A wind-solar power generation coefficient determination method and system, an electronic device, and a storage medium

Abstract

The application discloses a wind-solar power generation coefficient determination method and system, electronic equipment and a storage medium, and relates to the technical field of wind-solar power generation. The method comprises the following steps: determining target grids and initial grids based on multi-source time series data of all small grids in a target province and a constructed network structure; determining a plurality of Thiessen polygons; calculating the unit capacity construction coefficient according to the distance between each target grid and the center point of the Thiessen polygon; determining the average power generation line loss based on the average power generation coefficient of all wind-solar units; determining the time-series fault outage state of the wind-solar units on the transmission line in the corresponding target grid based on the time-series meteorological data of each fault judgment grid determined according to the initial grid; determining the annual average power generation coefficient of the target province based on each fault outage state and the rated power; and determining the wind-solar power generation coefficient according to the unit capacity construction coefficient, the average power generation line loss and the annual average power generation coefficient. The application improves the calculation accuracy and speed of the wind-solar power generation coefficient.

Description

Technical Field

[0001] This invention relates to the field of wind and solar power generation technology, and in particular to a method, system, electronic device and storage medium for determining wind and solar power generation coefficients. Background Technology

[0002] With the continuous transformation of the energy structure, the proportion of new energy units in the power system is gradually increasing. How to select more suitable regions for the construction of wind and solar power generators and accurately calculate the power generation coefficient of the units built in these regions has become an urgent problem to be solved in the construction of new energy units. However, the current calculation results of wind and solar coefficients for designated regions are very inaccurate and cannot provide a reliable reference for the construction of new energy units. On the one hand, the basic data used to calculate the construction coefficients of wind and solar units are relatively coarse in spatial granularity and have distortion problems, making it difficult to meet the accuracy requirements of power plant construction. On the other hand, the basic data used in the actual calculation process has a small time span and involves fewer types, resulting in short-term and uncertain results that cannot meet the needs of unit life cycle and long-term planning of the power system. Furthermore, the construction coefficient and losses of grid connection lines have not been considered in the coefficient calculation process for wind and solar units, making it impossible to match the actual grid structure and planning scheme of the power system.

[0003] To address the problem of calculating the wind and solar power generation coefficients in power systems, existing technologies include: (1) Using a mesoscale weather research and forecasting model (WRF), the grid is divided and covered at spatial scales of hundreds to thousands through data nesting. Numerical simulation is used to obtain a global map of wind and solar energy resources and power generation efficiency. The grid resolution is 9km×9km. The drawback is that the nested data sources are different, resulting in positional deviations and poor spatial resolution when projected onto the same space. (2) Using satellite remote sensing technology combined with ground observation data, a wind and solar energy resource database containing a series of high-resolution meteorological elements is established. This has the advantages of high temporal and spatial resolution and high-quality ground measurement. The disadvantage of this technology is that the calculation time is too long, and there is a problem of projection distortion for large-scale wind and solar resource calculation. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, electronic device, and storage medium for determining wind and solar power generation coefficients, thereby improving the accuracy and speed of wind and solar power generation coefficient calculation.

[0005] To achieve the above objectives, the present invention provides the following solution.

[0006] A method for determining wind and solar power generation coefficients includes: determining multi-source time-series data for each small grid cell from a time-series data table corresponding to all small grid cells in a target province; the multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data; the time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power; each time-series data table is established by sequentially performing inverse projection indexing on the small grid cells and acquiring multi-source time-series data; the small grid cells are determined by provincial zonal projection of the province where the small grid cell is located.

[0007] Based on the elevation data and land cover data of all the small grids, the target grid for the target province is determined.

[0008] The small grid cells through which the existing grid structure of the target province passes are identified as the initial grid cells.

[0009] The distance between each target grid and the center point of the corresponding Thiessen polygon is determined as the corresponding construction line length.

[0010] Based on the line length of all target grids, the construction coefficient per unit length of the line, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines, calculate the construction coefficient per unit capacity of the lines in the target province.

[0011] The average power generation coefficient of all wind and solar turbines is used to determine the average power generation line loss of the target province.

[0012] Each initial grid cell corresponding to the same Thiessen polygon as each target grid cell is identified as the fault detection grid cell.

[0013] Based on the time-series meteorological data of each fault judgment grid, the time-series fault outage status of the wind and solar turbines on the transmission lines in the corresponding target grid is determined; the wind and solar turbines include: wind turbines and photovoltaic units, and the fault outage status is either outage or normal operation.

[0014] The time-series average capacity factor of each wind and solar turbine is determined based on the fault outage status and rated power of each turbine.

[0015] The annual average power generation coefficient of the corresponding wind and solar turbine is determined based on the time-series average capacity coefficient of each wind and solar turbine, thereby determining the annual average power generation coefficient of the target province.

[0016] The wind and solar power generation coefficient of the target province is determined based on the unit capacity construction coefficient of the line, the average power generation line loss, and the annual average power generation coefficient.

[0017] Optionally, the process of dividing the small grid includes: performing provincial-level zonal projection on each province based on a preset projection method to obtain multiple corresponding large grids after projection; the preset projection method includes: Mercator projection method, Gauss-Kruger zonal projection method and general transverse Mercator zonal projection method.

[0018] The large grid after projection is divided into multiple small grids.

[0019] Optionally, the process of establishing the time series data table includes: assigning spatial coordinate system index values ​​to all the small grids to obtain a spatial coordinate system index table; one cell in the spatial coordinate system index table corresponds to one small grid; the spatial coordinate system index value of the small grid is the row and column number of the corresponding cell.

[0020] By performing inverse projection on the spatial coordinate system index value of the corresponding small grid according to the longitude zone where each small grid is located, the correspondence between the spatial coordinate system and the projected coordinate system is obtained.

[0021] For any current small grid cell in any province: Determine the inverse projection index matrix based on the correspondence between the spatial coordinate system and the projected coordinate system of the current small grid cell. Determine the latitude and longitude of the current small grid cell using the inverse projection index matrix. Acquire multi-source time-series data for the latitude and longitude, store the multi-source time-series data, and obtain a time-series data table for the current small grid cell.

[0022] Optionally, when the preset projection method is Mercator projection, provincial zonal projection is performed on any current province based on the preset projection method to obtain multiple corresponding large grids, including: determining the number of zonal zones contained in the current province according to the longitude range of the current province and the Mercator projection zonal zones.

[0023] When the number of longitude zones contained in the current province is greater than 2, the current province completely spans two longitude zones. The current province is divided into different regions with longitude zones as the boundary, and projection is performed according to the Mercator projection zone in which each region is located.

[0024] When the number of longitude zones contained in the current province is less than 3, the current province either completely spans a longitude zone or belongs entirely to a longitude zone, and the projection is performed according to the longitude zone in which the current province is located.

[0025] Optionally, based on the elevation data and land cover data of all the small grids, the target grid of the target province is determined, including: for any current small grid: based on the elevation data and land cover data of the current small grid, it is determined whether the current small grid meets the construction conditions of the wind and solar power units.

[0026] Each small grid that meets the construction conditions for wind and solar turbine units is designated as the target grid.

[0027] Optionally, based on the latitude and longitude boundary region of the target province and the existing grid structure, multiple Thiessen polygons are generated, including: determining the location coordinates of the busbar grid connection point and the substation on the existing grid structure within the latitude and longitude boundary region of the target province as the center point.

[0028] Based on the center points and the latitude and longitude boundary regions of the target provinces, multiple Thiessen polygons are generated.

[0029] A system for determining wind and solar power generation coefficients includes: a multi-source time-series data determination module, used to determine the multi-source time-series data of corresponding small grids from time-series data tables corresponding to all small grids in a target province; the multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data; the time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power; each time-series data table is established by sequentially performing inverse projection indexing of small grids and acquiring multi-source time-series data; the small grid is determined by provincial zonal projection of the province where the small grid is located.

[0030] The target raster determination module is used to determine the target raster of the target province based on the elevation data and land cover data of all the small rasters.

[0031] The initial grid determination module is used to determine the small grids through which the constructed grid structure of the target province passes as the initial grid.

[0032] The Thiessen polygon generation module is used to generate multiple Thiessen polygons based on the latitude and longitude boundary regions of the target province and the constructed grid structure.

[0033] The line length determination module is used to determine the distance between each target grid and the center point of the corresponding Thiessen polygon as the corresponding line length to be constructed.

[0034] The unit capacity construction coefficient calculation module is used to calculate the unit capacity construction coefficient of the lines in the target province based on the line length of all target grids, the construction coefficient per unit length of the lines, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines.

[0035] The average power generation line loss calculation module is used to determine the average power generation line loss of the target province based on the average power generation coefficient of all wind and solar turbines.

[0036] The fault diagnosis grid determination module is used to determine the initial grid corresponding to the same Thiessen polygon as each target grid as the fault diagnosis grid.

[0037] The fault outage status determination module is used to determine the time-series fault outage status of wind and solar turbines on the transmission lines in the corresponding target grid based on the time-series meteorological data of each fault judgment grid; the wind and solar turbines include wind turbines and photovoltaic units, and the fault outage status is outage or normal operation.

[0038] The time-series average capacity coefficient determination module is used to determine the time-series average capacity coefficient of the corresponding wind and solar turbine based on the fault outage status and rated power of each wind and solar turbine.

[0039] The annual average power generation coefficient determination module is used to determine the annual average power generation coefficient of the corresponding wind and solar turbine based on the time-series average capacity coefficient of each wind and solar turbine, thereby determining the annual average power generation coefficient of the target province.

[0040] The wind and solar power generation coefficient determination module is used to determine the wind and solar power generation coefficient of the target province based on the unit capacity construction coefficient of the line, the average power generation line loss, and the annual average power generation coefficient.

[0041] An electronic device comprising: one or more processors.

[0042] A storage device on which one or more programs are stored.

[0043] When the one or more programs are executed by the one or more processors, the one or more processors implement the wind and solar power generation coefficient determination method as described above.

[0044] A storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the wind and solar power generation coefficient determination method as described above.

[0045] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention discloses a method, system, electronic device and storage medium for determining wind and solar power generation coefficients. By using Mercator zonal projection technology, it solves the distortion problem that exists when projecting large-scale meteorological and geographical data; by establishing an inverse projection index matrix, it solves the position offset problem that exists when resampling multi-source time-series data of the raster; and by combining raster time-series meteorological data and transmission lines, it proposes a method for judging the fault status of wind and solar turbine units considering line icing, thereby improving the accuracy and speed of calculating the power generation coefficient of wind and solar construction. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method for determining the wind and solar power generation coefficients provided in Embodiment 1 of the present invention.

[0048] Figure 2 This is a flowchart illustrating the process of constructing a time-series data table using a multi-process parallel approach.

[0049] Figure 3 This is a schematic diagram of nearest neighbor interpolation.

[0050] Figure 4 This is a schematic diagram of bilinear interpolation.

[0051] Figure 5 This is a schematic diagram of cubic convolution interpolation.

[0052] Figure 6 This is a wind speed-power curve diagram for a wind turbine.

[0053] Figure 7 A schematic diagram of the model structure for determining the wind and solar power generation coefficients. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The purpose of this invention is to provide a method, system, electronic device, and storage medium for determining wind and solar power generation coefficients, aiming to improve the accuracy and speed of wind and solar power generation coefficient calculation.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1 provides a method for determining the wind and solar power generation coefficient.

[0058] Figure 1 This is a schematic flowchart of the method for determining the wind and solar power generation coefficients provided in Embodiment 1 of the present invention. Figure 1 As shown, the method for determining the wind and solar power generation coefficient in this embodiment includes:

[0059] Step 1: Determine the multi-source time series data for the corresponding small grid from the time series data table corresponding to all small grids in the target province.

[0060] The multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data; the time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power; each time-series data table is established by sequentially performing inverse projection indexing on small grids and acquiring multi-source time-series data; the small grids are determined by provincial zonal projection of the province where the small grid is located.

[0061] As an optional implementation method, the process of establishing a time series data table includes: (1) assigning spatial coordinate system index values ​​to all small grids to obtain a spatial coordinate system index table; one cell in the spatial coordinate system index table corresponds to one small grid. The spatial coordinate system index value of the small grid is the row and column number of the corresponding cell. (2) Performing inverse projection on the spatial coordinate system index value of the corresponding small grid according to the longitude zone where each small grid is located to obtain the correspondence between the spatial coordinate system and the projected coordinate system. (3) For any current small grid in any province: determine the inverse projection index matrix according to the correspondence between the spatial coordinate system and the projected coordinate system of the current small grid. Determine the latitude and longitude of the current small grid through the inverse projection index matrix. Obtain multi-source time series data of latitude and longitude, store the multi-source time series data, and obtain the time series data table of the current small grid.

[0062] Specifically, the time-series data tables are determined using a multi-process parallel approach, such as... Figure 2As shown, the process of establishing the time series data table includes: Step 1: Based on parallel acceleration of the process pool, a fast indexing model for multi-source data inverse projection after zonal projection and grid division is constructed: (1.1) Mercator inverse projection is performed on the grid after division according to its longitude zone: For the 1km×1km small grid after reprojection, the corresponding spatial coordinate system index value is inversely projected according to its longitude zone to form a one-to-one correspondence between the spatial coordinate system and the projected coordinate system. This ensures the accuracy of the grid spatial area while achieving the accuracy of its latitude and longitude information. (1.2) The grid inverse projection index matrix is ​​formed: Based on the one-to-one correspondence between the spatial coordinate system and the projected coordinate system, a spatial-projected coordinate correspondence index matrix is ​​generated for each grid, so that the corresponding latitude and longitude coordinates can be indexed by accessing the relative position of the grid spatial coordinates. (1.3) Create a process pool and import data sampling processes: In order to improve the speed of raster indexing of multi-source data and achieve "parallel operation and low memory usage", a process pool is created by importing parallel program processes. The specific explanation is as follows: A process pool (Multiprocess Pool) is a technology application that implements resource management and consists of management processes. Define a pool in the program logic, put a fixed number of processes in advance, and call the relevant processes in the pool to process tasks when needed. After the process is completed, it is not closed, but put back into the process pool to continue waiting for tasks. In this mode, the number of tasks processed at the same time depends on the total number of processes in the pool, ensuring low memory usage of the overall tasks; the concept of entering and leaving the pool saves the time of switching processes on and off, and realizes the effect of concurrent operation of the program. Step 2: (2.1) Raster inverse projection index: Traverse all the small rasters after division in the selected province, and obtain the latitude and longitude values ​​of the small rasters through the inverse projection index matrix. Index the value of the corresponding multi-source time series data under the latitude and longitude, and store the value of the multi-source time series data in the time series data table of the raster. (2.2) Multi-process resampling of raster multi-source data indexing: When indexing multi-source time-series data based on raster latitude and longitude, the cell size and starting index point of the indexed raster may differ from the multi-source time-series data. This raster may contain multiple values ​​for the same data content, necessitating data resampling. This involves changing the spatial resolution of the multi-source time-series data corresponding to the raster and setting rules for aggregation or interpolation between the new pixel sizes. Specifically, the resampling process includes nearest-neighbor interpolation (e.g., ...). Figure 3 As shown), bilinear interpolation (such as...) Figure 4 (as shown) and cubic convolution interpolation (such as) Figure 5The three interpolation methods are shown in the figure below, with the following advantages and disadvantages: Nearest neighbor interpolation: Advantages include simple calculation and fast speed; disadvantages include low interpolation accuracy and jagged edges. Bilinear interpolation: Advantages include relatively high interpolation accuracy; disadvantages include relatively slow calculation speed. Cubic convolution interpolation: Advantages include the highest interpolation accuracy; disadvantages include time-consuming calculation and potential alteration of the original image reflectivity. Considering the advantages and disadvantages of the above interpolation methods, and taking into account the uniformity of the index format and pixel size between the projected small grid and the multi-source time-series data requiring resampling, the interpolation method can be reasonably selected based on the required spatial precision: bilinear interpolation is used when the multi-source time-series data has low precision requirements and high indexing speed requirements; bilinear interpolation is used when high precision requirements are required and sufficient time is available for indexing data. Finally, the resampling program of the selected interpolation method is embedded into the data indexing program of the grid and imported into the process pool for execution. A data indexing process is created for each grid, realizing a "high-speed, low-memory" parallel operation multi-source time-series data resampling method.

[0063] Step 2: Based on the elevation data and land cover data of all small graticles, determine the target graticles for the target province.

[0064] Among them, the small grid is determined by provincial-level zoning projection of the province where the small grid is located.

[0065] As an optional implementation, step 2 includes: for any current small grid cell: based on the elevation data and land cover data of the current small grid cell, determine whether the current small grid cell meets the construction conditions for wind and solar power units.

[0066] Each small grid that meets the construction conditions for wind and solar turbine units is designated as the target grid.

[0067] Specifically, as shown in Tables 1 and 2, based on engineering practice standards, the elevation data and land cover data of any initial grid are used to determine whether the initial grid meets the construction conditions for wind and solar turbine units.

[0068] Table 1: Key Indicators and Recommended Parameters for the Construction of Wind Turbine Units

[0069]

[0070] Table 2. Key Indicators and Recommended Parameters for the Construction of Photovoltaic Units

[0071]

[0072] As an optional implementation method, the process of dividing the small grid includes: (1) performing provincial zonal projection on each province based on a preset projection method to obtain multiple corresponding large grids after projection; the preset projection methods include: Mercator projection method, Gauss-Kruger zonal projection method and general transverse Mercator zonal projection method. (2) dividing each large grid after projection to obtain multiple small grids.

[0073] As an optional implementation method, when the preset projection method is Mercator projection, provincial-level zonal projection is performed on any current province based on the preset projection method to obtain multiple corresponding large grids, including: (1) determining the number of zonal zones contained in the current province based on the longitude range and Mercator projection zonal zones of the current province. (2) when the number of zonal zones contained in the current province is greater than 2, the current province completely spans two longitude zones, and the current province is divided into different regions with the longitude zones as the boundary, and projection is performed according to the Mercator projection zonal zone in which each region is located. (3) when the number of zonal zones contained in the current province is less than 3, the current province completely spans one longitude zone or belongs entirely to one longitude zone, and projection is performed according to the longitude zone in which the current province is located.

[0074] Specifically, the provincial-level zonal projection for any current province includes: (1.1) Dividing the selected province into Mercator longitude zones: Based on the principle of the Mercator projection method, the global longitude is divided into 60 Mercator projection zones (UTM ZONE) according to international standards. Regions within the Mercator projection zones experience the least distortion when projected using the corresponding Mercator projection method. Based on the longitude range of the selected province, the longitude zones are divided according to the given Mercator projection zones, and the number (quantity) of the zones included in the province is recorded. (1.2) Determining whether the province spans more than two longitude zones: A. If the selected province contains no less than three zone numbers, that is, it completely spans more than two longitude zones, then the province is divided into different regions based on the longitude zones. Projection is performed according to the Mercator projection zones in which each region is located, and the coordinate system of the region is projected from the "spatial coordinate system" to the corresponding "projected coordinate system". B. If the selected province contains fewer than three zone numbers, that is, it only completely spans one longitude zone or belongs entirely to one longitude zone, then the entire area of ​​the province is projected according to its projection zone. (1.3) Divide the projected grid into 1km×1km sections: After the province completes the Mercator projection, a grid of the corresponding size is generated in the projection coordinate system. All grids are re-divided with a precision of 1km×1km and the spatial coordinate system index is assigned. A CSV format table is output. Each cell in the table represents a 1km×1km small grid after projection and subdivision. The spatial coordinate system index value of each small grid is the row and column number of the cell. The small grid corresponds to the spatial coordinate system before projection. If the precision of the spatial coordinate system before projection is 0.01°×0.01°, then the size of the small grid after projection is the size of the 0.01°×0.01° area in the projection coordinate system (in meters). (1.4) Rapid evaluation method for distortion before and after projection: For all small grids after Mercator projection and 1km×1km subdivision, calculate their relative positions, i.e., the aspect ratio of the spatial index, and compare it with the ratio of the latitude and longitude ranges corresponding to all small grids before projection to obtain the distortion coefficient κ. Due to the nature of the Mercator coordinate system, there is no latitude distortion before and after projection. Therefore, the magnitude of the distortion coefficient κ can better reflect the severity of longitude distortion before and after projection, and make a rapid judgment and evaluation of the distortion before and after projection. If the distortion coefficient κ before and after projection is large, that is, longitude distortion occurs before and after projection, the "Gauss-Kruger" or "General Transverse Mercator" zonal projection method can be selected according to the projection situation of the province. The distortion coefficients of the two methods can be quickly evaluated and the optimal one can be selected to eliminate the severe longitude distortion problem caused by "Mercator projection" at high latitudes and achieve provincial zonal projection with minimal distortion.

[0075] Step 3: Determine the small grid cells that the existing grid structure of the target province passes through as the initial grid cells.

[0076] Step 4: Generate multiple Thiessen polygons based on the latitude and longitude boundary regions of the target province and the existing grid structure.

[0077] As an optional implementation, step 4 includes: (1) determining the location coordinates of the busbar grid connection point and substation on the existing grid structure within the latitude and longitude boundary area of ​​the target province as the center point; (2) generating multiple Thiessen polygons based on each center point and the latitude and longitude boundary area of ​​the target province.

[0078] Specifically, based on the property that the distance from any point within a Thiessen polygon to the center point constituting that Thiessen polygon is less than the distance to the center points of other Thiessen polygons, the center point of the Thiessen polygon to which the small grid belongs is determined to be its grid connection point in the power system, that is, the system access point of the wind and solar turbine transmission line here.

[0079] Step 5: Determine the distance between each target grid and the center point of the corresponding Thiessen polygon as the corresponding construction line length.

[0080] Step 6: Based on the line length of all target grids, the construction coefficient per unit length of the line, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines, calculate the construction coefficient per unit capacity of the line in the target province.

[0081] Specifically, the formula for calculating the unit capacity construction factor of the line is as follows:

[0082]

[0083] Among them, C line The unit capacity construction coefficient for the line; l line C is the line length; mile Construction coefficient per unit length of the line; CF i CY represents the capacity factor of the i-th type of wind and solar turbine unit; line This represents the maximum capacity of the line; L i The service life of the i-th type of wind and solar turbine unit.

[0084] Step 7: Determine the average power generation line loss of the target province based on the average power generation coefficient of all wind and solar turbines.

[0085] Specifically, based on engineering experience, the average energy loss is taken as 5% of the total energy flowing through the line, i.e., LCOE. i，loss =0.05·LCCE i .

[0086] Among them, LCOE i，loss LCOE represents the average power generation line loss of the i-th type of wind and solar turbine. iLet be the average power generation coefficient of the i-th type of wind and solar turbine.

[0087] Step 8: Determine the initial grid cell corresponding to the same Thiessen polygon as each target grid cell as the fault judgment grid cell.

[0088] Step 9: Based on the time-series meteorological data of each fault judgment grid, determine the time-series fault outage status of the wind and solar turbines on the transmission lines in the corresponding target grid.

[0089] Among them, wind and solar power units include wind turbine units and photovoltaic units, and the fault outage status is either out of service or in normal operation.

[0090] Specifically, the time-series meteorological data (including time-series precipitation, time-series temperature, and time-series wind speed) of the small grid where the power line is located is indexed from the time-series data table to calculate the icing status of all power lines at each moment, thereby obtaining the outage status of the wind and solar turbines connected to that power line. The calculation model for power line icing is as follows:

[0091] Where ΔR is the uniform ice thickness per hour; V is the wind speed; P is the precipitation rate per hour; and d is the droplet diameter.

[0092] When rainfall occurs and the temperature of the small grid is below zero, calculate the uniform icing thickness of the corresponding line every hour. According to the "Technical Specification for Design of Overhead Transmission Lines with Heavy Icing (DL / T5440-2020)", when the icing thickness of a section of the line reaches 20mm or more, it is called a heavy icing zone. At this time, the conductor faces a greater risk of line breakage, so the transmission line is considered to be in an out-of-service state, that is, the wind turbines and photovoltaic units connected to it are in a fault outage state.

[0093] Furthermore, for wind turbines, too low a wind speed will prevent the turbine from reaching the cut-in speed, resulting in zero active power output. Conversely, too high a wind speed may cause the turbine to malfunction due to stress, requiring it to actively cut off, also resulting in zero active power output. In other words, when the wind speed in the area where the wind turbine is located is lower than the cut-in speed or higher than the cut-out speed, the wind turbine is in a shutdown state.

[0094] Step 10: Determine the time-series average capacity factor of each wind and solar turbine based on the fault outage status and rated power of each turbine.

[0095] Specifically, the formula for calculating the time-averaged capacity factor (CF) is as follows:

[0096] Among them, P r This refers to the rated power of the wind and solar turbine units; This represents the time-series average power of the wind and solar turbine units.

[0097] The calculation methods include: (1) Time-series power of wind turbine: First, the air density is converted, and the formula is: Where, n meas p is the equivalent density coefficient. meas Atmospheric pressure; p water R is the water vapor pressure. dry R is the drying coefficient; water Here, V represents the humidity coefficient; T is the Kelvin temperature. Then, altitude is converted using the formula: V = Vh o ·(H / H o ) a Among them, V o The wind speed at the current altitude; H o The current altitude is given; α is the surface friction coefficient; H is the altitude of the corresponding wind turbine model. Finally, the wind speed V is substituted into the equation... Figure 6 From the known wind speed-power curve of the wind turbine shown, the power generation P of the wind turbine at that wind speed can be obtained. W Value. (2) Time-series power of photovoltaic units: First, calculate the temperature coefficient, the calculation formula is: TEM coef =1-δ×(T) cell -25). Among them, 7EM coef δ represents the temperature coefficient of the photovoltaic panels in the photovoltaic unit; δ is the power conversion rate per unit temperature change; TEM coef This refers to the optimal operating temperature of the photovoltaic panels in the photovoltaic system. Then, the time-series average power is calculated using the following formula: Among them, SYS coef The system loss factor is typically set to 0.8056.

[0098] Specifically, when the timing icing status of the wind and solar turbines connected to the input lines of the small grid is determined to be out of service, and the corresponding P value of the wind and solar turbine is [not specified]. W It is zero.

[0099] Step 11: Determine the annual average power generation coefficient of the corresponding wind and solar turbines based on the time-series average capacity coefficient of each turbine, thereby determining the annual average power generation coefficient of the target province.

[0100] Specifically, the formula for calculating the annual average power generation coefficient of wind and solar turbines is as follows: Among them, LCOE i C is the annual average power generation coefficient of the i-th type of wind and solar turbine; build C represents the average annual construction coefficient for wind and solar power units. run This represents the annual average operating coefficient of wind and solar power units.

[0101] Step 12: Determine the wind and solar power generation coefficients for the target province based on the unit capacity construction coefficient, average power generation line loss, and annual average power generation coefficient.

[0102] Specifically, the wind and solar power generation coefficient (LCOE) of the target grid. all The calculation formula is: LCOE all =LCOE t +LCOE t，lass +C line The sum of the wind and solar power generation coefficients of all target grids is the wind and solar power generation coefficient of the target province.

[0103] Furthermore, the above method can be used as follows: Figure 7 The model structure shown is used to determine the wind and solar power generation coefficients. The model structure involves a total of 5 modules.

[0104] Module 1: Based on the Mercator projection coordinate system, the selected provinces are divided into zones for projection. Then, the projected grid is re-divided into 1km×1km grids and the degree of projection distortion is evaluated to obtain a 1km×1km high-precision grid.

[0105] Module 2: Establish a fast indexing model for multi-source data through inverse projection. Mercator inverse projection is performed on the projected high-precision raster to obtain a one-to-one raster inverse projection index matrix. A process pool is created and the data sampling process is imported.

[0106] Module 3: Perform multi-source data resampling for raster data. Select the interpolation method that best meets the current requirements, use a process pool to run the data sampling process asynchronously and in parallel, resample the raster data, and save the elevation and time-series meteorological data obtained from all raster resampling.

[0107] Module 4: Determine the outage status of wind and solar turbine units. Based on the multi-source data obtained from resampling of all grids, calculate the icing situation of the lines connected to the wind and solar turbine units, and consider the wind speed switching in and out of the wind turbine units to determine the outage status of the wind and solar turbine units at the corresponding time.

[0108] Module 5: Calculation of standardized wind and solar resources and power generation coefficients. Based on the elevation data obtained from resampling of all grids, select feasible grids for wind and solar power generation, and calculate the upper limit of exploitable wind and solar resource capacity. Draw Thiessen polygons based on the power system grid structure, and calculate the line construction coefficient and power loss of wind and solar turbines. Simultaneously, based on grid time-series meteorological data and considering turbine outages, calculate the capacity coefficient of wind and solar turbines, and measure the power generation coefficient of the selected grids. Add the line construction coefficient and the turbine power generation coefficient to obtain the standardized wind and solar coefficient for a 1km×1km precision grid within the selected province.

[0109] Example 2 provides a system for determining wind and solar power generation coefficients, including: a multi-source time-series data determination module, used to determine the multi-source time-series data of the corresponding small grid from the time-series data tables corresponding to all small grids in the target province; the multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data; the time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power; any time-series data table is established by sequentially performing inverse projection indexing of the small grids and obtaining multi-source time-series data; the small grids are determined by provincial zonal projection of the province where the small grid is located.

[0110] The target raster determination module is used to determine the target raster for a target province based on the elevation data and land cover data of all small rasters.

[0111] The initial grid determination module is used to determine the small grids through which the existing grid structure of the target province passes as the initial grid.

[0112] The Thiessen polygon generation module is used to generate multiple Thiessen polygons based on the latitude and longitude boundary regions of the target province and the existing grid structure.

[0113] The line length determination module is used to determine the distance between each target grid and the center point of the corresponding Thiessen polygon as the corresponding line length to be constructed.

[0114] The unit capacity construction coefficient calculation module is used to calculate the unit capacity construction coefficient of the lines in the target province based on the line length of all target grids, the construction coefficient per unit length of the lines, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines.

[0115] The average power generation line loss calculation module is used to determine the average power generation line loss of a target province based on the average power generation coefficient of all wind and solar turbines.

[0116] The fault diagnosis grid determination module is used to determine the initial grid corresponding to the same Thiessen polygon as each target grid as the fault diagnosis grid.

[0117] The fault outage status determination module is used to determine the time-series fault outage status of wind and solar turbines on the transmission lines in the corresponding target grid based on the time-series meteorological data of each fault judgment grid; wind and solar turbines include wind turbines and photovoltaic units, and the fault outage status is outage or normal operation.

[0118] The time-series average capacity coefficient determination module is used to determine the time-series average capacity coefficient of each wind and solar turbine based on the fault outage status and rated power of each wind and solar turbine.

[0119] The annual average power generation coefficient determination module is used to determine the annual average power generation coefficient of the corresponding wind and solar turbine based on the time-series average capacity coefficient of each wind and solar turbine, thereby determining the annual average power generation coefficient of the target province.

[0120] The wind and solar power generation coefficient determination module is used to determine the wind and solar power generation coefficient of a target province based on the unit capacity construction coefficient of the line, the average power generation line loss, and the annual average power generation coefficient.

[0121] Example 3 provides an electronic device, including: one or more processors.

[0122] A storage device on which one or more programs are stored.

[0123] When one or more programs are executed by one or more processors, the one or more processors implement the wind and solar power generation coefficient determination method as in Example 1.

[0124] Example 4 provides a storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the wind and solar power generation coefficient determination method as in Example 1.

[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0126] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining wind and solar power generation coefficients, characterized in that, The method includes: The multi-source time-series data for each small grid cell in the target province is determined from the time-series data tables corresponding to all small grid cells. The multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data. The time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power. Each time-series data table is established by sequentially performing inverse projection indexing on the small grid cells and acquiring multi-source time-series data. Each small grid cell is determined by performing provincial zonal projection on the province where the small grid cell is located. Based on the elevation data and land cover data of all the small grids, the target grids of the target province are determined. The small grid cells through which the existing grid structure of the target province passes are determined as the initial grid cells; Based on the latitude and longitude boundary region of the target province and the constructed grid structure, multiple Thiessen polygons are generated; The distance between each target grid and the center point of the corresponding Thiessen polygon is determined as the corresponding construction line length; Based on the line length of all target grids, the construction coefficient per unit length of the line, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines, calculate the construction coefficient per unit capacity of the line in the target province. The average power generation line loss of the target province is determined based on the average power generation coefficient of all wind and solar turbines. Each initial grid cell corresponding to the same Thiessen polygon as each target grid cell is identified as a fault detection grid cell; Based on the time-series meteorological data of each fault judgment grid, the time-series fault outage status of the wind and solar turbines on the transmission lines in the corresponding target grid is determined; the wind and solar turbines include: wind turbines and photovoltaic units, and the fault outage status is either outage or normal operation. The time-series average capacity factor of each wind and solar turbine is determined based on the fault outage status and rated power of each wind and solar turbine. The annual average power generation coefficient of the corresponding wind and solar turbine is determined based on the time-series average capacity coefficient of each wind and solar turbine, thereby determining the annual average power generation coefficient of the target province. The wind and solar power generation coefficient of the target province is determined based on the unit capacity construction coefficient of the line, the average power generation line loss, and the annual average power generation coefficient.

2. The method for determining the wind and solar power generation coefficient according to claim 1, characterized in that, The process of dividing the grid into smaller grids includes: Based on a preset projection method, provincial-level zonal projection is performed on each province to obtain multiple corresponding large grids after projection; the preset projection method includes: Mercator projection method, Gauss-Kruger zonal projection method and general transverse Mercator zonal projection method. The large grid after projection is divided into multiple small grids.

3. The method for determining the wind and solar power generation coefficient according to claim 1, characterized in that, The process of creating a time-series data table includes: All the small grid cells are assigned spatial coordinate system indices to obtain a spatial coordinate system index table; one cell in the spatial coordinate system index table corresponds to one small grid cell; the spatial coordinate system index value of the small grid cell is the row and column number of the corresponding cell. The spatial coordinate system index value of each small grid is inversely projected according to the longitude zone where each small grid is located, so as to obtain the correspondence between the spatial coordinate system and the projected coordinate system; For any current small grid cell in any province: Based on the correspondence between the spatial coordinate system and the projected coordinate system of the current small grid, determine the inverse projection index matrix; The latitude and longitude of the current small grid cell are determined by the inverse projection index matrix. Obtain multi-source time-series data of the latitude and longitude, store the multi-source time-series data, and obtain the time-series data table of the current small grid.

4. The method for determining the wind and solar power generation coefficient according to claim 2, characterized in that, When the preset projection method is Mercator projection, provincial-level zonal projection is performed on any current province based on the preset projection method to obtain multiple corresponding projected large grids, including: Based on the longitude range and Mercator projection zoning of the current province, determine the number of zoning zones included in the current province; When the number of longitude zones contained in the current province is greater than 2, the current province completely spans two longitude zones. The current province is divided into different regions with longitude zones as the boundary, and projection is performed according to the Mercator projection zone in which each region is located. When the number of longitude zones contained in the current province is less than 3, the current province either completely spans a longitude zone or belongs entirely to a longitude zone, and the projection is performed according to the longitude zone in which the current province is located.

5. The method for determining the wind and solar power generation coefficient according to claim 1, characterized in that, Based on the elevation data and land cover data of all the aforementioned small graticles, the target graticles for the target province are determined, including: For any current small grid cell: Based on the elevation data and land cover data of the current small grid, determine whether the current small grid meets the construction conditions for wind and solar turbine units; Each small grid that meets the construction conditions for wind and solar turbine units is designated as the target grid.

6. The method for determining the wind and solar power generation coefficient according to claim 1, characterized in that, Based on the latitude and longitude boundary region of the target province and the constructed grid structure, multiple Thiessen polygons are generated, including: The location coordinates of the busbar grid connection points and substations on the existing grid structure within the latitude and longitude boundary area of ​​the target province are all determined as the center point; Based on the center points and the latitude and longitude boundary regions of the target provinces, multiple Thiessen polygons are generated.

7. A system for determining wind and solar power generation coefficients, characterized in that, The system includes: The multi-source time-series data determination module is used to determine the multi-source time-series data of the corresponding small grid from the time-series data tables corresponding to all small grids in the target province. The multi-source time-series data includes: elevation data, land cover data, and time-series meteorological data. The time-series meteorological data includes: time-series precipitation, time-series temperature, time-series wind speed, time-series atmospheric pressure, time-series water vapor pressure, and time-series solar radiation power. Each time-series data table is established by sequentially performing inverse projection indexing on the small grids and acquiring multi-source time-series data. The small grids are determined by provincial zonal projection of the province where the small grid is located. The target grid determination module is used to determine the target grid of the target province based on the elevation data and land cover data of all the small grids. The initial grid determination module is used to determine the small grids through which the existing grid structure of the target province passes as the initial grid. The Thiessen polygon generation module is used to generate multiple Thiessen polygons based on the latitude and longitude boundary regions of the target province and the constructed grid structure. The line length determination module is used to determine the distance between each target grid and the center point of the corresponding Thiessen polygon as the corresponding line length to be constructed. The unit capacity construction coefficient calculation module is used to calculate the unit capacity construction coefficient of the lines in the target province based on the line length of all target grids, the construction coefficient per unit length of the lines, the upper limit of the line capacity, and the capacity coefficient and service life of all wind and solar turbines. The average power generation line loss calculation module is used to determine the average power generation line loss of the target province based on the average power generation coefficient of all wind and solar turbines. The fault judgment grid determination module is used to determine the initial grid corresponding to the same Thiessen polygon as each target grid as the fault judgment grid; The fault outage status determination module is used to determine the time-series fault outage status of wind and solar turbines on the transmission lines in the corresponding target grid based on the time-series meteorological data of each fault judgment grid; the wind and solar turbines include wind turbines and photovoltaic units, and the fault outage status is outage or normal operation. The time-series average capacity coefficient determination module is used to determine the time-series average capacity coefficient of the corresponding wind and solar turbine based on the time-series fault outage status and rated power of each wind and solar turbine. The annual average power generation coefficient determination module is used to determine the annual average power generation coefficient of the corresponding wind and solar turbine based on the time-series average capacity coefficient of each wind and solar turbine, thereby determining the annual average power generation coefficient of the target province. The wind and solar power generation coefficient determination module is used to determine the wind and solar power generation coefficient of the target province based on the unit capacity construction coefficient of the line, the average power generation line loss, and the annual average power generation coefficient.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the wind and solar power generation coefficient determination method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, It stores a computer program, wherein the computer program, when executed by a processor, implements the method for determining the wind and solar power generation coefficient as described in any one of claims 1 to 6.

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