Method and System for Evaluating Photovoltaic Carrying Capacity of Typical Mountain Distribution Networks with Multi-Dimensional Sources, Networks and Loads

Abstract

The present invention discloses a method and system for evaluating the photovoltaic carrying capacity of a typical mountain distribution network with multi-dimensional source-network-load, which relates to the field of new energy technologies. The method includes identifying a typical mountain with multi-dimensional source-network-load, collecting and preprocessing the grid operation data of the typical mountain distribution network with multi-dimensions; constructing a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the typical mountain distribution network with multi-dimensional source-network-load, and analyzing and adjusting the evaluation results of the photovoltaic carrying capacity. The present invention has high adaptability and scalability, can be customized and optimized according to the actual needs of different regions, and has broad practical application value. Especially in the context of large-scale access of photovoltaic power generation, it has a significant promoting effect on optimizing grid operation, improving the stability and economy of photovoltaic systems.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and specifically to a method and system for evaluating the photovoltaic carrying capacity of a typical mountain distribution network with multi-dimensional source-network-load. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection awareness, photovoltaic power generation, as a clean and renewable energy form, has received extensive attention and application. Under this background, photovoltaic power generation technology has developed rapidly and has gradually been applied in distribution networks. As the proportion of photovoltaic power generation in the power system increases, the indirect challenges brought to the operation of the distribution network are becoming increasingly apparent. Especially in mountainous areas with complex geographical conditions, due to the diversity of terrain, the variability of climate, and the complexity of the power grid structure, the evaluation of the photovoltaic carrying capacity has become an extremely challenging task.

[0003] The existing technologies mainly have the following deficiencies in the evaluation of the photovoltaic carrying capacity in the multi-dimensional source-network-load. Most of the existing evaluation methods rely on the analysis of single or a few parameters, ignoring the mutual coupling relationship between parameters in different dimensions and unable to accurately reflect the complex operating state in the mountain distribution network. In the process of data processing, traditional methods often fail to fully consider the dynamic changes of environmental factors, resulting in a large deviation between the evaluation results and the actual situation. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the problems to be solved by the present invention are that most of the existing evaluation methods rely on the analysis of single or a few parameters, ignoring the mutual coupling relationship between parameters in different dimensions and unable to accurately reflect the complex operating state in the mountain distribution network. In the process of data processing, traditional methods often fail to fully consider the dynamic changes of environmental factors, resulting in a large deviation between the evaluation results and the actual situation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network with multi-dimensional source-network-load, including:

[0007] Identifying a typical mountain with multi-dimensional source-network-load, collecting grid operation data of the typical mountain distribution network with multi-dimensions for preprocessing;

[0008] Constructing a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the typical mountain distribution network with multi-dimensional source-network-load, and analyzing and adjusting the evaluation results of the photovoltaic carrying capacity;

[0009] Constructing a visual interface to display the results of the photovoltaic carrying capacity in real time, storing the data generated during the asset evaluation process and performing access control.

[0010] As a preferred solution of the method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-grid-load in the present invention, wherein: the power grid operation data includes the power grid operation stability, the power grid load, and the photovoltaic access data;

[0011] Compare the power grid operation data with the typical mountain range one by one. If the power grid operation data meets the typical mountain range at the same time, it is determined as a multi-dimensional typical mountain of source-grid-load; if the power grid operation data does not meet the typical mountain range, it is determined as a multi-dimensional ordinary mountain of source-grid-load;

[0012] Identifying the multi-dimensional typical mountain of source-grid-load includes setting the typical mountain range according to the historical power grid operation data and expert experience, and the typical mountain range includes the power grid operation stability, the power grid load, and the photovoltaic access range.

[0013] As a preferred solution of the method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-grid-load in the present invention, wherein: collecting the power grid operation data of the multi-dimensional typical mountain distribution network includes installing smart meters in the photovoltaic power stations in the area of the multi-dimensional typical mountain distribution network of source-grid-load, and collecting the photovoltaic power generation power, the electricity consumption load, the electricity consumption data, and the voltage data of each power equipment in real time. Install a pyranometer and a temperature sensor in the photovoltaic power station to collect environmental data in real time, including solar radiation intensity and temperature data;

[0014] Collect the spatial position of each power equipment through a GPS device.

[0015] As a preferred solution of the method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-grid-load in the present invention, wherein: the preprocessing includes calculating the load demand data based on the collected electricity consumption data ;

[0016] Use the linear interpolation method to fill in the missing data of the photovoltaic power generation power, the electricity consumption load, the electricity consumption, the voltage, the environment, and the load demand data;

[0017] Use the z-score method to detect and delete the outliers in the photovoltaic power generation power, the electricity consumption load, the electricity consumption, the voltage, the environment, and the load demand data;

[0018] Use a moving average filter to denoise the photovoltaic power generation power, the electricity consumption load, the electricity consumption, the voltage, the environment, and the load demand data;

[0019] Standardize the denoised photovoltaic power generation power, electricity consumption load, electricity consumption, voltage, environment, and load demand data.

[0020] As an optimal solution of the photovoltaic carrying capacity evaluation method for the source-network-load multi-dimensional typical mountain distribution network described in the present invention, wherein: constructing a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the source-network-load multi-dimensional typical mountain distribution network includes using a constant temperature environmental laboratory to design and construct an optimal temperature laboratory, setting the initial temperature using the precise temperature control adjustment method, gradually increasing the temperature while keeping the solar radiation power unchanged, recording the output power of the photovoltaic at different temperatures, and calculating the efficiency of the photovoltaic at each temperature point. ;

[0021] Correlate the calculated efficiency values with the temperatures and plot an efficiency-temperature curve, and select the temperature corresponding to the highest efficiency point as the optimal operating temperature of the photovoltaic system;

[0022] Calculate the environmental impact factor based on the standardized environmental data , and the formula is:

[0023] ;

[0024] Wherein, is the solar radiation intensity of the i-th power device at time t, is the standard solar radiation intensity, is the temperature of the i-th power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system;

[0025] Calculate the ratio A of the photovoltaic power generation of the power device to the load demand, and the formula is:

[0026] ;

[0027] Wherein, is the photovoltaic power generation of the i-th power device at time t, N is the total number of power devices, is the load demand of the i-th power device at time t, is an extremely small positive number;

[0028] Construct a probability distribution for the standardized voltage data, and select the voltage value with the highest frequency of occurrence as the reference voltage;

[0029] Calculate the voltage change by subtracting the reference voltage from the actual voltage of the power device ;

[0030] Perform a linear transformation B on the voltage change, and the formula is:

[0031] ,

[0032] Wherein, is the reference voltage value of the power grid under normal operating conditions;

[0033] Calculate the second derivative of the load data , where is the load data at position x and time t;

[0034] Calculate the spatial derivative of the photovoltaic power data , where is the photovoltaic power generation data at position x and time t;

[0035] Combine the second derivative of the load data and the spatial derivative of the photovoltaic power data to construct a spatially varying comprehensive impact factor C, and the formula is:

[0036] ,

[0037] where and are the minimum and maximum spatial positions of the distribution network respectively, and x is the spatial position;

[0038] Construct a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the source-network-load multi-dimensional typical mountain distribution network at the i-th power equipment , and the formula is:

[0039] ,

[0040] where and are the start and end times of the evaluation period respectively.

[0041] As a preferred solution of the photovoltaic carrying capacity evaluation method for the source-network-load multi-dimensional typical mountain distribution network described in the present invention, wherein: the analysis and adjustment of the photovoltaic carrying capacity evaluation result include randomly collecting historical data for a period of time from the actually operating distribution network;

[0042] Use the weighted average method to calculate the actual observed value of the historical data;

[0043] The historical data includes photovoltaic power generation data, load demand data, environmental data, and voltage data and is preprocessed to generate a test set;

[0044] Input the test set into the photovoltaic carrying capacity evaluation model to obtain the evaluation result of the photovoltaic carrying capacity;

[0045] Set the observation threshold based on historical observation values. Subtract the actual observation value from the evaluation result of the photovoltaic carrying capacity to obtain an error value. Compare the error value with the observation threshold. If the error value is greater than the observation threshold, adjust the temperature range in the environmental impact factor. If the error value is less than or equal to the observation threshold, continue to observe.

[0046] As a preferred solution of the method for evaluating the photovoltaic carrying capacity of a source-network-load multi-dimensional typical mountain distribution network according to the present invention, wherein: the construction of the visualization interface to display the result of the photovoltaic carrying capacity in real time includes using D3.js to construct the visualization interface, using a data visualization tool to display the evaluation result of the photovoltaic carrying capacity and the error value in real time, and using Plotly.js to draw a line chart of the evaluation result of the photovoltaic carrying capacity that is updated in real time for real-time display;

[0047] The storage of the data generated during the asset evaluation of the data and the access control include storing the photovoltaic power generation, power consumption load, power consumption, voltage, environment, load demand data, and the evaluation result of the photovoltaic carrying capacity in the database in chronological order, and setting security access measures. The database backs up the stored data to the cloud and regularly detects the integrity of the stored data and the backup data.

[0048] A system for evaluating the photovoltaic carrying capacity of a source-network-load multi-dimensional typical mountain distribution network adopting the method as described above, wherein:

[0049] An acquisition unit, which identifies a source-network-load multi-dimensional typical mountain, collects the grid operation data of the multi-dimensional typical mountain distribution network for preprocessing;

[0050] An analysis unit, which constructs an evaluation model for the photovoltaic carrying capacity, evaluates the photovoltaic carrying capacity of the source-network-load multi-dimensional typical mountain distribution network, and analyzes and adjusts the evaluation result of the photovoltaic carrying capacity;

[0051] A visualization unit, which constructs a visualization interface to display the result of the photovoltaic carrying capacity in real time, stores the data generated during the asset evaluation of the data and performs access control.

[0052] A computer device, including: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the method described in any one of the present invention are implemented.

[0053] A computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by a processor, the steps of the method described in any one of the present invention are implemented.

[0054] Advantages of the present invention: Multiple operations extracted and standardized from power grid operation data, combined with the calculation of environmental impact factors and the analysis of the ratio of photovoltaic power to load demand, provide a scientific basis for the adaptation of photovoltaic systems under specific geographical and climatic conditions. By applying efficient data preprocessing methods and outlier detection techniques, the accuracy and reliability of the data are ensured. The construction of the visualization interface enables the real-time display of the results of photovoltaic carrying capacity assessment, helping operators to promptly grasp the system status and make adjustments when the error exceeds the standard. Through regular data storage and cloud backup, the long-term preservation and security of the data can be ensured. In addition, the present invention has high adaptability and scalability, can be customized and optimized according to the actual needs of different regions, and has extensive practical application value. Especially in the context of large-scale access of photovoltaic power generation, it has a significant promoting effect on optimizing power grid operation, improving the stability and economy of photovoltaic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0056] Figure 1 It is the overall flowchart of the method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-network-load provided by the first embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of the analysis process of the method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-network-load provided by the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0059] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention. This embodiment provides a method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-network-load. The method for evaluating the photovoltaic carrying capacity of a multi-dimensional typical mountain distribution network of source-network-load includes

[0060] S1: Identify the multi-dimensional typical mountain areas of the source-grid-load, and collect and preprocess the grid operation data of the multi-dimensional typical mountain distribution network.

[0061] Specifically, identifying the multi-dimensional typical mountain areas of the source-grid-load means collecting historical grid operation data, including grid operation stability, grid load, and photovoltaic access data. Set the scope of typical mountain areas according to historical grid operation data and expert experience, including the scope of grid operation stability, grid load, and photovoltaic access.

[0062] Compare the grid operation data with the scope of typical mountain areas one by one. If the grid operation data meets the scope of typical mountain areas at the same time, it is determined as a multi-dimensional typical mountain area of the source-grid-load; if the grid operation data does not meet the scope of typical mountain areas, it is determined as a multi-dimensional ordinary mountain area of the source-grid-load. The identification of the multi-dimensional typical mountain areas of the source-grid-load includes setting the scope of typical mountain areas according to historical grid operation data and expert experience, and the scope of typical mountain areas includes the scope of grid operation stability, grid load, and photovoltaic access.

[0063] The accumulation and analysis of historical data can reveal the long-term trends and seasonal variations in grid operation, helping to determine which areas have the potential to become multi-dimensional typical mountain areas of the source-grid-load. By combining historical data analysis with expert experience to set the scope of typical mountain areas, areas with high potential can be accurately defined in different dimensions. These scopes, as the basis for judgment, can significantly improve the accuracy of identifying multi-dimensional typical mountain areas of the source-grid-load, reducing the risk of misjudgment or omission. Through precise on-site data collection, the grid operation status of the area can be more realistically reflected, providing high-quality basic data for subsequent analysis and judgment, ensuring the reliability and accuracy of the identification results, making the premise of photovoltaic carrying capacity assessment more solid. The multi-dimensional grid operation data of the source-grid-load in the target area can be collected, providing dynamic feedback on the grid operation status. Compared with historical data, real-time data can reflect the current operation status and its change trend, especially in terms of the volatility brought by photovoltaic access. This has an important dynamic monitoring role in identifying typical mountain areas. Comparing the real-time collected data with the preset scope of typical mountain areas one by one can efficiently and accurately determine whether the area is a multi-dimensional typical mountain area of the source-grid-load. This comparison method can quickly screen out representative areas, providing decision support for the optimal operation of the grid.

[0064] Furthermore, collecting the grid operation data of the multi-dimensional typical mountain distribution network means installing smart meters in the photovoltaic power stations in the area of the multi-dimensional typical mountain distribution network of the source-grid-load to collect the photovoltaic power generation power, electricity load, electricity consumption, and voltage data of each power device in real time, and installing solar radiation meters and temperature sensors in the photovoltaic power stations to collect environmental data, including solar radiation intensity and temperature data.

[0065] Collect the spatial positions of each power device through GPS devices.

[0066] The real-time monitoring of the operation status of the distribution network ensures high timeliness and high precision of data. By real-time monitoring temperature data and solar radiation intensity data, the operation strategy of the photovoltaic system can be adjusted in a timely manner to ensure that the system operates within the optimal temperature range, thereby maximizing the power generation efficiency, reducing power generation losses caused by temperature fluctuations, and enhancing the overall economic benefits of the system. Real-time operation data of the distribution network in multiple dimensions are obtained, including photovoltaic power generation, electricity consumption load, electricity consumption, voltage, solar radiation intensity, and temperature data. These data can not only reflect the operation status of each dimension separately, but also reveal the mutual relationships and influences among various dimensions through integrated analysis, thus providing rich and detailed data support for the comprehensive evaluation of the photovoltaic carrying capacity. The integrated application of such multi-dimensional data enables the photovoltaic carrying capacity evaluation model to more comprehensively consider complex factors in actual operation, improving the reliability and applicability of the evaluation results.

[0067] Furthermore, preprocessing refers to calculating the load demand data based on the collected electricity consumption data , and the formula is:

[0068] ;

[0069] where is the j-th electricity consumption data of power equipment i at time t, and M is the total number of electricity consumption data.

[0070] Use linear interpolation method to fill in the missing data of photovoltaic power generation, electricity consumption load, electricity consumption, voltage, environment, and load demand data. Use the z-score method to detect and delete outliers in the photovoltaic power generation, electricity consumption load, electricity consumption, voltage, environment, and load demand data. Use a moving average filter to denoise the photovoltaic power generation, electricity consumption load, electricity consumption, voltage, environment, and load demand data. Standardize the denoised photovoltaic power generation, electricity consumption load, electricity consumption, voltage, environment, and load demand data.

[0071] By accurately predicting the load demand, the operating efficiency of the power system is optimized, energy waste is reduced, and the overall system reliability and stability are enhanced. The linear interpolation method is simple and efficient, and can provide accurate supplementary data when there is less missing data, thus maintaining the consistency of the dataset. By filling in the missing data, the integrity of the data is improved, making subsequent analysis and modeling more accurate, and ultimately enhancing the reliability of the entire evaluation process. By deleting outliers, the data quality is significantly improved, and the analysis bias caused by abnormal values is reduced. By cleaning the abnormal values in the data, the representativeness of the data is ensured, further improving the accuracy of model evaluation. By smoothing the data, short-term random fluctuations are reduced, making the data more representative and predictable. By denoising, smoother time series data is obtained, enhancing the interpretability of the data and the prediction ability of the model, and ultimately strengthening the accuracy of the photovoltaic carrying capacity assessment. By standardizing the data, the data becomes more uniform, eliminating the influence of dimensional differences on the model results, and enhancing the unity of the data and the effectiveness of the analysis.

[0072] S2: Construct a photovoltaic carrying capacity assessment model to evaluate the photovoltaic carrying capacity of a multi-dimensional typical mountainous distribution network of the power source, grid, and load, and analyze and adjust the assessment results of the photovoltaic carrying capacity.

[0073] Specifically, constructing a photovoltaic carrying capacity assessment model to evaluate the photovoltaic carrying capacity of a multi-dimensional typical mountainous distribution network of the power source, grid, and load includes using a constant temperature environmental laboratory to design and construct an optimal temperature laboratory, using the precise temperature control adjustment method to set the initial temperature, gradually increasing the temperature while keeping the solar radiation power unchanged, recording the output power of the photovoltaic at different temperatures, and calculating the efficiency of the photovoltaic at each temperature point. , the formula is:

[0074] ;

[0075] where is the input solar radiation power, is the output power of the photovoltaic at temperature T.

[0076] Correlate the calculated efficiency values with the temperature, plot the efficiency-temperature curve, and select the temperature corresponding to the highest efficiency as the optimal operating temperature of the photovoltaic system.

[0077] Based on the standardized environmental data, calculate the environmental impact factor , the formula is:

[0078] ;

[0079] where is the solar radiation intensity of the i-th power equipment at time t, is the standard solar radiation intensity, is the temperature of the i-th power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system.

[0080] Existing technologies usually rely on fixed environmental correction factors and lack real-time response to actual environmental conditions. The environmental impact factor construction method combines the actually measured optimal operating temperature and dynamic environmental data, and is flexibly adjusted through an exponential function, significantly enhancing the self-adaptability of the model. The photovoltaic efficiency usually does not decrease linearly, but as the temperature deviation increases, the rate of efficiency decrease accelerates. Using an exponential decay model to describe this non-linear relationship is the most reasonable choice. Existing technologies usually rely on fixed environmental correction factors and lack real-time response to actual environmental conditions, making it difficult to accurately reflect the impact of environmental changes at different times and locations on the photovoltaic system.

[0081] Calculate the ratio A of the photovoltaic power generation of the power device to the load demand. The formula is:

[0082] ;

[0083] where is the photovoltaic power generation of the i-th power device at time t, N is the total number of power devices, is the load demand of the i-th power device at time t, is an extremely small positive number.

[0084] Directly comparing the absolute values of the power generation and the load demand cannot effectively reflect the relative balance relationship between them. Especially when the load demand and the power generation fluctuate greatly, it may lead to distorted evaluation. Considering only the data of photovoltaic power generation or load demand alone, ignoring their mutual relationship, may lead to deviation in the evaluation of the system's carrying capacity. Through the ratio, not only can it reflect the ability of photovoltaic power generation to meet the load demand, but also provide a direct and dimensionless evaluation index, which is suitable for comparison between different power devices. Existing technologies use average values or historical data for static evaluation and lack sensitivity to real-time dynamic changes. Existing technologies only consider the photovoltaic power generation capacity under standard conditions and do not fully consider the impact of environmental factors on power generation.

[0085] Construct a probability distribution graph for the standardized voltage data, and select the voltage value with the highest frequency of occurrence as the reference voltage. Calculate the voltage change amount by subtracting the reference voltage from the actual voltage of the power device , and the formula is:

[0086] ;

[0087] where is the actual voltage of the i-th power device at time t, is the reference voltage value of the power grid under normal operating conditions.

[0088] Perform a linear transformation B on the voltage change amount. The formula is:

[0089] ;

[0090] Using linear transformation cannot capture the non-linear characteristics of the impact of voltage changes on the system. Especially when the voltage fluctuates greatly, the linear method may not be able to reflect the cumulative effect of voltage changes and the complex impact on system stability, resulting in a large deviation in the evaluation results in practical applications. The non-linear relationship ensures the adaptability of the model to voltage fluctuations of different degrees, making the evaluation of the photovoltaic carrying capacity more in line with the complexity of actual power grid operation. Using a direct linear form will weaken the response ability of the model to large voltage fluctuations and cannot accurately reflect the cumulative effect of voltage changes in the system. The linear form treats positive and negative voltage deviations asymmetrically. Through squaring, the formula reacts more strongly to voltage changes, especially when the fluctuations are large, and can accurately capture its impact on the system. Moreover, the squaring operation has a smooth non-linear characteristic, which helps to control the output stability of the system. The introduction of the non-linear function makes the model respond gently to small voltage changes and quickly to large voltage changes, accurately reflecting the dynamic requirements in actual power grid operation.

[0091] Calculate the second derivative of the load data , the formula is:

[0092] ;

[0093] where is the spatial interval between power devices, is the load data at position x + Δx and time t, is the load data at position x and time t, is the load data at position x - Δx and time t.

[0094] Calculate the spatial derivative of the photovoltaic power data , the formula is:

[0095] ;

[0096] where is the photovoltaic power generation data at position x + Δx and time t, is the photovoltaic power generation data at position x - Δx and time t.

[0097] Combining the second derivative of the load data and the spatial derivative of the photovoltaic power data, a spatially varying comprehensive impact factor C is constructed, and the formula is:

[0098] ;

[0099] where and are the minimum and maximum spatial positions of the distribution network respectively, and x is the spatial position.

[0100] Analyzing only the spatial distribution of the load power cannot fully reflect the impact of photovoltaic power access on the entire system. Especially when there is a spatial mismatch between photovoltaic power generation and load demand, considering only the load will miss the key impacts of the photovoltaic system. Analyzing only the spatial variation of photovoltaic power is also insufficient to comprehensively understand its impact on grid stability, especially in areas where the load distribution is uneven or there are significant changes. Ignoring the load distribution will lead to inaccurate assessment results. Using a simple weighted average method instead of integration may not be able to capture the cumulative effect of the spatial distribution, especially when the load and photovoltaic system are unevenly distributed, and the assessment results are prone to distortion. The integration operation can accumulate and quantify the spatial variation effects within the entire distribution network, providing a global comprehensive assessment index that reflects the impact of the distribution of load and photovoltaic power generation on stability in the entire system. Through the square root function processing, the weight of the spatial variation of photovoltaic power in the overall impact is enhanced, while avoiding excessive sensitivity to small changes, making the response of the model smoother and more stable. By combining the second and first derivatives, this solution can simultaneously analyze the spatial distribution characteristics of the load and photovoltaic power, more comprehensively reflecting the complex dynamics of the distribution network. Through the integration over the entire spatial range, this solution can comprehensively evaluate the variation effects at all spatial positions, avoiding the limitations of single-point or local analysis. Through the square root processing, this solution can enhance the flexible response ability of the model while avoiding excessive amplification of small changes, providing more reliable assessment results.

[0101] Construct a photovoltaic carrying capacity assessment model to evaluate the photovoltaic carrying capacity of power equipment in a multi-dimensional typical mountain distribution network of the source, grid, and load , and the formula is:

[0102] ;

[0103] where and are the start and end times of the evaluation period respectively.

[0104] Time integration is the only method that can accumulate dynamic changes into the overall effect. Simple weighted averaging or instantaneous value superposition will ignore the persistence and cumulative effect of changes in the time dimension. Many methods in the prior art only analyze a specific moment or average value, lacking sensitivity to the dynamic changes of the system. Through time integration, the model can capture the dynamic changes during the operation of the system and provide a more accurate assessment of the photovoltaic carrying capacity. Especially in the case of large load fluctuations, the prior art often only focuses on one or a few single-dimensional factors and lacks comprehensive integrated analysis, resulting in one-sided evaluation results. Through the comprehensive analysis of multi-dimensional factors, it can provide a global assessment of the grid operation status, avoid deviations caused by ignoring key factors, improve the integrity of the assessment, and can effectively evaluate the photovoltaic carrying capacity under different operation scenarios and conditions, adapting to more complex actual situations.

[0105] Identify the optimal operating temperature of the photovoltaic system, enabling the photovoltaic system to operate under optimal conditions, thereby maximizing the power generation efficiency and extending the service life of the system. This not only improves the economic efficiency of the photovoltaic system but also enhances the adaptability of the system under different environmental conditions. Convert complex environmental variables into quantifiable indicators, providing a scientific basis for the optimization of the photovoltaic system, enabling the operating parameters of the photovoltaic system to be dynamically adjusted to adapt to the actual environmental conditions and ensuring that the system can maintain efficient operation under various conditions. The calculation of the ratio makes the resource allocation of the power system more reasonable, reduces unnecessary power waste, and improves the power supply reliability of the overall system. Accurately identify weak power equipment in the power grid and foresee potential power supply shortage problems, thereby providing key data support for power dispatching. Accurately capture the spatial change characteristics during the operation of the power grid, especially in complex mountainous terrains. By comprehensively considering the change factors in the spatial and time dimensions, the system can achieve a more accurate assessment of the photovoltaic carrying capacity and ensure the stability and reliability of the distribution network during actual operation.

[0106] Furthermore, analyzing and adjusting the evaluation result of the photovoltaic carrying capacity means randomly collecting historical data for a period of time from the actually operating distribution network and using the weighted average method to calculate the actual observed values of the historical data; the historical data includes photovoltaic power generation data, load demand data, environmental data, and voltage data and is preprocessed to generate a test set.

[0107] Input the test set into the constructed photovoltaic carrying capacity evaluation model to obtain the evaluation result of the photovoltaic carrying capacity.

[0108] Set an observation threshold based on historical observations. Subtract the actual observed value from the evaluation result of the photovoltaic carrying capacity to obtain an error value. Compare the error value with the observation threshold. If the error value is greater than the observation threshold, then adjust the temperature range in the environmental impact factor. If the error value is less than or equal to the observation threshold, then conduct continuous observation. The specific process is asFigure 2 as shown

[0109] The generation and application of the test set can provide real operating data for evaluating the model, verify the performance of the model under different environmental and load conditions, ensure the robustness and adaptability of the model, enable the model to maintain high accuracy and stability in practical applications, avoid misjudgments caused by overfitting or data bias, objectively evaluate the prediction ability of the model by quantifying the error, and timely detect the deviations and deficiencies in the model. The dynamic adjustment mechanism ensures the adaptive ability of the model, enabling the model to continuously optimize according to the actual situation and improve its prediction accuracy. By optimizing the temperature range, the model can better adapt to the actual environmental changes, thereby improving the accuracy of photovoltaic carrying capacity assessment. The adjustment not only enhances the flexibility of the model but also provides a feasible parameter optimization path for the application of future similar systems, ensuring the best evaluation results can be obtained in different environments. Through long-term monitoring, potential performance degradation or the impact of environmental changes on the model prediction accuracy can be detected in a timely manner, and corresponding adjustments and optimizations can be made. This continuous observation mechanism ensures the stability and reliability of the model in actual operation and provides important data support and experience accumulation for future system upgrades and improvements.

[0110] S3: Construct a visual interface to display the results of photovoltaic carrying capacity in real time, store the data generated during the data asset evaluation process, and perform access control.

[0111] Specifically, constructing a visual interface to display the results of photovoltaic carrying capacity in real time means using D3.js to construct the visual interface, using a data visualization tool to display the photovoltaic carrying capacity evaluation results and error values in real time, and using Plotly.js to draw a line chart of the photovoltaic carrying capacity evaluation results with real-time updates for real-time display.

[0112] Among them, D3.js (Data-Driven Documents) is a data-driven JavaScript library used to generate complex, dynamic, and interactive data visualizations. D3.js allows developers to control the content and appearance of a page by binding data to DOM elements (such as SVG, Canvas, etc.). It supports various types of visualization charts (such as bar charts, line charts, scatter plots, tree diagrams, etc.) and provides powerful functions for data processing, transformation, and formatting. Plotly.js is an open-source JavaScript chart library built on D3.js and stack.gl, specifically for quickly building beautiful and highly interactive charts. Different from D3.js, Plotly.js provides a more simplified API and supports the rapid generation of interactive charts and dashboards. It has a rich set of built-in chart types (such as line charts, bar charts, pie charts, scatter plots, etc.) and can be easily integrated into web pages.

[0113] Dynamically generate and update the photovoltaic carrying capacity assessment results and error values, enabling users to obtain key data instantly, improving the readability and comprehensibility of the data, allowing users to quickly identify and analyze the performance of the photovoltaic system. The dynamic visualization function of D3.js can significantly improve work efficiency and reduce misjudgments caused by information asymmetry. By visually displaying the error values, the performance of the assessment model can be monitored in real time, and adjustments and optimizations can be made when necessary. This not only improves the efficiency of information transfer but also provides an intuitive basis for the continuous optimization of the assessment results, helping to ensure the best performance of the photovoltaic system in a complex environment. By updating the chart in real time, users can promptly identify any abnormal changes and quickly take corresponding measures, improving the flexibility of data analysis and enabling users at different levels to obtain the information they need according to their own requirements, and then make more accurate judgments. Through this comprehensive display and interaction function, the system further enhances its user-friendliness and application value.

[0114] Furthermore, storing the data generated during the asset evaluation process of the data and performing access control means storing the photovoltaic power generation, electricity load, electricity consumption, voltage, environment, load demand data, and photovoltaic carrying capacity assessment results in the database in chronological order and setting security access measures. The database backs up the stored data to the cloud and regularly conducts integrity checks on the stored data and the backup data.

[0115] Storing data in chronological order can facilitate subsequent analysis and tracking, ensuring data integrity and consistency. Through this systematic storage method, the long-term operation of the photovoltaic system can be comprehensively monitored, providing basic data support for historical data analysis and prediction. Through strict access control, illegal access to data and potential security threats are prevented, protecting the sensitive data of the photovoltaic system from external attacks or internal leaks. The secure access measures also make data management more standardized and transparent, enhancing the overall security of the system. Cloud backup not only provides double protection for data but also improves data persistence and recoverability. By restoring data through cloud backup, it is ensured that the evaluation data of the photovoltaic system will not be lost or damaged, thus maintaining the normal operation of the system. Through regular detection, data corruption or anomalies can be promptly discovered and repaired, ensuring data continuity and accuracy, enhancing the security and reliability of data assets, and providing strong guarantee for the long-term stable operation of the photovoltaic system.

[0116] On the other hand, this embodiment also provides a source-network-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity evaluation system, which includes:

[0117] A collection unit that identifies the source-network-load multi-dimensional typical mountain and collects and preprocesses the grid operation data of the multi-dimensional typical mountain distribution network.

[0118] An analysis unit that constructs a photovoltaic carrying capacity evaluation model, evaluates the photovoltaic carrying capacity of the source-network-load multi-dimensional typical mountain distribution network, and analyzes and adjusts the evaluation results of the photovoltaic carrying capacity.

[0119] A visualization unit that constructs a visualization interface to display the results of the photovoltaic carrying capacity in real time, stores the data generated during the data asset evaluation process, and performs access control.

[0120] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0122] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0123] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0124] Example 2, an embodiment of the present invention, provides a method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network with multiple dimensions of source, network, and load. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0125] In this embodiment, three different environmental conditions (temperate zone, mountain area, desert) are designed for evaluating the photovoltaic carrying capacity, and two methods (the prior art method and the method of the present invention) are used to test the same power grid under these three environments. The goal is to verify the stability of the two methods for evaluating the photovoltaic carrying capacity.

[0126] Environmental Design

[0127] Temperate environment: In typical temperate regions, the four seasons are distinct, the climate is mild, the temperature is moderate, and the solar radiation intensity is relatively stable.

[0128] Mountainous environment: In high-altitude areas, the temperature difference is large, the solar radiation intensity is low, and the weather fluctuates greatly.

[0129] Desert environment: High-temperature weather, small daily temperature difference, extremely high solar radiation intensity, and dry climate.

[0130] The grid carrying capacity under each environmental condition remains the same. Therefore, in the three different environments, the PV carrying capacity of the grid should have no substantial difference. The stability of the evaluation results of the two methods in different environments is mainly investigated.

[0131] Implementation process:

[0132] Environmental setting: Set up the grid and conduct tests in temperate, mountainous, and desert environments respectively. Ensure that the PV system carrying capacity in each environment is the same to ensure the comparability of the evaluation results.

[0133] Data collection: Install devices such as smart meters, radiometers, and temperature sensors to collect data on PV power generation, voltage, temperature, solar radiation intensity, etc. in real time.

[0134] Data processing and analysis:

[0135] Existing technology method: Directly calculate the ratio of PV power generation to load demand to obtain the evaluation value.

[0136] Method of the present invention: Adjust the evaluation model according to environmental data (temperature, radiation intensity, etc.) and calculate the corresponding PV carrying capacity.

[0137] Experimental comparison: Compare the evaluation results of the method of the present invention and the existing technology method in the three environments to evaluate the stability of both. Specifically, as shown in Table 1 and Table 2.

[0138] Table 1 Test results of the existing technology method

[0139]

[0140] Table 2 Test results of the method of the present invention

[0141]

[0142] The evaluation results of the traditional method show significant fluctuations. For example, in a temperate environment, the evaluation value is 1.06, while in a mountainous environment, the evaluation value is 1.18, and the evaluation value in a desert environment further rises to 1.24. This indicates that the traditional method fails to effectively consider the impact of the environment, resulting in significant fluctuations in the evaluation results under different environments. The traditional method fails to dynamically adjust environmental factors, leading to a high sensitivity of its evaluation results to environmental changes and large fluctuations.

[0143] In contrast, the evaluation results of the method of the present invention show smaller fluctuations. In a temperate environment, the evaluation value is 1.08, in a mountainous environment it is 1.09, and in a desert environment it is 1.07. This indicates that the method of the present invention can adapt to changes in different environmental conditions, dynamically adjust the evaluation model, and thus maintain relatively consistent evaluation results in the three environments. Although there are small fluctuations, these fluctuations are within a reasonable range and can more accurately reflect the photovoltaic carrying capacity.

[0144] It can be seen from the data that the method of the present invention has significantly smaller fluctuations compared to the traditional method. The average evaluation value of the traditional method is 1.20, while the average evaluation value of the method of the present invention is 1.07, and the fluctuation range is significantly reduced. This indicates that the method of the present invention can better adapt to environmental changes, thereby providing more stable and reliable evaluation results of the photovoltaic carrying capacity.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A multi-dimensional source-grid-load photovoltaic carrying capacity assessment method for typical mountain distribution networks, characterized by: include: Identify multi-dimensional typical mountainous areas of source, grid and load, and collect multi-dimensional typical mountainous distribution network operation data for pre-processing; Construct a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyze and adjust the photovoltaic carrying capacity assessment results; Build a visual interface to display the results of PV carrying capacity in real time, store the data generated during the data asset assessment process and perform access control; The photovoltaic carrying capacity evaluation model is constructed to evaluate the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, including using a constant temperature environment laboratory to design and construct an optimal temperature laboratory, using a precise temperature control method to set the initial temperature, gradually increasing the temperature while keeping the solar radiation power constant, recording the output power of the photovoltaic temperature, and calculating the efficiency of the photovoltaic at each temperature point. ; The calculated efficiency value is matched with the temperature, and the efficiency-temperature curve is drawn. The temperature corresponding to the highest efficiency point is selected as the optimal operating temperature of the photovoltaic system; Calculate environmental impact factors based on standardized environmental data , the formula is: ； in, is the solar radiation intensity of the ith power equipment at time t, is the standard solar radiation intensity, is the temperature of the ith power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system; Calculate the ratio A of the photovoltaic power generation power of the power equipment to the load demand, the formula is: ； in, is the photovoltaic power generation power of the ith power device at time t, N is the total number of power devices, is the load demand of the ith power equipment at time t, is a very small positive number; The standardized voltage data is constructed into a probability distribution, and the voltage value with the highest frequency is selected as the reference voltage; Calculate the voltage change by subtracting the reference voltage from the actual voltage of the power equipment ; The voltage change is linearly transformed B, and the formula is: ， in, It is the reference voltage value of the power grid under normal working condition; Calculate the second derivative of load data ;in is the load data at position x and time t; Calculate the spatial derivative of photovoltaic power data ;in is the photovoltaic power data at position x and time t; The second-order derivative of load data and the spatial derivative of photovoltaic power data are combined to construct the comprehensive impact factor C of spatial variation. The formula is: ， in, and are the minimum and maximum spatial positions of the distribution network, respectively, and x is the spatial position; Construct a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the i-th power equipment in a typical mountain distribution network with multiple dimensions of source, grid and load. , the formula is: ， in, and are the start and end time of the evaluation period, respectively.

2. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 1, characterized in that: The grid operation data includes grid operation stability, grid load and photovoltaic access data; The grid operation data is compared with the typical mountain range one by one. If the grid operation data meets the typical mountain range at the same time, it is determined to be a typical mountain with multiple dimensions of source, grid and load; if the grid operation data does not meet the typical mountain range, it is determined to be a common mountain with multiple dimensions of source, grid and load; The identifying of multi-dimensional typical mountains of source, grid and load includes setting a typical mountain range based on historical grid operation data and expert experience, and the typical mountain range includes grid operation stability, grid load and photovoltaic access range.

3. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 2 is characterized by: The collecting of grid operation data of a typical multi-dimensional mountain distribution network includes installing smart meters in photovoltaic power stations within the source-grid-load multi-dimensional typical mountain distribution network area to collect photovoltaic power generation power, power load, power consumption data and voltage data of each power device in real time, and installing solar radiation meters and temperature sensors in photovoltaic power stations to collect environmental data in real time, including solar radiation intensity and temperature data; The spatial location of each power device is collected through GPS equipment.

4. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 3 is characterized by: The preprocessing includes calculating load demand data based on the collected power consumption data. ; Linear interpolation was used to fill in missing data for photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data; Use the z-score method to detect and delete outliers in the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data; Use a moving average filter to denoise the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data; The denoised photovoltaic power generation, power load, power consumption, voltage, environment and load demand data are standardized.

5. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 4, characterized in that: The analysis and adjustment of the photovoltaic load capacity assessment results includes randomly collecting historical data within a period of time from the actual operating distribution network; Use the weighted average method to calculate the actual observed values ​​of historical data; The historical data includes photovoltaic power generation data, load demand data, environmental data and voltage data and is pre-processed to generate a test set; Inputting the test set into the photovoltaic carrying capacity evaluation model to obtain an evaluation result of the photovoltaic carrying capacity; The observation threshold is set based on the historical observation value, and the error value is obtained by subtracting the actual observation value from the evaluation result of the photovoltaic carrying capacity. The error value is compared with the observation threshold. If the error value is greater than the observation threshold, the temperature range in the environmental impact factor is adjusted; if the error value is less than or equal to the observation threshold, continuous observation is carried out.

6. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 5, characterized in that: The construction of a visualization interface to display the results of the photovoltaic carrying capacity in real time includes using D3.js to construct a visualization interface, using a data visualization tool to display the photovoltaic carrying capacity evaluation results and error values ​​in real time, and using Plotly.js to draw a line graph of the photovoltaic carrying capacity evaluation results that is updated in real time for real-time display; The data generated during the storage of data asset assessment and access control include storing photovoltaic power generation power, power load, power consumption, voltage, environment, load demand data and photovoltaic carrying capacity assessment results in the database in chronological order, and setting security access measures. The database will back up the stored data in the cloud and regularly perform integrity checks on the stored data and backup data.

7. A source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment system using the method as described in any one of claims 1 to 6, characterized in that: The acquisition unit identifies multi-dimensional typical mountainous areas of source, grid and load, and collects the grid operation data of multi-dimensional typical mountainous distribution networks for pre-processing; The analysis unit builds a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyzes and adjusts the photovoltaic carrying capacity assessment results; The visualization unit builds a visualization interface to display the results of PV carrying capacity in real time, stores the data generated during the data asset assessment process and performs access control.

8. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 1-6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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