Photovoltaic power mutation prediction method

Abstract

The present invention belongs to the field of electrical engineering technology and provides a photovoltaic power mutation prediction method. The photovoltaic power mutation prediction method includes obtaining cloud-related information data observed by meteorological satellites, assimilating the cloud-related information data into a numerical prediction mode as initial field data, and in the numerical prediction mode, predicting the cloud layer data in a future preset time period of a target area based on the initial field data, and calculating the total cloud amount of the cloud layer data, determining the irradiance of the target area in a future preset time period according to the total cloud amount, and judging whether to issue a photovoltaic power mutation warning according to the numerical value of the irradiance. Through the photovoltaic power mutation prediction method in the present invention, photovoltaic power mutations can be predicted more intuitively and accurately.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical engineering, and in particular relates to a photovoltaic power mutation prediction method. Background Art

[0002] At present, my country's installed capacity of solar power generation equipment ranks first in the world, but there is a drawback to solar power generation. Sudden weather changes can cause a sharp drop in the power generation of photovoltaic power stations, affecting the stability of the power grid. In order to improve the level of solar power grid connection, it is urgent to study the power mutation prediction technology of photovoltaic power stations so that the power grid can know and prepare in advance before the power generation of photovoltaic power stations suddenly changes. The key to predicting power mutations lies in the simulation of clouds. At present, satellite cloud map observation data is generally used to directly extrapolate and predict the trajectory of clouds, but clouds dissipate quickly, and this prediction method cannot accurately reflect the changing trend of solar irradiance, which makes it difficult for existing technicians to accurately predict the changing trend of photovoltaic power generation. Summary of the invention

[0003] In view of the above-mentioned defects or shortcomings, the present invention provides a photovoltaic power mutation prediction method, which aims to solve the technical problem that the existing prediction methods are difficult to accurately predict the changing trend of photovoltaic power generation.

[0004] To achieve the above object, the present invention provides a photovoltaic power mutation prediction method, wherein the photovoltaic power mutation prediction method comprises:

[0005] S100: Acquire cloud-related information data observed by meteorological satellites;

[0006] S200: Assimilate the cloud-related information data into the numerical forecast model as initial field data;

[0007] S300: In the numerical prediction mode, cloud data of the target area within a preset time period in the future is predicted based on the initial field data, and the total cloud amount of the cloud data is calculated;

[0008] S400: determining the irradiance of the target area in a future preset time period according to the total cloud cover;

[0009] S500: Determine whether to issue a photovoltaic power sudden change warning based on the irradiance value.

[0010] In the embodiment of the present invention, predicting the cloud data of the target area within a preset time period in the future based on the initial field data and calculating the total cloud amount of the cloud data specifically includes:

[0011] S301: predict all cloud layers in the target area and calculate the cloud amount information of each cloud layer;

[0012] S302: Calculate the total cloud cover in the target area based on the cloud cover information of all cloud layers.

[0013] In the embodiment of the present invention, the target area is predicted to have two cloud layers, and the calculation formula for calculating the total cloud amount in the target area based on the cloud amount information of the two cloud layers is:

[0014]

[0015] in, is the total cloud cover, and are the maximum overlapping cloud amount and random overlapping cloud amount in two cloud layers (K layer and L layer), It is the overlap coefficient of two layers of clouds, reflecting the degree of overlap between the two layers of clouds.

[0016] In the embodiment of the present invention, the maximum overlapping cloud amount and random overlapping cloud cover The calculation formula is:

[0017]

[0018] Among them, C k ,C l are the cloud amounts of K-layer and L-layer respectively.

[0019] In the embodiment of the present invention, the overlap coefficient The calculation formula is:

[0020]

[0021] Where Z is the vertical height, L cf (Z) is the anti-correlation thickness of cloud cover, which means that the overlap coefficient of two layers of cloud is reduced to e -1 time distance.

[0022] In the embodiment of the present invention, L cf The value of (Z) is 2km.

[0023] In the embodiment of the present invention, the calculation formula of irradiance is:

[0024]

[0025] Among them, F s is the irradiance, F c is the initial irradiance of the sun entering the atmosphere, The solar irradiance adjustment parameter varies with the solar altitude angle α and the latitude of the photovoltaic power station. The function of change, is the total cloud cover, and S is the effective area of ​​the photovoltaic power station.

[0026] In an embodiment of the present invention, the cloud-related information data includes cloud top temperature, cloud top height, cloud area, cloud optical thickness, and aerosol optical thickness.

[0027] In an embodiment of the present invention, the method of assimilating cloud-related information data as initial field data into a numerical forecast model is a three-dimensional variational numerical assimilation method.

[0028] In the embodiment of the present invention, the numerical prediction model is the WRF-CHEM numerical prediction model.

[0029] Through the above technical solution, the photovoltaic power mutation prediction method provided by the embodiment of the present invention has the following beneficial effects:

[0030] Cloud cover refers to the percentage of clouds that obstruct the view of the sky, and the total cloud cover in the target area directly affects the irradiance of the target area. Therefore, accurately simulating and calculating the total cloud cover in the target area in the future preset time period can make the final photovoltaic power mutation prediction result more accurate. The photovoltaic power mutation prediction method provided by the present invention uses the cloud-related information data recently observed by meteorological satellites as the initial field data for prediction, and the numerical forecast model predicts on the basis of the initial field data, so that the predicted cloud layer data can be made more accurate. After predicting the cloud layer data, the cloud layer data is processed to calculate the total cloud cover of the cloud layer data and the irradiance of the target area in the future preset time period, so as to better reflect the trend of photovoltaic power changes. In summary, the photovoltaic power mutation prediction method provided by the present invention uses a numerical forecast model to predict the cloud data of the target area, and uses the cloud-related information data observed by meteorological satellites as the initial field data for the prediction, thereby increasing the accuracy of the prediction. After the cloud data is predicted, the cloud data is converted and processed, so that the changing trend of the photovoltaic power can be more intuitively and accurately reflected. That is, the photovoltaic power mutation prediction method in the present invention can more intuitively and accurately predict the photovoltaic power mutation.

[0031] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide an understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 is a flowchart of the steps of a photovoltaic power mutation prediction method according to an embodiment of the present invention;

[0034] Figure 2 is a specific step diagram of step S300 in an embodiment of the present invention;

[0035] Figure 3 It is one of the prediction result diagrams of photovoltaic power generation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0037] The photovoltaic power mutation prediction method of the present invention is described below with reference to the accompanying drawings.

[0038] The present invention provides a photovoltaic power mutation prediction method, such as Figure 1 As shown, the photovoltaic power mutation prediction method includes:

[0039] S100: Acquire cloud-related information data observed by meteorological satellites;

[0040] S200: Assimilate the cloud-related information data into the numerical forecast model as initial field data;

[0041] S300: In the numerical prediction mode, cloud data of the target area within a preset time period in the future is predicted based on the initial field data, and the total cloud amount of the cloud data is calculated;

[0042] S400: determining the irradiance of the target area in a future preset time period according to the total cloud cover;

[0043] S500: Determine whether to issue a photovoltaic power sudden change warning based on the irradiance value.

[0044] Cloud cover refers to the percentage of clouds that obstruct the view of the sky, and the total cloud cover in the target area directly affects the irradiance of the target area. Therefore, accurately simulating and calculating the total cloud cover in the target area in the future preset time period can make the final photovoltaic power mutation prediction result more accurate. The photovoltaic power mutation prediction method provided by the present invention uses the cloud-related information data recently observed by meteorological satellites as the initial field data for prediction, and the numerical forecast model predicts on the basis of the initial field data, which can make the predicted cloud data more accurate. After predicting the cloud layer data, the cloud layer data is processed to calculate the total cloud cover of the cloud layer data and the irradiance of the target area in the future preset time period, so as to better reflect the trend of photovoltaic power changes. In summary, the photovoltaic power mutation prediction method provided by the present invention uses a numerical forecast model to predict the cloud data of the target area, and uses the cloud-related information data observed by meteorological satellites as the initial field data for the prediction, thereby increasing the accuracy of the prediction. After the cloud data is predicted, the cloud data is converted and processed, so that the changing trend of the photovoltaic power can be more intuitively and accurately reflected. That is, the photovoltaic power mutation prediction method in the present invention can more intuitively and accurately predict the photovoltaic power mutation.

[0045] like Figure 2 As shown, in an embodiment of the present invention, predicting the cloud data of the target area within a preset time period in the future based on the initial field data and calculating the total cloud amount of the cloud data specifically includes:

[0046] S301: predict all cloud layers in the target area and calculate the cloud amount information of each cloud layer;

[0047] S302: Calculate the total cloud cover in the target area based on the cloud cover information of all cloud layers.

[0048] That is, the above-mentioned cloud data includes the number of clouds and the cloud amount information of each cloud layer. When conducting forecasts, multiple layers of clouds may be simulated in the target area. There may be partial or complete overlap between the multiple layers of clouds, resulting in a decrease in the irradiance intensity in the area. In order to specifically calculate the impact of clouds on irradiance, the cloud amount information of the multiple layers of clouds can be first converted into the total cloud amount, and then the irradiance can be calculated based on the total cloud amount.

[0049] In the embodiment of the present invention, the target area is predicted to have two cloud layers, and the calculation formula for calculating the total cloud amount in the target area based on the cloud amount information of the two cloud layers is:

[0050]

[0051] in, is the total cloud cover, and are the maximum overlapping cloud amount and random overlapping cloud amount in two cloud layers (K layer and L layer), It is the overlap coefficient of two layers of clouds, reflecting the degree of overlap between the two layers of clouds.

[0052] Specifically, taking the upper and lower layers of clouds as an example, for the convenience of distinction, the two layers of clouds are recorded as layer k and layer L, and the total cloud amount of layer k and layer L is It can be written as a linear combination of the maximum overlapping cloud amount and the random overlapping cloud amount, that is,

[0053]

[0054] The total cloud amount of two layers of clouds can be calculated by the above formula: It should be noted that in photovoltaic power generation areas, it is common for single or double layers of clouds to overlap, and when more layers of clouds overlap, the illuminance will be too low and the photovoltaic equipment will not be able to generate electricity. Therefore, when the predicted number of cloud layers is greater than the preset value, it can be assumed that the photovoltaic power will definitely change suddenly. When the predicted result is a single layer of cloud or a double layer of cloud, it is necessary to make a specific analysis based on the calculated irradiance results. When the cloud layer predicted in the target area is a single layer of cloud, the total cloud amount of the single layer of cloud can be directly obtained from the predicted cloud information.

[0055] In the embodiment of the present invention, the maximum overlapping cloud amount and random overlapping cloud cover The calculation formula is:

[0056]

[0057] Among them, C k ,C l are the cloud amounts of K-layer and L-layer respectively. The specific values ​​can be directly obtained from the predicted cloud information. max(C k ,C l ) refers to C k ,C l The maximum value in .

[0058] In the embodiment of the present invention, the overlap coefficient The calculation formula is:

[0059]

[0060] Where Z is the vertical height, L cf (Z) is the anti-correlation thickness of cloud cover, which means that the overlap coefficient of two layers of cloud is reduced to e -1 Specifically, Z l is the vertical height of L layer clouds, Z kis the vertical height of the K-layer cloud, which can be directly obtained from the cloud layer data predicted by the numerical forecast model. That is, the cloud layer data predicted by the numerical forecast model includes not only the number of cloud layers and the cloud amount information of each cloud layer, but also the vertical height information of each cloud layer.

[0061] In the embodiment of the present invention, L cf The value of (Z) is 2 km. The analysis of satellite data and ground radar data shows that the global average value is about L cf (Z) = 2 km, L cf (Z) = 2km can give a total cloud cover that is relatively close to the actual amount and is currently the most reasonable approximation.

[0062] In the embodiment of the present invention, the calculation formula of irradiance is:

[0063]

[0064] Among them, F s is the irradiance, F c is the initial irradiance of the sun entering the atmosphere, The solar irradiance adjustment parameter varies with the solar altitude angle α and the latitude of the photovoltaic power station. The function of change, is the total cloud cover, and S is the effective area of ​​the photovoltaic power station.

[0065] After sunlight enters the atmosphere, the irradiance will be attenuated due to factors such as atmospheric refraction, cloud cover, changes in the solar altitude angle, and the location of the target area. Therefore, in order to accurately calculate the irradiance of the target area, it is necessary to first introduce Function, in order to consider the influence of factors such as position and solar altitude angle, the function can be directly obtained in the comparison table in the prior art, and It refers to considering the influence of total cloud cover on irradiance. When it is 0, This indicates that the target area is cloudless and the total cloud cover has no attenuation effect on irradiance. When it is 1, it means that the target area is completely blocked by clouds and the photovoltaic power generation power is 0. It should be noted that the solar altitude angle α will change with time, so the calculated irradiance F s It is a curve that changes with time. Photovoltaic power generation is also related to irradiance F s is positively correlated, so through the irradiance F s The changing trend of PV power can be known by looking at the changing trend of . Taking a specific calculation case as an example, Figure 3 As shown in the figure, the prediction results show that the photovoltaic power generation power will fluctuate greatly around 10 am to 12 pm. The power grid can prepare in advance after predicting the fluctuation.

[0066] In an embodiment of the present invention, cloud-related information data includes cloud top temperature, cloud top height, cloud area, cloud optical thickness, and aerosol optical thickness. Specifically, taking the sunflower-8 synchronous meteorological satellite as an example, when obtaining cloud-related information data, first obtain the visible light and infrared channel observation data of the sunflower-8 synchronous meteorological satellite, and then invert the cloud top temperature, cloud top height, cloud area, cloud optical thickness, and aerosol optical thickness based on these data, and then format these data to generate gridded pre-processed data with a resolution of 2 kilometers. Of course, the above-mentioned meteorological satellite can also be other, such as my country's Fengyun series meteorological satellites.

[0067] In the embodiment of the present invention, the method of assimilating cloud-related information data as initial field data into the numerical forecast model is a three-dimensional variational numerical assimilation method. Specifically, the three-dimensional variational assimilation objective function is defined as:

[0068]

[0069] In the formula, x is the analysis variable, x b is the background field (i.e., initial field), y0 is the observation value, B is the background error covariance matrix, R is the observation error covariance matrix, and H is the observation operator. Among them, the observation values ​​may include cloud top temperature, cloud top height, cloud area, cloud optical thickness, aerosol optical thickness, etc. Through the three-dimensional variational numerical assimilation method, the observation values ​​can be assimilated into the initial field as the initial field data. Specifically, the main calculation steps of three-dimensional variational assimilation are:

[0070] Read in background field x b And the observed value y0:

[0071] Calculate and update the variable d. The calculation formula of d is: d = y0-H(x b );

[0072] Perform optimization cycle calculation, mainly including:

[0073] Assume the initial value v = 0, calculate J b

[0074]

[0075] Complete the transformation from control variable to mode variable δx=Lv, including horizontal filtering, vertical processing and balance transformation;

[0076] Complete the change HLv from the pattern space to the observation space and calculate the residual vector HLv-d;

[0077] calculate

[0078] Using the adjoint operator LT and H T calculate

[0079] calculate

[0080] After completing the minimization calculation, we get the analytical increment δx = Lv;

[0081] Complete the analysis variables, output the analysis increments, and update the initial forecast field.

[0082] Among them, the specific calculation method of three-dimensional variational assimilation has been disclosed in the prior art and is only briefly described here.

[0083] In an embodiment of the present invention, the numerical prediction model is a WRF-CHEM numerical prediction model. By using the WRF-CHEM numerical prediction model with a chemical model, a three-dimensional variational numerical assimilation method is used to assimilate cloud-related information inverted by a meteorological satellite into the initial field in the model, thereby improving the accuracy of the initial field cloud data. At the same time, in the chemical module of the numerical prediction model, the assimilation of aerosol optical thickness is added to improve the accuracy of the description of the extinction ability of the atmosphere and clouds to solar radiation. Through the above two methods, the initial field is improved and its forecast accuracy is improved.

[0084] It should be noted that the prediction of any data cannot be separated from the support of basic data (initial field data). The more representative the basic data is, the more accurate the prediction result will be. In the present invention, the cloud-related information data recently observed by the satellite is used as the initial field data to make the prediction result more accurate. In addition, the present invention can accurately predict the change trend of photovoltaic power generation by studying the relationship between cloud cover and sunlight irradiance. After calculating the irradiance, a short-term early warning of power mutation caused by cloud cover in photovoltaic power stations can be achieved according to the calculation results.

[0085] In the embodiment of the present invention, many meteorological centers have carried out numerical forecast model business. The present invention is based on the existing business technology and analyzes and processes the predicted data, so as to express the changes of photovoltaic power generation power more intuitively and accurately.

[0086] In the embodiment of the present invention, the target area mainly refers to a region where a photovoltaic power station is installed.

[0087] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0088] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0089] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0090] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A photovoltaic power mutation prediction method, characterized in that: The photovoltaic power mutation prediction method comprises: Obtain cloud-related information data observed by meteorological satellites; Assimilating the cloud-related information data into a numerical forecast model as initial field data; In the numerical forecast mode, cloud data of a target area within a preset time period in the future is predicted based on the initial field data, and the total cloud amount of the cloud data is calculated; Determine the irradiance of the target area in a preset time period in the future according to the total cloud amount; Determining whether to issue a photovoltaic power mutation warning according to the numerical value of the irradiance; The calculation formula of the irradiance is: Among them, F s is the irradiance, F c is the initial irradiance of the sun entering the atmosphere, The solar irradiance adjustment parameter varies with the solar altitude angle α and the latitude of the photovoltaic power station. The function of change, is the total cloud cover, and S is the effective area of ​​the photovoltaic power station.

2. The photovoltaic power mutation prediction method according to claim 1 is characterized in that: Predicting the cloud data of the target area within a preset time period in the future based on the initial field data and calculating the total cloud amount of the cloud data specifically includes: Predict all cloud layers in the target area and calculate the cloud amount information of each cloud layer; The total cloud amount in the target area is calculated based on the cloud amount information of all cloud layers.

3. The photovoltaic power mutation prediction method according to claim 2 is characterized in that: The target area is predicted to have two cloud layers, and the calculation formula for calculating the total cloud amount in the target area based on the cloud amount information of the two cloud layers is: Among them, the two layers of clouds are defined as K layer and L layer. is the total cloud cover, and are the maximum overlapping cloud amount and random overlapping cloud amount in K-layer and L-layer respectively, It is the overlap coefficient of two layers of clouds, reflecting the degree of overlap between the two layers of clouds.

4. The photovoltaic power mutation prediction method according to claim 3 is characterized in that: The maximum overlapping cloud amount and the random overlapping cloud cover The calculation formula is: Among them, C k ,C l are the cloud amounts of K-layer and L-layer respectively.

5. The photovoltaic power mutation prediction method according to claim 3 is characterized in that: The overlap coefficient The calculation formula is: Where Z is the vertical height, Z k is the vertical height of the K layer of clouds, Z l is the vertical height of L layer clouds, L cf (Z) is the anti-correlation thickness of cloud cover, which means that the overlap coefficient of two layers of cloud is reduced to e -1 time distance.

6. The photovoltaic power mutation prediction method according to claim 5 is characterized in that: The L cf The value of (Z) is 2km.

7. The photovoltaic power mutation prediction method according to any one of claims 1 to 6, characterized in that: The cloud-related information data include cloud top temperature, cloud top height, cloud area, cloud optical thickness and aerosol optical thickness.

8. The photovoltaic power mutation prediction method according to any one of claims 1 to 6, characterized in that: The method of assimilating the cloud-related information data as initial field data into the numerical forecast model is a three-dimensional variational numerical assimilation method.

9. The photovoltaic power mutation prediction method according to any one of claims 1 to 6, characterized in that: The numerical prediction model is the WRF-CHEM numerical prediction model.

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