Single-sided photovoltaic module performance aging evaluation method considering multiple environment variables

Abstract

The invention provides a single-sided photovoltaic module performance aging evaluation method considering multiple environment variables, and the method comprises the steps: S1, forming initial data set input based on collected and obtained operation environment data; s2, after data screening, obtaining screened operation environment parameters, forming a data set, and correcting the working temperature of the photovoltaic module according to the input data set; s3, establishing a photovoltaic module physical aging rate model according to physical and chemical hypotheses based on an aging mechanism causing performance reduction, and correcting a fixed aging rate in the photovoltaic module physical aging rate model according to the corrected working temperature of the photovoltaic module so as to improve the evaluation precision; and S4, constructing a photovoltaic module performance aging evaluation model, obtaining a corresponding evaluation result through the obtained module aging rate, and achieving long-acting operation evaluation of the photovoltaic module under the current working condition. According to the method, the fixed parameters in the aging model can be corrected in combination with the actual operation condition, and the evaluation accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection awareness, photovoltaic power generation, as a clean and renewable energy form, has received extensive attention and rapid development. Photovoltaic modules are the core components of photovoltaic power generation systems, and their performance and lifespan directly affect the power generation efficiency and economic benefits of the entire photovoltaic power station.

[0003] However, during actual operation, photovoltaic modules are affected by various environmental factors, such as solar irradiance, environmental temperature, humidity, wind speed, and ultraviolet intensity. These environmental variables will cause the performance of photovoltaic modules to gradually decay and exhibit aging phenomena. Aging not only reduces the power generation efficiency of photovoltaic modules but may also affect their reliability and safety. Therefore, accurately evaluating the performance aging of photovoltaic modules is of great significance for formulating maintenance strategies, predicting power generation, and extending the lifespan of modules.

[0004] Currently, the evaluation of the performance aging of photovoltaic modules mainly relies on empirical constant models or laboratory accelerated aging tests. These methods are usually based on fixed aging rates or conducted in a controlled environment, and do not fully consider the various complex environmental variables in the actual operating environment. Therefore, the evaluation results often deviate from the actual situation and are difficult to meet the evaluation requirements in actual working conditions. In addition, traditional methods have limitations in dealing with the combined effects of multiple environmental factors and cannot accurately quantify the contributions of each environmental parameter to module aging. Summary of the Invention

[0005] The object of the present invention is to provide a method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables, which can combine the actual operating conditions, correct the fixed parameters in the aging model, and improve the accuracy and reliability of the evaluation. To achieve the above object of the invention, the present invention adopts the following technical solutions:

[0006] A method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables combines a basic aging model with the actual operating conditions to realize the correction of the photovoltaic aging rate considering the influence of environmental factors of the photovoltaic module, so as to achieve a comprehensive evaluation of the performance aging of the photovoltaic module that is more matched with the actual operating scenario of the photovoltaic power station.

[0007] The method includes the following steps:

[0008] S1: Based on the collected operating environment data, form an initial data set input, and the operating environment data includes solar irradiance, environmental temperature, dew point temperature, relative humidity, wind speed, and ultraviolet intensity;

[0009] S2: After data screening, obtain the screened operating environment parameters to form a data set, and correct the working temperature of the photovoltaic module according to the input data set.

[0010] S3: Establish a physical aging rate model of the photovoltaic module based on the physical and chemical assumptions underlying the aging mechanism that causes performance degradation, and correct the fixed aging rate in the physical aging rate model of the photovoltaic module according to the corrected working temperature of the photovoltaic module to improve the evaluation accuracy.

[0011] S4: Construct a performance aging evaluation model of the photovoltaic module, obtain the corresponding evaluation results through the obtained component aging rate, and realize the long-term operation evaluation of the photovoltaic module under the current working conditions.

[0012] On the basis of adopting the above technical solutions, the present invention can also adopt the following further technical solutions, or use these further technical solutions in combination:

[0013] In step S2, estimate the working temperature of the photovoltaic module under the environmental conditions where it is located according to the Sandia model:

[0014]

[0015] where, T mod is the working temperature of the photovoltaic module, in °C, T amb is the environmental temperature where the photovoltaic module is located, in °C, G POA is the plane irradiance of the photovoltaic module, in W / m 2 ; a and b are empirical coefficients, v wind is the wind speed, in m / s.

[0016] Among them, the coefficients a and b are preferably the coefficients in the case of the recommended open rack installation configuration, that is, a = -3.47 and b = -0.0594.

[0017] Furthermore, obtain the daily cycle temperature ΔT daily of the photovoltaic module, that is

[0018] ΔT daily = max[T mod,daily - min[T mod,daily

[0019] In step S3, the physical aging rate of the photovoltaic module is evaluated according to the Bala model by considering the daily maximum temperature of the photovoltaic module, the corrected thermal cycle temperature of the photovoltaic module, ultraviolet irradiation, and relative humidity factors to evaluate the influence of different environmental conditions on the expected life of the photovoltaic module. The specific content includes:

[0020]

[0021] Among them, k is the power degradation rate of the photovoltaic module; T max is the daily maximum temperature of the photovoltaic module, in K, which can be obtained through the calculation in step S2; ΔT daily is the daily cyclic temperature of the photovoltaic module, in K, which can be obtained through the calculation in step S2; UV daily is the daily average ultraviolet irradiance, unit: W / m 2 ; RH daily is the daily average relative humidity, unit: %; k B is the Boltzmann constant, which is 8.62×10 -5 eV; β 0 is the frequency factor, unit: s -1 ; β 1 is the activation energy, unit: eV; β 2 is the influence coefficient of the cyclic temperature, and its range is set between 2 and 5; β 3 is the influence of ultraviolet radiation, and its range is set between 0.6 and 1; β 4 is the influence of relative humidity, and its range is set between 0 and 2.

[0022] The β coefficients required by the Bala model, that is, β 0、 β 1、 β 2、 β 3、 β 4 should be determined by comparing the estimated degradation trend with the actual degradation trend observed in a photovoltaic power station in a similar environment.

[0023] In step S4, the photovoltaic module performance aging evaluation model includes a linear model, an exponential model, an improved model Pan model considering the influence of material factors, and an improved model Kaaya model considering the influence of material and shape factors;

[0024] The specific content of the linear model in the photovoltaic module performance aging evaluation model includes:

[0025]

[0026] Among them, P(t) is the output power of the photovoltaic module at time t, P 0 is the initial power of this module, and k is the power degradation rate of the photovoltaic module;

[0027] The specific content of the exponential model in the photovoltaic module performance aging evaluation model includes:

[0028]

[0029] Among them, P(t) is the output power of the photovoltaic module at time t, P 0 is the initial power of this module, and k is the power degradation rate of the photovoltaic module;

[0030] The specific content of the Pan model in the photovoltaic module performance aging evaluation model includes:

[0031]

[0032] Among them, P(t) is the output power of the photovoltaic module at time t, P 0 is the initial power of the module, k is the power degradation rate of the photovoltaic module, and λ is a parameter related to the photovoltaic module material;

[0033] The specific content of the Kaaya model in the photovoltaic module performance aging evaluation model includes:

[0034]

[0035] Among them, P(t) is the output power of the photovoltaic module at time t, P 0 is the initial power of the module, k is the power degradation rate of the photovoltaic module, Γ is a parameter related to the photovoltaic module material, and μ is a parameter related to the shape of the photovoltaic module.

[0036] Preferably, in an actual photovoltaic system, the power degradation of photovoltaic modules often exhibits a non-linear degradation characteristic. Therefore, other different photovoltaic performance degradation models can be selected for the power aging evaluation of photovoltaic modules to improve the accuracy of the evaluation.

[0037] The beneficial effects of the present invention are: (1) Using the correction model makes the influence of environmental variable parameters on photovoltaic modules more in line with the actual situation; (2) Combining the basic aging model with the actual operating conditions realizes a more accurate evaluation of the photovoltaic aging rate performance; (3) Solves the problem that the traditional mathematical model-based solution cannot meet the requirements of photovoltaic module performance aging evaluation in actual working condition scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The flowchart of the evaluation method of the present invention.

[0039] Figure 2 Are the environmental condition data of a certain place in the East China Sea, where (a) solar irradiance; (b) ambient temperature; (c) wind speed; (d) relative humidity.

[0040] Figure 3 Is the change in the working temperature of the photovoltaic module corrected based on the Sandia model.

[0041] Figure 4 Is the change in the daily degradation rate of the photovoltaic module performance obtained based on the Bala model.

[0042] Figure 5 Is the evaluation result of the performance degradation of the photovoltaic module in a 5-year operation scenario obtained based on the proposed method.

[0043] Figure 6 It is the evaluation result of the performance degradation of the photovoltaic module under the 20-year operation scenario obtained based on the proposed method. Detailed implementation manners

[0044] To describe the present invention more specifically, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] As Figure 1 shown, the method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables provided by the present invention includes the following steps:

[0046] First, obtain environmental detection data, including plane irradiance G POA , wind speed V wind , environmental temperature T amb , relative humidity RH; then, correct the working temperature of the photovoltaic module to obtain the actual working temperature of the photovoltaic module; afterwards, substitute the corrected data, that is, the data related to the working temperature of the photovoltaic module, into the Bala aging rate analysis model to obtain the estimated actual aging rate of the module; finally, substitute the corrected data, that is, the power degradation rate of the photovoltaic module in the actual scenario, into the linear model, exponential model, improved model Pan model considering the influence of material factors, and improved model Kaaya model considering the influence of material and shape factors to obtain the actual performance degradation of the module under different aging models.

[0047] To describe the present invention more specifically, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Taking the environmental data of a certain place in the East China Sea obtained from the NASA database as the input of the complex working conditions of the model, analyze the power aging law of the photovoltaic module under this operation scenario. The relevant data of this place used, plane irradiance G POA , wind speed V wind , environmental temperature T amb , relative humidity RH, as Figure 2 shown, (a)-(d) are the curves of solar irradiance, environmental temperature, wind speed, and relative humidity in a year with days as the unit respectively.

[0049] 1. First, based on the actual environmental working condition data of this place and substitute it into the Sandia model. The model expression is as follows:

[0050]

[0051] Among them, T mod is the temperature of the photovoltaic module (unit: °C), T amb is the environmental temperature where the photovoltaic module is located (unit: °C), G POAFor the plane irradiance of the photovoltaic module (unit: W / m 2 ). a and b are two empirical coefficients, and v wind is the wind speed (unit: m / s). Among them, the coefficients a and b are the coefficients in the case of the recommended open rack installation configuration, that is, a = -3.47 and b = -0.0594.

[0052] Correct the operating temperature of the photovoltaic module under the current working conditions to obtain the corresponding working temperature change curve, as Figure 3 shown is the working temperature change of the photovoltaic module corrected based on the Sandia model. The data is in days, and the corrected working temperature of the photovoltaic module for a whole year is plotted.

[0053] 2. Secondly, substitute the obtained corrected working temperature change of the photovoltaic module into the Bala model to estimate the component power degradation rate. The model expression is:

[0054]

[0055] Among them, k is the power degradation rate of the photovoltaic module; T max is the daily maximum temperature of the photovoltaic module (unit: K); ΔT daily is the daily cyclic temperature of the component (unit: K); UV daily is the daily average ultraviolet irradiance (unit: W / m 2 ); RH daily is the daily average relative humidity (unit: %); k B is the Boltzmann constant (8.62×10 -5 eV); β 0 is the frequency factor (unit: s -1 ); β 1 is the activation energy (unit: eV); β 2 is the influence coefficient of the cyclic temperature, and its range is usually set between 2 and 5; β 3 is the influence of ultraviolet radiation, and its range is usually set between 0.6 and 1; β 4 is the influence of relative humidity, and its range is usually set between 0 and 2.

[0056] Among them, the values of the β coefficients used in the Bala model calculation in this case are shown in Table 1:

[0057] Table 1 Value table of β coefficients used in Bala model calculation

[0058] β coefficient β0 β1 <![CDATA[β 2 > <![CDATA[β 3 > <![CDATA[β 4 > value 0.35 0.7 2.41 0.75 1.52

[0059] Based on the local environmental data and the photovoltaic module temperature correction data obtained according to the Sandia model, obtain the daily degradation rate change law of the local photovoltaic module performance, asFigure 4 Shown is the daily degradation rate change of the performance of a photovoltaic module obtained based on the Bala model. The data is in days, and the curve of the degradation rate change of the photovoltaic module over a whole year of historical data is plotted.

[0060] 3. According to the obtained daily degradation rate change model, substitute it into the relevant photovoltaic module power aging evaluation models respectively to obtain the power degradation situation of the photovoltaic module under the current operating scenario. In this embodiment, the four photovoltaic module power degradation models mentioned are compared. The values of other parameters in the model except the component aging rate k are shown in Table 2.

[0061] Table 2 Parameter values of each model

[0062]

[0063] (1) The specific content of the linear model in the photovoltaic module performance aging evaluation model includes:

[0064]

[0065] Among them, P(t) is the output power of the photovoltaic module at time t, and P 0 is the initial power of this component, and k is the power degradation rate of the photovoltaic module.

[0066] (2) The specific content of the exponential model in the photovoltaic module performance aging evaluation model includes:

[0067]

[0068] Among them, P(t) is the output power of the photovoltaic module at time t, and P 0 is the initial power of this component, and k is the power degradation rate of the photovoltaic module.

[0069] (3) The specific content of the Pan model in the photovoltaic module performance aging evaluation model includes:

[0070]

[0071] Among them, P(t) is the output power of the photovoltaic module at time t, and P 0 is the initial power of this component, k is the power degradation rate of the photovoltaic module, and λ is a parameter related to the photovoltaic module material.

[0072] (4) The specific content of the Kaaya model in the photovoltaic module performance aging evaluation model includes:

[0073]

[0074] Among them, P(t) is the output power of the photovoltaic module at time t, and P 0$P_0$ is the initial power of the component, $k$ is the power degradation rate of the photovoltaic module, $\Gamma$ is a parameter related to the photovoltaic module material, and $\mu$ is a parameter related to the shape of the photovoltaic module.

[0075] As Figure 5 shown is the evaluation result of the performance degradation of the photovoltaic module in a 20-year operation scenario obtained based on the proposed method. The data plots the component performance degradation curves under the evaluation of the Linear model, Exponational model, Pan model, and Kaaya model over a 20-year time span.

[0076] As Figure 6 shown is the evaluation result of the performance degradation of the photovoltaic module in a 25-year operation scenario based on different models obtained with a fixed component power degradation rate. The results show that its evaluation results highly depend on the value of the component degradation rate and it is difficult to characterize the change of the component power performance under actual working conditions.

[0077] Since there are certain differences in the power degradation laws of the photovoltaic module operation scenarios obtained by different models, and different models also have different performance characteristics in the deduction process of the degradation laws. Therefore, in actual analysis and application, it is necessary to further select a model suitable for the aging change law of the component according to the results of the accelerated aging tests of different photovoltaic modules, and make targeted corrections to the relevant parameters of the model to achieve a more accurate digital evaluation of the photovoltaic module aging. The parameter selections in the present invention are all common or typical values in the research, and the used component power degradation evaluation models are common basic models.

[0078] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables, characterized in that: The method comprises the steps of: S1: Based on the collected operating environment data, an initial data set input is formed, where the operating environment data includes solar irradiance, ambient temperature, dew point temperature, relative humidity, wind speed, ambient temperature of the component, and ultraviolet intensity; S2: After data screening, the screened operating environment parameters are obtained to form a data set, and the operating temperature of the photovoltaic module is corrected according to the input data set; S3: Establish a PV module physical aging rate model based on the physical and chemical assumptions underlying the aging mechanism that causes performance degradation, and correct the fixed aging rate in the PV module physical aging rate model based on the corrected PV module operating temperature to improve the evaluation accuracy; S4: Construct a photovoltaic module performance aging evaluation model, obtain the corresponding evaluation results through the obtained module aging rate, and realize the long-term operation evaluation of photovoltaic modules under current working conditions.

2. The method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables according to claim 1, characterized in that: In step S2, the operating temperature of the photovoltaic module under the ambient conditions is estimated according to the Sandia model: Among them, T mod is the operating temperature of the photovoltaic module, unit: °C, T amb is the ambient temperature of the photovoltaic module, in °C, G POA is the irradiance of the photovoltaic module plane, in W / m 2 ; a and b are empirical coefficients, v wind is the wind speed in m / s.

3. The method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables according to claim 2, characterized in that: The coefficients a and b use the coefficients for the recommended open bracket mounting configuration, namely a = -3.47 and b = -0.0594.

4. The method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables according to claim 1, characterized in that: In step S3, the physical aging rate of the photovoltaic module is evaluated according to the Bala model by considering the maximum temperature, thermal cycle temperature of the photovoltaic module, ultraviolet radiation and relative humidity factors to evaluate the impact of different environmental conditions on the expected life of the photovoltaic module. The specific contents include: Where, k is the power degradation rate of the photovoltaic module; T max is the maximum daily temperature of the PV module, in K; ΔT daily is the daily cycle temperature of the photovoltaic module, unit K; UV daily is the daily average ultraviolet irradiance, unit: W / m 2 RH daily is the daily average relative humidity, unit %; k B is the Boltzmann constant, which is 8.62×10 -5 eV; β0 is the frequency factor, unit: s -1 ; β1 is the activation energy, unit is eV; β2 is the influence coefficient of cycle temperature, which is set in the range of 2 to 5; β3 is the influence of ultraviolet radiation, which is set in the range of 0.6 to 1; β4 is the influence of relative humidity, which is set in the range of 0 to 2.

5. The method for evaluating the performance aging of a single-sided photovoltaic module considering multiple environmental variables according to claim 1, characterized in that: In step S4, the photovoltaic module performance aging assessment model includes a linear model, an exponential model, an improved model Pan model considering the influence of material factors, and an improved model Kaaya model considering the influence of material and shape factors; The specific contents of the linear model in the photovoltaic module performance aging assessment model include: Where P(t) is the output power of the PV module at time t, P0 is the initial power of the module, and k is the power degradation rate of the PV module; The specific contents of the exponential model in the photovoltaic module performance aging assessment model include: Where P(t) is the output power of the PV module at time t, P0 is the initial power of the module, and k is the power degradation rate of the PV module; The specific contents of the Pan model in the photovoltaic module performance aging assessment model include: Where P(t) is the output power of the photovoltaic module at time t, P0 is the initial power of the module, k is the power degradation rate of the photovoltaic module, and λ is the material-related parameter of the photovoltaic module; The specific contents of the Kaaya model in the photovoltaic module performance aging assessment model include: Wherein, P(t) is the output power of the photovoltaic module at time t, P0 is the initial power of the module, k is the power degradation rate of the photovoltaic module, Γ is the material-related parameter of the photovoltaic module, and μ is the shape-related parameter of the photovoltaic module.

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