Photovoltaic module installation angle determination method, device, storage medium and electronic equipment

Abstract

The present disclosure relates to a method, device, storage medium and electronic device for determining the installation angle of a photovoltaic component, which can effectively determine the optimal installation angle of the photovoltaic component, thereby improving the power generation and economic benefits of the photovoltaic system. The method includes: obtaining multiple installation angle combinations of the photovoltaic component, wherein each installation angle combination includes an azimuth angle and an inclination angle; determining a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target time period and the time-of-use electricity price corresponding to the target time period; determining the installation angle of the photovoltaic component according to the target installation angle combination.

Description

Technical Field

[0001] The present disclosure relates to the field of optimized design of distributed photovoltaic systems, and in particular, to a method, device, storage medium and electronic device for determining the installation angle of a photovoltaic component. Background Art

[0002] As the global energy crisis and environmental pollution become increasingly serious, the development and utilization of renewable energy, especially solar energy, has received widespread attention. As one of the important forms of solar energy utilization, distributed photovoltaic systems have the advantages of flexible installation, clean and environmentally friendly, and have gradually become an important direction for the development of new energy. However, the power generation efficiency of photovoltaic systems is affected by many factors, among which the installation angle and orientation of photovoltaic modules are one of the decisive factors.

[0003] In the related art, the design method for the inclination and orientation of photovoltaic modules is usually based on static environmental parameters such as local latitude, annual average solar radiation, etc., which are simplified and approximated, resulting in low power generation returns of photovoltaic projects. In addition, the dynamic changes of parameters such as solar radiation intensity and ambient temperature in different time periods will also make it difficult for the design results to achieve the best economic benefits. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method, device, storage medium and electronic device for determining the installation angle of a photovoltaic module to solve the problems in the related art.

[0005] In order to achieve the above objectives, in a first aspect, the present disclosure provides a method for determining an installation angle of a photovoltaic assembly, the method comprising: Acquire a plurality of installation angle combinations of the photovoltaic assembly, wherein each of the installation angle combinations comprises an azimuth angle and an inclination angle; Determine a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target period and the time-of-use electricity price corresponding to the target period; The installation angle of the photovoltaic assembly is determined according to the target installation angle combination.

[0006] Optionally, determining a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm includes: Obtaining a chromosome in the preset genetic algorithm corresponding to each of the installation angle combinations; Determining an initial population based on the chromosomes; Determining the fitness value corresponding to each of the chromosomes in the initial population according to the preset optimization function; When the preset termination condition is met, the maximum value of the fitness values ​​corresponding to each chromosome is determined as the target fitness value, and the target installation angle combination is determined according to the chromosome corresponding to the target fitness value.

[0007] Optionally, the method further comprises: When the preset termination condition is not met, a selection operation is performed according to the fitness value to retain the target chromosome within the preset fitness threshold range; According to the target chromosome, a new population is generated through a crossover operation and a mutation operation, wherein the crossover operation includes exchanging some genes of the chromosome according to a preset crossover probability, and the mutation operation includes randomly changing the genes in the chromosome according to a preset mutation probability; The new population is determined as the initial population for a new round of iteration, and the step of determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function is returned to be executed until the preset termination condition is met.

[0008] Optionally, determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function includes: Inputting the installation angle combination represented by each chromosome in the initial population into a preset photovoltaic simulation model to determine the target power generation data corresponding to each installation angle combination; A maximum benefit value is calculated according to the target power generation data and the preset optimization function, and the maximum benefit value is determined as the fitness value corresponding to each chromosome in the initial population.

[0009] Optionally, the acquiring a plurality of installation angle combinations of the photovoltaic assembly includes: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic assembly, a plurality of installation angle combinations of the photovoltaic assembly are obtained.

[0010] Optionally, the time periods of the time-of-use electricity price include valley period, low valley period, parity period, peak period and spike period.

[0011] In a second aspect, the present disclosure provides a photovoltaic assembly installation angle determination device, the device comprising: An acquisition module, used to acquire a plurality of installation angle combinations of the photovoltaic assembly, wherein each installation angle combination includes an azimuth angle and an inclination angle; A first determination module is used to determine a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target time period and the time-of-use electricity price corresponding to the target time period; The second determination module is used to determine the installation angle of the photovoltaic assembly according to the target angle combination.

[0012] Optionally, the acquisition module is used to: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic assembly, a plurality of installation angle combinations of the photovoltaic assembly are obtained.

[0013] In a third aspect, the present disclosure further provides a non-temporary computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0014] In a fourth aspect, the present disclosure further provides an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of any one of the methods in the first aspect.

[0015] Through the above technical solution, multiple installation angle combinations of photovoltaic modules are obtained, and the target installation angle combination is determined according to the installation angle combination, the preset optimization function and the preset genetic algorithm, and the installation angle of the photovoltaic module is determined according to the target installation angle combination. Among them, the installation angle combination of photovoltaic modules is determined by using the preset genetic algorithm and the preset optimization function, and taking into account the impact of the time-of-use electricity price mechanism on the power generation income of the photovoltaic system, the optimal photovoltaic module installation angle can be effectively determined, thereby improving the power generation and economic benefits of the photovoltaic system.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The figure is a flow chart of a method for determining a photovoltaic assembly installation angle according to an exemplary embodiment of the present disclosure.

[0018] Figure 2 The figure is a process diagram of a genetic algorithm according to an exemplary embodiment of the present disclosure.

[0019] Figure 3 The figure is a schematic diagram showing the distribution of time-of-use electricity prices in Shandong Province according to an exemplary embodiment of the present disclosure.

[0020] Figure 4It is a schematic diagram of a parameter page when the inclination angle of a photovoltaic component is 30° and the azimuth angle is 0° according to an exemplary embodiment of the present disclosure.

[0021] Figure 5 It is a schematic diagram of a parameter page when the inclination angle of a photovoltaic component is 30° and the azimuth angle is 25° according to an exemplary embodiment of the present disclosure.

[0022] Figure 6 It is a schematic diagram of a parameter page when a photovoltaic module has an inclination angle of 30° and an azimuth angle of 45° according to an exemplary embodiment of the present disclosure.

[0023] Figure 7 It is a schematic diagram of a parameter page when a photovoltaic assembly has an inclination angle of 30° and an azimuth angle of 75° according to an exemplary embodiment of the present disclosure.

[0024] Figure 8 It is a schematic diagram of a parameter page when the inclination angle of a photovoltaic component is 30° and the azimuth angle is 90° according to an exemplary embodiment of the present disclosure.

[0025] Fig. 9 It is a page diagram showing a simulation configuration parameter according to an exemplary embodiment of the present disclosure.

[0026] Fig.10 The figure is a graph showing the relationship between power generation and time at different azimuth angles when the inclination angle of a photovoltaic module is 30° according to an exemplary embodiment of the present disclosure.

[0027] Fig.11 The present invention is a block diagram of a photovoltaic assembly installation angle determination device according to an exemplary embodiment of the present invention.

[0028] Fig.12 It is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0030] As mentioned in the background technology, in the related technology, the design method for the inclination and orientation of photovoltaic modules is usually based on static environmental parameters, such as local latitude, annual average solar radiation, etc., which are simplified and approximated, resulting in low power generation returns of photovoltaic projects. In addition, the dynamic changes of parameters such as solar radiation intensity and ambient temperature in different time periods will also make it difficult for the design results to achieve the best economic benefits.

[0031] In view of this, the embodiments of the present disclosure provide a method, device, storage medium and electronic device for determining the installation angle of a photovoltaic module, which can effectively determine the optimal installation angle of the photovoltaic module, thereby improving the power generation and economic benefits of the photovoltaic system.

[0032] Figure 1 is a flow chart of a method for determining a photovoltaic assembly installation angle according to an exemplary embodiment of the present disclosure. Figure 1 As shown, the method may include the following steps: Step S101, obtaining a plurality of installation angle combinations of photovoltaic modules, wherein each installation angle combination includes an azimuth angle and an inclination angle.

[0033] Step S102, determining a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target period and the time-of-use electricity price corresponding to the target period.

[0034] Among them, the time periods for time-of-use electricity prices include valley period, low period, parity period, peak period and peak period.

[0035] It should be understood that existing photovoltaic projects are generally designed to face south for maximum power generation. In order to maximize the economic benefits of photovoltaic power generation, distributed photovoltaic arrays need to deviate from a certain direction to achieve the best azimuth, while minimizing the energy loss. Therefore, to maximize the economic benefits of photovoltaic power generation, it is necessary to find the best azimuth and best inclination of photovoltaic modules.

[0036] It should also be understood that with the continuous development of smart grids and the power market, the time-of-use electricity price mechanism has become an important part of the power market. The time-of-use electricity price mechanism sets different electricity prices in different time periods to guide users to reasonably adjust their electricity demand and achieve optimal allocation of power resources. For distributed photovoltaic systems, incorporating the time-of-use electricity price mechanism into the design considerations can further optimize the inclination and orientation of photovoltaic modules, so that they can generate electricity efficiently in different time periods and increase the power generation revenue of photovoltaic power stations.

[0037] For example, the preset optimization function can be expressed as follows: (1) Among them, maxP t Indicates the maximum photovoltaic benefit in one day, Q n Indicates the power generation at a certain hour, K n Indicates the time-of-use electricity price for a certain hour, and n can represent the number of hours from 6 to 18.

[0038] For example, regarding Q nIt can be expressed by the following relationship (2): (2) in, Indicates the azimuth angle of the photovoltaic module, represents the inclination angle of the photovoltaic module, and f(*) represents the constraint equation of power generation. And, The range can be set to , The range can be set to .

[0039] For example, regarding K n The specific type of can be shown in the following relation (3): (3) Among them, K 深谷 Indicates the time-of-use electricity price during the valley period in the region (may not be set in some regions), K 低谷 represents the low time-of-use electricity price in the area, K 平价 represents the time-of-use electricity price in the region, K 高峰 represents the peak time-of-use electricity price in the region, K 尖峰 Indicates the peak time-of-use electricity price in the area.

[0040] Step S103, determining the installation angle of the photovoltaic module according to the target installation angle combination.

[0041] Through the above method, multiple installation angle combinations of photovoltaic modules are obtained, and the target installation angle combination is determined according to the installation angle combination, the preset optimization function and the preset genetic algorithm, and the installation angle of the photovoltaic module is determined according to the target installation angle combination. Among them, the installation angle combination of photovoltaic modules is determined by using the preset genetic algorithm and the preset optimization function, and taking into account the impact of the time-of-use electricity price mechanism on the power generation income of the photovoltaic system, the optimal photovoltaic module installation angle can be effectively determined, thereby improving the power generation and economic benefits of the photovoltaic system.

[0042] In a possible implementation, determining a target installation angle combination according to the installation angle combination, a preset optimization function, and a preset genetic algorithm may include: Obtaining chromosomes in a preset genetic algorithm corresponding to each installation angle combination; Determine the initial population based on chromosomes; According to the preset optimization function, determine the fitness value corresponding to each chromosome in the initial population; When the preset termination condition is met, the maximum value of the fitness value corresponding to each chromosome is determined as the target fitness value, and the target installation angle combination is determined according to the chromosome corresponding to the target fitness value.

[0043] It should be understood that the actual installation angle value is converted into a chromosome code that can be processed by the genetic algorithm, usually using binary encoding. Each angle can be converted into a binary string of fixed length. When encoding, it is necessary to consider the range and accuracy requirements of the angle value and reasonably set the number of binary bits.

[0044] For example, the preset termination condition may be set to when the maximum number of iterations is reached or the fitness value is no longer significantly improved.

[0045] For example, the size and number of the initial population can be set according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0046] In a possible implementation, the method may further include: When the preset termination condition is not met, a selection operation is performed according to the fitness value, and the target chromosomes within the preset fitness threshold range are retained; According to the target chromosome, a new population is generated through a crossover operation and a mutation operation, wherein the crossover operation includes exchanging some genes of the chromosome according to a preset crossover probability, and the mutation operation includes randomly changing the genes in the chromosome according to a preset mutation probability; The new population is determined as the initial population for a new round of iterations, and the step of determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function is returned to be executed until the preset termination condition is met.

[0047] It should be understood that the optimization problem of the inclination and azimuth of photovoltaic modules in distributed photovoltaic systems is actually a multi-variable, multi-constrained nonlinear optimization problem. The complexity of this problem is mainly reflected in the following aspects: 1) Multivariability: The variables that need to be considered during the optimization process include not only the inclination and azimuth of the photovoltaic modules, but may also involve multiple dimensions such as time-of-use electricity prices and photovoltaic irradiance.

[0048] 2) Nonlinear relationship: There is a complex nonlinear relationship between the power generation efficiency of the photovoltaic system and the solar radiation intensity, ambient temperature, component inclination and azimuth, which is difficult to accurately describe with a simple mathematical formula.

[0049] 3) Multiple constraints: In actual design, the installation of photovoltaic systems must meet multiple constraints such as building structural safety, lighting conditions, and roof utilization.

[0050] 4) Impact of time-of-use electricity prices: The introduction of the time-of-use electricity price mechanism has further complicated the problem, because the power generation income in different time periods is different, and it is necessary to comprehensively consider economic benefits and power generation efficiency.

[0051] Therefore, given the complexity of the above problems, traditional optimization methods often find it difficult to obtain the global optimal solution. As a random search algorithm that simulates the biological evolution process, genetic algorithms are suitable for solving such multi-polar nonlinear optimization problems, and have the following advantages: 1) Strong versatility: Genetic algorithms do not rely on specific domain knowledge of the problem and only need to use fitness functions to evaluate the quality of the solution. Therefore, they can be widely used in various optimization problems.

[0052] 2) Good robustness: Genetic algorithms search through group evolution, which can effectively avoid falling into local optimal solutions and improve the globality of the solution.

[0053] 3) Good parallelism: The genetic algorithm operates on a set of solutions, namely the population, which can be evaluated and evolved simultaneously, making it easy to implement parallel computing and improve solution efficiency.

[0054] 4) Adaptability: Operations such as crossover and mutation in genetic algorithms are random and can adaptively adjust the search direction and step size to increase the diversity of solutions.

[0055] For example, applying genetic algorithms to the optimization of the inclination and azimuth of distributed photovoltaic modules can design Figure 2 Steps shown: Step S201 : encoding the inclination angle and azimuth angle of the photovoltaic module into chromosomes in a genetic algorithm.

[0056] It should be understood that the inclination and azimuth of the photovoltaic module can be encoded as chromosomes in the genetic algorithm, and each chromosome corresponds to a set of specific inclination and azimuth, that is, each chromosome represents a possible solution.

[0057] Step S202, randomly generate a certain number of chromosomes as an initial population.

[0058] Step S203, calculating the fitness value of each chromosome.

[0059] For example, the fitness value of each chromosome can be calculated and its advantages and disadvantages can be evaluated based on factors such as the power generation of the photovoltaic system, the time-of-use electricity price mechanism, and the operating cost.

[0060] Step S204, determine whether the stop criterion is met, if so, execute step S205, otherwise execute step S206.

[0061] Step S205, output the result.

[0062] It should be understood that outputting the optimal chromosome in the final population can determine the optimal inclination and azimuth of the photovoltaic module.

[0063] Step S206, selecting some chromosomes to enter the next generation population according to the fitness value.

[0064] It should be understood that selecting some excellent chromosomes to enter the next generation population based on fitness values ​​can ensure the inheritance of excellent genes and improve accuracy.

[0065] Step S207, performing crossover and mutation operations on the chromosomes in the next generation population, and returning to step S203.

[0066] It should be understood that by exchanging some genes of two chromosomes through the crossover operation, a new chromosome can be generated; by randomly modifying some genes in the chromosome through the mutation operation, the diversity of the population can be increased. The fitness evaluation, selection, crossover and mutation operations can be repeated until the stopping criteria are met.

[0067] Through the above method, the effective operation of the genetic algorithm can be ensured and better optimization results can be obtained.

[0068] In a possible implementation, determining the fitness value corresponding to each chromosome in the initial population according to a preset optimization function may include: Input the installation angle combination represented by each chromosome in the initial population into a preset photovoltaic simulation model to determine the target power generation data corresponding to each installation angle combination; The maximum benefit value is calculated according to the target power generation data and the preset optimization function, and the maximum benefit value is determined as the fitness value corresponding to each chromosome in the initial population.

[0069] Through the above method, the performance of different installation angle combinations can be accurately evaluated, providing a reliable fitness evaluation basis for the genetic algorithm.

[0070] For example, PVsyst software can be used for the preset photovoltaic simulation model. PVsyst is a software used to design and simulate photovoltaic systems. It can be used to evaluate the performance and economy of photovoltaic systems. Users can use PVsyst to design photovoltaic systems, including selecting photovoltaic modules, inverters, batteries and other equipment, and setting the layout and parameters of the system.

[0071] Specifically, a typical day can be selected in each month, the azimuth angle range is set to -90°~90°, the inclination angle range is set to 0°~90°, and there are 181×91=16471 combinations of inclination angle and azimuth angle. The Python programming software provides a function for calculating the number of combinations, and the combination is generated through Python's itertools.combinations, while math.comb can directly calculate the number of combinations.

[0072] Specifically, we can first calculate the lengths of the two intervals, and then multiply the two lengths to get the number of possible combinations of number pairs. That is, there are a total of 16,471 different combinations of any number in the integer range from -90 to 90 (including -90 and 90) and any number in the integer range from 0 to 90 (including 0 and 90).

[0073] For example, Figure 3 is a schematic diagram of the distribution of time-of-use electricity prices in Shandong Province according to an exemplary embodiment of the present disclosure. Figure 3 As shown, the time-of-use electricity prices in Shandong Province from January to February and December can be specifically as follows: 6:00 to 9:00 and 15:00 are flat-section electricity prices, 10:00 and 14:00 are low-valley electricity prices, 11:00 to 13:00 is deep-valley electricity prices, and 16:00 to 18:00 is peak electricity prices; the time-of-use electricity prices from March to May can be specifically as follows: 6:00 to 9:00, 15:00 and 16:00 are flat-section electricity prices, 10:00 and 14:00 are low-valley electricity prices, and 11:00 to 13:00 is deep-valley electricity prices. The peak electricity price is from 17:00 to 18:00; the specific time-of-use electricity price from June to August can be: 6:00 to 15:00 is the flat electricity price, 16:00 is the peak electricity price, and 17:00 to 18:00 is the peak electricity price; the specific time-of-use electricity price from September to November can be: 6:00 to 9:00 and 15:00 is the flat electricity price, 10:00 and 14:00 is the low-valley electricity price, 11:00 to 13:00 is the deep-valley electricity price, 16:00 is the peak electricity price, and 17:00 to 18:00 is the peak electricity price. It can be seen that the peak and peak of the electricity price are distributed in the afternoon, so the azimuth can be considered to be set in the west direction, and the power generation will be higher in the afternoon.

[0074] For example, in actual applications, the photovoltaic simulation model can be preset, that is, the combination of different azimuth angles and inclination angles can be simulated by PVsyst software to complete the project setting and obtain the radiation situation. The specific parameter page diagram can be shown as follows: Figures 4 to 9 As shown. Among them, Figure 4 This is a schematic diagram of the parameter page when the photovoltaic module inclination angle is 30° and the azimuth angle is 0°; Figure 5 This is a schematic diagram of the parameter page when the photovoltaic module has an inclination angle of 30° and an azimuth angle of 25°; Figure 6 This is a schematic diagram of the parameter page when the photovoltaic module has an inclination angle of 30° and an azimuth angle of 45°; Figure 7 This is a schematic diagram of the parameter page when the photovoltaic module has an inclination angle of 30° and an azimuth angle of 75°; Figure 8 This is a schematic diagram of the parameter page when the photovoltaic module has an inclination angle of 30° and an azimuth angle of 90°; Fig. 9 It is a page diagram of simulation configuration parameters.

[0075] For example, a typical day can be selected according to actual conditions, and the inclination angle of the photovoltaic module can be selected to be 30° toward the south, and different azimuth angles can be selected: 0°, 25° south-west, 45° southwest, 75° west-southwest, 90° due west, etc. The embodiments of the present disclosure are not limited to this.

[0076] For example, when the photovoltaic module has a tilt angle of 30°, the hourly power generation in different time periods is derived according to different azimuth angles and radiation conditions, and the corresponding electricity prices and electricity revenues in each time period can be shown in Tables 1 and 2 below.

[0077] Table 1

[0078] Among them, it can be obtained from Table 1 that when the inclination angle of the photovoltaic module is fixed at 30° and the azimuth angles are 0°, 25° and 45° respectively, the hourly power generation, electricity price and electricity fee income corresponding to the time period.

[0079] Table 2

[0080] Among them, it can be obtained from Table 2 that when the inclination angle of the photovoltaic module is fixed at 30° and the azimuth angles are 75° and 90° respectively, the hourly power generation, electricity price and electricity fee income corresponding to the time period.

[0081] Fig.10 The figure is a graph showing the relationship between power generation and time at different azimuth angles when the inclination angle of a photovoltaic module is 30° according to an exemplary embodiment of the present disclosure.

[0082] By combining Table 1 and Fig.10 It can be seen from the content that the total power generation will decrease when the azimuth is biased to the west, but the total investment return is the largest at the azimuth of 25°. Therefore, the 25° azimuth can be selected as the benchmark to consider narrowing the azimuth range, and the hourly power generation corresponding to the time periods of multiple angle combinations can be derived through simulation again to find the maximum return value. At the same time, the conditions of different seasons and different inclinations should also be considered. Finally, the optimal inclination and azimuth of the photovoltaic module can be obtained to maximize the economic benefits and power generation efficiency of the photovoltaic system.

[0083] In a possible implementation, obtaining multiple installation angle combinations of photovoltaic components may include: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic module, a plurality of installation angle combinations of the photovoltaic module are obtained.

[0084] It should be understood that by presetting the inclination angle range and the azimuth angle range, multiple installation angle combinations can be generated, and these combinations will be used as candidate solutions for the input of the genetic algorithm, thereby improving the optimization efficiency and meeting engineering requirements.

[0085] It should also be understood that the solution in this embodiment comprehensively considers multiple factors such as the time-of-use electricity price mechanism, solar radiation intensity, ambient temperature, etc., and can maximize the economic benefits and power generation efficiency of the photovoltaic system by accurately calculating the optimal inclination and orientation of the photovoltaic components. It can also provide new ideas and methods for the optimal design of distributed photovoltaic systems and promote the further development of the photovoltaic industry.

[0086] Fig.11 A photovoltaic assembly installation angle determination device 1100 is shown according to an exemplary embodiment of the present disclosure. Fig.11 As shown, the photovoltaic assembly installation angle determination device 1100 includes: An acquisition module 1101 is used to acquire multiple installation angle combinations of photovoltaic modules, wherein each installation angle combination includes an azimuth angle and an inclination angle; A first determination module 1102 is used to determine a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target period and the time-of-use electricity price corresponding to the target period; The second determination module 1103 is used to determine the installation angle of the photovoltaic module according to the target angle combination.

[0087] Optionally, the first determining module 1102 includes: An acquisition submodule, used to acquire chromosomes in a preset genetic algorithm corresponding to each installation angle combination; The first determination submodule is used to determine the initial population according to the chromosome; The second determination submodule is used to determine the fitness value corresponding to each chromosome in the initial population according to a preset optimization function; The third determination submodule is used to determine the maximum value of the fitness values ​​corresponding to each chromosome as the target fitness value when the preset termination condition is met, and determine the target installation angle combination according to the chromosome corresponding to the target fitness value.

[0088] Optionally, the first determining module 1102 further includes a fourth determining submodule, configured to: When the preset termination condition is not met, a selection operation is performed according to the fitness value, and the target chromosomes within the preset fitness threshold range are retained; According to the target chromosome, a new population is generated through a crossover operation and a mutation operation, wherein the crossover operation includes exchanging some genes of the chromosome according to a preset crossover probability, and the mutation operation includes randomly changing the genes in the chromosome according to a preset mutation probability; The new population is determined as the initial population for a new round of iterations, and the step of determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function is returned to be executed until the preset termination condition is met.

[0089] Optionally, the second determining submodule is used for: Input the installation angle combination represented by each chromosome in the initial population into a preset photovoltaic simulation model to determine the target power generation data corresponding to each installation angle combination; The maximum benefit value is calculated according to the target power generation data and the preset optimization function, and the maximum benefit value is determined as the fitness value corresponding to each chromosome in the initial population.

[0090] Optionally, the acquisition module 1101 is used to: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic module, a plurality of installation angle combinations of the photovoltaic module are obtained.

[0091] Optionally, the time periods of the time-of-use electricity price include valley period, low valley period, parity period, peak period and spike period.

[0092] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0093] Fig.12 FIG. 1 is a block diagram of an electronic device 1200 according to an exemplary embodiment. Fig.12 As shown, the electronic device 1200 may include: a processor 1201 , a memory 1202 . The electronic device 1200 may also include one or more of a multimedia component 1203 , an input / output (I / O) interface 1204 , and a communication component 1205 .

[0094] Among them, the overall operation of the processing electronic device 1200 is to complete all or part of the steps in the above-mentioned photovoltaic module installation angle determination method. The memory 1202 is used to store various types of data to support the operation of the electronic device 1200, and these data may include, for example, instructions for any application or method for operating on the electronic device 1200, and application-related data. The memory 1202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 1203 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may be further stored in the memory 1202 or sent through the communication component 1205. The audio component also includes at least one speaker for outputting an audio signal. The I / O interface 1204 provides an interface between the processor 1201 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, a button, etc. These buttons may be virtual buttons or physical buttons. The communication component 1205 is used for wired or wireless communication between the electronic device 1200 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 1205 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0095] In an exemplary embodiment, the electronic device 1200 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned method for determining the installation angle of photovoltaic components.

[0096] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for determining the installation angle of a photovoltaic component are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 1202 including program instructions, and the above-mentioned program instructions may be executed by the processor 1201 of the electronic device 1200 to complete the above-mentioned method for determining the installation angle of a photovoltaic component.

[0097] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0098] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0099] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for determining the installation angle of a photovoltaic module, characterized in that: The method comprises: Acquire a plurality of installation angle combinations of the photovoltaic assembly, wherein each of the installation angle combinations comprises an azimuth angle and an inclination angle; Determine a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target period and the time-of-use electricity price corresponding to the target period; The installation angle of the photovoltaic assembly is determined according to the target installation angle combination.

2. The method according to claim 1, characterized in that Determining a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm includes: Obtaining a chromosome in the preset genetic algorithm corresponding to each of the installation angle combinations; According to the chromosome, determining an initial population; Determining the fitness value corresponding to each of the chromosomes in the initial population according to the preset optimization function; When the preset termination condition is met, the maximum value of the fitness values ​​corresponding to each chromosome is determined as the target fitness value, and the target installation angle combination is determined according to the chromosome corresponding to the target fitness value.

3. The method according to claim 2, characterized in that The method further comprises: When the preset termination condition is not met, a selection operation is performed according to the fitness value to retain the target chromosome within the preset fitness threshold range; According to the target chromosome, a new population is generated through a crossover operation and a mutation operation, wherein the crossover operation includes exchanging some genes of the chromosome according to a preset crossover probability, and the mutation operation includes randomly changing the genes in the chromosome according to a preset mutation probability; The new population is determined as the initial population for a new round of iteration, and the step of determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function is returned to be executed until the preset termination condition is met.

4. The method according to claim 2, characterized in that: Determining the fitness value corresponding to each chromosome in the initial population according to the preset optimization function includes: Inputting the installation angle combination represented by each chromosome in the initial population into a preset photovoltaic simulation model to determine the target power generation data corresponding to each installation angle combination; A maximum benefit value is calculated according to the target power generation data and the preset optimization function, and the maximum benefit value is determined as the fitness value corresponding to each chromosome in the initial population.

5. The method according to claim 1, characterized in that The obtaining of multiple installation angle combinations of the photovoltaic components includes: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic assembly, a plurality of installation angle combinations of the photovoltaic assembly are obtained.

6. The method according to claim 1, characterized in that The time periods of the time-of-use electricity price include deep valley period, low valley period, parity period, peak period and peak period.

7. A photovoltaic module installation angle determination device, characterized in that: The device comprises: An acquisition module, used to acquire a plurality of installation angle combinations of the photovoltaic assembly, wherein each installation angle combination includes an azimuth angle and an inclination angle; A first determination module is used to determine a target installation angle combination according to the installation angle combination, a preset optimization function and a preset genetic algorithm, wherein the preset optimization function is a function that sums the product of the power generation in each target time period and the time-of-use electricity price corresponding to the target time period; The second determination module is used to determine the installation angle of the photovoltaic assembly according to the target angle combination.

8. The device according to claim 7, characterized in that The acquisition module is used for: According to the preset inclination angle range and the preset azimuth angle range of the photovoltaic assembly, a plurality of installation angle combinations of the photovoltaic assembly are obtained.

9. A non-transitory 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 the method according to any one of claims 1 to 6 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 6.

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