Optimum photovoltaic array size for a hybrid wind/PV system

A methodology for calculation of the optimum size of a PV array for a stand-alone hybrid wind/PV power system is developed. Long term data of wind speed and irradiance recorded for every hour of the day for 30 years were used. These data were used to calculate the probability density functions of the wind speed and the irradiance for each hour of a typical day in a month. The wind speed and irradiance probability density functions and manufacturer's specification on a wind turbine and a PV module were used to calculate the average power generated by the wind turbine and the PV module for each hour of a typical day in a month. The least square method is used to determine the best fit of the PV array and wind turbine to a given load. On the basis of the energy concept an algorithm was developed to find the optimum size of the PV array in the system     

Methodology for optimally sizing the combination of a battery bankand PV array in a wind/PV hybrid system

In this paper, a methodology for calculation of the optimum size of a battery bank and the PV array for a standalone hybrid wind/PV power system is developed. Long term data of wind speed and irradiance recorded for every hour of the day for 30 years were used. These data were used to calculate the average power generated by a wind turbine and a PV module for every hour of a typical day in a month. A load of a typical house in Massachusetts, USA, was used as a load demand of the hybrid system. For a given load and a desired loss of power supply probability, an optimum number of batteries and PV modules was calculated based on the minimum cost of the power system     

Mapping the performance of PV modules, effects of module type and data averaging

A method is presented for estimating the energy yield of photovoltaic (PV) modules at arbitrary locations in a large geographical area. The method applies a mathematical model for the energy performance of PV modules as a function of in-plane irradiance and module temperature and combines this with solar irradiation estimates from satellite data and ambient temperature values from ground station measurements. The method is applied to three different PV technologies: crystalline silicon, CuInSe₂ and CdTe based thin-film technology in order to map their performance in fixed installations across most of Europe and to identify and quantify regional performance factors. It is found that there is a clear technology dependence of the geographical variation in PV performance. It is also shown that using long-term average values of irradiance and temperature leads to a systematic positive bias in the results of up to 3%. It is suggested to use joint probability density functions of temperature and irradiance to overcome this bias.     

A power-rating model for crystalline silicon PV modules

A model for the performance of generic crystalline silicon photovoltaic (PV) modules is proposed. The model represents the output power of the module as a function of module temperature and in-plane irradiance, with a number of coefficients to be determined by fitting to measured performance data from indoor or outdoor measurements. The model has been validated using data from 3 different modules characterized through extensive measurements in outdoor conditions over several seasons. The model was then applied to indoor measurement data for 18 different PV modules to investigate the variability in modeled output from different module types. It was found that for a Central European climate the modeled output of the 18 modules varies with a standard deviation (SD) of 1.22%, but that the between-module variation is higher at low irradiance (SD of 3.8%). The variability between modules of different types is thus smaller than the uncertainty normally found in the total solar irradiation per year for a given site. We conclude that the model can therefore be used for generalized estimates of PV performance with only a relatively small impact on the overall uncertainty of such estimates resulting from different module types.     

A Probabilistic Approach to Photovoltaic Generator Performance Prediction

This paper describes a methodology to predict the performance of photovoltic (PV) generator based on long term climatological data and expected cell performance. The methodology uses long term historical data on insolation to calculate the probability distribution function parameters for each hour of a typical day of any season, week or day. Once the probability distribution function parameters are calculated, they are used to evaluate the predicted hourly, daily, weekly and seasonal capacity factors of a particular design of a PV panel/array at a particular site. Long term insolation data from Sterling, Virginia have been utilized with Solarex SX-110 panel designs to predict PV array performance.     

Optimum windmill-site matching

A methodology for the selection of the optimum windmill for a specific site is developed. The selection is based on finding the capacity factors of the available windmills. This is done by using long-term wind speed data recorded at different hours of the day for many years. These data are then used to generate mean wind speeds for a typical day in a month. Probability density functions for the mean wind speeds for the different hours of the day are generated with the manufacturer's specifications on the windmills used to calculate the capacity factors for the windmills. The windmill with the highest average capacity factor for the specific site is the optimum one and should be recommended     

A novel model for photovoltaic array performance prediction

Based on the I–V curves of a photovoltaic (PV) module, a novel and simple model is proposed in this paper to predict the PV module performance for engineering applications. Five parameters are introduced in this model to account for the complex dependence of the PV module performance upon solar-irradiance intensity and PV module temperature. Accordingly, the most important parameters, i.e. the short-circuit current, open-circuit voltage, fill factor and maximum power-output of the PV module, may be determined under different solar irradiance intensities and module temperatures. To validate the developed model, field measured data from one existing building-integrated photovoltaic system (BIPV) in Hong Kong was studied, and good agreements between the simulated results and the field data are found. This model is simple and especially useful for engineers to calculate the actual performances of the PV modules under operating conditions, with limited data provided by the PV module manufacturers needed.     

单面和双面单晶硅光伏组件的发电性能实证

针对p型PERC单面单晶硅光伏组件和n型双面单晶硅光伏组件,利用光伏组件户外实证测试系统,分析了2016年12月15日~2018年7月20日期间,上海市嘉定区某屋顶的地面采用白板背景时双面和单面组件,以及水泥背景时双面组件的等效发电时长,并对白板背景和水泥背景时双面组件较单面组件的发电量增益情况进行了分析;计算了组件的PR值;分析了阴天和晴天时组件最大输出功率与组件背板温度、太阳辐照度和环境温度的关系;最后对比了单面和双面组件运行13个月后的衰减值。该实证结果为单面和双面组件的户外实证发电性能提供了数据支撑,并对双面组件较单面组件的发电量增益情况进行了有效证明。     

Impact of environment factors on solar cell parameters of a-Si∥μc-Si photovoltaic modules

The behavior of amorphous silicon∥micro crystalline silicon (a-Si∥μc-Si) tandem-type photovoltaic (PV) module is complex because the output current is limited by the lower current component cell. Also, the outdoor behaviors are not fully understood. The impact of environment factors on solar cell parameters of a-Si∥μc-Si PV module was quantitatively analyzed and the module was compared with other silicon-based PV modules (single crystalline silicon (sc-Si) and amorphous silicon (a-Si)). The contour maps of solar cell parameters were constructed as a function of irradiance and module temperature. The contour map of a-Si∥μc-Si PV modules is similar to that of a-Si modules. The results imply that output characteristics of a-Si∥μc-Si PV modules are mainly influenced by the a-Si top cell. Furthermore, the efficiency of a-Si∥μc-Si PV modules was compared other solar cell parameters and the contour map of efficiency is similar to that of fill factor.     

The effect of the windmill's parameters on the capacity factor

In this paper a methodology to study the effect of the windmill's parameters on the capacity factor is presented. The study is based on finding the capacity factors (CF) of the identically rated available windmills. This is done by using long term wind speed data recorded at different hours of the day for many years. This data is then used to generate mean wind speeds for a typical day in a month. Probability density functions for the mean wind speeds for the different hours of the day are generated and used to calculate the capacity factors for the windmills taking into account the manufacturer's parameters of the windmills. The study shows that although the windmills have the same rating they have different capacity factors. The windmill with the highest average capacity factor for the specific site is to be recommended     

Improved artificial neural network method for predicting photovoltaic output performance

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi- neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I–V curves, P– V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.     

Thermal aspects of c-Si photovoltaic module energy rating

Standard test conditions (STC) of photovoltaic (PV) modules are not representative of field conditions; PV module operating temperature often rises up to 30 °C above STC temperature (25 °C), causing a performance drop of 0.5%/°C for crystalline silicium modules. Normal operating cell temperature (NOCT) provides better estimates of PV module temperature rise. It has nevertheless to be measured; moreover NOCT wind speed conditions do not always fit field conditions. The purpose of this work is to model average PV module temperature at given irradiance levels as a function of meteorological parameters and PV module implementation. Thus, no empirical knowledge of PV module thermal behaviour is required for energy rating basing on irradiation distributions over irradiance levels.     

An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)

To accurately model the PV module, it is crucial to include the effects of irradiance and temperature when computing the value of the model parameters. Considering the importance of this issue, this paper proposes an improved modeling approach using differential evolution (DE) method. Unlike other PV modeling techniques, this approach enables the computation of model parameters at any irradiance and temperature point using only the information provided by the manufacturer’s data sheet. The key to this improvement is the ability of DE to simultaneously compute all the model parameters at different irradiance and temperature. To validate the accuracy of the proposed model, three PV modules of different types (multi-crystalline, mono-crystalline and thin-film) are tested. The performance of the model is evaluated against the popular single diode model with series resistance R_s. It is found that the proposed model gives superior results for any irradiance and temperature variations. The modeling method is useful for PV simulator developers who require comprehensive and accurate model for the PV module.     

光伏电站中光伏组件串联数的优化设计

目前在光伏电站设计中,光伏组件串联数的计算广泛使用GB 50797-2012或IEC 62548-2016中的计算方式,假定在极端环境低温的情况下对光伏组串的开路电压、最大功率点电压等进行计算,进而确定光伏组件成串数量,即光伏组件串联数.但随着对光伏电站度电成本的要求日益严苛,上述对于计算光伏电站的光伏组件串联数的要求...     

Seasonal variation analysis of the outdoor performance of amorphous Si photovoltaic modules using the contour map

The effects of module temperature (T_mod) and spectral irradiance distribution on the outdoor performance of amorphous Si (a-Si) photovoltaic (PV) modules were investigated using contour maps. Compared to PV modules based on crystalline Si, such as single-crystalline Si (sc-Si) and multicrystalline Si, a-Si PV modules exhibit complex behavior with seasonal variation. In this study, we statistically analyzed the outdoor performance of a-Si and sc-Si PV modules. The influence of environmental factors on outdoor performance of a-Si PV modules was analyzed for two seasons, spring and autumn, in which the data periods had nearly the same average T_mod and integrated irradiation. The outdoor performance of the a-Si PV module depends on both temperature history and light-induced degradation.     

Efficiency model for photovoltaic modules and demonstration of its application to energy yield estimation

A new method has been proposed [W. Durisch, K.H. Lam, J. Close, Behaviour of a copper indium gallium diselenide module under real operating conditions, in: Proceedings of the World Renewable Energy Congress VII, Pergamon Press, Oxford, Elsevier, Amsterdam, 2002, ISBN 0-08-044079-7] for the calculation of the annual yield of photovoltaic (PV) modules at selected sites, using site-specific meteorological data. These yields are indispensable for calculating the expected cost of electricity generation for different modules, thus allowing the type of module to be selected with the highest yield-to-cost ratio for a specific installation site. The efficiency model developed and used for calculating the yields takes three independent variables into account: cell temperature, solar irradiance and relative air mass. Open parameters of the model for a selected module are obtained from current/voltage (I/V) characteristics, measured outdoors at Paul Scherrer Institute's test facility under real operating conditions. From the model, cell and module efficiencies can be calculated under all relevant operating conditions. Yield calculations were performed for five commercial modules (BP Solar BP 585 F, Kyocera LA361K54S, Uni-Solar UPM-US-30, Siemens CIS ST40 and Wuerth WS11003) for a sunny site in Jordan (Al Qawairah) for which reliable measured meteorological data are available. These represent mono-crystalline, poly-crystalline and amorphous silicon as well as with copper–indium-diselenide, CuInSe₂ PV modules. The annual yield for these modules will be presented and discussed.     

Estimation of irradiance and outdoor performance of photovoltaic modules by meteorological data

Three environmental factors of irradiance, solar spectral distribution and module temperature greatly affect the performances of photovoltaic (PV) modules. If the environmental factors can be estimated by basic meteorological data (BMD) announced by an official organization in various areas, the performances of PV modules can be estimated easily. In this study, a relationship between the environmental factors and the BMD was analyzed. The performances of Si-based (crystalline Si and thin-film Si) PV modules were estimated by the relationship. As a result, errors between the estimation and actual performances in the crystalline Si and thin-film PV modules were within 1.88% and 0.58% in Kusatsu city, Japan (34°58′N, 135°57′E). This methodology can be useful for rating the performance of PV modules at various areas.     

异质结光伏组件的发电效果研究

为研究异质结光伏组件的实际发电效果,收集某光伏发电站近两年的发电量数据,得到单块组件的月均发电量.通过对比分析,验证该异质结光伏组件相对于多晶光伏组件在发电效率方面的优越性,且与理论增益相符.通过计算冬夏两季的月均发电量和月均辐照度,证明相比多晶光伏组件,温度升高对该异质结光伏组件的功率影响更小,与理论上异质结光伏组件...     

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

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.     

The coupling factor: A new metric for determining and controlling the efficiency of solar photovoltaic power utilization

The production of electricity and hydrogen in a renewable fashion, such as using solar energy, can provide a clean and sustainable energy source for electric-powered vehicles, including fuel-cell and battery-electric vehicles. Our research on generating hydrogen and charging batteries using renewable solar photovoltaic (PV) electricity has led to the development of a simple and convenient new metric called the coupling factor that describes the fraction of the maximum PV power transferred to electrical loads. The keystone of the coupling factor concept is a regression model to calculate the maximum PV voltage, current, and power as a function of the instantaneous incident solar irradiance and the photovoltaic module temperature. The coupling factor can range from zero to one, i.e., no transfer of power from the PV system to the load, to complete transfer of the PV power. We describe the derivation of regression models to compute important PV electrical output variables, such as the open circuit voltage, the short circuit current, the maximum power point voltage, the maximum power point current, and the coupling factor as a function of the fundamental measured variables affecting those quantities. The models are derived for PV modules used in our previous research to power an electrolyzer and charge high-voltage batteries. In addition, we develop models for other modules using PV cell technologies different from those used in our PV system. Some of the calculated quantities are compared to measurements for our PV system. The usefulness of these quantities, and especially the coupling factor, in rating the transfer of PV power to electrolyzer and battery loads, is illustrated. Finally, we discuss how the predicted maximum power point voltage can be used for real-time control and efficiency optimization of a dynamic PV-load system.