A wind power consumption processing method, device, equipment and storage medium

Abstract

The application provides a wind power consumption processing method, device, equipment and storage medium. The method comprises the following steps: collecting wind power resource data of a to-be-processed sub-region, and collecting wind farm operation data of the to-be-processed sub-region, wherein the to-be-processed sub-region belongs to a to-be-processed wind farm region, and the to-be-processed wind farm region is divided into a plurality of to-be-processed sub-regions; performing first calculation processing according to the wind power resource data to obtain a wind power resource representation value of the to-be-processed sub-region; performing second calculation processing according to the wind farm operation data to obtain a power generation adjustment index and an energy storage adjustment index of the to-be-processed sub-region; outputting a first target operation parameter according to the wind power resource representation value and the power generation adjustment index to regulate wind power equipment of the to-be-processed sub-region; and outputting a second target operation parameter according to the wind power resource representation value and the energy storage adjustment index to regulate an energy storage system of the to-be-processed sub-region. The method improves the accuracy and management efficiency of wind power consumption.

Description

Technical Field

[0001] This application relates to the field of wind power equipment technology, and in particular to a method, apparatus, equipment and storage medium for wind power consumption. Background Technology

[0002] Wind power integration refers to the process of connecting electricity generated by wind farms to the power grid and effectively transmitting it to other electricity-consuming areas. This process is of great significance for improving wind power utilization, ensuring the stable operation of the power system, and promoting the sustainable development of renewable energy. Improper management of offshore wind power integration can directly affect the stability of the power system and may lead to grid frequency fluctuations. Timely and accurate monitoring and control can reduce the risk of grid frequency fluctuations.

[0003] In related technologies, only the operating status of wind farms is usually considered. For example, the power receiving parameters of the power grid and the state of charge data of the energy storage power station are directly obtained. By analyzing the above data, the scheduling and management of wind power consumption can be realized.

[0004] However, existing technologies cannot achieve accurate scheduling of wind power consumption, resulting in low utilization of wind resources. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and storage medium for wind power consumption processing, in order to solve the problem that the existing technology cannot achieve accurate scheduling of wind power consumption, resulting in low utilization of wind resources.

[0006] Firstly, this application provides a method for wind power consumption, comprising:

[0007] Collect wind resource data for the sub-region to be processed, and collect wind farm operation data for the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed.

[0008] Based on the wind resource data, the first calculation process is performed to obtain the wind resource characterization value of the sub-region to be processed;

[0009] Based on the wind farm operation data, a second calculation process is performed to obtain the power generation adjustment index and energy storage adjustment index of the sub-region to be processed;

[0010] Based on the wind resource characterization value and power generation adjustment index, the first target operating parameters are output to regulate the wind power equipment in the sub-region to be treated;

[0011] Based on the wind resource characterization value and energy storage adjustment index, the second target operating parameters are output to regulate the energy storage system of the sub-region to be treated.

[0012] In one possible implementation, wind resource data includes wind speed data and air density data;

[0013] Accordingly, based on the wind resource data, a first calculation process is performed to obtain the wind resource characterization value of the sub-region to be processed, including:

[0014] The wind energy density of the sub-region to be processed is calculated based on wind speed and air density data.

[0015] Based on the wind speed data, the turbulence intensity of the sub-region to be processed is calculated;

[0016] Based on wind speed data, wind energy density, and turbulence intensity, the wind resource characterization value of the sub-region to be processed is determined.

[0017] In one possible implementation, the wind resource characterization value of the sub-region to be processed is determined based on wind speed data, wind energy density, and turbulence intensity, including:

[0018] Obtain the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed, and the preset influence factor corresponding to the wind speed.

[0019] Based on the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed and the preset influence factor corresponding to the wind speed, as well as wind speed data, wind energy density and turbulence intensity, the wind resource characterization value of the sub-region to be processed is calculated.

[0020] In one possible implementation, the wind farm operation data includes power generation adjustment data and energy storage adjustment data; the power generation adjustment data includes the output power, rated power, and power generation of the wind turbines; the energy storage adjustment data includes the remaining battery capacity, total energy input, and total energy output of the energy storage system, as well as the charging and discharging energy of the sub-region to be processed.

[0021] Based on wind farm operation data, a second calculation process is performed to obtain the power generation adjustment index and energy storage adjustment index for the sub-region to be processed, including:

[0022] The power factor of the wind power equipment is calculated based on the output power and rated power.

[0023] Based on the power generation, the total power generation of the sub-region to be processed is calculated;

[0024] The power generation adjustment index of the sub-region to be processed is calculated based on the power coefficient and total power generation.

[0025] The energy conversion efficiency of the sub-region to be processed is calculated based on the total energy input and total energy output.

[0026] The average energy storage flow rate of the sub-region to be processed is calculated based on the charging energy and discharging energy.

[0027] The energy storage adjustment index of the sub-region to be processed is calculated based on the remaining battery capacity, energy conversion efficiency, and average energy storage flow.

[0028] In one possible implementation, based on wind resource characterization values ​​and power generation adjustment indices, a first target operating parameter is output, including:

[0029] Obtain the preset reference power generation adjustment index, the preset weight factor corresponding to the preset power generation adjustment index, the preset reference wind resource characterization value, and the preset weight factor corresponding to the preset wind resource characterization value.

[0030] Based on the power generation adjustment index, the preset reference power generation adjustment index, the weight factor corresponding to the preset power generation adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the weight factor corresponding to the preset wind resource characterization value, the power generation scheduling optimization index value of the sub-region to be processed is calculated.

[0031] Based on the power generation dispatch optimization index value, a matching is performed in the preset mapping table between the power generation dispatch optimization index value range and the power generation dispatch parameters to determine the power generation dispatch parameters of the sub-region to be processed. The power generation dispatch parameters include the rotor speed and output power of the wind power equipment.

[0032] Based on the power generation dispatch parameters, the first target operating parameters are output to regulate the rotor speed and output power of the wind power equipment in the sub-region to be processed.

[0033] In one possible implementation, based on wind resource characterization values ​​and energy storage adjustment indices, a second target operating parameter is output, including:

[0034] Obtain the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value.

[0035] Based on the energy storage adjustment index, the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value, the energy storage scheduling optimization index value of the sub-region to be processed is calculated.

[0036] Based on the energy storage scheduling optimization index value, a matching is performed in the preset mapping table between the energy storage scheduling optimization index value range and the energy storage scheduling parameters to determine the energy storage scheduling parameters of the sub-region to be processed. The energy storage scheduling parameters include the charging and discharging frequency and power of the energy storage system.

[0037] Based on the energy storage scheduling parameters, the second target operating parameters are output to regulate the charging and discharging frequency and power of the energy storage system in the sub-region to be processed.

[0038] In one possible implementation, before collecting wind resource data for the sub-region to be processed and collecting wind farm operation data for the sub-region to be processed, the method further includes:

[0039] Obtain the distribution information of wind power equipment in the wind farm area to be processed;

[0040] Based on the distribution information, the wind farm area to be processed is divided into multiple sub-areas to be processed.

[0041] Secondly, embodiments of this application provide a processing device for wind power consumption, comprising:

[0042] The data acquisition module is used to collect wind resource data of the sub-region to be processed and to collect wind farm operation data of the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed.

[0043] The first calculation module is used to perform a first calculation process based on wind resource data to obtain the wind resource characterization value of the sub-region to be processed.

[0044] The second calculation module is used to perform a second calculation based on the wind farm operation data to obtain the power generation adjustment index and energy storage adjustment index of the sub-region to be processed.

[0045] The first control module is used to output the first target operating parameters based on the wind resource characterization value and the power generation adjustment index, so as to regulate the wind power equipment in the sub-area to be processed.

[0046] The second control module is used to output the second target operating parameters based on the wind power resource characterization value and the energy storage adjustment index, so as to regulate the energy storage system of the sub-region to be treated.

[0047] Thirdly, this application provides a wind power consumption processing device, comprising:

[0048] At least one processor; and

[0049] A memory that is communicatively connected to at least one processor; wherein,

[0050] The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform the first aspect or various possible implementations of the first aspect as described above.

[0051] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect or various possible embodiments thereof.

[0052] This application provides a method, apparatus, equipment, and storage medium for wind power consumption processing. By collecting wind resource data and wind farm operation data of the sub-region to be processed, calculations are performed to obtain the wind resource characterization value, power generation adjustment index, and energy storage adjustment index of the sub-region. Based on the wind resource characterization value and power generation adjustment index, a first target operating parameter is output to regulate the wind power equipment in the sub-region. Based on the wind resource characterization value and energy storage adjustment index, a second target operating parameter is output to regulate the energy storage system in the sub-region. This achieves accurate regulation of wind power consumption. Compared to existing technologies that only analyze the operating status of wind farms to achieve wind power consumption scheduling and management, this application divides the region into sub-regions to be processed, and comprehensively calculates and processes the wind resource data and wind farm operation data in the sub-regions to obtain the first and second target operating parameters, respectively, to regulate the energy storage system in the sub-region, thereby improving the accuracy of wind power consumption management. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] Figure 1 A wind power consumption processing system architecture diagram provided in this application embodiment;

[0055] Figure 2 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0056] Figure 3 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0057] Figure 4 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0058] Figure 5 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0059] Figure 6 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0060] Figure 7 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0061] Figure 8 Example diagram of energy storage scheduling optimization index values ​​provided in the embodiments of this application;

[0062] Figure 9 A schematic flowchart illustrating a wind power consumption processing method provided in an embodiment of this application;

[0063] Figure 10 A schematic diagram of a wind power consumption processing device provided in an embodiment of this application;

[0064] Figure 11 This is a schematic diagram of a wind power consumption processing device provided in an embodiment of this application.

[0065] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0066] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0067] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0068] Offshore wind power integration management refers to the process of effectively utilizing and managing the electrical energy generated by offshore wind power. Due to the instability and intermittency of offshore wind power, offshore wind power integration management is necessary to ensure that the electrical energy generated by offshore wind power can be efficiently absorbed and utilized, and to avoid energy waste.

[0069] In existing technologies, wind power consumption is typically managed by analyzing the operational status of wind farms, such as grid power receiving parameters and energy storage power station state-of-charge data. However, existing technologies lack analysis of wind resource data and have a relatively singular analytical perspective, which may lead to insufficient accuracy of the analysis results and reduced efficiency in wind power consumption.

[0070] As a result, existing technologies suffer from the inability to accurately schedule wind power consumption and low utilization of wind resources.

[0071] To address the aforementioned issues, the core concept of this application is to collect wind resource data and wind farm operation data from the sub-region to be processed, perform calculations and processing to obtain a first target operation parameter and a second target operation parameter, and then regulate the energy storage system of the sub-region to be processed based on the first target operation parameter and the second target operation parameter. This comprehensively considers the impact of wind resource data and wind farm operation data on wind power consumption, thereby improving the accuracy of wind power consumption management.

[0072] Optionally, Figure 1 This is a schematic diagram of a wind power consumption processing system architecture provided in an embodiment of this application. The wind power consumption processing system is a computer device. Figure 1 In this framework, at least one of a data acquisition device 101, a processing device 102, and a display device 103 is included.

[0073] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the architecture of the wind power consumption processing system. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0074] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface, and the data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0075] The processing device 102 can perform a first calculation on wind resource data and a second calculation on wind farm operation data to obtain the wind resource characterization value, power generation adjustment index, and energy storage adjustment index of the sub-region to be processed; based on the wind resource characterization value and power generation adjustment index, a first target operating parameter is obtained to regulate the wind power equipment in the sub-region to be processed; based on the wind resource characterization value and energy storage adjustment index, a second target operating parameter is obtained to regulate the energy storage system in the sub-region to be processed, so as to achieve accurate scheduling of wind power consumption and improve the utilization rate of wind resources.

[0076] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0077] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0078] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 2 A flowchart illustrating a wind power consumption method provided in this application embodiment is shown below. Figure 2 As shown, the method includes:

[0081] S201. Collect wind resource data of the sub-region to be processed and collect wind farm operation data of the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed.

[0082] In this embodiment, the preset monitoring period is divided into multiple monitoring time periods.

[0083] Optional, wind resource data includes wind speed data and air density data.

[0084] Wind speed data in the sub-region to be processed is collected in real time using an anemometer.

[0085] Multiple monitoring points are set up in the sub-region to be processed. Each monitoring point is equipped with a pressure sensor and a temperature sensor. During each monitoring time period, the pressure and temperature data of each monitoring point in the sub-region to be processed are collected using the pressure and temperature sensors. The ratio of the pressure data P to the product of the dry air gas constant R and the temperature data T is taken as the air density data ρ. The calculation formula is as follows:

[0086]

[0087] Optionally, wind farm operation data includes power generation adjustment data and energy storage adjustment data; power generation adjustment data includes the output power, rated power, and power generation of wind turbines; energy storage adjustment data includes the remaining battery capacity, total energy input, and total energy output of the energy storage system, as well as the charging and discharging energy of the sub-region to be processed.

[0088] Output power refers to the actual electricity generated by the wind turbine during the monitoring period, which is monitored and collected in real time by sensors and data loggers in the wind turbine control system. Rated power refers to the maximum electricity that the wind turbine can continuously generate under optimal wind speed conditions.

[0089] Power generation refers to the total electrical energy generated by the wind turbine during the monitoring period.

[0090] S202. Based on the wind resource data, perform the first calculation process to obtain the wind resource characterization value of the sub-region to be processed.

[0091] In this embodiment, the wind resource characterization value is used to evaluate the wind turbine power generation capacity and adjust the wind turbine power generation strategy, thereby improving the power generation efficiency of the wind farm.

[0092] S203. Based on the wind farm operation data, perform a second calculation process to obtain the power generation adjustment index and energy storage adjustment index of the sub-region to be processed.

[0093] In this embodiment, by performing a second calculation on the wind farm operation data, the power generation adjustment index and energy storage adjustment index of the area to be processed are obtained, so as to clarify the power generation, energy demand and supply of the area to be processed, thereby improving the accuracy of wind power consumption management.

[0094] S204. Based on the wind resource characterization value and power generation adjustment index, output the first target operating parameters to regulate the wind power equipment in the sub-region to be processed.

[0095] In this embodiment, the wind power resource characterization value and the power generation adjustment index are used to calculate and output the first target operating parameters. Based on the first target operating parameters, the wind power equipment in the sub-region to be processed is adjusted to accurately assess the wind energy potential of the sub-region to be processed, thereby optimizing the operating strategy of the wind power equipment and improving the power generation efficiency of the wind farm.

[0096] S205. Based on the wind power resource characterization value and energy storage adjustment index, output the second target operating parameters to regulate the energy storage system of the sub-region to be treated.

[0097] In this embodiment, the second target operating parameters are calculated by using wind resource characterization values ​​and energy storage adjustment index. Based on the second target operating parameters, the energy storage system of the sub-region to be treated is adjusted. The energy storage system is adjusted in a timely manner according to different needs, thereby improving the rationality of the energy storage system of the sub-region to be treated.

[0098] In this embodiment, wind resource data and wind farm operation data of the sub-region to be processed are collected and processed accordingly to obtain wind resource characterization value, power generation adjustment index and energy storage adjustment index. Based on the wind resource characterization value, power generation adjustment index and energy storage adjustment index, the first target operation parameter and the second target operation parameter are obtained, thereby realizing precise control of wind power equipment and energy storage system in the sub-region to be processed and improving the wind energy utilization rate of wind farm.

[0099] Figure 3 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 2 Based on the illustrated embodiments, as Figure 3 As shown, step S202 above, which involves performing a first calculation based on wind resource data to obtain the wind resource characterization value of the sub-region to be processed, includes:

[0100] S301. Calculate the wind energy density of the sub-region to be processed based on wind speed and air density data.

[0101] In this embodiment, the air density data ρ at each monitoring point is averaged to obtain the average air density ρ′ of each sub-region to be processed.

[0102] The formula for calculating the wind energy density A of the sub-region to be processed is as follows:

[0103]

[0104] Where C represents the wind speed data of the sub-region to be processed.

[0105] S302. Based on the wind speed data, the turbulence intensity of the sub-region to be processed is calculated.

[0106] In this embodiment, turbulence intensity refers to an index that measures wind speed fluctuations. Using wind speed data, the standard deviation of wind speed σ and the average wind speed C′ are calculated. The formula for calculating the turbulence intensity B of the sub-region to be processed is as follows:

[0107]

[0108] S303. Determine the wind resource characterization value of the sub-region to be processed based on wind speed data, wind energy density, and turbulence intensity.

[0109] In this embodiment, for example, if the wind energy density of the sub-region to be processed is greater, it indicates that the wind speed of the sub-region to be processed is greater, which indicates that the sub-region to be processed has higher wind resources, and the wind resource characterization value of the sub-region to be processed is higher; the turbulence intensity of the sub-region to be processed reflects the stability of the wind speed. If the wind speed is more stable, it indicates that the sub-region to be processed has higher wind resources, and the wind resource characterization value of the sub-region to be processed is higher.

[0110] In this embodiment, by calculating the wind resource characterization value, the accuracy of assessing the wind turbine power generation capacity can be improved, the wind turbine power generation strategy can be optimized, and thus the power generation efficiency of the wind farm can be improved.

[0111] Figure 4 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 3 Based on the illustrated embodiments, as Figure 4 As shown, step S303 above, which determines the wind resource characterization value of the sub-region to be processed based on wind speed data, wind energy density, and turbulence intensity, includes:

[0112] S401. Obtain the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed, and the preset influence factor corresponding to the wind speed.

[0113] In this embodiment, i represents the identifier of the sub-region to be processed, j represents the identifier of the monitoring time period, and τ i This represents the wind resource characterization value of the i-th sub-region to be processed;

[0114] A ij Θ1 represents the wind energy density of the i-th sub-region to be processed within the j-th monitoring time period, ΔA represents the preset reference wind energy density, Θ1 represents the influence factor corresponding to the preset reference wind energy density ΔA, and Θ1 represents the numerical value of the degree of influence of wind energy density on the wind resource characterization value of the sub-region to be processed, which is obtained through the preset first mapping relationship.

[0115] B ijΘ2 represents the turbulence intensity of the i-th sub-region to be processed within the j-th monitoring time period, ΔB represents the preset reference turbulence intensity, Θ2 represents the influence factor corresponding to the preset reference turbulence intensity ΔB, and Θ2 represents the numerical value of the degree of influence of turbulence intensity on the wind resource characterization value of the sub-region to be processed, which is obtained through the preset second mapping relationship.

[0116] C ij This represents the wind speed data of the i-th sub-region to be processed within the j-th monitoring time period. ΔC represents the preset reference wind speed data. Θ3 represents the influence factor corresponding to the preset reference wind speed data ΔC, which refers to the numerical value of the degree of influence of the wind speed data on the wind resource characterization value of the sub-region to be processed. It is obtained through a preset third mapping relationship.

[0117] Where i is greater than or equal to 1 and less than or equal to the total number of sub-regions n to be processed; j is greater than or equal to 1 and less than or equal to the total number of monitoring time periods m; Θ1, Θ2, and Θ3 are all greater than 0 and less than 1, and ΔA, ΔB, ΔB, Θ1, Θ2, and Θ3 are all obtained from the preset offshore wind power consumption database.

[0118] S402. Based on the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed and the preset influence factor corresponding to the wind speed, as well as the wind speed data, wind energy density and turbulence intensity, calculate the wind resource characterization value of the sub-region to be processed.

[0119] In this embodiment, the wind resource characterization value τ of the sub-region to be processed i The calculation formula is as follows:

[0120]

[0121] In this embodiment, the wind resource characterization value of the sub-region to be processed is calculated, which accurately reflects the wind energy situation, thereby adjusting the wind power consumption in a timely manner and improving the power generation efficiency of the wind farm.

[0122] Figure 5 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 2 Based on the illustrated embodiments, as Figure 5 As shown, step S203 above involves performing a second calculation based on wind farm operation data to obtain the power generation adjustment index and energy storage adjustment index for the sub-region to be processed, including:

[0123] S501. Calculate the power factor of the wind power equipment based on the output power and rated power.

[0124] In this embodiment, the power factor of the wind turbine is used to measure the efficiency of the wind turbine in converting wind energy into electrical energy. The formula for calculating the power factor F of the wind turbine is as follows:

[0125]

[0126] Where D is the output power and P is the rated power.

[0127] S502. Calculate the total power generation of the sub-region to be processed based on the power generation.

[0128] In this embodiment, the sub-region to be processed includes at least one wind turbine. The total power generation of the sub-region to be processed is the sum of the power generation of each wind turbine in the sub-region to be processed. The power generation of the wind turbine refers to the total electrical energy generated by the wind turbine during the monitoring period, which is monitored and collected by the energy meter of the wind turbine.

[0129] S503. Calculate the power generation adjustment index of the sub-region to be processed based on the power coefficient and total power generation.

[0130] In this embodiment, i represents the identifier of the sub-region to be processed, j represents the identifier of the monitoring time period, and k represents the identifier of the wind power equipment in the sub-region to be processed.

[0131] D ij→k ΔD represents the output power of the k-th fan in the i-th sub-region to be processed during the j-th monitoring time period, ΔD represents the preset reference output power, and ε1 represents the influence factor corresponding to the preset reference output power.

[0132] F ij→k ΔF represents the power coefficient of the k-th fan in the i-th sub-region to be processed within the j-th monitoring time period, ΔF represents the preset reference power coefficient, and ε2 represents the influence factor corresponding to the preset reference power coefficient.

[0133] H ij ΔH represents the total power generation of the i-th sub-region to be processed within the j-th monitoring time period, ΔH represents the preset reference total power generation, and ε3 represents the influencing factor corresponding to the preset reference total power generation.

[0134] Where i is greater than or equal to 1 and less than or equal to the total number of sub-regions to be processed n; j is greater than or equal to 1 and less than or equal to the total number of monitoring time periods m; k is greater than or equal to 1 and less than or equal to the total number of wind power devices h; ε1, ε2, and ε3 are all greater than 0 and less than 1, and ΔD, ΔF, ΔH, ε1, ε2, and ε3 are all obtained from the preset offshore wind power consumption database.

[0135] Then the power generation adjustment index P of the i-th sub-region to be processed i The calculation formula is as follows:

[0136]

[0137] In this embodiment, the power generation adjustment index of the sub-region to be processed is a numerical quantification result obtained by analyzing the power coefficient and total power generation of the sub-region to be processed.

[0138] S504. Calculate the energy conversion efficiency of the sub-region to be processed based on the total energy input and total energy output.

[0139] In this embodiment, the total energy input E1 refers to the total energy received by the energy storage system during charging, and the total energy output E2 refers to the total energy released by the energy storage system during discharging; the formula for calculating the energy conversion efficiency M is as follows:

[0140]

[0141] The system measures current and voltage using current and voltage sensors, and calculates the total energy input E1 and total energy output E2 based on the current and voltage.

[0142] S505. Calculate the average energy storage flow rate of the sub-region to be processed based on the charging energy and discharging energy.

[0143] In this embodiment, the charging energy and discharging energy of the sub-region to be processed are added together to obtain the total energy storage flow of the sub-region to be processed. The total energy storage flow of the sub-region to be processed is divided by the duration of the monitoring period to obtain the average energy storage flow of the sub-region to be processed.

[0144] S506. Calculate the energy storage adjustment index of the sub-region to be processed based on the remaining battery capacity, energy conversion efficiency, and average energy storage flow.

[0145] In this embodiment, i represents the identifier of the sub-region to be processed, and j represents the identifier of the monitoring time period;

[0146] M ij ΔM represents the energy conversion efficiency in the i-th sub-region to be processed during the j-th monitoring time period, ΔM represents the preset reference energy conversion efficiency, and ω1 represents the weighting factor corresponding to the preset reference energy conversion efficiency.

[0147] P ij ω2 represents the average energy storage flow rate in the i-th sub-region to be processed during the j-th monitoring time period, ΔP represents the preset reference average energy storage flow rate, and ω2 represents the weighting factor corresponding to the preset reference average energy storage flow rate.

[0148] Q jΔQ represents the remaining battery capacity of the energy storage system during the j-th monitoring period, ω3 represents the preset reference battery remaining capacity, and ω3 represents the weighting factor corresponding to the preset reference battery remaining capacity.

[0149] Where i is greater than or equal to 1 and less than or equal to the total number of sub-regions n to be processed; j is greater than or equal to 1 and less than or equal to the total number of monitoring time periods m; ω1, ω2, and ω3 are all greater than 0 and less than 1, and ΔM, ΔP, ΔQ, ω1, ω2, and ω3 are all obtained from the preset offshore wind power consumption database.

[0150] Then the energy storage adjustment index ρ of the i-th sub-region to be processed i The calculation formula is as follows:

[0151]

[0152] For example, the higher the energy conversion efficiency of the sub-region to be processed, the more effectively wind energy can be converted into electrical energy, thereby improving the charging efficiency of the energy storage system, and the smaller the energy storage adjustment index; the higher the average energy storage flow, the higher the utilization rate of the energy storage system during charging and discharging, effectively regulating the fluctuations in power supply and demand; the remaining battery capacity reflects the current charging status and energy reserve level of the energy storage system, which facilitates precise adjustment of the energy storage system, thereby improving energy storage efficiency.

[0153] In this embodiment, by calculating the energy storage adjustment index of the sub-region to be processed, the overall performance of the energy storage system in the sub-region to be processed can be evaluated, thereby achieving precise scheduling of the energy storage system and improving energy storage efficiency.

[0154] Figure 6 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 2 Based on the illustrated embodiments, as Figure 6 As shown, step S204 above, which outputs the first target operating parameters based on the wind resource characterization value and the power generation adjustment index, includes:

[0155] S601. Obtain the preset reference power generation adjustment index, the preset weight factor corresponding to the preset power generation adjustment index, the preset reference wind resource characterization value, and the preset weight factor corresponding to the preset wind resource characterization value.

[0156] In this embodiment, i represents the identifier of the sub-region to be processed, and Δβ represents the preset reference power generation adjustment index. This represents the weighting factor corresponding to the preset power generation adjustment index, and Δτ represents the preset reference wind resource characterization value. This represents the weighting factor corresponding to the preset wind resource characterization value; where i is greater than or equal to 1 and less than or equal to the total number n of the sub-regions to be processed. Greater than 0 and less than 1, and Δβ, Δτ, as well as All data were obtained from a pre-set offshore wind power consumption database.

[0157] S602. Based on the power generation adjustment index, the preset reference power generation adjustment index, the weight factor corresponding to the preset power generation adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the weight factor corresponding to the preset wind resource characterization value, calculate the power generation scheduling optimization index value for the sub-region to be processed.

[0158] In this embodiment, β i τ represents the power generation adjustment index of the i-th sub-region to be processed. i Let α represent the wind resource characterization value of the i-th sub-region to be processed; then α represents the power generation dispatch optimization index value of the i-th sub-region to be processed. i The calculation formula is as follows:

[0159]

[0160] S603. Based on the power generation dispatch optimization index value, match it in the preset mapping table between the power generation dispatch optimization index value range and the power generation dispatch parameters to determine the power generation dispatch parameters of the sub-region to be processed. The power generation dispatch parameters include the rotor speed and output power of the wind power equipment.

[0161] In this embodiment, the power generation dispatch parameters include positive and negative values.

[0162] S604. Based on the power generation dispatch parameters, output the first target operating parameters to regulate the rotor speed and output power of the wind power equipment in the sub-region to be processed.

[0163] In this embodiment, the actual operating parameters of the wind power equipment are obtained, and the sum of the power generation scheduling parameters and the actual operating parameters is used as the first target operating parameter. The first target operating parameter is used as the target operating parameter of the wind power equipment, thereby realizing the regulation of the rotor speed and output power of the wind power equipment.

[0164] In this embodiment, the first target operating parameters are obtained by calculating the wind power resource characterization value and the power generation adjustment index, thereby realizing the accurate control of the rotor speed and output power of the wind power equipment to improve power generation efficiency.

[0165] Figure 7 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 2 Based on the illustrated embodiments, as Figure 7 As shown, in step S205 above, the second target operating parameters are output based on the wind resource characterization value and the energy storage adjustment index, including:

[0166] S701. Obtain the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value.

[0167] In this embodiment, i represents the identifier of the sub-region to be processed, Δρ represents the preset reference energy storage adjustment index, μ1 represents the compensation factor corresponding to the preset energy storage adjustment index, Δτ represents the preset reference wind resource characterization value, and μ2 represents the compensation factor corresponding to the preset wind resource characterization value; wherein i is greater than or equal to 1 and less than or equal to the total number n of the sub-regions to be processed, μ1 and μ2 are greater than 0 and less than 1, and Δρ, Δτ, μ1, and μ2 are all obtained from the preset offshore wind power consumption database.

[0168] S702. Based on the energy storage adjustment index, the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value, calculate the energy storage scheduling optimization index value for the sub-region to be processed.

[0169] In this embodiment, ρ i τ represents the energy storage adjustment index of the i-th sub-region to be processed. i Let represent the wind resource characterization value of the i-th sub-region to be processed, then δ represents the energy storage scheduling optimization index value of the i-th sub-region to be processed. i The calculation formula is as follows:

[0170]

[0171] Where e is the natural constant.

[0172] In this embodiment, for example, i is 1, 2, 3, 4, Δρ is 1.3, μ1 is 0.7, Δτ is 1.4, and μ2 is 0.3; then the energy storage adjustment index and wind resource characterization value of each sub-region to be processed, as well as the energy storage scheduling optimization index value corresponding to the sub-region to be processed, can be shown in the following table:

[0173]

[0174] like Figure 8As shown, the x-axis represents the wind resource characterization value, and the y-axis represents the energy storage scheduling optimization index value. When the energy storage index is 1.3, the functional relationship between the wind resource characterization value and the energy storage scheduling optimization index value is shown as curve a; when the energy storage index is 1.6, the functional relationship between the wind resource characterization value and the energy storage scheduling optimization index value is shown as curve b; and when the energy storage index is 1.9, the functional relationship between the wind resource characterization value and the energy storage scheduling optimization index value is shown as curve c.

[0175] It should be noted that as the energy storage adjustment index of each sub-region to be processed increases, the energy storage scheduling optimization index value increases accordingly; as the difference between the wind resource characterization value of each sub-region to be processed and the preset reference wind resource characterization value increases, the energy storage scheduling optimization index value increases accordingly.

[0176] S703. Based on the energy storage scheduling optimization index value, match it in the preset mapping table between the energy storage scheduling optimization index value range and the energy storage scheduling parameters to determine the energy storage scheduling parameters of the sub-region to be processed. The energy storage scheduling parameters include the charging and discharging frequency and power of the energy storage system.

[0177] In this embodiment, the energy storage scheduling parameters include positive and negative values.

[0178] S704. Based on the energy storage scheduling parameters, output the second target operating parameters to regulate the charging and discharging frequency and power of the energy storage system in the sub-region to be processed.

[0179] In this embodiment, the actual energy storage operation parameters in the sub-region to be processed are obtained, and the sum of the energy storage scheduling parameters and the actual energy storage operation parameters is used as the second target operation parameter. The second target operation parameter is used as the target operation parameter of the energy storage system in the sub-region to be processed, thereby realizing the regulation of the charging and discharging frequency and power of the energy storage system in the sub-region to be processed.

[0180] In this embodiment, the accuracy of the energy storage scheduling optimization index value is improved by using wind power resource characterization value and energy storage adjustment index to obtain the second target operating parameters, thereby realizing the precise control of the charging and discharging frequency and power of the energy storage system in the sub-region to be treated.

[0181] Figure 9 This is a flowchart illustrating a wind power consumption method provided in an embodiment of this application. Figure 2 Based on the illustrated embodiments, as Figure 9 As shown, before collecting wind resource data and wind farm operation data of the sub-region to be processed in step S201 above, the following steps are also included:

[0182] S901. Obtain the distribution information of wind power equipment in the wind farm area to be processed.

[0183] In this embodiment, wind power equipment can refer to wind turbines. A planar image of the area to which the offshore wind farm belongs is extracted using geographic information system software and image processing technology, and the location of each wind turbine in the planar image is marked.

[0184] S902. Based on the distribution information, the wind farm area to be processed is divided into multiple sub-areas to be processed.

[0185] In this embodiment, based on the preset number of wind turbines and the position of each wind turbine in the planar image, the Delaunay triangulation algorithm is used to divide the wind farm area to be processed into multiple sub-regions to be processed.

[0186] In this implementation, by dividing the wind farm area to be processed into multiple sub-areas to be processed, the accuracy of data analysis results can be improved, thereby achieving accurate scheduling of wind power consumption.

[0187] Figure 10 This is a schematic diagram of the structure of a wind power consumption processing device provided in an embodiment of this application, as shown below. Figure 10 As shown, the wind power consumption processing device includes:

[0188] The acquisition module 1001 is used to acquire wind resource data of the sub-region to be processed and to acquire wind farm operation data of the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed.

[0189] The first calculation module 1002 is used to perform a first calculation process based on wind resource data to obtain the wind resource characterization value of the sub-region to be processed.

[0190] The second calculation module 1003 is used to perform a second calculation based on the wind farm operation data to obtain the power generation adjustment index and energy storage adjustment index of the sub-region to be processed.

[0191] The first control module 1004 is used to output the first target operating parameters based on the wind resource characterization value and the power generation adjustment index, so as to regulate the wind power equipment in the sub-area to be processed.

[0192] The second control module 1005 is used to output the second target operating parameters based on the wind power resource characterization value and the energy storage adjustment index, so as to regulate the energy storage system of the sub-region to be processed.

[0193] Optionally, the first calculation module 1002 is further used for:

[0194] The wind energy density of the sub-region to be processed is calculated based on wind speed and air density data.

[0195] Based on the wind speed data, the turbulence intensity of the sub-region to be processed is calculated;

[0196] Based on wind speed data, wind energy density, and turbulence intensity, the wind resource characterization value of the sub-region to be processed is determined.

[0197] Optionally, the first calculation module 1002 is further used for:

[0198] Obtain the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed, and the preset influence factor corresponding to the wind speed.

[0199] Based on the preset reference wind energy density, the preset influence factor corresponding to the wind energy density, the preset reference turbulence intensity, the preset influence factor corresponding to the turbulence intensity, the preset reference wind speed and the preset influence factor corresponding to the wind speed, as well as wind speed data, wind energy density and turbulence intensity, the wind resource characterization value of the sub-region to be processed is calculated.

[0200] Optionally, the second calculation module 1003 is further used for:

[0201] The power factor of the wind power equipment is calculated based on the output power and rated power.

[0202] Based on the power generation, the total power generation of the sub-region to be processed is calculated;

[0203] The power generation adjustment index of the sub-region to be processed is calculated based on the power coefficient and total power generation.

[0204] The energy conversion efficiency of the sub-region to be processed is calculated based on the total energy input and total energy output.

[0205] The average energy storage flow rate of the sub-region to be processed is calculated based on the charging energy and discharging energy.

[0206] The energy storage adjustment index of the sub-region to be processed is calculated based on the remaining battery capacity, energy conversion efficiency, and average energy storage flow.

[0207] Optionally, the second calculation module 1003 is further used for:

[0208] Obtain the preset reference power generation adjustment index, the preset weight factor corresponding to the preset power generation adjustment index, the preset reference wind resource characterization value, and the preset weight factor corresponding to the preset wind resource characterization value.

[0209] Based on the power generation adjustment index, the preset reference power generation adjustment index, the weight factor corresponding to the preset power generation adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the weight factor corresponding to the preset wind resource characterization value, the power generation scheduling optimization index value of the sub-region to be processed is calculated.

[0210] Based on the power generation dispatch optimization index value, a matching is performed in the preset mapping table between the power generation dispatch optimization index value range and the power generation dispatch parameters to determine the power generation dispatch parameters of the sub-region to be processed. The power generation dispatch parameters include the rotor speed and output power of the wind power equipment.

[0211] Based on the power generation dispatch parameters, the first target operating parameters are output to regulate the rotor speed and output power of the wind power equipment in the sub-region to be processed.

[0212] Optionally, the second calculation module 1003 is further used for:

[0213] Obtain the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value.

[0214] Based on the energy storage adjustment index, the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value, the energy storage scheduling optimization index value of the sub-region to be processed is calculated.

[0215] Based on the energy storage scheduling optimization index value, a matching is performed in the preset mapping table between the energy storage scheduling optimization index value range and the energy storage scheduling parameters to determine the energy storage scheduling parameters of the sub-region to be processed. The energy storage scheduling parameters include the charging and discharging frequency and power of the energy storage system.

[0216] Based on the energy storage scheduling parameters, the second target operating parameters are output to regulate the charging and discharging frequency and power of the energy storage system in the sub-region to be processed.

[0217] Optionally, before the acquisition module 1001 acquires wind resource data of the sub-region to be processed and acquires wind farm operation data of the sub-region to be processed, the above-mentioned device further includes:

[0218] The segmentation module is used to obtain the distribution information of wind power equipment in the wind farm area to be processed; based on the distribution information, the wind farm area to be processed is divided into multiple sub-areas to be processed.

[0219] Figure 11 This is a schematic diagram of a wind power consumption processing device provided in an embodiment of this application. Figure 11 As shown, the wind power consumption processing equipment provided in this embodiment includes:

[0220] The device includes at least one processor 1101 and a memory 1102. Optionally, the device also includes a communication component 1103. The processor 1101, memory 1102, and communication component 1103 are connected via a bus 1104.

[0221] In a specific implementation, at least one processor 1101 executes computer execution instructions stored in memory 1102, causing at least one processor 1101 to perform the above-described method.

[0222] The specific implementation process of processor 1101 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0223] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0224] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0225] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0226] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0227] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0228] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0229] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0230] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0231] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0232] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as a portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0233] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0234] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for wind power consumption, characterized in that, include: Collect wind resource data of the sub-region to be processed, and collect wind farm operation data of the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed. The wind resource characterization value of the sub-region to be processed is calculated based on wind speed data, wind energy density, turbulence intensity, preset reference wind energy density and its corresponding influence factor, preset reference turbulence intensity and its corresponding influence factor, preset reference wind speed and its corresponding influence factor. The power generation adjustment index is calculated based on the power coefficient of the wind power equipment and the total power generation of the sub-region to be treated. The energy storage adjustment index is calculated based on the remaining battery capacity, energy conversion efficiency and average energy storage flow. Based on the wind resource characterization value and the power generation adjustment index, the first target operating parameters are output to regulate the wind power equipment in the sub-region to be treated. Based on the wind resource characterization value and the energy storage adjustment index, a second target operating parameter is output to regulate the energy storage system of the sub-region to be processed.

2. The method according to claim 1, characterized in that, The wind farm operation data includes power generation adjustment data and energy storage adjustment data; the power generation adjustment data includes the output power, rated power, and power generation of the wind power equipment; the energy storage adjustment data includes the remaining battery capacity, total energy input, and total energy output of the energy storage system, as well as the charging energy and discharging energy of the sub-region to be processed. The power factor of the wind power equipment is calculated based on the output power and the rated power. The energy conversion efficiency of the sub-region to be processed is calculated based on the total energy input and the total energy output. The average energy storage flow rate of the sub-region to be processed is calculated based on the charging energy and the discharging energy.

3. The method according to claim 2, characterized in that, The step of outputting the first target operating parameters based on the wind resource characterization value and the power generation adjustment index includes: Obtain the preset reference power generation adjustment index, the preset weight factor corresponding to the preset power generation adjustment index, the preset reference wind resource characterization value, and the preset weight factor corresponding to the preset wind resource characterization value. The power generation scheduling optimization index value of the sub-region to be processed is calculated based on the power generation adjustment index, the preset reference power generation adjustment index, the weight factor corresponding to the preset power generation adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the weight factor corresponding to the preset wind resource characterization value. Based on the power generation scheduling optimization index value, a matching is performed in a preset mapping table between the power generation scheduling optimization index value range and the power generation scheduling parameters to determine the power generation scheduling parameters of the sub-region to be processed, wherein the power generation scheduling parameters include the rotor speed and output power of the wind power equipment; Based on the power generation scheduling parameters, the first target operating parameters are output to regulate the rotor speed and output power of the wind power equipment in the sub-region to be processed.

4. The method according to claim 2, characterized in that, The step of outputting the second target operating parameters based on the wind resource characterization value and the energy storage adjustment index includes: Obtain the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value. The energy storage scheduling optimization index value of the sub-region to be processed is calculated based on the energy storage adjustment index, the preset reference energy storage adjustment index, the compensation factor corresponding to the preset energy storage adjustment index, the wind resource characterization value, the preset reference wind resource characterization value, and the compensation factor corresponding to the preset wind resource characterization value. Based on the energy storage scheduling optimization index value, a matching is performed in a preset mapping table between the energy storage scheduling optimization index value range and the energy storage scheduling parameters to determine the energy storage scheduling parameters of the sub-region to be processed, wherein the energy storage scheduling parameters include the charging and discharging frequency and power of the energy storage system; Based on the energy storage scheduling parameters, a second target operating parameter is output to regulate the charging and discharging frequency and power of the energy storage system in the sub-region to be processed.

5. The method according to claim 1, characterized in that, Before collecting wind resource data for the sub-region to be processed and collecting wind farm operation data for the sub-region to be processed, the following steps are also included: Obtain the distribution information of wind power equipment in the wind farm area to be processed; Based on the distribution information, the wind farm area to be processed is divided into multiple sub-areas to be processed.

6. A wind power consumption processing device, characterized in that, include: The data acquisition module is used to acquire wind resource data of the sub-region to be processed and to acquire wind farm operation data of the sub-region to be processed. The sub-region to be processed belongs to the wind farm area to be processed, and the wind farm area to be processed is divided into multiple sub-regions to be processed. The first calculation module is used to calculate the wind resource characterization value of the sub-region to be processed based on wind speed data, wind energy density and turbulence intensity, and preset reference wind energy density and its corresponding influence factor, preset reference turbulence intensity and its corresponding influence factor, preset reference wind speed and its corresponding influence factor. The second calculation module is used to calculate the power generation adjustment index based on the power coefficient of the wind power equipment and the total power generation of the sub-region to be processed, and to calculate the energy storage adjustment index based on the remaining battery capacity, energy conversion efficiency and average energy storage flow. The first control module is used to output the first target operating parameters based on the wind resource characterization value and the power generation adjustment index, so as to regulate the wind power equipment in the sub-area to be processed. The second control module is used to output second target operating parameters based on the wind power resource characterization value and the energy storage adjustment index, so as to regulate the energy storage system of the sub-region to be processed.

7. A wind power consumption processing device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the wind power consumption processing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the wind power consumption processing method as described in any one of claims 1 to 5.

Images