A method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas.

Abstract

This invention discloses a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas, comprising: S1, data collection and classification, i.e., collecting key meteorological data, PV output data, distribution network operation data, and PV-related installation, scrapping, and operation data of active power distribution areas; S2, performing data preprocessing and feature extraction on the data collected in step S1; S3, establishing an indicator system and developing a model based on the features extracted in step S2; S4, performing model verification, optimization, and case analysis on the model obtained in step S3; and S5, applying the results and optimizing strategies to the verified and optimized model from step S4. This invention not only improves the monitoring and management accuracy of PV power generation systems but also greatly enhances the stability and reliability of the power grid when renewable energy penetration is high, effectively promoting the optimization of the energy structure and the sustainability of energy use.

Description

Technical Field

[0001] This invention relates to the field of electricity anomaly identification, and in particular to a method for constructing evaluation indicators for the photovoltaic acceptance capacity of active power distribution areas. Background Technology

[0002] As a crucial component of renewable energy, photovoltaic (PV) power generation systems are influenced by various environmental factors, particularly weather conditions. For example, factors such as solar radiation intensity, temperature variations, cloud cover, and wind speed directly affect the power generation efficiency and output power of PV cells, leading to volatility and unpredictability in PV output. These changes not only impact electricity production but also pose challenges to grid management and operation. Particularly in active power distribution areas, the high volatility of PV power generation necessitates efficient peak-shaving and load management capabilities from the grid.

[0003] In existing technical solutions, most power grid operation and management systems adopt traditional power grid management strategies, which focus on static power grid design and operation modes. However, these modes are often unable to effectively cope with the dynamic changes caused by the volatility of photovoltaic power generation. Furthermore, traditional methods rely mainly on empirical rules or simple statistical models when dealing with uncertainties and anomalies in photovoltaic power generation, which limits their adaptability and flexibility in complex environments.

[0004] Furthermore, although machine learning-based predictive models have been explored in some areas to predict photovoltaic output and attempt to optimize grid operation, these methods still face challenges in practical applications. While machine learning models can process large amounts of data and identify complex patterns, they typically require substantial training data, and their interpretability and adaptability to new changes are relatively weak. Moreover, these models often ignore the specific operational rules and safety standards of the grid, potentially leading to discrepancies between the expected and actual application results.

[0005] Therefore, developing an evaluation method that comprehensively considers the potential of photovoltaic (PV) power generation, the grid's peak-shaving potential, and the potential for PV integration is particularly important. This method should integrate the explicitness of traditional grid management strategies with the data processing capabilities of machine learning algorithms, providing a more dynamic and adaptive grid management tool to better address the challenges of PV power generation in active distribution areas and improve the overall operational efficiency and reliability of the grid. This approach enables a comprehensive assessment of the grid's peak-shaving capacity and distributed generation integration capacity, thereby providing a scientific basis for grid planning and operational decisions. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas. This method can solve the problem that existing power grid management strategies cannot fully realize the identification, analysis, and utilization of PV power generation potential, power grid peak-shaving potential, and PV consumption, resulting in inaccurate power grid allocation and insufficient resource utilization.

[0007] To achieve the above objectives, this invention provides a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas, comprising:

[0008] S1, Data Acquisition and Classification, which involves collecting key meteorological data, photovoltaic output data, distribution network operation data, and photovoltaic-related installation, scrapping, and operation data for active power distribution areas;

[0009] S2, perform data preprocessing and feature extraction on the data collected in step S1;

[0010] S3. Based on the features extracted in step S2, establish an indicator system and develop a model.

[0011] S4. Perform model validation, optimization, and case analysis on the model obtained in step S3.

[0012] S5. Apply the results and optimize the strategy based on the validated and optimized model from step S4.

[0013] Optionally, in step S1, the key meteorological data of the active monitoring area includes: the current season, weather type, solar radiation intensity, temperature, humidity, cloud cover, and wind speed;

[0014] The photovoltaic output data includes: photovoltaic power generation;

[0015] The power distribution network operation data includes: electricity load data;

[0016] The photovoltaic-related installation, scrapping, and operation data include: photovoltaic installed capacity.

[0017] Optionally, step S2 includes:

[0018] S2.1 Performs outlier removal, missing data completion, data normalization and standardization, and time alignment of time series data on the collected data.

[0019] S2.2 Measure and calculate the daily average radiation intensity, radiation intensity fluctuation rate, daily average temperature, daily average humidity, temperature fluctuation rate, average cloud cover rate, and average wind speed of the active power distribution area under a certain season and a certain weather type to measure the photovoltaic power generation potential of the active power distribution area.

[0020] S2.3 Using the photovoltaic power generation, photovoltaic installed capacity, and electricity load data of the active distribution area, calculate the photovoltaic output power stability and maximum photovoltaic penetration rate of the region to assess the grid peak-shaving potential of the active distribution area.

[0021] S2.4 Construct indicators for the proportion of photovoltaic power generation in active power distribution areas and the utilization rate of photovoltaic power generation in active power distribution areas to assess the photovoltaic absorption potential of active power distribution areas;

[0022] S2.5 Input the indicators that measure the photovoltaic power generation potential of the active transformer area, the indicators that assess the grid peak-shaving potential of the active transformer area, and the indicators that assess the photovoltaic absorption potential of the active transformer area into the XGboost model to obtain the importance ranking results of all indicators.

[0023] Optionally, the formula for calculating the radiation intensity fluctuation rate in step S2.2 is as follows:

[0024]

[0025] In equation (1), α 辐射 μ is the standard deviation of the total radiation intensity throughout the day. 辐射 This represents the average radiation intensity over the entire day.

[0026] The formula for calculating the temperature fluctuation rate is:

[0027]

[0028] In equation (2), α 温度 μ represents the standard deviation of the temperature throughout the day. 温度 This represents the average temperature over the entire day.

[0029] Optionally, the formula for calculating the photovoltaic output power stability G1 in step S2.3 is:

[0030]

[0031] In equation (3), P i (t) represents the photovoltaic power generation of the active power distribution area at time t, P 平均 It is the average power generation of the active power distribution area during the same period of the day; N in the summation symbol indicates that there are N data points at time t;

[0032] The formula for calculating the maximum photovoltaic penetration rate G2 is as follows:

[0033]

[0034] Optionally, the formula for calculating the proportion of active photovoltaic power generation R1 in step S2.4 is as follows:

[0035]

[0036] In equation (5), t is time; P i (t) represents the photovoltaic power generation of the active transformer area at time t; D j (t) represents the power of the active distribution area's electrical load at time t; z in the summation symbol indicates that there are z data points at time t; m in the summation symbol indicates that there are m data points at time t;

[0037] The formula for calculating the utilization rate R2 of the active photovoltaic power generation area is as follows:

[0038]

[0039] In equation (6), D i (t) represents the available power generation of the distributed power source at time t, i.e., the photovoltaic installed capacity; s in the summation symbol indicates that there are s data points at time t.

[0040] Optionally, step S3 includes:

[0041] S3.1 Statistical analysis and correlation test are performed on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak-shaving potential and photovoltaic absorption potential, and indicator data with correlation coefficient values ​​exceeding the threshold are extracted.

[0042] S3.2, The extracted indicator data are evaluated using a comprehensive evaluation system for weight assessment;

[0043] S3.3 Perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight values ​​of the indicator data.

[0044] Optionally, step S3.3 includes:

[0045] S3.3.1, The AHP (Analytic Hierarchy Process) is used to construct a multi-level analysis framework consisting of a target layer, a criterion layer, and a feature layer for the data indicators after weight evaluation.

[0046] S3.3.2, establish a comparison matrix between the criterion layer and the feature layer, and use the scaling method in the Saaty scaling system to compare the importance of the indicator data after weight evaluation, so as to form a consistency judgment matrix;

[0047] S3.3.3 Calculate the maximum eigenvalue and consistency ratio CI of the consistency judgment matrix, and ensure that the consistency ratio CI ≤ 0.1;

[0048] S3.3.4, obtain the final weight values ​​of the indicator data.

[0049] Optionally, the weight calculation formula for the final indicator data in step S3.3.4 is as follows:

[0050]

[0051] In equation (7), w i Let a represent the weight of the i-th feature, i.e., the data indicator in the consistency judgment matrix. ij It is an element in the consistency judgment matrix, λ max Let be the largest eigenvalue in the consistency judgment matrix, and n be the order of the consistency judgment matrix.

[0052] Optionally, in step S3.1, statistical methods such as Pearson correlation coefficient and Spearman rank correlation are used to obtain the correlation coefficient values ​​of each indicator data in assessing photovoltaic acceptance capacity.

[0053] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This invention provides a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas. This method combines PV power generation characteristics with grid operation data, analyzing not only historical data but also considering the impact of real-time environmental changes on PV output. This approach is more comprehensive than traditional static evaluation models and can more accurately predict the performance of PV systems under different environmental conditions.

[0055] 2. This invention provides a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas. By using machine learning algorithms to analyze and optimize the grid's peak-shaving response, it improves the grid's adaptability to high load and high PV output periods. Compared with existing technologies, this strategy utilizes renewable energy more effectively while reducing dependence on traditional power generation resources.

[0056] 3. This invention provides a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas. By offering a decision support system that integrates business rules and advanced data analysis, grid operators can more effectively plan and manage operations. Implementing this method can help the grid better cope with the volatility of renewable energy and optimize grid operating efficiency and economics. Attached Figure Description

[0057] Figure 1 This is an operation flowchart of the method for constructing evaluation indicators for the photovoltaic reception capacity of active power distribution areas according to the present invention;

[0058] Figure 2 This is the result of the importance of the XGboost feature variables in this invention;

[0059] Figure 3 This is an example diagram illustrating a key aspect of photovoltaic grid connection in this invention. Detailed Implementation

[0060] The following will be combined with the appendix Figures 1-3 The technical content, structural features, objectives and effects of the present invention will be described in detail through preferred embodiments.

[0061] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0062] In the description of this invention, it should be noted that the terms "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0063] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0064] This invention provides a method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas, such as... Figure 1 As shown, the method for constructing this evaluation index includes:

[0065] S1: Data collection and classification;

[0066] S2: Perform data preprocessing and feature extraction on the data collected in step S1;

[0067] S3: Based on the features extracted in step S2, establish an indicator system and develop a model;

[0068] S4: Perform model validation, optimization, and case analysis on the model obtained in step S3;

[0069] S5: Apply the results and optimize the strategy based on the validated and optimized model from step S4.

[0070] Specifically:

[0071] In step S1, the collected data includes: key meteorological data of active power distribution areas, photovoltaic output data, power distribution network operation data, photovoltaic-related installation, scrapping and operation data, etc.

[0072] Furthermore, the key meteorological data for the active power distribution area includes: current season, weather type, solar radiation intensity, temperature (including maximum and minimum temperatures), humidity, cloud cover, wind speed, etc.; the photovoltaic output data includes: photovoltaic output power, photovoltaic power generation, etc.; the power distribution network operation data includes: peak shaving operation records and electricity load data; the photovoltaic-related installation, scrapping, and operation data includes: photovoltaic installed capacity.

[0073] Step S2 specifically includes the following steps:

[0074] S2.1 performs outlier removal, missing data completion, data normalization and standardization, and time alignment of time series data on the collected data.

[0075] S2.2 Measure and calculate the daily average radiation intensity, radiation intensity fluctuation rate, daily average temperature, daily average humidity, temperature fluctuation rate, average cloud cover rate, and average wind speed of the active power distribution area under a certain season and a certain weather type to measure the photovoltaic power generation potential of the active power distribution area; wherein, the daily average radiation intensity, daily average temperature, daily average humidity, average cloud cover rate, and average wind speed are all data that are readily available using existing technologies, and therefore will not be specifically described.

[0076] Among them, radiation intensity fluctuation rate and temperature fluctuation rate are key environmental variables in the key meteorological data.

[0077] Furthermore, the formula for calculating the radiation intensity fluctuation rate is as follows:

[0078]

[0079] In the formula, α 辐射 μ is the standard deviation of the total radiation intensity throughout the day. 辐射 This represents the average radiation intensity throughout the day.

[0080] Furthermore, the formula for calculating the temperature fluctuation rate is as follows:

[0081]

[0082] In the formula, α 温度 μ represents the standard deviation of the temperature throughout the day. 温度 This represents the average temperature over the entire day.

[0083] S2.3 Using the photovoltaic power generation, photovoltaic installed capacity, and electricity load data of the active power distribution area, calculate the photovoltaic output power stability and maximum photovoltaic penetration rate of the region to assess the grid peak-shaving potential of the active power distribution area.

[0084] The formula for calculating the photovoltaic output power stability G1 is as follows:

[0085]

[0086] In the formula, P i (t) represents the photovoltaic power generation of the active power distribution area at time t, P 平均 It is the average power generation of the active power distribution area during the same period of the day; N in the summation symbol indicates that there are N data points at time t.

[0087] Among them, the maximum photovoltaic penetration rate G2 is a key indicator used to assess the ratio of the maximum photovoltaic power generation that the grid can safely accommodate to the maximum load, thereby helping grid planners and operators to determine the electrical energy that can be absorbed from the photovoltaic power generation system without affecting grid stability and power supply reliability. The formula for calculating the maximum photovoltaic penetration rate G2 is as follows:

[0088]

[0089] In the formula, the maximum acceptable photovoltaic power refers to the maximum photovoltaic output power that the power grid can accept under the premise of maintaining the stability and safe operation of the power grid. The maximum acceptable photovoltaic power is obtained by power grid analysis, which includes power flow analysis and stability analysis. The maximum load refers to the highest power demand of the power grid in a specific time period (usually the peak period). The maximum load is obtained through the historical operation data of the distribution network.

[0090] S2.4 Construct indicators for the proportion of photovoltaic power generation in active power distribution areas and the utilization rate of photovoltaic power generation in active power distribution areas to assess the photovoltaic absorption potential of active power distribution areas.

[0091] The term "active photovoltaic power generation ratio" refers to the proportion of active photovoltaic power generation in a given area to the total electricity consumption of the region within a certain period. The formula for calculating the active photovoltaic power generation ratio R1 is as follows:

[0092]

[0093] In the formula, t represents time; P i (t) represents the photovoltaic power generation of the active transformer area at time t; D j (t) represents the power of the active transformer area's electrical load at time t; z in the summation symbol indicates that there are z data points at time t; m in the summation symbol indicates that there are m data points at time t.

[0094] The active photovoltaic power generation utilization rate refers to the real-time absorption of photovoltaic power within the active photovoltaic power generation area. The formula for calculating the active photovoltaic power generation utilization rate R2 is as follows:

[0095]

[0096] In the formula, D i (t) represents the available power generation of the distributed power source at time t, i.e., the photovoltaic installed capacity; s in the summation symbol indicates that there are s data points at time t.

[0097] S2.5 Input the indicators that measure the photovoltaic power generation potential of the active transformer area, the indicators that assess the grid peak-shaving potential of the active transformer area, and the indicators that assess the photovoltaic absorption potential of the active transformer area into the XGboost model to obtain the importance ranking results of all indicators.

[0098] Specifically, the above indicators, including average daily radiation intensity, average daily temperature, average daily humidity, average cloud cover, average wind speed, active power grid photovoltaic (PV) power generation utilization rate, radiation intensity fluctuation rate, temperature fluctuation rate, PV output power stability, maximum PV penetration rate, active power grid PV power generation ratio, weather type, season type, and the power feedback rate obtained by combining the above indicator data, are input into the XGboost (Limited Gradient Boosting) model for regression to obtain the importance values ​​corresponding to each indicator. Based on these importance values, the indicators affecting the PV acceptance capacity of active power grids are ranked. The results are as follows: Figure 2 As shown.

[0099] In step S3, after obtaining the indicator data for the three dimensions of photovoltaic power generation potential, grid peak-shaving potential, and photovoltaic absorption potential, statistical analysis and multi-criteria decision-making techniques are used to construct an indicator system and determine the weights.

[0100] S3.1 Statistical analysis and correlation test are performed on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak-shaving potential and photovoltaic absorption potential, and indicator data with absolute values ​​of correlation coefficients exceeding the threshold are extracted.

[0101] S3.1.1, apply advanced statistical methods to conduct a comprehensive review of the indicator data, including but not limited to mean analysis, variance analysis and outlier detection, in order to clean up the dataset and ensure the reliability of subsequent analysis.

[0102] S3.1.2, using Pearson correlation coefficient and Spearman rank correlation, the correlation coefficient values ​​of each indicator data in assessing photovoltaic acceptance capacity were obtained. The results are shown in Table 1 below:

[0103] Table 1

[0104]

[0105] S3.1.3 Extract indicator data whose absolute value of the correlation coefficient exceeds the preset threshold.

[0106] In an embodiment of the present invention, index data with correlation coefficients exceeding 0.3 and high importance thresholds are extracted from the table above. The results are shown in Table 2 below:

[0107] Table 2

[0108]

[0109] S3.2, The extracted indicator data are evaluated using a comprehensive evaluation system to assess their weights.

[0110] Specifically, this invention introduces a comprehensive evaluation mechanism, using a standardized scoring table with a range of 1 to 10 points, where 10 indicates that the feature is of extremely high importance for the assessment of photovoltaic acceptance capacity.

[0111] S3.3 Perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight values ​​of the indicator data.

[0112] S3.3.1, the Analytic Hierarchy Process (AHP) is used to construct a multi-level analysis framework consisting of a target layer, a criterion layer, and a feature layer for the data indicators after weight evaluation.

[0113] S3.3.2 Establish a comparison matrix between the criterion layer and the feature layer, and use the scaling method in the Saaty scaling system to compare the importance of the indicator data after weight evaluation, so as to form a consistency judgment matrix.

[0114] S3.3.3 Calculate the maximum eigenvalue and consistency ratio CI of the consistency judgment matrix, and ensure that the consistency ratio CI ≤ 0.1.

[0115] S3.3.4, the final weight values ​​of the indicator data are obtained through the following calculation formula (7):

[0116]

[0117] In the formula, w i a represents the weight of the i-th feature (i.e., the data index in the consistency judgment matrix). ij It is an element in the consistency judgment matrix, λ max Let be the largest eigenvalue in the consistency judgment matrix, and n be the order of the consistency judgment matrix.

[0118] This process enables precise quantification of feature weights, ensuring the balance of the comprehensive evaluation model and the effectiveness of decision-making.

[0119] Step S4 specifically includes the following steps:

[0120] S4.1 After the assessment model for the photovoltaic acceptance capacity of active power distribution areas is completed, an adjustment, optimization, prediction and monitoring mechanism for the model is established to regularly collect new operating data and update the parameters and algorithms of the model.

[0121] S4.2 Compare and analyze the prediction results of the model with the actual operating data of the active distribution area power grid to evaluate the accuracy of the model in predicting photovoltaic power output, power grid load fluctuations and assessing power grid stability. By integrating multi-dimensional meteorological data and refining spatial resolution and time step, the model can better capture and predict the dynamic characteristics of the local power grid, thereby further improving the prediction accuracy and robustness of the model.

[0122] In step S5, as described above... Figure 3 As shown, a validated and optimized assessment model for the photovoltaic (PV) integration capacity of active distribution areas is used to score and evaluate the scale and layout of PV construction in different regions. The impact of different expansion schemes on grid stability and economy is simulated, and strategies for PV power generation construction, expansion and grid peak shaving are optimized. This helps to achieve coordinated development of distributed power sources and the main grid, reduce the impact of PV access on grid load fluctuations, and improve the flexibility and reliability of grid operation planning.

[0123] In summary, the evaluation index construction method for the photovoltaic integration capacity of active power distribution areas provided by this invention can not only improve the monitoring and management accuracy of photovoltaic power generation systems, but also greatly enhance the stability and reliability of the power grid when renewable energy penetration is high, and effectively promote the optimization of energy structure and the sustainability of energy use.

[0124] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for constructing evaluation indicators for the photovoltaic (PV) uptake capability of active power distribution areas, characterized in that, Include: S1, Data Acquisition and Classification, which involves collecting key meteorological data, photovoltaic output data, distribution network operation data, and photovoltaic-related installation, scrapping, and operation data for active power distribution areas; S2, perform data preprocessing and feature extraction on the data collected in step S1; S3. Based on the features extracted in step S2, establish an indicator system and develop a model. S4. Perform model validation, optimization, and case analysis on the model obtained in step S3. S5. Apply the results and optimize the strategy of the model that has been verified and optimized in step S4. Step S2 includes: S2.1 Performs outlier removal, missing data completion, data normalization and standardization, and time alignment of time series data on the collected data. S2.2 Measure and calculate the daily average radiation intensity, radiation intensity fluctuation rate, daily average temperature, daily average humidity, temperature fluctuation rate, average cloud cover rate, and average wind speed of the active power distribution area under a certain season and a certain weather type to measure the photovoltaic power generation potential of the active power distribution area. S2.3 Using the photovoltaic power generation, photovoltaic installed capacity, and electricity load data of the active distribution area, calculate the photovoltaic output power stability and maximum photovoltaic penetration rate of the region to assess the grid peak-shaving potential of the active distribution area. S2.4 Construct indicators for the proportion of photovoltaic power generation in active power distribution areas and the utilization rate of photovoltaic power generation in active power distribution areas to assess the photovoltaic absorption potential of active power distribution areas; S2.5, input the indicators that measure the photovoltaic power generation potential of the active transformer area, the indicators that assess the grid peak-shaving potential of the active transformer area, and the indicators that assess the photovoltaic absorption potential of the active transformer area into the XGboost model to obtain the importance ranking results of all indicators; Step S3 includes: S3.1 Statistical analysis and correlation test are performed on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak-shaving potential and photovoltaic absorption potential, and indicator data with correlation coefficient values ​​exceeding the threshold are extracted. S3.2, The extracted indicator data are evaluated using a comprehensive evaluation system for weight assessment; S3.3, Perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight values ​​of the indicator data; Step S3.3 includes: S3.3.1, The AHP (Analytic Hierarchy Process) is used to construct a multi-level analysis framework consisting of a target layer, a criterion layer, and a feature layer for the data indicators after weight evaluation. S3.3.2, establish a comparison matrix between the criterion layer and the feature layer, and use the scaling method in the Saaty scaling system to compare the importance of the indicator data after weight evaluation, so as to form a consistency judgment matrix; S3.3.3 Calculate the maximum eigenvalue and consistency ratio CI of the consistency judgment matrix, and ensure that the consistency ratio CI ≤ 0.1; S3.3.4, obtain the final weight values ​​of the indicator data; The final weight calculation formula for the indicator data in step S3.3.4 is as follows: In equation (7), w i Let a represent the weight of the i-th feature, i.e., the data indicator in the consistency judgment matrix. ij It is an element in the consistency judgment matrix, λ max Let be the largest eigenvalue in the consistency judgment matrix, and n be the order of the consistency judgment matrix; In step S3.1, statistical methods such as Pearson correlation coefficient and Spearman rank correlation are used to obtain the correlation coefficient values ​​of each indicator data in assessing photovoltaic acceptance capacity.

2. The method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas as described in claim 1, characterized in that, In step S1, the key meteorological data of the active weather station area includes: current season, weather type, sunshine intensity, temperature, humidity, cloud cover, and wind speed; The photovoltaic output data includes: photovoltaic power generation; The power distribution network operation data includes: electricity load data; The photovoltaic-related installation, scrapping, and operation data include: photovoltaic installed capacity.

3. The method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas as described in claim 1, characterized in that, The formula for calculating the radiation intensity fluctuation rate in step S2.2 is as follows: In equation (1), α 辐射 μ is the standard deviation of the total radiation intensity throughout the day. 辐射 This represents the average radiation intensity over the entire day. The formula for calculating the temperature fluctuation rate is: In equation (2), α 温度 μ represents the standard deviation of the temperature throughout the day. 温度 This represents the average temperature over the entire day.

4. The method for constructing evaluation indicators for the photovoltaic (PV) integration capacity of active power distribution areas as described in claim 1, characterized in that, The formula for calculating the photovoltaic output power stability G1 in step S2.3 is as follows: In equation (3), P i (t) represents the photovoltaic power generation of the active power distribution area at time t, P 平均 It is the average power generation of the active power distribution area during the same period of the day; N in the summation symbol indicates that there are N data points at time t; The formula for calculating the maximum photovoltaic penetration rate G2 is as follows:

5. The method for constructing evaluation indicators for the photovoltaic (PV) uptake capacity of active power distribution areas as described in claim 1, characterized in that, The formula for calculating the proportion of active photovoltaic power generation R1 in step S2.4 is as follows: In equation (5), t is time; P i (t) represents the photovoltaic power generation of the active transformer area at time t; D j (t) represents the power of the active distribution area's electrical load at time t; z in the summation symbol indicates that there are z data points at time t; m in the summation symbol indicates that there are m data points at time t; The formula for calculating the utilization rate R2 of the active photovoltaic power generation area is as follows: In equation (6), D i (t) represents the available power generation of the distributed power source at time t, i.e., the photovoltaic installed capacity; s in the summation symbol indicates that there are s data points at time t.

Images