Photovoltaic panel arrangement method and system in same wind and light field

Abstract

The invention provides a photovoltaic panel arrangement method and system under the same wind and light field, and relates to the technical field of photovoltaic panels, and the method comprises the steps: obtaining fan parameters and a photovoltaic panel arrangement region; based on the fan parameters, shadow data of a photovoltaic panel arrangement area within a preset duration are obtained; establishing a mapping relation between the arrangement parameters of the photovoltaic panel and the generating capacity per unit area in the presence of shadow data; clustering the shadow regions to obtain a plurality of shadow clustering regions; determining optimized photovoltaic panel arrangement parameters of each shadow clustering region by combining the mapping relationship and taking maximization of the generating capacity per unit area as a target; and arranging the photovoltaic panels in each shadow clustering area according to the optimized photovoltaic panel arrangement parameters. The power generation loss and the hot spot effect caused by shielding are reduced, and land waste caused by large-scale distribution forbidding is avoided.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic panel technology, and in particular to a method and system for arranging photovoltaic panels under simultaneous wind and solar power conditions. Background Technology

[0002] Wind-solar hybrid power generation (also known as wind-solar hybrid power generation) refers to the simultaneous deployment of wind power and photovoltaic power generation equipment on the same site. This approach utilizes the complementarity of wind and solar energy, aiming to improve the stability and efficiency of energy production.

[0003] In scenarios where wind and solar power coexist, the placement of photovoltaic (PV) panels is of paramount importance. A well-planned arrangement not only ensures that PV panels receive as much solar radiation as possible, improving power generation efficiency, but also avoids interference from wind turbine blade shadows or air turbulence, thus guaranteeing long-term stable system operation. Furthermore, a scientifically designed layout can improve land utilization, reduce mutual interference between equipment, and lower operation and maintenance costs. By optimizing the angle, spacing, and orientation of PV panels, the maximum utilization of wind and solar energy can be achieved within limited space, ensuring that the comprehensive benefits of wind-solar complementarity are fully realized.

[0004] However, existing photovoltaic panel layout methods under the combined wind and solar power conditions have obvious shortcomings. They mainly rely too much on static and simplified shadow divisions and lack sufficient consideration of dynamic changes in shadows. In addition, while conventional no-displacement zone methods avoid shading, they cause land waste, while radial layouts are too one-size-fits-all, resulting in low utilization rates and failing to balance power generation efficiency and intensive land use. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a photovoltaic panel arrangement method under simultaneous wind and solar power, which can solve the obvious shortcomings of the existing photovoltaic panel arrangement methods under simultaneous wind and solar power. These shortcomings are mainly reflected in the over-reliance on static and simplified shadow division, the lack of sufficient consideration of the dynamic changes of shadow, the conventional no-displacement zone method, although it avoids shading, it causes land waste, while the radial arrangement is too one-size-fits-all, resulting in low utilization rate and failing to take into account both power generation efficiency and intensive land use.

[0006] A first aspect of this invention provides a method for arranging photovoltaic panels under simultaneous wind and solar power conditions, comprising:

[0007] S1: Obtain wind turbine parameters and photovoltaic panel layout area;

[0008] S2: Based on wind turbine parameters, obtain the shading data of the photovoltaic panel layout area within a preset time period;

[0009] S3: Establish the mapping relationship between photovoltaic panel layout parameters and power generation per unit area in the presence of shadow data;

[0010] S4: Cluster the shaded areas to obtain multiple shaded cluster regions;

[0011] S5: Based on the mapping relationship, with the goal of maximizing power generation per unit area, determine the optimal photovoltaic panel layout parameters for each shaded cluster region;

[0012] S6: Arrange the photovoltaic panels in each shaded cluster area according to the optimized photovoltaic panel layout parameters.

[0013] A second aspect of this invention provides a photovoltaic panel arrangement system for simultaneous wind and solar power deployment, comprising: a processor and a memory;

[0014] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the photovoltaic panel arrangement method under simultaneous wind and solar power as described in the first aspect.

[0015] A third aspect of the present invention provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the photovoltaic panel arrangement method under simultaneous wind and solar power as described in the first aspect.

[0016] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0017] In this embodiment of the invention, by acquiring wind turbine parameters and deployment area data, and combining this with dynamic shading simulation, the shading situation under different times and seasons can be accurately characterized. Furthermore, by establishing a mapping relationship between deployment parameters and power generation per unit area, and clustering shading areas, deviations caused by static division are avoided. Moreover, the system optimizes deployment within each shading cluster with the goal of maximizing power generation, reducing power generation losses and hotspot effects caused by shading, while avoiding land waste caused by large-scale bans on deployment. This achieves power generation efficiency, intensive land use, and system security. Attached Figure Description

[0018] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0019] Figure 1 This is a schematic flowchart of a photovoltaic panel arrangement method under simultaneous wind and solar power conditions provided by an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a photovoltaic panel arrangement system under simultaneous wind and solar power conditions provided in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The photovoltaic panel arrangement method under simultaneous wind and solar power conditions provided by the present invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0023] Reference manual attached Figure 1 The diagram shows a flowchart of a photovoltaic panel arrangement method under simultaneous wind and solar power conditions provided by an embodiment of the present invention.

[0024] This invention provides a method for arranging photovoltaic panels under simultaneous wind and solar power conditions, which may include the following steps:

[0025] S1: Obtain wind turbine parameters and photovoltaic panel layout area.

[0026] The wind turbine parameters mainly refer to the structural parameters of the wind turbine. The deployment area refers to the geographical area planned for the installation of photovoltaic panels. Obtaining this data ensures accurate analysis of the wind turbine's impact on the photovoltaic panels in subsequent steps, especially the impact of shading. By acquiring the wind turbine parameters and deployment area, foundational data can be provided for subsequent shading analysis and optimized deployment, ensuring that the deployment plan accurately reflects the actual situation.

[0027] In one possible implementation, the fan parameters include fan height, fan location, and fan impeller diameter.

[0028] S2: Based on the wind turbine parameters, obtain the shadow data of the photovoltaic panel layout area within a preset time period.

[0029] Among them, shadow data refers to the dynamic shading information generated by the wind turbine in the photovoltaic panel arrangement area, obtained based on the wind turbine's geometric parameters within a preset time range (such as a day, a year, or several typical periods).

[0030] It should be noted that those skilled in the art can set the preset duration according to actual needs, and this invention does not impose any limitations on it. Optionally, it can be set to a quarter or a year.

[0031] In one possible implementation, the shadow data includes the shadow region, the duration of the shadow region's existence, and the interval between the existence of the shadow region. S2 specifically includes:

[0032] Input the wind turbine parameters into the photovoltaic simulation tool and output the shadow data. The photovoltaic simulation tool includes the PVsyst tool, SolarPro tool and Helioscope tool.

[0033] For example, regarding tilt angle: use a small tilt angle in areas with severe shadows to improve the utilization of diffused light. Regarding orientation: the orientation of components in areas shaded in the morning and evening can be slightly east or west. Regarding spacing: adjust row and column spacing according to the duration of shadows.

[0034] Specifically, PVsyst (photovoltaic design software) supports inputting the geometric parameters of shading objects (such as wind turbine towers and blades) to simulate the impact of shading on photovoltaic arrays. SolarPro and Helioscope can import external models for photovoltaic layout and shading simulation. Alternatively, a general-purpose 3D modeling and simulation platform can be used; for example, SketchUp with a photovoltaic plugin can create 3D models of wind turbines, which can then be overlaid with solar path tools to simulate shadows. AutoCAD and Revit can be used for precise modeling, combined with solar radiation analysis modules. ArcGIS and QGIS, in conjunction with terrain data and solar radiation models, can establish spatial analysis of wind turbine shadow coverage areas.

[0035] S3: Establish the mapping relationship between photovoltaic panel layout parameters and power generation per unit area in the presence of shadow data.

[0036] In this context, power generation per unit area refers to the actual electrical energy output that a photovoltaic panel can generate within a given area (e.g., 1 square meter), after considering factors such as solar irradiance, temperature, and shading. It is used to measure the efficiency of a photovoltaic (PV) panel arrangement and is a crucial indicator for judging the merits of different arrangement methods. The mapping relationship refers to using mathematical models or data calculation methods to correlate the arrangement parameters of PV panels (such as tilt angle, azimuth angle, and spacing) with the power generation per unit area under shading conditions, thus forming an "input-output" functional relationship. This relationship can be used to predict and optimize the power generation effect under different arrangement parameters. By establishing the mapping relationship between arrangement parameters and power generation per unit area, the system can quantitatively evaluate the power generation effect under different arrangement methods.

[0037] In one possible implementation, the photovoltaic panel arrangement parameters include the arrangement angle, arrangement direction, and arrangement spacing of the photovoltaic panels.

[0038] This stage involves simulating the shadow cast by wind turbines and recording its changes within the photovoltaic area, essentially creating a shadow map to show how long each location is affected by shadow at different times. This data forms the basis for subsequent optimization of photovoltaic panel placement.

[0039] In one possible implementation, S3 specifically includes:

[0040] S301: Coefficient of shadow energy loss per unit area due to quantization of shadow data:

[0041] .

[0042] in, Indicates position Energy loss coefficient per unit area of ​​shadow at that location. Indicates position The spatiotemporal matrix at time t indicates whether the object is occluded. If occluded, the element has a value of 1; otherwise, it has a value of 0. Let represent the intensity of direct solar radiation at time t, and let cos represent the cosine function. This represents the angle of solar incidence at time t. Indicates the duration of the shadow area.

[0043] S302: Determine the effective power generation per unit area under different photovoltaic panel arrangement parameters by combining the unit area shading energy loss coefficient:

[0044] .

[0045] .

[0046] .

[0047] in, Indicates the angle of arrangement Layout direction and arrangement spacing The effective power generation under the given conditions is the mapping relationship. Indicates the angle of arrangement and layout direction Angle efficiency correction term, and This indicates the planned angle and planned direction of the current location. Represents the sine function. This represents the compensated power generation from the scattered radiation in the shaded area. This represents the spacing attenuation term of the arrangement spacing d, and e represents the natural constant. This represents the length of the inter-panel light field interference characteristic related to the properties of photovoltaic panels. This represents the maximum theoretical power generation of a photovoltaic panel under ideal, unshaded conditions.

[0048] The compensated power generation from scattered radiation in the shaded area represents the compensatory electrical energy generated by photovoltaic modules through scattered radiation under shaded conditions. It effectively mitigates power generation losses caused by the lack of direct sunlight, making the model more closely reflect reality. The inter-panel light field interference characteristic length, related to photovoltaic panel properties, represents the characteristic scale by which photovoltaic panels influence the light field distribution in a spatial arrangement. Specifically, changes in the spacing between photovoltaic panels lead to attenuation of the light distribution or mutual interference, and this influence typically exhibits an exponential decay law. The inter-panel light field interference characteristic length is a physical parameter describing this degree of attenuation.

[0049] Specifically, in the entire S3 process, the shadow data is first quantified in S301 to obtain the shadow energy loss coefficient per unit area, which dynamically reflects the degree of shading and energy loss at different locations at different times. Then, in S302, this coefficient is combined with factors such as the maximum power of the photovoltaic panel, angle correction, scattering compensation, and spacing attenuation to establish a mapping relationship between the photovoltaic panel layout parameters and the effective power generation per unit area G. This fully considers the dynamic characteristics of shadows, the influence of angle and direction, the compensation effect of scattered light, and the spacing effect, allowing layout optimization to be based on a more accurate and scientific foundation. This not only allows for a more realistic evaluation of the power generation benefits of different layout schemes but also effectively reduces losses caused by shading in practical applications.

[0050] S4: Cluster the shaded areas to obtain multiple shaded cluster regions.

[0051] The shadow clustering region refers to the area within the photovoltaic deployment area affected by the dynamic shadows of wind turbines, which is classified and grouped according to shadow distribution characteristics (such as shading frequency, shading duration, and shading intensity). By clustering shadow regions, shading areas with different characteristics are distinguished, providing a data foundation for formulating optimized deployment parameters.

[0052] In one possible implementation, the total area of ​​each shaded cluster region is greater than the total area of ​​a single photovoltaic panel.

[0053] It should be noted that the total area of ​​the shaded cluster regions is larger than the total area of ​​a single photovoltaic panel because the impact of shading is not limited to a single panel but covers a large area. Clustering allows these areas to be merged into a larger shading zone, enabling more precise analysis and optimization of photovoltaic panel placement to maximize the use of available space and minimize shading effects.

[0054] In one possible implementation, S4 specifically includes:

[0055] S401: Determine the first largest shaded cluster region:

[0056] .

[0057] .

[0058] in, This represents the first largest shaded cluster region. This indicates taking j at the maximum function value. , and These represent the shadow area, shadow duration, and shadow occlusion intensity of the j-th shadow region, respectively. , and These represent the weights of the corresponding items. This represents the total area of ​​the shaded region. This indicates the total duration of the shaded area.

[0059] S402: Construct a shadow region feature vector, which includes five elements: the ratio of the shadow region area to the total shadow region area, the ratio of the shadow region existence duration to the total shadow region duration, the shadow occlusion intensity, the principal direction angle of the shadow region, and the shape flatness ratio of the shadow region.

[0060] S403: Calculate the similarity between the feature vector of each shaded region and the feature vector of the largest shaded region:

[0061] .

[0062] in, These represent the feature vectors of the j-th shadow region and the largest shadow region, respectively. This represents the natural exponential function. They represent The element value corresponding to the m-th shaded region feature in the image. This represents the standard deviation of the characteristics of the m-th shaded region. express The similarity between them.

[0063] S404: Calculate the quantized value of shadow region density for each shadow region based on similarity:

[0064] .

[0065] in, This represents the quantized value of the shadow density of the j-th shadow region. This represents the spatial distance between the j-th shaded region and the largest shaded region. This represents a custom spatial attenuation coefficient.

[0066] S405: Determine the dynamic density threshold by combining the quantized value of the maximum shaded area density:

[0067] .

[0068] in, This represents the quantized value of the maximum shaded area density. Indicates the dynamic density threshold. This represents the threshold coefficient.

[0069] Optionally, the threshold coefficient can be set to 0.5.

[0070] S406: The shadow regions whose density quantization value is greater than the dynamic density threshold are classified into the cluster corresponding to the first maximum shadow cluster region.

[0071] S407: Remove the first largest shaded cluster region, return to step S401, redetermine the second largest shaded cluster region from the remaining shaded regions, until each shaded region is assigned to a different cluster.

[0072] S408: Output the obtained clusters as clustering results.

[0073] Specifically, this process clusters the shaded areas of wind turbines to accurately identify and optimize the arrangement of photovoltaic panels. First, the largest shaded cluster area is determined by weighting the shaded area, duration, and shading intensity. Then, feature vectors of the shaded areas are constructed, incorporating five key parameters such as area, duration, and shading intensity, to further describe the characteristics of the shaded areas. Step S403 calculates the similarity between feature vectors of different shaded areas to ensure that similar feature areas are clustered together. Step S404 combines similarity and spatial distance to quantify the density of shaded areas, ensuring accurate grouping of similar areas. Finally, by setting a dynamic density threshold, areas with densities higher than the threshold are included in the largest shaded cluster until all areas are effectively divided. The advantage of this method lies in its precise and dynamic shaded clustering, making the layout optimization more consistent with actual shading conditions, avoiding the coarse division in traditional methods, and improving the overall efficiency and land utilization of the photovoltaic system.

[0074] S5: Based on the mapping relationship, with the goal of maximizing power generation per unit area, determine the optimal photovoltaic panel layout parameters for each shaded cluster region.

[0075] Optimizing photovoltaic panel layout parameters refers to determining the optimal panel arrangement for maximizing power generation within each shaded area, based on the characteristics and mapping relationships of the shaded regions. These optimized parameters can reduce the impact of shading and improve the power generation efficiency of photovoltaic modules.

[0076] In one possible implementation, S5 specifically includes:

[0077] S501: Determine the range of photovoltaic panel placement angles and the minimum spacing between adjacent photovoltaic panels.

[0078] S502: Under the constraints of the arrangement angle range and minimum spacing, determine the optimal photovoltaic panel arrangement parameters with the goal of maximizing power generation per unit area:

[0079] .

[0080] in, This indicates taking the value of the maximum function. , Symbols representing constraints. Indicates the minimum spacing. Represents pi (π). This represents the effective power generation of the i-th shaded cluster region.

[0081] Specifically, the gradient descent method can be used to solve this problem. The specific steps are as follows: First, based on the differentiability of the mapping relationship, calculate... For each parameter The analytical gradient. Then, in the shadow boundary region (i.e. (Positions where the gradient is greater than the preset gradient) Iteratively adjust each parameter. This allows us to obtain optimized photovoltaic panel layout parameters.

[0082] It should be noted that this process mainly consists of two parts: parameter constraints and optimization solution. First, the angular range of the photovoltaic panels and the minimum spacing between adjacent panels are clearly defined to ensure optimization is performed under physically feasible conditions. Then, under these constraints, an optimization function is constructed with the goal of maximizing power generation per unit area, and solved using the gradient descent method. Specifically, based on the differentiability of the mapping relationship, the system can calculate the gradient of the power generation function with respect to angle, direction, and spacing, and iteratively adjust the parameters in the shadow boundary region, thereby gradually approaching the optimal solution. The advantage of this process is that it not only considers the dynamic characteristics of shadows and geometric constraints but also utilizes mathematical optimization methods to achieve rapid parameter convergence, thus obtaining an arrangement scheme that better suits actual site conditions. Ultimately, this significantly reduces power generation losses caused by shading, improves land use efficiency, and ensures the overall power generation efficiency and safety of the photovoltaic system.

[0083] S6: Arrange the photovoltaic panels in each shaded cluster area according to the optimized photovoltaic panel layout parameters.

[0084] In practical applications, the process begins by acquiring wind turbine parameters and the designated area to provide accurate input for subsequent analysis. Next, shadow data is obtained by simulating the wind turbine structure and solar trajectory, forming a basic "shadow map." Then, a mapping relationship is established between the placement parameters and the power generation per unit area, comprehensively quantifying the impact of shadows, angles, scattering, and spacing. Subsequently, the shadowed areas are clustered to identify shading zones with different characteristics. Optimal placement parameters within each cluster are obtained using gradient descent under constraints. Finally, photovoltaic panels are deployed based on the optimization results. The advantage of this method is that it considers both the dynamic changes in wind turbine shadows and integrates physical geometric constraints with mathematical optimization, making the photovoltaic panel placement more scientific and rational. This not only effectively reduces power generation losses and hot spot risks but also improves land use efficiency and system safety, thereby significantly enhancing the overall benefits and reliability of wind-solar co-location projects.

[0085] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0086] In this embodiment of the invention, by acquiring wind turbine parameters and deployment area data, and combining this with dynamic shading simulation, the shading situation under different times and seasons can be accurately characterized. Furthermore, by establishing a mapping relationship between deployment parameters and power generation per unit area, and clustering shading areas, biases caused by static division are avoided. Moreover, the system optimizes deployment within each shading cluster with the goal of maximizing power generation, reducing power generation losses and hotspot effects caused by shading, while avoiding land waste caused by large-scale bans on deployment. This achieves power generation efficiency, intensive land use, and system security.

[0087] Reference manual attached Figure 2 The diagram shows a schematic of a photovoltaic panel arrangement system under simultaneous wind and solar power conditions provided by an embodiment of the present invention.

[0088] This invention provides a photovoltaic panel arrangement system 20 for simultaneous wind and solar power application, including: a processor 201 and a memory 202;

[0089] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the photovoltaic panel arrangement method under the above-mentioned wind and solar co-location and can achieve the same technical effect. To avoid repetition, the present invention will not repeat the above-mentioned steps.

[0090] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0091] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0093] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0094] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0099] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described photovoltaic panel arrangement method under simultaneous wind and solar power, and can achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for arranging photovoltaic panels under simultaneous wind and solar power conditions, characterized in that, include: S1: Obtain wind turbine parameters and photovoltaic panel layout area; S2: Based on the wind turbine parameters, obtain the shadow data of the photovoltaic panel arrangement area within a preset time period; S3: Establish a mapping relationship between photovoltaic panel layout parameters and power generation per unit area in the presence of the aforementioned shadow data; S4: Cluster the shadowed regions to obtain multiple shadow clustered regions; S5: Based on the mapping relationship, with the goal of maximizing the power generation per unit area, determine the optimal photovoltaic panel layout parameters for each of the shadow clustering regions; S6: Arrange the photovoltaic panels in each of the shadow clustering regions according to the optimized photovoltaic panel arrangement parameters.

2. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, The fan parameters include fan height, fan location, and fan impeller diameter.

3. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, The shadow data includes the shadow region, the duration of the shadow region, and the interval between shadow regions; S2 specifically refers to: The wind turbine parameters are input into a photovoltaic simulation tool, and the shadow data is output. The photovoltaic simulation tool includes the PVsyst tool, the SolarPro tool, and the Helioscope tool.

4. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, The photovoltaic panel arrangement parameters include the arrangement angle, arrangement direction, and arrangement spacing of the photovoltaic panels.

5. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, S3 specifically includes: S301: Quantify the unit area shadow energy loss coefficient caused by the shadow data: ； in, Indicates position Energy loss coefficient per unit area of ​​shadow at that location. Indicates position The spatiotemporal matrix at time t indicates whether the object is occluded. If occluded, the element has a value of 1; otherwise, it has a value of 0. Let represent the intensity of direct solar radiation at time t, and let cos represent the cosine function. This represents the angle of solar incidence at time t. Indicates the duration of the shadow area; S302: Determine the effective power generation per unit area under different photovoltaic panel arrangement parameters based on the aforementioned unit area shading energy loss coefficient. ； ； ； in, Indicates the angle of arrangement Layout direction and arrangement spacing The effective power generation under the given conditions is the mapping relationship. Indicates the angle of arrangement and layout direction Angle efficiency correction term, and This indicates the planned angle and planned direction of the current location. Represents the sine function. This represents the compensated power generation from the scattered radiation in the shaded area. This represents the spacing attenuation term of the arrangement spacing d, and e represents the natural constant. This represents the length of the inter-panel light field interference characteristic related to the properties of photovoltaic panels. This represents the maximum theoretical power generation of a photovoltaic panel under ideal, unshaded conditions.

6. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, The total area of ​​each of the said shadow cluster regions is greater than the total area of ​​a single said photovoltaic panel.

7. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, S4 specifically includes: S401: Determine the first largest shaded cluster region: ； ； in, This represents the first largest shaded cluster region. This indicates taking j at the maximum function value. , and These represent the shadow area, shadow duration, and shadow occlusion intensity of the j-th shadow region, respectively. , and These represent the weights of the corresponding items. This represents the total area of ​​the shaded region. Indicates the total duration of the shaded area; S402: Construct a shadow region feature vector, wherein the shadow region feature vector includes five elements: the ratio of the shadow region area to the total shadow region area, the ratio of the shadow region existence duration to the total shadow region duration, the shadow occlusion intensity, the shadow region principal direction angle, and the shadow region shape flatness ratio. S403: Calculate the similarity between the feature vector of each shaded region and the feature vector of the largest shaded region: ； in, These represent the feature vectors of the j-th shadow region and the largest shadow region, respectively. This represents the natural exponential function. They represent The element value corresponding to the m-th shaded region feature in the image. This represents the standard deviation of the characteristics of the m-th shaded region. express The similarity between them; S404: Calculate the shadow region density quantization value for each shadow region based on the aforementioned similarity: ； in, This represents the quantized value of the shadow density of the j-th shadow region. This represents the spatial distance between the j-th shaded region and the largest shaded region. This represents a custom spatial attenuation coefficient; S405: Determine the dynamic density threshold by combining the quantized value of the maximum shaded area density: ； in, This represents the quantization value of the maximum shaded area density. Indicates the dynamic density threshold. Indicates the threshold coefficient; S406: The shadow regions whose density quantization values ​​are greater than the dynamic density threshold are classified into the cluster corresponding to the first maximum shadow clustering region; S407: Remove the first maximum shaded cluster region, return to step S401, and redetermine the second maximum shaded cluster region from the remaining shaded regions until each shaded region is assigned to a different cluster. S408: Output the obtained clusters as clustering results.

8. The photovoltaic panel arrangement method under simultaneous wind and solar power conditions according to claim 1, characterized in that, S5 specifically includes: S501: Determine the range of photovoltaic panel arrangement angles and the minimum spacing between adjacent photovoltaic panels; S502: Under the constraints of the stated arrangement angle range and the minimum spacing, determine the optimized photovoltaic panel arrangement parameters with the objective of maximizing the power generation per unit area: ； in, This indicates taking the value of the maximum function. , Symbols representing constraints. Indicates the minimum spacing. Represents pi (π). This represents the effective power generation of the i-th shaded cluster region.

9. A photovoltaic panel arrangement system for simultaneous wind and solar power application, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the photovoltaic panel arrangement method under the simultaneous wind and solar conditions as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the photovoltaic panel arrangement method under the simultaneous wind and solar conditions as described in any one of claims 1 to 8.

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