Fuling river water area flexible photovoltaic power station integrated optimization method and system

Abstract

The invention discloses a Fuling River water area flexible photovoltaic power station integrated optimization method and system, and relates to the technical field of photovoltaic power generation optimization, and the method comprises the steps: collecting power generation data, building a stress and deformation rule model of a flexible photovoltaic support system under different working conditions, carrying out the finite element analysis through combining a decomposition-aggregation method, and carrying out the integrated optimization of the flexible photovoltaic power station according to the analysis result. The method comprises the steps of determining design parameters of a support, establishing a function relation model of power generation efficiency and power station investment income, analyzing the power generation amount of a battery, establishing a preliminary photovoltaic power generation model, optimizing the model by utilizing a genetic algorithm, analyzing the comprehensive cost and income of a photovoltaic power station, and determining the optimal power station system type selection. By integrating data collection, model establishment, support design parameter optimization, preliminary photovoltaic power generation model analysis and genetic algorithm optimization, accurate simulation of the flexible photovoltaic support system under various working conditions is realized, the reliability and safety of support design are ensured, and the overall stability of a photovoltaic power station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation optimization, and in particular to a method and system for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters. Background Art

[0002] With the continuous growth of global energy demand and the increasing attention to environmental protection, the research and application of solar photovoltaic power generation as a clean and renewable energy form has been rapidly developed. Especially in water areas, flexible photovoltaic power stations have attracted much attention due to their good environmental adaptability and high power generation efficiency. However, the existing design of flexible photovoltaic power stations in water areas still has certain limitations in optimizing photovoltaic support systems, improving power generation efficiency and reducing the levelized cost of electricity.

[0003] At present, the design of photovoltaic support systems often relies on empirical formulas and simplified models. These methods are difficult to accurately simulate the support stress conditions under complex terrain and changeable weather conditions, resulting in the inability to fully guarantee the stability and durability of the support system. At the same time, the existing photovoltaic power generation models often lack refined and systematic analysis when considering factors such as component performance degradation and irradiance changes, resulting in a large deviation between the prediction of power generation efficiency and the actual situation. In addition, the existing optimization methods fail to effectively combine market dynamics and specific regional characteristics when dealing with the comprehensive cost and benefit analysis of photovoltaic power stations, resulting in insufficient economy and practicality of the optimization results. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method and system for integrated optimization of flexible photovoltaic power stations in the Fu River waters, so as to solve the problems in the prior art that the stability and durability of the support system cannot be fully guaranteed, the prediction of power generation efficiency deviates from the actual situation, and the economy and practicality of the optimization results are insufficient.

[0005] In order to solve the above technical problems, a method for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters is proposed, including:

[0006] Collect power generation data, establish a model of the force and deformation law of the flexible photovoltaic support system under different working conditions, and perform finite element analysis in combination with the decomposition-aggregation method; based on the analysis results, determine the design parameters of the support, establish a functional relationship model between power generation efficiency and power station investment returns, analyze battery power generation, and establish a preliminary photovoltaic power generation model; use genetic algorithms to optimize the model, analyze the comprehensive costs and benefits of the photovoltaic power station, and determine the optimal power station system selection.

[0007] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, the collection of power generation data includes collecting the solar radiation intensity in different time periods in the Fu River waters, the actual power generation of PERC and HJT battery modules under irradiance, the efficiency of converting solar energy into electrical energy, the power output of photovoltaic modules at different time points, the open circuit voltage of photovoltaic modules, the short circuit current of photovoltaic modules, the law of changes in the maximum output power of photovoltaic modules with temperature, and the performance degradation of photovoltaic modules.

[0008] The establishment of the stress and deformation law model of the flexible photovoltaic support system under different working conditions includes analyzing the stress of the support under different working conditions based on the variability of terrain and weather using geology, material mechanics and fluid mechanics theories, studying the integration principles of the comprehensive energy system under coupling factors and constraints, establishing differential algebraic equations under different conditions through the decomposition-aggregation method of system theory, and aggregating the subsystems through coupling transformation matrix equations to establish the overall stress and deformation model of the support system.

[0009] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, the decomposition-aggregation method includes decomposing the system according to different conditions, and establishing their respective differential algebraic equations separately, aggregating them through coupled transformation matrix equations, and finally establishing the force and deformation model of the support system under different working conditions.

[0010] The differential algebraic equation is:

[0011]

[0012] Among them, M i is the mass matrix, C i is the damping matrix, K i is the stiffness matrix, x i is the displacement vector, is the acceleration vector, is the velocity vector, F i (t) is the force vector, t is the time, and i is the variable index.

[0013] The coupling conversion matrix equation formula is:

[0014] F ij =T ij x ij

[0015] Among them, F ij is the force vector of subsystem j on subsystem i, T ij is the coupling transformation matrix, x ij is the displacement vector of subsystem j, i and j are variable indices.

[0016] The finite element analysis formula is:

[0017] s=γ G S GK +γ w Ψ w S wk ++γ s Ψ s S sk +γ t Ψ t S tk

[0018] Among them, γ G and S GK is the standard combined design value of permanent action effect, γ w , w and S wk is the combined design value of the variable action effect control, γ s , s and S sk is the combined design value of the control of frequent action effects, γ t , t and S tk is the combined design value controlled by accidental effects, and s is the load combination.

[0019] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, wherein: the determination of the design parameters of the bracket includes, according to the finite element analysis results, determining the stress conditions of the bracket under different working conditions, and based on the stress analysis results, determining the structural parameters of the bracket; the structural parameters of the bracket include material properties, cross-sectional dimensions and support point positions.

[0020] The functional relationship model for establishing power generation efficiency and power station investment income includes determining the research object and the main influencing factors, using on-site construction elements, PVSYS photovoltaic simulation platform, photovoltaic power station monitoring system integration platform and MATLAB simulation software, and through data comparison, data collection and analysis, and analysis of the characteristics of component installation capacity and inverter coefficient ratio, to establish a functional relationship model for the power generation efficiency of high-efficiency components and the investment income of the power station.

[0021] The research object is determined to be the average cost per kilowatt-hour; the main influencing factors include component type, solar irradiance and on-grid electricity price.

[0022] The function of power generation efficiency and power station investment return is expressed as:

[0023] R=η·E·PCI

[0024] Among them, R is the investment return, η is the power generation efficiency, E is the total power generation, P is the on-grid electricity price, C is the operation and maintenance cost, and I is the initial investment cost.

[0025] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, the establishment of a preliminary photovoltaic power generation model includes simulating and screening the data, analyzing the power generation performance under different irradiances, simulating and analyzing the most suitable installation and laying conditions and regional conditions for power generation efficiency, and establishing a preliminary photovoltaic power generation model for the Fu River basin.

[0026] The analysis of power generation performance under different irradiances includes establishing irradiance conditions, installation angles, water vapor thickness loss factors, water surface change influence factors and tracking frequencies as screening factors to analyze power generation performance under different irradiances.

[0027] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, the optimization of the model using a genetic algorithm includes market analysis and data collection, genetic binary encoding of power generation data using a genetic algorithm, initializing the population, selecting the most optimized individuals to enter the next generation according to the fitness, performing combined crossover and mutation on the selected individuals to generate a new solution population, evaluating individual fitness, evolving from generation to generation until the termination conditions are met, optimizing the target setting, and setting constraints.

[0028] The market analysis and data collection include a comprehensive analysis of the geographical and climatic characteristics of the Fujiang River water area, determining the potential for photovoltaic power generation in the Fujiang River water area, investigating the market price trends of photovoltaic modules and brackets, as well as supplier conditions, providing cost analysis data, collecting historical photovoltaic irradiance data in the Fujiang River water area through field measurements and inquiries to public databases, and obtaining performance data of PERC modules and HJT modules from manufacturers.

[0029] The genetic binary coding includes designing a coding scheme, converting the power generation data of the PERC component and the HJT component into a binary string, and determining the coding length.

[0030] The optimization goal setting includes clarifying that the optimization goal is to minimize the system averaged electricity cost, and setting the objective function as:

[0031] f(X)=1 / LCOE(X)

[0032] Where f(X) is the target value, LCOE(X) is the levelized cost of electricity, and X is the decision variable.

[0033] The agreed conditions are expressed as:

[0034] X min ≤X≤Xmax

[0035] Among them, X is the decision variable, X min is the minimum value of the decision variable, X max is the maximum value of the decision variable.

[0036] As a preferred scheme of the method for integrated optimization of the flexible photovoltaic power station in the Fu River waters described in the present invention, wherein: the determination of the optimal power station system selection includes configuring the economic parameters of the components, designing the chromosome structure, and encoding and decoding, running the genetic algorithm, analyzing the results output by the genetic algorithm, decoding the optimal individuals in the final generation population of the genetic algorithm, obtaining the specific values ​​of its decision variables, determining the optimal solution area of ​​the power generation curve and the power sales revenue curve of the high-efficiency components, and determining the power station system selection with the lowest system averaged electricity cost based on the optimal solution area.

[0037] The economic parameters of the component include the initial investment cost, operation and maintenance cost, expected life, depreciation rate, loan interest rate and electricity price of the component; the designed chromosome structure includes mapping the number of PERC components, the number of HJT components and the total installed capacity of the system onto a chromosome, and each decision variable corresponds to a gene segment on the chromosome.

[0038] Another object of the present invention is to provide a system for integrated optimization of flexible photovoltaic power stations in the Fu River waters. The present invention optimizes the design accuracy of photovoltaic brackets and realizes the practicality and economy of photovoltaic power station system selection. The system of the present invention solves the problems of photovoltaic bracket design accuracy, power generation efficiency prediction deviation and economic optimization by establishing a refined bracket force and deformation law model, an accurate photovoltaic power generation model, and optimizing the power station cost and benefit using genetic algorithms, thereby ensuring the practicality and economy of photovoltaic power station system selection in the Fu River waters.

[0039] As a preferred solution of the system for integrated optimization of a flexible photovoltaic power station in the Fu River waters described in the present invention, it is characterized by including a data collection and model building module, a bracket design parameter determination module, a power generation efficiency and investment return model building module, a preliminary photovoltaic power generation model building module and a genetic algorithm optimization module.

[0040] The data collection and model building module is used to collect solar radiation intensity and battery module power generation data in the Fujiang River waters, and to build a model of the force and deformation law of the flexible photovoltaic support system.

[0041] The support design parameter determination module is used to determine the optimal design parameters of the photovoltaic support under different working conditions based on the finite element analysis results, so as to ensure the stability and economy of the support.

[0042] The power generation efficiency and investment income model establishment module is used to establish a functional relationship model between power generation efficiency and power station investment income, and provide an economic evaluation standard for optimization.

[0043] The preliminary photovoltaic power generation model establishment module is used to establish a preliminary photovoltaic power generation model for the Fujiang River Basin through simulation and data screening and processing, and to analyze power generation performance under different irradiances.

[0044] The genetic algorithm optimization module is used to analyze the comprehensive cost and benefit of the photovoltaic power station through the genetic algorithm to determine the most optimized power station system selection.

[0045] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method described in the integrated optimization of a flexible photovoltaic power station in the Fu River waters are implemented.

[0046] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method described in the integrated optimization of a flexible photovoltaic power station in the Fu River waters are implemented.

[0047] Beneficial effects of the present invention: By integrating the steps of data collection, model building, bracket design parameter optimization, power generation efficiency and investment return model construction, preliminary photovoltaic power generation model analysis and genetic algorithm optimization, the present invention realizes the accurate simulation of the flexible photovoltaic bracket system under various working conditions, ensures the reliability and safety of the bracket design, and improves the overall stability of the photovoltaic power station. At the same time, by optimizing the bracket structure, material waste is reduced, the service life of the bracket is extended, the operation and maintenance efficiency is improved, and the long-term operation and maintenance cost is reduced. In addition, the established functional relationship model provides a scientific basis for the economic evaluation and revenue prediction of the power station, optimizes the layout of the power station, and improves the power generation efficiency. Finally, the optimal configuration of the power station system selection is found by using the genetic algorithm, which maximizes the investment return, reduces the averaged cost per kilowatt-hour, and improves the overall competitiveness of the power station. This integrated optimization and automation system not only improves the efficiency and quality of power station construction, but also realizes the automation and intelligence of the optimization design through computer equipment and storage media, greatly improves the design efficiency and reduces the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0049] Figure 1 An overall flow chart of a method for integrated optimization of a flexible photovoltaic power station in the Fu River waters provided by an embodiment of the present invention.

[0050] Figure 2 A diagram of photovoltaic power generation in the Fu River Basin, showing a method for integrated optimization of flexible photovoltaic power stations in the Fu River waters provided by an embodiment of the present invention.

[0051] Figure 3 A genetic algorithm flow chart of a method for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters provided by an embodiment of the present invention.

[0052] Figure 4 A system solution flow chart of a system for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0056] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0057] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0058] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] Example 1, reference Figure 1-Figure 3 , which is the first embodiment of the present invention, and provides a method for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters, comprising:

[0060] S1: Collect power generation data, establish the stress and deformation law model of the flexible photovoltaic support system under different working conditions, and perform finite element analysis using the decomposition-aggregation method.

[0061] The steps for studying the stress and deformation laws are as follows: first, fully consider the terrain conditions of the support system under different working conditions, the characteristics of the variability of weather conditions, and comprehensively apply theories such as geology, material mechanics, and fluid mechanics to obtain the stress conditions under different working conditions; secondly, explore the principles and methods of integrated energy system integration under different constraints under coupling factors.

[0062] Then, the decomposition-coordination or decomposition-aggregation method in system theory is used to study the modeling of the stress and deformation laws of the support system under different working conditions. On the one hand, the system is decomposed according to different conditions, and their respective differential algebraic equations are established separately; then, the subsystems are assembled through coupling transformation matrix equations, and finally the stress and deformation model of the support system under different working conditions is established.

[0063] The flexible bracket is a method of replacing the purlins of the traditional rigid bracket with steel strands, which provide pre-tensioning force. After the assembly is installed, the steel strands are allowed to have a certain deformation under different working conditions, thereby realizing a large span bracket of 10 to 30 meters. The flexible bracket improves land utilization, reduces steel consumption, reduces construction costs, saves costs, facilitates the formation of a larger span, and adapts to complex terrain environments.

[0064] The power generation data collected includes collecting the solar radiation intensity in different time periods in the Fu River waters, the actual power generation of PERC and HJT battery modules under irradiance, the efficiency of converting solar energy into electrical energy, the power output of photovoltaic modules at different time points, the open circuit voltage of photovoltaic modules, the short circuit current of photovoltaic modules, the law of changes in the maximum output power of photovoltaic modules with temperature, and the performance degradation of photovoltaic modules.

[0065] The establishment of the stress and deformation law model of the flexible photovoltaic support system under different working conditions includes analyzing the stress of the support under different working conditions based on the variability of terrain and weather using geology, material mechanics and fluid mechanics theories, studying the integration principles of the comprehensive energy system under coupling factors and constraints, establishing differential algebraic equations under different conditions through the decomposition-aggregation method of system theory, and aggregating the subsystems through coupling transformation matrix equations to establish the overall stress and deformation model of the support system.

[0066] The decomposition-aggregation method includes decomposing the system according to different conditions, and establishing respective differential algebraic equations separately, aggregating them through coupled transformation matrix equations, and finally establishing the stress and deformation model of the support system under different working conditions.

[0067] The differential algebraic equation is:

[0068]

[0069] Among them, M i is the mass matrix, C i is the damping matrix, K i is the stiffness matrix, x i is the displacement vector, is the acceleration vector, is the velocity vector, F i (t) is the force vector, t is the time, and i is the variable index.

[0070] The coupling conversion matrix equation formula is:

[0071] F ij =T ij x ij

[0072] Among them, F ij is the force vector of subsystem j on subsystem i, T ij is the coupling transformation matrix, x ij is the displacement vector of subsystem j, i and j are variable indices.

[0073] The finite element analysis formula is:

[0074] s=γ G SGK +γ w Ψ w S wk ++γ s Ψ s S sk +γ t Ψ t S tk

[0075] Among them, γ G and S GK is the standard combined design value of permanent action effect, γ w , w and S wk is the combined design value of the variable action effect control, γ s , s and S sk is the combined design value of the control of frequent action effects, γ t , t and S tk is the combined design value controlled by accidental effects, and s is the load combination.

[0076] S2: Based on the analysis results, determine the design parameters of the bracket, establish a functional relationship model between power generation efficiency and power station investment returns, analyze the battery power generation, and establish a preliminary photovoltaic power generation model.

[0077] Furthermore, determining the design parameters of the bracket includes determining the stress conditions of the bracket under different working conditions according to the finite element analysis results, and determining the structural parameters of the bracket based on the stress analysis results; the structural parameters of the bracket include material properties, cross-sectional dimensions and support point locations.

[0078] The functional relationship model for establishing power generation efficiency and power station investment income includes determining the research object and the main influencing factors, using on-site construction elements, PVSYS photovoltaic simulation platform, photovoltaic power station monitoring system integration platform and MATLAB simulation software, and through data comparison, data collection and analysis, and analysis of the characteristics of component installation capacity and inverter coefficient ratio, to establish a functional relationship model for the power generation efficiency of high-efficiency components and the investment income of the power station.

[0079] The research object is determined to be the average cost per kilowatt-hour; the main influencing factors include component type, solar irradiance and on-grid electricity price.

[0080] The function of power generation efficiency and power station investment return is expressed as:

[0081] R=η·E·PCI

[0082] Among them, R is the investment return, η is the power generation efficiency, E is the total power generation, P is the on-grid electricity price, C is the operation and maintenance cost, and I is the initial investment cost.

[0083] like Figure 2 The establishment of the preliminary photovoltaic power generation model includes simulating and screening the data, analyzing the power generation performance under different irradiances, simulating and analyzing the most suitable installation and laying conditions and power generation efficiency regional conditions, and establishing a preliminary photovoltaic power generation model for the Fu River Basin.

[0084] The analysis of power generation performance under different irradiances includes establishing irradiance conditions, installation angles, water vapor thickness loss factors, water surface change influence factors and tracking frequencies as screening factors to analyze power generation performance under different irradiances.

[0085] S3: Use genetic algorithms to optimize the model, analyze the comprehensive costs and benefits of photovoltaic power stations, and determine the optimal power station system selection.

[0086] like Figure 3 , the optimization of the model using genetic algorithm includes, conducting market analysis and data collection, using genetic algorithm to perform genetic binary encoding on power generation data, initializing the population, selecting the most optimized individuals to enter the next generation according to the fitness, performing combined crossover and mutation on the selected individuals to generate a new solution set population, evaluating individual fitness, evolving from generation to generation until the termination conditions are met, and optimizing the target setting and setting constraints.

[0087] The market analysis and data collection include a comprehensive analysis of the geographical and climatic characteristics of the Fujiang River water area, determining the potential for photovoltaic power generation in the Fujiang River water area, investigating the market price trends of photovoltaic modules and brackets, as well as supplier conditions, providing cost analysis data, collecting historical photovoltaic irradiance data in the Fujiang River water area through field measurements and inquiries to public databases, and obtaining performance data of PERC modules and HJT modules from manufacturers.

[0088] The genetic binary coding includes designing a coding scheme, converting the power generation data of the PERC component and the HJT component into a binary string, and determining the coding length.

[0089] The optimization goal setting includes clarifying that the optimization goal is to minimize the system averaged electricity cost, and setting the objective function as:

[0090] f(X)=1 / LCOE(X)

[0091] Where f(X) is the target value, LCOE(X) is the levelized cost of electricity, and X is the decision variable.

[0092] The agreed conditions are expressed as:

[0093] X min ≤X≤X max

[0094] Among them, X is the decision variable, X min is the minimum value of the decision variable 0, X max is the maximum value of the decision variable 5.

[0095] The method of determining the most optimized power station system selection includes configuring economic parameters of components, designing chromosome structure, encoding and decoding, running genetic algorithm, analyzing the results output by genetic algorithm, decoding the best individuals in the final generation population of genetic algorithm, obtaining the specific values ​​of its decision variables, determining the optimal solution area of ​​power generation curve and power sales revenue curve of high-efficiency components, and determining the power station system selection with the lowest system averaged electricity cost based on the optimal solution area.

[0096] The economic parameters of the components include the initial investment cost, operation and maintenance cost, expected life, depreciation rate, loan interest rate and electricity price of the components; the design of the chromosome structure includes mapping the number of PERC components, the number of HJT components and the total installed capacity of the system to a chromosome, and each decision variable corresponds to a gene segment on the chromosome. Considering that there are three decision variables, the three variables are placed on one chromosome in this optimization. The chromosome length is set to 15 and is divided into three segments: x, y, and z. During the encoding and decoding process, the three segments of data are encoded and decoded separately, and the results are stored in a two-dimensional data to simplify the algorithm programming process.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0098] Example 2 is an embodiment of the present invention, which provides a method for integrated optimization of flexible photovoltaic power stations in the Fu River waters. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0099] The finite element analysis method is established in Matlab to conduct mechanical analysis on the established finite element. Under normal and limit conditions, mechanical analysis is conducted and the combined factors are shown in the following table:

[0100]

[0101] γ in the above four working conditions G Both are 1.2.

[0102] The finite element analysis formula is:

[0103] s=γ G S GK +γ w Ψ w S wk ++γ s Ψ s S sk +γ t Ψ t S tk

[0104] Among them, γ G and S GK is the standard combined design value of permanent action effect, γ w , w and S wk is the combined design value of the variable action effect control, γ s , s and S sk is the combined design value of the control of frequent action effects, γ t , t and S tk is the combined design value controlled by accidental effects, and s is the load combination.

[0105]

[0106] γ in the above four working conditions G Both are 1.0.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0108] Example 3, reference Figure 4 , which is the third embodiment of the present invention, provides a system for integrated optimization of flexible photovoltaic power stations in the Fu River waters, including a data collection and model building module 100, a bracket design parameter determination module 200, a power generation efficiency and investment return model building module 300, a preliminary photovoltaic power generation model building module 400 and a genetic algorithm optimization module 500.

[0109] The data collection and model building module 100 is used to collect solar radiation intensity and battery module power generation data in the Fujiang River waters, and to build a stress and deformation law model of the flexible photovoltaic support system.

[0110] The support design parameter determination module 200 is used to determine the optimal design parameters of the photovoltaic support under different working conditions based on the finite element analysis results to ensure the stability and economy of the support.

[0111] The power generation efficiency and investment income model building module 300 is used to build a functional relationship model between power generation efficiency and power station investment income, so as to provide an economic evaluation standard for optimization.

[0112] The preliminary photovoltaic power generation model establishment module 400 is used to establish a preliminary photovoltaic power generation model of the Fujiang River Basin through simulation and data screening and processing, and analyze the power generation performance under different irradiances.

[0113] The genetic algorithm optimization module 500 is used to analyze the comprehensive cost and benefit of the photovoltaic power station through a genetic algorithm to determine the most optimized power station system selection.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0115] Embodiment 4, the fourth embodiment of the present invention, is different from the first three embodiments in that:

[0116] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0119] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters, characterized by: include, Collect power generation data, establish the stress and deformation law model of the flexible photovoltaic support system under different working conditions, and perform finite element analysis using the decomposition-aggregation method; According to the analysis results, determine the design parameters of the bracket, establish a functional relationship model between power generation efficiency and power station investment income, analyze the battery power generation, and establish a preliminary photovoltaic power generation model; Genetic algorithms are used to optimize the model, analyze the comprehensive costs and benefits of photovoltaic power stations, and determine the optimal power station system selection.

2. The method for integrated optimization of flexible photovoltaic power stations in the Fujiang River waters as claimed in claim 1, characterized in that: The power generation data collected includes collecting the solar radiation intensity in different time periods in the Fu River waters, the actual power generation of PERC and HJT battery modules under irradiance, the efficiency of converting solar energy into electrical energy, the power output of photovoltaic modules at different time points, the open circuit voltage of photovoltaic modules, the short circuit current of photovoltaic modules, the law of maximum output power of photovoltaic modules changing with temperature, and the performance degradation of photovoltaic modules; The establishment of the stress and deformation law model of the flexible photovoltaic support system under different working conditions includes analyzing the stress of the support under different working conditions based on the variability of terrain and weather using geology, material mechanics and fluid mechanics theories, studying the integration principles of the comprehensive energy system under coupling factors and constraints, establishing differential algebraic equations under different conditions through the decomposition-aggregation method of system theory, and aggregating the subsystems through coupling transformation matrix equations to establish the overall stress and deformation model of the support system.

3. The method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as claimed in claim 2, characterized in that: The decomposition-aggregation method includes decomposing the system according to different conditions, and establishing the respective differential algebraic equations separately, aggregating them through coupling transformation matrix equations, and finally establishing the force and deformation model of the support system under different working conditions; The differential algebraic equation is: Among them, M i is the mass matrix, C i is the damping matrix, K i is the stiffness matrix, x i is the displacement vector, is the acceleration vector, is the velocity vector, F i (t) is the force vector, t is time, and i is the variable index; The coupling conversion matrix equation formula is: F ij =T ij x ij Among them, F ij is the force vector of subsystem j on subsystem i, T ij is the coupling transformation matrix, x ij is the displacement vector of subsystem j, i and j are variable indices; The finite element analysis formula is: s=γ G S GK +g w P w S wk ++c s P s S sk +g t P t S tk Among them, γ G and S GK is the standard combined design value of permanent action effect, γ w , w and S wk is the combined design value of the variable action effect control, γ s , s and S sk is the combined design value of the control of frequent action effects, γ t , t and S tk is the combined design value controlled by accidental effects, and s is the load combination.

4. The method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as claimed in claim 3, characterized in that: Determining the design parameters of the bracket includes determining the stress conditions of the bracket under different working conditions according to the finite element analysis results, and determining the structural parameters of the bracket based on the stress analysis results; the structural parameters of the bracket include material properties, cross-sectional dimensions and support point positions; The establishment of the functional relationship model between power generation efficiency and power station investment income includes determining the research object and the main influencing factors, using on-site construction elements, PVSYS photovoltaic simulation platform, photovoltaic power station monitoring system integration platform and MATLAB simulation software, and through data comparison, data collection and analysis, and the characteristics of the component installation capacity and inverter coefficient ratio, establishing the functional relationship model between the power generation efficiency of the high-efficiency component and the power station investment income; The research object is determined to be the average cost per kWh; the main influencing factors include component type, solar irradiance and grid-connected electricity price; The function of power generation efficiency and power station investment return is expressed as: R=η·E·PCI Among them, R is the investment return, η is the power generation efficiency, E is the total power generation, P is the on-grid electricity price, C is the operation and maintenance cost, and I is the initial investment cost.

5. The method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as claimed in claim 4, characterized in that: The establishment of the preliminary photovoltaic power generation model includes simulating and screening the data, analyzing the power generation performance under different irradiances, simulating and analyzing the most suitable installation and laying conditions and power generation efficiency regional conditions, and establishing a preliminary photovoltaic power generation model for the Fujiang River Basin; The analysis of power generation performance under different irradiances includes establishing irradiance conditions, installation angles, water vapor thickness loss factors, water surface change influence factors and tracking frequencies as screening factors to analyze power generation performance under different irradiances.

6. The method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as claimed in claim 5, characterized in that: The optimization of the model by using the genetic algorithm includes market analysis and data collection, using the genetic algorithm to perform genetic binary coding on the power generation data, initializing the population, selecting the most optimized individuals to enter the next generation according to the fitness, performing combination crossover and mutation on the selected individuals to generate a new solution population, evaluating the fitness of the individuals, evolving from generation to generation until the termination conditions are met, and optimizing the target setting and setting the constraints; The market analysis and data collection include conducting a comprehensive analysis of the geographical and climatic characteristics of the Fujiang River watershed area, determining the potential for photovoltaic power generation in the Fujiang River watershed area, investigating the market price trends of photovoltaic modules and brackets, as well as the supplier situation, providing cost analysis data, collecting historical photovoltaic irradiance data in the Fujiang River watershed area through field measurements and inquiries to public databases, and obtaining performance data of PERC modules and HJT modules from manufacturers; The genetic binary encoding includes designing an encoding scheme, converting the power generation data of the PERC component and the HJT component into a binary string, and determining the encoding length; The optimization goal setting includes clarifying that the optimization goal is to minimize the system averaged electricity cost, and setting the objective function as: f(X)=1 / LCOE(X) Where f(X) is the target value, LCOE(X) is the levelized cost of electricity, and X is the decision variable; The agreed conditions are expressed as: X min ≤X≤X max Among them, X is the decision variable, X min is the minimum value of the decision variable, X max is the maximum value of the decision variable.

7. The method for integrated optimization of flexible photovoltaic power stations in the Fujiang River area as claimed in claim 6, characterized in that: The method of determining the optimal power station system selection includes configuring economic parameters of components, designing chromosome structure, encoding and decoding, running a genetic algorithm, analyzing the results output by the genetic algorithm, decoding the optimal individual in the final generation population of the genetic algorithm, obtaining the specific values ​​of its decision variables, determining the optimal solution area of ​​the power generation curve and the power sales revenue curve of the high-efficiency components, and determining the power station system selection with the lowest system averaged electricity cost according to the optimal solution area; The economic parameters of the component include the initial investment cost, operation and maintenance cost, expected life, depreciation rate, loan interest rate and electricity price of the component; the designed chromosome structure includes mapping the number of PERC components, the number of HJT components and the total installed capacity of the system onto a chromosome, and each decision variable corresponds to a gene segment on the chromosome.

8. A system using the method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and model building module, bracket design parameter determination module, power generation efficiency and investment return model building module, preliminary photovoltaic power generation model building module and genetic algorithm optimization module; The data collection and model building module is used to collect solar radiation intensity and battery module power generation data in the Fujiang River waters, and to establish a force and deformation law model of the flexible photovoltaic support system; The support design parameter determination module is used to determine the optimal design parameters of the photovoltaic support under different working conditions based on the finite element analysis results to ensure the stability and economy of the support; The power generation efficiency and investment income model establishment module is used to establish a functional relationship model between power generation efficiency and power station investment income, and provide an economic evaluation standard for optimization; The preliminary photovoltaic power generation model establishment module is used to establish a preliminary photovoltaic power generation model for the Fujiang River Basin through simulation and data screening and processing, and analyze the power generation performance under different irradiances; The genetic algorithm optimization module is used to analyze the comprehensive cost and benefit of the photovoltaic power station through the genetic algorithm to determine the most optimized power station system selection.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for integrated optimization of a flexible photovoltaic power station in the Fujiang River waters as described in any one of claims 1 to 7 are implemented.

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