Layout method and device of photovoltaic module, electronic equipment and medium

Abstract

The application provides a layout method and device of a photovoltaic assembly, electronic equipment and a medium, and the method comprises at least: obtaining target interval weight distribution of each layout parameter of the photovoltaic assembly in a target area; generating an optimization parameter population according to the target interval weight distribution of the layout parameter; forming a plurality of optimization parameter sub-population combinations and calculating power generation target values of each optimization parameter sub-population combination; and performing convergence calculation on the power generation target values of each optimization parameter sub-population combination to find out an optimization parameter sub-population combination that reaches a convergence target. In the application, the target interval weight distribution of each layout parameter of the photovoltaic assembly in the target area is obtained based on the maximum value of the power generation amount, then the corresponding optimization parameter population is generated according to the target interval weight distribution, and population combination, power generation target value calculation, population optimization and power generation target value convergence calculation are performed, thereby reducing the workload of population optimization, quickly finding out the target combination of the layout parameters, and improving the optimization efficiency and speed.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant design technology, specifically to a method, apparatus, electronic device, and medium for the layout of photovoltaic modules. Background Technology

[0002] In the design of photovoltaic power plant arrays, there are many types of layout parameters for each component (such as installation tilt angle, array spacing, bottom edge height of the component, azimuth angle, and capacity ratio), and the corresponding parameter ranges are quite wide. We need to find an optimal combination of layout parameters to maximize the power generation revenue after installation. Currently, two methods are commonly used: manual selection based on experience or exhaustive calculation. Manually selecting one or more combinations based on experience is generally difficult to find the optimal parameter combination and is inefficient. On the other hand, calculating the power generation revenue of all combinations through exhaustive calculation is too computationally intensive and cannot be done quickly enough, also resulting in low efficiency.

[0003] Therefore, there is an urgent need for a simple and efficient photovoltaic module layout optimization technology solution. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the present invention provides a layout optimization technology for photovoltaic modules to quickly and efficiently find the target combination of layout parameters that maximizes power generation revenue.

[0005] To achieve the above and other related objectives, the technical solution provided by this invention is as follows.

[0006] A method for arranging photovoltaic modules, comprising at least the following steps:

[0007] Obtain the target interval weight allocation for each layout parameter of photovoltaic modules within the target area;

[0008] For each of the layout parameters, a corresponding optimization parameter population is generated according to the target interval weight allocation of the layout parameter, and the optimization parameter population includes multiple optimization parameter subpopulations.

[0009] Each of the layout parameters is selected and combined to form multiple combinations of optimization parameter subpopulations, and the power generation target value corresponding to each combination of optimization parameter subpopulations is calculated.

[0010] The power generation target value of each optimization parameter subpopulation combination is converged by using a population optimization algorithm to find the optimization parameter subpopulation combination that has reached the convergence target.

[0011] The photovoltaic modules are arranged according to the optimal parameter subpopulation combination that achieves the convergence objective.

[0012] Optionally, the step of obtaining the target interval weight allocation of each layout parameter of the photovoltaic modules within the target area includes:

[0013] Based on maximizing power generation revenue or minimizing the cost per kilowatt-hour, a target interval weight allocation set is obtained. The target interval weight allocation set includes multiple regions and the target interval weight allocation of each layout parameter of the photovoltaic module in each region when the power generation revenue reaches the maximum value.

[0014] Obtain the target area where the photovoltaic module is located, and read the target interval weight allocation of each layout parameter of the photovoltaic module from the target interval weight allocation set according to the target area.

[0015] Optionally, the step of obtaining the target interval weight allocation set based on maximizing power generation revenue or minimizing the cost per kilowatt-hour includes:

[0016] Obtain the value range of each of the layout parameters of the photovoltaic modules in multiple regions and each region;

[0017] For each region, one piece of data for each layout parameter is selected and combined to form multiple optimization parameter combinations, and the power generation revenue or cost per kilowatt-hour corresponding to each optimization parameter combination is calculated.

[0018] For each region, find the optimal parameter combinations that correspond to the maximum value of the power generation revenue or the minimum value of the cost per kilowatt-hour, and denot them as the target optimal parameter combinations.

[0019] For each region, the target optimization parameter combinations of each group are clustered to obtain multiple segmented intervals for each layout parameter;

[0020] For each region, within each segment interval of each layout parameter, the ratio of the number of target optimization parameter combinations falling within the segment interval to the total number of target optimization parameter combinations is calculated, and the ratio is used as the target weight coefficient for the maximum value of the power generation revenue or the minimum value of the cost per kilowatt-hour within the segment interval, thus obtaining the target interval weight allocation for each layout parameter.

[0021] The target interval weight allocation set consists of the target interval weight allocation for each region and the target interval weight allocation for each layout parameter of the photovoltaic module within each region.

[0022] Optionally, the step of generating a corresponding optimization parameter population for each of the layout parameters according to the target interval weight allocation of the layout parameters includes:

[0023] For each segment interval of the layout parameters, an optimization parameter subpopulation is generated according to the corresponding target weight coefficient, and each optimization parameter subpopulation corresponding to the layout parameters constitutes the optimization parameter population.

[0024] Optionally, the population optimization algorithm includes at least one of particle swarm optimization and genetic algorithm.

[0025] Optionally, when performing convergence calculations on the power generation target values ​​of each of the optimization parameter subpopulation combinations, if the convergence calculation result fails to reach the convergence target, the photovoltaic module layout method further includes the following steps:

[0026] The target weight coefficient corresponding to each optimization parameter subgroup is adjusted according to the proportion of the power generation target value of each optimization parameter subgroup in each layout parameter to the total power generation target value of all optimization parameter subgroups.

[0027] The optimization parameter population is updated according to the adjusted target weight coefficients. The updated optimization parameter population includes multiple updated optimization parameter subpopulations.

[0028] One updated subpopulation of optimization parameters for each of the layout parameters is selected and combined to form multiple combinations of updated subpopulations of optimization parameters, and the power generation target value corresponding to each combination of updated subpopulations of optimization parameters is calculated.

[0029] The power generation target value of each updated subpopulation combination of the optimization parameters is calculated by using a population optimization algorithm. If the convergence target is reached, the process ends. If the convergence target is not reached, the process is repeated until the convergence target is reached.

[0030] Optionally, the step of adjusting the target weight coefficient corresponding to each optimization parameter subgroup based on the proportion of the power generation target value of each optimization parameter subgroup in each layout parameter to the total power generation target value of all optimization parameter subgroups includes:

[0031] For each of the layout parameters, if the proportion of the power generation target value of the optimization parameter subpopulation to the total power generation target value of all the optimization parameter subpopulations is greater than the corresponding target weight coefficient, then the target weight coefficient is increased to obtain the adjusted target weight coefficient.

[0032] For each of the layout parameters, if the proportion of the power generation target value of the optimization parameter subpopulation to the total power generation target value of all the optimization parameter subpopulations is less than or equal to the corresponding target weight coefficient, then the target weight coefficient is reduced to obtain the adjusted target weight coefficient.

[0033] Optionally, the target weight coefficient is greater than zero and the target weight coefficient is less than one.

[0034] A photovoltaic module layout apparatus, the apparatus comprising:

[0035] The data acquisition module is used to acquire the target area where the photovoltaic module is located, multiple areas where the photovoltaic module is located, and the value range of each layout parameter of the photovoltaic module in each area.

[0036] The first processing module is used to perform optimization weight calculation based on the value range of each layout parameter of the photovoltaic module in each region, based on the maximum power generation revenue or the minimum cost per kilowatt-hour, to obtain the target interval weight allocation of each layout parameter in each region. The target interval weight allocation of each region and each layout parameter of the photovoltaic module in each region constitutes the target interval weight allocation set.

[0037] The second processing module is used to generate a corresponding optimization parameter population based on the target interval weight allocation of each layout parameter in the target area. The optimization parameter population includes multiple optimization parameter subpopulations. Multiple optimization parameter subpopulation combinations are formed based on the combination of optimization parameter populations of each layout parameter. The power generation target value corresponding to each optimization parameter subpopulation combination is calculated. Then, the power generation target value of each optimization parameter subpopulation combination is converged through a population optimization algorithm to find the optimization parameter subpopulation combination that has reached the convergence target.

[0038] The output module is used to output the optimal parameter subpopulation combination that achieves the convergence target and the corresponding power generation target value;

[0039] The storage module is used to save the target interval weight allocation set, and also to save the optimization parameter subpopulation combination and the corresponding power generation target value that achieve the convergence target.

[0040] An electronic device, comprising:

[0041] One or more processors;

[0042] A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the photovoltaic module layout method described in any of the preceding claims.

[0043] A computer-readable storage medium storing computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the photovoltaic module layout method described in any of the preceding claims.

[0044] The beneficial effects of this invention are as follows: First, the target interval weight allocation of each layout parameter of the photovoltaic module within the target area is obtained. Then, based on the target interval weight allocation of each layout parameter, a corresponding optimization parameter population is generated, and population combination and power generation target value calculation are performed. Finally, population optimization and power generation target value convergence calculation are performed. Because the population is generated based on the target interval weight allocation of the optimized layout parameters, rather than based on the overall value range of the unoptimized layout parameters, the generated population can be optimized. The entire population optimization is not a global optimization, which reduces the workload of population optimization and can quickly find the target combination of layout parameters. The optimization efficiency is high and the speed is fast.

[0045] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0047] Figure 1 This is a schematic diagram illustrating the steps of a photovoltaic module layout parameter optimization method according to an exemplary embodiment of the present invention;

[0048] Figure 2 This is a flowchart illustrating a method for optimizing the layout parameters of a photovoltaic module according to an exemplary embodiment of the present invention;

[0049] Figure 3 yes Figure 2 A schematic diagram of step S11;

[0050] Figure 4 This is a schematic diagram illustrating the segmented intervals of the photovoltaic module with respect to the height of the lower edge of the module from the ground, as shown in an exemplary embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of a photovoltaic module segmented with respect to azimuth angle, illustrating an exemplary embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of the segmented intervals of a photovoltaic module with respect to the capacity ratio, as shown in an exemplary embodiment of the present invention;

[0053] Figure 7 This is a schematic diagram of a photovoltaic module with respect to the installation tilt angle, illustrating an exemplary embodiment of the present invention.

[0054] Figure 8This is a block diagram illustrating a photovoltaic module layout parameter optimization device according to an exemplary embodiment of the present invention;

[0055] Figure 9 This is a schematic diagram illustrating the structure of a computer system 900 used to implement the electronic device of the present invention, as shown in an exemplary embodiment of the present invention. Detailed Implementation

[0056] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0057] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0058] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0059] As mentioned in the background section, the inventors discovered that in the design process of photovoltaic arrays for photovoltaic power plants, there are many types of layout parameters for each photovoltaic module, and the corresponding parameter range is relatively wide. For example, the layout parameters of photovoltaic modules in a certain target area are as follows: the installation tilt angle of the photovoltaic modules ranges from 0 to 60 degrees, with a granularity of 1 degree; the array spacing is set according to the actual project, with the minimum to maximum spacing being 5 meters and a granularity of 0.2 meters; the height of the lower edge of the photovoltaic modules from the ground is between 0.5 meters and 3 meters, with a granularity of 0.2 meters; the azimuth angle is between -30 degrees and 30 degrees, with a granularity of 1 degree; and the capacity ratio is between 1 and 2, with a granularity of 0.01.

[0060] It's important to note that the average annual sunshine duration varies across different regions, resulting in different values ​​for the layout parameters of photovoltaic (PV) modules. We need to find the optimal combination of layout parameters within the range of values ​​for each PV module to maximize power generation revenue or minimize the cost per kilowatt-hour after installation. Currently, this is often achieved through manual selection based on experience or exhaustive calculation. However, manually selecting one or more layout parameter combinations based on experience for comparison is generally inefficient and rarely yields the optimal combination. On the other hand, exhaustively calculating the power generation revenue or cost per kilowatt-hour for all combinations is computationally intensive and difficult to perform in a short time, also resulting in low efficiency.

[0061] Based on this, the present invention proposes a layout optimization technology for photovoltaic modules: First, based on the maximum value of power generation revenue or the minimum value of cost per kilowatt-hour, the target interval weight allocation of each layout parameter of the photovoltaic modules in the target area is obtained. Then, based on the target interval weight allocation of each layout parameter, a corresponding optimization parameter population is generated, so that the generated population can be optimized to reduce the workload of population optimization. Then, population combination and power generation target value calculation are performed. Finally, population optimization and power generation target value convergence calculation are performed. The entire population optimization is not a global optimization, which can quickly and efficiently find the optimal combination of layout parameters.

[0062] In detail, embodiments of the present invention provide a photovoltaic module layout method, a photovoltaic module layout apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which will be described in detail below.

[0063] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of a photovoltaic module layout method according to an exemplary embodiment of the present invention. Figure 1 As shown, in an exemplary embodiment of the present invention, the photovoltaic module layout method includes at least the following steps:

[0064] S1. Obtain the target interval weight allocation of each layout parameter of the photovoltaic module within the target area;

[0065] S2. For each layout parameter, generate a corresponding optimization parameter population based on the target interval weight allocation of the layout parameter. The optimization parameter population includes multiple optimization parameter subpopulations.

[0066] S3. Select one optimization parameter subpopulation for each layout parameter and combine them to form multiple optimization parameter subpopulation combinations, and calculate the power generation target value corresponding to each optimization parameter subpopulation combination.

[0067] S4. Using a population optimization algorithm, the power generation target value of each subpopulation combination of optimization parameters is converged, and the subpopulation combination of optimization parameters that achieves the convergence target is found.

[0068] S5. Layout photovoltaic modules based on the optimal parameter subpopulation combination that achieves the convergence target.

[0069] In detail, in an exemplary embodiment of the present invention, as Figure 2 As shown, step S1, which involves obtaining the target interval weight allocation for each layout parameter of photovoltaic modules within the target area, further includes:

[0070] S11. Based on maximizing power generation revenue or minimizing the cost per kilowatt-hour, obtain the target interval weight allocation set. The target interval weight allocation set includes multiple regions and the target interval weight allocation of each layout parameter of the photovoltaic module in each region when the power generation revenue reaches the maximum value.

[0071] S12. Obtain the target area where the photovoltaic module is located, and read the target interval weight allocation of each layout parameter of the photovoltaic module from the target interval weight allocation set according to the target area.

[0072] The layout parameters include at least the installation tilt angle, array spacing, bottom edge height of the components, azimuth angle, and capacity ratio.

[0073] More specifically, in an exemplary embodiment of the invention, as Figure 3 As shown, step S11, which obtains the target interval weight allocation set based on maximizing power generation revenue or minimizing the cost per kilowatt-hour, further includes:

[0074] S111. Obtain the value range of each layout parameter of the photovoltaic module in multiple regions and each region.

[0075] S112. For each region, select one data point for each layout parameter and combine them to form multiple optimization parameter combinations, and calculate the power generation revenue or cost per kilowatt-hour corresponding to each optimization parameter combination.

[0076] S113. For each region, find the optimal parameter combinations that correspond to the maximum value of power generation revenue or the minimum value of cost per kilowatt-hour, and denot them as the target optimal parameter combinations.

[0077] S114. For each region, cluster the combination of target optimization parameters for each group to obtain multiple segment intervals for each layout parameter.

[0078] S115. For each region, in each segment interval of each layout parameter, calculate the ratio of the number of target optimization parameter combinations falling into the segment interval to the total number of target optimization parameter combinations, and use it as the target weight coefficient for the maximum value of power generation revenue or the minimum value of cost per kilowatt-hour in the segment interval, so as to obtain the target interval weight allocation for each layout parameter.

[0079] S116, the target interval weight allocation set is composed of the target interval weight allocation for each region and the layout parameters of each photovoltaic module within each region.

[0080] Furthermore, since the average annual sunshine hours vary in different regions (regions with different latitudes and longitudes and different altitudes), the layout parameters of photovoltaic modules installed in photovoltaic power stations in different regions need to be adapted to local conditions. That is, the layout parameters of photovoltaic modules differ in different regions, and the value range of the layout parameters of photovoltaic modules is different. For example, the installation tilt angle range of photovoltaic modules in region A is 0-60 degrees, while the installation tilt angle range of photovoltaic modules in region B is 0-70 degrees.

[0081] Therefore, in step S111, it is necessary to obtain the value range of each layout parameter of the photovoltaic module in multiple regions and each region, and to perform optimization preprocessing on each region where a photovoltaic power station may be installed, so as to expand the applicability of the photovoltaic module layout parameter optimization method.

[0082] Furthermore, in step S112, for each region, one piece of data for each layout parameter is selected and combined to form multiple optimization parameter combinations covering all layout parameters, and the power generation revenue (positively correlated with power generation) or cost per kilowatt-hour of each optimization parameter combination is roughly calculated.

[0083] Furthermore, in step S113, for each region, multiple rough optimization calculations are performed to find each set of optimization parameter combinations (such as N sets, where N is an integer greater than or equal to 2) corresponding to the maximum value of power generation revenue or the minimum value of cost per kilowatt-hour, and these are denoted as the target optimization parameter combinations.

[0084] Furthermore, in step S114, for each region, a clustering algorithm is used to cluster and divide each group of target optimization parameter combinations to obtain multiple segmented intervals corresponding to each layout parameter.

[0085] Further, in step S115, for each region, within each segment interval of each layout parameter, the ratio of the number of target optimization parameter combinations falling within that segment interval (i.e., the number of samples) to the total number of target optimization parameter combinations (i.e., the total number of samples) is calculated, and this ratio is used as the target weight coefficient for the maximum value of power generation revenue or the minimum value of per kilowatt-hour cost within that segment interval, thereby obtaining the target interval weight allocation for each layout parameter.

[0086] Furthermore, in step S116, the target interval weight allocation of each region and each layout parameter of the photovoltaic module in each region is combined into a dataset, that is, the target interval weight allocation set is obtained. The target interval weight allocation set is saved in the database for easy retrieval and retrieval in subsequent step S12.

[0087] More specifically, in step S11, based on the maximum value of power generation revenue or the minimum value of cost per kilowatt-hour, optimization preprocessing is performed on each layout parameter of photovoltaic modules in each region, resulting in each segment interval of layout parameters and the corresponding target weight coefficient, forming an optimization reference standard, which can appropriately reduce the workload of subsequent real-time optimization in each region.

[0088] It should be noted that in step S11, the target interval weight allocation set is obtained based on one of the principles of the maximum value of power generation revenue and the minimum value of the cost per kilowatt-hour. That is, the target interval weight allocation set is obtained based on the maximum value of power generation revenue or the minimum value of the cost per kilowatt-hour.

[0089] More specifically, in step S12, the target area where the photovoltaic module is located is first obtained, and then index matching is performed based on the target area to read the target interval weight allocation of each layout parameter of the corresponding photovoltaic module from the target interval weight allocation set.

[0090] In detail, step S2, which generates a corresponding population of optimization parameters for each layout parameter based on the target interval weight allocation of the layout parameter, includes:

[0091] For each segment interval of the layout parameters, an optimization parameter subpopulation is generated according to the corresponding target weight coefficient. The various optimization parameter subpopulations corresponding to the layout parameters constitute the optimization parameter population. In other words, according to the target interval weight allocation of the layout parameters, the value range of the layout parameters is divided into multiple corresponding segment intervals, and multiple optimization parameter subpopulations are generated according to the corresponding target weight coefficients. The number of individuals in each optimization parameter subpopulation is proportional to the corresponding target weight coefficient.

[0092] In detail, in step S3, one optimization parameter subpopulation for each layout parameter is selected and combined to form multiple optimization parameter subpopulation combinations covering all layout parameters, and the power generation target value corresponding to each optimization parameter subpopulation combination is calculated.

[0093] It should be emphasized that there are two types of target values ​​for optimization: the maximum target value and the minimum target value. Among them, the target values ​​for power generation, power generation revenue, and power generation profit are generally to seek the maximum value; while the target values ​​for cost per kilowatt-hour are generally to seek the minimum value.

[0094] Therefore, in step S3, the power generation target value includes at least one of power generation, power generation revenue, and cost per kilowatt-hour. These can be freely combined according to actual calculation needs and are not limited here. For example, the power generation target value may only include power generation (or power generation revenue), or it may only include cost per kilowatt-hour, with subsequent optimization calculations based solely on maximizing power generation or minimizing cost per kilowatt-hour. The power generation target value may also include any combination of two of power generation, power generation revenue, and cost per kilowatt-hour, such as power generation target value = power generation revenue - A × cost per kilowatt-hour, where A is a constant. Furthermore, the power generation target value may include a combination of all three, such as power generation target value = power generation profit = power generation revenue - power generation × cost per kilowatt-hour.

[0095] In an optional embodiment of the present invention, the step of calculating the power generation profit of each optimal parameter subpopulation combination, where the power generation target value = power generation profit = power generation revenue - power generation × cost per kilowatt-hour, further includes:

[0096] St1. For each subpopulation combination of optimization parameters, calculate the power generation and power generation revenue corresponding to each subpopulation combination of optimization parameters. The power generation revenue is positively correlated with the power generation.

[0097] St2. For each subpopulation combination of optimization parameters, calculate the cost per kilowatt-hour for each subpopulation combination of optimization parameters.

[0098] St3. For each optimal parameter subpopulation combination, calculate the power generation profit corresponding to each optimal parameter subpopulation combination based on power generation, power generation revenue, and cost per kilowatt-hour. Power generation profit = power generation revenue - power generation × cost per kilowatt-hour.

[0099] Specifically, in step S4, a population optimization algorithm is used to calculate the convergence of the power generation target value for each optimal parameter subpopulation combination, and to find the optimal parameter subpopulation combination that achieves the convergence target. The population optimization algorithm includes at least one of particle swarm optimization and genetic algorithms.

[0100] More in detail, such as Figure 2As shown, in step S4, when performing convergence calculations on the power generation target values ​​of each optimization parameter subpopulation combination, if the convergence calculation result fails to reach the convergence target, the photovoltaic module layout method further includes the following steps:

[0101] Stp1: Adjust the target weight coefficient corresponding to each optimization parameter subgroup according to the proportion of the power generation target value of each optimization parameter subgroup in each layout parameter to the total power generation target value of all optimization parameter subgroups.

[0102] Step 2: Based on the adjusted target weight coefficients, update the optimization parameter population. The updated optimization parameter population includes multiple updated optimization parameter subpopulations. Repeat steps S3-S4 again, selecting one updated optimization parameter subpopulation for each layout parameter and combining them to form multiple updated optimization parameter subpopulation combinations. Calculate the power generation target value corresponding to each updated optimization parameter subpopulation combination. Using a population optimization algorithm, perform convergence calculation on the power generation target value of each updated optimization parameter subpopulation combination. If the convergence target is reached, the process ends; otherwise, repeat this step until the final convergence target is reached.

[0103] The convergence target is not a fixed value; it is related to factors such as the corresponding component material, specifications, and quantity. For example, the convergence target for the cost per kilowatt-hour is its minimum value, the convergence target for power generation or power generation revenue is its maximum value, and the convergence target for power generation profit is its maximum value. In general, several calculations are required to reach the convergence target.

[0104] Furthermore, such as Figure 2 As shown, the step Stp1, which adjusts the target weight coefficient corresponding to each optimization parameter subgroup according to the proportion of the power generation target value of each optimization parameter subgroup in each layout parameter to the total power generation target value of all optimization parameter subgroups, includes:

[0105] Stp11. For each layout parameter, if the power generation target value of the optimization parameter subgroup accounts for a proportion of the total power generation target value of all optimization parameter subgroups that is greater than the corresponding target weight coefficient, then increase the target weight coefficient to obtain the adjusted target weight coefficient, but it shall not exceed 1 and is generally less than 1.

[0106] Stp12. For each layout parameter, if the power generation target value of the optimization parameter subpopulation is less than or equal to the total power generation target value of all optimization parameter subpopulations, then the target weight coefficient is reduced to obtain the adjusted target weight coefficient, but it is not lower than 0, and is generally greater than 0.

[0107] It should be noted that in step Stp1, the adjusted target weight coefficient is neither 1 nor 0, that is, the target weight coefficient is greater than zero and less than one. This is to increase the optimization range outside of theoretical experience and avoid falling into the experience trap, which would prevent the optimization from converging. For example, the installation tilt angle of a photovoltaic module has three intervals: [0,20], [20,40], and (40,60]. The target weight coefficient of the first interval is 0.1, the target weight coefficient of the second interval is 0.8, and the target weight coefficient of the third interval is 0.1. In actual multi-round calculations, the value is the largest in the interval [20,40], and the values ​​of the other two intervals are the lowest. The target weight coefficient of the second interval can be increased, and the target weight coefficients of the first and third intervals can be decreased, such as 0.96, 0.02, and 0.02. However, the target weight coefficient of the second interval cannot be directly set to 1 and the latter two to 0, as this may lead to the experience trap.

[0108] In an exemplary embodiment of the present invention, the optimization range for various layout parameters of photovoltaic modules in a certain area is set as follows: the installation tilt angle of the photovoltaic modules ranges from 0 to 60 degrees, with a granularity of 1 degree; the array spacing is set according to the actual project, with a minimum to maximum spacing of 5 meters and a granularity of 0.2 meters; the height of the lower edge of the photovoltaic modules from the ground is between 0.5 meters and 3 meters, with a granularity of 0.2 meters; the azimuth angle is between -30 degrees and 30 degrees, with a granularity of 1 degree; and the capacity ratio is between 1 and 2, with a granularity of 0.01. Meanwhile, the power generation target value in this embodiment of the present invention is the power generation revenue.

[0109] First, retrieve the target interval weight allocation for photovoltaic modules (e.g., piles) regarding the height of the lower edge of the module above the ground from the target interval weight allocation set: for example, if the height optimization range is three segments, then... Figure 4 As shown, the target weight coefficient in the minimum value interval h1 is 0.7, corresponding to the generation of 70% of the optimal sub-species in this interval. The target weight coefficient in the maximum value interval h3 is 0.2, corresponding to the generation of 20% of the optimal sub-species in this interval. The target weight coefficient in the middle range h2 is 0.1, corresponding to the generation of 10% of the optimal sub-species in this interval. If the power generation revenue of the h1 segment accounts for more than 70% of the total power generation revenue, it can be appropriately increased, but cannot exceed 100%. If the power generation revenue of the h3 segment accounts for more than 20% of the total power generation revenue of all sub-species, the number of sub-species generated in the next h3 segment can be appropriately increased. If the power generation revenue of the h2 segment accounts for less than 10% of the total power generation revenue, the proportion of the sub-species generated in the next segment can be appropriately reduced, but cannot be reduced to 0. The parameter optimization of the array spacing is similar to the parameter optimization of the component bottom edge height above the ground, and will not be described in detail here.

[0110] Similarly, extract the target interval weight allocation of the photovoltaic module with respect to the azimuth angle from the target interval weight allocation set: such as Figure 5As shown, the target weight coefficient is divided into [-10, 10] and other angle intervals. For example, the target weight coefficient for the [-10, 10] segment interval is 0.8, which corresponds to generating 80% of the optimal subpopulation in this interval. The other intervals correspond to generating 20% ​​of the optimal subpopulation. The adjustment method of the target weight coefficient for the next subpopulation generation is similar to the above, and will not be repeated here. If the target weight coefficient for the [-10, 10] segment interval is 1, the target weight coefficient for this interval can be appropriately reduced according to project needs (such as setting it to 0.9 or 0.8, etc.), and the other intervals will use the remaining target weight coefficient.

[0111] Similarly, extract the target interval weight allocation of photovoltaic modules with respect to capacity ratio from the target interval weight allocation set: such as Figure 6 As shown, its distribution ratio is divided into 3 segmented intervals. The target weight coefficient in the [C1,C2] segmented interval is 0.7, which generates more than 70% of the optimal subpopulations in this interval, and generates 30% of the optimal subpopulations in other ranges. The adjustment method of the target weight coefficient for the next generation of subpopulations is similar to that described above, and will not be repeated here.

[0112] Similarly, extract the target interval weight allocation of the photovoltaic modules with respect to the installation tilt angle from the target interval weight allocation set: such as Figure 7 As shown, if the target weight coefficient in the segment interval [angle1, angle2] is 0.8, more than 80% of the optimization subpopulations will be generated in this segment interval, and less than 20% of the optimization subpopulations will be generated in other intervals. The adjustment method of the target weight coefficient for the next generation of subpopulations is similar to that above, and will not be repeated here.

[0113] In the aforementioned method for optimizing the layout parameters of photovoltaic modules, the target interval weight allocation for each layout parameter of the photovoltaic modules within the target area is first obtained based on the maximum value of power generation revenue or the minimum value of levelized cost of electricity (LCOE), performing optimization preprocessing. Then, based on the target interval weight allocation of each layout parameter, a corresponding optimization parameter population is generated, which optimizes the generated population and effectively reduces the workload of population optimization. This reduces the workload of subsequent population combination, power generation target value calculation, population optimization, and power generation target value convergence calculation, enabling the rapid and efficient identification of the optimal combination of layout parameters. Furthermore, the target interval weight allocation for each layout parameter can be adjusted according to the actual proportion of the power generation target value, improving the accuracy of parameter optimization.

[0114] Figure 8 This is a block diagram illustrating a layout arrangement of a photovoltaic module, as shown in an exemplary embodiment of the present invention. Figure 8 As shown, the exemplary photovoltaic module layout apparatus includes:

[0115] The data acquisition module 11 is used to acquire the target area where the photovoltaic module is located, multiple areas where the photovoltaic module is located, and the value range of various layout parameters of the photovoltaic module in each area.

[0116] The first processing module 12 is used to perform optimization weight calculation based on the value range of each layout parameter of the photovoltaic module in each region, based on the maximum power generation revenue or the minimum cost per kilowatt-hour, to obtain the target interval weight allocation of each layout parameter in each region. The target interval weight allocation of each region and each layout parameter of the photovoltaic module in each region constitutes the target interval weight allocation set.

[0117] The second processing module 13 is used to generate a corresponding optimization parameter population based on the target interval weight allocation of each layout parameter in the target area. The optimization parameter population includes multiple optimization parameter subpopulations. Multiple optimization parameter subpopulation combinations are formed based on the optimization parameter population combinations of each layout parameter. The power generation target value corresponding to each optimization parameter subpopulation combination is calculated. Then, the power generation target value of each optimization parameter subpopulation combination is converged through the population optimization algorithm to find the optimization parameter subpopulation combination that has reached the convergence target.

[0118] Output module 14 is used to output the optimal parameter subpopulation combination that achieves the convergence target and the corresponding power generation target value;

[0119] Storage module 15 is used to store the target interval weight allocation set, and also to store the optimization parameter subpopulation combination that achieves the convergence target and the corresponding power generation target value;

[0120] The layout parameters include at least the installation tilt angle, array spacing, bottom edge height of the components, azimuth angle, and capacity ratio.

[0121] It should be noted that the photovoltaic module layout device and the photovoltaic module layout method provided in the above embodiments belong to the same concept. The specific operation of each module has been described in detail in the method embodiments and will not be repeated here. In practical applications, the photovoltaic module layout device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0122] The present invention also provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the photovoltaic module layout method provided in the above embodiments.

[0123] Figure 9A schematic diagram of a computer system suitable for implementing embodiments of the present invention is shown. It should be noted that... Figure 9 The computer system 900 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0124] like Figure 9 As shown, the computer system 900 includes a Central Processing Unit (CPU) 901, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 902 or programs loaded from storage portion 908 into Random Access Memory (RAM) 903, such as performing the methods described in the above embodiments. The RAM 903 also stores various programs and data required for system operation. The CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An Input / Output (I / O) interface 905 is also connected to the bus 904.

[0125] The following components are connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 910 as needed so that computer programs read from them can be installed into storage section 908 as needed.

[0126] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by central processing unit (CPU) 901, it performs various functions defined in the system of the present invention.

[0127] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0129] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0130] The present invention also provides a computer-readable storage medium storing computer-readable instructions thereon. When the computer-readable instructions are executed by a computer processor, the computer performs the photovoltaic module layout method provided in the above embodiments. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0131] Furthermore, the present invention provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the photovoltaic module layout method provided in the various embodiments described above.

[0132] In summary, in the photovoltaic module layout method, apparatus, electronic equipment, and medium provided by this invention, the target interval weight allocation of each layout parameter of the photovoltaic module within the target area is first obtained based on the maximum value of power generation revenue or the minimum value of levelized cost. Then, based on the target interval weight allocation of each layout parameter, a corresponding optimization parameter population is generated, and population combination, power generation target value calculation, population optimization, and power generation target value convergence calculation are performed. Because the population is generated based on the optimized target interval weight allocation of the layout parameters, rather than based on the overall value range of the unoptimized layout parameters, the workload of population optimization is reduced, and the target combination of layout parameters can be found quickly, resulting in high optimization efficiency and speed. The target interval weight allocation of each layout parameter can be adjusted according to the actual power generation target value ratio, which can improve the accuracy of parameter optimization. The adjusted target weight coefficient is not 1 or 0, which can increase the optimization of the range outside of theoretical experience, effectively avoid omissions, and further improve the accuracy of parameter optimization.

[0133] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention. It should be emphasized that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions, and operations that a system according to various embodiments of the present invention may implement. Each block in the flowchart or block diagram may represent a module, program segment, or part of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each box in a block diagram or flowchart, as well as combinations of boxes in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0134] The modules and units described in the embodiments of the present invention can be implemented in software or hardware, and can also be located in a processor. The names of these modules and units do not necessarily constitute a limitation on the module or unit itself.

[0135] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method of laying out a photovoltaic assembly, characterized in that, At least comprising steps of: obtaining target interval weight distribution of each layout parameter of a photovoltaic module in a target area; for each layout parameter, generating a corresponding optimization parameter population according to the target interval weight distribution of the layout parameter, the optimization parameter population comprising a plurality of optimization parameter sub-populations; selecting each optimization parameter sub-population of each layout parameter one by one and combining to form a plurality of optimization parameter sub-population combinations, and calculating the power generation target value corresponding to each optimization parameter sub-population combination; performing convergence calculation on the power generation target value of each optimization parameter sub-population combination through a population optimization algorithm to find out the optimization parameter sub-population combination that reaches the convergence target; arranging the photovoltaic module according to the optimization parameter sub-population combination that reaches the convergence target; wherein the step of obtaining the target interval weight distribution of each layout parameter of the photovoltaic module in the target area comprises: based on maximum power generation benefit or minimum unit power cost, obtaining a target interval weight distribution set, the target interval weight distribution set comprising a plurality of regions and target interval weight distribution of each layout parameter of the photovoltaic module in each region when the power generation benefit reaches a maximum value; obtaining the target area where the photovoltaic module is located, and reading the target interval weight distribution of each layout parameter of the photovoltaic module from the target interval weight distribution set according to the target area; the step of obtaining the target interval weight distribution set based on maximum power generation benefit or minimum unit power cost comprises: obtaining a plurality of regions and the value interval of each layout parameter of the photovoltaic module in each region; for each region, selecting one data of each layout parameter one by one and combining to form a plurality of optimization parameter combinations, and calculating the power generation benefit or unit power cost corresponding to each optimization parameter combination; for each region, finding out each group of optimization parameter combinations corresponding to the maximum value of the power generation benefit or the minimum value of the unit power cost, and recording as target optimization parameter combinations; for each region, clustering and dividing each group of target optimization parameter combinations to obtain a plurality of segmented intervals of each layout parameter; for each region, in each segmented interval of each layout parameter, calculating the ratio of the number of target optimization parameter combinations falling into the segmented interval to the total number of target optimization parameter combinations, and taking the ratio as the target weight coefficient of the maximum value of the power generation benefit or the minimum value of the unit power cost appearing in the segmented interval, to obtain the target interval weight distribution of each layout parameter; the target interval weight distribution of each layout parameter of the photovoltaic module in each region and each region form the target interval weight distribution set.

2. The layout method of a photovoltaic assembly according to claim 1, characterized in that, the step of generating a corresponding optimization parameter population for each layout parameter according to the target interval weight distribution of the layout parameter comprises: For each of the segment intervals of the layout parameters, a sub-population of the optimization parameters is generated according to a corresponding target weight coefficient, and each of the sub-populations of the optimization parameters corresponding to the layout parameters constitutes the population of the optimization parameters.

3. The layout method of a photovoltaic assembly according to claim 1, characterized in that, The population optimization algorithm at least includes one of a particle swarm optimization algorithm and a genetic algorithm.

4. The layout method of a photovoltaic assembly according to claim 1, characterized in that, When the convergence calculation of the power generation target values of each of the sub-populations of the optimization parameters is performed, if the result of the convergence calculation does not reach the convergence target, the layout method of the photovoltaic module further includes the steps of: According to the proportion of the power generation target value of each of the sub-populations of the optimization parameters in each of the layout parameters in the total power generation target value of all the sub-populations of the optimization parameters, the target weight coefficient corresponding to each of the sub-populations of the optimization parameters is adjusted; According to the adjusted target weight coefficient, the population of the optimization parameters is updated, and the updated population of the optimization parameters includes multiple updated sub-populations of the optimization parameters; Each of the updated sub-populations of the optimization parameters in each of the layout parameters is selected one by one and combined to form multiple updated sub-populations of the optimization parameters, and the power generation target value corresponding to each of the updated sub-populations of the optimization parameters is calculated; The convergence calculation of the power generation target values of each of the updated sub-populations of the optimization parameters is performed by a population optimization algorithm, if the convergence target is reached, the process is ended, if the convergence target is not reached, the step is repeated until the convergence target is reached.

5. The method of layout of a photovoltaic assembly according to claim 4, characterized in that, The step of adjusting the target weight coefficient corresponding to each of the sub-populations of the optimization parameters according to the proportion of the power generation target value of each of the sub-populations of the optimization parameters in each of the layout parameters in the total power generation target value of all the sub-populations of the optimization parameters includes: For each of the layout parameters, if the proportion of the power generation target value of the sub-population of the optimization parameters in the total power generation target value of all the sub-populations of the optimization parameters is greater than the corresponding target weight coefficient, the target weight coefficient is increased to obtain the adjusted target weight coefficient; For each of the layout parameters, if the proportion of the power generation target value of the sub-population of the optimization parameters in the total power generation target value of all the sub-populations of the optimization parameters is less than or equal to the corresponding target weight coefficient, the target weight coefficient is reduced to obtain the adjusted target weight coefficient.

6. The method of layout of a photovoltaic assembly according to claim 5, characterized in that, The target weight coefficient is greater than zero and less than one.

7. A layout device for a photovoltaic assembly, characterized in that The device includes: A data acquisition module is configured to acquire a target area where a photovoltaic module is located, a plurality of areas where the photovoltaic module is located, and a value interval of each layout parameter of the photovoltaic module in each area; A first processing module is configured to perform optimization weight calculation based on maximum power generation benefit or minimum cost per kilowatt according to the value interval of each layout parameter of the photovoltaic module in each area to obtain a target interval weight distribution of each layout parameter in each area, and the target interval weight distribution of each layout parameter of the photovoltaic module in each area and each area constitute a target interval weight distribution set; a second processing module, configured to generate a corresponding optimization parameter population according to target interval weight distribution of each layout parameter in the target region, the optimization parameter population comprising a plurality of optimization parameter sub-populations, a plurality of optimization parameter sub-population combinations being formed according to the optimization parameter population of each layout parameter, and a power generation target value corresponding to each optimization parameter sub-population combination being calculated, and a population optimization algorithm being used to perform convergence calculation on the power generation target value of each optimization parameter sub-population combination to find out the optimization parameter sub-population combination reaching a convergence target; an output module, configured to output the optimization parameter sub-population combination reaching the convergence target and the corresponding power generation target value; a storage module, configured to save the target interval weight distribution set and also configured to save the optimization parameter sub-population combination reaching the convergence target and the corresponding power generation target value; wherein the step of obtaining the target interval weight distribution of each layout parameter of a photovoltaic module in a target region comprises: obtaining a target interval weight distribution set based on maximum power generation benefit or minimum unit power cost, the target interval weight distribution set comprising a plurality of regions and target interval weight distribution of each layout parameter of the photovoltaic module in each region when the power generation benefit reaches a maximum value; obtaining the target region where the photovoltaic module is located and reading the target interval weight distribution of each layout parameter of the photovoltaic module from the target interval weight distribution set according to the target region; the step of obtaining the target interval weight distribution set based on maximum power generation benefit or minimum unit power cost comprises: obtaining a plurality of regions and a value interval of each layout parameter of the photovoltaic module in each region; for each region, selecting one data of each layout parameter one by one and combining to form a plurality of optimization parameter combinations, and calculating power generation benefit or unit power cost corresponding to each optimization parameter combination; for each region, finding out each group of optimization parameter combinations corresponding to the maximum value of the power generation benefit or the minimum value of the unit power cost, and recording as target optimization parameter combinations; for each region, clustering and dividing each group of target optimization parameter combinations to obtain a plurality of segmented intervals of each layout parameter; for each region, in each segmented interval of each layout parameter, calculating a ratio of the number of target optimization parameter combinations falling into the segmented interval to the total number of target optimization parameter combinations, and taking the ratio as a target weight coefficient of the segmented interval where the maximum value of the power generation benefit or the minimum value of the unit power cost appears, to obtain the target interval weight distribution of each layout parameter; the target interval weight distribution of each layout parameter of the photovoltaic module in each region and each region constitute the target interval weight distribution set.

8. An electronic device, comprising: comprises: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the method of layout of a photovoltaic assembly as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, a computer readable medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the method of layout of a photovoltaic assembly as claimed in any one of claims 1 to 6.

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