Distributed photovoltaic array arrangement method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of photovoltaic array arrangement, and provides a distributed photovoltaic array arrangement method and device, electronic equipment and a storage medium, and the method comprises the steps: building a three-dimensional roof model, and determining an unshielded roof available region in combination with an illumination condition; determining a final roof available area from the available areas which do not shield the roof based on the roof obstacle and the constraint conditions of the eave distance and the ridge spacing; according to a preset arrangement rule, the distributed photovoltaic array is arranged in the final usable area of the roof, and a corresponding initial arrangement scheme is obtained; and optimizing the initial arrangement scheme by using a preset optimization algorithm to obtain a final arrangement scheme. According to the embodiment of the invention, the accuracy and efficiency of roof photovoltaic array arrangement can be remarkably improved, and the method is especially suitable for a complex distributed scene.

Description

Technical Field

[0001] The present disclosure relates to the technical field of photovoltaic array arrangement, and in particular to a distributed photovoltaic array arrangement method and device, electronic equipment, and storage medium. Background Art

[0002] The layout of photovoltaic arrays is a core element in photovoltaic power generation system design, directly impacting power generation efficiency, investment costs, and ease of operation and maintenance. With the rapid development of distributed photovoltaic systems, rooftop power stations, due to limited space, variable shadowing, and complex building structures, place higher demands on the layout of photovoltaic arrays.

[0003] Traditional photovoltaic array layout methods often rely on human experience and manual operations, which are not only inefficient and prone to errors, but also have difficulty adapting to complex scenarios and meeting the actual needs of complex scenarios as the scale and complexity of photovoltaic power stations expand.

[0004] With the rapid development of automation and intelligent technologies, automated photovoltaic array layout methods have gradually become a research hotspot. These automated layout methods, by introducing advanced algorithms and technical means, can achieve efficient and accurate layout of photovoltaic arrays, thereby improving the overall performance of photovoltaic power generation systems.

[0005] For example, existing automatic layout methods for photovoltaic arrays include rule-based layout methods, intelligent optimization methods, layout methods based on Building Information Modeling (BIM), and layout methods for specific scenarios.

[0006] Rule-based layout methods typically use a set of pre-set rules to guide the placement of PV arrays. While this approach can improve layout efficiency to a certain extent, the pre-set rules are often overly simplistic and rigid. For example, they only pre-set fixed rules for spacing and orientation, lacking dynamic adaptability and struggling to cope with complex roof structures and dynamic shadow changes, making them unsuitable for complex and ever-changing application scenarios.

[0007] Intelligent optimization methods have been widely used in the field of photovoltaic array layout in recent years. They can automatically search for the optimal layout plan based on the actual needs and constraints of the photovoltaic array. Common intelligent optimization methods typically use genetic algorithms, particle swarm optimization algorithms, and ant colony algorithms. These algorithms have the advantages of strong global search capabilities and good adaptability, and can find high-quality solutions in complex and changing application scenarios. However, they are computationally complex, making it difficult to generate solutions in real time, and they lack support for three-dimensional spatial modeling.

[0008] With the popularization of BIM technology, some researchers have begun to try to apply BIM technology to the automatic layout of photovoltaic arrays, which has led to the development of BIM-based layout methods. This method can extract the building's geometric information and spatial relationships by obtaining the building's BIM model, thereby providing accurate data support for the layout of the photovoltaic array, achieving precise matching and efficient integration of the photovoltaic array and the building. However, this method relies on the building information model, but the cost of obtaining BIM data is high, and it has poor compatibility with non-structured roofs (such as old buildings).

[0009] For specific scenarios (such as rooftop photovoltaics and mountain photovoltaics), some researchers have developed specialized layout algorithms, which have led to layout methods for specific scenarios. For example, for rooftop photovoltaic scenarios, some researchers have proposed a method and system for automatically optimizing the layout of rooftop photovoltaic arrays. By collecting rooftop point cloud data, simulating shadow occlusion, and automatically setting the orientation and inclination of photovoltaic panels, efficient layout of rooftop photovoltaic arrays is achieved. However, these rooftop photovoltaic scenario-specific methods are mostly limited to two-dimensional plane analysis and lack the ability to avoid three-dimensional space, which can lead to shadow occlusion and component overlap issues.

[0010] In summary, although existing technologies also provide some methods for arranging photovoltaic arrays, there are still some industry pain points in PV array arrangement, such as low efficiency of manual design, easy neglect of local occlusion, difficulty in dynamic integration of three-dimensional terrain, building structure and lighting data, and lack of fast and accurate arrangement tools for distributed rooftop power stations. Summary of the Invention

[0011] The present disclosure aims to solve at least one of the problems existing in the prior art and provides a distributed photovoltaic array arrangement method and device, electronic equipment, and storage medium.

[0012] In one aspect of the present disclosure, a distributed photovoltaic array arrangement method is provided, the distributed photovoltaic array arrangement method comprising:

[0013] Build a three-dimensional roof model and determine the unobstructed usable area of the roof based on the lighting conditions;

[0014] Determine the final available area of the roof from the available area of the unobstructed roof based on roof obstacles and eaves distance and ridge spacing constraints;

[0015] Arranging distributed photovoltaic arrays in the final available area of the roof according to a preset arrangement rule to obtain a corresponding initial arrangement plan;

[0016] The initial arrangement scheme is optimized using a preset optimization algorithm to obtain a final arrangement scheme.

[0017] Optionally, the step of establishing a three-dimensional roof model and determining the unobstructed usable roof area in combination with lighting conditions includes:

[0018] Building the three-dimensional roof model based on any one or more of the drone point cloud data, the building CAD model, and the roof input parameters, and marking the available roof area;

[0019] Based on the available roof range, three-dimensional ray tracing is used to simulate the dynamic change of shadows under different lighting conditions to generate a light occlusion map within a specified time;

[0020] The corresponding range of the light blocking map within a specified time is removed from the available roof range to obtain the unblocked roof available area.

[0021] Optionally, determining the final roof usable area from the unobstructed roof usable area based on roof obstacles and eaves distance and ridge spacing constraints includes:

[0022] Marking the roof obstacles in the three-dimensional roof model to obtain roof obstacle marking results;

[0023] Inputting the roof obstacle annotation result into a convolutional neural network to perform obstacle boundary recognition to obtain a corresponding obstacle boundary recognition result;

[0024] Determining the layout area boundary according to the eaves distance and the ridge spacing constraint conditions;

[0025] According to the obstacle boundary recognition result and the layout area boundary, the unusable area is removed from the unobstructed roof available area to obtain the final available area of the roof.

[0026] Optionally, the preset arrangement rules include starting direction selection rules, array unit and electrical matching rules, multi-format component adaptation and optimization rules, gap compensation and secondary filling rules;

[0027] The starting direction selection rule includes: using any one of a unilateral starting mode, a symmetrical starting mode, and a dynamic selection logic mode to determine the arrangement starting direction;

[0028] The electrical matching rules for the array units include: giving priority to arranging the photovoltaic strings as array units, ensuring that the photovoltaic modules within the same photovoltaic string have the same orientation and inclination; if the remaining space in the final usable area of the roof is insufficient to arrange the entire photovoltaic string, then adjusting the length of the photovoltaic string or arranging the photovoltaic strings in parallel;

[0029] The multi-format component adaptation and optimization rules include: calculating the densest arrangement quantity and coverage rate of each format component for the current available area in the final available area of the roof, and optimizing the optimal format component solution by using a multi-objective optimization algorithm combined with economic parameters based on the densest arrangement quantity and coverage rate;

[0030] The gap compensation and secondary filling rules include: when the photovoltaic modules that have been arranged in the final usable area of the roof are deleted, the corresponding gap boundary is detected, and an attempt is made to fill the vacant area corresponding to the gap boundary by reducing the distance between adjacent photovoltaic modules, and ensuring that the distance between adjacent photovoltaic modules is not less than the minimum safety value. If the vacant area cannot be filled, the vacant area is marked as an area to be optimized; for the remaining narrow space at the edge of the roof in the final usable area of the roof, different arrangement patterns are used for arrangement.

[0031] Optionally, the unilateral starting mode includes: starting from a unilateral edge of an area with the highest lighting efficiency on the roof, arranging the distributed photovoltaic array in a final usable area of the roof in a serpentine path;

[0032] The symmetrical starting mode includes: taking the ridge line or the roof center line as the symmetry axis, arranging the roof synchronously from both sides of the symmetry axis in the final usable area of the roof, and maximizing the use of the symmetrical space in the final usable area of the roof;

[0033] The dynamic selection logic mode includes: selecting the optimal starting mode from the unilateral starting mode and the symmetrical starting mode according to the aspect ratio of the roof and the shape of the final available area of the roof to arrange the distributed photovoltaic array.

[0034] Optionally, the optimizing the initial arrangement scheme by using a preset optimization algorithm to obtain a final arrangement scheme includes:

[0035] With the goal of maximizing the number of photovoltaic modules, minimizing shadow loss, and minimizing cable costs, the particle swarm optimization algorithm is used to adjust the photovoltaic module spacing, panel combination, and starting direction in the initial arrangement scheme to obtain the corresponding solution set;

[0036] Perform frontier screening according to the Pareto principle and retain the non-inferior solution set in the solution set;

[0037] The arrangement effect and corresponding indicators of at least one alternative arrangement scheme corresponding to the non-inferior solution set are displayed to the user, and the alternative arrangement scheme selected by the user is used as the final arrangement scheme.

[0038] Another aspect of the present disclosure provides a distributed photovoltaic array arrangement device, the distributed photovoltaic array arrangement device comprising:

[0039] Establish a module for building a three-dimensional roof model and determining the unobstructed usable area of the roof based on the lighting conditions;

[0040] A determination module, configured to determine a final usable roof area from the unobstructed roof area based on roof obstacles, eaves distance, and ridge spacing constraints;

[0041] An initial arrangement module is used to arrange the distributed photovoltaic array in the final available area of the roof according to a preset arrangement rule to obtain a corresponding initial arrangement plan;

[0042] The optimization module is used to optimize the initial arrangement scheme using a preset optimization algorithm to obtain a final arrangement scheme.

[0043] Another aspect of the present disclosure provides an electronic device, including:

[0044] at least one processor; and,

[0045] a memory communicatively connected to at least one processor; wherein,

[0046] The memory stores instructions that can be executed by at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the distributed photovoltaic array arrangement method described above.

[0047] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the distributed photovoltaic array arrangement method described above when executed by a processor.

[0048] Another aspect of the present disclosure provides a computer program product, including a computer program, which implements the distributed photovoltaic array arrangement method described above when executed by a processor.

[0049] Compared with the existing technology, the present invention can realize adaptive switching of multiple versions by pre-setting arrangement rules, and combined with multi-objective dynamic optimization, it can significantly improve the accuracy and efficiency of rooftop photovoltaic array layout, which is particularly suitable for complex distributed scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0051] Figure 1 A flowchart of a distributed photovoltaic array arrangement method provided in one embodiment of the present disclosure;

[0052] Figure 2 A schematic diagram of starting direction selection in a unilateral starting mode provided in another embodiment of the present disclosure;

[0053] Figure 3 A schematic diagram of starting direction selection in a symmetrical starting mode provided in another embodiment of the present disclosure;

[0054] Figure 4 A schematic structural diagram of a distributed photovoltaic array arrangement device provided in another embodiment of the present disclosure;

[0055] Figure 5 A schematic structural diagram of an electronic device provided in another embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in each embodiment of the present disclosure, many technical details are provided to enable readers to better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present disclosure can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present disclosure. The various embodiments can be combined and referenced with each other under the premise that there is no contradiction.

[0057] One embodiment of the present disclosure relates to a distributed photovoltaic array arrangement method, the process of which is as follows: Figure 1 As shown, it includes steps S110 to S140.

[0058] Step S110: Create a three-dimensional roof model and determine the available area of the roof that is not blocked based on the lighting conditions.

[0059] Specifically, step S110 mainly involves performing three-dimensional modeling on the rooftop used to arrange the distributed photovoltaic array, and performing statistics on the available area to determine the area that is not blocked by light.

[0060] Exemplarily, step S110 includes: establishing a three-dimensional roof model based on any one or more of the drone point cloud data, the building CAD model, and the roof input parameters, and marking the available roof range; based on the available roof range, simulating the dynamic change of shadows under different lighting conditions through three-dimensional ray tracing, and generating a light occlusion map within a specified time; removing the corresponding range of the light occlusion map within the specified time from the available roof range to obtain an unobstructed available roof area.

[0061] Specifically, after obtaining the three-dimensional roof model, the available roof area can also be marked to determine the unobstructed roof area on this basis.

[0062] Step S120 : determining the final available area of the roof from the available area of the unobstructed roof based on roof obstacles, eaves distance, and ridge spacing constraints.

[0063] Specifically, step S120 mainly performs intelligent identification on the unusable area, and determines the final usable area of the roof in combination with the unblocked usable area of the roof determined in step S110.

[0064] Exemplarily, step S120 includes: marking roof obstacles in the three-dimensional roof model to obtain roof obstacle marking results; inputting the roof obstacle marking results into a convolutional neural network to perform obstacle boundary recognition to obtain corresponding obstacle boundary recognition results; determining the layout area boundary based on the eaves distance and ridge spacing constraints; based on the obstacle boundary recognition results and the layout area boundary, removing the unusable area from the unobstructed roof available area to obtain the final usable area of the roof.

[0065] Specifically, when intelligently identifying unusable areas, the input data includes roof obstacle labeling results, eaves distance, and ridge spacing constraints. The roof obstacle labeling results are input into a convolutional neural network, which identifies obstacle boundaries and outputs obstacle boundary identification results. The eaves distance and ridge spacing constraints are used to determine the layout area boundaries. The ridge spacing constraints can be obtained through an interactive interface based on user input data. After obtaining the obstacle boundary identification results and layout area boundaries, the area where the obstacle is located and the layout area boundary are removed from the unobstructed roof available area as unusable areas to obtain the final usable area of the roof.

[0066] Step S130: Arrange the distributed photovoltaic arrays in the final available area of the roof according to the preset arrangement rules to obtain a corresponding initial arrangement plan.

[0067] Specifically, the preset arrangement rules can be set according to actual needs.

[0068] Exemplarily, the preset arrangement rules include starting direction selection rules, array unit and electrical matching rules, multi-format component adaptation and optimization rules, gap compensation and secondary filling rules.

[0069] The starting direction selection rules include: using any one of the unilateral starting mode, the symmetrical starting mode, and the dynamic selection logic mode to determine the arrangement starting direction.

[0070] Among them, the unilateral starting mode may include: starting from the unilateral edge of the roof with the highest lighting efficiency, and arranging the distributed photovoltaic array in the final available area of the roof in a serpentine path. Figure 2 When the eaves are located on the south side of the ridge, the area with the highest lighting efficiency is usually the south-facing slope of the roof. In this case, the single-side starting mode can be started from the single-side edge of the south-facing slope of the roof, such as Figure 2 The roof edge or Figure 2 Starting from the eaves in the middle, the distributed photovoltaic array is filled in the final available area of the roof in a serpentine path along the direction of the arrow.

[0071] Symmetrical starting modes include: using the ridge line or the center line of the roof as the symmetry axis, arranging the roofs synchronously on both sides of the symmetry axis in the final available area of the roof, and maximizing the use of the symmetrical space in the final available area of the roof. Figure 3 The symmetric starting pattern can be based on the centerline of the roof as the symmetry axis, and the final available area of the roof can be arranged synchronously from both sides of the symmetry axis, i.e., the centerline of the roof. For example, Figure 3 As shown, the symmetrical starting pattern allows the distributed photovoltaic arrays to be arranged in the direction of the arrows, from the roof edges on both sides of the roof centerline, simultaneously toward the roof centerline, within the roof's final usable area. Alternatively, the symmetrical starting pattern can also be used to arrange the distributed photovoltaic arrays from the roof centerline toward the roof edges on both sides within the roof's final usable area. Those skilled in the art can choose the method based on actual needs.

[0072] Dynamically select the logic mode: Based on the roof's aspect ratio and the shape of the final usable area, the optimal starting mode is selected from the unilateral starting mode and the symmetrical starting mode for the distributed PV array layout. Users can also choose to use the unilateral starting mode or the symmetrical starting mode.

[0073] The rules for array unit and electrical matching include: giving priority to arranging array units with photovoltaic strings, ensuring that the photovoltaic modules within the same photovoltaic string have the same orientation and inclination to reduce circuit losses. If the remaining space in the final available area of the roof is insufficient to arrange the entire photovoltaic string, the length of the photovoltaic string is adjusted or the photovoltaic strings are arranged in parallel. Each photovoltaic string usually contains several photovoltaic modules. The number of photovoltaic modules contained in a photovoltaic string is the length of the photovoltaic string. For example, a photovoltaic string can contain 20 photovoltaic modules. In this case, the length of the photovoltaic string is 20. In particular, for areas arranged by adjusting the length of the photovoltaic strings or areas where photovoltaic strings are arranged in parallel, they can also be marked as non-standard areas to distinguish them from areas with normal arrangements.

[0074] The multi-version component adaptation and optimization rules include: for the current available area in the final available area of the roof, respectively calculating the densest arrangement number and coverage of each version component, and optimizing based on the densest arrangement number and coverage using a multi-objective optimization algorithm combined with economic parameters to select the comprehensive optimal version component solution. Among them, the component sizes and efficiency parameters involved in the existing component versions can be saved in a pre-set component version database, and user-defined additions are supported. Components of different versions can have different sizes. For example, the component sizes of different versions can include but are not limited to 2.382m×1.134m, 2.278m×1.134m, etc. Economic parameters can include but are not limited to component cost, power generation efficiency, cable length, etc. For example, the multi-objective optimization algorithm can adopt the second version of the non-dominated sorting genetic algorithm (NSGA-II). Of course, those skilled in the art can also choose other types of multi-objective optimization algorithms, and this embodiment does not limit this.

[0075] The gap compensation and secondary filling rules include: when the photovoltaic modules that have been arranged in the final usable area of the roof are deleted, the corresponding gap boundary is detected, and the vacant area corresponding to the gap boundary is attempted to be filled by reducing the spacing between adjacent photovoltaic modules, and the spacing between adjacent photovoltaic modules is guaranteed to be not less than the minimum safety value. If the vacant area cannot be filled, the vacant area is marked as an area to be optimized; for the remaining narrow space at the edge of the roof in the final usable area of the roof, different arrangement modes are used for arrangement. Among them, the arrangement of the photovoltaic array or the module version can be manually intervened in the area to be optimized. Different arrangement modes include but are not limited to vertical mode and horizontal mode. For example, for the remaining narrow space at the edge of the roof in the final usable area of the roof, the initial vertical mode can be changed to horizontal mode to improve the utilization rate of the remaining narrow space.

[0076] After the above arrangement rules are determined, step S130 can complete the layout of the distributed photovoltaic array according to these arrangement rules, obtain the corresponding initial arrangement plan, and further perform a three-dimensional visualization of the initial arrangement plan and count the number of photovoltaic modules involved.

[0077] By pre-setting arrangement rules, safety logic such as skylight avoidance and edge constraints can be implemented through rule-driven automation, and flexible adaptation can be achieved through deep collaboration between symmetrical / asymmetrical arrangement strategies and PV string design.

[0078] Step S140: Optimize the initial arrangement plan using a preset optimization algorithm to obtain a final arrangement plan.

[0079] Exemplarily, step S140 may include: with the goal of maximizing the number of photovoltaic modules, minimizing shadow loss, and minimizing cable costs, adjusting the photovoltaic module spacing, version combination, and starting direction in the initial arrangement scheme through a particle swarm optimization algorithm to obtain a corresponding solution set; performing frontier screening according to the Pareto principle to retain the non-inferior solution set in the solution set; displaying the arrangement effect and corresponding indicators of at least one alternative arrangement scheme corresponding to the non-inferior solution set to the user, and using the alternative arrangement scheme selected by the user as the final arrangement scheme.

[0080] Specifically, maximizing the number of photovoltaic modules represents the total number of photovoltaic modules covering the final usable area of the roof, which can be marked as N. Minimizing shadow loss represents the proportion of power generation efficiency reduction caused by shadow obstruction under the sunlight of the winter solstice, which can be marked as L. Minimizing cable cost represents the total length of cables required to connect each photovoltaic string, which can be marked as C. When optimizing the initial arrangement scheme, the values of N, L, and C are first calculated respectively, and then a neighborhood search is performed. The spacing, version combination, and starting direction of the photovoltaic modules are adjusted by the particle swarm optimization algorithm to obtain the corresponding solution set. The frontier screening is then performed using the Pareto principle to retain the non-inferior solution set in the solution set obtained by the particle swarm optimization algorithm, realizing intelligent optimization from "single coverage" to "multi-objective balance". Users can select the final arrangement scheme from the alternative arrangement schemes corresponding to the non-inferior solution set based on their preferences.

[0081] In particular, each time the layout plan is adjusted, the 3D platform can be used to update the layout effect and layout index comparison in real time, supporting users to optimize the layout plan through interaction.

[0082] Compared with the existing technology, the distributed photovoltaic array arrangement method provided by the embodiment of the present disclosure can realize adaptive switching of multiple versions by pre-setting arrangement rules, and combined with multi-objective dynamic optimization, it can significantly improve the accuracy and efficiency of rooftop photovoltaic array arrangement, which is particularly suitable for complex distributed scenarios.

[0083] Another embodiment of the present disclosure relates to a distributed photovoltaic array arrangement device, such as Figure 4 As shown, it includes an establishment module 410 , a determination module 420 , an initial arrangement module 430 , and an optimization module 440 .

[0084] The building module 410 is used to build a three-dimensional roof model and determine the available area of the roof that is not blocked in combination with the lighting conditions.

[0085] The determination module 420 is used to determine the final available area of the roof from the available area of the unobstructed roof based on the roof obstacles and the constraints of the eaves distance and the ridge spacing.

[0086] The initial arrangement module 430 is used to arrange the distributed photovoltaic arrays in the final available area of the roof according to a preset arrangement rule to obtain a corresponding initial arrangement plan.

[0087] The optimization module 440 is used to optimize the initial arrangement scheme using a preset optimization algorithm to obtain a final arrangement scheme.

[0088] The specific implementation method of the distributed photovoltaic array arrangement device provided in the embodiment of the present disclosure can be found in the description of the distributed photovoltaic array arrangement method provided in the embodiment of the present disclosure, and will not be repeated here.

[0089] Compared with the existing technology, the distributed photovoltaic array layout device provided in the embodiment of the present disclosure can realize adaptive switching of multiple versions by pre-setting arrangement rules, and combined with multi-objective dynamic optimization, it can significantly improve the accuracy and efficiency of rooftop photovoltaic array layout, which is particularly suitable for complex distributed scenarios.

[0090] Another embodiment of the present disclosure relates to an electronic device, such as Figure 5 Shown, including:

[0091] at least one processor 501; and,

[0092] A memory 502 in communication with at least one processor 501; wherein,

[0093] The memory 502 stores instructions that can be executed by the at least one processor 501 . The instructions are executed by the at least one processor 501 so that the at least one processor 501 can execute the distributed photovoltaic array arrangement method described in the above embodiment.

[0094] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0095] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0096] Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the distributed photovoltaic array arrangement method described in the above embodiment.

[0097] That is, those skilled in the art will understand that all or part of the steps in the methods described in the above embodiments can be implemented by instructing related hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps in the methods described in the various embodiments of the present disclosure. The aforementioned storage medium includes: a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0098] Another embodiment of the present disclosure relates to a computer program product, including a computer program, which implements the distributed photovoltaic array arrangement method described in the above embodiment when the computer program is executed by a processor.

[0099] Those skilled in the art will appreciate that the above-mentioned embodiments are specific embodiments for implementing the present disclosure, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present disclosure.

Claims

1. A distributed photovoltaic array arrangement method, characterized in that: The distributed photovoltaic array arrangement method comprises: Build a three-dimensional roof model and determine the unobstructed usable area of the roof based on the lighting conditions; Determine the final available area of the roof from the available area of the unobstructed roof based on roof obstacles and eaves distance and ridge spacing constraints; Arranging distributed photovoltaic arrays in the final available area of the roof according to a preset arrangement rule to obtain a corresponding initial arrangement plan; The initial arrangement scheme is optimized using a preset optimization algorithm to obtain a final arrangement scheme.

2. The distributed photovoltaic array arrangement method according to claim 1, characterized in that: The three-dimensional roof model is established, and the unobstructed roof usable area is determined in combination with the lighting conditions, including: Building the three-dimensional roof model based on any one or more of the drone point cloud data, the building CAD model, and the roof input parameters, and marking the available roof area; Based on the available roof range, three-dimensional ray tracing is used to simulate the dynamic change of shadows under different lighting conditions to generate a light occlusion map within a specified time; The corresponding range of the light blocking map within a specified time period is removed from the available roof range to obtain the unblocked roof available area.

3. The distributed photovoltaic array arrangement method according to claim 1, characterized in that: The method of determining the final available roof area from the unobstructed roof area based on the constraints of roof obstacles, eaves distance, and ridge spacing includes: Marking the roof obstacles in the three-dimensional roof model to obtain roof obstacle marking results; Inputting the roof obstacle annotation result into a convolutional neural network to perform obstacle boundary recognition to obtain a corresponding obstacle boundary recognition result; Determining the layout area boundary according to the eaves distance and the ridge spacing constraint conditions; According to the obstacle boundary recognition result and the layout area boundary, the unusable area is removed from the unobstructed roof available area to obtain the final available area of the roof.

4. The distributed photovoltaic array arrangement method according to claim 1, characterized in that: The preset arrangement rules include starting direction selection rules, array unit and electrical matching rules, multi-format component adaptation and optimization rules, gap compensation and secondary filling rules; The starting direction selection rule includes: using any one of a unilateral starting mode, a symmetrical starting mode, and a dynamic selection logic mode to determine the arrangement starting direction; The electrical matching rules for the array units include: giving priority to arranging the photovoltaic strings as array units, ensuring that the photovoltaic modules within the same photovoltaic string have the same orientation and inclination; if the remaining space in the final usable area of the roof is insufficient to arrange the entire photovoltaic string, then adjusting the length of the photovoltaic string or arranging the photovoltaic strings in parallel; The multi-format component adaptation and optimization rules include: calculating the densest arrangement quantity and coverage rate of each format component for the current available area in the final available area of the roof, and optimizing the optimal format component solution by using a multi-objective optimization algorithm combined with economic parameters based on the densest arrangement quantity and coverage rate; The gap compensation and secondary filling rules include: when the photovoltaic modules that have been arranged in the final usable area of the roof are deleted, the corresponding gap boundary is detected, and an attempt is made to fill the vacant area corresponding to the gap boundary by reducing the distance between adjacent photovoltaic modules, and ensuring that the distance between adjacent photovoltaic modules is not less than the minimum safety value. If the vacant area cannot be filled, the vacant area is marked as an area to be optimized; for the remaining narrow space at the edge of the roof in the final usable area of the roof, different arrangement patterns are used for arrangement.

5. The distributed photovoltaic array arrangement method according to claim 4, characterized in that: The unilateral starting mode includes: starting from the unilateral edge of the rooftop area with the highest lighting efficiency, arranging the distributed photovoltaic array in the final usable area of the rooftop in a serpentine path; The symmetrical starting mode includes: taking the ridge line or the roof center line as the symmetry axis, arranging the roof synchronously from both sides of the symmetry axis in the final usable area of the roof, and maximizing the use of the symmetrical space in the final usable area of the roof; The dynamic selection logic mode includes: selecting the optimal starting mode from the unilateral starting mode and the symmetrical starting mode according to the aspect ratio of the roof and the shape of the final available area of the roof to arrange the distributed photovoltaic array.

6. The distributed photovoltaic array arrangement method according to claim 1, characterized in that: The method of optimizing the initial arrangement scheme by using a preset optimization algorithm to obtain a final arrangement scheme includes: With the goal of maximizing the number of photovoltaic modules, minimizing shadow loss, and minimizing cable costs, the particle swarm optimization algorithm is used to adjust the photovoltaic module spacing, panel combination, and starting direction in the initial arrangement scheme to obtain the corresponding solution set; Perform frontier screening according to the Pareto principle and retain the non-inferior solution set in the solution set; The arrangement effect and corresponding indicators of at least one alternative arrangement scheme corresponding to the non-inferior solution set are displayed to the user, and the alternative arrangement scheme selected by the user is used as the final arrangement scheme.

7. A distributed photovoltaic array arrangement device, characterized in that: The distributed photovoltaic array arrangement device comprises: Establish a module for building a three-dimensional roof model and determining the unobstructed usable area of the roof based on the lighting conditions; A determination module, configured to determine a final usable roof area from the unobstructed roof area based on roof obstacles, eaves distance, and ridge spacing constraints; An initial arrangement module is used to arrange the distributed photovoltaic array in the final available area of the roof according to a preset arrangement rule to obtain a corresponding initial arrangement plan; The optimization module is used to optimize the initial arrangement scheme using a preset optimization algorithm to obtain a final arrangement scheme.

8. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the distributed photovoltaic array arrangement method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the distributed photovoltaic array arrangement method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the distributed photovoltaic array arrangement method according to any one of claims 1 to 6 is implemented.

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