A wind-solar installed capacity configuration method, device and electronic equipment

Abstract

The application discloses a wind and light installed capacity configuration method and device and electronic equipment, and relates to the technical field of wind and light installed capacity configuration. The method comprises the following steps: acquiring wind resource data and light resource data, and presetting a plurality of groups of wind and light installed capacity ratios under the condition of current total wind and light installed capacity; calculating wind power output data and photovoltaic output data corresponding to each group of wind and light installed capacity ratios based on the wind resource data, the light resource data and each group of wind and light installed capacity ratios; calculating difference data of each group of wind power output data and photovoltaic output data relative to load data, and determining fluctuation data of each group of difference data in a preset time period; fitting the probability distribution of each group of fluctuation data, calculating source and load matching degree indexes corresponding to each group of wind and light installed capacity based on the probability distribution; and determining the optimal wind and light installed capacity ratio from the plurality of groups of wind and light installed capacity ratios according to the relationship between the source and load matching degree indexes. The technical scheme provided by the application realizes the optimal configuration of wind and light installed capacity based on the relationship between wind and light power generation and load.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to a wind-solar installed capacity configuration method and device and electronic equipment. BACKGROUND

[0002] Under the low-carbon target, the power system will gradually transform into a new type of power system with new energy as the main body, and wind energy and solar energy will become the main power source. However, the random fluctuation of wind energy and solar energy itself brings great challenges to the safe and economic operation of the power system. In order to reduce the adverse effects of wind-solar grid connection, the temporal and spatial complementary effects of wind-solar power generation should be fully tapped, and the wind-solar installed capacity should be reasonably optimized and configured in the planning stage.

[0003] Current research on wind-solar installed capacity optimization mainly focuses on the complementary characteristics of wind-solar itself, and is usually based on the correlation between wind-solar power generation or the fluctuation of total wind-solar power generation: when the correlation coefficient between wind-solar power generation is maximum or the fluctuation of total wind-solar power generation is minimum, the wind-solar installed capacity is optimally configured. Most existing researches ignore the coordinated matching between wind-solar power generation and load. Therefore, how to optimize the wind-solar installed capacity based on the relationship between wind-solar power generation and load is a problem to be solved. SUMMARY

[0004] Therefore, the embodiments of the present application provide a wind-solar installed capacity configuration method, device and electronic equipment, so as to realize the optimization of wind-solar installed capacity based on the relationship between wind-solar power generation and load.

[0005] According to a first aspect, the embodiments of the present application provide a wind-solar installed capacity configuration method, which comprises: obtaining wind resource data and light resource data, and pre-setting a plurality of groups of wind-solar installed capacity ratios under the condition of current total wind-solar installed capacity; calculating wind power output data and photovoltaic output data corresponding to each group of wind-solar installed capacity ratios based on the wind resource data, light resource data and each group of wind-solar installed capacity ratios; calculating the difference data of each group of wind power output data and photovoltaic output data relative to load data, and determining the fluctuation data of each group of difference data within a preset time period; fitting the probability distribution of each group of fluctuation data to calculate the source-load matching degree index corresponding to each group of wind-solar installed capacity by using the probability distribution; and determining the optimal wind-solar installed capacity ratio from the plurality of groups of wind-solar installed capacity ratios according to the relationship between each source-load matching degree index.

[0006] Optionally, the probability distribution of each group of the fluctuation data is fitted to calculate the source-load matching degree index corresponding to each group of wind-solar installed capacity through the probability distribution, comprising: constructing a probability density function of the current group of wind-solar installed capacity based on a kernel density estimation method using the current group of fluctuation data; integrating the probability density function to obtain a cumulative probability distribution function of the current group of wind-solar installed capacity; and calculating the source-load matching degree index of the current group of wind-solar installed capacity at a preset confidence level according to the cumulative probability distribution function.

[0007] Optionally, the window width parameter in the kernel density estimation method is determined by: solving the optimal window width parameter through the average integral square error between the constructed probability density function and the real frequency distribution of the fluctuation data.

[0008] Optionally, the optimal wind-solar installed capacity is determined from a plurality of preset groups of wind-solar installed capacity according to the relationship between each of the source-load matching degree indexes, comprising: taking the wind-solar installed capacity corresponding to the minimum source-load matching degree index as the optimal wind-solar installed capacity.

[0009] Optionally, the optimal wind-solar installed capacity is determined from a plurality of preset groups of wind-solar installed capacity according to the relationship between each of the source-load matching degree indexes, comprising: extracting the minimum source-load matching degree index and other source-load matching degree indexes with a deviation rate less than a preset threshold from the minimum source-load matching degree index; taking the wind-solar installed capacity corresponding to the extracted source-load matching degree indexes as candidate wind-solar installed capacities; calculating the wind-solar power generation penetration rate corresponding to each group of candidate wind-solar installed capacities according to the wind power output data and the photovoltaic output data corresponding to each group of candidate wind-solar installed capacities; and taking the candidate wind-solar installed capacity corresponding to the maximum wind-solar power generation penetration rate as the optimal wind-solar installed capacity.

[0010] Optionally, the method further comprises: changing the current total wind-solar installed capacity, and returning to the step of presetting a plurality of groups of wind-solar installed capacities under the condition of the current total wind-solar installed capacity.

[0011] Optionally, the kernel function in the kernel density estimation method adopts a Gaussian kernel function.

[0012] According to a second aspect, an embodiment of the present application provides a wind and light installed capacity configuration device, the device comprising: a data initialization module configured to obtain wind resource data and light resource data, and preset a plurality of groups of wind and light installed capacity ratios under a current total wind and light installed capacity condition; a wind and light output calculation module configured to calculate wind power output data and photovoltaic output data corresponding to each group of wind and light installed capacity ratios based on the wind resource data, the light resource data, and each group of wind and light installed capacity ratios respectively; a load difference fluctuation module configured to calculate difference data of each group of wind power output data and photovoltaic output data relative to load data, and determine fluctuation data of each group of the difference data within a preset time period; a source and load matching degree index module configured to fit a probability distribution of each group of the fluctuation data, so as to calculate a source and load matching degree index corresponding to each group of wind and light installed capacity by using the probability distribution; and an installed capacity ratio determination module configured to determine an optimal wind and light installed capacity ratio from the plurality of groups of wind and light installed capacity ratios according to a relationship between each of the source and load matching degree indexes.

[0013] According to a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the method of the first aspect or any one of the optional embodiments of the first aspect.

[0014] According to a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make the computer execute the method of the first aspect or any one of the optional embodiments of the first aspect.

[0015] The technical scheme provided in the present application has the following advantages:

[0016] The technical scheme provided in the present application has the following advantages:

[0017] Further, after calculating the source-load matching degree indexes corresponding to each installed capacity ratio, the minimum source-load matching degree index is found, and other source-load matching degree indexes with a deviation rate less than a preset threshold from the minimum source-load matching degree index are extracted; the wind-solar installed capacity ratio corresponding to the extracted source-load matching degree index is taken as a candidate wind-solar installed capacity ratio; then, the wind-solar power generation penetration rates corresponding to each group of candidate wind-solar installed capacity ratios are calculated according to the wind power output data and the photovoltaic output data corresponding to each group of candidate wind-solar installed capacity ratios; and finally, the candidate wind-solar installed capacity ratio corresponding to the maximum wind-solar power generation penetration rate is taken as the optimal wind-solar installed capacity ratio. Thus, the accuracy of optimizing the wind-solar installed capacity configuration is improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] The features and advantages of the present application will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, which are given by way of illustration and are not to be considered limiting of the present application, in which:

[0019] Figure 1 a schematic diagram of steps of a wind-solar installed capacity configuration method in an embodiment of the present application is shown;

[0020] Figure 2 a source-load matching degree index curve diagram of a 2000 MW total wind-solar installed capacity under different wind-solar installed capacity ratios in an embodiment of the present application is shown;

[0021] Figure 3 a structural schematic diagram of a wind-solar installed capacity configuration device in an embodiment of the present application is shown;

[0022] Figure 4 a structural schematic diagram of an electronic device in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0024] Please refer to Figure 1 In an embodiment, a wind-solar installed capacity configuration method specifically includes the following steps:

[0025] Step S101: Obtain wind resource data and light resource data, and preset a plurality of groups of wind-solar installed capacity ratios under the condition of a current total wind-solar installed capacity.

[0026] Step S102: Based on the wind resource data, light resource data and each group of wind and light installed capacity ratio, the wind power output data and photovoltaic output data corresponding to each group of wind and light installed capacity ratio are calculated respectively;

[0027] Step S103: The difference data of each group of wind power output data and photovoltaic output data relative to the load data is calculated, and the fluctuation data of each group of difference data in the preset time period is determined;

[0028] Step S104: The probability distribution of each group of fluctuation data is fitted to calculate the source-load matching degree index corresponding to each group of wind and light installed capacity by the probability distribution;

[0029] Step S105: According to the relationship between each source-load matching degree index, the best wind and light installed capacity ratio is determined from the preset several groups of wind and light installed capacity ratio.

[0030] Specifically, the wind resource data and light resource data are obtained, including but not limited to obtaining the time series data of wind speed, irradiance and temperature in the target area, and cleaning and preprocessing the above data. In this embodiment, the data preprocessing includes inspection and screening, error data elimination and reasonable interpolation and correction. Then, the total wind and light installed capacity, several groups of wind power installed proportion and photovoltaic installed proportion are set. For example, the total wind and light installed capacity is Cap wt,st , a group of wind power installed proportion is α, and photovoltaic installed proportion is 1-α. Then, based on the obtained wind resource data and light resource data, the wind power output data and photovoltaic output data under each group of wind and light installed capacity ratio are calculated. The specific calculation formula is as follows:

[0031] The calculation formula of wind power output data P w at t time is:

[0032]

[0033] In the formula, Cap w is the installed capacity of a single wind turbine, unit: MW; n w is the total number of wind turbine installed capacity, the calculation method is C p is the wind energy utilization coefficient; ρ is the air density, unit: kg / m 3 ; A w is the wind wheel swept area, unit: m 2 ; v(t) is the wind speed at t time, unit: m / s; v i , v o and v r are the wind speed threshold values.

[0034] The calculation formula of photovoltaic output data P s at t time is:

[0035] P s (t)=n s ×η×A s ×r(t)×τ×[1-0.0045×(T(t)-25)]×10 -6

[0036] In the formula, Cap s n represents the installed capacity of a single photovoltaic module. s The total number of photovoltaic modules installed is calculated as follows: η is the photoelectric conversion efficiency; A s τ is the surface area of ​​the photovoltaic cell module; r(t) is the total solar radiation at time t; τ is the light transmittance of the photovoltaic cell module; T(t) is the operating temperature of the photovoltaic cell module at time t, which can be replaced by the ambient temperature.

[0037] Next, load data is acquired and preprocessed. Then, the load power at each moment within the data acquisition period is calculated based on the load data. The difference between the wind power output data and photovoltaic power output data calculated in the above steps and the load data at each moment is then used to characterize the difference between the current total wind and solar power output and the actual demand load. The specific calculation formula is as follows:

[0038] P d (t)=P l (t)-P w (t)-P s (t)

[0039] In the formula, P l (t) represents the load at time t, P d (t) represents the difference between wind and solar power output being higher or lower than the load.

[0040] Then, this embodiment characterizes the relationship between wind and solar installed capacity and load by calculating the real-time fluctuation of the wind-solar load difference data. For example, for a certain group of wind and solar installed capacity ratios, the fluctuation data ΔP of the difference data at time t is... d The calculation method for (t) is as follows:

[0041] ΔP d (t)=P d (t)-P d (t-1)

[0042] Then, the probability distribution of each group of fluctuation data is fitted to calculate the source-load matching degree index corresponding to each group of wind and light installed capacity through the probability distribution. Thus, the best wind and light installed capacity ratio is determined from the pre-set several groups of wind and light installed capacity ratio according to the relationship between the source-load matching degree indexes. For example, the best wind and light installed capacity ratio can be determined by the minimum source-load matching degree index in the embodiment, so as to calculate the best wind power installed capacity and the best photovoltaic installed capacity corresponding to the best wind and light installed capacity ratio in combination with the current total wind and light installed capacity, and the optimal configuration of the wind and light installed capacity is realized based on the relationship between the wind and light power generation and the load.

[0043] Specifically, in an embodiment, the above step S104 specifically includes the following steps:

[0044] Step one: based on the kernel density estimation method, the current group of fluctuation data is used to construct the probability density function of the current group of wind and light installed capacity ratio.

[0045] Step two: the probability density function is integrated to obtain the cumulative probability distribution function of the current group of wind and light installed capacity ratio.

[0046] Step three: the source-load matching degree index of the current group of wind and light installed capacity ratio under the pre-set confidence level is calculated according to the cumulative probability distribution function.

[0047] Specifically, in the embodiment, the kernel density estimation method is used to fit the probability distribution function of the real-time fluctuation amount of the wind and light load difference data, and then the source-load matching degree index representing the matching degree of wind and light output and load is calculated. Based on the kernel density estimation method, the probability density function of the fluctuation amount data of the wind and light load difference data is constructed as follows: d

[0048]

[0049] In the formula, N is the total number of samples, h is the window width parameter, ΔP d,i is the sample data, and K() is the kernel function.

[0050] It is considered that the window width parameter h in the kernel density estimation directly affects the smoothing degree of , and it is crucial to select a suitable window width. Therefore, in the embodiment, the best window width parameter is solved by constructing the average integral square error of the probability density function and the real frequency distribution of the fluctuation data, and the solving process is as follows:

[0051]

[0052] In the formula, f(ΔP d ) is the real frequency distribution of the population, the average integral square error MISE is a function of the window width h, and the minimum point of the average integral square error MISE is the estimated value of the best window width.​

[0053] In addition, in order to further improve the accuracy of the fluctuation data probability density function, the kernel function used by the kernel density estimation is a Gaussian kernel

[0054] After obtaining the probability density function, the cumulative probability distribution function of the current group of wind and light installation proportion can be calculated by integrating the probability density function, and the specific calculation formula is as follows:

[0055]

[0056] Finally, according to the cumulative probability distribution function, the source-load matching degree index MVoSL of the current group of wind and light installation proportion under the pre-set confidence level is calculated, and the calculation process is as follows:

[0057]

[0058] In this embodiment, the pre-set confidence level is 1-β, wherein F -1 () is ΔP d The inverse function of the cumulative probability distribution function, is the upper quantile, is the lower quantile, and R is the real number space.

[0059] Specifically, in an embodiment, the above step S105 specifically includes the following steps:

[0060] Step four: taking the wind and light installation proportion corresponding to the minimum source-load matching degree index as the optimal wind and light installation proportion.

[0061] Specifically, after calculating the source-load matching degree index corresponding to each group of wind and light installation proportion, the wind and light installation proportion corresponding to the minimum source-load matching degree index is taken as the optimal proportion, so that the wind and light output power output by the corresponding wind and light installation proportion has the highest matching degree with the load power, thereby realizing the optimization of the wind and light installation capacity.

[0062] Specifically, in an embodiment, the above step S105 specifically includes the following steps:

[0063] Step five: extracting the minimum source-load matching degree index and other source-load matching degree indexes with a deviation rate less than a pre-set threshold from the minimum source-load matching degree index.

[0064] Step six: taking the wind and light installation proportion corresponding to the extracted source-load matching degree index as the candidate wind and light installation proportion.

[0065] Step seven: calculating the wind and light power generation penetration rate corresponding to each group of candidate wind and light installation proportion according to the wind power output data and the photovoltaic output data corresponding to each group of candidate wind and light installation proportion.

[0066] Step eight: taking the candidate wind-solar installed capacity ratio corresponding to the maximum wind-solar power generation penetration rate as the optimal wind-solar installed capacity ratio.

[0067] Specifically, in order to further optimize the wind-solar installed capacity, a wind-solar power generation penetration rate index is introduced for optimization. The source-load matching degree indexes under different wind power and photovoltaic installed capacities at the same confidence level are calculated to obtain a set as follows After finding the minimum value Then, the wind-solar installed capacity ratio corresponding to the minimum value is found according to the following formula All source-load matching degree indexes with a deviation rate not exceeding a preset threshold λ are taken as candidate source-load matching degree indexes.

[0068]

[0069] Then, the corresponding wind power installed capacity set and photovoltaic installed capacity set are stored, the corresponding wind power installed capacity set is The photovoltaic installed capacity set is Finally, the wind power processing data and photovoltaic processing data under the above wind power and photovoltaic installed capacities are calculated, and then the wind-solar power generation penetration rate (i.e. the percentage of wind-solar power generation capacity to power consumption, and it is assumed that there is no wind and light abandonment factor) under each wind-solar installed capacity is calculated, and the calculation formula is as follows:

[0070]

[0071] In the formula, N is the number of time points. The optimal ratio of wind-solar installed capacity is the wind-solar power generation penetration rate R α The wind power installed capacity and the photovoltaic installed capacity corresponding to the maximum time are obtained. Thus, the accuracy of optimizing the wind-solar installed capacity configuration is further improved.

[0072] Specifically, in an embodiment, the wind-solar installed capacity configuration method provided by the embodiment of the present application further includes the following steps:

[0073] Step nine: changing the current total wind-solar installed capacity, and returning to the step of presetting a plurality of wind-solar installed capacity ratios under the condition of the current total wind-solar installed capacity.

[0074] Specifically, by changing the total wind-solar installed capacity, the above steps S101 to S105 are repeated to obtain the optimal ratio of wind-solar installed capacity under different wind-solar power generation penetration rates, and the accuracy of the total wind-solar installed capacity configuration is further improved.

[0075] The following is explained by taking an actual scene embodiment as an example:

[0076] It is assumed that the total wind-solar installed capacity is 2000MW, the wind-solar installed capacity ratio varies from 0 to 1 at equal intervals, and the Cap w = 2.5MW, C p=0.40, ρ=1.225kg / m 3 A w =13471m 2 v i =3m / s, v r = 9.1 m / s, v o =22m / s, Cap s =300Wp, η=0.17, A s =1.93m 2 With τ = 0.92, a confidence level of 95%, an upper quantile of 2.5%, and a lower quantile of 97.5%, through steps S101 to step eight above, the source-load matching index is calculated at the same confidence level (95%) under different wind power and photovoltaic installed capacity ratios, resulting in the following set {602.35MW, 564.01MW, ..., 780.44MW}. The trend of the source-load matching index under different installed capacity ratios is as follows: Figure 2 As shown, the minimum source-load matching index in the set is 431.23MW. All source-load matching indices with a deviation rate of no more than 0.5% from the minimum are identified, and the corresponding wind and solar power installation ratio sets are stored. The wind power installation ratio set is {0.35, 0.4}, and the solar power installation ratio set is {0.65, 0.6}. Finally, based on the maximum wind and solar power penetration rate, the optimal wind and solar power installation ratio is determined to be 0.4:0.6 when the total wind and solar installed capacity is 2000MW.

[0077] Through the above steps, the solution provided by this invention considers the detailed matching characteristics between wind and solar power output and load, accurately reflecting the differences in source-load matching degree under different wind and solar installed capacity configurations, which is beneficial to reducing the spinning reserve cost of the power system. The proposed wind and solar installed capacity optimization configuration method can improve the penetration rate of wind and solar power generation while ensuring source-load matching degree, contributing to the achievement of low-carbon goals. Furthermore, this method can be directly applied to power system planning and has stronger applicability to any time and spatial scale.

[0078] like Figure 3 As shown, this embodiment also provides a wind and solar installed capacity configuration device, which includes:

[0079] The data initialization module 101 is used to acquire wind resource data and solar resource data, and to preset several sets of wind and solar installed capacity ratios under the current total installed capacity of wind and solar power. For details, please refer to the relevant description of step S101 in the above method embodiment, which will not be repeated here.

[0080] The wind and light output calculation module 102 is configured to calculate wind power output data and photovoltaic output data corresponding to each group of wind and light installation ratios based on wind resource data, light resource data and each group of wind and light installation ratios. For details, refer to the related description of step S102 in the above method embodiment, which will not be repeated here.

[0081] The load difference fluctuation module 103 is configured to calculate difference data of each group of wind power output data and photovoltaic output data relative to load data, and determine fluctuation data of each group of difference data in a preset time period. For details, refer to the related description of step S103 in the above method embodiment, which will not be repeated here.

[0082] The source and load matching degree index module 104 is configured to fit the probability distribution of each group of fluctuation data, so as to calculate the source and load matching degree index corresponding to each group of wind and light installation capacities through the probability distribution. For details, refer to the related description of step S104 in the above method embodiment, which will not be repeated here.

[0083] The installation ratio determination module 105 is configured to determine the optimal wind and light installation ratio from the preset several groups of wind and light installation ratios according to the relationship between each source and load matching degree index. For details, refer to the related description of step S105 in the above method embodiment, which will not be repeated here.

[0084] The wind and light installation capacity configuration device provided in the embodiment of the application is used to execute the wind and light installation capacity configuration method provided in the above embodiment, and the implementation manner and principle are the same, and the details are described in the related description of the above method embodiment, which will not be repeated here.

[0085] Through the cooperation of the above components, the technical scheme provided in the application presets several groups of wind and light installation ratios under the current total wind and light installation capacity, and calculates the wind power output data and photovoltaic output data corresponding to each installation ratio based on wind resource data and light resource data. Then, the difference fluctuation data is determined according to the difference data of each group of wind power output data and photovoltaic output data relative to load data; then the probability distribution of each group of fluctuation data is fitted, so as to calculate the source and load matching degree index corresponding to each group of wind and light installation capacities through the probability distribution, so as to measure the matching degree between the output and load of the current wind and light installation ratio through the source and load matching degree index. Finally, the optimal wind and light installation ratio with the highest load matching degree is determined from the preset several groups of wind and light installation ratios according to the relationship between each source and load matching degree index, so as to realize the optimal configuration of the wind and light installation capacity based on the relationship between wind and light power generation and load.

[0086] In addition, after calculating the source-load matching degree indexes corresponding to the various installed capacities of wind and light, the minimum source-load matching degree index is found, and other source-load matching degree indexes with a deviation rate less than a preset threshold from the minimum source-load matching degree index are extracted; the wind and light installed capacity corresponding to the extracted source-load matching degree indexes is taken as a candidate wind and light installed capacity; then, the wind and light power generation penetration rates corresponding to various groups of candidate wind and light installed capacities are calculated according to the wind power output data and the photovoltaic output data corresponding to the various groups of candidate wind and light installed capacities; finally, the candidate wind and light installed capacity corresponding to the maximum wind and light power generation penetration rate is taken as the optimal wind and light installed capacity. Thus, the matching degree of the wind and light installed capacity and the load is further improved, and the accuracy of the optimized wind and light installed capacity configuration is improved.

[0087] Figure 4 An electronic device according to an embodiment of the present application is shown. The device includes a processor 901 and a memory 902, which can be connected through a bus or other means, Figure 4 For example, the connection through the bus is taken as an example.

[0088] The processor 901 can be a central processing unit (CPU). The processor 901 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, or combinations thereof.

[0089] The memory 902 is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the methods in the above method embodiments. The processor 901 performs various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above method embodiments.

[0090] The memory 902 can include a program storage area and a data storage area, where the program storage area can store an operating system, application programs required by at least one function, and the data storage area can store data created by the processor 901 and the like. In addition, the memory 902 can include a high-speed random access memory, and can further include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 902 can optionally include a memory disposed remotely from the processor 901, which can be connected to the processor 901 through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0091] One or more modules are stored in the memory 902, which, when executed by the processor 901, perform the methods in the above method embodiments.

[0092] The above electronic device specific details can be understood in correspondence with the relevant description and effects of the corresponding description in the above method embodiments, which will not be described here.

[0093] Those skilled in the art can understand that all or part of the processes in the above embodiments can be completed by a computer program instructing related hardware, and the implemented program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above types of memories.

[0094] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A wind-solar installed capacity configuration method, characterized by, The method comprises: obtaining wind resource data and light resource data, and presetting a plurality of groups of wind-light installed capacity ratios under the condition of the current total wind-light installed capacity; calculating wind power output data and photovoltaic output data corresponding to each group of wind-light installed capacity ratios based on the wind resource data, the light resource data and the wind-light installed capacity ratios; calculating the difference data of each group of wind power output data and photovoltaic output data relative to the load data, and determining the fluctuation data of each group of difference data within a preset time period; fitting the probability distribution of each group of fluctuation data to calculate the source-load matching degree index corresponding to each group of wind-light installed capacity by the probability distribution; the fitting of the probability distribution of each group of fluctuation data to calculate the source-load matching degree index corresponding to each group of wind-light installed capacity by the probability distribution comprises: constructing the probability density function of the current group of wind-light installed capacity ratios by using the current group of fluctuation data based on the kernel density estimation method, and the probability density function is as follows: where N is the total number of samples, h is the window width parameter, ΔP d,i is the i-th sample data, K() is the kernel function, ΔP d is the current group fluctuation data; wherein the window width parameter in the kernel density estimation method is determined by the following method: solving the optimal window width parameter by constructing the average integral square error of the probability density function and the true frequency distribution of the fluctuation data, and the solving process is as follows: where f(ΔP d ) is the true frequency distribution of the population, and the mean integrated squared error (MISE) is a function of the window width parameter h, whose minimum point is the estimate of the optimal window width. the kernel function in the kernel density estimation method adopts a Gaussian kernel function: wherein u represents the parameter in the kernel function; integrating the probability density function to obtain the cumulative probability distribution function of the current group of wind-light installed capacity ratios, and the specific calculation formula is as follows: calculating the source-load matching degree index of the current group of wind-light installed capacity ratios under the preset confidence level according to the cumulative probability distribution function; determining the optimal wind-light installed capacity ratio from the plurality of preset groups of wind-light installed capacity ratios according to the relationship between the source-load matching degree indexes; the determination of the optimal wind-light installed capacity ratio from the plurality of preset groups of wind-light installed capacity ratios according to the relationship between the source-load matching degree indexes comprises: taking the wind-light installed capacity ratio corresponding to the minimum source-load matching degree index as the optimal wind-light installed capacity ratio.

2. The method of claim 1, wherein, determining the optimal wind-light installed capacity ratio from the plurality of preset groups of wind-light installed capacity ratios according to the relationship between the source-load matching degree indexes comprises: extracting the minimum source-load matching degree index and other source-load matching degree indexes with a deviation rate less than a preset threshold from the minimum source-load matching degree index; taking the wind-light installed capacity ratio corresponding to the extracted source-load matching degree index as the candidate wind-light installed capacity ratio; calculating the wind-light power penetration rate corresponding to each group of candidate wind-light installed capacity ratios according to the wind power output data and the photovoltaic output data corresponding to each group of candidate wind-light installed capacity ratios; taking the candidate wind-light installed capacity ratio corresponding to the maximum wind-light power penetration rate as the optimal wind-light installed capacity ratio.

3. The method of claim 2, wherein, The method further comprises: changing the current total wind-light installed capacity, and returning to the step of presetting a plurality of groups of wind-light installed capacity ratios under the condition of the current total wind-light installed capacity.

4. A wind-solar installed capacity configuration device, characterized by, The device comprises: a data initialization module for obtaining wind resource data and light resource data, and presetting a plurality of groups of wind-light installed capacity ratios under the condition of the current total wind-light installed capacity; The wind and light output calculation module is configured to calculate wind power output data and photovoltaic output data corresponding to each group of wind and light installation ratios based on the wind resource data, the light resource data, and the wind and light installation ratios. The load difference fluctuation module is configured to calculate difference data of each group of wind power output data and photovoltaic output data relative to load data, and determine fluctuation data of each group of the difference data within a preset time period. The source-load matching degree index module is configured to fit a probability distribution of each group of the fluctuation data, and calculate a source-load matching degree index corresponding to each group of wind and light installation capacities through the probability distribution. The fitting of the probability distribution of each group of the fluctuation data and the calculation of the source-load matching degree index corresponding to each group of wind and light installation capacities through the probability distribution include: constructing a probability density function of a current group of wind and light installation ratios based on a kernel density estimation method using current group fluctuation data, the probability density function being as follows: where N is the total number of samples, h is the window width parameter, ΔP d,i is the i-th sample data, K() is the kernel function, ΔP d is the current group fluctuation data; wherein a window width parameter in the kernel density estimation method is determined by solving an optimal window width parameter through an average integral square error between the constructed probability density function and a true frequency distribution of the fluctuation data, and the solving process being as follows: where f(ΔP d ) is the true frequency distribution of the population, and the mean integrated squared error (MISE) is a function of the window width parameter h, whose minimum point is the estimate of the optimal window width. The kernel function in the kernel density estimation method adopts a Gaussian kernel function: wherein u represents a parameter in the kernel function. Integrating the probability density function to obtain a cumulative probability distribution function of the current group of wind and light installation ratios, and a specific calculation formula being as follows: According to the cumulative probability distribution function, calculating a source-load matching degree index of the current group of wind and light installation ratios at a preset confidence level. The installation ratio determination module is configured to determine an optimal wind and light installation ratio from a plurality of preset groups of wind and light installation ratios according to relationships between the source-load matching degree indexes. The determination of the optimal wind and light installation ratio from the plurality of preset groups of wind and light installation ratios according to the relationships between the source-load matching degree indexes includes: taking a wind and light installation ratio corresponding to a minimum source-load matching degree index as the optimal wind and light installation ratio.

5. An electronic device, comprising: The computer readable storage medium stores computer instructions for causing the computer to execute the method of any one of claims 1-3. The computer readable storage medium stores computer instructions for causing the computer to execute the method of any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, ​

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