Photovoltaic resource screening and sorting method for power grid access

Abstract

The invention discloses a photovoltaic resource screening and sorting method for power grid access, and belongs to the technical field of photovoltaic land screening. The method aims to solve the problem of grid connection failure caused by lack of power grid compatibility evaluation in the prior art. Dynamic matching of a photovoltaic group and a power grid infrastructure is ensured by integrating a power grid permitted access judgment mechanism, including access capacity check, power generation load matching check and light abandoning proportion constraint check. Adjacent developable lands are clustered into a group through land clustering, and parameters such as installed capacity and generating capacity are calculated; combining the plot groups, carrying out power grid access analysis, and judging whether the group combination meets a power grid operation condition or not; and evaluating the contribution degree of the grouping combination to the static stability margin of the power grid, the power supply reliability and the n-1 power flow transfer rate, and recommending the land use grouping combination according to the comprehensive benefit index sequence. According to the invention, through dynamic constraint and engineering optimization, efficient screening of photovoltaic resources is realized, and the grid-connected risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic land screening technology, and in particular to a method for screening and ranking photovoltaic resources for grid connection. Background Technology

[0002] With the acceleration of the global energy transition, photovoltaic (PV) power generation, as an important component of clean energy, has attracted increasing attention for its resource screening and site selection technologies. Existing technologies typically rely on Geographic Information Systems (GIS) and remote sensing data to assess the suitability of PV sites through spatial analysis. For example, Chinese patent authorization document CN115660367A discloses a macro-level site selection method for PV power plants based on GIS technology. This method collects meteorological, geographical, and socio-economic factors, uses the Analytic Hierarchy Process (AHP) to determine weights, and combines overlay analysis to generate a site selection result map. Similarly, CN119579361A discloses a large-area PV site selection method that automatically selects priority areas through land resource data, topographic factors (such as slope, aspect, and mountain shadow), and concentration analysis. These technologies focus on natural conditions and land physical characteristics, improving site selection efficiency and addressing issues such as land use compliance and topographic influence to some extent.

[0003] However, existing technologies have significant shortcomings and cannot effectively solve the compatibility issues between photovoltaic projects and grid connection. Specifically, methods such as CN115660367A and CN119579361A only focus on static land suitability analysis, failing to consider the dynamic constraints of grid infrastructure. This can lead to congestion, overload, or curtailment issues in the selected areas during later grid connection. For example, while CN119579361A optimizes land clustering through concentration analysis, it does not assess the matching degree between clusters and grid capacity, nor can it verify the balance between power generation output and real-time load. This can easily lead to grid connection failure in practical applications. Furthermore, existing technologies ignore grid operating parameters (such as hourly load fluctuations and curtailment control) and lack quantitative assessments of grid support functions such as peak-shaving capacity and power flow stability, making it difficult to guarantee grid security with the selection results. Another pain point is the lack of integrated quantification of construction and operation costs. The concentration analysis in CN119579361A is based solely on land area and distance, failing to consider grid engineering costs such as access distance and line length as optimization targets, which may result in low economic feasibility of the site selection scheme. These shortcomings indicate that existing technologies cannot fully address core pain points such as grid compatibility, dynamic constraints, and engineering economics, and there is an urgent need for an efficient screening method that integrates grid access dimensions. Summary of the Invention

[0004] The purpose of this invention is to solve the problem of grid connection failure caused by the lack of grid compatibility assessment in the prior art, and to achieve effective matching of photovoltaic resources and grid infrastructure by integrating a grid access permission discrimination mechanism.

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies that ignore the dynamic constraints of grid operation, and to ensure real-time coordination between photovoltaic power generation and grid dispatching needs by introducing power generation load matching and curtailment ratio control.

[0006] The purpose of this invention is to address the pain point of the lack of quantitative integration of power grid construction and operation conditions in the prior art. By incorporating engineering parameters such as access distance and line length into the optimization model, the technical feasibility of site selection schemes and the efficiency of engineering implementation can be improved.

[0007] This invention proposes a photovoltaic (PV) resource screening and ranking method for grid connection. The method includes: aggregating potential PV development land into land use clusters through land use clustering; combining these clusters; determining grid access permission for each cluster combination, and checking against curtailment ratio constraints; if a cluster combination passes the grid access permission determination, further calculations are performed; otherwise, the next cluster combination is considered; net power generation is calculated based on PV output curves and curtailment limits; the grid connection engineering workload is estimated based on the ratio of installed capacity to grid connection distance for each cluster combination; and the system operation mode is adjusted in conjunction with grid frequency and voltage stability requirements; the contribution of each cluster combination to static stability margin, power supply reliability, and n-1 power flow transfer rate is evaluated; and recommended land use cluster combinations are output in order of benefit indicators. This reduces the risk of grid connection failure by ensuring dynamic matching between PV cluster combinations and grid infrastructure through land use clustering and grid access permission determination mechanisms, thereby improving the grid's capacity to accept PV power generation and enabling the screening results to be directly used for project implementation.

[0008] As a preferred approach, this method uses remote sensing image data to screen out undevelopable land, resulting in a set of undeveloped land. Natural conditions are analyzed for this undeveloped land, and suitable land for development is selected through multi-factor comprehensive calculations. These calculations involve an integrated assessment of natural conditions, determining land suitability through raster reclassification and suitability calculations. This improves screening efficiency and data objectivity by automatically screening out undevelopable land based on remote sensing image data and multi-factor comprehensive calculations, accurately identifying land with suitable natural conditions, and reducing human error and resource waste.

[0009] As a preferred method, the method performs adjacent merging processing on the selected map patches suitable for development to form preliminary aggregation units; removes scattered plots whose area does not reach the preset threshold; clusters and merges map patches that meet the proximity conditions based on spatial distance to form complete land use clusters; assigns a number to each cluster and calculates its total area and spatial agglomeration index.

[0010] As a preferred approach, the grid allows for sequential checks on grid access capacity, power generation load matching, and curtailment ratio constraints. If a photovoltaic cluster fails any of these checks, the process moves to the next cluster for evaluation. This enables real-time compatibility verification between photovoltaic projects and the grid. Through multi-level checks on access capacity, load matching, and curtailment constraints, the system ensures that the photovoltaic clusters meet grid safety standards, reducing the cost of later commissioning and trial-and-error.

[0011] Preferably, this method confirms that the total installed capacity of the cluster combination does not exceed the upper limit of the carrying capacity of the target substation or T-connection line; the method verifies whether the hourly power generation exceeds the current grid load power. This prevents grid overload and frequency fluctuations, and improves grid dynamic stability and enhances system resilience by verifying the matching of the installed capacity upper limit and hourly power generation with the load power.

[0012] As a preferred approach, this method selects cluster combinations with a curtailment rate of not less than zero and not exceeding the potential maximum power generation rate. It optimizes power generation efficiency by controlling the curtailment rate within a reasonable range, avoiding energy waste, balancing power generation benefits with grid dispatch requirements, and improving the economic viability and operational sustainability of photovoltaic projects.

[0013] Preferably, this method determines net power generation based on photovoltaic output curves and curtailment limits, and assesses the output characteristics and grid support capabilities of photovoltaic clusters in conjunction with grid dispatch requirements. This enhances the grid support function of photovoltaic systems by calculating net power generation based on photovoltaic output curves and curtailment limits, and assessing output characteristics in conjunction with grid dispatch requirements, enabling photovoltaic clusters to participate in grid peak shaving and alleviate load peak-valley differences.

[0014] As a preferred approach, this method estimates the required line length and substation capacity for grid connection based on the ratio of installed capacity to access distance in clustered power grids, thus assessing the implementation complexity of grid connection projects. It quantifies grid construction costs, estimates the amount of grid connection work through the ratio of installed capacity to access distance, optimizes line length and substation capacity planning, reduces resource waste during implementation, and improves project feasibility.

[0015] Preferably, this method calculates the comprehensive grid benefit index of the cluster combination by evaluating the degree to which the cluster combination improves the static stability margin of the power grid, enhances the power supply reliability, and improves the n-1 power flow transfer rate. This improves the overall technical performance of the power grid by evaluating the contribution of the cluster combination to static stability margin, power supply reliability, and n-1 power flow transfer rate, thereby enhancing the grid's disturbance rejection capability and fault recovery speed.

[0016] As a preferred approach, this method ranks the land use clusters in descending order based on a comprehensive power grid benefit index, prioritizing the clusters and their corresponding grid parameters that offer the greatest technical benefits to power grid operation. This achieves optimal allocation of photovoltaic resources by ranking the clusters based on comprehensive power grid benefit indicators, prioritizing the clusters with the highest technical benefits, guiding power grid planning decisions, and maximizing the positive impact of photovoltaic power generation on power grid stability and reliability.

[0017] This invention achieves multiple beneficial effects in the photovoltaic resource selection process through an innovative method integrating grid access dimensions. The specific beneficial effects are as follows: 1. This invention effectively solves the grid connection failure problem caused by the lack of grid compatibility assessment in existing technologies by introducing a grid access permission judgment mechanism. This enables the selected photovoltaic land use clusters to be directly matched with the grid infrastructure, reducing the need for later adjustments and improving the certainty of project implementation.

[0018] 2. By integrating dynamic constraints such as power generation load matching and curtailment ratio control, this invention achieves coordination between photovoltaic output and real-time grid demand. This avoids the impact of power generation fluctuations on grid stability, increases the grid penetration rate of renewable energy, and reduces operational risks.

[0019] 3. This invention significantly improves the technical feasibility of site selection schemes by quantifying engineering parameters such as access distance and line length and incorporating them into a screening model. This reduces the complexity of grid connection projects, shortens project implementation cycles, and reduces resource waste.

[0020] 4. By evaluating the contribution of photovoltaic (PV) cluster combinations to indicators such as grid static stability margin and power supply reliability, this invention ensures that PV grid integration not only meets power generation needs but also proactively enhances the grid's disturbance immunity and fault recovery capabilities. This provides data support for grid planning and maximizes the positive technological impact of PV power generation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 This is a schematic diagram of the power grid access analysis process of the present invention. Detailed Implementation

[0023] Example 1 Embodiment 1 of this invention provides a method for screening and ranking photovoltaic resources for grid connection. This method achieves efficient screening and ranking of photovoltaic resources by integrating grid connection conditions. The following is a detailed description in conjunction with the appendix. Figure 1The flowchart below provides a detailed description of this embodiment. This method aims to solve the grid connection failure problem caused by the lack of grid compatibility assessment in the prior art. By introducing dynamic constraints and engineering optimization, it improves the practicality and reliability of the screening results.

[0024] This method begins with a preliminary screening stage. In this stage, developable land data is input, including land policy information and remote sensing imagery. Based on land policies, policy-restricted areas, such as basic farmland and ecological protection zones, are screened out as undevelopable areas. Simultaneously, developed land is identified using image processing techniques combined with the remote sensing imagery data. Remote sensing image preprocessing includes atmospheric correction, thin cloud removal, noise reduction, and shadow correction to ensure data quality. The preprocessed images are used to analyze radiance values; areas with higher radiance values ​​are typically areas covered by buildings or solar panels. Range patches are generated using vectorization tools to distinguish between developed and undeveloped land. Overlay analysis of developable land patches and vectorization results is performed; overlapping areas are identified as developed land and screened out. The final output is a set of undeveloped land with suitable natural conditions. This step, through automation, improves screening efficiency, avoids errors caused by manual intervention, and provides clean input data for subsequent analysis.

[0025] Next, the land use clustering and parameter calculation phase begins. First, small, scattered plots are eliminated to improve the economies of scale of the clusters. Second, the eliminated undeveloped land is clustered: adjacent plots are grouped based on boundary distances to form land use clusters. Adjacency is determined by spatial distance thresholds to ensure concentrated and contiguous land within each cluster. Clustering operations include merging adjacent patches using a geographic information system (GIS) and merging patches that meet distance criteria through aggregation analysis, thus integrating discrete land use. Then, the plot clusters are combined, and the technical parameters of each cluster combination are calculated: the area of ​​the cluster combination is measured using a GIS, and the installed capacity of the cluster combination is estimated based on the installed capacity density per unit area; power generation is estimated using system efficiency conversion and regional effective utilization hours to reflect the power generation potential of the cluster combination; furthermore, the cluster agglomeration degree is assessed, an indicator calculated based on the relationship between the area of ​​plots within the cluster and the distance to the cluster centroid, used to quantify the spatial concentration of land use. This step optimizes the land use layout, forming standardized cluster units, laying the foundation for grid connection analysis.

[0026] After land use clustering is completed, grid access analysis is performed, which is the core distinguishing feature of this invention. First, grid access permission is determined, including three sub-checks: access capacity check, power generation load matching check, and curtailment ratio constraint check. The access capacity check confirms whether the total installed capacity of the cluster exceeds the upper limit of the target grid node (such as a collection station or T-connection line), ensuring that photovoltaic power generation will not cause grid overload. The power generation load matching check verifies whether the hourly power generation output matches the current grid load power, avoiding power generation fluctuations affecting grid stability. The curtailment ratio constraint check ensures that the curtailment ratio is within a reasonable range, neither lower than zero nor exceeding the maximum power generation ratio, to balance power generation efficiency and grid dispatching needs. If the cluster passes all checks, it proceeds to subsequent calculations; otherwise, it returns to the land use clustering stage to readjust the cluster. This determination mechanism solves the pain point of lack of grid compatibility in existing technologies, achieving dynamic matching between photovoltaic projects and grid infrastructure, and significantly reducing the risk of grid connection failure.

[0027] If the photovoltaic cluster passes the grid access approval judgment, it enters the grid technical parameter calculation stage. This stage includes power generation output calculation, grid connection engineering quantity calculation, and system operation status calculation. Power generation output calculation is based on the photovoltaic output curve and curtailment limit to determine the net power generation of the cluster, and considers grid dispatching requirements (such as peak shaving requirements) to evaluate the peak shaving capacity and grid support value of the cluster. Grid connection engineering quantity calculation estimates the required line length, substation capacity, and other engineering quantities based on the ratio of the cluster's installed capacity to the connection distance, reflecting the complexity of grid construction. System operation status calculation combines grid frequency response and voltage stability requirements to adjust the system operation mode to ensure stable grid operation after photovoltaic access.

[0028] Finally, an overall grid benefit assessment is conducted. In this stage, the comprehensive grid benefit index of the cluster is calculated, including assessing the degree of improvement of the grid's static stability margin, the enhancement of power supply reliability, and the improvement effect on the n-1 power flow transfer rate. These indicators quantify the contribution of photovoltaic (PV) cluster combinations to the grid's technical performance, ensuring that grid connection schemes not only meet power generation demands but also proactively enhance the grid's resilience to disturbances and its fault recovery capabilities. Based on benefit indicators, the cluster combinations are ranked in descending order, prioritizing those with the greatest technical benefits to grid operation, and their grid parameters are output. This step achieves optimal allocation of PV resources, guides grid planning decisions, and maximizes the positive impact of renewable energy.

[0028] This embodiment fully demonstrates the implementation of the technical solution of the present invention through the above process, highlighting the integration and optimization of grid access conditions. Each step is described in detail with its technical features, especially the core distinguishing features such as grid access permission determination and benefit assessment, which directly correspond to the invention's objectives of improving grid compatibility, enhancing dynamic coordination capabilities, and optimizing engineering feasibility. This method ensures the practicality and reliability of the screening results, providing technical support for photovoltaic resource development.

[0029] Example 2 This method begins with a preliminary screening stage. In this stage, developable land data is input, including land policy information and remote sensing imagery. Based on land policies, policy-restricted areas, such as basic farmland and ecological protection zones, are screened out as undevelopable areas. Simultaneously, combined with remote sensing imagery data, image processing techniques are used to identify developed land, such as areas covered by photovoltaic power plants or buildings. Remote sensing image preprocessing includes atmospheric correction, thin cloud processing, noise reduction, and shadow correction to ensure data quality. The preprocessed images are used to analyze radiance values; areas with higher radiance values ​​are identified as areas covered by buildings or solar panels. Range patches are generated using vectorization tools to distinguish between developed and undeveloped land. Overlay analysis of developable land patches and vectorization results is performed; overlapping areas are identified as developed land and screened out. The final output is a set of undeveloped land with suitable natural conditions. This step, through automation, improves screening efficiency, avoids manual intervention, and provides clean input data for subsequent analysis.

[0030] Next, a multi-factor comprehensive calculation is performed. This stage involves the assessment of slope, aspect, hill shadow, and solar radiation to determine the suitability of the land use. The suitable development factors are all stored as raster data for easy comprehensive calculation. Slope, aspect, and hill shadow data are obtained through surface analysis based on a digital elevation model, while solar radiation is calculated using total horizontal radiation data. According to relevant regulations, the essential factors for suitable development land must meet specific conditions, such as a slope between 0 and 35 degrees and an aspect between 135 and 225 degrees being preferred. During the multi-factor comprehensive calculation, the raster values ​​of all factors are reclassified and calculated using a raster calculator, with the reclassified raster sizes remaining consistent. Specifically, after slope reclassification, values ​​are assigned as follows: 0-10 degrees is assigned 2, 10-35 degrees is assigned 1, and 35-90 degrees is assigned 0. After aspect reclassification, values ​​are assigned as follows: 0-135 degrees is assigned 0.5, 135-225 degrees is assigned 1, and 225-360 degrees is assigned 0.5. After mountain shadow reclassification, shadowed areas are assigned 0, and other areas are assigned 1. The total horizontal radiance is capped at 800 kWh / m², with values ​​above 800 kWh / m² assigned 1 and below 800 kWh / m² assigned 0. The suitability grid, considering priority factors, is calculated by multiplying the slope reclassification value, aspect reclassification value, mountain shadow reclassification value, and total horizontal radiance reclassification value. The portion of the suitability grid with a value greater than or equal to 1 is vectorized and overlaid with undeveloped land to distinguish suitable and unsuitable land for development; the overlapping portion is considered unsuitable land for development. This step, through multi-factor integrated assessment, accurately identifies land use with suitable natural conditions, providing a foundation for subsequent clustering.

[0031] Then, the land use clustering and parameter calculation stage begins. First, suitable development land is merged and clustered: adjacent land uses are merged using a geographic information system, grouping patches with boundary distances within 250 meters into clusters, and filtering out scattered plots smaller than 20 mu (approximately 1.3 hectares) to improve cluster concentration and economies of scale. Through aggregation analysis, adjacent patches are fused based on distance thresholds to achieve clustering of discrete land uses. Each cluster is numbered and its land area is calculated. Next, the technical parameters of the clusters are calculated: the estimated installed capacity is determined by multiplying the installed capacity density per unit area by the cluster area, reflecting the cluster's power generation potential; the estimated power generation is calculated using the system efficiency conversion factor and the region's effective utilization hours, representing the cluster's annual power generation capacity; simultaneously, the cluster's agglomeration is assessed. This indicator is calculated as the ratio of the sum of the areas of all plots within the cluster to the sum of the products of the distance from each plot to the cluster's centroid multiplied by the corresponding plot area, used to quantify the spatial concentration of land use. The results of these parameter calculations are stored in GIS land use elements, providing structured data for power grid access analysis. This step optimizes land use layout, forms standardized cluster units, and enhances the reliability of subsequent power grid analysis.

[0032] Finally, an optimization analysis of the construction and operation of the power plant and grid is conducted. This stage includes grid access permission determination, calculation of individual technical parameters, and overall grid benefit assessment. First, the land parcels are grouped together, and grid access permission determination is performed on each group: The total installed capacity of the group is checked to ensure it does not exceed the maximum capacity of the target substation or T-connection line, ensuring that photovoltaic power generation will not overload the grid; the hourly power output is verified to match the current grid load power, avoiding fluctuations in power generation that could affect grid stability; simultaneously, the curtailment rate is ensured to be within a reasonable range, neither lower than zero nor exceeding the maximum power generation rate, to balance power generation efficiency and grid dispatch requirements. If the group passes all checks, it proceeds to subsequent calculations; otherwise, it returns to the land use clustering stage to reselect group combinations. This determination mechanism solves the grid compatibility problem and achieves dynamic matching. Secondly, the technical parameters are calculated as follows: Based on the photovoltaic output curve and curtailment restrictions, the net power generation is calculated, and grid dispatch requirements such as peak shaving are considered to evaluate the peak shaving capacity and grid support value of the photovoltaic cluster combination. Based on the ratio of installed capacity to access distance of the photovoltaic cluster combination, the required line length, substation capacity, and other engineering quantities are estimated to reflect the complexity of grid construction. Combined with grid frequency response and voltage stability requirements, the system operation mode is adjusted to ensure stable grid operation after photovoltaic access. Finally, the overall grid benefit assessment is performed: The improvement of the grid's static stability margin, the enhancement of power supply reliability, and the improvement of the n-1 power flow transfer rate by the photovoltaic cluster combination are calculated to obtain a comprehensive grid benefit index. Based on the benefit index, the cluster combinations are ranked in descending order, and the cluster combination with the greatest technical benefit to grid operation is recommended first, and its grid parameters are output.

[0033] Grid access approval determination is a crucial step in ensuring the compatibility of photovoltaic (PV) clusters with grid operation. It involves dynamically checking and eliminating clusters that do not meet the requirements, thus preventing grid connection failures. This determination includes three sub-checks.

[0034] Confirm that the total installed capacity of the photovoltaic cluster does not exceed the maximum capacity of the target grid node (such as a substation or T-connection line). Specifically, check whether the sum of the installed capacity of all plots within the cluster exceeds the maximum allowable capacity of the grid node. If it does, remove the cluster to ensure that photovoltaic power generation will not cause grid overload or equipment damage.

[0035] For each hourly power generation output, verify whether it matches the grid load power at that time. For any given hour, calculate the total power generation output of the cluster combination (considering the output ratio and curtailment ratio caused by changes in sunlight) and check whether it exceeds the load power of the grid node at that time (for step-up substations, the load power is considered infinite). If there is a mismatch, filter out the cluster combination to avoid power generation fluctuations affecting grid stability.

[0036] The curtailment rate of solar power is ensured to be within a reasonable range, i.e., neither lower than zero nor exceeding the maximum generation rate. For any given hour and for each plot of land, the curtailment rate is checked to see if it falls between zero and the maximum generation rate. If it exceeds this range, the cluster combination is eliminated to balance generation efficiency and grid dispatch requirements. If the cluster combination passes all checks, it proceeds to the next calculation; otherwise, it returns to the land use clustering stage to reselect cluster combinations. This discrimination mechanism solves the grid compatibility problem and achieves dynamic matching.

[0037] according to Figure 2 As shown, after the cluster combination passes the access judgment, the present invention calculates the grid technical parameters to quantify the power generation capacity, engineering complexity and operating status of the cluster combination.

[0038] Based on photovoltaic output curves and curtailment limits, the net power generation of photovoltaic (PV) clusters is calculated. Taking into account variations in irradiance and the curtailment rate, the actual hourly power generation is determined and incorporated into grid dispatch requirements (such as peak-shaving requirements) to assess the peak-shaving capacity and grid support value of the clusters. For example, by integrating output curves and curtailment data, net power generation is calculated to reflect the real-time contribution of the clusters to the grid.

[0039] Based on the ratio of installed capacity to access distance in a power grid cluster, the required engineering quantities, such as line length and substation capacity, are estimated for connection to the system. Specifically, based on the installed capacity and access distance, the line laying length and substation scale are calculated to reflect the complexity and difficulty of power grid construction.

[0040] In accordance with the requirements of grid frequency response and voltage stability, the system operation mode is adjusted. By analyzing grid frequency fluctuations and voltage changes, the operation strategy of the photovoltaic cluster combination is optimized to ensure the stable operation of the grid after photovoltaic integration. For example, inverter settings or scheduling plans are adjusted to maintain system balance.

[0041] The overall power grid benefit assessment aims to quantify the contribution of cluster combinations to the technical performance of the power grid, and to rank and recommend based on benefit indicators to achieve optimal resource allocation.

[0042] Static stability margin refers to the ability of a power grid to maintain stable operation after being subjected to disturbances (such as load shifts or generator outages), and is usually assessed as a percentage of the stability limit. Calculating the improvement in static stability margin due to cluster combination involves the following steps: Power flow calculations and stability simulations are performed before and after the cluster integration. Voltage and frequency stability of the power grid are evaluated by simulating common disturbance scenarios (such as load increases or line faults). After the cluster integration, the stability limits of the power grid are calculated; for example, the maximum percentage of load increase the system can withstand before collapse is determined.

[0043] The improvement level is expressed as the percentage increase in the stability limit after the cluster combination is connected, relative to the original stability limit. For example, if the original stability limit is 100MW and it increases to 110MW after connection, the improvement level is 10%. The larger this value, the greater the contribution of the cluster combination to the static stability of the power grid.

[0044] The calculations combine the actual power generation output of the solar power clusters (based on photovoltaic output curves and curtailment limits) with peak-shaving requirements to ensure that the calculations reflect real-time operating conditions. This process uses power system analysis software (such as PSAT or PSS®E) for simulation and outputs stability margin indicators.

[0045] Power supply reliability refers to the grid's ability to continuously supply power, and is commonly measured by indicators such as the probability of load shedding (LOLP) and expected power shortage (EENS). The enhancement effect of grid cluster combination on power supply reliability includes: Based on historical operating data or Monte Carlo simulations, the probability of power grid load shedding before and after cluster integration is assessed. Specifically, random events (such as equipment failures or power generation fluctuations) are simulated to calculate the changes in the number and duration of power outages after cluster integration.

[0046] The enhancement effect is expressed as a percentage reduction in the probability of load shedding or a reduction in the expected amount of power shortage. For example, if the original probability of load shedding is 0.5% and it drops to 0.3% after grid connection, the enhancement effect is 0.2 percentage points. This value is calculated using reliability assessment software (such as ETAP or CYME), taking into account the actual generating capacity of the cluster (considering curtailment constraints) and grid load demand.

[0047] By combining grid dispatch requirements (such as peak shaving requirements), the assessment should ensure that it reflects the hourly power generation output and load matching, avoiding purely theoretical calculations.

[0048] The n-1 power flow transfer rate refers to the ability of other parts of the power grid to handle additional loads without causing overload when one line or piece of equipment fails (N-1 contingency). Calculating the improvement effect of cluster combination on the n-1 power flow transfer rate involves: Before and after the grid integration, N-1 contingency analysis was performed. Faults in critical lines or transformers were simulated to examine changes in power flow distribution and identify any overloaded lines or voltage exceedance issues.

[0049] The improvement is expressed as a percentage increase in power flow transfer capability after a fault or a reduction in the number of overloaded lines. For example, if there are 5 overloaded lines in the original N-1 scenario, and this is reduced to 2 after the connection, the improvement is 60%. This calculation uses power flow analysis tools (such as PowerWorld or DigSILENT) in combination with the generation output of the cluster combination (calculated based on net generation) and the grid topology.

[0050] Considering the grid frequency response and voltage stability requirements, the system operation mode is adjusted to ensure that the assessment results reflect actual operating conditions. For example, by optimizing the reactive power output of the cluster combination, the voltage profile is improved, and the power flow transfer capability is enhanced.

[0051] Calculation and ranking of comprehensive benefit indicators: Based on the above three technical indicators, the comprehensive power grid benefit indicators of the cluster combination are calculated as follows: The improvement in static stability margin, power supply reliability, and n-1 power flow transfer rate are normalized to the same scale (e.g., 0-100 points) and weighted according to grid priority (e.g., stability weight 40%, reliability weight 30%, power flow transfer weight 30%). The weighted sum is used to obtain a comprehensive benefit index, which directly reflects the overall technical contribution of the cluster combination to the grid.

[0052] All cluster combinations are sorted in descending order of comprehensive benefit indicators. Cluster combinations with the highest indicators are prioritized and their grid parameters (such as installed capacity, grid connection distance, and net generation) are output. This process ensures that the selection results maximize the stability, reliability, and resilience of the power grid, guiding grid planning decisions.

Claims

1. A photovoltaic resource screening and ranking method for grid access, characterized in that, The method includes: Potential photovoltaic development land is aggregated into land use clusters through land use clustering, and then the land plot clusters are combined. For each cluster combination, grid access permission is determined, and checks are performed based on the curtailment ratio constraint. If the cluster combination passes the grid access permission determination, subsequent calculations are performed; otherwise, the process moves to determine the next cluster combination. Net power generation is calculated based on photovoltaic output curves and curtailment restrictions. The amount of grid connection work is estimated based on the ratio of installed capacity and grid connection distance of the photovoltaic clusters. The system operation mode is adjusted in combination with grid frequency and voltage stability requirements. The contribution of land use clusters to static stability margin, power supply reliability, and n-1 power flow transfer rate is evaluated, and recommended land use cluster combinations are output in order of benefit indicators.

2. The photovoltaic resource screening and ranking method for grid-connection according to claim 1, characterized in that, The method is based on remote sensing image data, which filters out undevelopable land to obtain a set of undeveloped land. Natural conditions are analyzed for undeveloped land, and suitable land for development is selected through multi-factor comprehensive calculation. The multi-factor comprehensive calculation involves an integrated assessment of natural conditions, and land suitability is determined through raster reclassification and suitability calculation.

3. The photovoltaic resource screening and ranking method for grid-connection according to claim 1 or 2, characterized in that, The method performs adjacent merging processing on the selected map patches suitable for development to form preliminary aggregation units; Remove scattered plots of land whose area does not reach the preset threshold; Based on spatial distance, map patches that meet the proximity criteria are clustered and merged to form complete land use groups; Each cluster is numbered and identified, and its total area and spatial agglomeration index are calculated.

4. The method of claim 1, wherein, The grid access approval process sequentially checks the access capacity, power generation load matching, and curtailment ratio constraints. If a cluster fails any of these checks, the process moves on to the next cluster.

5. The photovoltaic resource screening and ranking method for grid-connection according to claim 1 or 4, characterized in that, The method confirms that the total installed capacity of the cluster does not exceed the upper limit of the carrying capacity of the target collection station or T-connection line; the method verifies whether the hourly power generation output exceeds the power load of the power grid at that time.

6. The photovoltaic resource screening and ranking method for grid integration according to claim 1 or 4, characterized in that, The method selects cluster combinations with a curtailment rate of not less than zero and not exceeding the potential maximum power generation rate.

7. The method of claim 1, wherein, The method determines net power generation based on photovoltaic output curves and curtailment limits, and assesses the output characteristics and grid support capabilities of cluster combinations in conjunction with grid dispatch requirements.

8. The method of claim 1, wherein, The method estimates the required line length and substation capacity for grid connection based on the ratio of installed capacity to access distance, and assesses the implementation complexity of grid connection projects.

9. The method of claim 1, wherein, The method calculates the comprehensive power grid benefit index of the cluster combination by evaluating the degree to which the cluster combination improves the static stability margin of the power grid, enhances the reliability of power supply, and improves the n-1 power flow transfer rate.

10. The photovoltaic resource screening and ranking method for grid integration according to claim 1 or 9, characterized in that, The method sorts the land use clusters in descending order based on the comprehensive power grid benefit index, and prioritizes recommending the land use clusters and their power grid parameters that have the greatest technical benefits for power grid operation.

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