Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging manageme...Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.展开更多
In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical m...In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.展开更多
This article presents the results of comparative study of two PV solar modules technologies,namely monofacial and bifacial.This study main objective is to identify conditions and parameters that make it possible to ob...This article presents the results of comparative study of two PV solar modules technologies,namely monofacial and bifacial.This study main objective is to identify conditions and parameters that make it possible to obtain better energy and economic efficiency from one or other of two technologies.The study reason lies in revival observed on bifacial module in recent years where all the major manufacturers of PV solar panels are developing them where in a few years,this technology risks being at the same price as the monofacial solar panel with better efficiency.Economic indicator used is energy levelized cost(LCOE)which is function technology type,energy productivity,annual investment and operation cost.To achieve this,a 3.685 MWc solar PV power plant was dimensioned and simulated under Matlab for a 3.5 ha site with a 2,320,740,602 FCFA budget for monofacial installation,against 1,925,188,640 FCFA for 2.73 MWc bifacial installation.The LCOE comparative analysis of two technologies calculated over a period of 25 years,showed that plant with bifacial panels is more beneficial if bifacial gain is greater than 9%.It has further been found that it is possible to gain up to 40%of invested cost if bifacial gain reaches 45%.Finally,a loss of about 10%of invested cost could be recorded if bifacial gain is less than 9%.展开更多
Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connect...Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.展开更多
This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any...This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any nonlinear loads and at the same time guarantees the delivery of a part of the load request from the same PV source. A boost converter is used for maximum power point(MPP) tracking purposes under various climate conditions through a fuzzy logic technique. The suggested study is tested under a MATLAB/Simulink environment. The obtained results depict the efficacy of the proposed procedures to meet the IEEE 519-1992 standard recommendation on harmonic levels.展开更多
In order to fully comprehend the developing status of wind power and photovoltaic (PV) power generation, a special investigation on the integration of wind power and PV power was launched by the agencies of the State ...In order to fully comprehend the developing status of wind power and photovoltaic (PV) power generation, a special investigation on the integration of wind power and PV power was launched by the agencies of the State Electricity Regulatory Commission (SERC) throughout China during July-October 2010. This report is completed based on the investigation along with routine supervisory and management programs. There are totally 573 wind power projects and 94 PV power projects involved. Existing problems in these projects are pointed out and proposals for regulation are put forward.展开更多
The energy assessment of the PV power systems is carried out by using different types of performance indicators that benchmark the output of these systems against the PV panel maximum output at hypothetical operation ...The energy assessment of the PV power systems is carried out by using different types of performance indicators that benchmark the output of these systems against the PV panel maximum output at hypothetical operation conditions. In this paper, a comparative analysis of six types of performance indicators is conducted and a new performance indicator which considers PV panel slope and orientation is proposed. The proposed indicator is benchmarking the PV system actual output against the maximum output of the same system if it would operate in two axis tracking mode. The proposed performance indicator is used to develop a friendly user calculator of PV system output that can be used by, energy providers and PV system installers to evaluate the output of the PV grid connect network. The advantage of the developed calculator is high-lighted by a case study that estimates energy capacity of different residential rooftop PV systems installed in a residential suburb in Sydney.展开更多
Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent st...Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.展开更多
The output power variability of photovoltaic(PV)power plants(PVPPs)is one of the major challenges for the op-eration and control of power systems.The short-term power variations,mainly caused by cloud movements,affect...The output power variability of photovoltaic(PV)power plants(PVPPs)is one of the major challenges for the op-eration and control of power systems.The short-term power variations,mainly caused by cloud movements,affect voltage magnitude and frequency,which may degrade power quality and power system reliability.Comprehensive analyses of these power variations are crucial to formulate novel control ap-proaches and assist power system operators in the operation and control of power systems.Thus,this paper proposes a simu-lation-based approach to assessing short-term power variations caused by clouds in PV power plants.A comprehensive assess-ment of the short-term power variations in a PV power plant operating under cloud conditions is another contribution of this paper.The performed analysis evaluates the individual impact of multiple weather condition parameters on the magnitude and ramp rate of the power variations.The simulation-based ap-proach synthesizes the solar irradiance time series using three-dimensional fractal surfaces.The proposed assessment ap-proach has shown that the PVPP nominal power,timescale,cloud coverage level,wind speed,period of the day,and shadow intensity level significantly affect the characteristics of the pow-ervariations.展开更多
Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties...Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction,a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed.First,Pearson correlation coefficients and mutual information are used to select representative features,reducing the impact of redundant features onmodel performance.Then,the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,thereby obtaining feature representations rich in temporal information.Subsequently,a multi-layer cascade structure is developed,progressively integrating prediction results from base learners such as LightGBM,XGBoost,Random Forest(RF),and Extreme Random Forest(ERF)to enhance model performance.Finally,an XGBoost-based meta-learner is utilized to integrate the outputs of the base learners and generate the final prediction results.The entire cascading process adopts a dynamic expansion strategy,where the decision to add new cascade layers is based on the R2 performance criterion.Experimental results demonstrate that the proposed model achieves high prediction accuracy and robustness under various weather conditions,showing significant improvements over traditional models and providing an effective solution for short-term PV power forecasting.展开更多
To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic networ...To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic network,this paper proposes a novel ensemble prediction model,named gradient boosting dendritic network(GBDD)model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation.Unlike other machine learning models,the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation.In addition,based on the structure of GBDD,this paper proposes a strategy that can improve the prediction performance of other types of prediction models.The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models.The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.展开更多
A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there ...A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.展开更多
This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the iss...This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.展开更多
Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal p...Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.展开更多
The application of PV facades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand, this paper investigates PV fac...The application of PV facades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand, this paper investigates PV facade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV facade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV facades. The results show that with various cities, building orientations, building forms, materials and arrangements of PV modules, there is a distinct difference in the electrical output energy of PV facades. Weather conditions nlav a very important role in terms of PV generation nerformance of PV facades.展开更多
India is highly dependent on solar photovoltaics(PV)to harness its vast solar resource potential and combat climate change.However,∼90%of the installed PV capacity in India is concentrated in the top nine states,with...India is highly dependent on solar photovoltaics(PV)to harness its vast solar resource potential and combat climate change.However,∼90%of the installed PV capacity in India is concentrated in the top nine states,with the remaining states lagging behind.The research reveals that during monsoons,heavy cloud cover and rain lead to high solar resource variability,intermittency and the risk of very low PV generation,which can result in reliability issues in future PV-dominated electricity grids.Although energy storage can help in overcoming high intermittency,there are multiple challenges associated with it.The novelty of this study lies in demonstrating the benefits of combining multiple PV sites in various regions to mitigate the risks of low PV generation and high variability.The variability of individual sites was found to be up to∼3.5 times higher than the variability of combined generation.During noon,prominent solar park sites like Bhadla and NP Kunta experience a decrease in power generation to values as low as∼10%of the rated PV capacity.However,the minimum generation of the large-scale dispersed PV generation is>30%.Furthermore,the research identifies other benefits of dispersing PV generation across the country,viz.,reduction of seasonal variability by adding PV capacity in the southern region,widening of the PV generation span,more room for PV capacity addition,reduction in storage and ramping needs,utilization of hydroelectric potential of the north-east and PV potential of Ladakh,and creating opportunities for sustainable development in rural agrarian regions through agrivoltaics.展开更多
The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study propo...The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction.The proposed model,called DRAM,concatenates a dilated convolutional neural network(DCNN)module with a bidirectional long short-term memory(BiLSTM)module,and integrates an attention mechanism.First,the processed data are input into the DCNN layer,and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data.Subsequently,the temporal characteristics between the features are extracted in the BiLSTM layer.Finally,an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables.In addition,the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model.In this study,the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.展开更多
文摘Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.
基金Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)Innovation Foundation of CMA Public Meteorological Service Center(K2023002)+1 种基金“Tianchi Talents”Introduction Plan(2023)Key Innovation Team for Energy and Meteorology of China Meteorological Administration。
文摘In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.
文摘This article presents the results of comparative study of two PV solar modules technologies,namely monofacial and bifacial.This study main objective is to identify conditions and parameters that make it possible to obtain better energy and economic efficiency from one or other of two technologies.The study reason lies in revival observed on bifacial module in recent years where all the major manufacturers of PV solar panels are developing them where in a few years,this technology risks being at the same price as the monofacial solar panel with better efficiency.Economic indicator used is energy levelized cost(LCOE)which is function technology type,energy productivity,annual investment and operation cost.To achieve this,a 3.685 MWc solar PV power plant was dimensioned and simulated under Matlab for a 3.5 ha site with a 2,320,740,602 FCFA budget for monofacial installation,against 1,925,188,640 FCFA for 2.73 MWc bifacial installation.The LCOE comparative analysis of two technologies calculated over a period of 25 years,showed that plant with bifacial panels is more beneficial if bifacial gain is greater than 9%.It has further been found that it is possible to gain up to 40%of invested cost if bifacial gain reaches 45%.Finally,a loss of about 10%of invested cost could be recorded if bifacial gain is less than 9%.
基金supported by Nation Key R&D Program of China(2021YFE0102400).
文摘Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.
文摘This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any nonlinear loads and at the same time guarantees the delivery of a part of the load request from the same PV source. A boost converter is used for maximum power point(MPP) tracking purposes under various climate conditions through a fuzzy logic technique. The suggested study is tested under a MATLAB/Simulink environment. The obtained results depict the efficacy of the proposed procedures to meet the IEEE 519-1992 standard recommendation on harmonic levels.
文摘In order to fully comprehend the developing status of wind power and photovoltaic (PV) power generation, a special investigation on the integration of wind power and PV power was launched by the agencies of the State Electricity Regulatory Commission (SERC) throughout China during July-October 2010. This report is completed based on the investigation along with routine supervisory and management programs. There are totally 573 wind power projects and 94 PV power projects involved. Existing problems in these projects are pointed out and proposals for regulation are put forward.
文摘The energy assessment of the PV power systems is carried out by using different types of performance indicators that benchmark the output of these systems against the PV panel maximum output at hypothetical operation conditions. In this paper, a comparative analysis of six types of performance indicators is conducted and a new performance indicator which considers PV panel slope and orientation is proposed. The proposed indicator is benchmarking the PV system actual output against the maximum output of the same system if it would operate in two axis tracking mode. The proposed performance indicator is used to develop a friendly user calculator of PV system output that can be used by, energy providers and PV system installers to evaluate the output of the PV grid connect network. The advantage of the developed calculator is high-lighted by a case study that estimates energy capacity of different residential rooftop PV systems installed in a residential suburb in Sydney.
基金upported by the National Key R&D Program of China(2018YFB0905200)National Natural Science Foundation of China(51907099)Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City(University of Macao)(SKLIoTSC(UM)-2021-2023/ORPF/A11/2022).
文摘Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.
基金supported in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001UTFPR-Campus Pato Branco, FINEP, SETI, CNPq, and Fundacao Araucária for scholarships and funding
文摘The output power variability of photovoltaic(PV)power plants(PVPPs)is one of the major challenges for the op-eration and control of power systems.The short-term power variations,mainly caused by cloud movements,affect voltage magnitude and frequency,which may degrade power quality and power system reliability.Comprehensive analyses of these power variations are crucial to formulate novel control ap-proaches and assist power system operators in the operation and control of power systems.Thus,this paper proposes a simu-lation-based approach to assessing short-term power variations caused by clouds in PV power plants.A comprehensive assess-ment of the short-term power variations in a PV power plant operating under cloud conditions is another contribution of this paper.The performed analysis evaluates the individual impact of multiple weather condition parameters on the magnitude and ramp rate of the power variations.The simulation-based ap-proach synthesizes the solar irradiance time series using three-dimensional fractal surfaces.The proposed assessment ap-proach has shown that the PVPP nominal power,timescale,cloud coverage level,wind speed,period of the day,and shadow intensity level significantly affect the characteristics of the pow-ervariations.
基金2023 Sustainable Development Science and Technology Innovation Action Plan Project of Chongming District Science and Technology Committee,Shanghai(CKST2023-01)Shanghai Science and Technology Commission Funded Project(19DZ2254800).
文摘Short-term photovoltaic(PV)power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling.To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction,a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed.First,Pearson correlation coefficients and mutual information are used to select representative features,reducing the impact of redundant features onmodel performance.Then,the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,thereby obtaining feature representations rich in temporal information.Subsequently,a multi-layer cascade structure is developed,progressively integrating prediction results from base learners such as LightGBM,XGBoost,Random Forest(RF),and Extreme Random Forest(ERF)to enhance model performance.Finally,an XGBoost-based meta-learner is utilized to integrate the outputs of the base learners and generate the final prediction results.The entire cascading process adopts a dynamic expansion strategy,where the decision to add new cascade layers is based on the R2 performance criterion.Experimental results demonstrate that the proposed model achieves high prediction accuracy and robustness under various weather conditions,showing significant improvements over traditional models and providing an effective solution for short-term PV power forecasting.
基金supported by the National Natural Science Foundation of China(Grant Nos.61973322 and 62103443)the Natural Science Foundation of Hunan Province,China(Grant No.2022JJ40630).
文摘To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic network,this paper proposes a novel ensemble prediction model,named gradient boosting dendritic network(GBDD)model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation.Unlike other machine learning models,the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation.In addition,based on the structure of GBDD,this paper proposes a strategy that can improve the prediction performance of other types of prediction models.The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models.The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.
文摘A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.
文摘This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.
基金supported by National Science Foundation of China(61563032,61963025)Project supported by Gansu Basic Research Innovation Group(18JR3RA133)+1 种基金Industrial Support and Guidance Project for Higher Education Institutions of Gansu Province(2019C-05)Open Fund Project of Key Laboratory of Industrial Process Advanced Control of Gansu Province(2019KFJJ02).
文摘Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.
基金Sponsored by the National Natural Science Foundation of China (Grant No.51008136)the Graduates’ Innovation and Enterprise Fund of HUST (Grant No.HF-11-12-2013)
文摘The application of PV facades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand, this paper investigates PV facade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV facade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV facades. The results show that with various cities, building orientations, building forms, materials and arrangements of PV modules, there is a distinct difference in the electrical output energy of PV facades. Weather conditions nlav a very important role in terms of PV generation nerformance of PV facades.
基金Department of Science and Technology,Government of India,to carry out the research under the Project U.K.India Clean Energy Research Institute(UKICERI)under Grant DST/RCUK/JVCCE/2015/02(C).
文摘India is highly dependent on solar photovoltaics(PV)to harness its vast solar resource potential and combat climate change.However,∼90%of the installed PV capacity in India is concentrated in the top nine states,with the remaining states lagging behind.The research reveals that during monsoons,heavy cloud cover and rain lead to high solar resource variability,intermittency and the risk of very low PV generation,which can result in reliability issues in future PV-dominated electricity grids.Although energy storage can help in overcoming high intermittency,there are multiple challenges associated with it.The novelty of this study lies in demonstrating the benefits of combining multiple PV sites in various regions to mitigate the risks of low PV generation and high variability.The variability of individual sites was found to be up to∼3.5 times higher than the variability of combined generation.During noon,prominent solar park sites like Bhadla and NP Kunta experience a decrease in power generation to values as low as∼10%of the rated PV capacity.However,the minimum generation of the large-scale dispersed PV generation is>30%.Furthermore,the research identifies other benefits of dispersing PV generation across the country,viz.,reduction of seasonal variability by adding PV capacity in the southern region,widening of the PV generation span,more room for PV capacity addition,reduction in storage and ramping needs,utilization of hydroelectric potential of the north-east and PV potential of Ladakh,and creating opportunities for sustainable development in rural agrarian regions through agrivoltaics.
基金Science and Technology Project of State Grid Ningxia Electric Power Co.,Ltd Research on Distributed Photovoltaic Fine Power Prediction Technology for Day-Ahead Scheduling,5229NX230007.
文摘The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction.The proposed model,called DRAM,concatenates a dilated convolutional neural network(DCNN)module with a bidirectional long short-term memory(BiLSTM)module,and integrates an attention mechanism.First,the processed data are input into the DCNN layer,and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data.Subsequently,the temporal characteristics between the features are extracted in the BiLSTM layer.Finally,an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables.In addition,the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model.In this study,the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.
