Method and system for evaluating investment effect of power distribution network project

Abstract

The invention belongs to the technical field of power management, and provides a power distribution network project investment effect evaluation method and system, and the method comprises the steps: collecting and integrating enterprise historical data, calculating the correlation with the power utilization potential score, and building a positive and negative correlation table. And determining an electricity utilization potential evaluation target, selecting an evaluation criterion, and evaluating each enterprise in the park. Through fuzzy judgment of experts, a fuzzy judgment matrix is constructed and integrated, data are extracted, and weights are distributed. And formulating corresponding electricity price incentive measures for enterprises with different electricity utilization potentials based on the electricity utilization potential evaluation result. According to the method, various qualitative and quantitative factors can be comprehensively considered, a more comprehensive and objective evaluation result is provided, an enterprise is more effectively stimulated to adjust power consumption behaviors, and the enterprise is guided to reduce power consumption or transfer power consumption time in a power consumption peak period through a scientific and reasonable electricity price signal, so that power demand side management is realized, and the operation efficiency of a power grid is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power management, and in particular relates to a method and system for evaluating the investment effectiveness of a distribution network project. Background Art

[0002] The evaluation of the investment effectiveness of distribution network projects needs to select appropriate evaluation indicators according to the evaluation objectives, such as investment payback period, internal rate of return, grid asset income ratio, capital investment guarantee rate and other economic benefit indicators. At present, the evaluation methods mainly include fuzzy comprehensive evaluation method, hierarchical analysis method and data envelopment analysis method.

[0003] In the existing technology, the evaluation of the investment effectiveness of park distribution network projects is often affected by subjective judgment, data utilization limitations, insufficient adaptability to environmental changes, static evaluation models, and "one-size-fits-all" incentive policies. These problems can easily lead to inaccurate evaluation results and inefficient electricity price incentives.

[0004] Park enterprises generally face high electricity prices, which to some extent limits their competitiveness. In the park's comprehensive energy system, air-conditioning load users have great demand response and regulation potential.

[0005] To this end, the present invention provides a method and system for evaluating the investment effectiveness of distribution network projects. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for evaluating the investment effectiveness of distribution network projects, comprising:

[0008] Step 1: Collect relevant data used for electricity potential assessment in multiple historical periods of the enterprise, integrate the same relevant data in different periods and use them as independent relevant data sets, calculate the Pearson correlation coefficient between each data set and the electricity potential score, determine the correlation between each data set and the electricity potential assessment, and establish positive correlation tables and negative correlation tables based on the Pearson correlation coefficient;

[0009] Step 2: Construct a hierarchical model, determine the evaluation of the investment effectiveness of the distribution network project as the target layer, select the criterion layer based on the relevant data used for the electricity potential evaluation in multiple historical periods of the enterprise, and evaluate the enterprises in the park as the solution layer;

[0010] Step 3: Establish a fuzzy judgment matrix, select personnel to make fuzzy judgments, and use triangular fuzzy quantitative judgments; integrate judgments by calculating weighted averages to construct a comprehensive fuzzy judgment matrix;

[0011] Step 4: Extract data from the fuzzy judgment matrix; assign fuzzy weights to the criteria; judge the rationality of weight assignment through consistency index and consistency ratio; perform fuzzy comprehensive calculation on the weights of the criteria and the scores of the schemes to obtain the electricity consumption potential assessment of each enterprise in the park.

[0012] As a further technical solution of the present invention, the process of establishing the positive correlation table and the negative correlation table according to the Pearson correlation coefficient is as follows:

[0013] Standardize the data set; calculate the Pearson correlation coefficient r of the standardized data i ;

[0014] If the Pearson correlation coefficient r i ≥0, the relevant data are marked as positively correlated data;

[0015] If the Pearson correlation coefficient r i <0, the relevant data will be marked as negatively correlated data;

[0016] Sort the positive correlation data and negative correlation data from large to small according to the Pearson correlation coefficient to obtain the positive correlation table and the negative correlation table.

[0017] As a further technical solution of the present invention, the process of selecting the criterion layer according to the relevant data used for evaluating the electricity consumption potential in multiple historical periods of the enterprise is:

[0018] When obtaining the electricity consumption potential assessment of the enterprise in multiple historical periods, the data used in each criterion layer is selected and integrated into the usage data set, and the usage data set is processed and analyzed to obtain the usage fluctuation value;

[0019] comparing the usage fluctuation value to a usage fluctuation threshold;

[0020] If the usage fluctuation value is greater than the usage fluctuation threshold, it means that the relevant data used in selecting the criterion layer has strong volatility;

[0021] If the usage fluctuation value is less than or equal to the usage fluctuation threshold, it means that the volatility of the relevant data used in selecting the criterion layer is weak; the relevant data in each usage data set is averaged to obtain the number of data currently selected as the criterion layer;

[0022] If the volatility of the relevant data is strong, the mode processing of the relevant data in each use data set is performed to obtain the number of data currently selected as the criterion layer. If there is no mode in the quantity group, the number of relevant data in each use data set is summed and the average is taken;

[0023] After determining the number of data currently selected as the criterion layer, select data in order from the positive correlation table and the negative correlation table respectively;

[0024] The selection is made while the number of positive correlation data and negative correlation data is the same, and the total number of positive correlation data and negative correlation data is the same as the number of correlation data used in the criterion layer selection in the current period.

[0025] As a further technical solution of the present invention, the method for obtaining the usage fluctuation value is:

[0026] Count the number of relevant data Ki in each used data set; through the formula ΔKi=|K i -K i-1 |Calculate the quantitative deviation of relevant data between adjacent historical periods;

[0027] The quantity deviations of the relevant data between all adjacent historical periods are summed up and averaged to obtain the mean of the relevant data usage deviation;

[0028] Extract the maximum value SYmax of the relevant data in all used data sets;

[0029] Extract the minimum value SYmin of relevant data among all used data sets;

[0030] The maximum deviation value of the relevant data is obtained through the formula: SP = SYmax-SYmin;

[0031] The usage fluctuation value is obtained by summing the mean value of the usage deviation of the relevant data and the maximum value of the usage deviation of the relevant data.

[0032] As a further technical solution of the present invention, the specific process of constructing the comprehensive fuzzy judgment matrix is:

[0033] Select m persons from the enterprise and assessment related personnel;

[0034] Assign weights Wk based on the qualifications, experience and relevance of the field of expertise of the selected personnel;

[0035] Each selector makes a judgment on criterion i relative to criterion j and expresses it with a triangular fuzzy number (aij, bij, cij); both criterion i and criterion j come from the criterion layer; through the formula Perform weighted average processing to obtain the triangular fuzzy number of each person's comprehensive judgment;

[0036] Summarize the judgment results between all criteria; form a comprehensive fuzzy judgment matrix; and perform consistency check on the fuzzy judgment matrix.

[0037] As a further technical solution of the present invention, the specific process of checking the consistency of the fuzzy judgment matrix is ​​as follows:

[0038] By formula The consistency index CI of the fuzzy judgment matrix R is obtained; where λmax is the maximum eigenvalue of the judgment matrix and n is the number of criteria; the random consistency index RI is determined:

[0039] According to the order of the judgment matrix, the corresponding RI value is found from the random consistency index table; RI is obtained by statistical analysis of a large number of randomly generated judgment matrices;

[0040] By formula The consistency ratio CR is obtained; if the CR value is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency and is acceptable; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be readjusted.

[0041] As a further technical solution of the present invention, the specific process of obtaining the electricity consumption potential evaluation of each enterprise in the park is as follows:

[0042] Extract data from the constructed fuzzy judgment matrix R; by formula The fuzzy weight Wi of each criterion is obtained; where Rij is the triangular fuzzy number of criterion i relative to criterion j;

[0043] Assign fuzzy weights to the criteria and perform normalization; collect actual data from the criteria layer of each enterprise in the park; and score each criterion based on the actual data of each enterprise;

[0044] For each enterprise, the weight of each criterion and the score of the enterprise are subjected to fuzzy comprehensive calculation to obtain the comprehensive score of each enterprise;

[0045] Divide the lower bound, median and upper bound of the fuzzy comprehensive score of each enterprise by the maximum upper bound of all enterprise scores; obtain the score standard value of each enterprise; determine the score interval based on the standardized score; classify the interval into four levels; and classify the four evaluation categories of applied electricity potential respectively;

[0046] Take the median of each enterprise's scoring standard value to classify it into intervals; determine the enterprise's electricity consumption potential evaluation based on the divided intervals and formulate an electricity consumption potential evaluation table.

[0047] As a further technical solution of the present invention, the specific process of the fuzzy weight of the allocation criterion is as follows:

[0048] Extract data from the constructed fuzzy judgment matrix R; by formula The fuzzy weight Wi of each criterion is obtained; where Rij is the triangular fuzzy number of criterion i relative to criterion j;

[0049] For each criterion i, refine its fuzzy weight; by formula Get the geometric mean of the lower bound, median and upper bound of each criterion i; where Wi,min represents the geometric mean of the lower bound; W i,med W represents the geometric mean of the median; i,max Denotes the geometric mean of the upper bound.

[0050] As a further technical solution of the present invention, the specific process of the normalization process is: Normalize the fuzzy weight of each criterion i; where W ’ i,min The normalized value of the geometric mean representing the lower bound; W ’ i,med W represents the normalized value of the geometric mean of the median; ’ i,max The normalized value of the geometric mean representing the upper bound;

[0051] It is worth mentioning that W ’ i,min , W ’ i,med , W ’ i,max 1 of

[0052] An evaluation system for distribution network project investment effectiveness, comprising:

[0053] The data collection module is used to collect relevant data for the enterprise's electricity potential assessment in multiple historical periods, and integrate the same relevant data in different periods as independent relevant data sets;

[0054] The data processing module determines the correlation between each data set and the electricity consumption potential assessment by calculating the Pearson correlation coefficient between each data set and the electricity consumption potential score, and establishes a positive correlation table and a negative correlation table based on the Pearson correlation coefficient;

[0055] The evaluation module is used to construct a hierarchical model, determine the evaluation of the investment effectiveness of distribution network projects as the target layer, select the criterion layer based on the relevant data used for electricity potential evaluation in multiple historical periods of the enterprise, and evaluate the enterprises in the park as the solution layer; establish a fuzzy judgment matrix, select personnel for fuzzy judgment, and use triangular fuzzy quantitative judgment; integrate the judgment by calculating the weighted average to construct a comprehensive fuzzy judgment matrix; extract data from the fuzzy judgment matrix; assign fuzzy weights to the criteria; judge the rationality of the weight allocation through consistency indicators and consistency ratios; perform fuzzy comprehensive operations on the weights of the criteria and the scores of the solutions to obtain the electricity consumption potential evaluation of each enterprise in the park.

[0056] The beneficial effects of the present invention are as follows:

[0057] 1. This solution integrates the company's historical cycle data, uses the Pearson correlation coefficient to analyze the data set, and establishes a table of positive and negative and size correlations. Through standardization and correlation analysis, it can more accurately identify and measure the key factors that affect electricity efficiency.

[0058] 2. Construct a hierarchical model to more systematically evaluate and optimize the enterprise's electricity consumption potential and improve the efficiency and effectiveness of energy management. Constructing a fuzzy judgment matrix can integrate the opinions of experts from different backgrounds, improve the objectivity and reliability of the evaluation results, and the consistency test enhances the stability of the evaluation model. Through standardized scoring and grade classification, the evaluation results are easier to understand and apply. The electricity consumption potential score is obtained to enhance the objectivity and reliability of the evaluation results.

[0059] 3. Analyze the enterprise's score deviation value and compare it with its threshold value, combine the electricity consumption potential assessment and deviation category, and provide different levels of electricity price subsidies for different levels of electricity consumption potential. Through differentiated electricity price subsidies, enterprises with large electricity consumption potential and large room for improvement in electricity efficiency can be supported more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] Figure 1 is a flow chart of the steps of the present invention;

[0062] Figure 2 It is a flow chart of the steps of obtaining the positive correlation table and the negative correlation table in the present invention. DETAILED DESCRIPTION

[0063] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0064] Example 1

[0065] like Figure 1 As shown, a method for evaluating the investment effectiveness of a distribution network project according to an embodiment of the present invention includes:

[0066] Step 1: Collect relevant data used for electricity potential assessment in multiple historical periods of the enterprise, integrate the same relevant data in different periods and use them as independent relevant data sets, calculate the Pearson correlation coefficient between each data set and the electricity potential score, determine the correlation between each data set and the electricity potential assessment, and establish positive correlation tables and negative correlation tables based on the Pearson correlation coefficient;

[0067] It should be noted that the relevant data collected include, but are not limited to: the annual output value of the enterprise, the enterprise's operating costs, the number of employees, the enterprise's profitability, annual electricity consumption, and energy efficiency;

[0068] Integrate the same relevant data in different periods and treat them as independent relevant data sets. For example, treat the operating costs of enterprises in different historical periods as an independent data set.

[0069] According to the formula Standardize the data set; mark X' as standardized data, whose X' value is in the interval [0,1]; X is the original data in the data set, where Xmin is the minimum value in the data set and Xmax is the maximum value in the data set;

[0070] The standardized data is processed by the formula Calculate the Pearson correlation coefficient r i

[0071] If the Pearson correlation coefficient r i ≥0, the relevant data are marked as positively correlated data;

[0072] If the Pearson correlation coefficient r i <0, the relevant data will be marked as negatively correlated data;

[0073] Sort the positive correlation data and negative correlation data from large to small according to the Pearson correlation coefficient to obtain a positive correlation table and a negative correlation table;

[0074] Step 2: Construct a hierarchical model, determine the evaluation of the investment effectiveness of the distribution network project as the target layer, select the criterion layer based on the relevant data used for the electricity potential evaluation in multiple historical periods of the enterprise, and evaluate the enterprises in the park as the solution layer;

[0075] Among them, the specific scheme for the selection of the criterion layer is:

[0076] Obtain relevant data used in selecting the criterion layer when evaluating the electricity consumption potential of the enterprise in multiple historical periods, integrate the relevant data used in each historical period into a usage data set, process and analyze the usage data set to obtain a usage fluctuation value, judge the volatility of the relevant data used in selecting the criterion layer according to the usage fluctuation value, and determine the number of relevant data used in selecting the criterion layer in the current period according to the volatility, and select relevant data in the positive correlation table and the negative correlation table respectively according to the number of relevant data used in selecting the criterion layer in the current period;

[0077] Count the number of relevant data in each used data set, marked as Ki (i = 1, 2, .... n), where Ki represents the number of relevant data in the i-th used data set;

[0078] Through the formula ΔKi=|K i -K i-1|Calculate the quantitative deviation of relevant data between adjacent historical periods;

[0079] The quantity deviations of the relevant data between all adjacent historical periods are summed up and averaged to obtain the mean of the relevant data usage deviation;

[0080] In all used data sets, the maximum value of the relevant data is extracted and marked as SYmax;

[0081] Among all the used data sets, the minimum value of the relevant data is extracted and marked as SYmin;

[0082] The maximum deviation value of the relevant data is obtained through the formula: SP = SYmax-SYmin;

[0083] The usage fluctuation value is obtained by summing the mean value of the usage deviation of the relevant data and the maximum value of the usage deviation of the relevant data;

[0084] comparing the usage fluctuation value to a usage fluctuation threshold;

[0085] If the usage fluctuation value is greater than the usage fluctuation threshold, it means that the relevant data used in selecting the criterion layer has strong volatility;

[0086] If the use volatility value is less than or equal to the use volatility threshold, it means that the volatility of the relevant data used in selecting the criterion layer is weak;

[0087] If the volatility of the relevant data is weak, the number of relevant data in each used data set is summed and averaged to obtain the number of relevant data used for criterion layer selection in the current period;

[0088] If the volatility of the relevant data is strong, the number of relevant data in each use data set is integrated into a quantity group, the mode in the quantity group is obtained, and the number of relevant data used for criterion layer selection in the current period is obtained. If there is no mode in the quantity group, the number of relevant data in each use data set is summed and averaged to obtain the number of relevant data used for criterion layer selection in the current period;

[0089] After determining the number of relevant data used for criterion layer selection in the current cycle, positive relevant data and negative relevant data are selected in order from the positive relevant table and the negative relevant table respectively, wherein the selected constraint conditions are: the number of positive relevant data and negative relevant data is the same, and the total number of positive relevant data and negative relevant data is the same as the number of relevant data used for criterion layer selection in the current cycle;

[0090] The technical solution of the embodiment of the present invention is: integrating the historical periodic data of the enterprise into a data set, obtaining the Pearson correlation coefficient of the data set, establishing positive correlation and negative correlation tables according to the positive and negative and size of the coefficient, analyzing the volatility of the historical data, determining the criterion layer for evaluation, and selecting data from the correlation table according to the volatility results. Through standardized processing and correlation analysis, the solution can more accurately identify and measure the key factors affecting electricity efficiency, build a hierarchical model to more systematically evaluate and optimize the electricity potential of the enterprise, and improve the efficiency and effectiveness of energy management.

[0091] Example 2

[0092] like Figure 1 As shown, a method for evaluating the investment effectiveness of a distribution network project according to an embodiment of the present invention includes:

[0093] Step 3: Establish a fuzzy judgment matrix, select personnel to make fuzzy judgments, and use triangular fuzzy quantitative judgments; integrate judgments by calculating weighted averages to construct a comprehensive fuzzy judgment matrix;

[0094] Select m (m=1,2,3,..n) people from the enterprise and assessment related personnel; including but not limited to senior managers, middle managers, scientific researchers and professional technicians;

[0095] Assign weights based on the qualifications, experience and professional field relevance of the selected personnel; and mark their weights as Wk;

[0096] Each selector makes a judgment on criterion i relative to criterion j and represents it with a triangular fuzzy number (aij, bij, cij); aij represents the lower bound, bij represents the median, and cij represents the upper bound; both criterion i and criterion j come from the criterion layer; through the formula Perform weighted average processing to obtain the triangular fuzzy number (Aij, Bij, Cij) of each person's comprehensive judgment; k ij ,b k ij ,c k ij They are the selector’s judgment of criterion i relative to criterion j;

[0097] Summarize the judgment results between all criteria; form an n×n comprehensive fuzzy judgment matrix R; and mark the importance of criterion i relative to criterion j as Rij; where Rij is a triangular fuzzy number (aij, bij, cij);

[0098] Perform consistency check on the fuzzy judgment matrix R; through the formula The consistency index CI of the fuzzy judgment matrix R is obtained; where λmax is the maximum eigenvalue of the judgment matrix and n is the number of criteria; the random consistency index RI is determined:

[0099] According to the order of the judgment matrix, the corresponding RI value is found from the random consistency index table; RI is obtained by statistical analysis of a large number of randomly generated judgment matrices;

[0100] By formula The consistency ratio CR is obtained; if the CR value is less than 0.1, it is considered that the judgment matrix has satisfactory consistency and is acceptable; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be readjusted;

[0101] When the consistency test fails, that is, the consistency ratio CR is greater than or equal to 0.1, the fuzzy judgment matrix needs to be adjusted; the adjustments made should be recorded in detail, including the matrix before and after the adjustment and the reasons for the adjustment;

[0102] Step 4: Extract data from the fuzzy judgment matrix; assign fuzzy weights to the criteria; judge the rationality of weight assignment through consistency index and consistency ratio; perform fuzzy comprehensive calculation on the weights of the criteria and the scores of the schemes to obtain the electricity consumption potential assessment of each enterprise in the park;

[0103] Extract data from the constructed fuzzy judgment matrix R; by formula The fuzzy weight Wi of each criterion is obtained; where Rij is the triangular fuzzy number of criterion i relative to criterion j;

[0104] For each criterion i, refine its fuzzy weight; by formula Get the geometric mean of the lower bound, median and upper bound of each criterion i; where W i,min represents the geometric mean of the lower bound; W i,med W represents the geometric mean of the median; i,max represents the geometric mean of the upper bound;

[0105] use Normalize the fuzzy weight of each criterion i; where W ’ i,min The normalized value of the geometric mean representing the lower bound; W ’ i,med W represents the normalized value of the geometric mean of the median; ’ i,max The normalized value of the geometric mean representing the upper bound;

[0106] It is worth mentioning that W ’ i,min , W ’ i,med , W ’i,max 1 of;

[0107] Collect the actual data of each enterprise under each criterion in the park; then score each criterion according to the actual data of each enterprise; the score is expressed by triangular fuzzy numbers;

[0108] For each enterprise, the weight of each criterion and the score of the enterprise are subjected to fuzzy comprehensive calculation to obtain the comprehensive score of each enterprise;

[0109] Divide the lower bound, median and upper bound of the fuzzy comprehensive score of each enterprise by the maximum upper bound of all enterprise scores respectively; obtain the score standard value of each enterprise; determine the score interval according to the standardized score; classify the interval into four levels: low, medium, high and very high; corresponding to the four evaluation categories of low power consumption potential, medium power consumption potential, high power consumption potential and very high power consumption potential respectively;

[0110] It should be noted that the type of scoring standard value is triangular fuzzy value;

[0111] Take the median of the scoring standard values ​​of each enterprise; and classify them into intervals; determine the enterprise's electricity consumption potential evaluation based on the divided intervals and formulate an electricity consumption potential evaluation table;

[0112] The technical solution of the embodiment of the present invention is: construct a fuzzy judgment matrix to comprehensively evaluate the electricity consumption potential of an enterprise, assign fuzzy number weights and perform consistency checks, extract data from the matrix and perform fuzzy comprehensive operations to obtain the electricity consumption potential score of the enterprise. Constructing a fuzzy judgment matrix can integrate the opinions of experts from different backgrounds, improve the objectivity and reliability of the evaluation results, and the consistency check enhances the stability of the evaluation model. Through standardized scoring and grade classification, the evaluation results are easier to understand and apply.

[0113] Example 3

[0114] A method for evaluating the investment effectiveness of a distribution network project according to an embodiment of the present invention includes:

[0115] Formulate corresponding electricity price incentives based on the evaluation of the enterprise's electricity consumption potential;

[0116] Count the maximum and minimum values ​​in each interval; subtract the minimum value from each value in the interval and divide by the difference between the maximum and minimum values; get the score deviation value for each enterprise; the deviation value ranges from 0 to 1;

[0117] Compare the score deviation value with the score deviation threshold; if the score deviation value is greater than the score deviation threshold, the enterprise is classified as a high deviation class; if the score deviation value is less than or equal to the score deviation threshold, the enterprise is classified as a low deviation class; formulate a deviation table;

[0118] Combine the deviation table with the electricity potential assessment table to obtain a comprehensive assessment table;

[0119] According to the different levels of electricity consumption potential in the comprehensive evaluation table, different levels of electricity price subsidies are provided; for example, low electricity consumption potential: provide a small or no electricity price subsidy; medium electricity consumption potential: provide a medium electricity price subsidy, such as a 4% discount; high electricity consumption potential: provide a large electricity price subsidy, such as an 8% discount; very high electricity consumption potential: provide the largest electricity price subsidy, such as a 12% discount;

[0120] Then, for high-deviation enterprises, time-of-use electricity price discounts will be implemented according to the comprehensive evaluation table to encourage them to use electricity during periods of low electricity demand; such as providing additional electricity price discounts during off-peak hours, such as an additional 4% discount at night or on weekends;

[0121] The technical solution of the embodiment of the present invention is: analyze the score deviation value of the enterprise and compare it with its threshold, classify the enterprise into high deviation and low deviation categories to formulate electricity price incentives; combine the electricity potential assessment and deviation category to provide different degrees of electricity price subsidies for different levels of electricity potential. This solution can more accurately support those enterprises with large electricity potential and large room for improvement in electricity efficiency through differentiated electricity price subsidies. Time-of-use electricity price discounts help balance the load of the power grid and reduce the pressure of electricity demand during peak hours.

[0122] Example 4

[0123] This embodiment discloses a distribution network project investment effectiveness evaluation system, including:

[0124] The data collection module is used to collect relevant data for the enterprise's electricity potential assessment in multiple historical periods, and integrate the same relevant data in different periods as independent relevant data sets;

[0125] The data processing module determines the correlation between each data set and the electricity consumption potential assessment by calculating the Pearson correlation coefficient between each data set and the electricity consumption potential score, and establishes a positive correlation table and a negative correlation table based on the Pearson correlation coefficient;

[0126] The evaluation module is used to construct a hierarchical model, determine the evaluation of the investment effectiveness of distribution network projects as the target layer, select the criterion layer based on the relevant data used for electricity potential evaluation in multiple historical periods of the enterprise, and evaluate the enterprises in the park as the solution layer; establish a fuzzy judgment matrix, select personnel for fuzzy judgment, and use triangular fuzzy quantitative judgment; integrate the judgment by calculating the weighted average to construct a comprehensive fuzzy judgment matrix; extract data from the fuzzy judgment matrix; assign fuzzy weights to the criteria; judge the rationality of the weight allocation through consistency indicators and consistency ratios; perform fuzzy comprehensive operations on the weights of the criteria and the scores of the solutions to obtain the electricity consumption potential evaluation of each enterprise in the park.

[0127] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for evaluating the investment effectiveness of a distribution network project, characterized in that: include: Step 1: Collect relevant data used for electricity potential assessment in multiple historical periods of the enterprise, integrate the same relevant data in different periods and use them as independent relevant data sets, calculate the Pearson correlation coefficient between each data set and the electricity potential score, determine the correlation between each data set and the electricity potential assessment, and establish positive correlation tables and negative correlation tables based on the Pearson correlation coefficient; Step 2: Construct a hierarchical model, determine the evaluation of the investment effectiveness of the distribution network project as the target layer, select the criterion layer based on the relevant data used for the electricity potential evaluation in multiple historical periods of the enterprise, and evaluate the enterprises in the park as the solution layer; Step 3: Establish a fuzzy judgment matrix, select personnel to make fuzzy judgments, and use triangular fuzzy quantitative judgments; integrate judgments by calculating weighted averages to construct a comprehensive fuzzy judgment matrix; Step 4: Extract data from the fuzzy judgment matrix; assign fuzzy weights to the criteria; judge the rationality of weight assignment through consistency index and consistency ratio; perform fuzzy comprehensive calculation on the weights of the criteria and the scores of the schemes to obtain the electricity consumption potential assessment of each enterprise in the park.

2. The method for evaluating the investment effectiveness of a distribution network project according to claim 1, characterized in that: The process of establishing the positive correlation table and the negative correlation table according to the Pearson correlation coefficient is as follows: Standardize the data set; calculate the Pearson correlation coefficient r of the standardized data i ; If the Pearson correlation coefficient r i ≥0, the relevant data are marked as positively correlated data; If the Pearson correlation coefficient r i <0, the relevant data will be marked as negatively correlated data; Sort the positive correlation data and negative correlation data from large to small according to the Pearson correlation coefficient to obtain the positive correlation table and the negative correlation table.

3. The method for evaluating the investment effectiveness of a distribution network project according to claim 1, characterized in that: The process of selecting the criterion layer based on the relevant data used for the electricity consumption potential assessment in multiple historical periods of the enterprise is as follows: When obtaining the electricity consumption potential assessment of the enterprise in multiple historical periods, the data used in each criterion layer is selected and integrated into the usage data set, and the usage data set is processed and analyzed to obtain the usage fluctuation value; comparing the usage fluctuation value to a usage fluctuation threshold; If the usage fluctuation value is greater than the usage fluctuation threshold, it means that the relevant data used in selecting the criterion layer has strong volatility; If the usage fluctuation value is less than or equal to the usage fluctuation threshold, it means that the volatility of the relevant data used in selecting the criterion layer is weak; the relevant data in each usage data set is averaged to obtain the number of data currently selected as the criterion layer; If the volatility of the relevant data is strong, the mode processing of the relevant data in each use data set is performed to obtain the number of data currently selected as the criterion layer. If there is no mode in the quantity group, the number of relevant data in each use data set is summed and the average is taken; After determining the number of data currently selected as the criterion layer, select data in order from the positive correlation table and the negative correlation table respectively; The selection is made while the number of positive correlation data and negative correlation data is the same, and the total number of positive correlation data and negative correlation data is the same as the number of correlation data used in the criterion layer selection in the current period.

4. The method for evaluating the investment effectiveness of a distribution network project according to claim 2, characterized in that: The method for obtaining the usage fluctuation value is as follows: Count the number of relevant data Ki in each used data set; through the formula ΔKi=|K i -K i-1 |Calculate the quantitative deviation of relevant data between adjacent historical periods; The quantity deviations of the relevant data between all adjacent historical periods are summed up and averaged to obtain the mean of the relevant data usage deviation; Extract the maximum value SYmax of the relevant data in all used data sets; Extract the minimum value SYmin of relevant data among all used data sets; The maximum deviation value of the relevant data is obtained through the formula: SP = SYmax-SYmin; The usage fluctuation value is obtained by summing the mean value of the usage deviation of the relevant data and the maximum value of the usage deviation of the relevant data.

5. The method for evaluating the investment effectiveness of a distribution network project according to claim 1, characterized in that: The specific process of constructing a comprehensive fuzzy judgment matrix is ​​as follows: Select m persons from the enterprise and assessment related personnel; Assign weights Wk based on the qualifications, experience and relevance of the field of expertise of the selected personnel; Each selector makes a judgment on criterion i relative to criterion j and expresses it with a triangular fuzzy number (aij, bij, cij); both criterion i and criterion j come from the criterion layer; through the formula Perform weighted average processing to obtain the triangular fuzzy number of each person's comprehensive judgment; Summarize the judgment results among all criteria; Form a comprehensive fuzzy judgment matrix; perform consistency check on the fuzzy judgment matrix.

6. The method for evaluating the investment effectiveness of a distribution network project according to claim 5, characterized in that: The specific process of checking the consistency of the fuzzy judgment matrix is ​​as follows: By formula The consistency index CI of the fuzzy judgment matrix R is obtained; where λmax is the maximum eigenvalue of the judgment matrix and n is the number of criteria; the random consistency index RI is determined: According to the order of the judgment matrix, the corresponding RI value is found from the random consistency index table; RI is obtained by statistical analysis of a large number of randomly generated judgment matrices; By formula The consistency ratio CR is obtained; if the CR value is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency and is acceptable; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be readjusted.

7. The method for evaluating the investment effectiveness of a distribution network project according to claim 1, characterized in that: The specific process of evaluating the electricity consumption potential of each enterprise in the park is as follows: Extract data from the constructed fuzzy judgment matrix R; through the formula The fuzzy weight Wi of each criterion is obtained; where Rij is the triangular fuzzy number of criterion i relative to criterion j; Assign fuzzy weights to the criteria and perform normalization; Collect actual data from each enterprise level in the park; Each criterion is scored based on the actual data of each enterprise; For each enterprise, the weight of each criterion and the score of the enterprise are subjected to fuzzy comprehensive calculation; Get the comprehensive rating of each enterprise; Divide the lower bound, median and upper bound of the fuzzy comprehensive score of each enterprise by the maximum upper bound of all enterprise scores; obtain the score standard value of each enterprise; determine the score interval based on the standardized score; classify the interval into four levels; and classify the four evaluation categories of applied electricity potential respectively; Take the median of each enterprise's scoring standard value to classify it into intervals; determine the enterprise's electricity consumption potential evaluation based on the divided intervals and formulate an electricity consumption potential evaluation table.

8. The method for evaluating the investment effectiveness of a distribution network project according to claim 7, characterized in that: The specific process of allocating fuzzy weights of the criteria is as follows: Extract data from the constructed fuzzy judgment matrix R; through the formula The fuzzy weight Wi of each criterion is obtained; where Rij is the triangular fuzzy number of criterion i relative to criterion j; For each criterion i, refine its fuzzy weight; by formula Get the geometric mean of the lower bound, median and upper bound of each criterion i; where W i,min represents the geometric mean of the lower bound; W i,med W represents the geometric mean of the median; i,max Denotes the geometric mean of the upper bound.

9. The method for evaluating the investment effectiveness of a distribution network project according to claim 7, characterized in that: The specific process of the normalization process is as follows: Normalize the fuzzy weight of each criterion i; where W ’ i,min The normalized value of the geometric mean representing the lower bound; W ’ i,med W represents the normalized value of the geometric mean of the median; ’ i,max The normalized value of the geometric mean representing the upper bound; It is worth mentioning that W ’ i,min , W ’ i,med , W ’ i,max The sum of is 1. Count the maximum and minimum values ​​in each interval; subtract the minimum value from each value in the interval and divide it by the difference between the maximum and minimum values; Get the score deviation value of each enterprise; Compare the score deviation value with the score deviation threshold; if the score deviation value is greater than the score deviation threshold, the enterprise is classified as a high deviation class; if the score deviation value is less than or equal to the score deviation threshold, the enterprise is classified as a low deviation class; formulate a deviation table; Combine the deviation table with the electricity potential assessment table to obtain a comprehensive assessment table; Different degrees of electricity price subsidies will be provided according to the different levels of electricity consumption potential assessment in the comprehensive evaluation table; and time-of-use electricity price discounts will be implemented for high-deviation enterprises based on the comprehensive evaluation table.

10. A distribution network project investment effectiveness evaluation system, characterized in that: include: The data collection module is used to collect relevant data for the enterprise's electricity potential assessment in multiple historical periods, and integrate the same relevant data in different periods as independent relevant data sets; The data processing module determines the correlation between each data set and the electricity consumption potential assessment by calculating the Pearson correlation coefficient between each data set and the electricity consumption potential score, and establishes a positive correlation table and a negative correlation table based on the Pearson correlation coefficient; The evaluation module is used to build a hierarchical model, determine the evaluation of the investment effect of the distribution network project as the target layer, select the criterion layer based on the relevant data used for the electricity potential evaluation in multiple historical cycles of the enterprise, and evaluate the enterprises in the park as the solution layer; Establish a fuzzy judgment matrix, select personnel to make fuzzy judgments, and use triangular fuzzy quantitative judgments; integrate judgments by calculating weighted averages to construct a comprehensive fuzzy judgment matrix; extract data from the fuzzy judgment matrix; assign fuzzy weights to criteria; judge the rationality of weight allocation through consistency indicators and consistency ratios; perform fuzzy comprehensive operations on the weights of criteria and the scores of the plans to obtain an evaluation of the electricity consumption potential of each enterprise in the park.

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