converts the decay energy of a radioisotope into electric energy by using a phosphor and a photovoltaic converter.Many phosphors with high light yield and good environmental stability have been developed,but the performance of radio-photovoltaic cells remains far behind expectations in terms of power density and power conversion efficiency,because of the poor photoelectric conversion efficiency of traditional photovoltaic converters under low-light conditions.This paper reports an radio-photovoltaic cell based on an intrinsically stable formamidinium-cesium perovskite photovoltaic converter exhibiting a wide light wavelength response from 300 to 800 nm,high open-circuit voltage(V_(oc)),and remarkable efficiency at low-light intensity.When a He ions accelerator is adopted as a mimickedαradioisotope source with an equivalent activity of 0.83 mCi cm^(-2),the formamidinium-cesium perovskite radio-photovoltaic cell achieves a V_(oc)of 0.498 V,a short-circuit current(J_(sc))of 423.94 nA cm^(-2),and a remarkable power conversion efficiency of 0.886%,which is 6.6 times that of the Si reference radio-photovoltaic cell,as well as the highest among all radio-photovoltaic cells reported so far.This work provides a theoretical basis for enhancing the performance of radio-photovoltaic cells.
基金supported by the State Grid Science&Technology Project of China(5400-202224153A-1-1-ZN).
文摘Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.
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.
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.
文摘To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61902158,61806087).
文摘Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.
文摘Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a reliable amortization schedule. In the context of photovoltaic systems connected to the interconnected electricity grid, the quintessence of damping is the amount of energy injected into the grid. Thus it is fundamental to know the parameters of this network and their variation. This paper presents an evaluation of the impact of power grid disturbances on the performance of a solar PV plant under real conditions. The CICAD photovoltaic solar plant, connected to the Senelec distribution network, with an installed capacity of 2 MWp is the study setting. An energy audit of the plant is carried out. Then the percentage of each loss is determined: voltage drops, module degradation, inverter efficiency. The duration of each disconnection is measured and recorded daily. The corresponding quantity of lost energy is thus calculated from meteorological data (irradiation, temperature, wind speed, illumination) recorded by the measurement unit in one-minute steps. The observation period is three months. The total duration of disconnections related to the instability of the electrical network during the study period is 46.7 hours. The amount of energy lost is estimated at 22.6 MWh. This represents 2.4% of the actual calculated production.
文摘The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.
基金supported in part by the Natural Science Foundation of Shandong Province(ZR2021QE289)in part by State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22201).
文摘The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.
文摘The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.