Method and device for improving new energy consumption capability of district and county-level power distribution network considering low-carbon demand

Abstract

The invention relates to a district and county-level power distribution network new energy consumption capability improvement method considering low-carbon demands, and the method comprises the steps: obtaining the annual new energy output and load data of a district and county-level power distribution network, carrying out the standardization processing, and extracting the large-load and small-load typical days of the large new energy output and the small new energy output in each season; calculating an annual average correlation coefficient; and according to the annual average correlation coefficient, the correlation between the new energy output and the load is analyzed, and new energy access planning of the district and county level power distribution network is carried out. According to the method, the space-time coupling rule of the load type and the new energy output is effectively identified, the complementary characteristics of different load types and the new energy output are quantitatively evaluated, the new energy is preferentially connected to a high-correlation load line, and differential planning suggestions are formed; the new energy and load curve matching degree and the district and county level power distribution network new energy consumption capability can be improved, redundant line reconstruction is reduced, the district and county level power distribution network construction investment is reduced, and a scientific decision basis is provided for district and county level power distribution network low-carbon planning.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon planning of power systems, and in particular to a method and device for improving the new energy consumption capacity of district-level and county-level distribution networks considering low-carbon requirements. Background Technique

[0002] Traditional distribution network planning methods mostly rely on historical load data and fixed scenario simulations. With the continuous increase in the proportion of new energy in the energy structure, the inherent intermittency and randomness characteristics of new energy pose significant challenges to traditional power grid planning methods. Especially at the district-level and county-level distribution network level, distributed new energy needs to be consumed locally nearby. Existing planning methods are difficult to cope with dynamic factors such as new energy output fluctuations and load changes, resulting in an increasingly prominent problem of insufficient consumption capacity, which seriously restricts the realization of low-carbon goals.

[0003] As the end of the power system, the district-level and county-level power grids are directly connected to a large number of users. The effective implementation of low-carbon planning for district-level and county-level power grids is crucial for the realization of the carbon emission reduction goal of the entire power system. And improving the new energy consumption capacity is the key direction for low-carbon planning of district-level and county-level distribution networks. How to promote the effective construction of new energy in district-level and county-level distribution networks, improve the new energy consumption capacity of district-level and county-level distribution networks considering low-carbon requirements, and reduce the carbon emission level of district-level and county-level distribution networks. Therefore, there is an urgent need to develop a method for improving the new energy consumption capacity of district-level and county-level distribution networks considering low-carbon requirements. Summary of the Invention

[0004] To solve the problem that new energy cannot be consumed locally under low-carbon requirements and the new requirements for power grid operation and construction brought by energy transmission, the primary object of the present invention is to provide a method for improving the new energy consumption capacity of district-level and county-level distribution networks considering low-carbon requirements, which can improve the matching degree between new energy and load curves and the new energy consumption capacity of district-level and county-level distribution networks, reduce redundant line transformation, and reduce the construction investment of district-level and county-level distribution networks.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for improving the new energy consumption capacity of district-level and county-level distribution networks considering low-carbon requirements, the method includes the following steps in sequence:

[0006] (1) Obtain the annual new energy output and load data of the district-level and county-level distribution networks and perform standardized processing, extract the typical days with large new energy output and small load and the typical days with small new energy output and large load in each season, and obtain the typical days of the four seasons;

[0007] (2) Group the new energy output data of various types of the district-level and county-level distribution networks and the load data of various types in pairs, calculate the Spearman correlation coefficient for each typical day of each group of corresponding data, and obtain the annual average correlation coefficient

[0008] (3) According to the annual average correlation coefficient Analyze the correlation between new energy output and load, and conduct the new energy access planning for the district-level and county-level distribution networks.

[0009] Step (1) specifically includes the steps in the following order:

[0010] (1a) Determine the new energy types and load types of the district-level and county-level distribution networks, obtain the hourly data of each type of new energy output and each type of load data of the district-level and county-level distribution networks from the power grid company, and perform standardization processing on the annual new energy output and each type of load of the district-level and county-level distribution networks. The calculation formula is as follows:

[0011]

[0012] In the formula: i is the hourly data, i = 1, 2,..., 8760; is the per-unit value of new energy output per hour after standardization processing, P o,i is the new energy output per hour, P o,max is the new energy installed capacity; is the per-unit value of load per hour after standardization processing, P l,i is the load per hour, P l,max is the maximum value of the annual load;

[0013] (1b) Take the mean value of the per-unit value data of each type of load after standardization processing to obtain the mean value of the per-unit value of the load And divide the per-unit value data of each type of new energy output and the mean value data of the per-unit value of the load after standardization processing into 4 parts according to the four seasons of spring, summer, autumn, and winter to form a spring data set, a summer data set, an autumn data set, and a winter data set. Among them, spring is March, April, and May, summer is June, July, and August, autumn is September, October, and November, and winter is December, January, and February. Use the spring data set, summer data set, autumn data set, and winter data set to extract the typical days with large new energy output and small load and the typical days with small new energy output and large load in each season for each type of new energy, that is, the typical days of the four seasons. The calculation formula is as follows:

[0014]

[0015] In the formula: X is the maximum value of the difference between the per-unit value of new energy output per hour after standardization processing and the mean value of the per-unit value of the load ; Y is the per-unit value of new energy output per hour after standardization processing and the mean value of the per-unit value of the load The minimum value of the difference; when i is in the spring dataset, the typical day when X occurs is the typical day with large new energy output and small load in spring, and the typical day when Y occurs is the typical day with small new energy output and large load in spring, and so on, to obtain the typical days of the four seasons, with two typical days corresponding to one season.

[0016] Specifically, step (2) means: pairwise grouping the new energy output data of each type in the district and county-level distribution network with each type of load, that is, there are m types of new energy and n types of load, and each type of new energy is combined with each type of load, totaling m×n groups. Calculate the Spearman correlation coefficient for the normalized per-unit value data of new energy output and the per-unit value data of load corresponding to each typical day in each group. The calculation formula is as follows:

[0017]

[0018] In the formula: ρ s is the correlation coefficient; R is the normalized per-unit value data of new energy output corresponding to the typical day, S is the normalized load data corresponding to the typical day, R and S are the input variables for calculating the correlation coefficient; j is the variable observation value; R j is the descending order rank of the per-unit value of new energy output for the variable observation value j; S j is the descending order rank of the per-unit value of load for the variable observation value j; is the average rank of the per-unit value of new energy output; is the average rank of the per-unit value of load; N is the total number of observation values; d j is the rank difference of the variable observation value j in the per-unit value of new energy output and the per-unit value of load, d j =R j -S j ;

[0019] Take the average of the correlation coefficients of each typical day obtained for each new energy output and load combination to obtain the annual average correlation coefficient Obtain the correlation between new energy output and load. The calculation formula is as follows:

[0020]

[0021] In the formula: ρ s春1 is the correlation coefficient of the typical day with large new energy output and small load in spring; ρ s春2 is the correlation coefficient of the typical day with small new energy output and large load in spring; ρ s夏1 is the correlation coefficient of the typical day with large new energy output and small load in summer; ρ s夏2 is the correlation coefficient of the typical day with small new energy output and large load in summer; ρ s秋1is the correlation coefficient for a typical day in autumn with large new energy output and small load; ρ s秋2 is the correlation coefficient for a typical day in autumn with small new energy output and large load; ρ s冬1 is the correlation coefficient for a typical day in winter with large new energy output and small load; ρ s冬2 is the correlation coefficient for a typical day in winter with small new energy output and large load.

[0022] Step (3) specifically refers to: If it is determined that the new energy output of this type is negatively correlated with the load of this type, and the consumption capacity of this type of new energy cannot be improved, which does not meet the requirements; if it is determined that the new energy output of this type is independent of the load of this type and there is no correlation, and the consumption capacity of this type of new energy cannot be improved; if it is determined that the new energy output of this type is slightly correlated with the load of this type, and the consumption capacity of this new energy can be improved to a certain extent; if it is determined that the new energy output of this type is significantly correlated with the load of this type, and the consumption capacity of new energy can be improved better; if it is determined that the new energy output of this type is highly correlated with the load of this type, and the consumption capacity of new energy can be significantly improved;

[0023] Combined with the annual average correlation coefficient sort the load types suitable for the access of new energy, and calculate the proportion of the annual electricity consumption of each type of load on the line. The calculation formula is as follows:

[0024]

[0025] In the formula: a c is the proportion of the annual electricity consumption of the c-th type of load; c is the load type included on the line, c = 1, 2,..., C; C is the total type of load included on the line; Q c is the annual electricity consumption of the c-th type of load; Q is the total annual electricity consumption of all types of loads on the line;

[0026] In the new energy planning and construction, select the line with the annual average correlation coefficient between [0.5, 1] and the proportion of the annual electricity consumption of the load a c above 30% for connection.

[0027] Another object of the present invention is to provide an electronic device, including:

[0028] A processor; and

[0029] A memory in which computer program instructions are stored, and when the computer program instructions are run by the processor, the processor executes the method for improving the new energy consumption capacity of the district-level distribution network considering low-carbon requirements as described above.

[0030] The present invention also provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are run by a processor, the processor is caused to execute the method for improving the new energy consumption capacity of a district-level distribution network considering low-carbon requirements as described above.

[0031] As can be seen from the above technical solutions, the beneficial effects of the present invention are as follows: The present invention combines the time-series characteristics of historical new energy output and load data, effectively identifies the spatio-temporal coupling law between load types and new energy output, quantitatively evaluates the complementary characteristics of different load types and new energy output based on the correlation coefficient, and preferentially connects new energy to high-correlation load lines to form differential planning suggestions. Compared with the traditional empirical planning method, the present invention can improve the matching degree between new energy and load curves and the new energy consumption capacity of district-level distribution networks, reduce redundant line transformation, lower the construction investment of district-level distribution networks, provide a scientific decision-making basis for the low-carbon planning of district-level distribution networks, have both low-carbon benefits and economic benefits, and have important practical value for promoting the dual-carbon goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flowchart of the method of the present invention;

[0033] Figure 2 is a recommended graph for preferential access in new energy planning. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] As Figure 1 shown, a method for improving the new energy consumption capacity of a district-level distribution network considering low-carbon requirements, the method includes the following steps in sequence:

[0035] (1) Obtain the annual new energy output and load data of the district-level distribution network and perform standardized processing, extract the typical days with large new energy output and small load and the typical days with small new energy output and large load in each season to obtain the typical days of the four seasons;

[0036] (2) Group the new energy output data of each type in the district-level distribution network and the load data of each type in pairs, calculate the Spearman correlation coefficient for each typical day of each group of corresponding data to obtain the annual average correlation coefficient

[0037] (3) Analyze the correlation between new energy output and load according to the annual average correlation coefficient and conduct the new energy access planning for the district-level distribution network.

[0038] Step (1) specifically includes the following steps in sequence:

[0039] (1a) Determine the new energy types and load types at the district and county level distribution network, obtain the new energy output data and load data of each type per hour throughout the year for the district and county level distribution network from the power grid company, and perform standardization processing on the new energy output and load of each type in the district and county level distribution network throughout the year. The calculation formula is as follows:

[0040]

[0041] In the formula: i is the hourly data, i = 1, 2,..., 8760; is the per unit value of new energy output per hour after standardization processing, P o,i is the new energy output per hour, P o,max is the installed capacity of new energy; is the per unit value of load per hour after standardization processing, P l,i is the load per hour, P l,max is the maximum value of the annual load;

[0042] (1b) Take the mean value of the per unit value data of each type of load after standardization processing to obtain the mean value of the per unit value of the load And divide the per unit value data of each type of new energy output and the mean value data of the per unit value of the load after standardization processing into 4 parts according to the four seasons of spring, summer, autumn, and winter, forming a spring dataset, a summer dataset, an autumn dataset, and a winter dataset. Among them, spring is from March to May, summer is from June to August, autumn is from September to November, and winter is from December to January and February. Using the spring dataset, summer dataset, autumn dataset, and winter dataset, respectively extract the typical days when the new energy output is large and the load is small and the typical days when the new energy output is small and the load is large for each type of new energy in each season, that is, the typical days of the four seasons. The calculation formula is as follows:

[0043]

[0044] In the formula: X is the per unit value of new energy output per hour after standardization processing and the mean value of the per unit value of the load The maximum value of the difference; Y is the per unit value of new energy output per hour after standardization processing and the mean value of the per unit value of the load The minimum value of the difference; When i is in the spring dataset, extract the typical day when X occurs as the typical day when the new energy output is large and the load is small in spring, and extract the typical day when Y occurs as the typical day when the new energy output is small and the load is large in spring. And so on, to obtain the typical days of the four seasons. One season corresponds to two typical days.

[0045] Step (2) specifically refers to: pairwise grouping the new energy output data of each type in the district and county-level distribution network with each type of load. That is, there are m types of new energy and n types of load. Each type of new energy is combined with each type of load, totaling m×n groups. Calculate the Spearman correlation coefficient for the normalized per-unit value data of new energy output and the per-unit value data of load corresponding to each typical day in each group. The calculation formula is as follows:

[0046]

[0047] In the formula: ρ s is the correlation coefficient; R is the normalized per-unit value data of new energy output corresponding to the typical day, S is the normalized load data corresponding to the typical day, R and S are the input variables for calculating the correlation coefficient; j is the variable observation value; R j is the descending order rank of the per-unit value of new energy output for the variable observation value j; S j is the descending order rank of the per-unit value of load for the variable observation value j; is the average rank of the per-unit value of new energy output; is the average rank of the per-unit value of load; N is the total number of observation values; d j is the difference in the rank of the variable observation value j between the per-unit value of new energy output and the per-unit value of load, d j =R j -S j ;

[0048] Take the mean of the correlation coefficients of each typical day obtained for each new energy output and load combination to obtain the annual average correlation coefficient Obtain the correlation between new energy output and load. The calculation formula is as follows:

[0049]

[0050] In the formula: ρ s春1 is the correlation coefficient for the typical day with large new energy output and small load in spring; ρ s春2 is the correlation coefficient for the typical day with small new energy output and large load in spring; ρ s夏1 is the correlation coefficient for the typical day with large new energy output and small load in summer; ρ s夏2 is the correlation coefficient for the typical day with small new energy output and large load in summer; ρ s秋1 is the correlation coefficient for the typical day with large new energy output and small load in autumn; ρ s秋2 is the correlation coefficient for the typical day with small new energy output and large load in autumn; ρ s冬1 is the correlation coefficient for the typical day with large new energy output and small load in winter; ρ s冬2 is the correlation coefficient for the typical day with small new energy output and large load in winter.

[0051] Step (3) specifically refers to: If it is determined that the output of this type of new energy is negatively correlated with this type of load, and the consumption capacity of this type of new energy cannot be improved, which does not meet the requirements; if it is determined that the output of this type of new energy is independent of this type of load and there is no correlation, and the consumption capacity of this type of new energy cannot be improved; if it is determined that the output of this type of new energy is slightly correlated with this type of load, and the consumption capacity of this new energy can be improved to a certain extent; if it is determined that the output of this type of new energy is significantly correlated with this type of load, and the consumption capacity of new energy can be improved better; if it is determined that the output of this type of new energy is highly correlated with this type of load, and the consumption capacity of new energy can be significantly improved;

[0052] Combined with the annual average correlation coefficient Sort the load types suitable for the access of new energy, and calculate the proportion of the annual electricity consumption of each type of load on the line. The calculation formula is as follows:

[0053]

[0054] In the formula: a c is the proportion of the annual electricity consumption of the c-th type of load; c is the load type included on the line, c = 1, 2,..., C; C is the total load type included on the line; Q c is the annual electricity consumption of the c-th type of load; Q is the total annual electricity consumption of all types of loads on the line;

[0055] In the new energy planning and construction, select the line with the annual average correlation coefficient between [0.5, 1] and the proportion of the annual electricity consumption of the load a c above 30% for access.

[0056] Another object of the present invention is to provide an electronic device, including:

[0057] A processor; and

[0058] A memory, in which computer program instructions are stored, and when the computer program instructions are run by the processor, the processor executes the method for improving the new energy consumption capacity of the district-level distribution network considering low-carbon requirements as described above.

[0059] The present invention also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are run by a processor, the processor executes the method for improving the new energy consumption capacity of the district-level distribution network considering low-carbon requirements as described above.

[0060] Example 1

[0061] In the current year of the typical districts and counties, for new energy, photovoltaic is selected; for load types, 9 types in industrial loads are selected, including non-metallic mineral products, transportation electrical and electronic equipment, automobile manufacturing, etc.; hotels and shopping malls in commercial loads; high-rise buildings and villas in residential loads; office buildings, schools, hospitals, electric vehicles in other loads, and livestock breeding, food processing, farms, and tea manufacturing in agricultural loads.

[0062] The annual new energy output and data of various types of loads in the district and county distribution network are provided and standardized as shown in Table 1 below.

[0063] Table 1 Standardized processing of new energy output and load (partial display)

[0064] Date Time Photovoltaic output Industrial load - Metal products industry Industrial load - Footwear industry Industrial load - Textile industry 4 / 26 0:00 0.000 0.681 0.452 0.471 4 / 26 1:00 0.000 0.609 0.539 0.487 4 / 26 2:00 0.000 0.676 0.486 0.412 4 / 26 3:00 0.000 0.678 0.503 0.538 4 / 26 4:00 0.000 0.680 0.518 0.500 4 / 26 5:00 0.000 0.646 0.582 0.482 4 / 26 6:00 0.028 0.665 0.396 0.667 4 / 26 7:00 0.172 0.650 0.371 0.501 4 / 26 8:00 0.378 0.675 0.473 0.524 4 / 26 9:00 0.549 0.613 0.536 0.610 4 / 26 10:00 0.672 0.619 0.549 0.707

[0065] The per-unit value data of various types of loads after standardized processing are averaged, and the per-unit value of new energy output and the average per-unit value data of load are divided into 4 parts according to the four seasons to form 4 data sets. The typical days with large new energy output and small load and small new energy output and large load in each season are extracted respectively, as shown in Table 2 below.

[0066] Table 2 Extraction of typical days in four seasons

[0067] Season Typical day Spring March 30th, April 26th Summer July 2nd, July 25th Autumn October 19th, November 1st Winter January 13th, December 25th

[0068] The new energy output data of various types in the district and county distribution network and the load data of various types are grouped in pairs. The Spearman correlation coefficient is calculated for each corresponding data of each group on each typical day, and the average value of the correlation coefficients obtained for each typical day is taken to obtain the annual average correlation coefficient as shown in Table 3 below.

[0069] Table 3 Calculation results of the correlation coefficient between new energy output and load

[0070]

[0071] Analyze the correlation between new energy output and load. Combining the correlation, sort the load types suitable for new energy access. In the new energy planning and construction, select the lines with an annual average correlation coefficient between [0.5, 1] with this type of new energy and a load annual electricity consumption ratio of more than 30%, as shown in Table 4 below and Figure 2 as shown, in Figure 2Among them, 1 to 8 represent the access recommendation ranking. Among them, 1 has the highest correlation with photovoltaic, and 1 is preferentially recommended for access. In the planning and construction of district and county-level distribution networks, to improve the consumption of new energy, when the new power source type is photovoltaic, it is preferentially connected to the lines where the annual electricity consumption of the relevant loads of hospitals, pharmaceutical manufacturing, food processing, livestock breeding, transportation electrical and electronic equipment, office buildings, shopping malls and hotels accounts for more than 30%.

[0072] Table 4 Correlation analysis results of new energy output and load

[0073]

[0074] To sum up, the present invention combines the time series characteristics of historical new energy output and load data, effectively identifies the spatio-temporal coupling law between load types and new energy output, quantitatively evaluates the complementary characteristics of different load types and new energy output based on the correlation coefficient, and preferentially connects new energy to the lines with high-correlation loads to form differential planning suggestions. Compared with the traditional empirical planning method, the present invention can improve the matching degree between the new energy and the load curve and the new energy consumption capacity of the district and county-level distribution network, reduce the transformation of redundant lines, reduce the construction investment of the district and county-level distribution network, provide a scientific decision-making basis for the low-carbon planning of the district and county-level distribution network, have both low-carbon benefits and economic benefits, and have important practical value for promoting the dual-carbon goal.

Claims

1. A method for improving the new energy consumption capacity of county-level distribution networks considering low-carbon demand, characterized by: The method comprises the following steps in order: (1) Obtain the annual renewable energy output and load data of the county-level distribution network and perform standardized processing to extract typical days with high renewable energy output and low load and low renewable energy output and high load in each season, and obtain typical days for the four seasons; (2) The various types of renewable energy output data and various types of load data of the district and county distribution network are grouped in pairs, and the Spearman correlation coefficient is calculated for each typical day of each group of corresponding data to obtain the annual average correlation coefficient. (3) Based on the average correlation coefficient throughout the year Analyze the correlation between renewable energy output and load, and carry out renewable energy access planning for district and county distribution networks.

2. The method for improving the new energy consumption capacity of district and county-level distribution networks considering low-carbon demand according to claim 1 is characterized in that: Step (1) specifically includes the following steps in order: (1a) Determine the types of renewable energy and loads in the county-level distribution network. Obtain the hourly data of renewable energy output and loads of each type in the county-level distribution network throughout the year from the power grid company. Standardize the output of each type of renewable energy and loads of each type in the county-level distribution network throughout the year. The calculation formula is as follows: Where: i is hourly data, i=1,2,...,8760; P is the unit value of renewable energy output per hour after standardization. o,i is the renewable energy output per hour, P o,max Installed capacity for new energy sources; P is the load per hour after standardization. l,i is the hourly load, P l,max The maximum load of the whole year; (1b) Take the average of the load per unit value data of each type after standardization to obtain the load per unit value mean The standardized per-unit data of each type of new energy output and per-unit load mean data are divided into four parts according to the four seasons of spring, summer, autumn and winter, forming spring data set, summer data set, autumn data set and winter data set. Spring is March, April and May, summer is June, July and August, autumn is September, October and November, and winter is December, January and February. The spring data set, summer data set, autumn data set and winter data set are used to extract the typical days with large new energy output and small load and the typical days with small new energy output and large load in each season of each type of new energy, that is, the typical days of the four seasons. The calculation formula is as follows: Where: X is the unit value of renewable energy output per hour after standardization Average value of load per unit The maximum value of the difference; Y is the unit value of renewable energy output per hour after standardization The mean value of load per unit When i is in the spring data set, the typical day when X occurs is the typical day with large output of new energy and small load in spring, and the typical day when Y occurs is the typical day with small output of new energy and large load in spring, and so on, the typical days of the four seasons are obtained, and one season corresponds to two typical days.

3. The method for improving the new energy consumption capacity of county-level distribution networks considering low-carbon demand according to claim 1 is characterized in that: Step (2) specifically refers to: grouping the output data of each type of renewable energy in the county-level distribution network with each type of load in pairs, that is, there are m types of renewable energy and n types of load, and each type of renewable energy is combined with each type of load, for a total of m×n groups, and calculating the Spearman correlation coefficient of the standardized renewable energy output per unit value data and the load per unit value data corresponding to each typical day of each group, and the calculation formula is as follows: Where: s is the correlation coefficient; R is the standardized per unit value data of new energy output corresponding to a typical day. S is the standardized load data corresponding to a typical day. R and S are used as input variables for correlation coefficient calculation; j is the observed value of the variable; R j S is the descending order of the unit value of the new energy output of the variable observation value j; j Arrange the load per unit value of variable observation j in descending order; It is the average ranking level of the per unit value of renewable energy output; is the average ranking of the load per unit value; N is the total number of observations; d j is the ranking difference of variable observation value j in the unit value of renewable energy output and unit value of load, d j =R j -S j ; Take the average of the correlation coefficients of each typical day for each new energy output and load combination to get the average correlation coefficient for the whole year. The correlation between the output of new energy and the load is obtained, and the calculation formula is as follows: Where: s春1 is the correlation coefficient of a typical day with high renewable energy output and low load in spring; ρ s春2 is the correlation coefficient of a typical day with small output and large load of renewable energy in spring; ρ s夏1 is the correlation coefficient of a typical day with high renewable energy output and low load in summer; ρ s夏2 is the correlation coefficient for a typical day in summer when the output of renewable energy is small and the load is large; ρ s秋1 is the correlation coefficient of a typical day with high renewable energy output and low load in autumn; ρ s秋2 is the correlation coefficient of a typical day with low renewable energy output and high load in autumn; ρ s冬1 is the correlation coefficient of a typical day with high output of renewable energy and low load in winter; ρ s冬2 It is the correlation coefficient of a typical day with low output of renewable energy and high load in winter.

4. The method for improving the new energy consumption capacity of district and county-level distribution networks considering low-carbon demand according to claim 1 is characterized in that: Step (3) specifically means: if It is judged that the output of this type of new energy is negatively correlated with this type of load, and the consumption capacity of this type of new energy cannot be improved, which does not meet the requirements; if It is judged that the output of this type of new energy is independent of this type of load, there is no correlation, and the consumption capacity of this type of new energy cannot be improved; if Judging that the output of this type of new energy is lowly related to this type of load, the absorption capacity of this new energy can be improved to a certain extent; if It is judged that the output of this type of new energy is significantly related to this type of load, which can better improve the new energy consumption capacity; if Judging that this type of new energy output is highly correlated with this type of load, it can significantly improve the new energy consumption capacity; Combined with the average correlation coefficient for the whole year Sort the load types that are suitable for new energy access, and calculate the annual electricity consumption proportion of each type of load on the line. The calculation formula is as follows: Where: a c is the annual electricity consumption proportion of the c-type load; c is the load type included in the line, c = 1, 2, ..., C; C is the total load type included in the line; Q c is the annual power consumption of the c-type load; Q is the total annual power consumption of all types of loads on the line; In the planning and construction of new energy, select the annual average correlation coefficient of this type of new energy Between [0.5,1] and the load annual electricity consumption accounts for a c More than 30% of the lines are accessible.

5. An electronic device comprising: processor; as well as A memory, in which computer program instructions are stored, and when the computer program instructions are executed by the processor, the processor executes the method for improving the new energy consumption capacity of the district and county level distribution network considering low-carbon needs as described in any one of claims 1-4.

6. A computer-readable storage medium having computer program instructions stored thereon, wherein when the computer program instructions are executed by a processor, the processor is enabled to execute the method for improving the new energy absorption capacity of a county-level distribution network taking into account low-carbon needs as described in any one of claims 1-4.

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