Evaluation index construction method for photovoltaic acceptance capability of active transformer area

Abstract

The invention discloses an evaluation index construction method for the photovoltaic acceptance capability of an active transformer area, and the method comprises the steps: S1, data collection and classification: collecting key meteorological data, photovoltaic output data, power distribution network operation data, and photovoltaic-related installation scrapping and operation data of the active transformer area; s2, performing data preprocessing and feature extraction on the data acquired in the step S1; s3, performing index system establishment and model development according to the features extracted in the step S2; s4, performing model verification optimization and case analysis on the model obtained in the step S3; and S5, performing result application and strategy optimization on the model verified and optimized in the step S4. According to the invention, the monitoring and management precision of the photovoltaic power generation system can be improved, the stability and reliability of the power grid during high penetration of renewable energy sources can be greatly enhanced, and the optimization of an energy structure and the sustainability of energy use are effectively promoted.

Description

Technical Field

[0001] The present invention relates to the field of power consumption anomaly recognition, and in particular to a method for constructing an evaluation index of photovoltaic acceptance capacity of an active station area. Background Art

[0002] As an important component of renewable energy, the output characteristics of photovoltaic power generation systems are affected by a variety of environmental factors, especially weather conditions. For example, factors such as sunshine intensity, temperature changes, cloud cover and wind speed will directly affect the power generation efficiency and output power of photovoltaic cells, resulting in volatility and unpredictability of photovoltaic output. These changes not only affect power production, but also bring challenges to the management and operation of the power grid, especially in active areas. The high volatility of photovoltaic power generation requires the power grid to have efficient peak load regulation and load management capabilities.

[0003] Among the 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. In addition, traditional methods mainly rely on empirical rules or simple statistical models when dealing with the uncertainty and abnormal conditions of photovoltaic power generation, which limits their adaptability and flexibility in complex environments.

[0004] In addition, although machine learning-based prediction models have begun to be explored in some areas to predict PV output and try to optimize grid operation, these methods still face challenges in practical application. Although machine learning models can process large amounts of data and identify complex patterns, they usually require a large amount of training data, and the models are less interpretable and adaptable to new changes. In addition, these models often ignore the specific business rules and safety standards of grid operation, which may lead to deviations from expectations in actual operation.

[0005] Therefore, it is particularly important to develop an evaluation method that comprehensively considers the potential of photovoltaic power generation, the potential of grid peak load regulation, and the potential of photovoltaic acceptance. This method should be able to integrate the clarity of traditional grid management strategies and the data processing capabilities of machine learning algorithms to provide a more dynamic and adaptable grid management tool, so as to better cope with the challenges of photovoltaic power generation in active areas and improve the overall operational efficiency and reliability of the grid. Through this method, a comprehensive evaluation of the grid peak load regulation capacity and the distributed power generation acceptance capacity can be achieved, thereby providing a scientific basis for grid planning and operation decisions. Summary of the invention

[0006] The purpose of the present invention is to provide a method for constructing an evaluation index of the photovoltaic acceptance capacity of an active substation, which can solve the problem that the existing power grid management strategy cannot fully realize the identification, analysis and utilization of photovoltaic power generation potential, power grid peak-shaving potential and photovoltaic absorption, thereby causing inaccurate power grid allocation and insufficient resource utilization.

[0007] To achieve the above-mentioned purpose, the present invention provides a method for constructing an evaluation index of photovoltaic acceptance capacity of an active area, comprising:

[0008] S1, data collection and classification, that is, collecting key meteorological data of active areas, photovoltaic output data, distribution network operation data, photovoltaic-related installation scrapping and operation data;

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

[0010] S3, establishing an indicator system and developing a model based on the features extracted in step S2;

[0011] S4, performing model verification optimization and case analysis on the model obtained in step S3;

[0012] S5, applying the results and optimizing the strategies of the model verified and optimized in step S4.

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

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

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

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

[0017] Optionally, step S2 comprises:

[0018] S2.1, remove outliers, fill in missing data, normalize and standardize the collected data, and align the time series data;

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

[0020] S2.3, using the PV power generation, PV installed capacity, and power load data of the active area, calculate the PV output power stability and maximum PV penetration rate of the area to assess the peak load regulation potential of the grid in the active area;

[0021] S2.4, construct the proportion of photovoltaic power generation in active areas and the utilization rate of photovoltaic power generation in active areas to evaluate the photovoltaic consumption potential in active areas;

[0022] S2.5, input the indicators for measuring the photovoltaic power generation potential of the active area, the indicators for evaluating the peak-shaving potential of the power grid in the active area, and the indicators for evaluating the photovoltaic consumption potential of the active area into the XGboost model to obtain the importance ranking results of all indicators.

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

[0024]

[0025] In formula (1), α 辐射 is the standard deviation of the radiation intensity throughout the day, μ 辐射 is the average value of radiation intensity throughout the day;

[0026] The calculation formula of the temperature fluctuation rate is:

[0027]

[0028] In formula (2), α 温度 is the standard deviation of the temperature throughout the day, μ 温度 The average temperature for the whole day.

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

[0030]

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

[0032] The calculation formula of the maximum photovoltaic penetration G2 is:

[0033]

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

[0035]

[0036] In formula (5), t is time; P i (t) is the photovoltaic power generation power of the active area at time t; D j (t) is the power of the active area power load at time t; the z in the summation symbol indicates that there are z data at time t; the m in the summation symbol indicates that there are m data at time t;

[0037] The calculation formula of the active area photovoltaic power generation utilization rate R2 is:

[0038]

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

[0040] Optionally, step S3 includes:

[0041] S3.1, conduct statistical analysis and correlation test on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak regulation potential and photovoltaic consumption potential, and extract indicator data with correlation coefficient values ​​exceeding the threshold;

[0042] S3.2, the extracted indicator data are weighted using a comprehensive evaluation system;

[0043] S3.3, perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight value of the indicator data.

[0044] Optionally, the step S3.3 comprises:

[0045] S3.3.1, the AHP hierarchical analysis method is used for the data indicators after weight evaluation to construct a multi-level analysis framework consisting of the target layer, criterion layer and feature layer;

[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 to form a consistency judgment matrix;

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

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

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

[0050]

[0051] In formula (7), w i represents the i-th feature, that is, the weight of the data indicator in the consistency judgment matrix, a ij is an element in the consistency judgment matrix, λ max is the maximum eigenvalue in the consistency judgment matrix, and n is 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 correlation coefficient values ​​of various indicator data in evaluating photovoltaic acceptance capacity.

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

[0054] 1. The present invention provides a method for constructing an evaluation index for the photovoltaic acceptance capacity of an active area, which combines photovoltaic power generation characteristics with grid operation data, and analyzes not only based on historical data, but also considers the impact of real-time environmental changes on photovoltaic output. This method is more comprehensive than the traditional static evaluation model and can more accurately predict the performance of photovoltaic systems under different environmental conditions.

[0055] 2. The present invention provides a method for constructing an evaluation index for the photovoltaic acceptance capacity of an active area. By using a machine learning algorithm to analyze and optimize the peak-shaving response of the power grid, the adaptability of the power grid to high load and high photovoltaic output periods is improved. Compared with the existing technology, this strategy makes more efficient use of renewable energy while reducing dependence on traditional power generation resources.

[0056] 3. The present invention provides a method for constructing an evaluation index for the photovoltaic acceptance capacity of an active area. By providing a decision support system that integrates business rules and advanced data analysis, power grid operators can more effectively plan and manage operations. The implementation of this method can help the power grid better cope with the volatility of renewable energy and optimize the operating efficiency and economy of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is an operation flow chart of the method for constructing the evaluation index of the photovoltaic acceptance capacity of the active area of ​​the present invention;

[0058] Figure 2 It is the XGboost feature variable importance result of the present invention;

[0059] Figure 3 This is an example diagram of the key links of the photovoltaic grid-connected system of the present invention. DETAILED DESCRIPTION

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

[0061] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0062] In the description of the present invention, it should be noted that the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0063] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] The present invention provides a method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area, such as Figure 1 As shown, the evaluation index construction method includes:

[0065] S1: Data collection and classification;

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

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

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

[0069] S5: Apply the results and optimize the strategy of the model verified and optimized in step S4.

[0070] Specific:

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

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

[0073] The step S2 specifically includes the following steps:

[0074] S2.1, remove outliers, fill in missing data, normalize and standardize the data, and align the time series data.

[0075] S2.2, measure and calculate the all-day average radiation intensity, radiation intensity fluctuation rate, all-day average temperature, all-day average humidity, temperature fluctuation rate, average cloud coverage rate, and average wind speed of the active substation in a certain season and a certain weather type, so as to measure the photovoltaic power generation potential of the active substation; among which, the all-day average radiation intensity, all-day average temperature, all-day average humidity, average cloud coverage rate, and average wind speed are data that are easily obtained using existing technologies, so they are not described in detail.

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

[0077] Furthermore, the calculation formula of the radiation intensity fluctuation rate is:

[0078]

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

[0080] Furthermore, the calculation formula of the temperature fluctuation rate is:

[0081]

[0082] In the formula, α 温度 is the standard deviation of the temperature throughout the day, μ 温度 The average temperature for the whole day.

[0083] S2.3, using the PV power generation power, PV installed capacity, and power load data of the active area, calculate the PV output power stability and maximum PV penetration rate in the area to assess the peak-shaving potential of the grid in the active area.

[0084] The calculation formula of the photovoltaic output power stability G1 is:

[0085]

[0086] Where P i (t) is the photovoltaic power generation power of the active area at time t, P 平均 It is the average value of the power generated by the active area during the power generation period of the day; the N in the summation symbol means that there are N data at time t.

[0087] Among them, the maximum photovoltaic penetration rate G2 is a key indicator that can be used to evaluate the ratio of the maximum photovoltaic power generation power that the power grid can safely accept to the maximum load, thereby helping power grid planners and operators to obtain the amount of electricity that can be accepted from the photovoltaic power generation system without affecting the stability and reliability of the power grid. The calculation formula of the maximum photovoltaic penetration rate G2 is:

[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 stable 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), and the maximum load is obtained through the historical operation data of the distribution network.

[0090] S2.4, construct indicators of the proportion of photovoltaic power generation in active areas and the utilization rate of photovoltaic power generation in active areas to evaluate the photovoltaic absorption potential of active areas.

[0091] The active area photovoltaic power generation ratio refers to the proportion of active area photovoltaic power generation to regional electricity consumption within a certain period of time. The calculation formula for the active area photovoltaic power generation ratio R1 is:

[0092]

[0093] Where t is time; P i (t) is the photovoltaic power generation power of the active area at time t; D j (t) is the power of the active area power load at time t; the z in the summation symbol indicates that there are z data at time t; and the m in the summation symbol indicates that there are m data at time t.

[0094] The active area photovoltaic power generation utilization rate refers to the real-time consumption of photovoltaic power generation in the active area. The calculation formula of the active area photovoltaic power generation utilization rate R2 is:

[0095]

[0096] Where D i (t) is the available power generation of the distributed power source at time t, that is, the photovoltaic installed capacity; the s in the summation symbol means that there are s data at time t.

[0097] S2.5, input the indicators for measuring the photovoltaic power generation potential of the active area, the indicators for evaluating the peak-shaving potential of the power grid in the active area, and the indicators for evaluating the photovoltaic consumption potential of the active area into the XGboost model to obtain the importance ranking results of all indicators.

[0098] Specifically, the above indicators, including the average radiation intensity throughout the day, the average temperature throughout the day, the average humidity throughout the day, the average cloud coverage, the average wind speed, the utilization rate of photovoltaic power generation in the active area, the fluctuation rate of radiation intensity, the fluctuation rate of temperature, the stability of photovoltaic output power, the maximum photovoltaic penetration rate, the proportion of photovoltaic power generation in the active area, the weather type, the season type, and the power return rate obtained by combining the data of the above indicators, are input into the XGboost (extreme gradient boosting) model for regression to obtain the importance value corresponding to each indicator, and the indicators affecting the photovoltaic acceptance capacity of the active area are ranked according to the importance value. The results are as follows: Figure 2 shown.

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

[0100] S3.1, conduct statistical analysis and correlation test on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak regulation potential and photovoltaic consumption potential, and extract indicator data with the absolute value of the correlation coefficient exceeding the threshold.

[0101] S3.1.1, Apply advanced statistical methods to conduct a comprehensive review of indicator data, including but not limited to mean analysis, variance analysis, and outlier detection, to purify the data set and ensure the reliability of subsequent analysis.

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

[0103] Table 1

[0104]

[0105] S3.1.3, extract indicator data whose absolute value of correlation coefficient exceeds a preset threshold.

[0106] In an embodiment of the present invention, the index data with a correlation coefficient exceeding 0.3 and a large importance threshold are extracted from the above table. The result is shown in Table 2 below:

[0107] Table 2

[0108]

[0109] S3.2, the extracted indicator data shall be weighted using a comprehensive assessment system.

[0110] Specifically, the present 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 extremely important for the evaluation of photovoltaic acceptance capacity.

[0111] S3.3, perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight value of the indicator data.

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

[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 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 value of the indicator data is obtained by the following calculation formula (7):

[0116]

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

[0118] Through this process, the accurate quantification of feature weights is achieved, ensuring the balance of the comprehensive evaluation model and the effectiveness of decision-making.

[0119] The step S4 specifically includes the following steps:

[0120] S4.1. After the construction of the active area photovoltaic acceptance capacity assessment model is completed, an adjustment optimization and prediction 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 operation data of the active substation power grid, evaluate the accuracy of the model in predicting photovoltaic power generation output, grid load fluctuations and evaluating grid stability, and better capture and predict the dynamic characteristics of the local grid by integrating multi-dimensional meteorological data, refining spatial resolution and time step, so as to further improve the prediction accuracy and robustness of the model.

[0122] In the step S5, Figure 3 As shown in the figure, the scale and layout of photovoltaic construction in different regions are scored and evaluated using a verified and optimized active area photovoltaic acceptance capacity assessment model, and the impact of different expansion plans on grid stability and economy is simulated to optimize photovoltaic power generation construction, expansion access and grid peak regulation strategies. This will help achieve the coordinated development of distributed power sources and the main grid, reduce the impact of photovoltaic access on grid load fluctuations, and improve the flexibility and reliability of grid operation planning.

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

[0124] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the invention. After reading the above content, it will be obvious to those skilled in the art for various modifications and substitutions of the present invention. Therefore, the protection scope of the present invention should be limited by the attached claims.

Claims

1. A method for constructing an evaluation index of photovoltaic acceptance capacity of an active area, characterized in that: Include: S1, data collection and classification, i.e., collecting key meteorological data of active substations, photovoltaic output data, distribution network operation data, photovoltaic-related installation scrapping and operation data; 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; S5, applying the results and optimizing the strategies of the model verified and optimized in step S4.

2. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 1, characterized in that: In step S1, the key meteorological data of the active area include: current season, weather type, sunshine intensity, temperature, humidity, cloud cover, and wind speed; The photovoltaic output data includes: photovoltaic power generation power; The distribution network operation data includes: power load data; The photovoltaic-related installation, scrapping and operation data include: photovoltaic installed capacity.

3. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 2, characterized in that: The step S2 comprises: S2.1, remove outliers, fill in missing data, normalize and standardize the collected data, and align the time series data; S2.2, measure and calculate the average radiation intensity, radiation intensity fluctuation rate, average temperature, average humidity, temperature fluctuation rate, average cloud coverage rate, and average wind speed of the active area in a certain season and weather type to measure the photovoltaic power generation potential of the active area; S2.3, using the PV power generation, PV installed capacity, and power load data of the active area, calculate the PV output power stability and maximum PV penetration rate of the area to assess the peak load regulation potential of the grid in the active area; S2.4, construct the proportion of photovoltaic power generation in active areas and the utilization rate of photovoltaic power generation in active areas to evaluate the photovoltaic consumption potential in active areas; S2.5, input the indicators for measuring the photovoltaic power generation potential of the active area, the indicators for evaluating the peak-shaving potential of the power grid in the active area, and the indicators for evaluating the photovoltaic consumption potential of the active area into the XGboost model to obtain the importance ranking results of all indicators.

4. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 3, characterized in that: The calculation formula of the radiation intensity fluctuation rate in step S2.2 is: In formula (1), α 辐射 is the standard deviation of the radiation intensity throughout the day, μ 辐射 is the average value of radiation intensity throughout the day; The calculation formula of the temperature fluctuation rate is: In formula (2), α 温度 is the standard deviation of the temperature throughout the day, μ 温度 The average temperature for the whole day.

5. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 3, characterized in that: The calculation formula of the photovoltaic output power stability G1 in step S2.3 is: In formula (3), P i (t) is the photovoltaic power generation power of the active area at time t, P 平均 It is the average value of the power generated by the active area during the power generation period of the day; the N in the summation symbol indicates that there are N data at time t; The calculation formula of the maximum photovoltaic penetration G2 is:

6. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 3, characterized in that: The calculation formula for the proportion of active area photovoltaic power generation R1 in step S2.4 is: In formula (5), t is time; P i (t) is the photovoltaic power generation power of the active area at time t; D j (t) is the power of the active area power load at time t; the z in the summation symbol indicates that there are z data at time t; the m in the summation symbol indicates that there are m data at time t; The calculation formula of the active area photovoltaic power generation utilization rate R2 is: In formula (6), D i (t) is the available power generation of the distributed power source at time t, that is, the photovoltaic installed capacity; the s in the summation symbol means that there are s data at time t.

7. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 3, characterized in that: The step S3 comprises: S3.1, conduct statistical analysis and correlation test on the indicator data of the three dimensions of photovoltaic power generation potential, grid peak regulation potential and photovoltaic consumption potential, and extract indicator data with correlation coefficient values ​​exceeding the threshold; S3.2, the extracted indicator data are weighted using a comprehensive evaluation system; S3.3, perform AHP hierarchical analysis on the indicator data after weight evaluation to obtain the final weight value of the indicator data.

8. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 7, characterized in that: The step S3.3 comprises: S3.3.1, the AHP hierarchical analysis method is used for the data indicators after weight evaluation to construct a multi-level analysis framework consisting of the target layer, criterion layer and feature layer; 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 to form a consistency judgment matrix; S3.3.3, calculating the maximum eigenvalue and consistency ratio CI of the consistency judgment matrix, and ensuring that the consistency ratio CI ≤ 0.1; S3.3.4, obtain the final weight value of the indicator data.

9. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 8, characterized in that: The final weight calculation formula of the indicator data in step S3.3.4 is: In formula (7), w i represents the i-th feature, that is, the weight of the data indicator in the consistency judgment matrix, a ij is an element in the consistency judgment matrix, λ max is the maximum eigenvalue in the consistency judgment matrix, and n is the order of the consistency judgment matrix.

10. The method for constructing an evaluation index of the photovoltaic acceptance capacity of an active area according to claim 7, characterized in that: In step S3.1, the statistical means of Pearson correlation coefficient and Spearman rank correlation are used to obtain the correlation coefficient value of each indicator data in evaluating the photovoltaic acceptance capacity.

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