Optimization method and system for utilizing large pressure on original fan position

Abstract

The invention belongs to the technical field of wind power generation. According to the optimization method and system, gridding is conducted on a wind power plant by means of large pressure and small pressure on the original draught fan position, a draught fan arrangement scheme serves as a single individual, the single individual is represented by character strings, a first character represents that draught fans of machine positions in a grid need to be dismantled, a second character represents that draught fans of machine positions in the grid are not dismantled, and a third character represents that draught fans of machine positions in the grid need to be dismantled. The third character represents that a fan needs to be newly built in the grid; setting an initial boundary condition, and randomly generating a set number of single individuals to form an initial population; and evaluating each individual in the initial population by taking the power generation capacity full-period income as a fitness value, selecting the single individual for breeding the next generation according to the goal of maximizing the fitness value, and obtaining a final fan arrangement scheme after an iteration termination condition is reached. According to the method, a large-pressure-small technical improvement scheme is optimized, and the process, the definition and the standardization are realized when the large-pressure-small scheme on the wind power plant is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to an optimization method using large pressure reduction at the original wind turbine position, an optimization system using large pressure reduction at the original wind turbine position, a computer device, a computer-readable storage medium and a computer program product. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Upgrading to large and reducing to small usually refers to replacing multiple small-capacity wind turbines with large-capacity wind turbines. This can achieve wind turbine renewal under limited site conditions while improving wind turbine power generation efficiency. The technical transformation of upgrading to large and reducing to small wind turbines can be divided into equal capacity transformation, capacity increase transformation, and capacity reduction transformation based on wind farm capacity. Different wind farm capacity transformations produce different power generation benefits.

[0004] If the technical transformation of upsizing and downsizing does not involve building new wind turbines at the original machine site, it will be necessary to reapply for the land area for installation and pay the relevant land acquisition fees. At present, technical transformation of wind farms is not allowed to encroach on ecological land, and in some areas, only transformation is allowed at the original machine site.

[0005] The technical transformation of upscaling and downscaling involves the selection of sites for the removal and construction of new wind turbines. Therefore, the technical transformation plan of upscaling and downscaling needs to comprehensively consider the optimization of wind farm micro-site selection. At present, the selection and optimization of the transformation plan mainly rely on manual experience. There is a lack of relevant optimization models, and it is impossible to systematically carry out the technical transformation of upscaling and downscaling from the perspective of the arrangement and distribution of new and removed sites. Summary of the invention

[0006] In order to address the deficiencies in the prior art, the present invention provides an optimization method and system for upscaling and downscaling using the original wind turbine position, and uses a genetic algorithm to optimize the upscaling and downscaling technical transformation plan with maximizing the full-cycle profit of the wind farm's power generation as the objective function, thereby achieving the process, clarification and standardization of the upscaling and downscaling plan for the wind farm when it is established.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides an optimization method utilizing the large pressure and small pressure at the original fan position.

[0008] An optimization method using the large pressure and small pressure at the original fan position includes the following process: The wind farm is gridded, and a wind turbine layout scheme is used as a single entity, and the single entity is represented by a string, wherein the first character represents that the wind turbines in the grid need to be removed, the second character represents that the wind turbines in the grid are not removed, and the third character represents that new wind turbines need to be built in the grid; Setting initial boundary conditions, and randomly generating a set number of the single individuals to form an initial population; Each individual in the initial population is evaluated with the full cycle benefit of power generation as the fitness value, and the single individual is selected for breeding the next generation based on the maximum fitness value. After reaching the iteration termination condition, the final wind turbine layout plan is obtained.

[0009] As a further limitation of the first aspect of the present invention, the full cycle income of power generation is: ,in, Q The full cycle income of power generation, H is the reasonable utilization hours of the original wind farm, Y is the remaining operating life of the original wind farm, W To subsidize electricity prices, w For electricity price parity, y is the operating life of the wind farm after transformation, h It is the annual utilization hours of the fan after transformation.

[0010] As a further limitation of the first aspect of the present invention, the subsidized electricity price is: ,in, e The benchmark feed-in tariff for renewable energy. e’ The benchmark price for local coal-fired power generation. V The applicable VAT rate.

[0011] As a further limitation of the first aspect of the present invention, the single individual is represented by a ternary string, where the first character is 0, the second character is 1, and the third character is 2.

[0012] As a further limitation of the first aspect of the present invention, calculating a crossover operator based on the selected single individual comprises: Select two pairs of parent individuals , Perform crossover to produce offspring, i.e. hybridization operator :

[0013] in, , , is a random number in the interval [0, 1].

[0014] As a further limitation of the first aspect of the present invention, the mutation probability As the boundary, adopt m Random numbers are used to replace the parent individuals to obtain the offspring individuals, including:

[0015] in, is the mutation probability, , is a random number uniformly distributed in [0, 1]. is the mutation operator.

[0016] In a second aspect, the present invention provides an optimization system utilizing the large pressure and small pressure at the original fan position.

[0017] An optimization system utilizing the large pressure and small pressure at the original fan position, comprising: The gridding encoding unit is configured to: grid the wind farm, take a wind turbine layout scheme as a single entity, and represent the single entity with a character string, wherein the first character represents that the wind turbine at the position in the grid needs to be removed, the second character represents that the wind turbine at the position in the grid is not to be removed, and the third character represents that a new wind turbine needs to be built in the grid; The initial population generation unit is configured to: set initial boundary conditions and randomly generate a set number of the single individuals to form an initial population; The optimization result generating unit is configured to: evaluate each individual in the initial population with the full cycle benefit of power generation as the fitness value, select the single individual for breeding the next generation according to the maximum fitness value, and obtain the final wind turbine layout plan after reaching the iteration termination condition.

[0018] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the optimization method using the large pressure and small pressure at the original fan position as described in the first aspect of the present invention is implemented.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the optimization method of utilizing the large pressure and small pressure at the original fan position as described in the first aspect of the present invention.

[0020] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the optimization method of utilizing the large pressure and small pressure at the original fan position as described in the first aspect of the present invention.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention innovatively proposes an optimization method and system for increasing the size of wind turbines at the original wind turbine positions, and uses a genetic algorithm to maximize the full-cycle revenue of the wind farm, thereby optimizing the technical transformation plan for increasing the size of wind turbines at the original wind turbine positions, and achieving the process, clarification and standardization of the establishment of the plan for increasing the size of wind turbines at the original wind turbine positions.

[0022] 2. The present invention uses genetic algorithm as the underlying logic, considers aspects such as micro-site selection, power generation income, and related policies, and takes the maximum full-cycle income of wind farm power generation as the standard to construct an optimization model that utilizes the original machine position to increase the size and reduce the size, thereby achieving the optimization of the technical transformation plan of increasing the size and reducing the size.

[0023] 3. The present invention innovatively proposes an optimization method and system for increasing the size of wind turbines and reducing the pressure using the original wind turbine position. Each individual in the population is evaluated based on the full-cycle benefit of power generation as the fitness value, and individuals are selected based on the maximization of the fitness value. Combined with the proposed hybridization operator and mutation operator calculation methods, a more accurate optimization of the technical transformation of increasing the size of wind turbines and reducing the pressure is achieved.

[0024] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0026] Figure 1 A schematic flow chart of an optimization method using a large pressure and a small pressure at an original fan position provided in Example 1 of the present invention; Figure 2 A schematic diagram of an optimization system using a large pressure and a small pressure at the original fan position provided in Example 2 of the present invention; Figure 3 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0029] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0030] Embodiment 1: This implementation proposes an optimization method that utilizes the large pressure and small pressure at the original fan position. The following is a brief introduction to the technical terms and related concepts involved in this treatment solution, including: Reasonable utilization hours: The reasonable utilization hours for wind power projects in Class I, Class II, Class III and Class IV resource areas over their entire life cycle are 48,000 hours, 44,000 hours, 40,000 hours and 36,000 hours respectively.

[0031] The goal of the optimization method described in this implementation is to make the transformation plan economically optimized and feasible in design under the premise of meeting the relevant regulations on the technical transformation of large-scale wind farms to small-scale wind farms.

[0032] If the technical transformation of upsizing and downsizing does not involve building new wind turbines at the original machine site, it is necessary to apply for a land acquisition consent and pay the relevant land acquisition fees. At present, most regions do not allow wind farm technical transformation to encroach on ecological land. At the same time, some regions do not allow technical transformation plans for old wind farms to expand the area of ​​the original wind farm. Therefore, it is not recommended to build new wind turbines at new machine sites.

[0033] The transformation of old wind turbines involves site optimization and the increase in power generation revenue after the transformation. This implementation method will build a technical solution model for upsizing and downsizing from the aspects of micro-site selection, transformation costs, power generation revenue, etc., and optimize the technical transformation solution for upsizing and downsizing. This implementation method mainly uses genetic algorithm as the underlying logic, takes maximizing the full-cycle profit of wind farm power generation as the objective function, uses existing wind farm wind resource data, conducts micro-site selection, and tries to use existing roads or collection systems as much as possible during the site selection process.

[0034] In terms of electricity price calculation, according to the "Management Measures for the Renovation, Upgrading and Decommissioning of Wind Farms", wind farm renovation and upgrading projects that have been connected to the grid for less than 20 years and whose cumulative power generation does not exceed the subsidized power generation over the entire life cycle will continue to receive central government subsidies, and the annual subsidized power generation will be based on the actual power generation and shall not exceed 5% of the subsidized power generation over the entire life cycle of the project before the renovation. The "Management Measures" also stipulate that the on-grid electricity price for the subsidized power generation of wind farm renovation and upgrading projects shall be implemented in accordance with the power price policy of the project before the renovation, and the on-grid electricity price for other power generation shall be implemented in accordance with the power price policy of the year in which the project is approved for change. Relying on this policy, the specific function of the full-cycle revenue of the wind farm power generation is established.

[0035] The transformation of a wind farm from a large-scale power plant to a small-scale power plant involves the demolition of machine sites. Generally, the demolition of the original machine sites and the construction of new ones can meet the relevant policy requirements for the technical transformation of wind farms. Proper selection of the sites for demolition and construction is conducive to maximizing the benefits of the wind farm.

[0036] The optimization method described in this implementation is specifically as follows: Figure 1 As shown, the following process is included: S1: Genetic coding, specifically, including: The wind farm is gridded with the original wind farm site as the center, and a wind turbine layout scheme is taken as a single individual. The individual is represented by a ternary string, where 0 represents no wind turbine in the grid (i.e., the wind turbine at the site is removed), 1 represents the original wind turbine in the grid (i.e., the wind turbine at the site is not removed), and 2 represents the newly built wind turbine in the grid (i.e., the original wind turbine at the site is replaced). An example is shown in Table 1.

[0037] Table 1: Ternary numbers of various machine positions.

[0038]

[0039] S2: Initialize the population, specifically, including: Set the initial boundary conditions and randomly generate a certain number of single individuals (solutions) to form the initial population.

[0040] S3: Evaluate fitness, including: Each individual in the population is evaluated based on the objective function of the full cycle revenue of power generation, and its fitness value (i.e., the full cycle revenue value of power generation) is calculated. The fitness value reflects the degree of excellence of the individual in the solution space. A higher fitness value indicates that the individual is more effective or better in solving the problem.

[0041] The revenue function of the whole cycle of power generation is: (1); in, Q The full cycle income of power generation, H is the reasonable utilization hours of the original wind farm, Y is the remaining operating life of the original wind farm, W To subsidize electricity prices, w For electricity price parity, y is the operating life of the wind farm after transformation, h It is the annual utilization hours of the fan after transformation.

[0042] The subsidy price W is: (2); Where: e The benchmark on-grid electricity price for renewable energy (including on-grid electricity prices determined through competitive methods such as bidding) e’ The benchmark price for local coal-fired power generation. V The applicable VAT rate.

[0043] S4: Individuals are selected based on their fitness values ​​for reproducing the next generation, where individuals with higher fitness are more likely to be selected.

[0044] S5: Design crossover operators and mutation operators, and select two pairs of parent individuals from step S4 , Crossover is performed to produce offspring, namely the hybrid operator , the crossover operator individuals are: (3); Where: , , is a random number in the interval [0,1].

[0045] In genetic algorithms, mutation operators are used to introduce random changes in individual genomes to increase population diversity and avoid falling into local optimal solutions. As the boundary, m random numbers are used to replace the parent individuals to obtain the offspring individuals, and the mutation probability is taken is 0.1.

[0046] The mutation operator individuals are: (4); Where: is the mutation probability, , is a random number uniformly distributed in [0, 1]. is the mutation operator.

[0047] S6: Termination condition check: The initial population size is set to m. When the number of algorithm iterations reaches m, the function converges to the optimal solution, that is, the maximum value of the objective function is obtained, and the algorithm terminates.

[0048] The present invention uses genetic algorithm as the underlying logic, considers micro-site selection, power generation income, related policies and other aspects, takes the maximum full-cycle income of wind farm power generation as the standard, constructs an optimization model that uses the original machine position to increase the size and reduce the size, and optimizes the technical transformation plan of increasing the size and reducing the size. From an engineering perspective, combined with actual conditions and in accordance with relevant regulations, the present invention relatively comprehensively constructs a model of the optimal method of increasing the size and reducing the size, and realizes more accurate optimization of increasing the size and reducing the size.

[0049] Embodiment 2: like Figure 2 As shown, this implementation provides an optimization system using the large pressure and small pressure at the original fan position, including: The gridding encoding unit is configured to: grid the wind farm, take a wind turbine layout scheme as a single entity, and represent the single entity with a character string, wherein the first character represents that the wind turbine at the position in the grid needs to be removed, the second character represents that the wind turbine at the position in the grid is not to be removed, and the third character represents that a new wind turbine needs to be built in the grid; The initial population generation unit is configured to: set initial boundary conditions and randomly generate a set number of the single individuals to form an initial population; The optimization result generating unit is configured to: evaluate each individual in the initial population with the full cycle benefit of power generation as the fitness value, select the single individual for breeding the next generation according to the maximum fitness value, and obtain the final wind turbine layout plan after reaching the iteration termination condition.

[0050] The specific working methods of the above-mentioned units are described in Example 1 and will not be repeated here.

[0051] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the optimization system using the large pressure and small pressure at the original fan position can also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0052] According to another embodiment of the present application, the system described in this embodiment can be constructed and the optimization method of utilizing the large pressure and small size at the original fan position of the embodiment of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0053] Embodiment 3: like Figure 3 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0054] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0055] The processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0056] The processor 1001 is configured to execute the following process: The wind farm is gridded, and a wind turbine layout scheme is used as a single entity, and the single entity is represented by a string, wherein the first character represents that the wind turbines in the grid need to be removed, the second character represents that the wind turbines in the grid are not removed, and the third character represents that new wind turbines need to be built in the grid; Setting initial boundary conditions, and randomly generating a set number of the single individuals to form an initial population; Each individual in the initial population is evaluated with the full cycle benefit of power generation as the fitness value, and the single individual is selected for breeding the next generation based on the maximum fitness value. After reaching the iteration termination condition, the final wind turbine layout plan is obtained.

[0057] The specific working method is described in Example 1 and will not be repeated here.

[0058] Embodiment 4: This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0059] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0060] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: The wind farm is gridded, and a wind turbine layout scheme is used as a single entity, and the single entity is represented by a string, wherein the first character represents that the wind turbines in the grid need to be removed, the second character represents that the wind turbines in the grid are not removed, and the third character represents that new wind turbines need to be built in the grid; Setting initial boundary conditions, and randomly generating a set number of the single individuals to form an initial population; Each individual in the initial population is evaluated with the full cycle benefit of power generation as the fitness value, and the single individual is selected for breeding the next generation based on the maximum fitness value. After reaching the iteration termination condition, the final wind turbine layout plan is obtained.

[0061] The specific working method is described in Example 1 and will not be repeated here.

[0062] Embodiment 5: The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process: The wind farm is gridded, and a wind turbine layout scheme is used as a single entity, and the single entity is represented by a string, wherein the first character represents that the wind turbines in the grid need to be removed, the second character represents that the wind turbines in the grid are not removed, and the third character represents that new wind turbines need to be built in the grid; Setting initial boundary conditions, and randomly generating a set number of the single individuals to form an initial population; Each individual in the initial population is evaluated with the full cycle benefit of power generation as the fitness value, and the single individual is selected for breeding the next generation based on the maximum fitness value. After reaching the iteration termination condition, the final wind turbine layout plan is obtained.

[0063] The specific working method is described in Example 1 and will not be repeated here.

[0064] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server, a data center, etc. that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method using the large pressure and small pressure at the original fan position, characterized in that: The process includes: The wind farm is gridded, and a wind turbine layout scheme is used as a single entity, and the single entity is represented by a string, wherein the first character represents that the wind turbines in the grid need to be removed, the second character represents that the wind turbines in the grid are not removed, and the third character represents that new wind turbines need to be built in the grid; Setting initial boundary conditions, and randomly generating a set number of the single individuals to form an initial population; Each individual in the initial population is evaluated with the full cycle benefit of power generation as the fitness value, and the single individual is selected for breeding the next generation based on the maximum fitness value. After reaching the iteration termination condition, the final wind turbine layout plan is obtained.

2. The optimization method of utilizing the large pressure and small pressure at the original fan position according to claim 1, characterized in that: The full cycle income of the power generation is: ,in, Q The full cycle income of power generation, H is the reasonable utilization hours of the original wind farm, Y is the remaining operating life of the original wind farm, W To subsidize electricity prices, w For electricity price parity, y is the operating life of the wind farm after transformation, h It is the annual utilization hours of the fan after transformation.

3. The optimization method of utilizing the large pressure and small pressure at the original fan position as claimed in claim 2, characterized in that: The subsidized electricity price is: ,in, e The benchmark feed-in tariff for renewable energy. e’ The benchmark price for local coal-fired power generation. V The applicable VAT rate.

4. The optimization method using the large pressure and small pressure at the original fan position according to any one of claims 1 to 3, characterized in that: The single individual is represented by a ternary string, where the first character is 0, the second character is 1, and the third character is 2.

5. The optimization method using the large pressure and small pressure at the original fan position according to any one of claims 1 to 3, characterized in that: According to the selected single individual, a hybridization operator is calculated, including: Select two pairs of parent individuals , Perform crossover to produce offspring, i.e. hybridization operator : in, , , is a random number in the interval [0,1].

6. The optimization method of utilizing the large pressure and small pressure at the original fan position as claimed in claim 5, characterized in that: The probability of mutation As the boundary, adopt m Random numbers are used to replace the parent individuals to obtain the offspring individuals, including: in, is the mutation probability, , is a random number uniformly distributed in [0, 1]. is the mutation operator.

7. An optimization system using the large pressure and small pressure at the original fan position, characterized in that: include: The gridding encoding unit is configured to: grid the wind farm, take a wind turbine layout scheme as a single entity, and represent the single entity with a character string, wherein the first character represents that the wind turbine at the position in the grid needs to be removed, the second character represents that the wind turbine at the position in the grid is not to be removed, and the third character represents that a new wind turbine needs to be built in the grid; The initial population generation unit is configured to: set initial boundary conditions and randomly generate a set number of the single individuals to form an initial population; The optimization result generating unit is configured to: evaluate each individual in the initial population with the full cycle benefit of power generation as the fitness value, select the single individual for breeding the next generation according to the maximum fitness value, and obtain the final wind turbine layout plan after reaching the iteration termination condition.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the optimization method using the large pressure and small pressure at the original fan position as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the optimization method using the large pressure and small pressure at the original fan position as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the optimization method using the large pressure and small pressure at the original fan position as claimed in any one of claims 1 to 6 is implemented.

Images