A random generation method and device for photovoltaic output curve

Abstract

The present invention provides a method and device for randomly generating photovoltaic output curves. This method calculates the vertical distance from all photovoltaic power stations to be evaluated to the typhoon center path, and based on this vertical distance, obtains the distance type corresponding to each photovoltaic power station to be evaluated. Based on the typhoon intensity and distance type, a pre-constructed photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated is obtained. Based on the pre-constructed photovoltaic output mode sequence sample library, all photovoltaic output modes in the photovoltaic output mode sequence sample library are obtained. For each photovoltaic output mode, a corresponding photovoltaic output curve sample is obtained from the pre-constructed photovoltaic output curve sample library. Based on all the photovoltaic output curve samples, a photovoltaic output curve corresponding to each photovoltaic power station is generated. Compared with the existing technology, the technical solution of the present invention accurately simulates the photovoltaic output curves of photovoltaic power stations in different regions during typhoons.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic output prediction of power grids, and in particular to a method and device for randomly generating a photovoltaic output curve. Background Art

[0002] my country is actively promoting energy transformation, with renewable energy sources like wind and solar power gradually replacing traditional fossil fuels, leading to significant changes in the power supply structure. However, in new power systems, primary energy from wind and photovoltaic power sources, which serve as the main power sources, is difficult to store. This makes their generated power highly sensitive to changes in weather conditions and is much more susceptible to emergencies such as extreme weather events and natural disasters than traditional power sources and loads. This introduces multiple new risks to the safety and reliability of the power system across different timescales. Therefore, when evaluating new power system planning schemes, it is necessary to consider the impact of extreme weather conditions on the grid's ability to provide reliable power and evaluate the grid's ability to accommodate renewable energy.

[0003] Typhoons are one of the extreme weather events that have the greatest impact on high-proportion renewable energy power systems. To conduct operational assessments of high-proportion renewable energy power grids during typhoons, it is first necessary to accurately simulate the changes in renewable energy power generation from before the typhoon arrives to 1-2 weeks after the typhoon leaves. Currently, there is insufficient research on the technology for generating scenarios of changes in large-scale photovoltaic output curves under the influence of typhoons, and there is a lack of models and methods. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for randomly generating a photovoltaic output curve, so as to realize accurate simulation of the photovoltaic output curves of photovoltaic power stations in different regions during a typhoon.

[0005] In order to solve the above technical problems, the present invention provides a method for randomly generating a photovoltaic output curve, comprising:

[0006] Obtain random typhoon data, wherein the random typhoon data includes a typhoon center path and typhoon intensity; calculate the vertical distance between all photovoltaic power stations to be evaluated and the typhoon center path, and obtain a distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance;

[0007] Obtaining, according to the typhoon intensity and the distance type, a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated, so as to randomly select a photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated from the pre-built photovoltaic output mode sequence sample library;

[0008] All photovoltaic output modes in the photovoltaic output mode sequence sample are obtained, and for each photovoltaic output mode, a corresponding photovoltaic output curve sample is obtained from a pre-built photovoltaic output curve sample library, and a photovoltaic output curve corresponding to each photovoltaic power station is generated based on all photovoltaic output curve samples.

[0009] In one possible implementation, a PV output curve sample library is pre-built, specifically including:

[0010] Acquire a historical photovoltaic output curve library of the photovoltaic power station, and perform normalization processing on each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain a historical photovoltaic output normalized curve;

[0011] Calculating the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalized curve, and clustering each historical photovoltaic output normalized curve based on the equivalent power generation and the peak coefficient to obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve;

[0012] According to the clustering pattern, all historical photovoltaic output normalized curves belonging to the clustering pattern are obtained to generate a photovoltaic output curve sample library.

[0013] In one possible implementation, the equivalent power generation and peak factor corresponding to each historical photovoltaic output normalization curve are calculated, specifically including:

[0014] The equivalent power generation corresponding to each historical photovoltaic output normalization curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows:

[0015]

[0016] Where A i is the equivalent power generation, S i (t) is the normalized curve of historical photovoltaic output;

[0017] A least squares fitting process is performed on each historical photovoltaic output normalized curve according to the standard curve to obtain a fitting coefficient, and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalized curve, wherein the standard curve is as follows:

[0018]

[0019] Where, t sr and t ss They are sunrise and sunset times respectively;

[0020] The least squares fitting process is as follows:

[0021]

[0022] Where C si is the fitting coefficient.

[0023] In one possible implementation, a PV output mode sequence sample library is pre-built, specifically including:

[0024] According to the vertical distance between the photovoltaic power station and the historical typhoon center path, the distance type of the photovoltaic power station is set, wherein the distance type includes short-distance photovoltaic power station, medium-distance photovoltaic power station and long-distance photovoltaic power station;

[0025] Obtaining a first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period, obtaining a first photovoltaic output normalized curve set corresponding to each photovoltaic power station, and calculating a first equivalent power generation and a first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set;

[0026] At the same time, calculating the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center to obtain a clustering pattern corresponding to each first photovoltaic output normalized curve, and generating a photovoltaic output pattern sequence corresponding to each photovoltaic power station based on all clustering patterns in the first photovoltaic output normalized curve set;

[0027] A typhoon intensity level is obtained, and the photovoltaic output mode sequence corresponding to each photovoltaic power station is classified according to the typhoon intensity level and the distance type to generate a photovoltaic output mode sequence sample library.

[0028] In a possible implementation, obtaining the first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period specifically includes:

[0029] Obtain the typhoon landing date and the typhoon departure date, and set the three days before the typhoon landing date, the typhoon landing date, the typhoon departure date, and the two days after the typhoon departure date as the typhoon period;

[0030] A historical photovoltaic output curve of each photovoltaic power station on each day during the typhoon period is obtained, and each historical photovoltaic output curve is normalized to obtain a first photovoltaic output normalized curve.

[0031] The present invention also provides a random generation device for a photovoltaic output curve, comprising: a photovoltaic power station distance type acquisition module, a photovoltaic output mode sequence sample acquisition module, and a photovoltaic output curve generation module;

[0032] The photovoltaic power station distance type acquisition module is used to obtain random typhoon data, wherein the random typhoon data includes the typhoon center path and typhoon intensity; calculate the vertical distance from all photovoltaic power stations to be evaluated to the typhoon center path, and obtain the distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance;

[0033] The photovoltaic output mode sequence sample acquisition module is configured to acquire a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated according to the typhoon intensity and the distance type, so as to randomly select the photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated from the pre-built photovoltaic output mode sequence sample library;

[0034] The photovoltaic output curve generation module is used to obtain all photovoltaic output modes in the photovoltaic output mode sequence sample, obtain the corresponding photovoltaic output curve sample for each photovoltaic output mode from the pre-built photovoltaic output curve sample library, and generate the photovoltaic output curve corresponding to each photovoltaic power station based on all photovoltaic output curve samples.

[0035] The present invention provides a random generation device for a photovoltaic output curve, further comprising: a photovoltaic output curve sample library construction module;

[0036] The photovoltaic output curve sample library construction module is used to obtain a historical photovoltaic output curve library of a photovoltaic power station, and normalize each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain a historical photovoltaic output normalized curve;

[0037] The photovoltaic output curve sample library construction module is used to calculate the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalized curve, and cluster each historical photovoltaic output normalized curve based on the equivalent power generation and the peak coefficient to obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve;

[0038] The photovoltaic output curve sample library building module is used to obtain all historical photovoltaic output normalized curves belonging to the clustering pattern according to the clustering pattern, and generate a photovoltaic output curve sample library.

[0039] In one possible implementation, the photovoltaic output curve sample library construction module is used to calculate the equivalent power generation and peak factor corresponding to each historical photovoltaic output normalized curve, specifically including:

[0040] The equivalent power generation corresponding to each historical photovoltaic output normalization curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows:

[0041]

[0042] Where A i is the equivalent power generation, S i (t) is the normalized curve of historical photovoltaic output;

[0043] A least squares fitting process is performed on each historical photovoltaic output normalized curve according to the standard curve to obtain a fitting coefficient, and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalized curve, wherein the standard curve is as follows:

[0044]

[0045] Where, t sr and t ss They are sunrise and sunset times respectively;

[0046] The least squares fitting process is as follows:

[0047]

[0048] Where C si is the fitting coefficient.

[0049] The present invention provides a random generation device for a photovoltaic output curve, further comprising: a photovoltaic output mode sequence sample library construction module;

[0050] The pre-PV output mode sequence sample library construction module is used to set the distance type of the PV power station according to the vertical distance from the PV power station to the historical typhoon center path, wherein the distance type includes short-distance PV power station, medium-distance PV power station and long-distance PV power station;

[0051] The pre-PV output mode sequence sample library construction module is used to obtain the first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period, obtain the first photovoltaic output normalized curve set corresponding to each photovoltaic power station, and calculate the first equivalent power generation and the first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set;

[0052] The pre-photovoltaic output mode sequence sample library construction module is used to calculate the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center, so as to obtain the clustering pattern corresponding to each first photovoltaic output normalized curve, and generate the photovoltaic output mode sequence corresponding to each photovoltaic power station based on all clustering patterns in the first photovoltaic output normalized curve set;

[0053] The pre-photovoltaic output mode sequence sample library construction module is used to obtain the typhoon intensity level, classify the photovoltaic output mode sequence corresponding to each photovoltaic power station according to the typhoon intensity level and the distance type, and generate a photovoltaic output mode sequence sample library.

[0054] In a possible implementation, the pre-PV output mode sequence sample library construction module is used to obtain the first PV output normalized curve corresponding to each PV power station every day during the typhoon period, specifically including:

[0055] Obtain the typhoon landing date and the typhoon departure date, and set the three days before the typhoon landing date, the typhoon landing date, the typhoon departure date, and the two days after the typhoon departure date as the typhoon period;

[0056] A historical photovoltaic output curve of each photovoltaic power station on each day during the typhoon period is obtained, and each historical photovoltaic output curve is normalized to obtain a first photovoltaic output normalized curve.

[0057] The present invention also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the random generation method of the photovoltaic output curve as described in any one of the above items is implemented.

[0058] The present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the random generation method of the photovoltaic output curve as described in any one of the above items.

[0059] Compared with the prior art, the random generation method and device of a photovoltaic output curve according to the embodiment of the present invention has the following beneficial effects:

[0060] By obtaining random typhoon data, calculating the vertical distance from all photovoltaic power stations to be evaluated to the typhoon center path, and obtaining the distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance, and obtaining a pre-constructed photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated based on the typhoon intensity and distance type, so that all photovoltaic output modes in the photovoltaic output mode sequence sample are obtained based on the pre-constructed photovoltaic output mode sequence sample library, and for each photovoltaic output mode, its corresponding photovoltaic output curve sample is obtained from the pre-constructed photovoltaic output curve sample library. Based on all photovoltaic output curve samples, a photovoltaic output curve corresponding to each photovoltaic power station is generated. Compared with the existing technology, the technical solution of the present invention uses the historical photovoltaic output curves of some photovoltaic power stations during typhoons to pre-construct a photovoltaic output curve sample library and a pre-constructed photovoltaic output mode sequence sample library. It can simulate and generate the power generation time series of photovoltaic stations under different typhoon intensities and typhoon paths for photovoltaic power stations in the planned grid, realize accurate simulation of photovoltaic output curves of photovoltaic power stations in different regions during typhoons, and reflect the impact of typhoons on the changes in photovoltaic output of the planned large power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of an embodiment of a random generation method of a photovoltaic output curve provided by the present invention;

[0062] Figure 2 This is a structural schematic diagram of an embodiment of a random generation device for a photovoltaic output curve provided by the present invention;

[0063] Figure 3 is a schematic diagram of a photovoltaic output curve according to an embodiment of the present invention;

[0064] Figure 4 It is a structural schematic diagram of another embodiment of a random generation device for photovoltaic output curve provided by the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] Example 1

[0067] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of a random generation method of a photovoltaic output curve provided by the present invention, such as Figure 1As shown, the method includes steps 101 to 103, which are specifically as follows:

[0068] In one embodiment, before executing step 101 , the method further includes: pre-building a photovoltaic output curve sample library.

[0069] In one embodiment, a historical photovoltaic output curve library of a photovoltaic power station is obtained, and each historical photovoltaic output curve in the historical photovoltaic output curve library is normalized to obtain a historical photovoltaic output normalized curve.

[0070] Specifically, set the typhoon season and month, such as June to September in the southern region. Include the historical photovoltaic output curves of all completed photovoltaic sites within the monthly range into the data statistical analysis scope, generate a historical photovoltaic output curve library, and normalize each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain the historical photovoltaic output normalized curve S i (t), (t=1, 2, ..., n), wherein the normalization process is as follows:

[0071]

[0072] In one embodiment, the equivalent power generation and peak factor corresponding to each historical photovoltaic output normalization curve are calculated.

[0073] Specifically, the equivalent power generation corresponding to each historical photovoltaic output normalization curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows:

[0074]

[0075] Where A i is the equivalent power generation, S i (t) is the normalized historical photovoltaic output curve.

[0076] Specifically, according to the standard curve K t For each historical photovoltaic output normalization curve S i (t) Perform least square fitting to obtain the fitting coefficient C si , and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalization curve, wherein the standard curve is as follows:

[0077]

[0078] Where, t sr and t ss They are sunrise and sunset times respectively;

[0079] The least squares fitting process has the following fitting objectives:

[0080]

[0081] Where C si is the fitting coefficient.

[0082] In one embodiment, each historical photovoltaic output normalized curve is clustered according to the equivalent power generation and the peak coefficient to obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve; based on the clustering pattern, all historical photovoltaic output normalized curves belonging to the clustering pattern are obtained to generate a photovoltaic output curve sample library.

[0083] Specifically, all historical photovoltaic output normalization curves are calculated according to the equivalent power generation A i , peak coefficient C si Clustering is performed based on the features; preferably, the k-order nearest neighbor method (k-NN) is used for clustering, the number of classifications is set to 5, the center value of each cluster is calculated respectively, and the cluster center values correspond to sunny, cloudy, overcast, rainy and continuous rainfall from high to low, with a total of five clustering modes; the historical photovoltaic output normalized curves belonging to each clustering mode are integrated to form a photovoltaic output curve sample library corresponding to the clustering mode.

[0084] Specifically, the photovoltaic output curve sample library includes five types: sunny-photovoltaic output curve sample library A, cloudy-photovoltaic output curve sample library B, overcast-photovoltaic output curve sample library C, overcast-rainy-photovoltaic output curve sample library D, and continuous rainfall-photovoltaic output curve sample library E.

[0085] In one embodiment, the pre-built photovoltaic output curve sample library is illustrated as follows:

[0086] The sunrise time of a certain city in June is 5:40 and the sunset time is 19:19. By substituting it into the above standard curve formula, a standard curve is formed. After fitting, the equivalent power generation A of the curve is obtained. i =4.20, crest factor C si =5.09.

[0087] By performing the above processing on all photovoltaic power stations in the city, the cluster center values corresponding to the five clustering patterns can be obtained; when the sample is clustered into the clustering pattern: cloudy, it is included in the cloudy-photovoltaic output curve sample library.

[0088] In one embodiment, before executing step 101 , the method further includes: pre-building a photovoltaic output model sequence sample library.

[0089] In one embodiment, the distance type of the photovoltaic power station is set according to the vertical distance from the photovoltaic power station to the historical typhoon center path, wherein the distance type includes a short-distance photovoltaic power station, a medium-distance photovoltaic power station, and a long-distance photovoltaic power station.

[0090] Specifically, according to the typhoon center path of the typhoon, the vertical distance from the photovoltaic power station to the typhoon center path is calculated, and the stations are divided into three categories according to the distance of the vertical distance. When the vertical distance from the photovoltaic power station to the typhoon center path is within 150km, the photovoltaic power station is judged to be a short-distance photovoltaic power station; when the vertical distance from the photovoltaic power station to the typhoon center path is between 150km and 250km, the photovoltaic power station is judged to be a medium-distance photovoltaic power station; when the vertical distance from the photovoltaic power station to the typhoon center path is more than 250km, the photovoltaic power station is judged to be a long-distance photovoltaic power station.

[0091] In one embodiment, a first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during a typhoon period is obtained to obtain a first photovoltaic output normalized curve set corresponding to each photovoltaic power station.

[0092] Specifically, the typhoon landing date and the typhoon departure date are obtained, and the three days before the typhoon landing date, the typhoon landing date, the typhoon departure date, and the two days after the typhoon departure date are set as the typhoon period; the historical photovoltaic output curve of each photovoltaic power station in each day during the typhoon period is obtained, and each historical photovoltaic output curve is normalized to obtain a first photovoltaic output normalized curve; for each photovoltaic power station, the first photovoltaic output normalized curve during the typhoon period is integrated to obtain a first normalized output curve set corresponding to each photovoltaic power station.

[0093] As an example in this embodiment: For the definition of typhoon period: D0 represents the typhoon landing day, and D1 represents the typhoon departure day. To simplify the analysis, it is assumed that the weather pattern remains unchanged during the typhoon landing period; therefore, the modeling of photovoltaic output changes during the typhoon period mainly considers the following 7 days: 3 days before typhoon landing: D0-3, D0-2, D0-1; the first and last day of typhoon landing: D0, D1; 2 days after typhoon departure: D1-1, D1-2.

[0094] The historical photovoltaic output curve records corresponding to each photovoltaic power station in the above 7 days are extracted to form the data sample for modeling analysis. The historical photovoltaic output curves are normalized to obtain the first normalized output curve of the photovoltaic power station during the typhoon period for 7 days. The first normalized output curve set is generated and recorded as: {T x,z,d (t), d=1,…,7}, where x is the typhoon and z is the photovoltaic power station.

[0095] In one embodiment, the first equivalent power generation and the first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set are calculated. The calculation process of the first equivalent power generation and the first peak coefficient is the same as the calculation of the equivalent power generation and the peak coefficient involved in the above-mentioned pre-constructed photovoltaic output curve sample library, and will not be repeated here.

[0096] In one embodiment, the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center is calculated to obtain a clustering pattern corresponding to each first photovoltaic output normalized curve.

[0097] Specifically, since the center value of each cluster has been calculated in the above-mentioned pre-constructed photovoltaic output curve sample library, any one of the first photovoltaic output normalized curves is selected in turn from the first normalized output curve set, and the Euclidean distance from the first equivalent power generation and the first peak coefficient corresponding to any one of the first photovoltaic output normalized curves to the five cluster centers is calculated; based on the principle of minimum Euclidean distance, the clustering mode corresponding to the minimum Euclidean distance is obtained, and the clustering mode is set to the clustering mode M∈(A, B, C, D, E) corresponding to any one of the first photovoltaic output normalized curves.

[0098] Repeat the calculation of the Euclidean distance above until the clustering pattern classification of each first normalized output curve in the first normalized output curve set is completed, and all clustering patterns in the first normalized output curve set are integrated to obtain the photovoltaic output model sequence corresponding to the first normalized output curve set: {M x,z, (d), d=1,…,7; M∈(A,B,C,D,E)}.

[0099] In one embodiment, a typhoon intensity level is obtained, and the photovoltaic output mode sequence corresponding to each photovoltaic power station is classified according to the typhoon intensity level and the distance type to generate a photovoltaic output mode sequence sample library.

[0100] Specifically, the typhoon intensity is set to tropical depression, tropical storm, severe tropical storm, typhoon and severe typhoon.

[0101] Preferably, for the classification of typhoon intensity levels: based on the average wind speed from 2 minutes before the hour to the hour, the national standard "Tropical Cyclone Scale" (GB / T19201-2006) is adopted, and it is divided into 5 categories: 1-Tropical Depression (TD, 10.8-17.1m / s); 2-Tropical Storm (TS, 17.2-24.4m / s); 3-Severe Tropical Storm (STS, 24.5-32.6m / s); 4-Typhoon (TY, 32.7-41.4m / s); 5-Severe Typhoon (STY, 41.5-50.9m / s).

[0102] Specifically, 15 photovoltaic output mode sequence sample libraries are set up according to three types of photovoltaic power station distances and five typhoon intensities, wherein the 15 photovoltaic output mode sequence sample libraries include: a short-distance photovoltaic power station-tropical depression mode sequence sample library, a short-distance photovoltaic power station-tropical storm mode sequence sample library, a short-distance photovoltaic power station-severe tropical storm mode sequence sample library, a short-distance photovoltaic power station-typhoon mode sequence sample library, a short-distance photovoltaic power station-typhoon mode sequence sample library, a medium-distance photovoltaic power station-tropical depression mode sequence sample library, a medium-distance photovoltaic power station-tropical storm mode sequence sample library, a medium-distance photovoltaic power station-severe tropical storm mode sequence sample library, a medium-distance photovoltaic power station-typhoon mode sequence sample library, a medium-distance photovoltaic power station-typhoon mode sequence sample library, a long-distance photovoltaic power station-tropical depression mode sequence sample library, a long-distance photovoltaic power station-tropical storm mode sequence sample library, a long-distance photovoltaic power station-severe tropical storm mode sequence sample library, a long-distance photovoltaic power station-typhoon mode sequence sample library, and a long-distance photovoltaic power station-typhoon mode sequence sample library.

[0103] Based on the first photovoltaic output normalized curve set corresponding to each photovoltaic power station during the existing typhoon period, a correlation analysis was performed between typhoon intensity and photovoltaic power station distance type, so that the model sequence samples corresponding to each photovoltaic power station were divided into 15 photovoltaic output mode sequence sample libraries according to typhoon intensity and photovoltaic power station distance category.

[0104] The pre-construction process of the photovoltaic output model sequence sample library is illustrated by taking Typhoon Ewiniar, which landed in Guangdong in June 2018, as an example.

[0105] Typhoon Ewiniar has a maximum wind speed of 20m / s and a typhoon intensity of tropical storm. After obtaining the typhoon center path of Typhoon Ewiniar, the vertical distance between the photovoltaic power station and the typhoon center path is calculated to be 130.04km. Therefore, the photovoltaic power station is classified as a close-range photovoltaic power station. The photovoltaic power generation normalization curve of the photovoltaic power station for 7 days from D0-3 to D1+2 during Typhoon Ewiniar is calculated, as well as the equivalent power generation A for each day. i , peak coefficient C si The geometric distances of each day's equivalent power generation and peak factor to the five cluster centers were calculated, and the clusters were grouped into the closest clusters. The resulting PV output pattern sequence for the station during Typhoon Ewiniar was: ADBDCBA. This sequence was included as a sample in the Short-Range PV Power Station - Tropical Storm Model Sequence Library.

[0106] Step 101: Obtain random typhoon data, wherein the random typhoon data includes the typhoon center path and typhoon intensity; calculate the vertical distance between all photovoltaic power stations to be evaluated and the typhoon center path, and obtain the distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance.

[0107] In one embodiment, the photovoltaic power stations to be evaluated may be all photovoltaic power stations in the planned grid.

[0108] In one embodiment, random typhoon data is obtained. Specifically, the recorded historical typhoon center paths are randomly sampled to obtain the typhoon center paths; and the preset typhoon intensities are randomly sampled to obtain the typhoon intensity.

[0109] Preferably, the acquired typhoon data may be randomly set based on human intervention.

[0110] In one embodiment, based on a randomly generated typhoon center path, the vertical distance between the photovoltaic power station to be evaluated in all planned power grids and the typhoon center path is calculated. When the vertical distance is within 150 km, the photovoltaic power station to be evaluated is judged to be a short-distance photovoltaic power station; when the vertical distance is between 150 km and 250 km, the photovoltaic power station to be evaluated is judged to be a medium-distance photovoltaic power station; when the vertical distance is greater than 250 km, the photovoltaic power station to be evaluated is judged to be a long-distance photovoltaic power station.

[0111] Step 102: According to the typhoon intensity and the distance type, a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated is obtained, so that the photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated is randomly selected from the pre-built photovoltaic output mode sequence sample library.

[0112] In one embodiment, based on the typhoon intensity randomly obtained in step 101 and the distance type of each photovoltaic power station to be evaluated, traverse the pre-built 15 photovoltaic output mode sequence sample libraries to obtain the photovoltaic output mode sequence sample library corresponding to the photovoltaic power station to be evaluated, and select any model sequence sample from the photovoltaic output mode sequence sample library, and use the any model sequence sample as the photovoltaic output mode sequence sample corresponding to the photovoltaic power station to be evaluated, denoted as: M x,z,i (d), d=1,…,7; M∈(A,B,C,D,E).

[0113] Step 103: Obtain all photovoltaic output modes in the photovoltaic output mode sequence sample, obtain the corresponding photovoltaic output curve sample for each photovoltaic output mode from the pre-built photovoltaic output curve sample library, and generate the photovoltaic output curve corresponding to each photovoltaic power station based on all photovoltaic output curve samples.

[0114] In one embodiment, since each photovoltaic output pattern sequence sample contains the clustering pattern corresponding to the first photovoltaic output normalized curve of the seven days during the typhoon period, the clustering pattern corresponding to the first photovoltaic output normalized curve of each day in the photovoltaic output pattern sequence sample is extracted according to the photovoltaic output pattern sequence sample, and the clustering pattern M corresponding to the first photovoltaic output normalized curve of each day is obtained. x,z, (d), d = 1, ..., 7, select the corresponding photovoltaic output curve sample library from the set five photovoltaic output curve sample libraries according to the clustering pattern M∈(A, B, C, D, E), and randomly extract a photovoltaic output curve sample from the photovoltaic output curve sample library.

[0115] In one embodiment, based on randomly selected photovoltaic output curve samples, each value in the sample is multiplied by the installed capacity of the photovoltaic power station to be evaluated, so that the photovoltaic output curve sample can be converted into a photovoltaic output curve with a nominal value.

[0116] In one embodiment, the above steps are repeated to randomly obtain photovoltaic output mode sequence samples corresponding to the typhoon period for all photovoltaic power stations to be evaluated, and the photovoltaic output mode sequence samples are converted into photovoltaic output curves. By superimposing the photovoltaic output areas corresponding to all photovoltaic power stations to be evaluated, the total photovoltaic output time series of the planned grid during the typhoon period can be obtained.

[0117] As an example of this embodiment: suppose a new photovoltaic power station is planned in a certain place in the future, with a known latitude and longitude position and a capacity of 230MW. A typhoon path is randomly extracted from the typhoon historical path library, and the typhoon intensity is randomly extracted to obtain a strong tropical storm. The vertical distance between the planned photovoltaic power station and the typhoon center path is calculated to be 245.8km, which belongs to a medium-distance station. From the pre-constructed 15 photovoltaic output mode sequence sample libraries, a medium-distance photovoltaic power station-tropical storm mode sequence sample library is selected, and the medium-distance photovoltaic power station-tropical storm mode sequence sample library is randomly sampled to obtain a photovoltaic output mode sequence sample: BBADDEA, which is used as the photovoltaic output mode sequence sample of the photovoltaic power station during this typhoon period. According to the clustering patterns (B), (B), (A), (D), corresponding to the seven days in the photovoltaic output mode sequence sample, (D), (E), (A), select the corresponding photovoltaic output curve sample library from the pre-built sunny-photovoltaic output curve sample library A, cloudy-photovoltaic output curve sample library B, overcast-photovoltaic output curve sample library C, overcast-rainy-photovoltaic output curve sample library D, and continuous rainfall-photovoltaic output curve sample library E, and randomly extract a photovoltaic output curve sample from the photovoltaic output curve sample library and multiply it by the installed capacity of the station 230MW to form the photovoltaic output curve for 7 days, including 3 days before the typhoon, the first day of the typhoon landing, the last day of the typhoon landing, and the first and second days after the typhoon leaves, as shown below: Figure 3 As shown, Figure 3 It is a schematic diagram of the photovoltaic output curve.

[0118] In summary, the present invention provides a random generation method for photovoltaic output curves. By adopting a data-driven modeling approach, a photovoltaic output curve sample library and a pre-constructed photovoltaic output mode sequence sample library can be pre-constructed through the historical photovoltaic output curves of some photovoltaic power stations during typhoons. This method can simulate and generate power generation time series of photovoltaic stations in different regions under different typhoon intensities and typhoon path combinations for a large number of newly added photovoltaic power stations in the planned grid, and well reflect the impact of typhoons on the changes in photovoltaic output of the planned large power grid.

[0119] Example 2

[0120] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of a random generation device for photovoltaic output curves provided by the present invention, such as Figure 2 As shown, the method includes a photovoltaic power station distance type acquisition module 201, a photovoltaic output mode sequence sample acquisition module 202 and a photovoltaic output curve generation module 203, which are specifically as follows:

[0121] The photovoltaic power station distance type acquisition module 201 is used to obtain random typhoon data, wherein the random typhoon data includes the typhoon center path and typhoon intensity; calculate the vertical distance from all photovoltaic power stations to be evaluated to the typhoon center path, and obtain the distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance.

[0122] The photovoltaic output mode sequence sample acquisition module 202 is used to obtain a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated based on the typhoon intensity and the distance type, so as to randomly select the photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated from the pre-built photovoltaic output mode sequence sample library.

[0123] The photovoltaic output curve generation module 203 is used to obtain all photovoltaic output modes in the photovoltaic output mode sequence sample, obtain the corresponding photovoltaic output curve sample for each photovoltaic output mode from the pre-built photovoltaic output curve sample library, and generate the photovoltaic output curve corresponding to each photovoltaic power station based on all photovoltaic output curve samples.

[0124] The embodiment of the present invention provides a random generation device for photovoltaic output curves, further comprising: a photovoltaic output curve sample library construction module; Figure 4 As shown, Figure 4 It is a structural schematic diagram of another embodiment of a random generation device for photovoltaic output curve provided by the present invention.

[0125] The photovoltaic output curve sample library construction module 204 is used to obtain a historical photovoltaic output curve library of a photovoltaic power station, normalize each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain a historical photovoltaic output normalized curve; calculate the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalized curve, cluster each historical photovoltaic output normalized curve based on the equivalent power generation and the peak coefficient, and obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve; based on the clustering pattern, obtain all historical photovoltaic output normalized curves belonging to the clustering pattern to generate a photovoltaic output curve sample library.

[0126] In one embodiment, the photovoltaic output curve sample library construction module 204 is used to calculate the equivalent power generation and peak factor corresponding to each historical photovoltaic output normalized curve; specifically, the equivalent power generation corresponding to each historical photovoltaic output normalized curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows:

[0127]

[0128] Where A i is the equivalent power generation, S i (t) is the normalized curve of historical photovoltaic output;

[0129] A least squares fitting process is performed on each historical photovoltaic output normalized curve according to the standard curve to obtain a fitting coefficient, and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalized curve, wherein the standard curve is as follows:

[0130]

[0131] Where, t sr and t ss They are sunrise and sunset times respectively;

[0132] The least squares fitting process is as follows:

[0133]

[0134] Where C si is the fitting coefficient.

[0135] The random generation device of a photovoltaic output curve provided by the embodiment of the present invention further includes: a photovoltaic output mode sequence sample library construction module 205; Figure 4 As shown, Figure 4 It is a structural schematic diagram of another embodiment of a random generation device for photovoltaic output curve provided by the present invention.

[0136] The pre-PV output mode sequence sample library construction module 205 is configured to set the distance type of the PV power station based on the vertical distance from the PV power station to the historical typhoon center path, where the distance types include short-distance PV power station, medium-distance PV power station, and long-distance PV power station; obtain the first photovoltaic output normalized curve corresponding to each PV power station every day during the typhoon period, obtain the first photovoltaic output normalized curve set corresponding to each PV power station, calculate the first equivalent power generation and the first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set; calculate the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center to obtain the clustering pattern corresponding to each first photovoltaic output normalized curve, and generate the photovoltaic output mode sequence corresponding to each PV power station based on all clustering patterns in the first photovoltaic output normalized curve set; obtain the typhoon intensity level, classify the photovoltaic output mode sequence corresponding to each PV power station according to the typhoon intensity level and the distance type, and generate a photovoltaic output mode sequence sample library.

[0137] In one embodiment, the pre-photovoltaic output mode sequence sample library construction module 205 is used to obtain the first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period; specifically, the typhoon landing day and the typhoon departure day are obtained, and the three days before the typhoon landing day, the typhoon landing day, the typhoon departure day, and the two days after the typhoon departure day are set as the typhoon period; the historical photovoltaic output curve of each photovoltaic power station on each day during the typhoon period is obtained, and each historical photovoltaic output curve is normalized to obtain the first photovoltaic output normalized curve.

[0138] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0139] It should be noted that the above embodiment of the apparatus for randomly generating a photovoltaic output curve is merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected to achieve the purpose of this embodiment as needed.

[0140] Based on the above-mentioned embodiment of the random generation method of the photovoltaic output curve, another embodiment of the present invention provides a terminal device for randomly generating a photovoltaic output curve. The terminal device for randomly generating a photovoltaic output curve includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the random generation method of the photovoltaic output curve of any embodiment of the present invention is implemented.

[0141] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device for randomly generating photovoltaic output curves.

[0142] The terminal device for randomly generating the photovoltaic output curve may be a computing device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device for randomly generating the photovoltaic output curve may include, but is not limited to, a processor and a memory.

[0143] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the photovoltaic output curve random generation terminal device, and utilizes various interfaces and lines to connect various parts of the entire photovoltaic output curve random generation terminal device.

[0144] The memory can be used to store the computer program and / or module. The processor realizes various functions of the terminal device for randomly generating photovoltaic output curves by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0145] Based on the above-mentioned embodiment of the random generation method of the photovoltaic output curve, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the random generation method of the photovoltaic output curve of any embodiment of the present invention.

[0146] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0147] In summary, the present invention provides a random generation method and device for a photovoltaic output curve. By acquiring random typhoon data, the vertical distances from all photovoltaic power stations to be evaluated to the typhoon center path are calculated, and the distance type corresponding to each photovoltaic power station to be evaluated is obtained based on the vertical distance; according to the typhoon intensity and distance type, a pre-constructed photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated is obtained, so that all photovoltaic output modes in the photovoltaic output mode sequence samples are obtained based on the pre-constructed photovoltaic output mode sequence sample library, and for each photovoltaic output mode, its corresponding photovoltaic output curve sample is obtained from the pre-constructed photovoltaic output curve sample library, and based on all photovoltaic output curve samples, a photovoltaic output curve corresponding to each photovoltaic power station is generated. Compared with the existing technology, the technical solution of the present invention pre-constructs a photovoltaic output curve sample library and a pre-constructed photovoltaic output mode sequence sample library through the historical photovoltaic output curves of some photovoltaic power stations during typhoons. It can simulate and generate the power generation time series of photovoltaic stations under different typhoon intensities and typhoon paths for photovoltaic power stations in the planned grid, realize accurate simulation of the photovoltaic output curves of photovoltaic power stations in different regions during typhoons, and reflect the impact of typhoons on the changes in photovoltaic output of the planned large power grid.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A random generation method for photovoltaic output curve, characterized in that: include: Obtain random typhoon data, wherein the random typhoon data includes a typhoon center path and typhoon intensity; calculate the vertical distance between all photovoltaic power stations to be evaluated and the typhoon center path, and obtain a distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance; Obtaining, according to the typhoon intensity and the distance type, a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated, so as to randomly select a photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated from the pre-built photovoltaic output mode sequence sample library; Acquire all photovoltaic output modes in the photovoltaic output mode sequence sample, acquire a photovoltaic output curve sample corresponding to each photovoltaic output mode from a pre-built photovoltaic output curve sample library, and generate a photovoltaic output curve corresponding to each photovoltaic power station based on all photovoltaic output curve samples; The pre-constructed photovoltaic output curve sample library includes: obtaining a historical photovoltaic output curve library of a photovoltaic power station, normalizing each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain a historical photovoltaic output normalized curve; calculating the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalized curve, clustering each historical photovoltaic output normalized curve according to the equivalent power generation and the peak coefficient to obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve; and obtaining all historical photovoltaic output normalized curves belonging to the clustering pattern according to the clustering pattern to generate a photovoltaic output curve sample library; The pre-constructed photovoltaic output mode sequence sample library specifically includes: setting the distance type of the photovoltaic power station based on the vertical distance between the photovoltaic power station and the historical typhoon center path, where the distance types include short-distance photovoltaic power stations, medium-distance photovoltaic power stations, and long-distance photovoltaic power stations; obtaining the first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period, obtaining a first photovoltaic output normalized curve set corresponding to each photovoltaic power station, and calculating the first equivalent power generation and first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set; simultaneously, calculating the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center to obtain a clustering pattern corresponding to each first photovoltaic output normalized curve, and generating a photovoltaic output mode sequence corresponding to each photovoltaic power station based on all clustering patterns in the first photovoltaic output normalized curve set; obtaining a typhoon intensity level, and classifying the photovoltaic output mode sequence corresponding to each photovoltaic power station according to the typhoon intensity level and the distance type to generate a photovoltaic output mode sequence sample library.

2. The random generation method of photovoltaic output curve according to claim 1, characterized in that: Calculate the equivalent power generation and peak factor corresponding to each historical photovoltaic output normalization curve, including: The equivalent power generation corresponding to each historical photovoltaic output normalization curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows: Where A i is the equivalent power generation, S i (t) is the normalized curve of historical photovoltaic output; A least squares fitting process is performed on each historical photovoltaic output normalized curve according to the standard curve to obtain a fitting coefficient, and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalized curve, wherein the standard curve is as follows: Where, t sr and t ss They are sunrise and sunset times respectively; The least squares fitting process is as follows: Where C si is the fitting coefficient.

3. The random generation method of photovoltaic output curve according to claim 1, characterized in that: Obtain the normalized first photovoltaic output curve for each photovoltaic power station every day during the typhoon period, including: Obtain the typhoon landing date and the typhoon departure date, and set the three days before the typhoon landing date, the typhoon landing date, the typhoon departure date, and the two days after the typhoon departure date as the typhoon period; A historical photovoltaic output curve of each photovoltaic power station on each day during the typhoon period is obtained, and each historical photovoltaic output curve is normalized to obtain a first photovoltaic output normalized curve.

4. A random generation device for photovoltaic output curve, characterized in that: include: Photovoltaic power station distance type acquisition module, photovoltaic output mode sequence sample acquisition module, photovoltaic output curve generation module, photovoltaic output curve sample library construction module and photovoltaic output mode sequence sample library construction module; The photovoltaic power station distance type acquisition module is used to obtain random typhoon data, wherein the random typhoon data includes the typhoon center path and typhoon intensity; calculate the vertical distance from all photovoltaic power stations to be evaluated to the typhoon center path, and obtain the distance type corresponding to each photovoltaic power station to be evaluated based on the vertical distance; The photovoltaic output mode sequence sample acquisition module is configured to acquire a pre-built photovoltaic output mode sequence sample library corresponding to each photovoltaic power station to be evaluated according to the typhoon intensity and the distance type, so as to randomly select the photovoltaic output mode sequence sample corresponding to each photovoltaic power station to be evaluated from the pre-built photovoltaic output mode sequence sample library; The photovoltaic output curve generation module is configured to obtain all photovoltaic output modes in the photovoltaic output mode sequence sample, obtain the corresponding photovoltaic output curve sample for each photovoltaic output mode from a pre-built photovoltaic output curve sample library, and generate a photovoltaic output curve corresponding to each photovoltaic power station based on all photovoltaic output curve samples; The photovoltaic output curve sample library construction module is used to obtain a historical photovoltaic output curve library of a photovoltaic power station, normalize each historical photovoltaic output curve in the historical photovoltaic output curve library to obtain a historical photovoltaic output normalized curve; calculate the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalized curve, cluster each historical photovoltaic output normalized curve based on the equivalent power generation and the peak coefficient, and obtain a clustering pattern corresponding to each historical photovoltaic output normalized curve; based on the clustering pattern, obtain all historical photovoltaic output normalized curves belonging to the clustering pattern to generate a photovoltaic output curve sample library; The photovoltaic output mode sequence sample library construction module is used to set the distance type of the photovoltaic power station based on the vertical distance between the photovoltaic power station and the historical typhoon center path, where the distance types include short-distance photovoltaic power stations, medium-distance photovoltaic power stations, and long-distance photovoltaic power stations; obtain the first photovoltaic output normalized curve corresponding to each photovoltaic power station every day during the typhoon period, obtain the first photovoltaic output normalized curve set corresponding to each photovoltaic power station, and calculate the first equivalent power generation and first peak coefficient corresponding to each first photovoltaic output normalized curve in the first photovoltaic output normalized curve set; at the same time, calculate the Euclidean distance from the first equivalent power generation and the first peak coefficient to each cluster center to obtain the clustering pattern corresponding to each first photovoltaic output normalized curve, and generate the photovoltaic output mode sequence corresponding to each photovoltaic power station based on all clustering patterns in the first photovoltaic output normalized curve set; obtain the typhoon intensity level, and classify the photovoltaic output mode sequence corresponding to each photovoltaic power station according to the typhoon intensity level and the distance type to generate a photovoltaic output mode sequence sample library.

5. The random generation device of photovoltaic output curve according to claim 4, characterized in that: The photovoltaic output curve sample library construction module is used to calculate the equivalent power generation and peak coefficient corresponding to each historical photovoltaic output normalization curve, specifically including: The equivalent power generation corresponding to each historical photovoltaic output normalization curve is calculated according to a preset equivalent power generation calculation formula, wherein the preset equivalent power generation calculation formula is as follows: Where A i is the equivalent power generation, S i (t) is the normalized curve of historical photovoltaic output; A least squares fitting process is performed on each historical photovoltaic output normalized curve according to the standard curve to obtain a fitting coefficient, and the fitting coefficient is used as the peak coefficient of each historical photovoltaic output normalized curve, wherein the standard curve is as follows: Where, t sr and t ss They are sunrise and sunset times respectively; The least squares fitting process is as follows: Where C si is the fitting coefficient.

6. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for randomly generating a photovoltaic output curve according to any one of claims 1 to 3 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the random generation method of the photovoltaic output curve according to any one of claims 1 to 3.

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