A method for evaluating solar radiation resources for a photovoltaic power plant

Abstract

The present application relates to photovoltaic planning technical field, especially to a kind of solar radiation resource evaluation method for photovoltaic power station.The method is first according to installed capacity target to determine ideal state laying area, and combined with the building coverage of target area and shading loss is modified, and delimitation redundant target area.Subsequently, candidate point is screened in area, using the energy efficiency evaluation index of expected radiation resource and three-dimensional topological grid line cost, point is divided into different spatial clustering cluster by clustering algorithm.Aiming at each cluster, the minimum circumscribed polygon area is fitted using the convex hull algorithm, and the site selection area set is formed.Further, the present application simulates dynamic shadow by digital elevation model and ray tracing algorithm, and calculates the actual radiation resource amount of each area;if the total resource amount does not meet the target, a multi-objective function is introduced for Pareto optimization iteration, and a matching solution is selected from the optimal solution set and the polygon boundary is adaptively adjusted until the demand is met.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic planning technology, and in particular to a method for assessing solar irradiance resources for photovoltaic power plants. Background Technology

[0002] With the green transformation of the global energy structure, distributed photovoltaic (PV) power generation has been widely used in urban buildings, high-tech parks, and industrial complexes due to its advantages of on-site development and consumption. However, in the complex urban environment, how to accurately assess solar irradiance resources and quickly and scientifically locate the installation area of ​​PV power stations remains a core issue for the large-scale implementation of distributed PV power stations in cities.

[0003] Existing methods for assessing and selecting solar irradiance resources suffer from the following technical bottlenecks. First, resource assessment is disconnected from spatial location. Traditional assessment methods often rely on total horizontal irradiance data from meteorological stations, combined with simple empirical reduction factors to estimate regional power generation. Second, the coupling between grid connection economics and geographical resource distribution is insufficient. Current site selection logic largely prioritizes areas with the highest irradiance, neglecting the cost of power grid connection. In building cluster environments, the installation of photovoltaic lines is constrained by building structures, vertical cable wells, and the location of existing power distribution facilities. Summary of the Invention

[0004] To overcome the above deficiencies, this invention provides a method for assessing solar irradiance resources for photovoltaic power plants. It aims to achieve accurate quantitative assessment of high-efficiency photovoltaic installation areas and automated optimization of site selection boundaries in complex urban environments by constructing a three-dimensional topological path cost and dynamic shading actuarial model.

[0005] In a first aspect, the present invention provides the following technical solution: a method for assessing solar irradiance resources in photovoltaic power plants, comprising: Based on the preset installed capacity target, determine the ideal laying area corresponding to the theoretical irradiance requirement; Based on the building coverage and shading loss of the target area, the ideal laying area is corrected to obtain a redundant target area; Candidate points are selected within the redundant target area, and a clustering algorithm is used, with energy efficiency evaluation indicators as the clustering basis, to divide the candidate points into different spatial clusters. For each of the spatial clusters, a minimum bounding polygon region containing candidate points within the cluster is constructed to form a set of site selection regions; The sum of actual irradiance resources within the selected site set is evaluated, and it is determined whether it meets the preset resource demand target. If the conditions are not met, the candidate point selection is re-executed, and the boundary of the minimum bounding polygon region is adjusted based on the optimization results. If the conditions are met, the current set of smallest bounding polygon regions will be determined as the site selection scheme for the photovoltaic power station.

[0006] Preferably, the step of correcting the ideal laying area includes: A redundancy evaluation model is constructed based on the building coverage and shading loss of the target area, and the redundancy coefficient is calculated. The redundancy target area is obtained by correcting the ideal laying area using the redundancy coefficient.

[0007] Preferably, the step of screening candidate points within the redundant target area includes: The redundant target area is divided into grid cells of a preset size, and each grid cell is defined as the initial point to be evaluated; Obtain historical irradiance data and geographic environmental shading parameters for each initial point, and calculate the expected available irradiance for each initial point; Set a resource admission threshold, remove initial points whose expected available irradiance is lower than the resource admission threshold, and retain the remaining initial points as candidate points.

[0008] Preferably, the step of dividing the candidate points into different spatial clusters includes: Based on the location of the grid connection point within the redundant target area, determine the grid connection line cost from each candidate point to the grid connection point; Calculate the energy efficiency evaluation index corresponding to each candidate site. The energy efficiency evaluation index is positively correlated with the expected irradiance resources of the candidate site and negatively correlated with the grid connection line cost. With the goal of maximizing the overall energy efficiency of each spatial cluster, candidate sites that are adjacent to each other and have similar energy efficiency evaluation index characteristics are divided into the same spatial cluster.

[0009] Preferably, the step of constructing the minimum bounding polygon region containing candidate points within the cluster includes: Obtain the set of spatial coordinates of candidate points located at the edge positions in each of the spatial clusters; Based on the set of spatial coordinates, the convex hull algorithm is used to fit and obtain a closed geometric boundary that can completely enclose all candidate points in the corresponding spatial cluster. The region enclosed by the closed geometric boundary is defined as the minimum circumscribed polygon region.

[0010] Preferably, the step of assessing the sum of actual irradiance resources within the set of site selection areas includes: Obtain the slope aspect, slope, and shadow occlusion duration of candidate points within each smallest bounding polygon region in the site selection area set; By combining historical irradiance data and the duration of shading, the effective area where photovoltaic modules can be deployed in each region is determined, and the corresponding annual cumulative irradiance is calculated to obtain the actual irradiance resources of each region. The actual irradiance resources of each region are summed to obtain the total actual irradiance resources.

[0011] Preferably, re-executing the candidate site screening step includes: A multi-objective function is established with the goal of maximizing the sum of expected irradiance for each spatial cluster and minimizing the total cost of grid-connected lines. The multi-objective function is used to optimize the distribution of points within the redundant target area, resulting in a Pareto optimal solution set containing multiple non-dominated site selection schemes. Based on the deviation between the current actual irradiation resource amount and the resource demand target, the solution with the closest new irradiation resource amount to the deviation value is selected from the Pareto optimal solution set as the optimal location scheme.

[0012] Preferably, the step of adjusting the boundary of the minimum circumscribed polygon region based on the optimization result includes: Based on the deviation between the current actual irradiation resources and the target resource demand, determine the incremental direction and step size parameters for boundary adjustment; Based on the optimal addressing scheme, candidate points within the current minimum bounding polygon area are added or removed to obtain an updated set of points. Based on the updated point set, the coordinates of the polygon vertices are refitted to achieve adaptive adjustment of the minimum bounding polygon region.

[0013] The present invention has the following beneficial effects: 1. This invention constructs an energy efficiency evaluation index that couples the cost of three-dimensional topological paths with expected irradiance resources, enabling the clustering process to automatically identify and avoid false high-value locations with high resources and high construction costs. This achieves the integration of the economics of power access and the distribution of geographical resources, thereby reducing the system's cost per kilowatt-hour.

[0014] 2. This invention introduces a multi-objective collaborative optimization mechanism. When there is a resource gap in the refined assessment, it can automatically find a non-dominated solution set between energy gain and construction cost. Through deviation feedback, it realizes the adaptive dynamic adjustment of the site selection boundary, replacing the traditional tedious manual adjustment process and greatly improving the scientific nature of site selection.

[0015] 3. This invention realizes full-process management from ideal area calculation to construction area output. It compensates for the accuracy loss of traditional planar assessment by using ray tracing and 3D modeling, and solves the pain point of the disconnect between resource assessment and engineering implementation in complex urban environments. It provides reliable technical support for the rapid positioning and precise development of distributed photovoltaics. Attached Figure Description

[0016] Figure 1 This is a flowchart of a solar irradiance resource assessment method for photovoltaic power plants proposed in this invention. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 In a first embodiment of the present invention, the present invention provides a method for assessing solar irradiance resources for photovoltaic power plants, such as... Figure 1 As shown, it includes the following steps: S1. Based on the preset installed capacity target, determine the ideal laying area corresponding to the theoretical irradiance requirement; In this embodiment, the system is based on a preset installed capacity target. By combining historical meteorological data of the target area, the ideal paving area corresponding to the theoretical irradiance requirement is determined. First, the optimal annual tilt irradiance of the target area under a standard meteorological year is obtained by searching geographic information systems or meteorological databases. Based on the nominal conversion efficiency of photovoltaic modules and the overall system efficiency of equipment such as inverters and transformers Calculate the expected annual production capacity per unit area. Then, set a target installed capacity. Converted into annual target power generation Based on the ratio between the annual target power generation and the expected annual power output per unit area, the ideal laying area was initially calculated. The ideal installation area refers to the minimum physical projected area required to meet the energy output target under extremely ideal conditions, without considering external environmental obstruction, building footprint limitations, and optimal spacing and non-overlapping installation of photovoltaic modules. S2. Based on the building coverage and shading loss of the target area, the ideal laying area is corrected to obtain a redundant target area; Preferably, the step of correcting the ideal laying area includes: A redundancy evaluation model is constructed based on the building coverage and shading loss of the target area, and the redundancy coefficient is calculated. The redundancy target area is obtained by correcting the ideal laying area using the redundancy coefficient.

[0019] In this embodiment, a redundancy evaluation model is constructed to calculate the comprehensive redundancy coefficient. This redundancy evaluation model comprehensively considers the building coverage rate of the target area and the expected shading loss factor. The building coverage rate is obtained by retrieving vector electronic maps or high-resolution satellite imagery data of the target area, and is defined as the ratio of the total area of ​​all building footprints within the target area to the total land area of ​​the area. The building coverage rate reflects the dilution effect of non-building plots (such as roads, green spaces, and squares) on the usable area for photovoltaics in urban space. Simultaneously, the redundancy evaluation model introduces a shading loss factor to characterize the decrease in effective irradiance due to surrounding tall buildings, vegetation, or parapet walls. This factor is initially estimated based on the average building height distribution of the target area and the solar altitude angle on the winter solstice corresponding to the local latitude. In one feasible implementation, the building coverage rate is denoted as... The average occlusion loss factor is Then the overall redundancy coefficient This can be expressed as: ; in, Additional redundancy weighting is applied, including the impact of factors such as rooftop utilization and regional compliance on the area. After obtaining the comprehensive redundancy coefficient, the system multiplies the ideal laying area by the comprehensive redundancy coefficient to obtain the redundancy target area. Subsequently, based on the geometric center of the target area or a preset reference point, the initial search radius is expanded outward according to the redundancy target area, thereby defining a geographically continuous redundant target area.

[0020] S3. Select candidate points within the redundant target area, use a clustering algorithm, and divide the candidate points into different spatial clusters based on energy efficiency evaluation indicators. Preferably, the step of screening candidate points within the redundant target area includes: The redundant target area is divided into grid cells of a preset size, and each grid cell is defined as the initial point to be evaluated; Obtain historical irradiance data and geographic environmental shading parameters for each initial point, and calculate the expected available irradiance for each initial point; Set a resource admission threshold, remove initial points whose expected available irradiance is lower than the resource admission threshold, and retain the remaining initial points as candidate points.

[0021] In this embodiment, firstly, a geographic information system is invoked to perform grid subdivision in the planar projection coordinate system of the redundant target area. The system sets grid cells of a preset size (e.g., based on the standard dimensions of the photovoltaic array or the minimum working unit on the roof). or The geometric center of each grid cell is defined as the initial point to be evaluated. Each initial point carries unique spatial coordinate information. This serves as a data index for subsequent correlation of meteorological data and geographic parameters.

[0022] For each initial location, the system extracts historical irradiance data from the past ten years to obtain the total horizontal irradiance and the ratio of direct to diffuse radiation. Simultaneously, based on a high-precision digital surface model, it extracts the building heights, vegetation distribution, and terrain undulations around the location. Combining this with the local solar altitude and azimuth angle patterns, it calculates the shadow loss caused by surrounding objects at different times of the year. By subtracting shadow loss and expected equipment conversion losses from the historical irradiance baseline, the expected available irradiance for that location is obtained. .

[0023] In order to eliminate inefficient areas that lack development value from an engineering economic perspective, this plan sets resource access thresholds. This threshold is typically set with reference to the local levelized cost of electricity (LCOE) benchmark for photovoltaic (PV) power generation. The system will calculate the expected available irradiance at each initial location. With resource access threshold Compare them. If If a point is determined to be severely occluded or lacking in natural light, it is removed from the point model and will no longer participate in subsequent clustering optimization; if If the location is deemed to have basic resource development value, it will be retained and defined as a candidate location.

[0024] Preferably, the step of dividing the candidate points into different spatial clusters includes: Based on the location of the grid connection point within the redundant target area, determine the grid connection line cost from each candidate point to the grid connection point; Calculate the energy efficiency evaluation index corresponding to each candidate site. The energy efficiency evaluation index is positively correlated with the expected irradiance resources of the candidate site and negatively correlated with the grid connection line cost. With the goal of maximizing the overall energy efficiency of each spatial cluster, candidate sites that are adjacent to each other and have similar energy efficiency evaluation index characteristics are divided into the same spatial cluster.

[0025] In this embodiment, the spatial location information of grid connection points (such as transformers, distribution rooms, or pre-set grid connection cabinets) within the redundant target area is first retrieved. For each candidate point The topological path length from the candidate point to the grid connection point is calculated using the three-dimensional path distance. This length is not a simple Euclidean or Manhattan distance, but rather the actual length of the photovoltaic power line. For example, the Euclidean distance from the candidate site to the grid connection point may be extremely small, but due to the high altitude of the candidate site, the topology path length can far exceed the corresponding Euclidean distance. Since this scheme uses similar line materials, the grid connection line cost is simplified to the topology path length. .

[0026] To identify optimal regions that combine high returns and low costs from a massive number of data points, this embodiment constructs an energy efficiency evaluation index. In one feasible implementation, this metric is defined by the following mathematical model: ; in, The expected available irradiance for each candidate site. The length of the topological path from the candidate point to the grid connection point.

[0027] This scheme employs a spatial clustering algorithm to logically group candidate points. The clustering process preferably follows these criteria: For any candidate point pair within the redundant target area Its comprehensive distance Weighted by spatial geometric distance and energy efficiency characteristic deviation, in one feasible implementation, it can be expressed as: ; in: Spatial geometric distance, representing the location of a point. and Spatial distance between them; This represents the deviation in energy efficiency characteristics, indicating the location. and Corresponding energy efficiency evaluation indicators The absolute value of the difference; and These are the corresponding weighting coefficients. Introducing... This ensures that points at different heights within the same building, or in adjacent buildings with significant height differences, are physically constrained, thus meeting the engineering requirements for centralized component placement and unified support structure design. (Introduction) This constrains the range of fluctuations in unit power generation costs for points within the same cluster.

[0028] The algorithm searches for the optimal clustering partitioning scheme through iterative search. This minimizes the sum of squares (SSE) within the entire cluster: ; in, For the first The centroid feature vectors of each spatial cluster. By continuously optimizing the cluster center position and cluster boundary, the overall energy efficiency of each spatial cluster (i.e., the energy efficiency of all points within the cluster) is improved. The weighted average value reaches a local maximum. Ultimately, all candidate points within the redundant target area are divided into at least one spatial cluster, with each cluster representing a potential photovoltaic array distribution unit.

[0029] S4. For each of the spatial clusters, construct the minimum bounding polygon region containing the candidate points within the cluster to form a set of site selection regions; Preferably, the step of constructing the minimum bounding polygon region containing candidate points within the cluster includes: Obtain the set of spatial coordinates of candidate points located at the edge positions in each of the spatial clusters; Based on the set of spatial coordinates, the convex hull algorithm is used to fit and obtain a closed geometric boundary that can completely enclose all candidate points in the corresponding spatial cluster. The region enclosed by the closed geometric boundary is defined as the minimum circumscribed polygon region.

[0030] In this embodiment, the system first traverses each spatial cluster. The process involves identifying and extracting candidate points located at the geometric edges of the cluster. Specifically, it involves obtaining the set of spatial coordinates of all candidate points within the cluster in three-dimensional space. To ensure that the closed geometric boundary of the subsequent fitting can cover the actual usable area of ​​the roof, the extraction process will prioritize retaining the coordinate points located inside the parapet wall and the edge of the obstacle to form the geometric contour point set of this cluster.

[0031] For the extracted set of spatial coordinates, this embodiment employs the convex hull algorithm for geometric fitting. This algorithm searches for reference points with extreme coordinate values ​​within the coordinate set, and using these as starting points, sequentially filters the outermost vertices that can enclose all internal points according to polar angle or coordinate order. The fitted closed geometric boundary exhibits minimum perimeter and complete envelopment characteristics, meaning that all candidate points within this cluster are located inside or on the edge of the closed geometric boundary.

[0032] The system identifies the closed polygonal region enclosed by each closed geometric boundary as the minimum bounding polygonal region corresponding to that cluster. Since there may be multiple independent building units or discrete clusters within the redundant target area, the system logically summarizes all generated minimum bounding polygonal regions to form a set of site selection areas.

[0033] S5. Evaluate the sum of actual irradiance resources within the selected area set and determine whether it meets the preset resource demand target. Preferably, the step of assessing the sum of actual irradiance resources within the set of site selection areas includes: Obtain the slope aspect, slope, and shadow occlusion duration of candidate points within each smallest bounding polygon region in the site selection area set; By combining historical irradiance data and the duration of shading, the effective area where photovoltaic modules can be deployed in each region is determined, and the corresponding annual cumulative irradiance is calculated to obtain the actual irradiance resources of each region. The actual irradiance resources of each region are summed to obtain the total actual irradiance resources.

[0034] In this embodiment, the system extracts core environmental parameters affecting photovoltaic conversion efficiency for each smallest bounding polygon region in the selected area set, specifically including the slope aspect, slope gradient, and shading duration of each candidate point. For slope aspect and slope gradient analysis, in one feasible implementation, a high-precision digital elevation model of the area is obtained, and the normal vector of the grid cell is calculated using a spatial difference algorithm to determine the tilt angle and orientation of each point. For shading duration simulation, the system combines the latitude and longitude information of the target area and the annual trajectory model of the sun, and uses a ray tracing algorithm to perform three-dimensional shading simulation of obstacles (such as parapet walls, elevator machine rooms, surrounding tall buildings, and vegetation) within the area. By statistically analyzing the hourly shading situation throughout the standard meteorological year, the annual cumulative shading duration of each candidate point is obtained.

[0035] Combining historical irradiance baseline data with the aforementioned environmental parameters, the system removes grid cells from the minimum bounding polygon region that face north, have excessively large slopes, or have an annual cumulative shading duration exceeding a preset threshold. The remaining physical space with practical installation conditions is determined as the effective area for photovoltaic module deployment. For the effective area, the corresponding historical irradiance data is retrieved, and combined with geometric corrections caused by slope aspect and slope gradient, as well as energy loss due to shading, the annual cumulative irradiance of the effective area under standard operating conditions is calculated. Spatial integration is performed on the effective area and the annual cumulative irradiance to obtain the actual irradiance resource corresponding to the minimum bounding polygon region. Subsequently, the actual irradiance resource of all minimum bounding polygon regions within the site selection area set is summed to obtain the sum of the actual irradiance resource.

[0036] Subsequently, the sum of the actual irradiated resources The comparison is made with the preset resource demand target. If... If the target resource requirement is greater than or equal to the target, it means that the current site selection boundary, even after considering complex slope constraints and shading losses, can still support the achievement of the initial installed capacity target. At this point, the system stops iterating and determines the current set of site selection areas as the final solution. If... If the energy demand is less than the target, it indicates that the current site selection plan has an energy gap.

[0037] S6. If the conditions are not met, the candidate point selection is re-executed, and the boundary of the minimum circumscribed polygon region is adjusted based on the optimization results. If the conditions are met, the current set of smallest bounding polygon regions will be determined as the site selection scheme for the photovoltaic power station.

[0038] Preferably, re-executing the candidate site screening step includes: A multi-objective function is established with the goal of maximizing the sum of expected irradiance for each spatial cluster and minimizing the total cost of grid-connected lines. The multi-objective function is used to optimize the distribution of points within the redundant target area, resulting in a Pareto optimal solution set containing multiple non-dominated site selection schemes. Based on the deviation between the current actual irradiation resource amount and the resource demand target, the solution with the closest new irradiation resource amount to the deviation value is selected from the Pareto optimal solution set as the optimal location scheme.

[0039] In this embodiment, the system establishes a mathematical model with the collaborative optimization objective of maximizing the total expected irradiance and minimizing the total cost of grid-connected lines. Let the set of candidate locations to be selected within the redundant target area be denoted as . Define decision variables Indicates whether to select the first There are several points. Construct the following multi-objective vector function. : Objective function 1 (energy gain): ; in, For the pre-calculated first The expected available irradiance at each location.

[0040] Objective function two (cost loss): ; in, The length of the aforementioned three-dimensional topological path. Cost per unit length of cable and construction cost. The fixed costs of individual point supports and installations are shared.

[0041] The NSGA-II multi-objective evolutionary algorithm was used for iterative processing. In each generation of the population, individuals... Dominant Individual The conditions are: ; The algorithm eventually converges to obtain the Pareto optimal solution set. Each solution Each represents a newly added combination of location distributions, and higher irradiance cannot be obtained without increasing costs.

[0042] Based on the calculated resource deviation value ,in For the preset resource requirement target, in the solution set The matching logic is executed within the system. The system calculates each non-dominated solution... New radiation Deviation value The Euclidean distance is used to select the optimal solution that satisfies the following formula. : ; If there are multiple solutions where the irradiation increment is close Then, the first choice is selected. The solution with the smallest value is taken as the optimal addressing scheme.

[0043] Preferably, the step of adjusting the boundary of the minimum circumscribed polygon region based on the optimization result includes: Based on the deviation between the current actual irradiation resources and the target resource demand, determine the incremental direction and step size parameters for boundary adjustment; Based on the optimal addressing scheme, candidate points within the current minimum bounding polygon area are added or removed to obtain an updated set of points. Based on the updated point set, the coordinates of the polygon vertices are refitted to achieve adaptive adjustment of the minimum bounding polygon region.

[0044] In this embodiment, after determining the optimal site selection scheme, the system needs to dynamically update the physical construction boundary based on the added or removed points. The direction and step size of the boundary adjustment depend on the deviation value. The positive and negative signs, if The system executes an expansion strategy and identifies the optimal addressing scheme. The spatial coordinates of the newly added points are used as support points for the outward topology of the boundary. If... This means that excessive resource redundancy leads to excessively high costs, prompting the implementation of a contraction strategy.

[0045] The system will The newly added candidate points are incorporated into the current spatial clusters to obtain the updated point set. For the updated set of vertices, the system re-invokes the convex hull algorithm. Let the updated set of vertex coordinates be... .

[0046] To ensure the adjusted boundaries meet actual engineering wiring requirements, the system performs topology smoothing on the refitted boundaries. If adding points results in extremely narrow sharp corners or corridors on the boundaries, the system calculates the interior angles of the boundaries. ,like Less than the preset minimum turning angle limit The system automatically corrects the boundary to a more regular geometry through normal expansion or vertex merging. Finally, the system completes the adaptive adjustment of the minimum bounding polygon region and outputs the final photovoltaic power plant site selection scheme.

[0047] Example 2 This embodiment uses a distributed photovoltaic power station in a high-tech industrial park in a city as an example to specifically illustrate the method of the present invention. The park has a preset total installed capacity of 1.2MW and includes 5 R&D buildings (20m-80m in height) and supporting parking lots. The building density is high and vertical shading is severe.

[0048] The system determines the ideal installation area based on the installation target and local meteorological data. A redundancy evaluation model is constructed using building coverage factor and shading loss factor. The calculated redundancy coefficient is used to amplify the ideal area, thereby delineating a redundant target area including rooftops, podiums, parking lot awnings, and connecting corridors. This area is then discretized into grid cells of a preset size as initial points. By acquiring historical average irradiance data and geographical environmental parameters, points with expected available irradiance below the threshold are eliminated, resulting in a candidate point set.

[0049] The system establishes an energy efficiency evaluation index, which is positively correlated with expected irradiance resources and negatively correlated with grid connection line costs. The grid connection line cost is determined based on the three-dimensional topology path from the candidate location to the grid-connected distribution room. Clustering is performed with the goal of maximizing overall energy efficiency. During the clustering process, although Building 5 (80m) and Building 1 (20m) are spatially adjacent, the topology path cost of vertical cable laying in Building 5 is much higher than that in Building 1, resulting in significant differences in their energy efficiency index characteristics. Therefore, they are classified into different clusters.

[0050] For each cluster, edge point coordinates were extracted and a convex hull algorithm was used to fit and generate closed geometric boundaries, determining the minimum bounding polygon region for each cluster. Furthermore, digital elevation models were retrieved for each region, and ray tracing algorithms were used to simulate the shadow drift over 8760 hours annually. Combining aspect and slope data, the effective area for deployable components and the annual cumulative irradiance were accurately calculated for each region. The results showed that the sum of actual irradiance resources in the initially generated regional set did not meet the resource demand target.

[0051] To address the resource gap, a multi-objective function was established with the collaborative optimization goals of maximizing total irradiance and minimizing total grid connection cost. The Pareto optimal solution set was obtained through optimization calculations. Based on the current resource deviation value, the system matched the solution set with the closest possible increase in irradiance and the most cost-effective solution. This scheme selected the area for the new parking lot sunshade and fine-tuned the installation boundary of Building 2. Based on this, the point set was updated and the vertex coordinates were refitted to achieve adaptive topology adjustment of the polygonal region. The adjusted scheme was evaluated and found to meet the resource requirement objectives.

[0052] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing solar irradiance resources in photovoltaic power plants, characterized in that, include: Based on the preset installed capacity target, determine the ideal laying area corresponding to the theoretical irradiance requirement; Based on the building coverage and shading loss of the target area, the ideal laying area is corrected to obtain a redundant target area; Candidate locations are selected within the redundant target area, and a clustering algorithm is used, with energy efficiency evaluation indicators as the clustering basis, to divide the candidate locations into different spatial clusters. For each of the spatial clusters, a minimum bounding polygon region containing candidate points within the cluster is constructed to form a set of site selection regions; The sum of actual irradiance resources within the selected site set is evaluated, and it is determined whether it meets the preset resource demand target. If the conditions are not met, the candidate point selection process is repeated, and the boundary of the minimum bounding polygon region is adjusted based on the optimization results. If the conditions are met, the current set of smallest bounding polygon regions will be determined as the site selection scheme for the photovoltaic power station.

2. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, The steps for correcting the ideal laying area include: A redundancy evaluation model is constructed based on the building coverage and shading loss of the target area, and the redundancy coefficient is calculated. The redundancy target area is obtained by correcting the ideal laying area using the redundancy coefficient.

3. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, The steps for selecting candidate points within a redundant target area include: The redundant target area is divided into grid cells of a preset size, and each grid cell is defined as the initial point to be evaluated; Obtain historical irradiance data and geographic environmental shading parameters for each initial point, and calculate the expected available irradiance for each initial point; Set a resource admission threshold, remove initial points whose expected available irradiance is lower than the resource admission threshold, and retain the remaining initial points as candidate points.

4. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, The step of dividing the candidate points into different spatial clusters includes: Based on the location of the grid connection point within the redundant target area, determine the grid connection line cost from each candidate point to the grid connection point; Calculate the energy efficiency evaluation index corresponding to each candidate site. The energy efficiency evaluation index is positively correlated with the expected irradiance resources of the candidate site and negatively correlated with the grid connection line cost. With the goal of maximizing the overall energy efficiency of each spatial cluster, candidate sites that are adjacent to each other and have similar energy efficiency evaluation index characteristics are divided into the same spatial cluster.

5. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, The steps for constructing the minimum bounding polygon region containing candidate points within the cluster include: Obtain the set of spatial coordinates of candidate points located at the edge positions in each of the spatial clusters; Based on the set of spatial coordinates, the convex hull algorithm is used to fit and obtain a closed geometric boundary that can completely enclose all candidate points in the corresponding spatial cluster. The region enclosed by the closed geometric boundary is defined as the minimum circumscribed polygon region.

6. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, The steps for assessing the sum of actual irradiance resources within the selected site set include: Obtain the slope aspect, slope, and shadow occlusion duration of candidate points within each smallest bounding polygon region in the site selection area set; By combining historical irradiance data and the duration of shading, the effective area where photovoltaic modules can be deployed in each region is determined, and the corresponding annual cumulative irradiance is calculated to obtain the actual irradiance resources of each region. The actual irradiance resources of each region are summed to obtain the total actual irradiance resources.

7. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 1, characterized in that, Re-execution of the candidate site screening process includes: A multi-objective function is established with the goal of maximizing the sum of expected irradiance for each spatial cluster and minimizing the total cost of grid-connected lines. The multi-objective function is used to optimize the distribution of points within the redundant target area, resulting in a Pareto optimal solution set containing multiple non-dominated site selection schemes. Based on the deviation between the current actual irradiation resource amount and the resource demand target, the solution with the closest new irradiation resource amount to the deviation value is selected from the Pareto optimal solution set as the optimal location scheme.

8. The method for assessing solar irradiance resources for photovoltaic power plants according to claim 7, characterized in that, The steps for adjusting the boundary of the minimum bounding polygon region based on the optimization results include: Based on the deviation between the current actual irradiation resources and the target resource demand, determine the incremental direction and step size parameters for boundary adjustment; Based on the optimal addressing scheme, candidate points within the current minimum bounding polygon area are added or removed to obtain an updated set of points. Based on the updated point set, the coordinates of the polygon vertices are refitted to achieve adaptive adjustment of the minimum bounding polygon region.

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