Photovoltaic arrangement method based on gradient grading and wiring feasibility analysis

Abstract

The invention relates to the technical field of large ground photovoltaic system engineering, in particular to a photovoltaic arrangement method based on gradient grading and wiring feasibility analysis. Comprising the following steps: acquiring and processing topographic data to obtain detailed topographic data of a target plot; on the basis of detailed topographic data of the target land parcel, calculating a gradient value of each grid unit by using a GIS tool to obtain a gradient grading result; a classification standard of the slope value is set; the classification standards of the gradient values include excellent, moderate, acceptable and improper; then, eliminating improper areas to generate a layout area graph; according to a gradient grading result, a differential arrangement strategy is adopted for optimal, moderate and acceptable areas to generate initial array arrangement; performing path optimization by adopting an ant colony algorithm, and outputting a lowest-cost wiring scheme by utilizing a path cost function; performing multi-objective optimization analysis; outputting an optimal arrangement scheme by using a multi-target fitness function; and the layout problem of the photovoltaic array on a complex terrain is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of large-scale ground photovoltaic system engineering, and in particular to a photovoltaic arrangement method based on slope grading and wiring feasibility analysis. Background Art

[0002] The photovoltaic industry is currently a strategic emerging sector. PV system design is a core pillar of the national photovoltaic strategy, directly impacting energy security, technological independence, and cost reduction and efficiency improvement. Through efficiency optimization, scenario adaptation, and intelligent grid connection, it maximizes clean energy output, accelerates grid parity, and promotes industrial chain upgrades. Furthermore, innovative design contributes to the dual carbon goals and enhances international competitiveness, making it a key component of energy transition and sustainable development.

[0003] Currently, available flat land for photovoltaic power plants is becoming increasingly scarce, while mountain photovoltaic applications are becoming increasingly widespread. With the rapid development of photovoltaic power generation technology, the demand for photovoltaic power plants in complex terrain conditions is increasing. Traditional photovoltaic layout methods are primarily designed for flat terrain and are difficult to adapt to areas with large terrain variations, such as mountains and hills. Existing technologies have the following problems: 1. They fail to fully consider the impact of terrain slope on photovoltaic array layout, resulting in low power generation efficiency; 2. Cabling design fails to take into account terrain slope, resulting in excessively long cable paths and increased losses; 3. A lack of systematic optimization methods makes it difficult to balance power generation efficiency and economic costs.

[0004] Therefore, how to achieve a more efficient and feasible photovoltaic array layout under complex terrain conditions remains a technical challenge in photovoltaic system design. Summary of the Invention

[0005] The problem to be solved by the present invention is to propose a method that can effectively solve the layout problem of photovoltaic arrays on complex terrain, minimize the shading between photovoltaic components and the difficulty of wiring the electrical system, thereby improving the power generation and operation and maintenance efficiency of photovoltaic power stations.

[0006] In view of the shortcomings of the prior art, the present invention solves the technical problems by adopting a technical solution: a photovoltaic arrangement method based on slope classification and wiring feasibility analysis, comprising the following steps:

[0007] S1: Topographic data acquisition and processing; using drone aerial survey, lidar or satellite remote sensing technology to obtain high-precision digital elevation model (DEM) and detailed topographic data of the target area;

[0008] S2: Slope analysis and classification: Based on the detailed terrain data of the target plot, GIS tools are used to calculate the slope value of each grid cell to obtain the slope classification results; and the slope classification standards are set; the slope classification standards include excellent, moderate, acceptable and unsuitable;

[0009] S3: Extraction of deployable areas; eliminating unsuitable areas and generating a deployable area map;

[0010] S4: Preliminary array arrangement; adopt differentiated layout strategies for excellent, moderate, and acceptable areas based on slope classification results;

[0011] S5: Wiring path simulation; using the ant colony algorithm to optimize the path, the photovoltaic array and electrical nodes are modeled as graph nodes, and the path cost function is used to output the minimum cost wiring solution;

[0012] S6: Multi-objective optimization analysis; output the optimal layout plan using the multi-objective fitness function;

[0013] S7: Generate outputs; generate construction drawings and 3D models;

[0014] S8: Drone inspection verification.

[0015] Preferably, the classification criteria of S2 are excellent, moderate, acceptable and unsuitable, respectively: excellent refers to a slope value of 0-5°, moderate refers to a slope value of 5-15°, acceptable refers to a slope value of 15-30°, and unsuitable refers to a slope value >30°.

[0016] Preferably, the influencing parameters of the path cost function in S5 include cable length, slope difficulty, turning radius, and obstacle avoidance.

[0017] Preferably, the dynamic path cost function Cost is expressed as:

[0018] Cost=ω1·Length+ω2·Slopt_Penalty+ω3·Obstacle_Avoidance;

[0019] Where: ω1 is the cable length weight; ω2 is the slope penalty weight; ω3 is the obstacle avoidance cost weight; Length is the cable length; Slope_Penalty is the slope penalty; Obstacle_Avoidance is the obstacle avoidance cost.

[0020] Preferably, the multi-objective fitness function Fitness is expressed as,

[0021] Fitness=k1·Powre_Output-k2·Cable_Cost-k3·Shadow_Loss-k4·Slope_Penalty;

[0022] Power_Output is the power generation forecast based on PVsyst simulation;

[0023] Cable_Cost is the cable cost, which is the total wiring length × unit cost;

[0024] Shadow_Loss is the shadow loss rate, which is the power generation loss caused by shadowing;

[0025] Slope_Penalty is the slope penalty term, which is the additional cost of the bracket in the steep slope area;

[0026] k1 is the power generation prediction weight; k2 is the cable cost weight; k3 is the shading loss rate weight; and k4 is the slope penalty weight.

[0027] Preferably, the slope analysis in S2 utilizes a slope analysis algorithm, which includes dynamic slope adaptive adjustment, east-west misalignment control, complex terrain support smoothing, and winter solstice shadow calculation model.

[0028] Preferably, the slope value Slop of each grid cell is calculated as follows:

[0029]

[0030] in: represents the rate of change of elevation z in the east-west direction (x-axis) (east-west slope), Represents the rate of change of elevation z in the north-south direction (y-axis) (north-south slope).

[0031] Preferably, the winter solstice shadow calculation model is as follows:

[0032] The minimum north-south shading distance P calculation model is used to ensure that there is no shadow all year round:

[0033] Based on geographic latitude The optimal array spacing is dynamically calculated based on the solar altitude angle sh and azimuth angle sa:

[0034]

[0035] Where L is the projected length of the photovoltaic panel on the slope, and ɑ is the actual inclination angle of the photovoltaic panel; if obstruction is detected, the height of the bracket of the obstructed row is automatically raised and the layout is re-optimized.

[0036] The beneficial effects of the present invention are as follows:

[0037] The present invention combines slope classification with wiring feasibility analysis to design the layout of photovoltaic arrays in a more scientific and reasonable manner, optimize array spacing, inclination angles, and cable wiring paths, overcome terrain obstacles to photovoltaic power station construction, and thus improve the power generation efficiency and economic benefits of the photovoltaic system.

[0038] The method of the present invention divides the site into several slope grade areas through terrain digitization and slope analysis algorithm. The slope value of each area is graded according to its adaptability to array layout, so as to select the appropriate photovoltaic array arrangement for different slope areas. The ant colony algorithm is used, combined with the slope classification results, to analyze the wiring feasibility of different areas to ensure the shortest cable path, the lowest cost and the minimum loss.

[0039] The method of the present invention uses a genetic algorithm to perform intelligent optimization layout of photovoltaic arrays based on the results of slope grading and wiring feasibility analysis, combined with factors such as the installation angle, orientation and array spacing of photovoltaic modules. Based on the results of slope grading and wiring feasibility analysis, construction drawings and three-dimensional models are generated, and drone inspections are used to verify installation accuracy. Finally, the optimization model is fed back based on measured data. This method can effectively solve the layout problem of photovoltaic arrays on complex terrain, minimize the shading between photovoltaic modules and the wiring difficulty of the electrical system, thereby improving the power generation and operation and maintenance efficiency of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention relates to a flow chart of a deployment method;

[0041] Figure 2 It is a schematic diagram of the genetic algorithm and ant colony algorithm involved in the present invention;

[0042] Figure 3 It is a schematic diagram of the slope classification diagram of the target area of ​​the method of the present invention;

[0043] Figure 4 It is a schematic diagram of the area that can be arranged after screening according to the method of the present invention;

[0044] Figure 5 This is a schematic diagram of the preliminary arrangement of the photovoltaic array according to the method of the present invention;

[0045] Figure 6 Schematic diagram of the wiring path analysis result of the method of the present invention;

[0046] Figure 7 It is a schematic diagram of the final layout optimization result of the method of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0048] Photovoltaic power generation is a new energy project that is strongly supported by the country. With the rapid development of photovoltaic power generation technology, more and more photovoltaic power stations have begun to be built in areas with complex terrain (such as mountains, hills and slopes, etc.). However, in the layout of photovoltaic arrays in complex terrain, due to factors such as terrain undulations, slope changes, and obstruction by surface objects, traditional layout methods often cannot maximize the use of terrain advantages, resulting in low power generation efficiency of photovoltaic systems, and even problems such as wiring difficulties and excessive costs. At present, there are existing photovoltaic array layout optimization methods, but most of them focus on flat terrain or ignore the mutual influence between slope grading and wiring feasibility. Therefore, how to achieve a more efficient and feasible photovoltaic array layout under complex terrain conditions is still a technical problem in photovoltaic system design; the method of the present invention is a photovoltaic array layout method based on slope grading and wiring feasibility analysis, which is particularly suitable for the design and planning of photovoltaic power stations in complex terrain (such as mountains, hills, etc.).

[0049] The technical solution of the present invention is applicable to mountain photovoltaic power generation systems, such as Figure 1 As shown, the specific steps include:

[0050] Topographic data collection and processing: Use drone aerial surveys, lidar, or satellite remote sensing technology to obtain high-precision digital elevation models (DEMs) with a resolution of at least 1 meter. Preprocess the DEM data, including noise filtering, interpolation, and terrain feature extraction.

[0051] Slope analysis and classification. The method utilizes remote sensing data, topographic survey data, or a digital elevation model (DEM) to obtain detailed topographic data for the target site. GIS tools are used to calculate the slope value for each grid cell, resulting in a slope classification result. Based on the slope classification criteria and in accordance with PV module installation requirements, slopes are divided into four levels: 0-5° (excellent, suitable for standard array layout), 5°-15° (moderate, requiring tilt adjustment), 15°-30° (acceptable, requiring special support design), and >30° (unsuitable, excluding the layout area). Terrain digitization and slope analysis algorithms are used to divide the site into several slope-graded regions. The slope value of each region is graded based on its suitability for array layout, allowing for the selection of appropriate PV array layouts for different slope regions. The slope analysis algorithm includes dynamic slope adaptive adjustment, east-west misalignment control, complex terrain support smoothing, and a winter solstice shadow calculation model.

[0052] Extract deployable areas. Exclude areas with slopes > 30°; perform secondary screening based on surface obstacles (rocks, vegetation, etc.); and generate a deployable area map.

[0053] Preliminary array layout. Adopt differentiated layout strategies based on slope classification results (Excellent: Standard south-facing layout for gently sloping areas, large array brackets for the module array to reduce costs, and tilt angle optimized for local latitude; Adaptive terrain tilt angle for moderately sloping areas, combining large and small arrays to maintain module flatness; Acceptable: Small array brackets or special installation methods for areas with larger slopes); Ensure no shadows from 9:00 AM to 3:00 PM.

[0054] Wiring path simulation. The method is based on the site's slope information and preset electrical design specifications, and uses an ant colony algorithm for path optimization [modeling photovoltaic arrays and electrical nodes (inverters or combiner boxes, box transformers, etc.) as graph nodes; the path cost function considers: cable length, slope difficulty, turning radius, obstacle avoidance; the pheromone update mechanism strengthens the optimal path]. Output the lowest cost wiring plan. By combining slope grading with wiring feasibility analysis, the layout of photovoltaic arrays can be designed more scientifically and reasonably, and the array spacing, inclination angle and cable wiring path can be optimized to overcome the obstacles of terrain to the construction of photovoltaic power stations, thereby improving the power generation efficiency and economic benefits of the photovoltaic system. Using the ant colony algorithm, combined with the slope grading results, the wiring feasibility of different areas is analyzed to ensure the shortest cable path, the lowest cost and the smallest loss.

[0055] Multi-objective optimization analysis. An evaluation index system is established using a multi-objective fitness function that considers power generation (based on sunlight simulation), wiring costs, construction difficulty, and land utilization. A genetic algorithm is used for optimization: the chromosomes encode array position, inclination, and type. The fitness function integrates these indicators and iteratively optimizes through selection, crossover, and mutation. The optimal layout solution is output. Based on the results of slope classification and wiring feasibility analysis, combined with factors such as PV module installation angle, orientation, and array spacing, a genetic algorithm is used to intelligently optimize the layout of the PV array.

[0056] Generate output, construction implementation and verification, and generate construction drawings and 3D models.

[0057] Drone inspections verify installation accuracy, using drone inspections to provide feedback to the optimization model based on measured data. Based on the slope classification and wiring feasibility analysis results, construction drawings and 3D models are generated. Drone inspections also verify installation accuracy, ultimately providing feedback to the optimization model based on measured data.

[0058] The present invention can design the layout of photovoltaic arrays more scientifically and rationally, optimize array spacing, inclination angles and cable routing, overcome terrain obstacles to photovoltaic power station construction, and thus improve the power generation efficiency and economic benefits of the photovoltaic system.

[0059] Based on the traditional photovoltaic layout method, this invention forms a complete set of slope classification

[0060] + Wiring feasibility analysis + Intelligent optimization layout solution. The following is a detailed description of the algorithm optimization and innovation points:

[0061] 1. Slope classification optimization algorithm

[0062] (1) Dynamic slope adaptive adjustment

[0063] The traditional method only divides the terrain into fixed slope intervals. The present invention adopts dynamic slope threshold adjustment and automatically optimizes the classification standard according to the complexity of the terrain: 0-5° (excellent): standard array arrangement, and the inclination angle is optimized according to the local latitude.

[0064] 5-15° (moderate): Adaptively adjust the bracket height to ensure the flatness of the module (east-west height difference ≤ 10cm).

[0065] 15-30° (acceptable): Use segmented bracket adjustment to ensure a smooth transition between adjacent photovoltaic panels.

[0066] >30° (unsuitable): automatically eliminated, and the available area is further optimized in combination with geological stability analysis (such as landslide risk).

[0067] (2) East-West Stagger Control

[0068] Introducing a staggered adjustment algorithm to ensure that the height difference between the north and south ends of the same row of photovoltaic panels is ≤10cm:

[0069] Using a step-by-step calculation, the bracket height (h1, h2, h3, h4) is dynamically adjusted. If the height difference exceeds the standard, the front and rear bracket heights are automatically adjusted to ensure array flatness and avoid uneven stress on the components due to uneven terrain.

[0070] (3) Smooth handling of complex terrain supports

[0071] For steep slopes (e.g., east-west gradients > 26°), a coordinated adjustment strategy for adjacent PV panels is employed: if the slope of PV panel N, I_N, exceeds SlopeHig (a preset threshold), the bracket height h4^M of the preceding panel, M, is adjusted simultaneously to ensure I_N ≤ SlopeHig. Through iterative optimization, the entire array maintains stability and power generation efficiency even in complex terrain.

[0072] (4) Winter Solstice Shadow Calculation Model

[0073] Adopt the minimum north-south shading distance (P) calculation model to ensure no shadows throughout the year: based on geographic latitude Dynamic calculation of optimal array spacing based on solar altitude angle (sh) and azimuth angle (sa):

[0074]

[0075] Where L is the projected length of the photovoltaic panel on the slope (in the north-south direction), and ɑ is the actual inclination angle of the photovoltaic panel (relative to the horizontal plane); if obstruction is detected, the height of the bracket of the obstructed row is automatically raised and the layout is re-optimized.

[0076] 2. Wiring feasibility optimization algorithm (based on ant colony algorithm improvement)

[0077] (1) Dynamic path cost function

[0078] Traditional routing only considers the shortest path. This invention combines factors such as slope, obstacles, and turning radius to optimize the path cost function of the ant colony algorithm:

[0079] Cost=ω1·Length+ω2·Slope_Penalty+ω3·Obstacle_Avoidance;

[0080] Among them: ω1 represents the cable length weight (value ranges from 0.5 to 0.7); ω2 represents the slope penalty weight (value ranges from 0.2 to 0.4); ω3 represents the obstacle avoidance cost weight (value ranges from 0.1 to 0.3); the value of each parameter depends on the weight ratio of the factors considered in the project.

[0081] Length: Cable length (primary optimization target),

[0082] Slope_Penalty: Slope penalty (e.g., routing cost increases in areas >15°),

[0083] Obstacle_Avoidance: Obstacle avoidance cost. Obstacle avoidance cost refers to the additional cost of bypassing obstacles such as rocks and gullies.

[0084] (2) Pheromone adaptive update mechanism

[0085] The traditional ant colony algorithm updates pheromones in a fixed manner, while the present invention adopts a dynamic volatility coefficient:

[0086] Initial stage: high volatility (ρ = 0.8), rapid exploration of feasible paths.

[0087] Late stage: low volatility (ρ = 0.3), strengthening the optimal path.

[0088] Elite ant strategy: retain the pheromones of the top 10% optimal paths to accelerate convergence.

[0089] (3) Multi-level wiring optimization

[0090] Main line optimization (box-type transformer → inverter or combiner box): An algorithm is used to preliminarily plan the trunk path.

[0091] Branch line optimization (inverter or combiner box → PV array): The ant colony algorithm refines wiring to ensure minimal losses.

[0092] Final verification: Combined with the 3D terrain model, check the feasibility of the path (such as whether it crosses a steep slope).

[0093] 3. Array intelligent optimization algorithm

[0094] (1) Chromosome coding optimization

[0095] The traditional genetic algorithm uses binary coding, and the present invention uses hybrid coding:

[0096] Positional gene: photovoltaic panel coordinates (x, y, z)

[0097] Angle genes: inclination (α), azimuth (γ)

[0098] Type gene: scaffold type (large array / small array)

[0099] (2) Multi-objective fitness function

[0100] Fitness=k1·Power_Output-k2·Cable_Cost-k3·Shadow_Loss-k4·Slope_Penalty;

[0101] Power_Output: Power generation prediction based on PVsyst simulation;

[0102] Cable_Cost: cable cost, which is the total wiring length × unit cost;

[0103] Shadow_Loss: shadow loss rate, which is the power generation loss caused by shadow;

[0104] Slope_Penalty: Slope penalty term, which is the additional cost of the support in the steep slope area;

[0105] k1 represents the power generation prediction weight (value ranges from 0.5 to 0.8), k2 represents the cable cost weight (value ranges from 0.1 to 0.3), k3 represents the shading loss rate weight (value ranges from 0.15 to 0.25), and k4 represents the slope penalty weight (value ranges from 0.05 to 0.2). The value of each weight parameter depends on the weight ratio of the factors considered in the project.

[0106] (3) Lighting analysis and shadow detection

[0107] Use ray tracing algorithms to simulate year-round lighting:

[0108] Input: 3D terrain model, sun trajectory data Output: Light intensity distribution map, shadow heat map Combined with drone inspection data, the model is dynamically corrected to improve accuracy.

[0109] (4) Construction route planning

[0110] Based on the 3D terrain model, the Dijkstra algorithm is used to plan the equipment transportation path:

[0111] Avoid steep slopes (>25°) and soft soil areas to optimize the lifting points and reduce the distance the machine moves. Figure 2 As shown, the specific implementation process of the method of the present invention includes the following steps:

[0112] S1: Terrain Data Collection and Processing

[0113] Obtain DEM data of the target terrain area through UAV aerial survey, satellite remote sensing or ground laser scanning technology, with a resolution of preferably 1m or finer.

[0114] S2: Slope Analysis and Grading

[0115] Use GIS tools (such as ArcGIS or QGIS) to calculate the slope of DEM data and use the Horn algorithm to calculate the slope value of each grid cell:

[0116]

[0117] represents the rate of change of elevation z in the east-west direction (x-axis) (east-west slope), Represents the rate of change of elevation z in the north-south direction (y-axis) (north-south slope).

[0118] To grade the terrain:

[0119] If terrain complexity > 0.7:

[0120] Slope threshold = [0°, 8°, 20°, 30°] # complex terrain fine classification;

[0121] else:

[0122] Slope threshold = [0°, 5°, 15°, 30°] # Standard classification.

[0123] S3: Extraction of deployable areas

[0124] Areas with slopes greater than 30° are eliminated, and the available areas are further modified based on existing obstacles on the site (such as rocks, trees, etc.) to form a candidate area map for deployment.

[0125] S4: Preliminary array arrangement

[0126] Perform a preliminary array layout within the candidate area, prioritizing south exposure and spacing rules (ensuring no shadows between 9:00 AM and 3:00 PM). Use an occlusion simulation tool (such as PVsyst software) to detect any shadows between arrays.

[0127] S5: Routing path simulation

[0128] Based on the arranged array, key electrical nodes such as inverters (combiner boxes) and box-type transformers are selected. An ant colony algorithm (which finds the most appropriate path during the wiring phase) is used to calculate the wiring path cost within the constraints of the slope map and obstacles, and output cable length and cost estimates.

[0129] Ant Colony Algorithm Optimizes Cable Routing:

[0130] (1) Path modeling:

[0131] Electrical nodes (inverters or combiner boxes, box-type transformers) as graph nodes

[0132] Path weight = length × slope coefficient × turn penalty

[0133] (2)Pheromone update:

[0134]

[0135] (3) Output results:

[0136] Shortest path cable layout

[0137] Special treatment plan for slope-sensitive areas

[0138] S6: Multi-objective optimization analysis

[0139] Establish comprehensive evaluation indicators (maximum power generation, minimum wiring cost, minimum obstruction, and smaller slope), use genetic algorithms (optimize array layout) and other multi-objective optimization methods to fine-tune the array position and output the optimized layout plan.

[0140] Genetic algorithm to optimize array layout:

[0141] (1) Define a chromosome (i.e. a candidate solution). It is necessary to encode a photovoltaic array layout into a "chromosome". For example:

[0142]

[0143] (2) Multi-objective optimization:

[0144] Fitness function Fitness:

[0145] Fitness=k1·Power_Output-k2·Cable_Cost-k3·Shadow_Loss-k4·Slope_Penalty;

[0146] in:

[0147] Power_Output is the power generation forecast based on PVsyst simulation (kWh);

[0148] Cable_Cost is the cable cost (10,000 yuan);

[0149] Shadow_Loss is the occlusion loss rate;

[0150] Slope_Penalty is the slope penalty term.

[0151] (3) Optimization process: The PV array layout is optimized through a genetic algorithm to maximize power generation, minimize wiring costs, shading losses, and slope penalties. Genetic algorithms use selection (roulette wheel selection or tournament selection can be used to select individuals with higher fitness to enter the next generation), crossover (crossover operations can use single-point crossover or multi-point crossover to generate new candidate individuals. The purpose of crossover is to mix the excellent characteristics of the parent generation into new individuals), and mutation (mutation operations are used to introduce diversity and prevent the algorithm from falling into local optimality; mutation can randomly adjust the position, angle, or bracket type of the array) to generate new candidate solutions. Based on the individuals generated by the genetic algorithm, the ant colony algorithm performs local optimization of wiring and shading losses. Ants select the best path based on the quality of the local solution, simulate the optimization wiring process, avoid shading and reduce slope penalties, and optimize from generation to generation. The genetic algorithm and the ant colony algorithm promote each other through information exchange to optimize the layout and wiring of the array.

[0152] S7: Generate output

[0153] The final output includes: optimized array location diagrams, wiring route diagrams, road route diagrams (with planned construction equipment and transportation plans), power generation estimates, wiring cost estimates, and construction proposals. The BIM model can also be used to construct a 3D model of the support foundation and cable trench paths.

[0154] S8: UAV inspection and verification: Use drones to quickly detect construction quality and ensure that: (1) the bracket installation position and angle error is ≤ 5cm; (2) the cable laying path is in accordance with the design; (3) deviations are discovered and corrected in real time.

[0155] like Figure 3 As shown, the slope of the land is analyzed and graded within the red line range where the survey map can be arranged, and the four-level terrain is evaluated.

[0156] like Figure 4 As shown, the arrangable areas in the surveying map are extracted.

[0157] like Figure 5 As shown, the deployable area is preliminarily arranged according to the pre-set longitude and latitude and the component single array information (at this stage, it is only necessary to ensure that the array is not shadowed between 9:00-15:00 during the day).

[0158] like Figure 6 As shown in the figure, based on the placement rules of the inverter (combiner box) and the box transformer, the ant colony algorithm (finding the most suitable path in the wiring stage) is used to calculate the wiring path cost under the constraints of the slope map and the obstacle map, and output the cable length and cost estimation.

[0159] Summary of the innovative points of the present invention:

[0160]

[0161] The method of the present invention is based on the experience and inspiration gained from continuous project design, and optimizes and improves the traditional photovoltaic equipment layout, thereby improving the PR (power generation efficiency) and economic benefits of the photovoltaic system; the method of the present invention divides the site into several slope grade areas through terrain digitization processing and slope analysis algorithm. The slope value of each area is graded according to its adaptability to array layout, so as to select a suitable photovoltaic array arrangement method for different slope areas; the method uses a wiring path optimization algorithm, combined with the slope classification results, to analyze the wiring feasibility of different areas to ensure the shortest cable path, the lowest cost and the smallest loss; the method is based on the slope classification and wiring feasibility analysis results, combined with factors such as the installation angle, orientation and array spacing of the photovoltaic modules, to perform intelligent optimization layout of the photovoltaic array. The present invention can effectively solve the layout problem of photovoltaic arrays on complex terrain, minimize the shading between photovoltaic modules and the difficulty of wiring the electrical system, thereby improving the power generation and operation and maintenance efficiency of the photovoltaic power station.

[0162] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A photovoltaic layout method based on slope grading and wiring feasibility analysis, characterized by: The following steps are included: S1: Topographic data acquisition and processing; using drone aerial surveys, lidar, or satellite remote sensing technology to obtain high-precision digital elevation models (DEMs) and detailed topographic data of the target area; S2: Slope analysis and classification: Based on the detailed terrain data of the target plot, GIS tools are used to calculate the slope value of each grid cell to obtain the slope classification results; and the slope classification standards are set; the slope classification standards include excellent, moderate, acceptable and unsuitable; S3: Extraction of deployable areas; eliminating unsuitable areas and generating a deployable area map; S4: Preliminary array arrangement; adopt differentiated layout strategies for excellent, moderate, and acceptable areas based on slope classification results; S5: Wiring path simulation; using the ant colony algorithm to optimize the path, the photovoltaic array and electrical nodes are modeled as graph nodes, and the path cost function is used to output the minimum cost wiring solution; S6: Multi-objective optimization analysis; output the optimal layout plan using the multi-objective fitness function; S7: Generate outputs; generate construction drawings and 3D models; S8: Drone inspection verification.

2. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 1, characterized in that: The classification criteria of S2 are excellent, moderate, acceptable and unsuitable, respectively: excellent refers to a slope value of 0-5°, moderate refers to a slope value of 5-15°, acceptable refers to a slope value of 15-30°, and unsuitable refers to a slope value >30°.

3. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 1, characterized in that: The parameters affecting the path cost function in S5 include cable length, slope difficulty, turning radius, and obstacle avoidance.

4. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 3 is characterized by: The dynamic path cost function Cost is expressed as: Cost=ω1·Length+ω2·Slope_Penalty+ω3·Obstacle_Avoidance; Where: ω1 is the cable length weight; ω2 is the slope penalty weight; ω3 is the obstacle avoidance cost weight; Length is the cable length; Slope_Penalty is the slope penalty; Obstacle_Avoidance is the obstacle avoidance cost.

5. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 1, characterized in that: The multi-objective fitness function Fitness is expressed as, Fitness=k1·Power_Output-k2·Cable_Cost-k3·Shabow_Loss-k4·Slope_Penalty; Power_Output is the power generation forecast based on PVsyst simulation; Cable_Cost is the cable cost, which is the total wiring length × unit cost; Shadow_Loss is the shadow loss rate, which is the power generation loss caused by shadowing; Slope_Penalty is the slope penalty term, which is the additional cost of the bracket in the steep slope area; k1 is the power generation prediction weight; k2 is the cable cost weight; k3 is the occlusion loss rate weight, and k4 is the slope penalty weight.

6. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 2, characterized in that: The slope analysis in S2 utilizes a slope analysis algorithm, which includes dynamic slope adaptive adjustment, east-west misalignment control, complex terrain support smoothing, and winter solstice shadow calculation model.

7. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 1, characterized in that: The slope value Slop of each grid cell is calculated as follows: in: represents the rate of change of elevation z in the east-west direction (x-axis) (east-west slope), Represents the rate of change of elevation z in the north-south direction (y-axis) (north-south slope).

8. The photovoltaic layout method based on slope grading and wiring feasibility analysis according to claim 6, characterized in that: The shadow calculation model for the winter solstice is as follows: The minimum north-south shading distance P calculation model is used to ensure that there is no shadow all year round: Based on geographic latitude The optimal array spacing is dynamically calculated based on the solar altitude angle sh and azimuth angle sa: Where L is the projected length of the photovoltaic panel on the slope, and ɑ is the actual inclination angle of the photovoltaic panel; if obstruction is detected, the height of the bracket of the obstructed row is automatically raised and the layout is re-optimized.

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