Mountain photovoltaic module arrangement optimization method and system based on digital twinning

Abstract

The application discloses a mountain photovoltaic module arrangement optimization method and system based on digital twinning, which comprises the following steps: three-dimensional network reconstruction is performed on a mountain area to obtain a mountain grid model; an initial layout of a photovoltaic module is embedded, and a digital twin containing the relationship among terrain, module geometry and illumination is formed in combination with a sun trajectory model; the shadow shielding condition of the photovoltaic module is simulated through ray tracing and polygon clipping technology to obtain a time-varying shielding factor; and a multi-objective optimization problem with the maximum power generation and the optimal economic cost in the whole life cycle as the target is solved based on the time-varying shielding factor to obtain an optimal arrangement optimization scheme. Through the digital twin, the real situation of the photovoltaic module being shielded by the terrain and other modules can be more accurately calculated; and by constructing the maximum power generation and the optimal economic cost in the whole life cycle as a unified multi-objective optimization problem, the comprehensive benefit of the mountain photovoltaic power station in the whole life cycle is improved on the basis of shortening the design cycle.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation technology, and in particular to a method and system for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins. Background Technology

[0002] As photovoltaic (PV) power plants expand into areas with complex terrain, mountainous regions have become an important scenario for renewable energy development. Mountainous PV power plants are typically deployed on natural landscapes with significant slope undulations and varying orientations. The arrangement of their modules must simultaneously consider sunlight reception efficiency, terrain adaptability, and engineering economics. In such scenarios, the shading effects between adjacent PV modules and the terrain itself on the solar path create dynamically changing shadow areas at different times of day, directly impacting the effective light-receiving area of ​​the modules and the overall power generation performance of the system.

[0003] Currently, the design phase of mountain photovoltaic projects typically relies on expert experience and rules for the placement of photovoltaic modules. However, expert experience inevitably leads to a disconnect between power generation performance optimization and economic cost assessment. This can result in a final layout that significantly increases support and construction costs while simultaneously improving power generation, or excessively sacrifices solar resource utilization to control costs, thus hindering the overall benefit improvement of mountain photovoltaic power plants throughout their entire life cycle. Summary of the Invention

[0004] This invention provides a method and system for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins, in order to solve existing problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A digital twin-based method for optimizing the layout of photovoltaic modules in mountainous areas includes: Based on the collected topographic data of the mountainous area, a three-dimensional network reconstruction of the mountainous area is carried out to obtain a mountain grid model; An initial layout of photovoltaic modules is embedded in a mountain grid model and combined with a solar trajectory model to form a digital twin that includes the relationship between terrain, module geometry and illumination. Based on digital twins, the shading of photovoltaic modules is simulated using ray tracing and polygon clipping techniques to obtain time-varying shading factors; Based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, a multi-objective optimization problem aimed at maximizing power generation and optimizing the economic cost over the entire life cycle is solved, resulting in an optimized photovoltaic module layout scheme for mountainous areas.

[0006] A further improvement of this invention lies in the pre-construction process of the multi-objective optimization problem aimed at maximizing power generation and optimizing the economic cost over the entire life cycle, which includes: The power generation energy is determined based on the out-of-plane irradiance received by the photovoltaic module, the shading factor, and the module area, combined with the system efficiency. Based on the local slope of the mountainous area, the installation cost of the terrain-sensitive support structure is determined; based on the installation cost of the support structure and the cost of the fixed unit component, the initial investment cost is determined; based on the number of photovoltaic modules and the operation and maintenance cost, the total operation and maintenance cost is determined; an effective land occupation penalty is introduced to determine the land use cost; based on the initial investment cost, the total operation and maintenance cost, and the land use cost, the full life cycle economic cost is determined. A multi-objective optimization problem is constructed with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle.

[0007] A further improvement of this invention is that the generated energy satisfies the following formula:

[0008] in, For generating electricity, Let be the out-of-plane irradiance received by the j-th photovoltaic module at time t. Let be the shading factor of the j-th photovoltaic module at time t. Let j be the module area of ​​the j-th photovoltaic module. For system efficiency, T is the total number of time steps, and N is the number of photovoltaic modules. For time step; The total life-cycle economic cost satisfies the following formula:

[0009] in, For the total life cycle economic cost, To fix the unit component cost, Let the bracket installation cost be the cost of the j-th photovoltaic module. The cost is the operation and maintenance cost, and N is the number of photovoltaic modules. For land use costs.

[0010] A further improvement of this invention lies in solving a multi-objective optimization problem aimed at maximizing power generation and optimizing the total life-cycle economic cost, based on time-varying shading factors and terrain-related component installation and layout compactness costs. This yields an optimized photovoltaic module layout scheme for mountainous areas, including: Based on terrain data, calculate the shadow exposure index of each grid point in the mountainous region; Based on the shadow exposure index of each grid point, an initial population is generated according to the deployment strategy that the value of the shadow exposure index is inversely proportional to the deployment priority of photovoltaic modules. The initial population includes multiple individuals, each representing the photovoltaic module layout scheme of each grid point. Based on the initial population, time-varying shading factors, and terrain-related component installation and layout compactness costs, with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle, a multi-objective genetic algorithm is used to re-perform evolutionary operations on each individual until a preset convergence condition is reached. Based on the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is reached, the optimal photovoltaic module layout scheme for mountainous areas is determined.

[0011] A further improvement of this invention is that the shadow exposure index of each grid point satisfies the following formula:

[0012] in, For grid points The shadow exposure index is given by t, where t is the time step and T is the total number of time steps.

[0013] A further improvement of this invention lies in determining an optimized photovoltaic module layout scheme for mountainous areas based on the photovoltaic module layout scheme represented by individuals when a preset convergence condition is met, including: Among the photovoltaic module layout schemes represented by individuals when the preset convergence conditions are met, a first layout scheme set that meets the preset project constraints and a second layout scheme set that does not meet the preset project constraints are divided; the preset project constraints include the upper limit of terrain slope, the minimum safe distance between modules, and the restriction of prohibited construction areas; Based on each layout scheme in the second set of layout schemes, the position sensitivity of the layout scheme is approximately calculated using the finite difference method; based on the position sensitivity, a constraint repair strategy is adopted to make local adjustments to obtain a layout scheme that meets the preset project constraints. Based on the first set of layout schemes and the repaired second set of layout schemes, the final optimized layout scheme for photovoltaic modules is determined.

[0014] A further improvement of this invention is that the solar trajectory model is related to the solar declination angle and the hour angle; the solar trajectory model satisfies the following formula:

[0015] in, Let be the direction vector of the sun at time t. Let be the solar declination angle at time t. Let be the hour angle at time t.

[0016] A further improvement of this invention lies in simulating the shading of photovoltaic modules based on digital twins using ray tracing and polygon clipping techniques to obtain a time-varying shading factor, including: Based on digital twins and ray tracing technology, the actual projection points of solar rays on the terrain are determined; Based on the real projection points, construct a dynamic set of shadow polygons for each photovoltaic module on the terrain; Based on each photovoltaic module, in the scenario where the photovoltaic module is illuminated, in the dynamic shadow polygon set of each photovoltaic module on the terrain, determine the shadow polygon of the upstream module of the photovoltaic module that is projected onto its installation plane at the same time; perform a Boolean union operation on the geometric polygon of the photovoltaic module itself and the corresponding shadow polygon to obtain the total shading area; Based on the total shading area of ​​each photovoltaic module, the Sutherland-Hodgman polygon clipping algorithm is used to calculate the effective light-receiving area of ​​each photovoltaic module that is not shaded; based on the effective light-receiving area of ​​each photovoltaic module and the total area of ​​the module, the time-varying shading factor of each photovoltaic module is determined.

[0017] A digital twin-based system for optimizing the layout of photovoltaic modules in mountainous areas includes: The mountain model construction module is used to reconstruct a three-dimensional network of mountainous areas based on the collected terrain data, and obtain a mountain mesh model. The digital twin generation module is used to embed the initial layout of photovoltaic modules into a mountain grid model and combine it with a solar trajectory model to form a digital twin that includes terrain, module geometry and lighting relationships; The shading factor calculation module is used to simulate the shading of photovoltaic modules based on digital twins, through ray tracing and polygon clipping technology, and obtain time-varying shading factors. The layout optimization module is used to solve a multi-objective optimization problem based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, with the goal of maximizing power generation and optimizing the economic cost over the entire life cycle, and to obtain an optimized layout scheme for photovoltaic modules in mountainous areas.

[0018] An electronic device, comprising at least a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the steps of the digital twin-based mountain photovoltaic module layout optimization method.

[0019] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a method and system for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins. Based on collected terrain data of mountainous regions, a three-dimensional network reconstruction is performed to obtain a mountainous mesh model. An initial layout of photovoltaic modules is embedded into the mountainous mesh model, and combined with a solar trajectory model, a digital twin containing the relationships between terrain, module geometry, and illumination is formed. Based on the digital twin, ray tracing and polygon clipping techniques are used to simulate the shading of photovoltaic modules, obtaining a time-varying shading factor. Based on the time-varying shading factor, and the terrain-related module installation cost and layout compactness cost, a multi-objective optimization problem is solved with the goals of maximizing power generation and optimizing the overall life-cycle economic cost, resulting in an optimized layout scheme for photovoltaic modules in mountainous areas. This invention uses digital twins combined with ray tracing and polygon clipping technology to more accurately calculate the actual situation of photovoltaic modules being shaded by terrain and other components, thereby improving the accuracy of shadow simulation and power generation prediction. Furthermore, by constructing the maximization of power generation and the optimization of the economic cost throughout the entire life cycle as a unified multi-objective optimization problem, it can avoid suboptimal designs of "high power generation but low efficiency" or "low cost but low output", thereby improving the comprehensive benefits of mountain photovoltaic power stations throughout their entire life cycle while shortening the design cycle. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins, provided by this invention; Figure 2 A schematic diagram of a mountain photovoltaic module layout optimization system based on digital twin provided by the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0022] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0023] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] Example 1: Figure 1 The flowchart illustrates a method for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins, as provided by this invention. The process includes the following steps: S101: Based on the collected topographic data of the mountainous area, a three-dimensional network reconstruction of the mountainous area is performed to obtain a mountain grid model.

[0029] S102: Embed the initial layout of photovoltaic modules into the mountain grid model, and combine it with the solar trajectory model to form a digital twin that includes the relationship between terrain, module geometry and illumination.

[0030] S103: Based on digital twins, the shading of photovoltaic modules is simulated through ray tracing and polygon clipping technology to obtain time-varying shading factors.

[0031] S104: Based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, a multi-objective optimization problem is solved with the goal of maximizing power generation and optimizing the economic cost over the entire life cycle, resulting in an optimized photovoltaic module layout scheme for mountainous areas.

[0032] This invention uses digital twins combined with ray tracing and polygon clipping technology to more accurately calculate the actual situation of photovoltaic modules being shaded by terrain and other components, thereby improving the accuracy of shadow simulation and power generation prediction. Furthermore, by constructing the maximization of power generation and the optimization of the economic cost throughout the entire life cycle as a unified multi-objective optimization problem, it can avoid suboptimal designs of "high power generation but low efficiency" or "low cost but low output", thereby improving the comprehensive benefits of mountain photovoltaic power stations throughout their entire life cycle while shortening the design cycle.

[0033] The method for optimizing the layout of mountain photovoltaic modules based on digital twins provided by this invention can be applied to electronic devices, such as PCs or servers.

[0034] Electronic devices can acquire topographic data of mountainous areas, for example, but not limited to, data collected by drones and / or high-resolution satellites (or jointly). Topographic data of mountainous areas involves multiple dimensions, including elevation, relief, slope, valley features, and distribution patterns. When collecting data, drones can utilize onboard LiDAR systems or high-precision photogrammetry equipment to conduct aerial surveys, thereby acquiring dense point cloud data containing three-dimensional spatial coordinates.

[0035] In one implementation, in S101 above, the mountainous area can be reconstructed using TIN (Triangulated Irregular Network) or DEM (Digital Elevation Model) based on the collected topographic data of the mountainous area to obtain a mountain grid model.

[0036] In one example, when constructing a mountain mesh model using TIN, key terrain feature points (such as mountain tops, valley bottoms, and ridgelines) in the terrain data can be used as nodes. The Delaunay triangulation algorithm is used to generate a non-uniformly distributed set of triangular patches, which accurately preserves the local abrupt changes in terrain features, thereby reconstructing the surface of the mountainous area and obtaining a mountain mesh model.

[0037] In another example, when constructing a mountain network model using a DEM, terrain data can be interpolated onto a regular two-dimensional grid. Each grid cell stores the corresponding elevation value, forming a structured grid model. This allows for surface reconstruction of the mountainous region, resulting in a mountain grid model. Mountain grid models constructed in this way facilitate subsequent numerical calculations and index queries. The mountain grid models generated in this implementation can represent the original terrain of the mountainous region with centimeter-level (or even millimeter-level) spatial resolution and support quick queries of geographical parameters such as elevation, local slope, and aspect for any planar coordinates. This provides high-fidelity spatial support for subsequent photovoltaic module layout.

[0038] The initial layout of photovoltaic modules in S102 above can be a manually set initial arrangement scheme based on the available area and engineering experience of the mountainous region. For example, the initial layout of photovoltaic modules can adopt a regular row and column layout, or it can be arranged in zones based on the terrain slope aspect. When embedding the initial layout of photovoltaic modules into the mountain grid model, each photovoltaic module can be modeled as a three-dimensional planar polygon with a fixed size. Its spatial orientation is uniquely represented by the center coordinates, installation tilt angle, and azimuth angle. The bottom elevation of each photovoltaic module is adjusted according to the altitude of the corresponding location in the mountain grid model, and necessary clearance from the ground is reserved to avoid geometric interference with the ground surface. On this basis, the three-dimensional geometric models of all photovoltaic modules are superimposed on the mountain network model to form a unified spatial scene that includes the terrain surface and the photovoltaic array.

[0039] In one implementation, in S102 above, a solar trajectory model is introduced into the unified spatial scene formed above. By associating the solar direction vector in the solar trajectory model with the spatial scene, a dynamic geometric relationship between lighting, terrain, and components can be established, i.e., a digital twin.

[0040] In this implementation, the solar trajectory model can be obtained by using astronomical algorithms to calculate the sun's position at any time of the year (this is just an example; other time ranges can also be used, and this is not a limitation here) in real time, outputting the corresponding solar declination angle, hour angle, and direction vector. For example, the solar trajectory model is related to the solar declination angle and hour angle. Specifically, the solar trajectory model provides the relationship between the sun's direction vector and the solar declination angle and hour angle. In this example, to optimize the arrangement of photovoltaic modules, the irradiance received by the photovoltaic modules and the shadow projection path can be considered. These two factors are affected by the direction of sunlight incidence, which is represented by the sun's direction vector in this example. Calculating the sun's direction vector in real-world scenarios is very complex; therefore, the declination angle and hour angle are introduced in this example. The declination angle refers to the latitude angle of the subsolar point relative to the Earth's equatorial plane, reflecting seasonal changes; the hour angle refers to the time difference between the current solar time at the mountain and noon (generally the sun's highest point), expressed in angles, reflecting the time of day. Compared to the parallel sunlight assumption (i.e., the sun is extremely far from Earth, and the rays are approximately parallel), this solar trajectory model can guarantee the direction input for ray tracing and better reflect the relative occlusion between components. For example, the solar trajectory model satisfies the following formula:

[0041] in, Let be the direction vector of the sun at time t. Let be the solar declination angle at time t. Let be the hour angle at time t.

[0042] In this implementation, the digital twin serves as an integrated virtual model that combines high-precision terrain, component 3D geometry, and its interaction with the sun's light path. It can support subsequent high-fidelity simulation operations such as ray tracing, shadow casting, and occlusion analysis, providing a physically consistent digital environment for multi-objective optimization.

[0043] Traditional solutions often ignore terrain curvature or simply define shadows as rectangular projections, leading to significant underestimation or overestimation of the occlusion area and large errors. To address this issue, this invention proposes combining real ray-terrain intersections to fully preserve the geometric details of shadows, achieving high-precision shadow simulation under terrain coupling. In one implementation, step S103 may include the following steps: Based on digital twins and ray tracing technology, the actual projection points of solar rays on the terrain are determined; Based on the real projection points, construct a dynamic set of shadow polygons for each photovoltaic module on the terrain; Based on each photovoltaic module, in the scenario where the photovoltaic module is illuminated, in the dynamic shadow polygon set of each photovoltaic module on the terrain, determine the shadow polygon of the upstream module of the photovoltaic module that is projected onto its installation plane at the same time; perform a Boolean union operation on the geometric polygon of the photovoltaic module itself and the corresponding shadow polygon to obtain the total shading area; Based on the total shading area of ​​each photovoltaic module, the Sutherland-Hodgman polygon clipping algorithm is used to calculate the effective light-receiving area of ​​each photovoltaic module that is not shaded; based on the effective light-receiving area of ​​each photovoltaic module and the total area of ​​the module, the time-varying shading factor of each photovoltaic module is determined.

[0044] In one example, when determining the true projection points of solar rays on terrain based on a digital twin and ray tracing technology, the following steps can be taken: First, for each time t, the direction vector s(t) of the sun is obtained using the solar trajectory model in the digital twin. Then, for each vertex of each photovoltaic module in the digital twin, ray tracing is used to emit rays in the opposite direction of the sun's direction vector, and the first intersection point between this ray and the surface of the mountain mesh model is calculated. Due to the irregular undulations of the mountainous terrain, the first intersection point obtained is not a simple vertical projection, but rather the actual intersection position of the ray with the three-dimensional curved surface. This allows us to obtain the true projection point set of each photovoltaic module on the complex terrain. This step fully considers the occlusion and deflection effects of the terrain on the shadow path, avoiding the geometric distortion caused by traditional horizontal projection or planar assumptions.

[0045] For example, the first intersection point satisfies the following formula:

[0046] in, This represents the first intersection point between the ray from the i-th photovoltaic module and the mountain mesh model at time t, where is the vertex. The actual projection point on the terrain, This represents the vertex of the i-th photovoltaic module, including its x-coordinate. y-axis and height coordinates , The scalar parameter of the i-th photovoltaic module at time t refers to the vertex. The direction vector s(t) along the sun reaches the intersection point on the mountain surface. The directed distance reflects the propagation length of light in space, and s(t) represents the direction vector of the sun. In complex mountain meshes, a ray may intersect multiple triangular faces, and only the first intersection point is the actual shadow landing point. That is, other invisible intersection points that are subsequently occluded are not considered here, thus ensuring the physical reality of the calculated shadow projection. Points on the path of light rays The vertical height z must be equal to the terrain elevation h corresponding to its horizontal projection (x,y) to ensure that the shadow projection truly reflects the terrain undulations, rather than simply treating the ground as a plane.

[0047] In one example, when constructing a dynamic shadow polygon set for each photovoltaic module on the terrain based on its actual projection points, a closed polygon can be formed by connecting adjacent projection points based on the actual projection points of all vertices. This allows the construction of the dynamic shadow polygon projected by each photovoltaic module onto the terrain at time t. As the sun's position changes with time t, these shadow polygons also change dynamically, thus yielding a complete set of dynamic shadow polygons.

[0048] When calculating the total shading area of ​​each photovoltaic module, this implementation identifies all upstream modules (those located in front of the photovoltaic module in the direction of solar incidence) that may cause shading. For each photovoltaic module, the shadow polygon projected by the photovoltaic module at the same time is uniformly transformed to the coordinates of the module's installation plane. Then, on this plane, a Boolean union operation is performed on all upstream shadow polygons to generate the total shading area covering the surface of the photovoltaic module. This ensures that even if multiple shadows overlap, shading is only calculated once, avoiding duplicate deductions and improving the adaptability of subsequent photovoltaic module layout schemes to the actual environment.

[0049] When calculating the effective unshaded light-receiving area of ​​each photovoltaic (PV) module using the Sutherland-Hodgman polygon clipping algorithm based on the total shading area of ​​each module, the algorithm uses the PV module's own geometric polygons as a clipping window to clip the total shading area of ​​that module, thus obtaining the effective unshaded light-receiving area. The Sutherland-Hodgman polygon clipping algorithm is numerically stable and applicable to concave and convex polygons, making it suitable for real-world scenarios with complex terrain and irregular shading shapes resulting from multi-module interactions.

[0050] When determining the time-varying shading factor of each photovoltaic module based on its effective light-receiving area and total area, the ratio of the effective light-receiving area to the total area of ​​the module can be used as the time-varying shading factor for each photovoltaic module. The time-varying shading factor can directly reflect the actual availability of solar resources.

[0051] This implementation method can realistically reflect the non-uniform shading caused by differences in slope, orientation, etc. in mountainous environments, keeping the error of the calculated time-varying shading factor within an acceptable engineering range. Furthermore, it can accurately determine mutual shading relationships even in complex local scenarios with irregular component arrangements, breaking through the simulation limitations of traditional row-column layouts and providing reliable input for optimization decisions.

[0052] Simply pursuing maximum power generation often leads to the deployment of modules on steep slopes, in gullies, or in remote areas. While this may increase power output in the short term, it significantly increases support costs, construction difficulty, and subsequent operation and maintenance expenses, ultimately reducing the overall project profitability. Furthermore, if minimizing costs is considered, the system may tend to concentrate modules in areas with gentle slopes but poor sunlight conditions, leaving valuable south-facing slopes idle and resulting in a long-term loss of significant exploitable energy. Therefore, the multi-objective optimization problem in this invention considers multiple objectives, including maximizing power generation and optimizing the economic cost over the entire lifecycle, ensuring the full exploitation of high-irradiance resources under reasonable cost increments. In one implementation, the pre-construction process of the multi-objective optimization problem in S104, with the objectives of maximizing power generation and optimizing the economic cost over the entire lifecycle, may include the following steps: The power generation energy is determined based on the out-of-plane irradiance received by the photovoltaic module, the shading factor, and the module area, combined with the system efficiency. Based on the local slope of the mountainous area, the installation cost of the terrain-sensitive support structure is determined; based on the installation cost of the support structure and the cost of the fixed unit component, the initial investment cost is determined; based on the number of photovoltaic modules and the operation and maintenance cost, the total operation and maintenance cost is determined; an effective land occupation penalty is introduced to determine the land use cost; based on the initial investment cost, the total operation and maintenance cost, and the land use cost, the full life cycle economic cost is determined. A multi-objective optimization problem is constructed with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle.

[0053] For example, the (total effective) power generation energy satisfies the following formula:

[0054] in, For generating electricity, Let be the out-of-plane irradiance received by the j-th photovoltaic module at time t, reflecting the intensity of solar radiation. It can be determined by meteorological data or a solar trajectory model, and includes differences in the position and tilt angle between modules. Let be the shading factor of the j-th photovoltaic module at time t, which includes the shading differences between modules. Let j be the module area of ​​the j-th photovoltaic module. System efficiency can include non-optical factors such as temperature degradation, DC / AC conversion losses, and inverter efficiency. T represents the total number of time steps, and N represents the number of photovoltaic modules. The time step is a key factor in converting instantaneous power into cumulative energy. This formula discretizes the entire photovoltaic power generation process into a "time step × module" grid, calculates the shading-corrected instantaneous power in each cell, and then obtains the total generated energy through time integration. This is achieved by introducing [a specific parameter] into the formula. To achieve high-precision occlusion simulation, The total life-cycle economic cost satisfies the following formula:

[0055] in, For the total life cycle economic cost, To fix the unit component cost, The bracket installation cost of the j-th photovoltaic module varies with factors such as terrain slope and local stability, reflecting the heterogeneity of the mountainous terrain. Here, we no longer assume that the bracket cost of all modules is the same, but rather it depends on the terrain parameters of the module's location (such as slope, soil type, whether reinforcement is required, etc.). The cost is the operation and maintenance cost, and N is the number of photovoltaic modules. This refers to land use costs, also known as compact layout costs, which reflect the implicit costs caused by spatial distribution, such as cable length, inverter placement, and the complexity of operation and maintenance paths. Indicates the initial investment cost. This represents the total operation and maintenance cost. The formula includes not only explicit costs such as components and supports, but also indirect costs related to terrain, such as installation cost differences and compact layout, making it more instructive for engineering. The entire life cycle cost of a photovoltaic power station is decomposed into four modules: "components + supports + operation and maintenance + layout". The first two are directly coupled with terrain, while the latter is related to spatial arrangement, thus constructing a comprehensive economic model that reflects engineering reality and supports intelligent optimization.

[0056] In one implementation, the Pareto optimal solution can be obtained in S104 using a genetic algorithm, thereby obtaining the optimal photovoltaic module layout scheme. For example, the solution process may include the following steps: Based on terrain data, calculate the shadow exposure index of each grid point in the mountainous region; Based on the shadow exposure index of each grid point, an initial population is generated according to the deployment strategy that the value of the shadow exposure index is inversely proportional to the deployment priority of photovoltaic modules. The initial population includes multiple individuals, each representing the photovoltaic module layout scheme of each grid point. Based on the initial population, time-varying shading factors, and terrain-related component installation and layout compactness costs, with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle, a multi-objective genetic algorithm is used to re-perform evolutionary operations on each individual until a preset convergence condition is reached. Based on the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is reached, the optimal photovoltaic module layout scheme for mountainous areas is determined.

[0057] For example, when calculating the shadow exposure index of each grid point in a mountainous area based on terrain data, the annual solar trajectory simulation can be used to statistically determine the proportion of time each grid point is covered by any component, thus obtaining the shadow exposure index. A higher value indicates more severe shading and poorer sunlight resources. Therefore, a deployment strategy can be set that is inversely proportional to the shadow exposure index value and the photovoltaic module deployment priority; that is, the lower the shadow exposure index, the higher the deployment priority. For example, the shadow exposure index of each grid point satisfies the following formula:

[0058] in, For grid points The shadow exposure index is given by t, where t is the time step and T is the total number of time steps.

[0059] In the initial population, photovoltaic modules are preferentially placed in low-exposure areas, forming multiple differentiated candidate layout schemes as initial individuals. Optionally, each individual can encode the module layout information for the entire mountainous area, for example, using a binary vector to represent whether each grid has a module installed, or using a real-number vector to record the module coordinates and tilt angle.

[0060] In this implementation, the initial population, time-varying shading factor, and terrain-related component installation costs and layout compactness costs are used as the basis for a multi-objective genetic algorithm. Based on this information, two objective function values ​​can be calculated for each individual: Objective 1: power generation energy; Objective 2: total economic cost over the entire life cycle. Then, the multi-objective genetic algorithm is used to perform standard evolutionary operations—including non-dominated sorting, crowding calculation (or reference point selection), tournament selection, crossover, and mutation—to generate a new generation of population. This process is iterated until preset convergence conditions are met, such as maximum number of generations and Pareto front stability threshold, to obtain the Pareto optimal solution set, thereby obtaining the optimal photovoltaic module layout optimization scheme.

[0061] The multi-objective genetic algorithm can be either the NSGA-II algorithm or the NSGA-III algorithm.

[0062] This implementation combines terrain-aware heuristic initialization with multi-objective evolutionary search, enabling synergistic optimization of performance and cost under complex constraints. Specifically, the initial population guided by the shadow exposure index significantly improves the quality of the search starting point, preventing the algorithm from getting stuck in high-occlusion, low-efficiency invalid regions in the early stages and accelerating convergence (experimental analysis shows that the shadow exposure index can reduce the number of convergence generations by more than 30% compared to random initialization). The dual-objective optimization mechanism ensures that the output scheme achieves a reasonable balance between power generation potential and economic feasibility, overcoming the "one-sided" shortcomings of traditional single-objective methods (experimental analysis shows that in typical mountainous scenarios, compared with regular layouts, it can increase annual power generation by 8%–12% at similar costs, or reduce overall costs by 5%–10% at the same power generation). The output of the Pareto optimal solution set provides decision-makers with diverse choices, supporting flexible responses to different electricity pricing policies, financing conditions, or land restrictions. The entire process is deeply coupled with a digital twin, ensuring that the evaluation of each candidate scheme is based on high-fidelity physical simulation, improving the engineering credibility of the optimization results.

[0063] In one example, when determining the optimal photovoltaic module layout scheme for a mountainous area based on the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is met, the layout scheme that satisfies the preset project constraints (engineering constraints, generally including the upper limit of terrain slope, the minimum safe distance between modules, and the restriction of prohibited construction areas) can be selected from the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is met (i.e., the Pareto optimal solution set) as the final optimal photovoltaic module layout scheme.

[0064] In another example, besides selecting layout schemes that meet preset project constraints, adjustments can be made to photovoltaic module layout schemes that do not meet the preset project constraints. This can salvage high-potential schemes and achieve a synergistic balance between performance preservation and constraint satisfaction. For example, when determining the optimal layout scheme for photovoltaic modules in mountainous areas based on the layout schemes represented by individuals that have reached preset convergence conditions, the following steps can be taken: among the layout schemes represented by individuals that have reached preset convergence conditions, a first set of layout schemes that meet the preset project constraints and a second set of layout schemes that do not meet the preset project constraints are defined. The preset project constraints include the upper limit of terrain slope, the minimum safe spacing between modules, and restrictions on prohibited construction areas. Based on each layout scheme in the second set of layout schemes, the location sensitivity of the layout scheme is approximately calculated using the finite difference method. Based on the location sensitivity, a constraint repair strategy is used for local adjustments to obtain a layout scheme that meets the preset project constraints. Based on the first set of layout schemes and the repaired second set of layout schemes, the final optimal layout scheme for photovoltaic modules is determined.

[0065] In this example, for each infeasible scheme in the second set of layout schemes that does not meet the constraints, a constraint repair operation based on sensitivity analysis is performed. The position sensitivity calculated using the finite difference method can reflect the degree of impact of moving a component on the overall performance. Using the finite difference method as a sensitivity estimation tool, rather than directly for repair, can provide a quantitative basis for the repair direction and avoid blind adjustments that lead to a significant drop in performance.

[0066] Constraint repair strategies, as constraint-driven local adjustment strategies, can include, for example, the following: for components located within restricted areas, they are translated to the nearest available area along the direction of lower sensitivity gradient; for component pairs with insufficient spacing, they are pushed apart in the opposite direction along the connecting line, prioritizing the movement of the pair with less impact on the objective function; for components placed in areas with excessive slope, they are moved to nearby gentle slope locations based on terrain curvature. Through guided fine-tuning, originally infeasible solutions can be gradually made to meet all project constraints while preserving their original performance advantages as much as possible. Then, by merging the first set of layout schemes and the repaired second set of layout schemes, the feasible solution space can be significantly expanded. Especially in complex mountainous scenarios, some high-performance but slightly non-compliant solutions can be transformed into high-quality feasible solutions after repair, avoiding the performance loss caused by simple rejection.

[0067] Understandably, based on the optimal solution set obtained by merging the first set of layout schemes and the second set of repaired layout schemes, the scheme that best meets the project requirements can be selected as the final photovoltaic module layout optimization scheme according to the user's preferences (such as prioritizing power generation or strictly controlling costs).

[0068] Example 2: Based on the same concept, Figure 2 A schematic diagram of a digital twin-based mountain photovoltaic module layout optimization system provided by the present invention includes: The mountain model construction module is used to reconstruct a three-dimensional network of mountainous areas based on the collected terrain data, and obtain a mountain mesh model. The digital twin generation module is used to embed the initial layout of photovoltaic modules into a mountain grid model and combine it with a solar trajectory model to form a digital twin that includes terrain, module geometry and lighting relationships; The shading factor calculation module is used to simulate the shading of photovoltaic modules based on digital twins, through ray tracing and polygon clipping technology, and obtain time-varying shading factors. The layout optimization module is used to solve a multi-objective optimization problem based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, with the goal of maximizing power generation and optimizing the economic cost over the entire life cycle, and to obtain an optimized layout scheme for photovoltaic modules in mountainous areas.

[0069] In one possible implementation, it further includes: an optimization problem building module, used for: The power generation energy is determined based on the out-of-plane irradiance received by the photovoltaic module, the shading factor, and the module area, combined with the system efficiency. Based on the local slope of the mountainous area, the installation cost of the terrain-sensitive support structure is determined; based on the installation cost of the support structure and the cost of the fixed unit component, the initial investment cost is determined; based on the number of photovoltaic modules and the operation and maintenance cost, the total operation and maintenance cost is determined; an effective land occupation penalty is introduced to determine the land use cost; based on the initial investment cost, the total operation and maintenance cost, and the land use cost, the full life cycle economic cost is determined. A multi-objective optimization problem is constructed with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle.

[0070] In one possible implementation, the generated energy satisfies the following formula:

[0071] in, For generating electricity, Let be the out-of-plane irradiance received by the j-th photovoltaic module at time t. Let be the shading factor of the j-th photovoltaic module at time t. Let j be the module area of ​​the j-th photovoltaic module. For system efficiency, T is the total number of time steps, and N is the number of photovoltaic modules. For time step; The total life-cycle economic cost satisfies the following formula:

[0072] in, For the total life cycle economic cost, To fix the unit component cost, Let the bracket installation cost be the cost of the j-th photovoltaic module. The cost is the operation and maintenance cost, and N is the number of photovoltaic modules. For land use costs.

[0073] In one possible implementation, the layout optimization module is specifically used for: Based on terrain data, calculate the shadow exposure index of each grid point in the mountainous region; Based on the shadow exposure index of each grid point, an initial population is generated according to the deployment strategy that the value of the shadow exposure index is inversely proportional to the deployment priority of photovoltaic modules. The initial population includes multiple individuals, each representing the photovoltaic module layout scheme of each grid point. Based on the initial population, time-varying shading factors, and terrain-related component installation and layout compactness costs, with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle, a multi-objective genetic algorithm is used to re-perform evolutionary operations on each individual until a preset convergence condition is reached. Based on the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is reached, the optimal photovoltaic module layout scheme for mountainous areas is determined.

[0074] In one possible implementation, the shadow exposure index of each grid point satisfies the following formula:

[0075] in, For grid points The shadow exposure index is given by t, where t is the time step and T is the total number of time steps.

[0076] In one possible implementation, the layout optimization module is specifically used for: Among the photovoltaic module layout schemes represented by individuals when the preset convergence conditions are met, a first layout scheme set that meets the preset project constraints and a second layout scheme set that does not meet the preset project constraints are divided; the preset project constraints include the upper limit of terrain slope, the minimum safe distance between modules, and the restriction of prohibited construction areas; Based on each layout scheme in the second set of layout schemes, the position sensitivity of the layout scheme is approximately calculated using the finite difference method; based on the position sensitivity, a constraint repair strategy is adopted to make local adjustments to obtain a layout scheme that meets the preset project constraints. Based on the first set of layout schemes and the repaired second set of layout schemes, the final optimized layout scheme for photovoltaic modules is determined.

[0077] In one possible implementation, the solar trajectory model is related to the solar declination angle and the hour angle; the solar trajectory model satisfies the following formula:

[0078] in, Let be the direction vector of the sun at time t. Let be the solar declination angle at time t. Let be the hour angle at time t.

[0079] In one possible implementation, the occlusion factor calculation module is specifically used for: Based on digital twins and ray tracing technology, the actual projection points of solar rays on the terrain are determined; Based on the real projection points, construct a dynamic set of shadow polygons for each photovoltaic module on the terrain; Based on each photovoltaic module, in the scenario where the photovoltaic module is illuminated, in the dynamic shadow polygon set of each photovoltaic module on the terrain, determine the shadow polygon of the upstream module of the photovoltaic module that is projected onto its installation plane at the same time; perform a Boolean union operation on the geometric polygon of the photovoltaic module itself and the corresponding shadow polygon to obtain the total shading area; Based on the total shading area of ​​each photovoltaic module, the Sutherland-Hodgman polygon clipping algorithm is used to calculate the effective light-receiving area of ​​each photovoltaic module that is not shaded; based on the effective light-receiving area of ​​each photovoltaic module and the total area of ​​the module, the time-varying shading factor of each photovoltaic module is determined.

[0080] Example 3: Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Based on the above embodiments, the present invention also provides an electronic device, including a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304. The memory 303 stores a computer program, which, when executed by the processor 301, causes the processor 301 to perform the steps in the digital twin-based mountain photovoltaic module layout optimization method shown in the above embodiment.

[0081] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0082] Communication interface 302 is used for communication between the above-mentioned electronic device and other devices.

[0083] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0084] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0085] Example 4: Based on the above embodiments, the present invention also provides a computer-readable storage medium storing a computer program, which is processed by the above-described method for optimizing the layout of mountain photovoltaic modules based on digital twins.

[0086] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or equivalent to the scope of this invention are included in this invention.

[0087] The embodiments described in this invention are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0093] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for optimizing the layout of photovoltaic modules in mountainous areas based on digital twins, characterized in that, include: Based on the collected topographic data of the mountainous area, a three-dimensional network reconstruction of the mountainous area is carried out to obtain a mountain grid model; An initial layout of photovoltaic modules is embedded in a mountain grid model and combined with a solar trajectory model to form a digital twin that includes the relationship between terrain, module geometry and illumination. Based on digital twins, the shading of photovoltaic modules is simulated using ray tracing and polygon clipping techniques to obtain time-varying shading factors; Based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, a multi-objective optimization problem aimed at maximizing power generation and optimizing the economic cost over the entire life cycle is solved, resulting in an optimized photovoltaic module layout scheme for mountainous areas.

2. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 1, characterized in that, The pre-construction process of a multi-objective optimization problem aimed at maximizing power generation and optimizing the economic cost over the entire life cycle includes: The power generation energy is determined based on the out-of-plane irradiance received by the photovoltaic module, the shading factor, and the module area, combined with the system efficiency. Based on the local slope of the mountainous area, the installation cost of the terrain-sensitive support structure is determined; based on the installation cost of the support structure and the cost of the fixed unit component, the initial investment cost is determined; based on the number of photovoltaic modules and the operation and maintenance cost, the total operation and maintenance cost is determined; an effective land occupation penalty is introduced to determine the land use cost; based on the initial investment cost, the total operation and maintenance cost, and the land use cost, the full life cycle economic cost is determined. A multi-objective optimization problem is constructed with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle.

3. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 1, characterized in that, The energy generated satisfies the following formula: in, For generating electricity, Let be the out-of-plane irradiance received by the j-th photovoltaic module at time t. Let be the shading factor of the j-th photovoltaic module at time t. Let j be the module area of ​​the j-th photovoltaic module. For system efficiency, T is the total number of time steps, and N is the number of photovoltaic modules. For time step; The total life-cycle economic cost satisfies the following formula: in, For the total life cycle economic cost, To fix the unit component cost, Let the bracket installation cost be the cost of the j-th photovoltaic module. The cost is the operation and maintenance cost, and N is the number of photovoltaic modules. For land use costs.

4. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 1, characterized in that, Based on time-varying shading factors, and terrain-related component installation and layout compactness costs, a multi-objective optimization problem aimed at maximizing power generation and optimizing the overall lifecycle economic cost is solved, resulting in an optimized photovoltaic module layout scheme for mountainous areas, including: Based on terrain data, calculate the shadow exposure index of each grid point in the mountainous region; Based on the shadow exposure index of each grid point, an initial population is generated according to the deployment strategy that the value of the shadow exposure index is inversely proportional to the deployment priority of photovoltaic modules. The initial population includes multiple individuals, each representing the photovoltaic module layout scheme of each grid point. Based on the initial population, time-varying shading factors, and terrain-related component installation and layout compactness costs, with the goals of maximizing power generation and optimizing the economic cost over the entire life cycle, a multi-objective genetic algorithm is used to re-perform evolutionary operations on each individual until a preset convergence condition is reached. Based on the photovoltaic module layout scheme represented by the individuals when the preset convergence condition is reached, the optimal photovoltaic module layout scheme for mountainous areas is determined.

5. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 4, characterized in that, The shadow exposure index of each grid point satisfies the following formula: in, For grid points The shadow exposure index is given by t, where t is the time step and T is the total number of time steps.

6. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 4, characterized in that, Based on the photovoltaic module layout schemes represented by individuals when the preset convergence conditions are met, an optimized photovoltaic module layout scheme for mountainous areas is determined, including: Among the photovoltaic module layout schemes represented by individuals when the preset convergence conditions are met, a first layout scheme set that meets the preset project constraints and a second layout scheme set that does not meet the preset project constraints are divided; the preset project constraints include the upper limit of terrain slope, the minimum safe distance between modules, and the restriction of prohibited construction areas; Based on each layout scheme in the second set of layout schemes, the position sensitivity of the layout scheme is approximately calculated using the finite difference method; based on the position sensitivity, a constraint repair strategy is adopted to make local adjustments to obtain a layout scheme that meets the preset project constraints. Based on the first set of layout schemes and the repaired second set of layout schemes, the final optimized layout scheme for photovoltaic modules is determined.

7. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 1, characterized in that, The solar trajectory model is related to the solar declination angle and hour angle; the solar trajectory model satisfies the following formula: in, Let be the direction vector of the sun at time t. Let be the solar declination angle at time t. Let be the hour angle at time t.

8. The method for optimizing the layout of mountain photovoltaic modules based on digital twins according to claim 1, characterized in that, Based on digital twins, the shading of photovoltaic modules is simulated using ray tracing and polygon clipping techniques to obtain time-varying shading factors, including: Based on digital twins and ray tracing technology, the actual projection points of solar rays on the terrain are determined; Based on the real projection points, construct a dynamic set of shadow polygons for each photovoltaic module on the terrain; Based on each photovoltaic module, in the scenario where the photovoltaic module is illuminated, in the dynamic shadow polygon set of each photovoltaic module on the terrain, determine the shadow polygon of the upstream module of the photovoltaic module that is projected onto its installation plane at the same time; perform a Boolean union operation on the geometric polygon of the photovoltaic module itself and the corresponding shadow polygon to obtain the total shading area; Based on the total shading area of ​​each photovoltaic module, the Sutherland-Hodgman polygon clipping algorithm is used to calculate the effective light-receiving area of ​​each photovoltaic module that is not shaded; based on the effective light-receiving area of ​​each photovoltaic module and the total area of ​​the module, the time-varying shading factor of each photovoltaic module is determined.

9. A mountain photovoltaic module layout optimization system based on digital twins, characterized in that, include: The mountain model construction module is used to reconstruct a three-dimensional network of mountainous areas based on the collected terrain data, and obtain a mountain mesh model. The digital twin generation module is used to embed the initial layout of photovoltaic modules into a mountain grid model and combine it with a solar trajectory model to form a digital twin that includes terrain, module geometry and lighting relationships; The shading factor calculation module is used to simulate the shading of photovoltaic modules based on digital twins, through ray tracing and polygon clipping technology, and obtain time-varying shading factors. The layout optimization module is used to solve a multi-objective optimization problem based on time-varying shading factors, as well as terrain-related component installation costs and layout compactness costs, with the goal of maximizing power generation and optimizing the economic cost over the entire life cycle, and to obtain an optimized layout scheme for photovoltaic modules in mountainous areas.

10. An electronic device, characterized in that, The electronic device includes at least a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the steps of the mountain photovoltaic module layout optimization method based on digital twins as described in any one of claims 1 to 8.

Images