Method and system for predicting developable capacity of distributed photovoltaic installation in region

Abstract

A method and a system for predicting developable capacity of distributed photovoltaic installation in a region accurately identify a land which can be used for photovoltaic installation by analyzing terrain and policy information of a target region, provide basic data for sunlight condition analysis by utilizing shielding object identification, and combine with evaluation of influence of different seasons and climate conditions on photovoltaic power generation efficiency to predict the developable capacity of the distributed photovoltaic installation in the region. The actual influence of the environmental condition on the installed capacity is identified, the power grid compatibility is evaluated and the maximum access capacity is identified by analyzing the actual performance of the regional power grid, and the accuracy of development capacity prediction is improved. According to the method, the developable capacity of photovoltaic installation can be obtained in combination with terrain and policy information and different seasons and climate conditions, and the developable capacity of photovoltaic installation can be evaluated by comprehensively considering the influence of terrain fluctuation and annual illumination receiving conditions on installation cost and equipment maintenance.

Description

Technical Field

[0001] The present invention relates to a method for predicting the developable capacity of photovoltaic power, and particularly to a method and system for predicting the developable capacity of distributed photovoltaic installations within a region. Background Art

[0002] The field of photovoltaic energy management technology focuses on achieving the optimal operation of photovoltaic systems through various management strategies and technical tools, including the monitoring, prediction, storage, and scheduling of photovoltaic power generation. The aim is to ensure the efficient use of energy and the stability of the power grid. By integrating meteorological data, energy consumption history, and real-time data, combined with data analysis, prediction algorithms, and intelligent control, the power output of photovoltaic devices is predicted and adjusted to match various environmental conditions and demands, analyzing the changes in energy supply and demand, enhancing the economic efficiency and reliability of photovoltaic systems. By integrating photovoltaic systems with existing power infrastructure, ensuring the stable supply of energy and maximizing the utilization of renewable resources, combined with equipment maintenance and fault diagnosis, promoting the sustainable development and technological innovation of the photovoltaic industry.

[0003] Among them, the method for predicting the developable capacity of distributed photovoltaic installations within a region aims to estimate the maximum photovoltaic installation capacity based on environmental conditions and technical feasibility within the target region, providing accurate data support for energy planners and investors, helping to evaluate the value and feasibility of photovoltaic projects, and providing data support for decision-making behaviors. By accurately predicting the developable capacity of distributed photovoltaic systems, effectively planning the expansion and upgrade of the power grid, optimizing the allocation of energy resources, enhancing the attractiveness and economic benefits of photovoltaic investments, helping to promote the utilization of renewable energy, and achieving the goals of optimizing the energy structure and environmental protection.

[0004] Although there is such a method, it still has the following defects:

[0005] Traditional methods for predicting the developable capacity of photovoltaic installations are insufficient in data processing and integration. In scenarios with complex geography and variable climate conditions, they cannot fully consider the actual impacts of terrain undulations and policy restrictions on photovoltaic power generation, resulting in poor accuracy of prediction results.

[0006] Disclosing the information of this background art section is only intended to increase the understanding of the overall background of the present application, and should not be regarded as an admission or any form of implication that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to overcome the defect in the prior art that the actual impacts of terrain undulations and policy restrictions on photovoltaic power generation cannot be fully considered, and to provide a method and system for predicting the developable capacity of distributed photovoltaic installations within a region that can fully consider the actual impacts of terrain undulations and policy restrictions on photovoltaic power generation.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] A method for predicting the developable capacity of distributed photovoltaic installations within a region, the prediction method comprising:

[0010] S1. Based on the regional geographical location information, collect the land use data and terrain data within the target region, analyze the influence of different terrain conditions on the photovoltaic installation capacity according to the land use data and terrain data, and combine the regional policy information to identify the available land locations, so as to obtain the terrain correlation analysis result;

[0011] S2. Based on the terrain correlation analysis result, collect the ground and aerial image data of the target region, identify various obstacles and simulate the solar radiation at multiple times and seasons, and generate the sunshine condition analysis result;

[0012] S3. Based on the sunshine condition analysis result, collect the meteorological data of the target region, analyze the influence of seasonal and climate changes on the power generation efficiency of photovoltaic devices, and combine the land availability and sunshine conditions at multiple locations to identify the feasibility of multiple installation locations, and generate the environmental impact assessment result;

[0013] S4. Based on the environmental impact assessment result, simulate and analyze the influence of multiple photovoltaic installation capacities on the grid load regulation ability, power stability and fault response ability after access, so as to obtain the access impact assessment information;

[0014] S5. Based on the access impact assessment information, collect and analyze the performance data of the target region's power grid, evaluate the influence of the existing grid conditions on the access of photovoltaic projects, and identify the maximum access capacity, and generate the grid access condition information;

[0015] S6. According to the grid access condition information, predict the developable capacity of photovoltaic installations within the target region by evaluating the influence of the terrain conditions, land availability, environmental conditions and grid performance of the target region on the installation capacity, and generate the installation capacity prediction result.

[0016] The S1 includes:

[0017] Based on the regional geographical location information, collect the land use data and terrain data within the target region, analyze the protection status of the land at multiple locations, identify the available land locations permitted by the policy, and generate a list of available land locations;

[0018] Based on the list of available land locations, analyze the undulation degree of the terrain at multiple locations, calculate the land slope, and detect the land cover types under various terrain conditions, and generate the terrain undulation degree data;

[0019] The land slope is obtained through the slope calculation algorithm of the digital elevation model and the analysis surface tool, and includes:

[0020]

[0021] In the above formula, S is the slope of the terrain, Δhx and Δhy are the elevation differences in the x and y directions, Δx and Δy are the horizontal distance differences in the x and y directions, the x and y directions are perpendicular to each other, and π is the pi;

[0022] Based on the terrain undulation data, considering the impact of terrain undulation on installation cost and equipment maintenance, evaluate the impact of various terrain features on the photovoltaic installed capacity, identify and record multiple candidate installation locations, and generate the terrain correlation analysis result;

[0023] The terrain correlation analysis result includes regional terrain condition information, land cover type distribution map, and available land area location information.

[0024] The S2 includes:

[0025] Based on the terrain correlation analysis result, collect and analyze the ground and aerial image data of the target area, identify various obstacles affecting sunlight, including trees, buildings, and mountains, and generate the obstacle identification data;

[0026] Based on the obstacle identification data, simulate sunlight and analyze the solar radiation paths in multiple seasons and time periods, evaluate the impact of various obstacles on sunlight conditions, and generate the sunlight occlusion impact data, including:

[0027] R = R0 × (1 - ∑(k i ×A i ));

[0028] In the above formula, R is the actually received solar radiation amount, R0 is the theoretical solar radiation amount without occlusion, k i is the occlusion coefficient of the i-th type of obstacle, and A i is the proportion of the i-th type of obstacle in the field of view;

[0029] Based on the sunlight occlusion impact data, simulate the annual sunlight reception situation by considering seasonal changes, evaluate the sunlight conditions at multiple locations, and generate the sunlight condition analysis result;

[0030] The sunlight condition analysis result includes the obstacle type identification result, sunlight intensity distribution map, and seasonal sunlight change simulation data.

[0031] The S3 includes:

[0032] Based on the sunlight condition analysis result, collect the meteorological data of the target area, including temperature, humidity, wind speed, and rainfall, and construct a serialized data set according to the time information to obtain the meteorological data set;

[0033] Based on the meteorological data set, analyze the climate change pattern of the target area, evaluate the impact of changes in temperature and humidity on the photovoltaic power generation efficiency, analyze the shading effect of cloud cover changes on solar radiation, and combine with the lighting conditions at the target location to calculate the expected power generation efficiency of photovoltaic devices at multiple locations, generating power generation efficiency prediction data;

[0034] Based on the power generation efficiency prediction data, consider the land availability and sunshine conditions at the target location, evaluate the feasibility of multiple installation locations, identify and mark multiple candidate installation locations, obtaining the environmental impact assessment results;

[0035] The environmental impact assessment results include the analysis results of seasonal temperature fluctuations, the prediction information of climate change trends, and the expected power generation efficiency of photovoltaic devices. The access impact assessment information includes the grid load regulation ability, the power system stability assessment information, and the analysis results of the fault response ability.

[0036] The S4 includes:

[0037] Based on the environmental impact assessment results, evaluate the impact of multiple photovoltaic installed capacities on the grid load regulation ability, generating grid impact simulation data;

[0038] Based on the grid impact simulation data, analyze the impact of multiple photovoltaic installed capacities on the grid fault response ability, including the response speed and recovery ability of the grid to fault events, generating the fault response analysis results;

[0039] Based on the fault response analysis results, evaluate the impact of multiple photovoltaic installed capacities on the power stability, predict the grid operation conditions under multiple scenarios, obtaining the access impact assessment information.

[0040] The S5 includes:

[0041] Based on the access impact assessment information, collect various state data of the target area grid, including power generation capacity, electricity demand, and energy storage facility capacity, analyze the current grid operation efficiency and load conditions, generating the grid operation data set;

[0042] Based on the grid operation data set, analyze the load status of transformers and transmission lines in the grid, analyze the impact of multiple photovoltaic capacities on the grid equipment load, generating the load condition analysis results;

[0043] According to the load condition analysis results, analyze and calculate the maximum accessible capacity of the photovoltaic project under the existing grid conditions, obtaining the grid access condition information;

[0044] The maximum accessible capacity of the photovoltaic project includes:

[0045]

[0046] In the above formula, P max is the maximum accessible capacity of the photovoltaic project, S rated is the rated capacity of the transformer or transmission line, P load is the current load, and SF is the safety margin factor;

[0047] The grid access condition information includes the evaluation information of the existing grid power generation capacity, the load data of transformers and transmission lines, and the analysis results of energy storage capacity. The installed capacity prediction results include the installable photovoltaic area, the expected annual average power generation, and the return on investment of the photovoltaic system.

[0048] The S6 includes:

[0049] According to the grid access condition information, for multiple installation candidate locations in the target area, by measuring the geometries of multiple locations, calculate the installable area of the target area and generate area installation area information;

[0050] The specific formula for calculating the installable area of the target area is:

[0051]

[0052] where represents the abscissa of the j-th vertex of the i-th location, represents the ordinate of the j-th vertex of the i-th location, represents the abscissa of the (j + 1)-th vertex of the i-th location, represents the ordinate of the (j + 1)-th vertex of the i-th location, m i represents the number of vertices of the i-th location, n represents the total number of candidate locations, A total is the total installation area of all candidate locations, j represents the index of the current vertex, j + 1 represents the loop of the vertex index to ensure that each vertex is calculated in pairs with the next vertex, and i represents the index of the current location;

[0053] Based on the area installation area information, according to the sunshine conditions and meteorological data of the target area, calculate the power generation amount and change pattern of the target area and generate power generation calculation results;

[0054] According to the power generation calculation results, combined with the regional grid conditions, by comparing with the maximum accessible capacity of the photovoltaic project, analyze the installable development capacity of the target area and generate installed capacity prediction results.

[0055] A distributed photovoltaic installable development capacity prediction system in a region, the system includes a terrain correlation analysis module, a sunshine condition analysis module, an environmental impact assessment module, an access impact assessment module, a grid access condition analysis module, and an installed capacity prediction module;

[0056] The terrain correlation analysis module is used to collect land use data and terrain data within the target area based on regional geographical location information, analyze the impact of different terrain conditions on the photovoltaic installed capacity according to the land use data and terrain data, and combine with regional policy information to identify available land locations, so as to obtain the terrain correlation analysis result;

[0057] The sunshine condition analysis module is used to collect ground and aerial image data of the target area based on the terrain correlation analysis result, identify various obstacles and simulate solar radiation at multiple times and seasons, and generate the sunshine condition analysis result;

[0058] The environmental impact assessment module is used to collect meteorological data of the target area based on the sunshine condition analysis result, analyze the impact of seasonal and climate changes on the power generation efficiency of photovoltaic equipment, and combine the land availability and sunshine conditions at multiple locations to identify the feasibility of multiple installation locations, and generate the environmental impact assessment result;

[0059] The access impact assessment module is used to simulate and analyze the impact of multiple photovoltaic installed capacities on the grid load regulation ability, power stability and fault response ability based on the environmental impact assessment result, so as to obtain the access impact assessment information;

[0060] The grid access condition analysis module is used to collect and analyze the performance data of the target area grid based on the access impact assessment information, evaluate the impact of the existing grid conditions on the access of photovoltaic projects, and identify the maximum access capacity, and generate the grid access condition information;

[0061] The installed capacity prediction module is used to predict the developable capacity of photovoltaic installed capacity in the target area by evaluating the impact of the terrain conditions, land availability, environmental conditions and grid performance of the target area on the installed capacity, and generate the installed capacity prediction result.

[0062] A device for predicting the developable capacity of distributed photovoltaic installed capacity within a region, the device includes a processor and a memory;

[0063] The memory is used to store computer program code and transmit the computer program code to the processor;

[0064] The processor is used to execute the aforementioned method for predicting the developable capacity of distributed photovoltaic installed capacity within a region according to the instructions in the computer program code.

[0065] A computer medium, on which a computer program is stored, and when the computer program is executed by a processor, the aforementioned method for predicting the developable capacity of distributed photovoltaic installed capacity within a region is implemented.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0067] 1. In the method for predicting the developable capacity of distributed photovoltaic installations within a region of the present invention, by analyzing the terrain and policy information of the target region, the land available for photovoltaic installations is accurately identified. The identification of obstacles provides basic data for the analysis of sunlight conditions. By combining the evaluation of the influence of different seasons and climate conditions on the photovoltaic power generation efficiency, the actual influence of environmental conditions on the installed capacity is identified. By analyzing the actual performance of the regional power grid, the grid compatibility is evaluated and the maximum access capacity is identified, improving the accuracy of the prediction of the developable capacity. Therefore, this design can obtain the developable capacity of photovoltaic installations by combining terrain and policy information and different seasons and climate conditions.

[0068] 2. In the method for predicting the developable capacity of distributed photovoltaic installations within a region of the present invention, the land slope is obtained through the slope calculation algorithm of the digital elevation model and the analysis surface tool. This algorithm can accurately obtain the planar undulation of the target region and consider the influence of the terrain undulation on the installation cost and equipment maintenance, evaluating the influence of various terrain features on the photovoltaic installed capacity. Therefore, this design can evaluate the developable capacity of photovoltaic installations by considering the influence of the terrain undulation on the installation cost and equipment maintenance.

[0069] 3. In the method for predicting the developable capacity of distributed photovoltaic installations within a region of the present invention, by collecting and analyzing the ground and aerial image data of the target region, various obstacles affecting sunlight are identified, including trees, buildings, and mountains, generating obstacle identification data. By evaluating the influence of various obstacles on sunlight conditions through the obstacle identification data, the annual light reception situation considering seasonal changes is obtained, and then the influence of light conditions on the developable capacity of photovoltaic installations is evaluated. Therefore, this design can evaluate the developable capacity of photovoltaic installations by considering the annual light reception situation considering seasonal changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is the flowchart of the method of the present invention.

[0071] Figure 2 is the detailed flowchart of S1 in Embodiment 1 of the present invention.

[0072] Figure 3 is the detailed flowchart of S2 in Embodiment 1 of the present invention.

[0073] Figure 4 is the detailed flowchart of S3 in Embodiment 1 of the present invention.

[0074] Figure 5 is the detailed flowchart of S4 in Embodiment 1 of the present invention.

[0075] Figure 6 is the detailed flowchart of S5 in Embodiment 1 of the present invention.

[0076] Figure 7 This is the detailed flowchart of S6 in Embodiment 1 of the present invention.

[0077] Figure 8 This is a schematic diagram of the spatial rectangular coordinate system in Embodiment 1 of the present invention.

[0078] Figure 9 This is the structure diagram of the system of the present invention.

[0079] Figure 10 This is the structure diagram of the device of the present invention. Detailed implementation manners

[0080] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0081] Embodiment 1:

[0082] Refer to Figure 1 , a method for predicting the developable capacity of distributed photovoltaic installations in a region, the prediction method comprising:

[0083] S1: Based on the regional geographical location information, collect the land use data and terrain data in the target region, analyze the influence of various terrain conditions on the photovoltaic installation capacity, and combine the regional policy information to identify the available land locations, so as to obtain the terrain correlation analysis result;

[0084] S2: Based on the terrain correlation analysis result, by collecting the ground and aerial image data of the target region, identify various obstacles and simulate the solar radiation at multiple times and seasons, and generate the sunshine condition analysis result;

[0085] S3: Based on the sunshine condition analysis result, collect the meteorological data of the target region, analyze the influence of seasonal and climate changes on the power generation efficiency of photovoltaic devices, and generate the environmental impact assessment result;

[0086] S4: Based on the environmental impact assessment result, simulate and analyze the influence on the grid load regulation ability, power stability and fault response ability after connecting various photovoltaic installation capacities, so as to obtain the access influence assessment information;

[0087] S5: Based on the access influence assessment information, collect and analyze the performance data of the target region power grid, evaluate the influence of the existing grid conditions on the access of photovoltaic projects, and identify the maximum access capacity, and generate the grid access condition information;

[0088] S6: According to the grid access condition information, by evaluating the influence of the terrain conditions, land availability, environmental conditions and grid performance of the target region on the installation capacity, predict the developable capacity of photovoltaic installations in the target region, and generate the installation capacity prediction result.

[0089] The results of terrain correlation analysis include regional terrain condition information, land cover type distribution maps, and available land area location information. The results of sunlight condition analysis include obstruction type identification results, sunlight intensity distribution maps, and seasonal sunlight change simulation data. The results of environmental impact assessment include seasonal temperature fluctuation analysis results, climate change trend prediction information, and expected power generation efficiency of photovoltaic devices. The results of access impact assessment information include grid load regulation capacity, power system stability assessment information, and fault response ability analysis results. The grid access condition information includes existing grid power generation capacity assessment information, load data of transformers and transmission lines, and energy storage capacity analysis results. The installed capacity prediction results include installable photovoltaic area, expected annual average power generation, and return on investment of the photovoltaic system.

[0090] See Figure 2 , based on the regional geographical location information, collect the land use data and terrain data within the target area, analyze the impact of various terrain conditions on the photovoltaic installed capacity, and combine the regional policy information to identify the available land locations. The specific steps to obtain the terrain correlation analysis results are as follows:

[0091] S101: Based on the regional geographical location information, collect the land use data and terrain data within the target area, analyze the protection status of the land at multiple locations, identify the available land locations permitted by policies, and generate a list of available land locations;

[0092] In the sub-step of geographical information analysis, based on the regional geographical location information, collect the land use data of the target area through the geographical information system, including land ownership, use classification, and historical usage. Use the land use database to cross-check the data with the geographical information system to ensure the accuracy and update of the data. Classify the land in the target area, identify the protection status of each type of land, and use spatial analysis tools to check the boundaries of each protected area to determine the available land locations permitted by policies. Use the land classification model to determine the availability of each piece of land based on the protection status and generate a list of available land locations. The list includes the locations of the land and usage suggestions, providing a scientific basis for subsequent land development.

[0093] S102: Based on the list of available land locations, analyze the undulation degree of the terrain at multiple locations, calculate the land slope, and detect the land cover types under various terrain conditions to generate undulation degree data of the terrain;

[0094] In the above content, based on the list of available land locations, analyze the undulation degree of the terrain at multiple locations, calculate the land slope, detect the land cover types under various terrain conditions, and generate undulation degree data of the terrain;

[0095] See Figure 8, a spatial rectangular coordinate system is established. The land slope is obtained through the slope calculation algorithm of the digital elevation model and the analysis surface tool, including:

[0096]

[0097] In the above formula, S is the slope of the terrain, Δhx and Δhy are the elevation differences in the x and y directions, Δx and Δy are the horizontal distance differences in the x and y directions. The x and y directions are perpendicular to each other, and π is the pi.

[0098] Detailed explanation of the formula and the derivation process of formula calculation:

[0099] Δh is the elevation difference. The elevation values between adjacent points are obtained using DEM data, and the elevation difference is obtained by subtraction. Assume that the elevation of the target point is 100 meters and the elevation of the adjacent point is 106 meters. The elevation difference in the east-west direction is Δh x = 106 - 100 = 6 meters. Δx and Δy are the horizontal distance differences, which are determined by the resolution of the DEM dataset. Assume that the resolution of the DEM is 100 meters per pixel, then the horizontal distance difference of each pixel is 100 meters. Calculate the slope:

[0100]

[0101] The result of 5.71° indicates that the slope of the target block is 5.71 degrees, which means that the terrain of the target area is relatively flat and suitable for the installation of photovoltaic devices. Through this slope analysis, the installation stability of the photovoltaic system can be ensured, and the sunlight receiving efficiency can be optimized. The value reflects the vertical change of the terrain. At the same time, the land cover type under the terrain conditions is detected and classified using the land cover classification system, including forest land, grassland, and built-up land. By combining the slope data and the land cover data, the terrain undulation data is generated. The data provides an important reference for judging the applicability and development potential of the land.

[0102] S103: Based on the terrain undulation data, considering the influence of the terrain undulation on the installation cost and equipment maintenance, evaluate the influence of various terrain features on the photovoltaic installed capacity, identify and record multiple candidate installation locations, and generate the terrain correlation analysis result;

[0103] In the sub - steps of the cost - benefit analysis tool, based on the terrain undulation data, using the cost model to consider the impact of terrain undulation on installation costs and equipment maintenance, execute the cost - analysis model, input the terrain data and the estimated equipment maintenance frequency and costs, estimate the installation and maintenance costs under different terrain features, use the photovoltaic installation capacity evaluation model to evaluate the impact of terrain features such as slope and land - cover type on photovoltaic installation capacity, including the initialization, parameter setting, and execution process of the model, such as the impact of slope on the installation angle of photovoltaic panels and the impact of land - cover type on the efficiency of photovoltaic panels, identify the candidate installation locations with the optimal cost - benefit ratio, record the location information, and generate the terrain - correlation analysis results. The results help decision - makers understand the return on investment and guide the equipment layout and optimization of photovoltaic projects.

[0104] See Figure 3 , based on the terrain - correlation analysis results, the steps of collecting ground and aerial image data of the target area, identifying various obstacles, and simulating solar radiation at multiple times and seasons to generate the sunlight - condition analysis results are as follows:

[0105] S201: Based on the terrain - correlation analysis results, collect and analyze the ground and aerial image data of the target area, identify various obstacles affecting sunlight, including trees, buildings, and mountains, and generate obstacle - identification data;

[0106] In the sub - steps of the image - processing and analysis tool, based on the terrain - correlation analysis results, collect the ground and aerial image data of the target area through drones and satellite images. The steps include adjusting the resolution and contrast of the images to improve the recognizability of obstacles, using the deep - learning model of image - recognition algorithms, and using convolutional neural networks to automatically identify and classify the obstacles in the images, including trees, buildings, and mountains. Extract the position and size information of the obstacles through image - segmentation technology, and generate obstacle - identification data. The data lists the type, position, and sunlight - blocking effect of each obstacle, providing the necessary input for subsequent sunlight analysis.

[0107] S202: Based on the obstacle - identification data, simulate the light and analyze the solar - radiation paths at multiple seasons and time periods, evaluate the impact of various obstacles on sunlight conditions, and generate sunlight - blocking - impact data;

[0108] R = R0×(1 - ∑(k i ×A i ));

[0109] In the above formula, R is the actual received solar - radiation amount, R0 is the theoretical solar - radiation amount without obstacles, k i is the blocking coefficient of the i - th type of obstacle, and A i is the proportion of the i - th type of obstacle in the field of view;

[0110] Detailed Explanation of the Formula and the Derivation Process of Formula Calculation:

[0111] Assume that the theoretical solar radiation R0 is 1000 W / m2, and the shading coefficients of trees, buildings, and mountains in the area are k trees = 0.3, k buildings = 0.5, k mountains = 0.7, and the view percentages of the obstacles are A trees = 0.1, A buildings = 0.2, A mountains = 0.05, calculate R:

[0112] R = 1000×(1 - (0.3×0.1 + 0.5×0.2 + 0.7×0.05))

[0113] R = 1000×(1 - (0.03 + 0.1 + 0.035))

[0114] R = 1000×(1 - 0.165)

[0115] R = 1000×0.835 = 835 W / m2

[0116] The result of 835 W / m2 indicates that under the given obstacle configuration, the actual received solar radiation is 835 W / m2. The calculation process is used to evaluate the influence of different obstacles on sunlight conditions and obtain the sunlight reception situation under the current obstacle distribution.

[0117] S203: Based on the sunlight occlusion influence data, simulate the annual sunlight reception situation by considering seasonal variations, evaluate the sunlight conditions at multiple locations, and generate the sunlight condition analysis results;

[0118] In the sub - steps of the sunlight condition evaluation tool, based on the sunlight occlusion influence data, use the annual sunlight simulation model SAM, consider seasonal variations, simulate the annual sunlight reception situation, calculate the predicted sunlight distribution using geographical location and historical climate data, set geographical and obstacle information for each location, conduct dynamic simulation of sunlight reception conditions, and generate the sunlight condition analysis results. The results include the seasonal variation diagram of sunlight reception amount and the estimated potential energy output, providing data support for selecting the optimal installation location of photovoltaic devices. The analysis results help optimize the layout and design of the photovoltaic system to ensure maximum energy collection and system efficiency.

[0119] See Figure 4 , based on the sunlight condition analysis results, collect the meteorological data of the target area, analyze the influence of seasonal and climate changes on the power generation efficiency of photovoltaic devices, and the specific steps to generate the environmental impact assessment results are as follows:

[0120] S301: Based on the sunshine condition analysis result, collect meteorological data of the target area, including temperature, humidity, wind speed and rainfall, and construct a serialized data set according to time information to obtain a meteorological data set;

[0121] In the sub-steps of the meteorological data processing module, based on the analysis results of sunshine conditions, the meteorological data of the target area, including temperature, humidity, wind speed and rainfall, are collected by using ground meteorological stations and satellite data. Time series analysis tools, such as the Pandas library in Python, are used to build a serialized data set, including data cleaning, interpolation and normalization processing to ensure the continuity and comparability of the data. The data set is regularly updated through an automatic data collection system to ensure the real-time and accuracy of the data, and a meteorological data set is generated. The data set provides a basis for analyzing climate change patterns and evaluating the impact of climate conditions on photovoltaic power generation efficiency. The data set records the meteorological conditions at each time point and provides a reliable input for subsequent climate analysis and decision-making.

[0122] S302: Based on the meteorological data set, analyze the climate change pattern of the target area, evaluate the impact of changes in temperature and humidity on photovoltaic power generation efficiency, analyze the shielding effect of cloud cover changes on solar radiation, and calculate the expected power generation efficiency of photovoltaic equipment at multiple locations based on the lighting conditions of the target location to generate power generation efficiency prediction data;

[0123] In sub-step S302, based on the meteorological data set, outliers are removed through data preprocessing, temperature and humidity, cloud cover and solar radiation data are screened, a geographic statistical model is constructed based on historical data of the same period, climate change patterns in the target area are identified and predicted, linear regression analysis and time series prediction are used to predict changes in climate conditions in multiple periods of time in the future, cloud cover changes at each location are analyzed, and physical basic models such as the Coulomb scattering model are used to evaluate the shielding effect of cloud cover on solar radiation. The cloud cover data is compared with ground solar radiation measurements to verify the accuracy of the model. In combination with actual lighting conditions, a nonlinear regression model is used to calculate the expected power generation efficiency of photovoltaic equipment at different locations. By inputting a variety of climate data, such as average temperature, average humidity and average daily cloud cover, the power generation efficiency of multiple locations is calculated to generate power generation efficiency prediction data.

[0124] S303: Based on the power generation efficiency prediction data, considering the lighting conditions and land availability of the target location, evaluating the feasibility of multiple installation locations, identifying and marking multiple candidate installation locations, and obtaining an environmental impact assessment result;

[0125] In sub-step S303, based on the power generation efficiency prediction data, using geographic information system technology, analyze the lighting conditions and land use types at the target location, including detailed geographic and environmental data such as terrain slope, surface cover category, and land ownership information. According to the specific requirements of photovoltaic equipment for land and lighting, determine the land availability through spatial analysis. At the same time, considering regulatory constraints and protected area division, through multi-factor decision analysis method, combining land availability, lighting conditions, and predicted power generation efficiency, evaluate the advantages and disadvantages of each candidate installation location. The process involves weight assignment and priority rating, select the most suitable location for photovoltaic installation, mark multiple locations as candidate installation sites, and generate an environmental impact assessment result through comprehensive analysis. The result lists the feasibility scores and key influencing factors for each location, providing a scientific basis for the installation decision.

[0126] See Figure 5 , based on the environmental impact assessment result, simulate and analyze the impact of multiple photovoltaic installed capacities on the grid load regulation ability, power stability, and fault response ability after access. The specific steps to obtain the access impact assessment information are as follows:

[0127] S401: Based on the environmental impact assessment result, evaluate the impact of multiple photovoltaic installed capacities on the grid load regulation ability, and generate grid impact simulation data;

[0128] In the sub-step of the grid simulation analysis tool, based on the environmental impact assessment result, use grid simulation software GridLAB-D or PSS / E to evaluate the impact of multiple photovoltaic installed capacities on the grid load regulation ability. The steps involve setting simulation parameters, including the size of the installed capacity, access location, and time variable, conducting grid load flow simulation, evaluating the impact of photovoltaic installation on the grid peak load and valley load, and examining the dynamic changes of the grid load during photovoltaic access in the simulation process. Generate grid impact simulation data, which shows the changes in the grid load regulation ability under different photovoltaic installed capacities, providing an important basis for grid planning and operation.

[0129] S402: Based on the grid impact simulation data, analyze the impact of multiple photovoltaic installed capacities on the grid fault response ability, including the response speed and recovery ability of the grid to fault events, and generate a fault response analysis result;

[0130] In the sub-steps of the power grid fault analysis tool, based on the power grid impact simulation data, the fault analysis model DIgSILENT PowerFactory is used to analyze the impact of various photovoltaic installed capacities on the power grid fault response ability, including the response speed and recovery ability of the power grid to fault events. The model evaluates the fault recovery time and the required control measures by simulating the voltage and current responses of the power grid during a fault, and generates the fault response analysis results. The results describe in detail the fault handling ability of the power grid after the photovoltaic access, including the specific performance of the response speed and recovery ability, providing a scientific basis for the formulation of power grid fault management and emergency response strategies.

[0131] S403: Based on the fault response analysis results, evaluate the impact of various photovoltaic installed capacities on power stability, predict the power grid operation conditions under multiple scenarios, and obtain the access impact assessment information;

[0132] In the sub-steps of the power grid stability assessment tool, based on the fault response analysis results, the system stability assessment tool, such as the Simulink environment in MATLAB, is used to evaluate the impact of various photovoltaic installed capacities on power stability, perform multi-scenario simulations, consider the power grid operation conditions under different seasons, day-night cycles, and climate conditions, evaluate the impact of the changes in photovoltaic power generation on the power grid frequency and voltage stability, generate the access impact assessment information, which provides decision support for the operation safety of the power grid, ensures the stable operation of the power grid under different conditions, and the prediction analysis results help the power grid operator optimize the dispatching strategy to ensure the reliability and stability of power supply.

[0133] See Figure 6 , based on the access impact assessment information, the steps of collecting and analyzing the performance data of the target area power grid, evaluating the impact of the existing power grid conditions on the photovoltaic project access, and identifying the maximum access capacity to generate the power grid access condition information are specifically as follows:

[0134] S501: Based on the access impact assessment information, collect various state data of the target area power grid, including power generation capacity, electricity demand, and energy storage facility capacity, analyze the current power grid operation efficiency and load conditions, and generate the power grid operation data set;

[0135] In the sub - steps of the data collection and analysis module, based on the access impact assessment information, various status data of the power grid in the target area are collected through the use of a data collection system such as the SCADA system, including real - time power generation capacity, power consumption demand, and energy storage facility capacity. The data is monitored and recorded in real - time through the power grid management system. Using data analysis software such as Tableau or Power BI, data visualization and trend analysis are carried out to evaluate the current operating efficiency and load conditions of the power grid. By analysis, the bottlenecks and efficiency optimization points of the power grid operation are determined, and a power grid operation dataset is generated. The dataset provides a comprehensive view of the power grid operation status, providing a basis for further power grid optimization and decision - making to ensure the high efficiency and reliability of the power grid operation.

[0136] S502: Based on the power grid operation dataset, analyze the load status of transformers and transmission lines in the power grid, analyze the impact of various photovoltaic capacity accesses on the load of power grid equipment, and generate the load condition analysis results;

[0137] In the sub - steps of the load analysis tool, based on the power grid operation dataset, use power system analysis software such as ETAP or DIgSILENT to conduct load status analysis of transformers and transmission lines. The process includes inputting the topological structure of the power grid, transformer capacity, and line parameters, and simulating and analyzing the impact on the load of power grid equipment under the condition of accessing various photovoltaic installed capacities, evaluating the thermal current - carrying, voltage drop, and overload conditions of power grid equipment caused by load changes, and generating the load condition analysis results. The results describe the load status of each power grid equipment after increasing the photovoltaic installed capacity, providing a scientific basis for the equipment maintenance and upgrade decision - making of the power grid.

[0138] S503: According to the load condition analysis results, analyze and calculate the maximum accessible capacity of the photovoltaic project under the existing power grid conditions to obtain the power grid access condition information;

[0139] In the sub - steps of the power grid capacity assessment, according to the load condition analysis results, use the power grid capacity calculation formula Analyze and calculate the maximum accessible capacity of the photovoltaic project under the existing power grid conditions.

[0140] Where P max represents the maximum accessible capacity of the photovoltaic project, S rated represents the rated capacity of the transformer or transmission line, P load represents the current load, and SF represents the safety margin factor.

[0141] Detailed explanation of the formula and the formula calculation derivation process:

[0142] Assume that the rated capacity S rated of the transformer is 100 MW, the current power grid load P load is 70 MW, and the safety margin factor SF is set to 0.1, calculate Pmax :

[0143]

[0144] The result of 27.27 MW indicates that under the given grid load and safety factor conditions, the maximum accessible capacity of the PV project is 27.27 MW. The formula is used to evaluate the admissible capacity of PV access to the existing grid, and the optimal PV access capacity under the current grid load and equipment capacity limit is obtained.

[0145] See Figure 7 , according to the grid access condition information, by evaluating the impact of the terrain conditions, land availability, environmental conditions, and grid performance of the target area on the installed capacity, the specific steps for predicting the developable capacity of PV installations in the target area and generating the installed capacity prediction result are as follows:

[0146] S601: According to the grid access condition information, based on multiple installation candidate locations in the target area, by measuring the geometries of multiple locations, calculate the installable area of the target area and generate area installation information;

[0147] The specific formula for calculating the installable area of the target area is:

[0148]

[0149] Where, represents the abscissa of the j-th vertex of the i-th location, represents the ordinate of the j-th vertex of the i-th location, which is obtained by collecting geographical location information and analyzing terrain data, and using land use data and terrain data to determine the coordinates of each vertex of the candidate installation plot for PV devices. represents the abscissa of the (j + 1)-th vertex of the i-th location, represents the ordinate of the (j + 1)-th vertex of the i-th location, m i represents the number of vertices of the i-th location. By analyzing the terrain of multiple locations and detecting land cover types through terrain undulation data, evaluating the complexity of the terrain, and determining the number of vertices of the polygon, n represents the total number of candidate locations, A total is the total installable area of all candidate locations, j represents the index of the current vertex, j + 1 represents the loop of the vertex index to ensure that each vertex is calculated in pairs with the next vertex, and i represents the index of the current location;

[0150] Detailed explanation of the formula and the derivation process of formula calculation:

[0151] The formula is used to calculate the total area of irregular polygon land, and the result is used to determine the total installable area available for PV devices.

[0152] Parameter meanings and setting values:

[0153] n is the total number of candidate positions, with a value of 2, indicating that there are two pieces of candidate land;

[0154] m i is the number of vertices of each piece of land. Assume that for the first piece of land m1 = 4 and for the second piece of land m2 = 3

[0155] and are the abscissa and ordinate of the j-th vertex of each piece of land respectively;

[0156] Assume that the vertex coordinates of the first piece of land are:

[0157]

[0158] The vertex coordinates of the second piece of land are:

[0159]

[0160] Substitute the parameters into the formula for calculation:

[0161] Calculate the area of the first piece of land:

[0162]

[0163] Calculate the area of the second piece of land:

[0164]

[0165] Calculate the total area:

[0166] A total = A1 + A2 = 12 + 6 = 18

[0167] The result A total = 18 indicates that the total installation area available for photovoltaic devices in the area is 18 square meters. The value is used to help determine the usable land area in the area and calculate the developable installed capacity of photovoltaics.

[0168] S602: Based on the area installation information of the area, calculate the power generation amount and change pattern of the target area according to the sunshine conditions and meteorological data of the target area, and generate a power generation calculation result;

[0169] In the sub - steps of the energy model analysis tool, based on the regional installation area information, combined with the sunshine conditions and meteorological data of the target area, using the photovoltaic power generation simulation software PVsyst, calculate the power generation amount and change pattern of the target area, involving inputting the efficiency, tilt angle and orientation of the photovoltaic panels, as well as sunshine and meteorological data. The software simulates the power generation efficiency under different weather conditions and generates the calculation results of the power generation amount. The results provide the daily, monthly and annual power generation forecasts, and the pattern of the power generation amount changing with seasons and meteorological conditions, providing data support for energy output planning and management to ensure that the evaluation of energy investment return is based on accurate and comprehensive analysis.

[0170] S603: According to the calculation results of the power generation amount, combined with the regional power grid conditions, by comparing with the maximum accessible capacity of the photovoltaic project, analyze the installable development capacity of the target area and generate the prediction results of the installed capacity.

[0171] In the sub - steps of the power system evaluation tool, according to the calculation results of the power generation amount, combined with the operating conditions of the regional power grid, use the power system analysis software DIgSILENT PowerFactory to compare with the maximum accessible capacity of the photovoltaic project, analyze the installable development capacity of the target area, including evaluating the load balance and stability of the power grid when receiving the maximum power generation amount, and generate the prediction results of the installed capacity. The results indicate the installable photovoltaic installed capacity of the target area without affecting the stability of the power grid, providing a scientific basis for regional energy strategies and photovoltaic expansion, and helping decision - makers optimize resource allocation and energy layout.

[0172] Embodiment 2:

[0173] See Figure 9 , a prediction system for the installable development capacity of distributed photovoltaic installations in a region, the system includes a terrain correlation analysis module, a sunshine condition analysis module, an environmental impact assessment module, an access impact assessment module, a grid access condition analysis module and an installed capacity prediction module;

[0174] The terrain correlation analysis module is used to collect land use data and terrain data within the target area based on the regional geographical location information, analyze the impact of different terrain conditions on the photovoltaic installed capacity according to the land use data and terrain data, and combine the regional policy information to identify the available land locations to obtain the terrain correlation analysis results.

[0175] The sunshine condition analysis module is used to collect ground and aerial image data of the target area based on the terrain correlation analysis results, identify various obstacles and simulate the solar radiation at multiple times and seasons to generate the sunshine condition analysis results.

[0176] The environmental impact assessment module is used to collect meteorological data of the target area based on the analysis result of the sunlight condition, analyze the impact of seasonal and climate changes on the power generation efficiency of photovoltaic devices, combine the land availability and sunlight condition at multiple locations, identify the feasibility of multiple installation locations, and generate an environmental impact assessment result;

[0177] The access impact assessment module is used to simulate and analyze the impact on the grid load regulation ability, power stability and fault response ability after multiple photovoltaic installed capacities are accessed based on the environmental impact assessment result, and obtain access impact assessment information;

[0178] The grid access condition analysis module is used to collect and analyze the performance data of the target area grid based on the access impact assessment information, evaluate the impact of the existing grid conditions on the access of photovoltaic projects, identify the maximum access capacity, and generate grid access condition information;

[0179] The installed capacity prediction module is used to predict the developable capacity of photovoltaic installed capacity in the target area by evaluating the impact of the terrain condition, land availability, environmental condition and grid performance of the target area on the installed capacity according to the grid access condition information, and generate an installed capacity prediction result.

[0180] Embodiment 3:

[0181] See Figure 10 , a device for predicting the developable capacity of distributed photovoltaic installed capacity in a region, the device includes a processor and a memory;

[0182] The memory is used to store computer program code and transmit the computer program code to the processor;

[0183] The processor is used to execute the method for predicting the developable capacity of distributed photovoltaic installed capacity in the region described in Embodiment 1 according to the instructions in the computer program code.

[0184] A computer medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for predicting the developable capacity of distributed photovoltaic installed capacity in the region described in Embodiment 1.

[0185] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modification or change made by those of ordinary skill in the art according to the content disclosed by the present invention shall be included in the protection scope recorded in the claims.

Claims

1. A method for predicting the developable capacity of distributed photovoltaic installations within a region, characterized in that: The prediction method includes: S1. Based on the regional geographical location information, collect the land use data and terrain data within the target region, analyze the influence of different terrain conditions on the photovoltaic installation capacity according to the land use data and terrain data, and combine the regional policy information to identify the available land locations, obtaining the terrain correlation analysis result; S2. Based on the terrain correlation analysis result, collect the ground and aerial image data of the target region, identify various obstacles and simulate the solar radiation at multiple times and seasons, generating the sunshine condition analysis result; S3. Based on the sunshine condition analysis result, collect the meteorological data of the target region, analyze the influence of seasonal and climate changes on the power generation efficiency of photovoltaic devices, and combine the land availability and sunshine conditions at multiple locations to identify the feasibility of multiple installation locations, generating the environmental impact assessment result; S4. Based on the environmental impact assessment result, simulate and analyze the influence of connecting multiple photovoltaic installation capacities on the grid load regulation ability, power stability and fault response ability, obtaining the access impact assessment information; S5. Based on the access impact assessment information, collect and analyze the performance data of the target region's power grid, evaluate the influence of the existing grid conditions on the access of photovoltaic projects, and identify the maximum access capacity, generating the grid access condition information; S6. According to the grid access condition information, predict the developable capacity of distributed photovoltaic installations within the target region by evaluating the influence of the terrain conditions, land availability, environmental conditions and grid performance of the target region on the installation capacity, generating the installation capacity prediction result.

2. The method for predicting the developable capacity of distributed photovoltaic installations within a region according to claim 1, characterized in that: S1 includes: Based on the regional geographical location information, collect the land use data and terrain data within the target region, analyze the protection status of the land at multiple locations, identify the available land locations permitted by the policy, generating a list of available land locations; Based on the list of available land locations, analyze the undulation degree of the terrain at multiple locations, calculate the land slope, and detect the land cover types under various terrain conditions, generating the terrain undulation degree data; The land slope is obtained through the slope calculation algorithm of the digital elevation model and the analysis surface tool, including: In the above formula, S is the slope of the terrain, Δhx and Δhy are the elevation differences in the x and y directions, Δx and Δy are the horizontal distance differences in the x and y directions, the x and y directions are perpendicular to each other, and π is the pi; Based on the terrain undulation degree data, consider the influence of the terrain undulation on the installation cost and equipment maintenance, evaluate the influence of various terrain features on the photovoltaic installation capacity, identify and record multiple candidate installation locations, generating the terrain correlation analysis result; The terrain correlation analysis result includes the regional terrain condition information, the land cover type distribution map and the available land area location information.

3. The method for predicting the developable capacity of distributed photovoltaic installations within a region according to claim 1, characterized in that: S2 includes: Based on the terrain correlation analysis results, collect and analyze ground and aerial image data of the target area, identify various obstacles affecting sunlight, including trees, buildings, and mountains, and generate obstacle identification data; Based on the obstacle identification data, simulate sunlight and analyze the solar radiation paths in multiple seasons and time periods, evaluate the impact of various obstacles on sunlight conditions, and generate sunlight occlusion impact data, including: R = R0×(1 - ∑(k i ×A i )); In the above formula, R is the actually received solar radiation, R0 is the theoretical solar radiation without occlusion, and k i is the occlusion coefficient of the i-th type of occluder, and A i is the proportion of the i-th type of occluder in the field of view; Based on the sunlight occlusion impact data, simulate the annual sunlight reception situation by considering seasonal changes, evaluate the sunlight conditions at multiple locations, and generate sunlight condition analysis results; The sunlight condition analysis results include obstacle type identification results, sunlight intensity distribution maps, and seasonal sunlight change simulation data.

4. The method for predicting the developable capacity of distributed photovoltaic installations in a region according to claim 1, wherein: The S3 includes: Based on the sunlight condition analysis results, collect the meteorological data of the target area, including temperature, humidity, wind speed, and rainfall, and construct a serialized data set according to the time information to obtain a meteorological data set; Based on the meteorological data set, analyze the climate change pattern of the target area, evaluate the impact of temperature and humidity changes on the photovoltaic power generation efficiency, analyze the shading effect of cloud cover changes on solar radiation, and combine with the sunlight conditions at the target location to calculate the expected power generation efficiency of photovoltaic devices at multiple locations, and generate power generation efficiency prediction data; Based on the power generation efficiency prediction data, consider the land availability and sunlight conditions at the target location, evaluate the feasibility of multiple installation locations, identify and mark multiple candidate installation locations, and obtain the environmental impact assessment results; The environmental impact assessment results include seasonal temperature fluctuation analysis results, climate change trend prediction information, and expected power generation efficiency of photovoltaic devices, and the access impact assessment information includes grid load regulation ability, power system stability assessment information, and fault response ability analysis results.

5. The method for predicting the developable capacity of distributed photovoltaic installations in a region according to claim 1, wherein: The S4 includes: Based on the environmental impact assessment results, evaluate the impact of various photovoltaic installation capacities on the grid load regulation ability, and generate grid impact simulation data; Based on the grid impact simulation data, analyze the impact of various photovoltaic installation capacities on the grid fault response ability, including the response speed and recovery ability of the grid to fault events, and generate fault response analysis results; Based on the fault response analysis results, evaluate the impact of various photovoltaic installation capacities on power stability, predict the grid operation conditions in multiple scenarios, and obtain the access impact assessment information.

6. The method for predicting the developable capacity of distributed photovoltaic installations in a region according to claim 1, wherein: The S5 includes: Based on the access impact assessment information, collect various state data of the target area grid, including power generation capacity, electricity demand, and energy storage facility capacity, analyze the current grid operation efficiency and load situation, and generate a grid operation data set; Based on the power grid operation data set, analyze the load status of transformers and transmission lines in the power grid, analyze the impact of multiple photovoltaic capacity accesses on the loads of power grid equipment, and generate a load condition analysis result; According to the load condition analysis result, analyze and calculate the maximum accessible capacity of the photovoltaic project under the existing power grid conditions to obtain power grid access condition information; The maximum accessible capacity of the photovoltaic project includes: In the above formula, P max is the maximum accessible capacity of the photovoltaic project, S rated is the rated capacity of the transformer or transmission line, P load is the current load, and SF is the safety margin factor; The power grid access condition information includes existing power grid power generation capacity evaluation information, load data of transformers and transmission lines, and energy storage capacity analysis results. The installed capacity prediction result includes the installable photovoltaic area, expected annual power generation, and the return on investment of the photovoltaic system.

7. The method for predicting the developable capacity of distributed photovoltaic installation in a region according to claim 1, characterized in that: The S6 includes: According to the power grid access condition information, based on multiple installation candidate locations in the target region, calculate the installable area of the target region by measuring the geometric shapes of multiple locations, and generate regional installation area information; The specific formula for calculating the installable area of the target region is: Among them, represents the abscissa of the j-th vertex at the i-th position, represents the ordinate of the j-th vertex at the i-th position, represents the abscissa of the (j + 1)-th vertex at the i-th position, represents the ordinate of the (j + 1)-th vertex at the i-th position, m i represents the number of vertices at the i-th position, n represents the total number of candidate positions, A total is the total installation area of all candidate positions, j represents the index of the current vertex, j + 1 represents the loop of the vertex index to ensure that each vertex is calculated in pairs with the next vertex, and i represents the index of the current position; Based on the regional installation area information, calculate the power generation amount and change pattern of the target region according to the sunshine conditions and meteorological data of the target region, and generate a power generation calculation result; According to the power generation calculation result, combined with the regional power grid conditions, analyze the developable capacity of the installed capacity in the target region by comparing with the maximum accessible capacity of the photovoltaic project, and generate an installed capacity prediction result.

8. The system for predicting the developable capacity of distributed photovoltaic installation in a region according to claims 1 to 7, characterized in that: The system includes a terrain correlation analysis module, a sunshine condition analysis module, an environmental impact assessment module, an access impact assessment module, a power grid access condition analysis module, and an installed capacity prediction module; The terrain correlation analysis module is used to collect land use data and terrain data in the target region based on the regional geographical location information, analyze the impact of different terrain conditions on the photovoltaic installed capacity according to the land use data and terrain data, and combine the regional policy information to identify the available land locations to obtain a terrain correlation analysis result; The sunshine condition analysis module is used to collect ground and aerial image data of the target region based on the terrain correlation analysis result, identify various obstacles and simulate the solar radiation at multiple times and seasons to generate a sunshine condition analysis result; The environmental impact assessment module is used to collect the meteorological data of the target region based on the sunshine condition analysis result, analyze the impact of seasonal and climate changes on the power generation efficiency of photovoltaic equipment, and combine the land availability and sunshine conditions at multiple locations to identify the feasibility of multiple installation locations to generate an environmental impact assessment result; The access impact assessment module is used to simulate and analyze the impact of multiple photovoltaic installed capacities on the power grid load regulation ability, power stability, and fault response ability based on the environmental impact assessment result to obtain access impact assessment information; The grid connection condition analysis module is used to collect and analyze the performance data of the target area power grid based on the access impact assessment information, evaluate the impact of the existing grid conditions on the access of the photovoltaic project, identify the maximum access capacity, and generate grid connection condition information; The installed capacity prediction module is used to predict the developable capacity of the photovoltaic installed capacity in the target area according to the grid connection condition information by evaluating the impact of the terrain conditions, land availability, environmental conditions, and grid performance of the target area on the installed capacity, and generate the installed capacity prediction result.

9. A device for predicting the developable capacity of distributed photovoltaic installed capacity in a region, characterized in that: The device includes a processor and a memory; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the method for predicting the developable capacity of distributed photovoltaic installed capacity in the region according to the instructions in the computer program code in claims 1 to 7.

10. A computer medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for predicting the developable capacity of distributed photovoltaic installed capacity in the region according to claims 1 to 7 is implemented.

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