A Method and System for Optimal Power Configuration of Wind Turbines Based on Transmission Tower Clusters

Abstract

This application discloses a method and system for optimal wind turbine power configuration based on a network of transmission towers. The method includes: determining a first node and a second node among multiple transmission tower nodes corresponding to the network of transmission towers; determining the power consumption of the UAV for transportation based on the power consumption coefficient of the UAV and the distance between the first and second nodes; determining the power supply and demand balance constraints based on the total power generation, the power of the energy storage battery, the power demand of the load, and the power consumption of transportation; determining a wind turbine cost function that matches the power of the wind turbine at the first node, and determining an objective function based on the wind turbine cost function; and performing optimization based on the objective function and the constraints to obtain the optimal wind turbine power configuration scheme corresponding to the first node. The constraints include the power supply and demand balance constraints, which enable the selection of suitable transmission towers for installing wind turbines and optimize the power configuration of the installed wind turbines, so as to improve wind energy utilization efficiency and reduce wind turbine installation costs.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method and system for optimal power configuration of wind turbines based on a group of transmission towers. Background Technology

[0002] Wind energy, as a clean and sustainable energy source, has been widely researched and applied in recent years. However, traditional large-scale wind power systems typically require substantial land resources and may face numerous limitations during construction and maintenance. Therefore, exploring more efficient and flexible ways to utilize wind energy has become a key focus of current research.

[0003] In related technologies, since transmission towers are a crucial component of power transmission networks and are widely distributed across various regions, wind turbines are typically installed on all transmission towers within a selected area for power generation. However, this method has low efficiency in utilizing wind energy and incurs high installation costs for the required wind turbines. Summary of the Invention

[0004] To address the aforementioned technical problems, this application proposes a method and system for optimal wind turbine power configuration based on a network of transmission towers. This method can select suitable transmission towers for installing wind turbines and optimize the power configuration of the installed wind turbines, thereby improving wind energy utilization efficiency and reducing wind turbine installation costs.

[0005] In a first aspect, embodiments of this application provide a method for optimal wind turbine power configuration based on a group of transmission towers, including:

[0006] Among the multiple transmission tower nodes corresponding to the transmission tower group, a first node and a second node are identified. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery comes entirely from the first node.

[0007] Based on the power consumption coefficient of the drone and the distance between the first node and the second node, the power consumption of the drone for transportation is determined.

[0008] Based on the total power generation, the energy of the energy storage battery, the energy demand of the load, and the energy consumed in transportation, the energy supply and demand balance constraints are determined.

[0009] Determine a wind turbine cost function that matches the wind turbine power of the first node, and determine an objective function based on the wind turbine cost function;

[0010] Based on the objective function and constraints, an optimization solution is performed to obtain the optimal power configuration scheme for the wind turbine corresponding to the first node. The constraints include the power supply and demand balance constraint.

[0011] Optionally, determining the transport power consumption of the drone based on the power consumption coefficient corresponding to the drone and the distance between the first node and the second node includes:

[0012] The energy consumption for transportation is determined based on the energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0013] Optionally, the power supply and demand balance constraint is adapted to indicate that the total power generation is greater than or equal to the power consumption, wherein the power consumption is the sum of the power of the energy storage battery, the power demand of the load, and the power consumption of the transportation.

[0014] Optionally, if all the electrical energy used by the second node comes from the energy storage battery, the constraint also includes an energy usage constraint, which is adapted to indicate that the total electrical energy is less than or equal to the electrical energy of the energy storage battery.

[0015] Optionally, the constraint conditions further include a wind turbine power constraint, which is adapted to indicate that the wind turbine power of the first node is less than or equal to a preset maximum wind turbine power.

[0016] Optionally, the first node includes an energy storage device, and the energy storage battery is contained in the energy storage device. The capacity of the energy storage device is determined based on the power consumption curve of the base tower and the power generation curve corresponding to the total power generation, wherein the power consumption curve of the base tower represents the total power consumption of the first node.

[0017] Optionally, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the power consumption curve of the base station.

[0018] Optionally, determining the first node and the second node among the multiple transmission tower nodes corresponding to the transmission tower group includes:

[0019] Determine the regional wind resource information corresponding to each of the multiple transmission tower nodes;

[0020] Among the plurality of transmission tower nodes, the node whose regional wind resource information meets the wind resource requirements is selected as the first node;

[0021] All nodes of the plurality of transmission tower nodes other than the first node are designated as the second node.

[0022] Optionally, the objective function is adapted to indicate that the goal is to minimize the wind turbine cost function.

[0023] Secondly, embodiments of this application provide a wind turbine power optimal configuration system based on a group of transmission towers, including:

[0024] The node determination module is used to determine a first node and a second node among multiple transmission tower nodes corresponding to a group of transmission towers. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery comes entirely from the first node.

[0025] The transportation power consumption determination module is used to determine the transportation power consumption of the UAV based on the power consumption coefficient corresponding to the UAV and the distance between the first node and the second node.

[0026] The power supply and demand balance constraint module is used to determine the power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the power demand of the load, and the power consumption of transportation.

[0027] The objective function determination module is used to determine a wind turbine cost function that matches the wind turbine power of the first node, and to determine an objective function based on the wind turbine cost function;

[0028] The wind turbine power optimal configuration module is used to perform optimization solutions based on the objective function and constraints to obtain the optimal wind turbine power configuration scheme corresponding to the first node, wherein the constraints include the power supply and demand balance constraints.

[0029] In summary, the embodiments of this application have at least the following beneficial effects:

[0030] In this embodiment, a first node and a second node are determined from multiple transmission tower nodes corresponding to a group of transmission towers. Within a preset time period, the total power generation of the first node exceeds its own load demand. The total power generation is determined by the wind turbine power of the first node. At least a portion of the power used by the second node comes from an energy storage battery transported from the first node to the second node by a drone. The energy storage battery's power originates entirely from the first node. The transportation power consumption of the drone is determined based on its corresponding power consumption coefficient and the distance between the first and second nodes. The system considers the total power generation, the energy stored in the battery, the load demand, and the transportation consumption to determine the power supply and demand balance constraints. It then determines a wind turbine cost function that matches the power of the wind turbine at the first node, and determines an objective function based on this function. Finally, based on the objective function and constraints, it performs an optimization solution to obtain the optimal wind turbine power configuration scheme corresponding to the first node. The constraints include the power supply and demand balance constraints, thereby enabling the selection of suitable transmission towers (i.e., the first node) for installing wind turbines and optimizing the power configuration of the installed wind turbines to improve wind energy utilization efficiency while reducing wind turbine installation costs. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the method for optimal wind turbine power configuration based on a group of transmission towers provided in this application.

[0032] Figure 2 This is a schematic diagram of a power transmission tower group provided in an embodiment of this application;

[0033] Figure 3 This is another schematic diagram of the power transmission tower group provided in the embodiments of this application;

[0034] Figure 4 This is a schematic diagram of the distribution of transmission towers and related information, as well as the load distribution, provided in the embodiments of this application.

[0035] Figure 5 This is a schematic diagram of the structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0037] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more. In the description of this application, the term "comprising" and its variations are open-ended, meaning "including but not limited to." The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments."

[0038] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0039] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing specific embodiments only and is not intended to limit the application. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0040] Firstly, see [the following] Figure 1 The diagram shows a flowchart of a method for optimal wind turbine power configuration based on a group of transmission towers, according to an embodiment of this application. The method includes steps S101-S105, as follows:

[0041] S101, among the multiple transmission tower nodes corresponding to the transmission tower group, a first node and a second node are determined. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery all comes from the first node.

[0042] It is easy to understand that a power transmission tower group includes multiple power transmission towers. In this embodiment, the multiple power transmission tower nodes can be represented by abstracting the multiple power transmission towers included in the power transmission tower group into corresponding nodes.

[0043] In some cases, related technologies typically install horizontally-axis wind power generation equipment on transmission towers. This type of equipment usually requires a large installation space and may be affected by wind direction changes during operation. In contrast, the embodiments of this application preferably install vertical-axis wind turbines on transmission tower nodes. Vertical-axis wind turbines have advantages such as compact structure, small footprint, and strong adaptability to wind direction, making them particularly suitable for use in complex terrain conditions. Furthermore, the widespread distribution and high height of transmission towers provide natural advantages for the installation of vertical-axis wind turbines.

[0044] See one example. Figure 2 The first node can be a transmission tower node located in an area rich in wind energy resources (such as a mountaintop) among multiple transmission tower nodes, and the second node can be a transmission tower node located in an area with insufficient wind energy resources (such as a foot of a mountain) among multiple transmission tower nodes.

[0045] In one example, the first node may also be equipped with other power generation equipment besides wind turbines, such as photovoltaic power generation equipment. The second node may not have power generation capabilities (i.e., it may not be equipped with any power generation equipment including wind turbines), or it may not generate electricity autonomously during normal times (e.g., it may have built-in batteries and / or generators for emergency use only), or it may deploy some power generation equipment with smaller power output, without specific limitations here.

[0046] In some cases, see Figure 2 Wind turbine 201 is installed on the first node 202, making full use of the tower height and space resources of the first node. Energy storage battery 203 is transported to the second node by drone 205. Excess energy storage batteries at the mountaintop can then be transported to transmission towers at lower wind speeds to meet the load demand of the area at the foot of the mountain. The area near the second node at the foot of the mountain typically has many high-power loads, such as street lighting systems 209, agricultural irrigation systems 208, and communication base stations 207. Additionally, there may be loads within the second node itself, such as monitoring devices 206. Furthermore, energy storage battery 203 can also power the power inspection drone 204, solving the endurance problem of the drone under traditional inspection methods. In this case, the installation of wind turbines on each transmission tower, the installed power of the wind turbines, and the size of the energy storage capacity can be considered to ensure that the load demand of each transmission tower is met and the total installation cost is minimized.

[0047] In one example, the energy storage battery may be detachably mounted on a first node, which is used to charge the energy storage battery when its real-time total power generation is greater than its real-time power consumption.

[0048] S102, based on the power consumption coefficient of the UAV and the distance between the first node and the second node, determine the power consumption of the UAV for transportation.

[0049] In one example, the specifications of each energy storage battery can be identical, and the number of energy storage batteries transported by a single drone each time can be fixed. In this case, the weight of the energy storage batteries transported by a single drone each time is constant. Thus, the energy consumption coefficient can be designed as a constant, which, multiplied by the aforementioned distance, yields the drone's transport energy consumption. Therefore, in this embodiment, the transport energy consumption can be determined solely based on the energy consumption coefficient and the distance. The transport energy consumption can then be expressed by the following formula:

[0050]

[0051] in, This indicates the electrical energy consumed during transportation (i.e., the drone transporting the energy storage battery from the first node). Transport to the second node (Electricity required) It is the energy consumption coefficient and is designed to be a constant, preferably, , Indicates the first node Transport to the second node The distance.

[0052] Because drones consume energy during the transport of energy storage batteries, this energy consumption usually needs to be replenished at the first node. In other words, drones can be charged directly at the first node (e.g., by deploying drones to guard airports at the first node to automatically charge the drones), or drones can directly use the energy storage batteries they are transporting to charge themselves.

[0053] Here, the power consumption of drones for transportation is not negligible. If it is ignored, the optimal power configuration would be to install wind turbines in areas with abundant wind resources. This would mean that energy storage batteries cannot be used in areas with scarce wind resources and long distances, which is obviously not practical.

[0054] See one example. Figure 3 As can be seen, the energy storage batteries are transported from the first node where the wind turbine is installed to the second node where the wind turbine is not installed. The arrows in the figure indicate the direction of transportation of the energy storage batteries.

[0055] S103, based on the total power generation, the energy of the energy storage battery, the energy demand of the load, and the energy consumed in transportation, determine the energy supply and demand balance constraint.

[0056] In one example, we first define: Indicates the first node, Indicates the second node, Indicates the first node The amount of electricity generated within a preset time period (e.g., one day) (i.e., the total amount of electricity generated). Indicates the first node The load's electrical energy demand within a preset time period (e.g., one day), Indicates starting from the first node Transport to the second node The electrical energy of the energy storage battery, This indicates the electrical energy consumed during transportation (i.e., the drone transporting the energy storage battery from the first node). Transport to the second node (The required electrical energy). Thus, it can be assumed that for the first node... The electricity it generates can not only meet its own load requirements, but the excess electricity can also be transported to other nodes. At this point, the following needs to be satisfied:

[0057]

[0058] For the second node At least a portion of the electrical energy it uses within a preset time period (e.g., one day). Originating from the first node ,Right now:

[0059]

[0060] For example, when at least a portion of the aforementioned electrical energy is all electrical energy, It can represent the second node. Load demand within a preset duration.

[0061] S104, determine a wind turbine cost function that matches the wind turbine power of the first node, and determine an objective function based on the wind turbine cost function.

[0062] In one example, the wind turbine cost function can be used to indicate the cost at the first node for each additional unit of installed power, where the cost may include installation costs and / or wind energy utilization efficiency costs.

[0063] S105, Based on the objective function and constraints, perform optimization to obtain the optimal power configuration scheme for the wind turbine corresponding to the first node, wherein the constraints include the power supply and demand balance constraint.

[0064] In one optional implementation, determining the transport power consumption of the drone based on the power consumption coefficient corresponding to the drone and the distance between the first node and the second node includes:

[0065] The energy consumption for transportation is determined based on the energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0066] In one example, the specifications of each energy storage battery can be different, and / or the number of energy storage batteries transported by a single drone each time can be variable. In this case, the weight of the energy storage batteries transported by a single drone each time is not constant. Therefore, the influence of the weight of the energy storage batteries needs to be considered when determining the energy consumption during transportation. This energy consumption can be expressed by the following formula:

[0067]

[0068] in, To and and Relevant variables, such as It should be noted here that... This is the energy consumption coefficient that takes into account the weight of the energy storage battery. With the above Both represent a constant, but compared to In other words, The values ​​of can be different. express arrive The distance. The calculation depends on the specific drone parameters and the distance from q to k. For example, the maximum flight distance of the drone in one round trip and the maximum number of energy storage batteries that can be carried should be considered to reduce the drone's power consumption.

[0069] In one alternative implementation, the power supply and demand balance constraint is adapted to indicate that the total power generation is greater than or equal to the power consumption, wherein the power consumption is the sum of the power of the energy storage battery, the power demand of the load, and the power consumption of the transportation.

[0070] In an alternative implementation, where all electrical energy used by the second node comes from the energy storage battery, the constraint further includes an energy usage constraint adapted to indicate that all electrical energy is less than or equal to the energy of the energy storage battery.

[0071] In one alternative implementation, the constraint further includes a wind turbine power constraint, which is adapted to indicate that the wind turbine power of the first node is less than or equal to a preset maximum wind turbine power.

[0072] In this embodiment, considering that excessively powerful or heavy wind turbines can damage transmission towers, the power of the wind turbines installed on each transmission tower can have a maximum value, denoted as . ,Right now:

[0073]

[0074] for The power of the fan installed on it.

[0075] In one optional implementation, the first node includes an energy storage device, the energy storage battery being contained within the energy storage device, the capacity of the energy storage device being determined based on the power consumption curve of the base tower and the power generation curve corresponding to the total power generation, wherein the power consumption curve of the base tower characterizes the total power consumption of the first node.

[0076] It should be noted that the energy storage battery in this embodiment is included in the energy storage device. That is, the energy storage battery is usually detachably installed on the first node and charged by the first node. When the drone transports the energy storage battery, it can bring back the energy storage battery that was transported to the second node each time it returns after the transport is completed. Therefore, the number of energy storage batteries owned by the first node can be regarded as dynamically balanced as a whole.

[0077] It is understandable that energy output in wind power systems is typically volatile. Since variations in wind speed can lead to unstable power generation, directly using wind energy for power supply may affect system reliability. Therefore, energy storage devices are particularly important. By configuring energy storage devices, electricity can be effectively stored and released, smoothing power output to a certain extent and ensuring power supply stability. Furthermore, various power loads may exist around transmission towers, such as monitoring equipment, communication base stations, and agricultural irrigation facilities. These loads typically have different power consumption characteristics and may be geographically dispersed. Therefore, this embodiment utilizes energy storage devices to achieve effective power distribution and management, which is key to improving the overall system efficiency.

[0078] In one alternative implementation, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the power consumption curve of the base station.

[0079] In this embodiment, due to the volatility of wind energy and the diversity of load demand, energy storage capacity can be configured for transmission towers to meet the time mismatch between the power consumption of the tower load and the power generation of the wind turbine.

[0080] In one example, the capacity of the energy storage device must satisfy the following formula:

[0081]

[0082] in, Indicates the capacity of the energy storage device. and These represent the power output of the power generation curve and the power consumption curve of the tower at time t, respectively. You can set a preset duration.

[0083] For the second node, such as a second node that does not have power generation capacity or does not generate power autonomously during normal times, it can also include a second energy storage device. In this case, the capacity of the second energy storage device... The following formula can be satisfied:

[0084]

[0085] Where at least a portion of the aforementioned electrical energy constitutes all electrical energy, It can represent the second node. Load demand within a preset duration.

[0086] Here, since the second node includes a second energy storage device, the energy storage battery can also be installed on the drone. In this case, the drone can charge the energy storage battery at the first node, and after charging is complete, transport the energy storage battery to the second node to charge the second energy storage device. Thus, the electrical energy of the energy storage battery can refer to all the electrical energy that the second energy storage device can obtain from the energy storage battery.

[0087] In one optional implementation, determining the first node and the second node among the multiple transmission tower nodes corresponding to the transmission tower group includes:

[0088] Determine the regional wind resource information corresponding to each of the multiple transmission tower nodes;

[0089] Among the plurality of transmission tower nodes, the node whose regional wind resource information meets the wind resource requirements is selected as the first node;

[0090] All nodes of the plurality of transmission tower nodes other than the first node are designated as the second node.

[0091] In one example, the wind resource requirement may include regional wind resource information indicating that the wind resource assessment value of the region is higher than a corresponding threshold, wherein the wind resource assessment value may be calculated from average wind speed, average wind power density and / or average wind frequency distribution, etc.

[0092] In one alternative implementation, the objective function is adapted to indicate that the goal is to minimize the wind turbine cost function.

[0093] In one example, assume the cost of the wind turbine is... 10,000 yuan / kWh, and assuming it only applies to the first node. If wind turbines are installed on the transmission tower group, then the total cost of the wind turbines (i.e., the wind turbine cost function) is:

[0094]

[0095] in, for The power of the fan installed on top, Let the cost be the cost of the wind turbine (e.g., installation cost). Thus, the objective function can be expressed as:

[0096] Based on the above-mentioned embodiments, the formulaic expression for the optimization solution can be as follows:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] in, express The duration of power generation (e.g., hours) within a preset time period (e.g., average daily).

[0104] In the relevant embodiments of this application, drones can be used to allocate batteries among various transmission towers, thereby distributing electrical energy. This fully utilizes the wind resources of each tower and minimizes the overall wind turbine cost of the tower group. Furthermore, to eliminate the time mismatch between load power consumption and wind turbine power generation, energy storage devices are also configured, enhancing practicality.

[0105] The following specific embodiments are provided to further illustrate the technical effects of the relevant embodiments of this application.

[0106] Wind turbines and energy storage devices are installed on a portion of 20 transmission towers. The goal is to minimize the cost of the wind turbines for the entire tower group while meeting load demand. Assume the following: transmission tower distribution (the towers with installed wind turbines have power generation capacity; additionally, transmission towers also have load consumption, such as monitoring loads), load distribution, and annual generating hours of the wind turbines on the transmission towers. Figure 4 As shown, Figure 4 The horizontal axis represents the lateral distance x / km, and the vertical axis represents the vertical distance y / km.

[0107] according to Figure 4 For transmission towers where wind turbines are installed, the average daily generating hours of the wind turbines are:

[0108] ,

[0109] in, It's not hard to understand. Figure 4 The description refers to the annual average number of hours the wind turbine generates electricity. The daily average number of hours the wind turbine generates electricity is obtained by dividing the annual average number of hours the wind turbine generates electricity by 365 days and then rounding to two decimal places.

[0110] by express arrive The distance is expressed in kilometers.

[0111] Assume the distance between transmission tower A and transmission tower B is... The drone transports energy storage batteries from A to B, and can transport a maximum of [number] batteries at a time. The amount of electricity consumed by the drone during transport between A and B is as follows: Furthermore, the depleted batteries need to be transported back. Therefore, the total electrical energy consumed by the drone for transporting A and B is:

[0112]

[0113] in, This is the maximum flight distance of the drone with full load. Let be the drone's battery capacity. Then, the corresponding energy consumption coefficient for the drone is:

[0114]

[0115] If a DJI FC30 drone is used, the relevant parameters can be found in Table 1. Regarding the distance between transmission tower A and transmission tower B... For ranges less than 4km, the drone uses a single-battery flight mode. , , For distance For ranges exceeding 4km, the drone uses a dual-battery flight mode. , , Therefore, the energy consumption coefficient in this embodiment is:

[0116]

[0117] Furthermore, assume that the distance between adjacent towers (e.g., tower 1 and tower 2, tower 5 and tower 6) is 0.5 km. The distance between each tower can be represented by the following distance matrix. The matrix represents the distance from the first node to the second node, the energy storage power station t1 / t2, and the agricultural pump station n1 / n2 / n3 / n4.

[0118]

[0119] Based on the obtained distance matrix, the matrix of energy consumption coefficients can be derived as follows:

[0120]

[0121] Next, we determine the load levels for the agricultural pumping station and the power transmission towers. Let's assume the agricultural pumping station... Let the load demand of the transmission tower where the wind turbine is installed be:

[0122] ;

[0123] The load requirement for transmission towers without wind turbines is:

[0124] ;

[0125] Assume the maximum generating capacity of a single transmission tower with a wind turbine installed is .

[0126] Based on the above calculations and given parameters, the optimal configuration scheme of wind turbine power for 20 transmission towers can be obtained by using integer programming to solve the optimization problem, as shown in Table 1.

[0127] Table 1

[0128]

[0129] For energy storage configurations on transmission towers, the general approach is to use energy storage devices to supplement the electricity required by the load when the wind turbine's power generation is too low, or to store the electricity that the load cannot consume when the wind turbine's power generation is too high, thus eliminating the time mismatch between wind turbine power generation and load power consumption. The following types of loads can be considered for transmission towers:

[0130] 1) Monitoring loads of transmission towers: These loads typically have a power of 5KW and operate 24 hours a day;

[0131] 2) Highway lighting and advertising loads: These loads typically have a power rating of 2KW and operate for 12 hours.

[0132] 3) Power supply calculation type of load for transmission towers: This type of load generally has a power of 2KW and operates 24 hours a day;

[0133] 4) Power line inspection drone load: Refer to DJI FC30, power is 2KW.

[0134] Assuming that the load types for transmission towers 1 through 20 are as follows: the load on transmission tower 1 is for inspection drones; the load on transmission tower 2 is for monitoring; the loads on transmission towers 4, 5, 13, 17, and 19 are for power calculation; and the loads on transmission towers 6, 7, 8, 9, 12, 15, 16, 18, and 20 are for highway lighting. Following the formulas in the above embodiments related to the capacity of the energy storage device, the capacity configuration of the energy storage device for each transmission tower can be obtained as shown in Table 2.

[0135] Table 2

[0136]

[0137] In conjunction with the above-described embodiments of this application, this application also possesses at least one of the following advantages:

[0138] (1) Due to the use of vertical axis wind turbines, they can capture wind energy from any direction without needing to adjust to the wind direction. Secondly, the blade and rotor layout of vertical axis wind turbines gives them better wind resistance. They can better cope with changes in wind speed and direction, reducing oscillations and vibrations when affected by strong winds. This wind resistance can improve the reliability and stability of the system. In addition, compared with horizontal axis wind turbines, vertical axis wind turbines have lower operating noise and simpler structure and components, making them easier to maintain and repair. The layout of blades and mechanical parts makes inspection and replacement more convenient, reducing maintenance costs and time.

[0139] (2) Integrating vertical axis wind turbines into transmission towers is beneficial because these towers are typically located in vast areas and are often quite tall, making them underutilized. Integrating vertical axis wind turbines into transmission towers allows for full utilization of these spatial resources, maximizing land use efficiency and avoiding the occupation of additional land. Secondly, transmission towers are usually located in open areas with good wind energy resources. Installing vertical axis wind turbines on transmission towers allows for full utilization of these wind energy resources, improving wind energy utilization efficiency and increasing power output. Furthermore, transmission towers are an important component of power transmission, possessing a stable structure and reliable infrastructure. Integrating vertical axis wind turbines into transmission towers allows for the use of the tower structure as support, improving system stability and reliability. Finally, traditional wind turbines typically require the construction of independent grid connection systems, including cables and substations. Integrating vertical axis wind turbines into transmission towers allows for direct connection of the generator's output to the transmission line, simplifying grid layout and reducing grid construction costs and workload.

[0140] (3) Based on factors such as the location and height of the transmission towers, the distribution of surrounding wind energy resources, and power load demand, this application uses mathematical modeling and optimization algorithms to determine which towers to install wind turbines on and the optimal parameters (such as power rating) for each turbine. This not only makes full use of existing infrastructure but also avoids over-investment. In areas with abundant wind energy resources, increasing the number of wind turbines and combining them with energy storage devices can maximize energy capture and storage capacity; while in areas with high load demand but limited wind energy resources, flexible power allocation can be achieved by using drones to transport batteries. This dynamic optimization method not only improves energy utilization efficiency but also reduces power transmission losses. As a transportation tool for mobile energy storage units, drones can quickly respond to changes in power demand in different areas and achieve dynamic balance of power resources. Compared with traditional fixed energy storage systems, this method has higher flexibility and scalability, and is particularly suitable for scenarios with uneven distribution of wind energy resources or large fluctuations in load demand.

[0141] (4) By making full use of existing transmission tower infrastructure and adopting distributed generation and energy storage methods, this application reduces the need for large-scale new facilities. In addition, by establishing a mathematical model to construct an optimization algorithm, a wind turbine installation strategy based on transmission tower groups is determined to minimize the total wind turbine installation cost.

[0142] Secondly, correspondingly, the embodiments of this application also provide a wind turbine power optimal configuration system based on a group of transmission towers, which can realize all the processes of the wind turbine power optimal configuration method based on a group of transmission towers provided in the above embodiments.

[0143] This application provides a wind turbine power optimization configuration system based on a group of transmission towers, including:

[0144] The node determination module is used to determine a first node and a second node among multiple transmission tower nodes corresponding to a group of transmission towers. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery comes entirely from the first node.

[0145] The transportation power consumption determination module is used to determine the transportation power consumption of the UAV based on the power consumption coefficient corresponding to the UAV and the distance between the first node and the second node.

[0146] The power supply and demand balance constraint module is used to determine the power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the power demand of the load, and the power consumption of transportation.

[0147] The objective function determination module is used to determine a wind turbine cost function that matches the wind turbine power of the first node, and to determine an objective function based on the wind turbine cost function;

[0148] The wind turbine power optimal configuration module is used to perform optimization solutions based on the objective function and constraints to obtain the optimal wind turbine power configuration scheme corresponding to the first node, wherein the constraints include the power supply and demand balance constraints.

[0149] In one optional implementation, determining the transport power consumption of the drone based on the power consumption coefficient corresponding to the drone and the distance between the first node and the second node includes:

[0150] The energy consumption for transportation is determined based on the energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0151] In one alternative implementation, the power supply and demand balance constraint is adapted to indicate that the total power generation is greater than or equal to the power consumption, wherein the power consumption is the sum of the power of the energy storage battery, the power demand of the load, and the power consumption of the transportation.

[0152] In an alternative implementation, where all electrical energy used by the second node comes from the energy storage battery, the constraint further includes an energy usage constraint adapted to indicate that all electrical energy is less than or equal to the energy of the energy storage battery.

[0153] In one alternative implementation, the constraint further includes a wind turbine power constraint, which is adapted to indicate that the wind turbine power of the first node is less than or equal to a preset maximum wind turbine power.

[0154] In one optional implementation, the first node includes an energy storage device, the energy storage battery being contained within the energy storage device, the capacity of the energy storage device being determined based on the power consumption curve of the base tower and the power generation curve corresponding to the total power generation, wherein the power consumption curve of the base tower characterizes the total power consumption of the first node.

[0155] In one alternative implementation, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the power consumption curve of the base station.

[0156] In one optional implementation, determining the first node and the second node among the multiple transmission tower nodes corresponding to the transmission tower group includes:

[0157] Determine the regional wind resource information corresponding to each of the multiple transmission tower nodes;

[0158] Among the plurality of transmission tower nodes, the node whose regional wind resource information meets the wind resource requirements is selected as the first node;

[0159] All nodes of the plurality of transmission tower nodes other than the first node are designated as the second node.

[0160] In one alternative implementation, the objective function is adapted to indicate that the goal is to minimize the wind turbine cost function.

[0161] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above-mentioned embodiments.

[0162] Fourthly, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described in any of the above-described embodiments.

[0163] Fifthly, embodiments of this application provide a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the preceding claims.

[0164] See Figure 5The computer device in this embodiment includes a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501, such as a wind turbine power optimization configuration program based on a transmission tower group. When the processor 501 executes the computer program, it implements the steps in the various embodiments of the wind turbine power optimization configuration method based on a transmission tower group described above, for example... Figure 1 The steps S101-S105 are shown.

[0165] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 502 and executed by the processor 501 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0166] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0167] The processor 501 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 501 can be any conventional processor. The processor 501 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0168] The memory 502 can be used to store the computer programs and / or modules. The processor 501 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 502 and calling the data stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0169] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 501, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0170] In summary, the embodiments of this application have at least the following beneficial effects:

[0171] In this embodiment, a first node and a second node are determined from multiple transmission tower nodes corresponding to a group of transmission towers. Within a preset time period, the total power generation of the first node exceeds its own load demand. The total power generation is determined by the wind turbine power of the first node. At least a portion of the power used by the second node comes from an energy storage battery transported from the first node to the second node by a drone. The energy storage battery's power originates entirely from the first node. The transportation power consumption of the drone is determined based on its corresponding power consumption coefficient and the distance between the first and second nodes. The system considers the total power generation, the energy stored in the battery, the load demand, and the transportation consumption to determine the power supply and demand balance constraints. It then determines a wind turbine cost function that matches the power of the wind turbine at the first node, and determines an objective function based on this function. Finally, based on the objective function and constraints, it performs an optimization solution to obtain the optimal wind turbine power configuration scheme corresponding to the first node. The constraints include the power supply and demand balance constraints, thereby enabling the selection of suitable transmission towers (i.e., the first node) for installing wind turbines and optimizing the power configuration of the installed wind turbines to improve wind energy utilization efficiency while reducing wind turbine installation costs.

[0172] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware platforms, or it can be implemented entirely by hardware. Based on this understanding, all or part of the technical solutions of this application that contribute to the background technology can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0173] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for optimal power allocation of wind turbines based on transmission tower groups, characterized in that, include: Among the multiple transmission tower nodes corresponding to the transmission tower group, a first node and a second node are identified. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery comes entirely from the first node. Based on the power consumption coefficient of the drone and the distance between the first node and the second node, the power consumption of the drone for transportation is determined. Based on the total power generation, the energy of the energy storage battery, the energy demand of the load, and the energy consumed in transportation, the energy supply and demand balance constraints are determined. Determine a wind turbine cost function that matches the wind turbine power of the first node, and determine an objective function based on the wind turbine cost function; Based on the objective function and constraints, an optimization solution is performed to obtain the optimal power configuration scheme for the wind turbine corresponding to the first node, wherein the constraints include the power supply and demand balance constraint. The first node includes an energy storage device, and the energy storage battery is contained in the energy storage device. The capacity of the energy storage device is determined based on the power consumption curve of the base tower and the power generation curve corresponding to the total power generation. The power consumption curve of the base tower represents the total power consumption of the first node.

2. The method according to claim 1, characterized in that, The step of determining the transport power consumption of the drone based on the power consumption coefficient corresponding to the drone and the distance between the first node and the second node includes: The energy consumption for transportation is determined based on the energy consumption coefficient, the distance, and the weight of the energy storage battery.

3. The method according to claim 1, characterized in that, The power supply and demand balance constraint is adapted to indicate that the total power generation is greater than or equal to the power consumption, wherein the power consumption is the sum of the power of the energy storage battery, the power demand of the load, and the power consumption of the transportation.

4. The method according to claim 1, characterized in that, When all the electrical energy used by the second node comes from the energy storage battery, the constraint also includes an energy usage constraint, which is adapted to indicate that the total electrical energy is less than or equal to the energy of the energy storage battery.

5. The method according to claim 1, characterized in that, The constraints also include a wind turbine power constraint, which is adapted to indicate that the wind turbine power of the first node is less than or equal to a preset maximum wind turbine power.

6. The method according to claim 1, characterized in that, The capacity of the energy storage device is determined by integrating the difference between the power generation curve and the power consumption curve of the tower.

7. The method according to claim 1, characterized in that, The determination of the first node and the second node among the multiple transmission tower nodes corresponding to the transmission tower group includes: Determine the regional wind resource information corresponding to each of the multiple transmission tower nodes; Among the plurality of transmission tower nodes, the node whose regional wind resource information meets the wind resource requirements is selected as the first node; All nodes of the plurality of transmission tower nodes other than the first node are designated as the second node.

8. The method according to claim 1, characterized in that, The objective function is adapted to indicate that the goal is to minimize the wind turbine cost function.

9. A wind turbine power optimal configuration system based on transmission tower groups, characterized in that, include: The node determination module is used to determine a first node and a second node among multiple transmission tower nodes corresponding to a group of transmission towers. Within a preset time period, the total power generation of the first node is greater than its own load demand. The total power generation is determined by the wind turbine power of the first node. At least part of the power used by the second node comes from the energy storage battery transported from the first node to the second node by a drone. The power of the energy storage battery comes entirely from the first node. The transportation power consumption determination module is used to determine the transportation power consumption of the UAV based on the power consumption coefficient corresponding to the UAV and the distance between the first node and the second node. The power supply and demand balance constraint module is used to determine the power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the power demand of the load, and the power consumption of transportation. The objective function determination module is used to determine a wind turbine cost function that matches the wind turbine power of the first node, and to determine an objective function based on the wind turbine cost function; The wind turbine power optimal configuration module is used to perform optimization based on the objective function and constraints to obtain the optimal wind turbine power configuration scheme corresponding to the first node, wherein the constraints include the power supply and demand balance constraints. The first node includes an energy storage device, and the energy storage battery is contained in the energy storage device. The capacity of the energy storage device is determined based on the power consumption curve of the base tower and the power generation curve corresponding to the total power generation. The power consumption curve of the base tower represents the total power consumption of the first node.

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