Sandy mountain photovoltaic field leveling optimization method based on plane arrangement constraint

Abstract

The invention discloses a sand hill photovoltaic field level optimization method based on plane arrangement constraints, and relates to the technical field of photovoltaic field level optimization. According to the sand hill photovoltaic field level collaborative optimization method based on the game theory and data driving, firstly, a field construction cost and power generation benefit double-target game model is constructed, and the counterbalance relation between economical efficiency and power generation efficiency is quantified through an incremental relation model of the field construction cost and the power generation benefit; and meanwhile, a site design gradient variable range is determined by considering a threat strategy. Meanwhile, in combination with a triangular irregular network model and Careola software, line spacing optimization driven by topographic data is realized, shadow shielding and gradient parameters of a photovoltaic array are dynamically coupled, and the limitation of a traditional empirical formula is broken through. The method not only responds to the demand of the industry trend of transformation from yield competition to cost optimization in the photovoltaic industry, but also opens up a new path for application of a multi-objective optimization algorithm in the field of engineering general drawings.

Description

Technical Field

[0001] This application relates to the field of photovoltaic site leveling optimization technology, specifically to a sand dune photovoltaic site leveling optimization method based on planar layout constraints. Background Technology

[0002] The site leveling design for sand dune terrain faces a contradiction between economic efficiency and power generation efficiency. Current mainstream technologies still rely on empirical formulas and static models, failing to quantify the dynamic relationship between terrain slope and photovoltaic panel shading. In photovoltaic site layout, issues often arise such as excessively large or small row spacing, leading to excessively high site leveling costs or unsatisfactory power generation efficiency. Furthermore, existing technologies typically use manually assigned weight coefficients to adjust the weights of the objective function. This method lacks a quantitative balancing mechanism based on game theory, failing to effectively address the problems of excessive leveling in low-slope areas or uncontrolled leveling costs in high-slope areas. While traditional multi-objective optimization algorithms have achieved some application in areas such as incremental distribution network resource allocation, these methods mainly focus on the interest game between investment entities and do not solve the problem of balancing the dynamic coupling of terrain parameters and engineering economics in photovoltaic site construction. Therefore, this application proposes a sand dune photovoltaic site leveling optimization method based on planar layout constraints. This method aims to optimize the design of photovoltaic module layout and site leveling by quantifying the dynamic influence of factors such as terrain slope and shading, thereby improving the economic efficiency and power generation efficiency of photovoltaic site construction.

[0003] Existing technologies, such as the invention application patent with announcement number CN118117621A, disclose a method for optimizing the configuration of energy storage in photovoltaic low-voltage distribution areas. This method includes obtaining historical data information of user nodes in photovoltaic low-voltage distribution areas through smart meters; constructing a three-phase linear model of voltage squared versus power for low-voltage distribution area nodes based on the historical data information and calculating the parameter values ​​of the linear model using the least squares method; obtaining typical daily scenario information of photovoltaic and load output in photovoltaic low-voltage distribution areas; and constructing an energy storage optimization configuration model based on the voltage three-phase linear model and typical daily scenario information, with the goal of minimizing the total economic operating cost under each typical daily scenario of photovoltaic low-voltage distribution areas, and solving the energy storage optimization configuration model to obtain relevant information on energy storage configuration in low-voltage distribution areas.

[0004] Existing technology, such as the invention application patent with announcement number CN118365095B, ​​discloses a method for automatic photovoltaic (PV) pile placement and intelligent calculation based on a three-dimensional platform. Specifically, it relates to the field of intelligent PV pile placement technology, including PV field pile coordinate synchronization, intelligent PV pile placement data acquisition, PV pile placement efficiency optimization processing, PV pile placement cost control processing, PV pile placement quality early warning processing, PV pile placement data analysis, automatic PV pile placement optimization, and PV pile placement data evaluation. By collecting PV pile coordinate data from the PV field piles, it calculates the PV automatic pile placement power generation efficiency optimization index, PV automatic pile placement cost optimization index, and PV automatic pile placement quality early warning index. It analyzes and obtains the PV pile placement intelligent monitoring coefficient. Through the PV field pile coordinate synchronization step, after providing survey data, it performs pile length calculation and pile placement. An early warning mechanism ensures the stable operation of the entire PV field. Through analysis of historical data and intelligent calculation results, it improves the operational efficiency of the PV field.

[0005] The above scheme has the following technical problems: (1) The estimation of cut and fill volume and the row spacing design are not dynamically coupled: Although the existing earthwork calculation model can estimate the cut and fill volume, it does not form a dynamic linkage with the photovoltaic row spacing design parameters, which leads to the failure to fully reflect the impact of slope change on shadow shading, resulting in the problem of redundant or insufficient row spacing setting, which restricts the photovoltaic array layout efficiency.

[0006] (2) The objective collaborative optimization mechanism lacks game theory support: Existing collaborative optimization methods mostly rely on manually setting weight coefficients to comprehensively compare economic efficiency and power generation efficiency. They lack a quantitative balancing mechanism based on game theory, making it difficult to achieve a balanced coordination between leveling costs and power generation revenue. This can easily lead to phenomena such as excessive leveling in low-slope areas and a surge in site construction costs in high-slope areas.

[0007] (3) Lack of adaptive design strategy for complex terrain: For complex terrain with large initial slope, existing technology relies on fixed leveling threshold to forcibly cut peaks and fill valleys without considering the spatial heterogeneity of terrain features, resulting in nonlinear growth of earthwork volume. Moreover, there is a lack of adaptive adjustment strategy based on terrain data, making it difficult to achieve a dynamic balance between local leveling and global economy. Summary of the Invention

[0008] The purpose of this application is to provide a planar layout constraint-based optimization method for photovoltaic fields in sand dunes, which solves the problems existing in the background technology.

[0009] To solve the above-mentioned technical problems, this application adopts the following technical solution: This application provides a sand dune photovoltaic site leveling optimization method based on planar layout constraints, including: Step 1: Obtain the terrain data and photovoltaic parameters of the target area from the data center, analyze and determine the row spacing of the target photovoltaic array based on the photovoltaic parameters, and calculate the site construction cost of the target photovoltaic array through the terrain data; Finally, comprehensively evaluate the annual power generation efficiency of the target photovoltaic array by combining the photovoltaic parameters and terrain data.

[0010] Step 2: Map the average annual site construction cost and annual power generation benefit of the target photovoltaic array to the benefit range, and construct a dual-objective game model of site construction cost and power generation benefit; consider the "threat strategy" to determine the range of changes in site design slope, so as to quantify the balance between the economics and power generation efficiency of the photovoltaic array.

[0011] Step 3: Conduct a comprehensive analysis of the site construction cost and annual economic benefits of the optimized photovoltaic construction site, evaluate and verify the site leveling optimization effect of the optimized target photovoltaic array, and ensure its best performance in practical applications.

[0012] The beneficial effects of this application are as follows: 1. This application provides a sand dune photovoltaic site leveling optimization method based on planar layout constraints. By combining game theory and data-driven collaborative optimization methods for sand dune photovoltaic site leveling, it first constructs a dual-objective game model of site construction cost and power generation efficiency, and quantifies the balance between economic efficiency and power generation efficiency through their annual incremental values. Simultaneously, a "threat strategy" is considered to ensure the output of the design slope range. Furthermore, by combining a triangular irregular network model and candela software, it achieves terrain data-driven row spacing optimization, dynamically coupling the shading and slope parameters of the photovoltaic array, breaking through the limitations of traditional empirical formulas. This application not only responds to the industry trend of the photovoltaic industry shifting from production competition to cost optimization, but also opens up new paths for the application of multi-objective optimization algorithms in the field of engineering master plans.

[0013] 2. This application solves the applicable range of the design slope by combining the utility function and the "threat strategy". Finally, the optimal design slope is selected based on the Nash equilibrium index to achieve Pareto optimality of economy and power generation efficiency, providing a solution for photovoltaic field leveling in sand dune terrain that is both theoretically rigorous and engineering-applicable.

[0014] 3. This application constructs a systematic technical framework for multi-objective collaborative optimization of photovoltaic power plant site leveling. First, it innovatively uses the annual average increment of both as utility through a dual-objective game model of site construction cost and power generation benefit, quantifying the dynamic balance between economic efficiency and power generation efficiency, thus overcoming the subjective limitations of the traditional weighting coefficient method. Second, it determines the site design slope range based on the consideration of "threat strategies," solving the problem of nonlinear surge in site construction costs in high-slope areas. Finally, it selects the optimal design slope by combining the Nash equilibrium index, achieving Pareto optimality of cost and benefit. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the implementation steps of the method described in this application.

[0017] Figure 2 Flowchart of the algorithm for optimizing site construction costs.

[0018] Figure 3 A graph showing the relationship between power generation revenue per square kilometer and incremental investment in site leveling when the site slope is changed, where: (1) Relationship between power generation revenue and site investment;

[0019] (2) Local relationship between power generation revenue and site investment;

[0020] (3) Relationship of changes in total revenue. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Reference Figure 1 As shown, this application provides a method for optimizing the layout of a sand dune photovoltaic site based on planar constraints, including the following steps: Step 1: Obtain and analyze the terrain data and photovoltaic parameters in the target area from the data management center, and then obtain the row spacing of the target photovoltaic array based on the photovoltaic parameters analysis, obtain the average annual site construction cost of the target photovoltaic array based on the terrain data analysis, and finally obtain the annual power generation efficiency of the target photovoltaic array through comprehensive analysis.

[0023] In a specific example, the terrain data includes the height difference between each vertex of each triangular grid corresponding to the existing terrain and the designed terrain, the area of ​​each triangular grid of the existing terrain, and the site leveling design slope; the photovoltaic parameters include the row spacing of the target photovoltaic array, the geographical latitude of the area where the target photovoltaic array is located, the solar declination, the solar time, and the elevation difference between the upper and lower edges of the target photovoltaic array.

[0024] It should be noted that solar declination is the latitude of the point where the sun is directly overhead.

[0025] In a specific example, the engineering economic indicators include the average annual site construction cost and the annual power generation benefit.

[0026] In a specific example, the row spacing of the target photovoltaic array is determined based on photovoltaic parameters. The specific analysis process is as follows: Based on the photovoltaic parameters, the elevation difference between the upper and lower edges of the target photovoltaic array, the geographical latitude, solar declination, and solar time of the region where the target photovoltaic array is located are obtained. The elevation difference between the upper and lower edges of the photovoltaic array is denoted as ΔH, and the geographical latitude of the region where the target photovoltaic array is located is denoted as... The solar declination is denoted as δ and the solar time as t′.

[0027] Substituting the solar time t′ into the conversion formula: h=15°*(t′-12), we can calculate the hour angle h of the target region. Then, according to the calculation formula: The solar declination δ of the target region is calculated, where π is the mathematical constant pi and N represents the number of days in a year, N∈[1, 365].

[0028] Furthermore, taking into account the geographical latitude of the target photovoltaic array area The solar declination δ and hour angle h are calculated using the following formulas: The solar elevation angle α is calculated, where sin is the sine function and cos is the cosine function.

[0029] It should be noted that the sun's elevation angle is the angle between the sun's rays and the horizontal plane.

[0030] Finally, the solar elevation angle α and the elevation difference between the upper and lower edges of the photovoltaic array are combined and denoted as ΔH, according to the calculation formula: The row spacing D of the target photovoltaic array is calculated.

[0031] In a specific example, the simultaneous calculation of the site construction cost of the target photovoltaic array using terrain data involves the following analysis: Based on the terrain data, the height difference between each vertex of each triangular grid corresponding to the existing terrain and the designed terrain, the area of ​​each triangular grid of the existing terrain, and the site leveling design slope are obtained. The height difference between each vertex of each triangular grid corresponding to the existing terrain and the designed terrain is denoted as... Where i is the triangular mesh number, i is a positive integer, and l is the number of each vertex of the tertiary mesh (l = 1, 2, 3). Therefore, the height differences of each triangular mesh vertex are respectively... and Let A denote the area of ​​each triangular grid in the existing terrain. i Let (x) be the slope of the site leveling design.

[0032] Based on the height differences of each vertex of the triangular grid corresponding to the existing terrain and the designed terrain, and the area of ​​each triangular grid, according to the calculation formula: The cut and fill volume V of the i-th triangular grid is calculated. i ± V i ± Including V i + and V i - V i + V represents the fill volume of the i-th triangular grid. i - This represents the excavation volume of the i-th triangular grid.

[0033] Combining the fill and cut volumes of each triangular grid, according to the calculation formula: and The total fill volume V is recorded separately. t Total excavation volume V w , where n is the total number of triangular grids.

[0034] It should be noted that the total amount of filling and excavation includes the amount of site leveling, the amount of surplus soil, and the amount of loose earthwork; among which, the amount of surplus soil includes the earthwork for building and equipment foundations, the earthwork for basements, the earthwork for roads, the earthwork for railway trenches, the earthwork for pipeline trenches, the earthwork for drainage ditches, the earthwork for replacement soil, and the earthwork for slopes.

[0035] Based on this, a site construction cost model is further constructed: C = c(V t +|V w |)+C wall +C road +C others Where c is the unit earthwork cost, C wall To support the project costs, C wall =c w ·A w +c ws ·V w A w V is the projected area of ​​the slope; w C represents the volume of the retaining wall or the volume of the concrete. w Cost per unit area of ​​slope projection; C wsCost per unit volume of support engineering; C road For road construction costs, C road =c r ·L r ·B r L r Length of roads within the venue; B r Average road width within the venue; C r C is the construction cost per unit area of ​​the site's roads; others For other related site construction costs, C others =l oth ·[c(V t +|V w |)+C wall +C road ].

[0036] In conjunction with the present value discounted annuity formula: The annual average site construction cost C is calculated when the site leveling design slope is (x). (x) r represents the benchmark discount rate, where C represents the site construction cost and n is the project period.

[0037] It should be noted that in the full life-cycle economic assessment of photovoltaic sites, civil engineering works such as site leveling, support structures, and road construction typically occur in the initial construction phase, with costs being one-time investments and accounting for a high proportion. If only the total one-time cost is used to measure the quality of the project, the impact of the time value of money over the project's operating cycle is easily overlooked. Therefore, this application converts the site construction cost model into an annual average cost value and uses the discounted annuity method to consider the time value of money over the project's life cycle. This allows the indicator to be compared with continuous revenue items such as annual power generation benefits, thereby achieving a dynamic balance between power generation benefits and project investment. This annualized processing method helps the multi-objective optimization model maintain a consistent dimension across the time dimension, improving the engineering applicability and economic explanatory power of the assessment results.

[0038] In a specific example, the final assessment, taking into account photovoltaic parameters, terrain data, and the project payback period, yields the annual power generation benefit of the target photovoltaic array. The specific analysis process is as follows: The solar elevation angle α and azimuth angle β are simulated using candela software, and then calculated according to the formula: The annual power generation benefit E corresponding to the design slope of the site leveling is calculated to be (x). (x) , where N h Let be the number of photovoltaic panels in each row, h be the number of rows in the photovoltaic array, h be a positive integer, P be the rated power of a single photovoltaic panel, q be the photovoltaic electricity price, T be the annual equivalent full-power hours, η be the system efficiency for widespread power generation, m be the length of the short side of a single photovoltaic panel, and D be the total power output. x± The row spacing of the photovoltaic array is adjusted for the slope of the site.

[0039] In a specific example, the calculation process for the row spacing of the photovoltaic array after slope correction in the site leveling design is as follows: When the slope of the land where the target photovoltaic array is located increases to positive from south to north, the calculation formula is used: D (x)- =ΔH*cotα*(1+cotβtan(x)) calculates the corrected row spacing D of the photovoltaic array corresponding to the site leveling design slope (x). (x)- .

[0040] When the slope of the land where the target photovoltaic array is located does not increase positively from south to north, then the calculation formula is used: D (x)+ =ΔH*cot*(α+(x))*(1+cotαtan(x)) calculates the corrected photovoltaic array row spacing D corresponding to the site leveling design slope (x). (x)+ Based on this, the corrected row spacing D of the photovoltaic array corresponding to the site leveling design slope (x) is obtained. x± .

[0041] Reference Figure 2 As shown, in step two, the average annual site construction cost and annual power generation benefit of the target photovoltaic array are mapped to the mapping interval, thereby constructing a dual-objective game model of site construction cost and power generation benefit. Based on the consideration of "threat strategy", the feasible range of design slope is output, thereby analyzing and obtaining the optimal design slope of the target photovoltaic array.

[0042] In a specific example, the process of mapping the average annual site construction cost and annual power generation benefit of the target photovoltaic array to a benefit range, and constructing a bi-objective game model of site construction cost and power generation benefit, is as follows: According to the calculation formula: U c (x)=ΔC (x) =C (x) -C (x0) By mapping the average annual site construction cost of the target photovoltaic array to the effect interval, the average annual site construction cost utility U of the target photovoltaic array is obtained. C (x), C (x0) This refers to the cost of leveling the site when the natural slope is 0.

[0043] According to the calculation formula: U E (x)=E (x) -E (x0) Mapping the annual power generation efficiency of the target photovoltaic array to the effect interval yields the average annual power generation efficiency U of the target photovoltaic array. E (x), E (x0) Annual revenue from photovoltaic power generation when the slope is 0 degrees.

[0044] To ensure that the benefits after leveling can cover the cost increment, only the design slope that satisfies {0≤x≤45, UE≥max[0,ΔC(x)]} is allowed to be included in the applicable range; if no feasible solution other than the terrain slope can be found, the "threat strategy" in game theory is applied to revert to the original terrain slope.

[0045] Finally, according to the calculation formula: (x * ) = argmax (x)∈[L,U] The optimal field slope (x) of the target photovoltaic array is calculated using N(x). * ), where N(x) represents the Nash equilibrium utility index, N(x) = U E (x)-U C (x), where w C and w E These are the weighting factors corresponding to costs and benefits, respectively.

[0046] Step 3: Analyze the average annual site construction cost and annual economic benefits of the optimized target photovoltaic array, and then analyze the site leveling optimization effect of the target photovoltaic array.

[0047] In a specific example, the comprehensive analysis of site construction costs and annual economic benefits of the optimized photovoltaic construction site is conducted to evaluate and verify the site leveling optimization effect of the optimized target photovoltaic array. The specific analysis process is as follows: Based on the optimal site leveling slope obtained from the analysis, the construction and renovation of the target photovoltaic array are optimized. After the construction is completed, the earthwork volume during the construction process is obtained. After the target photovoltaic array is put into use for a preset period, the power generation cost, power generation efficiency and average annual income of the target photovoltaic array are obtained from the management center.

[0048] By comparing the site construction cost of the photovoltaic array with that of the original slope photovoltaic array, the site leveling effect of the target slope is obtained.

[0049] The annual revenue of the photovoltaic array at the target slope is compared with the annual revenue of the photovoltaic array at the original slope to obtain the power generation efficiency at the target slope.

[0050] The optimal target slope is obtained when the power generation efficiency is greater than the site leveling efficiency and is maximized.

[0051] It should be noted that the technical solution of this application has been successfully implemented in the 1000MW photovoltaic project in Alxa Left Banner, Inner Mongolia, verifying its engineering applicability and optimization efficiency in complex terrain. The project is located in a desert sandy area with a DC-side installed capacity of 1200MWp, covering an area of ​​approximately 30,000 mu, and an initial terrain slope range of 0%-40%. During the implementation process, based on standardized site input terrain data and photovoltaic parameters, including tilt angle θ = 38°, azimuth angle β = 0°, and single-panel power Pp = 575Wp, the TIN model was used to accurately calculate the cut and fill volume under different design slopes, and the economic indicators were quantified by combining the annual average site construction cost function. At the same time, the solar trajectory was simulated using candela software to dynamically optimize the photovoltaic row spacing, ensuring minimal shading and improving power generation efficiency. Specific parameter values ​​are shown in the parameter value table.

[0052] Table 1: Parameter Value Table

[0053]

[0054]

[0055] During the optimization phase, a grid search algorithm is employed, with an initial target half-width Δx = 0.5%. The algorithm iterates through the candidate slope space to obtain a reasonable leveling range variation curve, as shown below. Figure 3 As shown, the final Nash equilibrium solution results are presented in the field leveling optimization solution table.

[0056] Table 2: Calculation Table of Relationship between Power Generation Revenue per Square Kilometer and Incremental Investment in Site Leveling When Changing Site Slope

[0057]

[0058] according to Figure 3 As shown in (1), with the increase of site slope: when the site slope is less than 38°, the spacing between the slabs decreases and the total revenue increment gradually increases. After the slope is greater than 38°, the revenue increment gradually decreases. The site construction cost increases rapidly with the increase of slope. After 15°, the increment quickly exceeds the revenue increment.

[0059] according to Figure 3 As shown in (2), the site slope changes within 12.5°, and the site construction cost is less than the increase in power generation revenue. This indicates that within this range, the investment cost will increase the site slope and obtain positive returns.

[0060] according to Figure 3 As shown in (3), the difference between the incremental power generation revenue and the incremental annualized investment in site treatment under the condition of site slope change is that when the site slope increases by 7° per square kilometer, the total revenue increases by RMB 3,077,560 per year.

[0061] It should be noted that the 1000MW photovoltaic project in Alashan, Inner Mongolia, verified that when the site construction cost is 10 yuan / m²... 3 When the slope is within the acceptable range, appropriately increasing the slope on the sunny side and appropriately decreasing the slope on the shady side can significantly increase the total power generation revenue. For the case study project, which covers 30,000 mu (approximately 200,000 square kilometers), 50% of the area can be optimized. By appropriately changing the slope, revenue can be increased by 30.7756 million yuan per year.

[0062] Furthermore, this application achieves engineering reproducibility through deep integration of modular algorithms with TIN models and candela software, and can be extended to the optimization of dynamic environmental parameters such as wind and sand, and snow accumulation.

[0063] This application provides a sand dune photovoltaic (PV) site leveling optimization method based on planar layout constraints. By combining game theory and a data-driven collaborative optimization method, it first constructs a dual-objective game model of site construction cost and power generation efficiency, and quantifies the trade-off between economic efficiency and power generation efficiency using an incremental relationship model. Simultaneously, it considers "threat strategies" to determine the variable range of the site design slope. Furthermore, by combining a triangular irregular network model and candela software, it achieves terrain data-driven row spacing optimization, dynamically coupling the shading and slope parameters of the PV array, thus overcoming the limitations of traditional empirical formulas. This application not only responds to the industry trend of the PV sector shifting from production competition to cost optimization, but also opens up new paths for the application of multi-objective optimization algorithms in the field of engineering site planning.

[0064] The above content is merely an example and illustration of the concept of this application. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in this application, they should all fall within the protection scope of this application.

Claims

1. A method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints, characterized in that, include: Step 1: Obtain topographic data and photovoltaic parameters of the target area from the data center. Analyze and determine the row spacing of the target photovoltaic array based on the photovoltaic parameters. At the same time, calculate the site construction cost of the target photovoltaic array using the topographic data. Finally, evaluate the annual power generation efficiency of the target photovoltaic array by combining the photovoltaic parameters, topographic data, and project payback period. Step 2: Map the average annual site construction cost and annual power generation benefit of the target photovoltaic array to the benefit range, and construct a bi-objective game model of site construction cost and power generation benefit; consider the "threat strategy" to determine the range of changes in site design slope, so as to quantify the balance between the economics and power generation efficiency of the photovoltaic array. Step 3: Conduct a comprehensive analysis of the site construction cost and annual economic benefits of the optimized photovoltaic construction site, evaluate and verify the site leveling optimization effect of the optimized target photovoltaic array, and ensure its best performance in practical applications.

2. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 1, characterized in that, The terrain data includes the height difference between each vertex of each triangular grid corresponding to the existing terrain and the designed terrain, the area of ​​each triangular grid of the existing terrain, and the site leveling design slope; the photovoltaic planar layout parameters include the row spacing of the target photovoltaic array, the geographical latitude, solar declination, solar time, and the elevation difference between the upper and lower edges of the target photovoltaic array.

3. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 1, characterized in that, The project's economic indicators include the average annual site construction cost and annual power generation benefits.

4. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 3, characterized in that, The row spacing of the target photovoltaic array is determined based on photovoltaic parameter analysis. The specific analysis process is as follows: Based on photovoltaic parameters, the elevation difference between the upper and lower edges of the target photovoltaic array, the geographical latitude, solar declination, and solar time of the area where the target photovoltaic array is located are obtained. The elevation difference between the upper and lower edges of the photovoltaic array is denoted as ΔH, and the geographical latitude of the area where the target photovoltaic array is located is denoted as... Solar declination is denoted as δ and solar time as t ′ ; Substituting the solar time t′ into the conversion formula: h=15°*(t′-12), we can calculate the hour angle h of the target region. Then, according to the calculation formula: The solar declination δ of the target region is calculated, where π is the mathematical constant pi, N represents the number of days in a year, and N∈[1, 365]. Furthermore, taking into account the geographical latitude of the target photovoltaic array area The solar declination δ and hour angle h are calculated using the following formulas: The solar elevation angle α is calculated, where sin is the sine function and cos is the cosine function; Finally, the solar elevation angle α and the elevation difference between the upper and lower edges of the photovoltaic array are combined and denoted as ΔH, according to the calculation formula: The minimum row spacing D of the target photovoltaic array is calculated. The formula for calculating D is explained as follows: Assuming the site leveling slope increases positively from south to north, the formula D is used. x+ =ΔH·cot(α+x)·(1+cotαtanx), otherwise use formula D x- =ΔH·cotα·(1+cotθtanx); where the design slope of the site leveling is x, the angle between the design ground line and the horizontal plane is arctanx, and the tilt angle of the photovoltaic array is θ.

5. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 4, characterized in that, The site construction cost of the target photovoltaic array is calculated simultaneously using terrain data. The specific analysis process is as follows: Site earthwork volume calculation: Based on topographic data, obtain the height difference of each vertex of each triangular grid corresponding to the existing topography and the design topography, as well as the area of ​​each triangular grid of the existing topography and the site leveling design slope. The height difference of each vertex of each triangular grid corresponding to the existing topography and the design topography is denoted as... Where i is the triangular mesh number, i is a positive integer, and l is the number of each vertex of the tertiary mesh (l = 1, 2, 3). Therefore, the height differences of each triangular mesh vertex are respectively... and Let A denote the area of ​​each triangular grid in the existing terrain. i Let the design slope of the site leveling be denoted as (x); combining the height difference of each vertex of each triangular grid corresponding to the existing terrain and the design terrain, and the area of ​​each triangular grid, according to the calculation formula: The cut and fill volume V of the i-th triangular grid is calculated. i ± V i ± Including V i + and V i - V i + V represents the fill volume of the i-th triangular grid. i - This represents the excavation volume of the i-th triangular mesh; Combining the fill and cut volumes of each triangular grid, according to the calculation formula: and The total fill volume V is recorded separately. t Total excavation volume V w , where n is the total number of triangular grids; Site construction cost model: C = c(V) t +|V w |)+C wall +C road +C others Where c is the unit earthwork cost, C wall To support the project costs, C wall =c w ·A w +c ws ·V w A w V represents the projected area of ​​the slope; w C represents the volume of the retaining wall or the volume of the concrete. w Cost per unit area of ​​slope projection; C ws Cost per unit volume of support engineering; C road For road construction costs, C road =c r ·L r ·B r L r Length of roads within the venue; B r Average road width within the venue; C r C is the construction cost per unit area of ​​the site's roads; others For other related site construction costs, C others =l oth ·[c(V t +|V w |)+C wall +C road ]; In conjunction with the present value discounted annuity formula: The annual average site construction cost C is calculated when the site leveling design slope is (x). (x) r represents the benchmark discount rate, where C represents the site construction cost and n is the project period.

6. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 5, characterized in that, Finally, by comprehensively considering photovoltaic parameters, terrain data, and the project's payback period, the annual power generation efficiency of the target photovoltaic array is evaluated. The specific analysis process is as follows: The solar elevation angle α and azimuth angle β were simulated using candela software, and then calculated according to the formula: The annual power generation benefit E corresponding to the design slope of the site leveling is calculated to be (x). (x) , where N h Let be the number of photovoltaic panels in each row, h be the number of rows in the photovoltaic array, h be a positive integer, P be the rated power of a single photovoltaic panel, q be the photovoltaic electricity price, T be the annual equivalent full-power hours, η be the system efficiency for widespread power generation, m be the length of the short side of a single photovoltaic panel, and D be the total power output. x± The row spacing of the photovoltaic array is adjusted for the slope of the site.

7. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 6, characterized in that, The calculation process for the row spacing of the photovoltaic array after slope correction in the site leveling design is as follows: When the slope of the land where the target photovoltaic array is located increases from south to north to a positive gradient, then the following formula is used for calculation: D (x)- =ΔH*cotα*(1+cotβtan(x)) calculates the corrected row spacing D of the photovoltaic array corresponding to the site leveling design slope (x). (x)- ; When the slope of the land where the target photovoltaic array is located does not increase positively from south to north, then the calculation formula is used: D (x)+ =ΔH*cot*(α+(x))*(1+cotαtan(x)) calculates the corrected photovoltaic array row spacing D corresponding to the site leveling design slope (x). (x)+ Based on this, the corrected row spacing D of the photovoltaic array corresponding to the site leveling design slope (x) is obtained. x± .

8. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 7, characterized in that, The process of mapping the average annual site construction cost and annual power generation benefit of the target photovoltaic array to the benefit range and constructing a bi-objective game model of site construction cost and power generation benefit is as follows: According to the calculation formula: U c (x)=ΔC (x) =C (x) -C (x0) Mapping the annual average site construction cost increment of the target photovoltaic array to the effect interval yields the annual average site construction cost utility U of the target photovoltaic array. C (x), C (x0) The cost of site leveling when the natural slope is 0; According to the calculation formula: U E (x)=E (x) -E (x0) Mapping the annual power generation efficiency increment of the target photovoltaic array to the mapping interval yields the average annual power generation efficiency utility U of the target photovoltaic array. E (x), E (x0) Annual revenue from photovoltaic power generation when the slope is 0 degrees.

9. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 8, characterized in that, The adaptive threshold search algorithm is used to determine a reasonable leveling range, and the optimal design slope is obtained based on this analysis. The specific analysis process is as follows: To ensure that the benefits after leveling can cover the cost increment, only the design slope that satisfies {0≤x≤45, UE≥max[0,ΔC(x)]} is allowed to be included in the applicable range; if no feasible solution other than the terrain slope can be found, the "threat strategy" in game theory is applied to revert to the original terrain slope. Finally, according to the calculation formula: (x * ) = argmax (x)∈[L,U] The optimal field slope (x) of the target photovoltaic array is calculated using N(x). * ), where N(x) represents the Nash equilibrium utility index, N(x) = U E (x)-U C (x).

10. The method for optimizing the layout of a sand dune photovoltaic field based on planar arrangement constraints according to claim 9, characterized in that, The optimization of the photovoltaic construction site involves a comprehensive analysis of site construction costs and annual economic benefits. The optimization effect of the target photovoltaic array is evaluated and verified. The specific analysis process is as follows: Based on the optimal site leveling slope obtained from the analysis, the construction and renovation of the target photovoltaic array are optimized. After the construction is completed, the site construction work volume during the construction process is obtained. After the target photovoltaic array is put into use for a preset period, the power generation cost, power generation efficiency and average annual income of the target photovoltaic array are obtained from the management center. By comparing the site construction workload of the target photovoltaic array with that of a photovoltaic array of the same scale, the optimization effect of the site construction workload of the target photovoltaic array is obtained. By comparing the power generation cost of the target photovoltaic array with the power generation cost before the upgrade, the optimization effect of the power generation cost of the target photovoltaic array can be obtained. The average annual revenue of the target photovoltaic array is compared with the average annual revenue of a photovoltaic array before the renovation or of the same scale to obtain the optimization effect of the average annual revenue of the target photovoltaic array.

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