Fan power optimal configuration method and system based on transmission tower group

Abstract

The invention discloses a fan power optimal configuration method and system based on a transmission tower group. The method comprises the following steps: determining a first node and a second node in a plurality of transmission tower nodes corresponding to the transmission tower group; determining transportation consumed electric energy of the unmanned aerial vehicle based on the electric energy consumption coefficient corresponding to the unmanned aerial vehicle and the distance between the first node and the second node; determining an electric energy supply-demand balance constraint based on the total generating capacity, the electric energy of the energy storage battery, the load demand electric energy and the transportation consumption electric energy; determining a fan cost function matched with the fan power of the first node, and determining a target function based on the fan cost function; on the basis of the target function and constraint conditions, optimization solution is carried out, a fan power optimal configuration scheme corresponding to the first node is obtained, the constraint conditions comprise electric energy supply and demand balance constraint, a proper transmission tower can be selected to be used for installing a fan, power configuration of the installed fan is optimized, the wind energy utilization efficiency is improved, and the power utilization rate of the fan is increased. Meanwhile, the installation cost of the fan is reduced.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method and system for optimally configuring wind turbine power based on a group of transmission towers. Background Art

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

[0003] In the prior art, because transmission towers are a crucial component of the power transmission network and are widely distributed, wind turbines are typically installed on all transmission towers within a selected area for power generation. However, this approach has low wind energy utilization efficiency and the required wind turbine installation costs are high. Summary of the Invention

[0004] In order to solve the above technical problems, the embodiments of the present application propose a method and system for optimal configuration of wind turbine power based on a group of transmission towers, which can select suitable transmission towers for installing wind turbines and optimize the power configuration of the installed wind turbines, so as to improve the efficiency of wind energy utilization and reduce the cost of wind turbine installation.

[0005] In a first aspect, an embodiment of the present application provides a method for optimally configuring wind turbine power based on a group of transmission towers, comprising: A first node and a second node are determined among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than the power demand of its own load, the total power generation being determined by the power of a wind turbine of the first node, and at least a portion of the power used by the second node is derived from an energy storage battery transported from the first node to the second node by a drone, and the power of the energy storage battery is derived entirely from the first node; Determining the transportation 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; Determining a power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the load power demand, and the transportation power consumption; Determining a wind turbine cost function that matches the wind turbine power of the first node, and determining an objective function based on the wind turbine cost function; Based on the objective function and the constraints, an optimization solution is performed to obtain an optimal wind turbine power configuration scheme corresponding to the first node, wherein the constraints include the power supply and demand balance constraint.

[0006] Optionally, determining the transportation 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 electric energy consumed for transportation is determined based on the electric energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0007] Optionally, the power supply and demand balance constraint is suitable for indicating that the total power generation is greater than or equal to the consumed power, and the consumed power is the sum of the power of the energy storage battery, the load demand power and the transportation consumed power.

[0008] Optionally, when all the electric energy used by the second node comes from the energy storage battery, the constraint condition further includes an electric energy usage constraint, and the electric energy usage constraint is suitable for indicating that the total electric energy is less than or equal to the electric energy of the energy storage battery.

[0009] Optionally, the constraint condition further includes a wind turbine power constraint, and the wind turbine power constraint is suitable for indicating that the wind turbine power of the first node is less than or equal to a preset wind turbine maximum power.

[0010] Optionally, the first node includes an energy storage device, the energy storage battery is contained in the energy storage device, and the capacity of the energy storage device is determined based on a base tower power consumption curve and a power generation curve corresponding to the total power generation, wherein the base tower power consumption curve represents the total power consumption of the first node.

[0011] Optionally, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the tower power consumption curve.

[0012] Optionally, determining the first node and the second node from a plurality of transmission tower nodes corresponding to the transmission tower group includes: Determining regional wind resource information corresponding to each of the plurality of transmission tower nodes; Among the multiple transmission tower nodes, a node whose regional wind resource information meets the wind resource requirement is selected as the first node; All nodes among the plurality of transmission tower nodes except the first node are used as the second nodes.

[0013] Optionally, the objective function is adapted to indicate that minimizing the wind turbine cost function is the goal.

[0014] In a second aspect, an embodiment of the present application provides a wind turbine power optimal configuration system based on a transmission tower group, comprising: a node determination module, configured to determine a first node and a second node among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than the power energy required by its own load, the total power generation being determined by the power of a wind turbine of the first node, and at least a portion of the power used by the second node is derived from an energy storage battery transported from the first node to the second node by a drone, the power of the energy storage battery being derived entirely from the first node; a transport power consumption determination module, configured to determine the transport power consumption of the drone based on a power consumption coefficient corresponding to the drone and a distance between the first node and the second node; an electric energy supply and demand balance constraint module, configured to determine an electric energy supply and demand balance constraint based on the total power generation, the electric energy of the energy storage battery, the electric energy required by the load, and the electric energy consumed by transportation; an objective function determination module, configured to 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; The wind turbine power optimal configuration module is used to perform an optimization solution based on the objective function and the constraint conditions to obtain the wind turbine power optimal configuration scheme corresponding to the first node, wherein the constraint conditions include the power supply and demand balance constraint.

[0015] In summary, the embodiments of the present application have at least the following beneficial effects: According to an embodiment of the present application, a first node and a second node are determined from a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, the total power generation of the first node is greater than the power demanded by its own load, 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 an energy storage battery transported from the first node to the second node by a drone, and the power of the energy storage battery all comes from the first node; based on the power consumption coefficient corresponding to 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 The total power generation, the electric energy of the energy storage battery, the load demand electric energy and the transportation consumption electric energy are used to determine the power supply and demand balance constraint; a wind turbine cost function that matches the wind turbine power of the first node is determined, and an objective function is determined based on the wind turbine cost function; an optimization solution is performed based on the objective function and the 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 constraint, so that a suitable transmission tower (i.e., the first node) can be selected for installing the wind turbine and the power configuration of the installed wind turbine can be optimized, so as to improve the wind energy utilization efficiency and reduce the wind turbine installation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for optimally configuring wind turbine power based on a group of transmission towers provided in an embodiment of the present application; Figure 2 This is a schematic diagram of a transmission tower group provided in an embodiment of the present application; Figure 3 This is another schematic diagram of a transmission tower group provided in an embodiment of the present application; Figure 4 This is a schematic diagram of the distribution of transmission towers and related information and load distribution provided by the embodiments of the present application; Figure 5 It is a structural diagram of the computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, "multiple" means two or more. In the description of this application, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "according to" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments".

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0020] 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 meanings as those commonly understood by those skilled in the art. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit this application. Those of ordinary skill in the art will understand the specific meanings of the above terms in this application in specific circumstances.

[0021] First, see Figure 1 , shows a flow chart of a method for optimally configuring wind turbine power based on a group of transmission towers provided by an embodiment of the present application, the method comprising steps S101-S105, specifically as follows: S101, determining a first node and a second node among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than its own load demand power, the total power generation is determined by the wind turbine power of the first node, and at least part 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, and the power of the energy storage battery all comes from the first node.

[0022] It is easy to understand that the transmission tower group includes multiple transmission towers. The multiple transmission tower nodes in this embodiment can be represented by abstracting the multiple transmission towers included in the transmission tower group into corresponding nodes.

[0023] In some cases, related art typically installs horizontal-axis wind turbines on transmission towers. This type of equipment typically requires a large installation space and may be affected by changes in wind direction during operation. In contrast, in the embodiments of the present application, vertical-axis wind turbines are preferably installed at 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. In addition, the widespread distribution and high height of transmission towers provide natural advantages for the installation of vertical-axis wind turbines.

[0024] In one example, see Figure 2 The first node may be a transmission tower node located in an area with abundant wind energy resources (such as a mountain top) among multiple transmission tower nodes, and the second node may be a transmission tower node located in an area with insufficient wind energy resources (such as a mountain foot) among multiple transmission tower nodes.

[0025] 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., not be equipped with any power generation equipment including wind turbines), or may not generate power autonomously on a daily basis (e.g., may have built-in batteries and / or generators for emergency use only), or may be deployed with some power generation equipment with lower power generation capacity, which is not specifically limited here.

[0026] In some cases, see Figure 2 , the wind turbine 201 is installed on the first node 202, which can fully utilize the tower height and space resources of the first node. The energy storage battery 203 is transported to the second node by the drone 205. At this time, the excess energy storage batteries at the top of the mountain can be transported to the transmission tower with low wind speed at the foot of the mountain to meet the load demand of the foot of the mountain. There are usually many high-power loads near the second node at the foot of the mountain, such as street lighting systems 209, agricultural irrigation systems 208 and communication base stations 207. In addition, there may also be the second node's own load, such as monitoring devices 206. In addition, the energy storage battery 203 can also be used to power the power inspection drone 204 to solve the endurance problem of the power inspection drone 204 under traditional inspection methods. In this case, it is possible to consider whether each transmission tower is equipped with a wind turbine, the size of the wind turbine installation power, and the size of the energy storage capacity configuration, so that the load demand of each transmission tower is met and the total installation cost can be minimized.

[0027] In one example, the energy storage battery may be detachably mounted on the first node, and the first node is configured to charge the energy storage battery when its real-time total generated power is greater than its real-time power consumption.

[0028] S102: Determine the transportation 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.

[0029] In one example, the specifications of the energy storage batteries can be the same, 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. In this way, the power consumption coefficient can be designed as a constant, and the power consumption coefficient multiplied by the above distance can be used to obtain the power consumption of the drone during transportation. Therefore, in this embodiment, the power consumption of transportation can be determined based solely on the power consumption coefficient and the distance. The power consumption of transportation can be expressed as the following formula: in, Indicates the energy consumed by transportation (i.e. the drone transfers the energy storage battery from the first node Transport to the second node The required electrical energy is the power consumption coefficient and is designed to be a constant, preferably, , Indicates the first node Transport to the second node distance.

[0030] Since the drone will consume a certain amount of energy in the process of transporting the energy storage battery, that is, the transportation of the drone consumes electrical energy, and the electrical energy consumed in transportation usually needs to be replenished at the first node, that is, the drone can be charged directly at the first node (for example, deploying a drone at the first node to guard the airport, etc., to automatically charge the drone), or the drone can also directly use the energy storage battery it transports to charge itself.

[0031] Here, the electricity consumption of drone transportation cannot be ignored. If it is ignored, the optimal power configuration plan is to install wind turbines in areas with rich wind resources as much as possible. This will lead to the inability to use energy storage batteries in areas with poor wind resources and long distances, which is obviously unrealistic.

[0032] In one example, see Figure 3 It can be seen that the energy storage battery is transported from the first node where the wind turbine is installed to the second node where the wind turbine is not installed. The arrow in the figure indicates the transportation direction of the energy storage battery.

[0033] S103 , determining a power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the load demand power, and the transportation consumption power.

[0034] In one example, first define: represents the first node, represents the second node, Indicates the first node The amount of electricity generated (i.e. total electricity generated) within a preset period of time (e.g. one day), Indicates the first node The load demand for electrical energy within a preset period of time (e.g. one day), From the first node Transport to the second node The energy of the energy storage battery, Indicates the energy consumed by transportation (i.e. the drone transfers the energy storage battery from the first node Transport to the second node 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 the node , then you need to meet the following requirements: For the second node , at least part of the electricity used within a preset period of time (such as one day) From the first node ,Right now: Exemplarily, when the at least part of the electrical energy is all the electrical energy, Can represent the second node Load demand over a preset period of time.

[0035] 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.

[0036] In an example, the wind turbine cost function may be used to indicate, at a first node, the cost of each additional installed unit power amount for its wind turbine power, wherein the cost may include installation cost and / or wind energy utilization efficiency cost.

[0037] S105 , performing an optimization solution based on the objective function and the constraint conditions to obtain an optimal wind turbine power configuration solution corresponding to the first node, wherein the constraint conditions include the power supply and demand balance constraint.

[0038] In an optional embodiment, determining the transportation 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 electric energy consumed for transportation is determined based on the electric energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0039] In one example, the specifications of the energy storage batteries may be different, and / or the number of energy storage batteries transported by a single drone may not be fixed. In this case, the weight of the energy storage batteries transported by a single drone is not constant. Therefore, the weight of the energy storage batteries needs to be considered when determining the transport energy consumption. In this case, the transport energy consumption can be expressed as the following formula: in, For and Related variables, such as , it should be noted here that the It is the power consumption coefficient considering the weight of the energy storage battery. With the above They all represent a constant, compared to In terms of The value of can be different. express arrive distance. The calculation of is related to the specific UAV parameters and the distance from q to k. For example, the maximum round-trip flight distance of the UAV and the maximum number of energy storage batteries that can be carried should be considered to reduce the UAV's power consumption.

[0040] In an optional embodiment, the electricity supply and demand balance constraint is suitable for indicating that the total power generation is greater than or equal to the consumed electricity, and the consumed electricity is the sum of the electricity of the energy storage battery, the load demand electricity and the transportation consumed electricity.

[0041] In an optional embodiment, when all the electric energy used by the second node comes from the energy storage battery, the constraint condition also includes an electric energy usage constraint, and the electric energy usage constraint is suitable for indicating that the total electric energy is less than or equal to the electric energy of the energy storage battery.

[0042] In an optional implementation, the constraint condition further includes a wind turbine power constraint, where the wind turbine power constraint 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.

[0043] In this embodiment, considering that a wind turbine with excessive power or weight may cause damage to the transmission tower, the power of the wind turbine installed on each transmission tower can have a maximum value, which is denoted as ,Right now: for The power of the fan installed.

[0044] In an optional embodiment, the first node includes an energy storage device, the energy storage battery is contained in the energy storage device, and the capacity of the energy storage device is determined based on a base tower power consumption curve and a power generation curve corresponding to the total power generation, wherein the base tower power consumption curve represents the total power consumption of the first node.

[0045] 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 detachably installed on the first node on a daily basis and is charged by the first node. When the drone transports the energy storage battery, it can bring back the energy storage battery last transported to the second node from the second node each time it returns after transportation. Therefore, the number of energy storage batteries owned by the first node can be regarded as dynamically balanced as a whole.

[0046] It is understandable that in a wind power generation system, energy output is usually volatile. Since changes in wind speed may lead to instability in power generation, directly using wind energy for power supply may affect the reliability of the system. For this reason, the role of energy storage devices is particularly important. By configuring energy storage devices, it is possible to achieve effective storage and release of electricity, smooth the power output to a certain extent, and ensure the stability of power supply. In addition, there may be a variety of power load demands around the transmission towers, such as monitoring equipment, communication base stations, agricultural irrigation facilities, etc. These loads usually have different power consumption characteristics and may be distributed more dispersedly. Therefore, this embodiment can achieve effective distribution and management of electricity by deploying energy storage devices, which is the key to improving the overall efficiency of the system.

[0047] In an optional embodiment, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the tower power consumption curve.

[0048] In this embodiment, due to the volatility of wind energy and the diversity of load demands, energy storage capacity can be configured for the transmission tower to meet the time mismatch between tower load power consumption and wind turbine power generation.

[0049] In one example, the capacity of the energy storage device must satisfy the following formula: in, Indicates the capacity of the energy storage device, and Respectively represent the power of the power generation curve and the tower power consumption curve at time t, The duration can be preset.

[0050] As for the second node, for example, a second node that does not have power generation capability or does not generate power autonomously in daily life, it can also include a second energy storage device. In this case, the capacity of the second energy storage device is The following formula can be satisfied: Wherein, when the above-mentioned at least part of the electric energy is all the electric energy, Can represent the second node Load demand over a preset period of time.

[0051] 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, transport the energy storage battery to the second node to charge the second energy storage device. In this way, the electric energy of the energy storage battery can refer to all the electric energy that the second energy storage device can obtain from the energy storage battery.

[0052] In an optional embodiment, determining the first node and the second node from the plurality of transmission tower nodes corresponding to the transmission tower group includes: Determining regional wind resource information corresponding to each of the plurality of transmission tower nodes; Among the multiple transmission tower nodes, a node whose regional wind resource information meets the wind resource requirement is selected as the first node; All nodes among the plurality of transmission tower nodes except the first node are used as the second nodes.

[0053] 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 by average wind speed, average wind power density and / or average wind frequency distribution, etc.

[0054] In an optional embodiment, the objective function is suitable for indicating that minimizing the wind turbine cost function is the goal.

[0055] In one example, assume the wind turbine cost is Ten thousand yuan / KWh, and assuming that only at the first node If wind turbines are installed on the transmission tower group, the total wind turbine cost (i.e. wind turbine cost function) is: in, for The power of the fan installed on the is the cost of the wind turbine (e.g., the installation cost of the wind turbine). Thus, the objective function can be expressed as: In combination with the above-mentioned related embodiments, the optimization solution can be formulated as follows: in, express The duration (e.g. number of hours) of power generation within a preset time period (e.g. daily average).

[0056] In the relevant embodiments of this application, drones can be used to dispatch batteries between transmission towers to achieve power distribution. This fully utilizes the wind resources of each tower and minimizes the cost of wind turbines for the entire tower cluster. Furthermore, to eliminate the temporal mismatch between load power consumption and wind turbine power generation, energy storage devices are also deployed, enhancing practicality.

[0057] Some specific embodiments are provided below to further illustrate the technical effects of the relevant embodiments of this application.

[0058] Install wind turbines and energy storage devices on a portion of 20 transmission towers to minimize the cost of wind turbines for the tower group while meeting the load demand. Assume that the distribution of transmission towers (the transmission towers where wind turbines are installed have power generation capacity, and the transmission towers also have loads that consume power, such as monitoring loads), load distribution, and the annual power generation hours of wind turbines on transmission towers are as follows: Figure 4 As shown, Figure 4 The horizontal axis represents the horizontal distance x / km, and the vertical axis represents the vertical distance y / km.

[0059] according to Figure 4 , for the transmission towers with wind turbines installed, the average daily generating hours of the wind turbines are: , in, It is not difficult to understand. Figure 4 The number described in the figure is the average annual generating hours of the wind turbine. The average daily generating hours of the wind turbine here are the average annual generating hours of the wind turbine divided by 365 days, with two decimal places retained.

[0060] by express arrive The distance in kilometers.

[0061] Assume that the distance between transmission tower A and transmission tower B is , the drone transports the energy storage battery from A to B, and can transport up to In addition, the exhausted batteries need to be transported back. Therefore, for A and B, the power consumption of the drone transportation is: in, The maximum flight distance of the drone when fully loaded. is the battery capacity of the drone. Then, the corresponding power consumption coefficient of the drone is: If a DJI FC30 drone is used, the relevant parameters can be found according to Table 1. For distances less than 4km, the drone uses a single battery flight mode. , , ; For distance For distances exceeding 4km, the drone uses dual battery flight mode. , , Therefore, the power consumption coefficient in this embodiment is: Furthermore, assume that the distance between adjacent towers (e.g., transmission tower 1 and transmission tower 2, transmission tower 5 and transmission tower 6) is 0.5 km. The distance between each transmission tower can be expressed as follows: Each element in the matrix represents the distance from the first node to the second node, the energy storage station t1 / t2, and the agricultural pumping station n1 / n2 / n3 / n4.

[0062] According to the obtained distance matrix, the matrix of power consumption coefficient can be obtained as follows: Next, set the load size of the agricultural pump station and the transmission tower. Assume that the agricultural pump station Assume that the load power demand of the transmission tower where the wind turbine is installed is: ; The load demand of the transmission tower without wind turbines is: ; Assume that the maximum power generation capacity of a single transmission tower with wind turbine installed is .

[0063] Based on the above calculations and given parameters, the integer programming method is used for optimization and the optimal configuration of wind turbine power for 20 transmission towers is obtained as shown in Table 1.

[0064] Table 1 Regarding the energy storage configuration of transmission towers, the general idea is that the energy storage device should supplement the power required by the load when the wind turbine power generation is too low, or store the power that the load cannot consume when the wind turbine power generation is too high, so as to eliminate the time mismatch between wind turbine power generation and load power consumption. For transmission tower loads, the following loads can be considered: 1) Transmission tower monitoring load: This type of load has a power of 5KW and works 24 hours a day; 2) Highway lighting and advertising loads: The power of such loads is generally 2KW and they work for 12 hours. 3) Transmission tower power supply calculation load: This type of load power is generally 2KW and works 24 hours a day; 4) Power inspection drone load: refer to DJI FC30, with a power of 2KW.

[0065] Assume that for transmission towers 1 through 20, the load types are: Tower 1's load is classified as an inspection drone load; Tower 2's load is classified as a monitoring load; Towers 4, 5, 13, 17, and 19's load is classified as a power calculation load; and Towers 6, 7, 8, 9, 12, 15, 16, 18, and 20's load is classified as a highway lighting load. Using the formulas from the aforementioned embodiment related to energy storage device capacity, the energy storage device capacity configuration for each transmission tower can be calculated as shown in Table 2: Table 2 In combination with the above-mentioned embodiments of the present application, the present application also has at least one of the following advantages: (1) Due to the use of vertical axis wind turbines, it can capture wind energy from winds of any direction without having to adjust to the direction of the wind. Secondly, the layout of the blades and rotors of vertical axis wind turbines makes them more wind-resistant. They can better cope with changes in wind speed and direction, reducing shock and vibration 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 structures and components, making them easier to maintain and service. The layout of blades and mechanical components makes inspection and replacement more convenient, reducing maintenance costs and time.

[0066] (2) Since vertical axis wind turbines are integrated into transmission towers, transmission towers are usually located in wide areas and are high in height, so they are rarely fully utilized. Integrating vertical axis wind turbines into transmission towers can make full use of these space resources, maximize land utilization benefits, and avoid occupying additional land. Secondly, transmission towers are usually located in open areas, which often have good wind energy resources. By installing vertical axis wind turbines on transmission towers, the wind energy resources in these areas can be fully utilized, wind energy utilization efficiency can be improved, and power production can be increased. In addition, transmission towers are an important part of power transmission, with stable structures and reliable infrastructure. Integrating vertical axis wind turbines into transmission towers can use the tower structure as support to improve the stability and reliability of the system. Finally, traditional wind turbines usually require the construction of an independent grid access system, including cables and substations. Integrating vertical axis wind turbines into transmission towers can directly connect the generator output to the transmission line, simplifying the grid layout and reducing the grid construction cost and engineering workload.

[0067] (3) This application determines which towers to install wind turbines on and the optimal parameters (such as power level) of each wind turbine based on factors such as the location and height of the transmission towers, the distribution of surrounding wind energy resources, and the power load demand, combined with mathematical modeling and optimization algorithms. 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 cooperating with energy storage devices can maximize energy capture and storage capabilities; while in areas with high load demand but limited wind energy resources, flexible power allocation can be achieved by transporting batteries through drones. This dynamic optimization method not only improves energy utilization efficiency but also reduces power transmission losses. As a means of transporting mobile energy storage units, drones can quickly respond to changes in power demand in different regions 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 where wind energy resources are unevenly distributed or load demand fluctuates greatly.

[0068] (4) By fully utilizing the existing transmission tower infrastructure and adopting distributed generation and energy storage, this application reduces the need for large-scale new facilities. In addition, by establishing a mathematical model and constructing an optimization algorithm, a wind turbine installation strategy based on the transmission tower cluster was determined to minimize the total wind turbine installation cost.

[0069] On the second aspect, accordingly, the embodiments of the present application further provide a system for optimally configuring wind turbine power based on a transmission tower group, which can implement all processes of the method for optimally configuring wind turbine power based on a transmission tower group provided in the above embodiments.

[0070] The embodiment of the present application provides a wind turbine power optimal configuration system based on a transmission tower group, including: a node determination module, configured to determine a first node and a second node among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than the power energy required by its own load, the total power generation being determined by the power of a wind turbine of the first node, and at least a portion of the power used by the second node is derived from an energy storage battery transported from the first node to the second node by a drone, the power of the energy storage battery being derived entirely from the first node; a transport power consumption determination module, configured to determine the transport power consumption of the drone based on a power consumption coefficient corresponding to the drone and a distance between the first node and the second node; an electric energy supply and demand balance constraint module, configured to determine an electric energy supply and demand balance constraint based on the total power generation, the electric energy of the energy storage battery, the electric energy required by the load, and the electric energy consumed by transportation; an objective function determination module, configured to 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; The wind turbine power optimal configuration module is used to perform an optimization solution based on the objective function and the constraint conditions to obtain the wind turbine power optimal configuration scheme corresponding to the first node, wherein the constraint conditions include the power supply and demand balance constraint.

[0071] In an optional embodiment, determining the transportation 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 electric energy consumed for transportation is determined based on the electric energy consumption coefficient, the distance, and the weight of the energy storage battery.

[0072] In an optional embodiment, the electricity supply and demand balance constraint is suitable for indicating that the total power generation is greater than or equal to the consumed electricity, and the consumed electricity is the sum of the electricity of the energy storage battery, the load demand electricity and the transportation consumed electricity.

[0073] In an optional embodiment, when all the electric energy used by the second node comes from the energy storage battery, the constraint condition also includes an electric energy usage constraint, and the electric energy usage constraint is suitable for indicating that the total electric energy is less than or equal to the electric energy of the energy storage battery.

[0074] In an optional implementation, the constraint condition further includes a wind turbine power constraint, where the wind turbine power constraint 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.

[0075] In an optional embodiment, the first node includes an energy storage device, the energy storage battery is contained in the energy storage device, and the capacity of the energy storage device is determined based on a base tower power consumption curve and a power generation curve corresponding to the total power generation, wherein the base tower power consumption curve represents the total power consumption of the first node.

[0076] In an optional embodiment, the capacity of the energy storage device is determined by integrating the difference between the power generation curve and the tower power consumption curve.

[0077] In an optional embodiment, determining the first node and the second node from the plurality of transmission tower nodes corresponding to the transmission tower group includes: Determining regional wind resource information corresponding to each of the plurality of transmission tower nodes; Among the multiple transmission tower nodes, a node whose regional wind resource information meets the wind resource requirement is selected as the first node; All nodes among the plurality of transmission tower nodes except the first node are used as the second nodes.

[0078] In an optional embodiment, the objective function is suitable for indicating that minimizing the wind turbine cost function is the goal.

[0079] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above-described methods when the computer program is executed by a processor.

[0080] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of any of the methods described above.

[0081] In a fifth aspect, an embodiment of the present application provides a computer device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0082] See also Figure 5 The computer device of 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 program for optimally configuring wind turbine power based on a group of transmission towers. When the processor 501 executes the computer program, the steps of the above-mentioned embodiments of the method for optimally configuring wind turbine power based on a group of transmission towers are implemented, such as Figure 1 Steps S101-S105 are shown.

[0083] 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 implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0084] The computer device may be a desktop computer, laptop, PDA, cloud server, or other computing device. The computer device may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not limit the computer device. The computer device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, and the like.

[0085] The processor 501 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor 501 may be any conventional processor. The processor 501 is the control center of the computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0086] The memory 502 can be used to store the computer programs and / or modules. The processor 501 implements the various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 502 and accessing 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 an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 502 may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0087] If the module / unit integrated into the computer device 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 present application can implement all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 501, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.

[0088] In summary, the embodiments of the present application have at least the following beneficial effects: According to an embodiment of the present application, a first node and a second node are determined from a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, the total power generation of the first node is greater than the power demanded by its own load, 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 an energy storage battery transported from the first node to the second node by a drone, and the power of the energy storage battery all comes from the first node; based on the power consumption coefficient corresponding to 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 The total power generation, the electric energy of the energy storage battery, the load demand electric energy and the transportation consumption electric energy are used to determine the power supply and demand balance constraint; a wind turbine cost function that matches the wind turbine power of the first node is determined, and an objective function is determined based on the wind turbine cost function; an optimization solution is performed based on the objective function and the 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 constraint, so that a suitable transmission tower (i.e., the first node) can be selected for installing the wind turbine and the power configuration of the installed wind turbine can be optimized, so as to improve the wind energy utilization efficiency and reduce the wind turbine installation cost.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary hardware platform, and of course, it can also be implemented entirely through hardware. Based on this understanding, all or part of the contribution of the technical solution of the present application to the background technology can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or certain parts of the embodiments.

[0090] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. A method for optimally configuring wind turbine power based on a group of transmission towers, characterized in that: include: A first node and a second node are determined among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than the power demand of its own load, the total power generation being determined by the power of a wind turbine of the first node, and at least a portion of the power used by the second node is derived from an energy storage battery transported from the first node to the second node by a drone, and the power of the energy storage battery is derived entirely from the first node; Determining the transportation 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; Determining a power supply and demand balance constraint based on the total power generation, the power of the energy storage battery, the load power demand, and the transportation power consumption; Determining a wind turbine cost function that matches the wind turbine power of the first node, and determining an objective function based on the wind turbine cost function; Based on the objective function and the constraints, an optimization solution is performed to obtain an optimal wind turbine power configuration scheme corresponding to the first node, wherein the constraints include the power supply and demand balance constraint.

2. The method according to claim 1, characterized in that The determining, based on the power consumption coefficient corresponding to the drone and the distance between the first node and the second node, the power consumption of the drone for transportation includes: The electric energy consumed for transportation is determined based on the electric 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 suitable for indicating that the total power generation is greater than or equal to the consumed power, and the consumed power is the sum of the power of the energy storage battery, the load demand power and the transportation consumed power.

4. The method according to claim 1, wherein In the case that all the electric energy used by the second node comes from the energy storage battery, the constraint condition further includes an electric energy usage constraint, and the electric energy usage constraint is suitable for indicating that the total electric energy is less than or equal to the electric energy of the energy storage battery.

5. The method according to claim 1, wherein The constraint condition further includes a wind turbine power constraint, where the wind turbine power constraint 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, wherein The first node includes an energy storage device, the energy storage battery is contained in the energy storage device, and the capacity of the energy storage device is determined based on a base tower power consumption curve and a power generation curve corresponding to the total power generation, wherein the base tower power consumption curve represents the total power consumption of the first node.

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

8. The method according to claim 1, characterized in that The step of determining the first node and the second node from among the plurality of transmission tower nodes corresponding to the transmission tower group includes: Determining regional wind resource information corresponding to each of the plurality of transmission tower nodes; Among the multiple transmission tower nodes, a node whose regional wind resource information meets the wind resource requirement is selected as the first node; All nodes among the plurality of transmission tower nodes except the first node are used as the second nodes.

9. The method according to claim 1, characterized in that The objective function is adapted to indicate a goal of minimizing the wind turbine cost function.

10. A wind turbine power optimal configuration system based on a transmission tower group, characterized in that: include: a node determination module, configured to determine a first node and a second node among a plurality of transmission tower nodes corresponding to a transmission tower group, wherein, within a preset time period, a total power generation of the first node is greater than the power energy required by its own load, the total power generation being determined by the power of a wind turbine of the first node, and at least a portion of the power used by the second node is derived from an energy storage battery transported from the first node to the second node by a drone, the power of the energy storage battery being derived entirely from the first node; a transport power consumption determination module, configured to determine the transport power consumption of the drone based on a power consumption coefficient corresponding to the drone and a distance between the first node and the second node; an electric energy supply and demand balance constraint module, configured to determine an electric energy supply and demand balance constraint based on the total power generation, the electric energy of the energy storage battery, the electric energy required by the load, and the electric energy consumed by transportation; an objective function determination module, configured to 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; The wind turbine power optimal configuration module is used to perform an optimization solution based on the objective function and the constraint conditions to obtain the wind turbine power optimal configuration scheme corresponding to the first node, wherein the constraint conditions include the power supply and demand balance constraint.

Images