A benchmarking evaluation method for dispatching benefits of cascade hydropower stations considering hydropower station characteristics

Abstract

This invention provides a method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group, taking into account the characteristics of hydropower stations. First, it collects and organizes basic and benefit indicators related to hydropower station scheduling benefits, thereby establishing a scientific and objective evaluation system for hydropower station scheduling benefits, and standardizes the indicators in the evaluation system. Second, considering the differences in hardware facilities of different hydropower stations, it quantifies the benefit indicator data of different hydropower stations and river basin entities into intensity values ​​based on complexity theory, enabling comparative analysis of different evaluation objects on the same dimension. Third, based on the intensity values ​​of benefit indicators, it calculates the combined weights of benefit indicators using the entropy weight method and the CRITIC method, obtaining indicator weights that take into account both data disorder and conflict. Finally, it uses an improved TOPSIS method to obtain the scheduling benefit level of different hydropower stations and river basin entities along the schedule, analyzing the changes in the benefit level of different hydropower stations and river basin entities from a time dimension.

Description

Technical Field

[0001] This invention belongs to the field of benchmarking evaluation of hydropower stations, and in particular relates to a benchmarking evaluation method for the scheduling benefits of a cascade hydropower station group that takes into account the characteristics of hydropower stations. Background Technology

[0002] Due to factors such as development difficulty and environmental pressures, securing new projects is becoming increasingly challenging for hydropower developers. To maximize profits, hydropower developers are shifting their focus from developing new power plants to meticulous management of existing assets. Benchmarking is an effective way to improve management efficiency. It can identify gaps between different companies, pinpoint their weaknesses, and help improve their operational efficiency. However, direct evaluation based on one or more indicators is not easy because there are significant differences in the distribution areas, social services, operating environments, hydraulic structures, mechanical equipment, and electrical equipment parameters among different hydropower stations. Therefore, establishing a standardized evaluation system for different hydropower station groups to improve the operational efficiency and management level of hydropower stations is a pressing theoretical and practical problem that needs to be solved. Summary of the Invention

[0003] This invention addresses the complexity of benchmarking and evaluating the scheduling efficiency of hydropower stations. First, it collects and organizes basic and efficiency indicators related to hydropower station scheduling efficiency, thus establishing a scientific and objective benchmarking and evaluation system for hydropower station scheduling efficiency, and standardizes the indicators within the evaluation system. Second, considering the differences in hardware facilities among different hydropower stations, it quantifies the efficiency indicator data of different hydropower stations and river basin entities into intensity values ​​based on complexity theory, enabling comparative analysis of different evaluation objects on the same dimension. Third, based on the efficiency indicator intensity values, it calculates the combined weights of efficiency indicators using the entropy weight method and the CRITIC method, obtaining indicator weights that take into account both data disorder and conflict. Finally, it uses an improved TOPSIS method to obtain the scheduling efficiency level of different hydropower stations and river basin entities over time, analyzing the changes in efficiency levels of different hydropower stations and river basin entities from a time perspective. This invention can identify hydropower stations with low efficiency and propose improvement suggestions, which has certain reference value for promoting the level of hydropower operation and management in my country.

[0004] To achieve the above-mentioned technical features, the objective of this invention is as follows: a method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations, comprising the following steps:

[0005] Step 1: Construct a benchmarking and evaluation system for the dispatching benefits of hydropower stations:

[0006] Collect and organize basic and benefit indicators related to the scheduling benefits of hydropower stations, construct a benchmarking evaluation system for the scheduling benefits of hydropower stations, and standardize the indicators in the evaluation system.

[0007] Step 2, quantify the scheduling efficiency indicators of hydropower stations:

[0008] Considering the differences in hardware facilities of different hydropower stations, the benefit index data of hydropower stations and the main body of the watershed are quantified into intensity values ​​through complexity theory;

[0009] Step 3: Determine the combined weights of hydropower station dispatch benefit indicators:

[0010] Based on the strength value of the benefit indicator, the weight of the benefit indicator is calculated by the entropy weight method and the CRITIC method. Then, the combined weight of the benefit indicator is obtained by taking the minimum sum of squared deviations of the weight results of the two methods as the criterion.

[0011] Step 4: Calculate the scheduling efficiency level of the hydropower station and its progress.

[0012] By combining the grey relational analysis method with the Euclidean distance method, the distance metric of the traditional TOPSIS method is improved, and the scheduling efficiency level of hydropower stations and the main body of the watershed is calculated for different months.

[0013] The detailed steps for constructing the hydropower station dispatch efficiency benchmarking evaluation system in step 1 are as follows:

[0014] Step 1.1 establishes an evaluation index system reflecting the comprehensive dispatch efficiency of hydropower stations. This system is divided into two categories: basic indicators and efficiency indicators. Basic indicators reflect the differences in hydraulic structure, mechanical equipment, and electrical equipment among different hydropower stations, including rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output. Efficiency indicators reflect the dispatch efficiency of hydropower stations based on power generation and water utilization, including water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate. Specific data for the basic indicators are obtained from the hydropower plant's technical parameter tables, while specific data for the efficiency indicators are calculated using historical dispatch data of the hydropower stations. The specific calculation method is as follows:

[0015] ① Power generation:

[0016]

[0017] In the formula, This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the average daily reservoir water level of the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); iThis represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0018] ②Water conservation and increased power generation:

[0019]

[0020] In the formula, This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0021] ③ Water energy utilization improvement rate:

[0022]

[0023] In the formula, This represents the water energy utilization improvement rate of the i-th hydropower station at time t, in percentage. This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0024] ④ Power generation water consumption rate:

[0025]

[0026] In the formula: This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h; This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters. 3 / s; T represents the calculation period, in hours;

[0027] ⑤ Wasted water power:

[0028]

[0029] In the formula, This represents the amount of water wasted by the i-th hydropower station at time t, expressed in ten thousand kWh. This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s; This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h;

[0030] ⑥ Installed utilization hours:

[0031]

[0032] In the formula: This represents the installed capacity utilization hours of the i-th hydropower station at time t, in hours (h). N represents the power generation of the i-th hydropower station at time t, in 10,000 kW·h. i This represents the installed capacity of the i-th hydropower station, in MW.

[0033] ⑦ Water utilization rate:

[0034]

[0035] In the formula, Water utilization rate, expressed as a percentage. This represents the average daily inflow to the i-th hydropower station, in meters (m³). 3 / s; This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s;

[0036] Step 1.2: The efficacy coefficient method is used to perform dimensionless processing on the indicators to eliminate the influence of differences in units, amplitudes, and trends between indicators on the evaluation results. The efficacy coefficient method is calculated as follows:

[0037] For positive indicators:

[0038]

[0039] In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,maxs and s represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1, c is used to shift the dimensionless value of the indicator, and d is used to scale the dimensionless value of the indicator.

[0040] For contrarian indicators:

[0041]

[0042] In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,max and u j,min Let c and d represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1. The function of c is to shift the dimensionless value of the indicator, and the function of d is to scale the dimensionless value of the indicator.

[0043] The detailed steps for quantifying the hydropower station dispatch efficiency indicators in step 2 are as follows:

[0044] Step 2.1: Based on the constructed indicator system, the basic indicators—rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output—are used as independent variables. The benefit indicators—water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate—are used as dependent variables in sequence. First, a univariate regression equation is constructed by selecting the j-th benefit indicator and each basic indicator, as shown below:

[0045] y j =β0+β q s q ,(q=1,2,···,K)(10);

[0046] In the formula, y j s represents the j-th benefit indicator; q β represents the q-th basic indicator; β0 represents the intercept; β q represents the regression coefficient of the q-th basic indicator; K represents the number of basic indicators;

[0047] Step 2.2: Calculate the deviation F of the basic index in each regression equation, and select the basic index with the largest deviation F. If F max >F 0.05 Then, this basic indicator is introduced into the model, and the deviation F value of each basic indicator in the model after introducing this indicator is tested. If F q <F 0.1 Then, the q-th basic indicator is removed until no indicator needs to be included in the equation or removed from the equation. After screening, the regression equations for the j-th benefit indicator and the basic indicators are as follows:

[0048] yj =β0+β1s1+···+β Q s Q (11);

[0049] In the formula, y j β represents the j-th benefit indicator; β0 represents the intercept; β Q This represents the regression coefficient of the Qth basic indicator; Q represents the number of basic indicators remaining after screening.

[0050] Step 2.3: Calculate the weight of each basic indicator based on the magnitude of its regression coefficient in the regression equation. The calculation method is as follows:

[0051]

[0052] In the formula, w Q It is the weight of the Qth basic indicator; β Q β represents the regression coefficient of the Qth basic indicator; sum This represents the sum of the absolute values ​​of the regression coefficients of the basic indicators; Q represents the number of basic indicators remaining after screening.

[0053] Step 2.4 introduces the variance inflation factor test index to determine whether it passes the collinearity test. The calculation method is as follows:

[0054]

[0055] In the formula, V q This represents the variance inflation factor of the q-th basic indicator; This represents the correlation coefficient between the q-th basic indicator and other basic indicators; when V q ≤10 indicates that the q-th basic index does not exhibit multicollinearity;

[0056] Step 2.5, considering the differences in hardware facilities between different hydropower stations and the temporal and spatial connections between different hydropower stations in the same river basin, constructs two types of intensity values ​​using complexity theory to standardize the benefit indicators of hydropower stations and the main body of the river basin. The two types of intensity values ​​are described in detail below:

[0057] Evaluation of the intensity values ​​of single benefit indicators for different hydropower stations:

[0058]

[0059] In the formula, This represents the intensity value of the j-th benefit indicator of the i-th hydropower station at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; qf represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; Q represents the number of basic indicators remaining after screening.

[0060] Evaluation of the intensity values ​​of single benefit indicators for different watershed subjects:

[0061]

[0062] In the formula, This represents the intensity value of the j-th benefit indicator of the g-th watershed entity at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; q f represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; p represents the total number of hydropower stations within the main body g of the basin; Q represents the number of remaining basic indicators after screening.

[0063] The detailed steps for determining the combined weights of hydropower station dispatch benefit indicators in step 3 are as follows:

[0064] Step 3.1: Based on the strength value of the benefit indicators, obtain the amount of useful information carried and transmitted by each benefit indicator. Then, calculate the weight of each benefit indicator using the entropy weight method, as follows:

[0065]

[0066] In the formula, α ej e represents the weight of the j-th benefit indicator obtained based on the entropy weight method; j Let J represent the entropy value of the j-th benefit indicator; J represents the total number of benefit indicators.

[0067] Step 3.2: Based on the strength values ​​of the benefit indicators, obtain the degree of conflict and volatility of each benefit indicator data. Then, calculate the weight of each benefit indicator using the CRITIC method, as follows:

[0068]

[0069] In the formula, α cj σ represents the weight of the j-th benefit indicator obtained based on the CRITIC method; j R represents the degree of fluctuation of the j-th benefit indicator; j S represents the degree of conflict for the j-th benefit indicator; j This represents the information content of the j-th benefit indicator; J represents the total number of benefit indicators.

[0070] Step 3.3 minimizes the heterogeneity of the weight results from the entropy weight method and the CRITIC method to obtain a combined weight that fully considers the disorder and conflict of the indicator data. The calculation method is as follows:

[0071]

[0072] In the formula, α j α represents the combined weight of the j-th benefit indicator; ej α represents the weight of the j-th benefit indicator obtained based on the entropy weight method; cj represents the weight of the j-th benefit indicator obtained based on the CRITIC method; J represents the total number of benefit indicators.

[0073] The detailed steps for calculating the hydropower station dispatch efficiency level and schedule in step 4 are as follows:

[0074] Step 4.1: Select J indicators, I hydropower stations or river basin entities, and T time periods to construct the original evaluation matrix:

[0075]

[0076] In the formula, R represents the original evaluation matrix; Let J represent the intensity value of the j-th benefit indicator of the i-th hydropower enterprise at time t; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period.

[0077] Step 4.2: The original evaluation matrix is ​​dimensionless to obtain the standardized evaluation matrix R. * The weighted evaluation matrix is ​​obtained by combining the standardized evaluation matrix with the weighted combination of benefit indicators, as shown below:

[0078]

[0079] In the formula, Z represents the weighted evaluation matrix; express The value after dimensionless processing; α j This represents the weight of the j-th benefit indicator; This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period.

[0080] Step 4.3: Obtain the positive and negative ideal solutions based on the maximum and minimum values ​​corresponding to each indicator in the weighted evaluation matrix.

[0081]

[0082] In the formula, Z + and Z- These represent the positive ideal solution and the negative ideal solution, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators.

[0083] Step 4.4, calculate the Euclidean distance and grey relational degree; the Euclidean distance is calculated as follows:

[0084]

[0085] In the formula, and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators.

[0086] The grey relational degree is calculated as follows:

[0087]

[0088] In the formula, and These are the grey relational degrees between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively. and It is the grey correlation coefficient; J represents the number of benefit indicators;

[0089] Step 4.5: Construct an improved distance metric using grey relational analysis and Euclidean distance. The calculation method is as follows:

[0090]

[0091] In the formula, and These are the new distance metrics of the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions; and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. and λ represents the grey relational degree between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively; λ represents the degree of emphasis on the two distances.

[0092] Step 4.6: Calculate the relative tracking progress using the new distance metric. A larger relative tracking progress value indicates that the benefit of the evaluated object i at time t is closer to the positive ideal solution, and the better the benefit. The calculation method for relative tracking progress is as follows:

[0093]

[0094] Where D i,t This represents the relative progress value of the i-th hydropower enterprise at time t. and These are the new distance metrics for the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions.

[0095] Beneficial effects of this invention:

[0096] 1. We collected and organized basic and benefit indicators related to the scheduling benefits of hydropower stations, and constructed a scientific, objective and reasonable benchmarking and evaluation system for the scheduling benefits of hydropower stations, providing a reference for the scheduling work of hydropower stations in my country.

[0097] 2. Considering the differences in hardware facilities of different hydropower stations, the benefit index data of different hydropower stations and river basin entities are quantified into intensity values ​​based on complexity theory, so that different evaluation objects can be compared and analyzed in the same dimension.

[0098] 3. Based on the strength values ​​of the benefit indicators, calculate the combined weights of the benefit indicators using the entropy weight method and the CRITIC method to obtain the indicator weights that take into account both the disorder and conflict of the data.

[0099] 4. The scheduling efficiency level of different hydropower stations and river basin entities is obtained by using the improved TOPSIS method, and the changes in efficiency level of different hydropower stations and river basin entities are analyzed from the time dimension. Attached Figure Description

[0100] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0101] Figure 1 This is a flowchart for benchmarking and evaluating scheduling efficiency.

[0102] Figure 2 This is a diagram of the scheduling efficiency benchmarking and evaluation index system.

[0103] Figure 3 This is a topology diagram of a hydroelectric power station.

[0104] Figure 4 This is a graph for verifying the effectiveness of the strength values ​​of the benefit indicators.

[0105] Figure 5 This is a graph showing the combined weighting of benefit indicators.

[0106] Figure 6This is a graph showing the progress results of the scheduling efficiency of hydropower stations and the main watershed operations. Detailed Implementation

[0107] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0108] Example 1:

[0109] like Figure 1-5 As shown, a benchmarking evaluation method for the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations is proposed. First, basic and benefit indicators related to hydropower station scheduling benefits are collected and organized to establish a scientific and objective benchmarking evaluation system for hydropower station scheduling benefits, and the indicators in the evaluation system are standardized. Second, considering the differences in hardware facilities of different hydropower stations, the benefit indicator data of different hydropower stations and river basin entities are quantified into intensity values ​​based on complexity theory, enabling different evaluation objects to be compared and analyzed on the same dimension. Third, based on the intensity values ​​of benefit indicators, the combined weights of benefit indicators are calculated using the entropy weight method and the CRITIC method to obtain indicator weights that take into account both data disorder and conflict. Finally, the improved TOPSIS method is used to obtain the scheduling benefit level of different hydropower stations and river basin entities along the schedule, analyzing the changes in the benefit level of different hydropower stations and river basin entities from a time dimension.

[0110] Example 2:

[0111] A method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group, considering the characteristics of hydropower stations, includes the following steps:

[0112] Step 1: Construct a benchmarking and evaluation system for the dispatching benefits of hydropower stations:

[0113] Collect and organize basic and benefit indicators related to the scheduling benefits of hydropower stations, construct a benchmarking evaluation system for the scheduling benefits of hydropower stations, and standardize the indicators in the evaluation system.

[0114] Step 2, quantify the scheduling efficiency indicators of hydropower stations:

[0115] Considering the differences in hardware facilities of different hydropower stations, the benefit index data of hydropower stations and the main body of the watershed are quantified into intensity values ​​through complexity theory;

[0116] Step 3: Determine the combined weights of hydropower station dispatch benefit indicators:

[0117] Based on the strength value of the benefit indicator, the weight of the benefit indicator is calculated by the entropy weight method and the CRITIC method. Then, the combined weight of the benefit indicator is obtained by taking the minimum sum of squared deviations of the weight results of the two methods as the criterion.

[0118] Step 4: Calculate the scheduling efficiency level of the hydropower station and its progress.

[0119] By combining the grey relational analysis method with the Euclidean distance method, the distance metric of the traditional TOPSIS method is improved, and the scheduling efficiency level of hydropower stations and the main body of the watershed is calculated for different months.

[0120] Furthermore, the detailed steps for constructing the hydropower station dispatch efficiency benchmarking evaluation system in step 1 are as follows:

[0121] Step 1.1 establishes an evaluation index system reflecting the comprehensive dispatch efficiency of hydropower stations. This system is divided into two categories: basic indicators and efficiency indicators. Basic indicators reflect the differences in hydraulic structure, mechanical equipment, and electrical equipment among different hydropower stations, including rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output. Efficiency indicators reflect the dispatch efficiency of hydropower stations based on power generation and water utilization, including water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate. Specific data for the basic indicators are obtained from the hydropower plant's technical parameter tables, while specific data for the efficiency indicators are calculated using historical dispatch data of the hydropower stations. The specific calculation method is as follows:

[0122] ① Power generation:

[0123]

[0124] In the formula, This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the average daily reservoir water level of the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0125] ②Water conservation and increased power generation:

[0126]

[0127] In the formula, This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0128] ③ Water energy utilization improvement rate:

[0129]

[0130] In the formula, This represents the water energy utilization improvement rate of the i-th hydropower station at time t, in percentage. This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours.

[0131] ④ Power generation water consumption rate:

[0132]

[0133] In the formula: This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h; This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters. 3 / s; T represents the calculation period, in hours;

[0134] ⑤ Wasted water power:

[0135]

[0136] In the formula, This represents the amount of water wasted by the i-th hydropower station at time t, expressed in ten thousand kWh. This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s; This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h;

[0137] ⑥ Installed utilization hours:

[0138]

[0139] In the formula: This represents the installed capacity utilization hours of the i-th hydropower station at time t, in hours (h). N represents the power generation of the i-th hydropower station at time t, in 10,000 kW·h. i This represents the installed capacity of the i-th hydropower station, in MW.

[0140] ⑦ Water utilization rate:

[0141]

[0142] In the formula, Water utilization rate, expressed as a percentage. This represents the average daily inflow to the i-th hydropower station, in meters (m³). 3 / s; This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s;

[0143] Step 1.2: The efficacy coefficient method is used to perform dimensionless processing on the indicators to eliminate the influence of differences in units, amplitudes, and trends between indicators on the evaluation results. The efficacy coefficient method is calculated as follows:

[0144] For positive indicators:

[0145]

[0146] In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,max s and s represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1, c is used to shift the dimensionless value of the indicator, and d is used to scale the dimensionless value of the indicator.

[0147] For contrarian indicators:

[0148]

[0149] In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,max and u j,min Let c and d represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1. The function of c is to shift the dimensionless value of the indicator, and the function of d is to scale the dimensionless value of the indicator.

[0150] Furthermore, the detailed steps for quantifying the hydropower station dispatch efficiency indicators in step 2 are as follows:

[0151] Step 2.1: Based on the constructed indicator system, the basic indicators—rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output—are used as independent variables. The benefit indicators—water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate—are used as dependent variables in sequence. First, a univariate regression equation is constructed by selecting the j-th benefit indicator and each basic indicator, as shown below:

[0152] y j =β0+β q s q ,(q=1,2,···,K)(10);

[0153] In the formula, y j s represents the j-th benefit indicator; q β represents the q-th basic indicator; β0 represents the intercept; β q represents the regression coefficient of the q-th basic indicator; K represents the number of basic indicators;

[0154] Step 2.2: Calculate the deviation F of the basic index in each regression equation, and select the basic index with the largest deviation F. If F max >F 0.05 Then, this basic indicator is introduced into the model, and the deviation F value of each basic indicator in the model after introducing this indicator is tested. If F q <F 0.1 Then, the q-th basic indicator is removed until no indicator needs to be included in the equation or removed from the equation. After screening, the regression equations for the j-th benefit indicator and the basic indicators are as follows:

[0155] y j =β0+β1s1+···+β Q s Q (11);

[0156] In the formula, y j β represents the j-th benefit indicator; β0 represents the intercept; β Q This represents the regression coefficient of the Qth basic indicator; Q represents the number of basic indicators remaining after screening.

[0157] Step 2.3: Calculate the weight of each basic indicator based on the magnitude of its regression coefficient in the regression equation. The calculation method is as follows:

[0158]

[0159] In the formula, w Q It is the weight of the Qth basic indicator; β Q β represents the regression coefficient of the Qth basic indicator; sum This represents the sum of the absolute values ​​of the regression coefficients of the basic indicators; Q represents the number of basic indicators remaining after screening.

[0160] Step 2.4 introduces the variance inflation factor test index to determine whether it passes the collinearity test. The calculation method is as follows:

[0161]

[0162] In the formula, V q This represents the variance inflation factor of the q-th basic indicator; This represents the correlation coefficient between the q-th basic indicator and other basic indicators; when V q ≤10 indicates that the q-th basic index does not exhibit multicollinearity;

[0163] Step 2.5, considering the differences in hardware facilities between different hydropower stations and the temporal and spatial connections between different hydropower stations in the same river basin, constructs two types of intensity values ​​using complexity theory to standardize the benefit indicators of hydropower stations and the main body of the river basin. The two types of intensity values ​​are described in detail below:

[0164] Evaluation of the intensity values ​​of single benefit indicators for different hydropower stations:

[0165]

[0166] In the formula, This represents the intensity value of the j-th benefit indicator of the i-th hydropower station at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; q f represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; Q represents the number of basic indicators remaining after screening.

[0167] Evaluation of the intensity values ​​of single benefit indicators for different watershed subjects:

[0168]

[0169] In the formula, This represents the intensity value of the j-th benefit indicator of the g-th watershed entity at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; q f represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; p represents the total number of hydropower stations within the main body g of the basin; Q represents the number of remaining basic indicators after screening.

[0170] Furthermore, the detailed steps for determining the combined weights of the hydropower station dispatch benefit indicators in step 3 are as follows:

[0171] Step 3.1: Based on the strength value of the benefit indicators, obtain the amount of useful information carried and transmitted by each benefit indicator. Then, calculate the weight of each benefit indicator using the entropy weight method, as follows:

[0172]

[0173] In the formula, α ej e represents the weight of the j-th benefit indicator obtained based on the entropy weight method; j Let J represent the entropy value of the j-th benefit indicator; J represents the total number of benefit indicators.

[0174] Step 3.2: Based on the strength values ​​of the benefit indicators, obtain the degree of conflict and volatility of each benefit indicator data. Then, calculate the weight of each benefit indicator using the CRITIC method, as follows:

[0175]

[0176] In the formula, α cj σ represents the weight of the j-th benefit indicator obtained based on the CRITIC method; j R represents the degree of fluctuation of the j-th benefit indicator; j S represents the degree of conflict for the j-th benefit indicator; j This represents the information content of the j-th benefit indicator; J represents the total number of benefit indicators.

[0177] Step 3.3 minimizes the heterogeneity of the weight results from the entropy weight method and the CRITIC method to obtain a combined weight that fully considers the disorder and conflict of the indicator data. The calculation method is as follows:

[0178]

[0179] In the formula, α j α represents the combined weight of the j-th benefit indicator; ejα represents the weight of the j-th benefit indicator obtained based on the entropy weight method; cj represents the weight of the j-th benefit indicator obtained based on the CRITIC method; J represents the total number of benefit indicators.

[0180] Furthermore, the detailed steps for calculating the hydropower station dispatch efficiency level and schedule in step 4 are as follows:

[0181] Step 4.1: Select J indicators, I hydropower stations or river basin entities, and T time periods to construct the original evaluation matrix:

[0182]

[0183] In the formula, R represents the original evaluation matrix; Let J represent the intensity value of the j-th benefit indicator of the i-th hydropower enterprise at time t; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period.

[0184] Step 4.2: The original evaluation matrix is ​​dimensionless to obtain the standardized evaluation matrix R. * The weighted evaluation matrix is ​​obtained by combining the standardized evaluation matrix with the weighted combination of benefit indicators, as shown below:

[0185]

[0186] In the formula, Z represents the weighted evaluation matrix; express The value after dimensionless processing; α j This represents the weight of the j-th benefit indicator; This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period.

[0187] Step 4.3: Obtain the positive and negative ideal solutions based on the maximum and minimum values ​​corresponding to each indicator in the weighted evaluation matrix.

[0188]

[0189] In the formula, Z + and Z - These represent the positive ideal solution and the negative ideal solution, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators.

[0190] Step 4.4, calculate the Euclidean distance and grey relational degree; the Euclidean distance is calculated as follows:

[0191]

[0192] In the formula, and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators.

[0193] The grey relational degree is calculated as follows:

[0194]

[0195] In the formula, and These are the grey relational degrees between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively. and It is the grey correlation coefficient; J represents the number of benefit indicators;

[0196] Step 4.5: Construct an improved distance metric using grey relational analysis and Euclidean distance. The calculation method is as follows:

[0197]

[0198] In the formula, and These are the new distance metrics of the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions; and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. and λ represents the grey relational degree between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively; λ is the degree of bias between the two distances, set to 0.5.

[0199] Step 4.6: Calculate the relative tracking progress using the new distance metric. A larger relative tracking progress value indicates that the benefit of the evaluated object i at time t is closer to the positive ideal solution, and the better the benefit. The calculation method for relative tracking progress is as follows:

[0200]

[0201] Where D i,t This represents the relative progress value of the i-th hydropower enterprise at time t. and These are the new distance metrics for the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions.

[0202] Example 3:

[0203] This study analyzed the scheduling efficiency of 37 hydropower stations in different river basins of two provinces in southwestern my country over 12 months of the year, focusing on different hydropower stations and the main body of the river basin. The topology and specific parameters of the hydropower stations are as follows: Figure 3 As shown, 37 hydropower stations are distributed in the AF basin, and the hardware facilities of different hydropower stations vary greatly.

[0204] Figure 4 The difference between the raw data and intensity values ​​of the benefit indicators is shown. It can be seen that the standard deviation of the intensity values ​​of each benefit indicator decreased by more than 0.01 compared with the raw data. This indicates that quantifying the benefit indicators into intensity values ​​through complexity theory effectively eliminates the impact of data fluctuations caused by differences in the basic indicators of different power plants on the evaluation results. Figure 5 The results show the differences between the combined weighting of benefit indicators and the weighting results of the entropy weighting method and the CRITIC method. It can be seen that the average difference between the combined weighting results and the weighting results of the entropy weighting method and the CRITIC method is smaller than the difference between the weighting results of the entropy weighting method and the CRITIC method. In particular, the weights of indicators CHU, HUR, and SH are reduced by 0.036, 0.029, and 0.031, respectively. This indicates that the combined weighting balances the importance of the indicators obtained by the entropy weighting method and the CRITIC method, effectively preventing the influence of extreme weights from a single method on the evaluation results. Figure 6 This study shows the changes in dispatch efficiency levels for different hydropower stations and different river basins over 12 months of the year. It can be seen that the efficiency level against the schedule values ​​of hydropower stations ranges from 0.387 to 0.619. The median efficiency level against the schedule values ​​of hydropower stations from June to December is generally higher than that from January to May, indicating that the efficiency level of most hydropower stations from June to December is higher than that from January to May. Hydropower Station No. 14's efficiency level against the schedule values ​​ranks in the top 25% every month, indicating that the efficiency level of Hydropower Station No. 14 is higher than that of most hydropower stations. From... Figure 6 It can also be seen that the benefit level of the main body of the watershed is distributed between 0.434 and 0.595. According to the median benefit level, the main body of the watershed also shows a pattern that the benefit level from 6 to 12 months is higher than that from 1 to 5 months. Among them, the benefit level of watershed C ranks in the top two in all 12 months, showing a high level of scheduling benefit.

Claims

1. A method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations, characterized in that, Includes the following steps: Step 1: Construct a benchmarking and evaluation system for the dispatching benefits of hydropower stations: Collect and organize basic and benefit indicators related to the scheduling benefits of hydropower stations, construct a benchmarking evaluation system for the scheduling benefits of hydropower stations, and standardize the indicators in the evaluation system. Step 2, quantify the scheduling efficiency indicators of hydropower stations: Considering the differences in hardware facilities of different hydropower stations, the benefit index data of hydropower stations and the main body of the watershed are quantified into intensity values ​​through complexity theory; Step 3: Determine the combined weights of hydropower station dispatch benefit indicators: Based on the strength value of the benefit indicator, the weight of the benefit indicator is calculated by the entropy weight method and the CRITIC method. Then, the combined weight of the benefit indicator is obtained by taking the minimum sum of squared deviations of the weight results of the two methods as the criterion. Step 4: Calculate the scheduling efficiency level of the hydropower station and its progress. The distance metric of the traditional TOPSIS method is improved by combining the grey relational analysis method with the Euclidean distance method, and the scheduling efficiency level of hydropower stations and the main body of the watershed is calculated according to the schedule in different months. The detailed steps for quantifying the hydropower station dispatch efficiency indicators in step 2 are as follows: Step 2.1: Based on the constructed indicator system, the basic indicators—rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output—are used as independent variables. The benefit indicators—water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate—are used as dependent variables in sequence. First, a univariate regression equation is constructed by selecting the j-th benefit indicator and each basic indicator, as shown below: y j =β0+β q s q ,(q=1,2,…,K)(10); In the formula, y j s represents the j-th benefit indicator; q β represents the q-th basic indicator; β0 represents the intercept; β q represents the regression coefficient of the q-th basic indicator; K represents the number of basic indicators; Step 2.2: Calculate the deviation F of the basic index in each regression equation, and select the basic index with the largest deviation F. If F max >F 0.05 Then, this basic indicator is introduced into the model, and the deviation F value of each basic indicator in the model after introducing this indicator is tested. If F q <F 0.1 Then, the q-th basic indicator is removed until no indicator needs to be included in the equation or removed from the equation. After screening, the regression equations for the j-th benefit indicator and the basic indicators are as follows: y j =β0+β1s1+…+β Q s Q (11); In the formula, y j β represents the j-th benefit indicator; β0 represents the intercept; β Q This represents the regression coefficient of the Qth basic indicator; Q represents the number of basic indicators remaining after screening. Step 2.3: Calculate the weight of each basic indicator based on the magnitude of its regression coefficient in the regression equation. The calculation method is as follows: In the formula, w Q It is the weight of the Qth basic indicator; β Q β represents the regression coefficient of the Qth basic indicator; sum This represents the sum of the absolute values ​​of the regression coefficients of the basic indicators; Q represents the number of basic indicators remaining after screening. Step 2.4 introduces the variance inflation factor test index to determine whether it passes the collinearity test. The calculation method is as follows: In the formula, V q This represents the variance inflation factor of the q-th basic indicator; This represents the correlation coefficient between the q-th basic indicator and other basic indicators; when V q ≤10 indicates that the q-th basic index does not exhibit multicollinearity; Step 2.5, considering the differences in hardware facilities between different hydropower stations and the temporal and spatial connections between different hydropower stations in the same river basin, constructs two types of intensity values ​​using complexity theory to standardize the benefit indicators of hydropower stations and the main body of the river basin. The two types of intensity values ​​are described in detail below: Evaluation of the intensity values ​​of single benefit indicators for different hydropower stations: In the formula, This represents the intensity value of the j-th benefit indicator of the i-th hydropower station at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; q f represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; Q represents the number of basic indicators remaining after screening. Evaluation of the intensity values ​​of single benefit indicators for different watershed subjects: In the formula, This represents the intensity value of the j-th benefit indicator of the g-th watershed entity at time t; This represents the value of the j-th benefit indicator of the i-th hydropower station at time t. w represents the value of the q-th basic index of the i-th hydropower station at time t; q f represents the weight of the q-th basic indicator; q It is a univariate function fitted with benefit indicators as dependent variables and basic indicators as independent variables; p represents the total number of hydropower stations within the main body g of the basin; Q represents the number of remaining basic indicators after screening.

2. The method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations, as described in claim 1, is characterized in that... The detailed steps for constructing the hydropower station dispatch efficiency benchmarking evaluation system in step 1 are as follows: Step 1.1 establishes an evaluation index system reflecting the comprehensive dispatch efficiency of hydropower stations. This system is divided into two categories: basic indicators and efficiency indicators. Basic indicators reflect the differences in hydraulic structure, mechanical equipment, and electrical equipment among different hydropower stations, including rated head, rated power generation flow, number of generating units, regulating reservoir capacity, inflow, installed capacity, and guaranteed output. Efficiency indicators reflect the dispatch efficiency of hydropower stations based on power generation and water utilization, including water-saving power generation, water energy utilization improvement rate, power generation water consumption rate, power generation, abandoned water volume, installed capacity utilization hours, and water utilization rate. Specific data for the basic indicators are obtained from the hydropower plant's technical parameter tables, while specific data for the efficiency indicators are calculated using historical dispatch data of the hydropower stations. The specific calculation method is as follows: ① Power generation: In the formula, This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the average daily reservoir water level of the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours. ②Water conservation and increased power generation: In the formula, This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours. ③ Water energy utilization improvement rate: In the formula, This represents the water energy utilization improvement rate of the i-th hydropower station at time t, in percentage. This represents the water-saving and power generation increase of the i-th hydropower station at time t, in ten thousand kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters (m³). 3 / s; Z represents the water level of the assessment reservoir for the i-th hydropower station, in meters (m). i The average daily downstream water level of the i-th hydropower station is expressed in meters (m) or h. i η represents the head loss of the i-th hydropower station, in meters (m); i This represents the overall efficiency of the i-th hydropower station generator unit; T represents the calculation period, in hours. ④ Power generation water consumption rate: In the formula: This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h; This represents the power generation of the i-th hydropower station at time t, expressed in 10,000 kW·h. This represents the average daily power generation flow of the i-th hydropower station, in meters. 3 / s; T represents the calculation period, in hours; ⑤ Wasted water power: In the formula, This represents the amount of water wasted by the i-th hydropower station at time t, expressed in ten thousand kWh. This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s; This represents the water consumption rate for power generation at time t, expressed in meters. 3 / kW·h; ⑥ Installed utilization hours: In the formula: This represents the installed capacity utilization hours of the i-th hydropower station at time t, in hours (h). N represents the power generation of the i-th hydropower station at time t, in 10,000 kW·h. i This represents the installed capacity of the i-th hydropower station, in MW. ⑦ Water utilization rate: In the formula, Water utilization rate, expressed as a percentage. This represents the average daily inflow to the i-th hydropower station, in meters (m³). 3 / s; This represents the average daily water discharge of the i-th hydropower station, in meters. 3 / s; Step 1.2: The efficacy coefficient method is used to perform dimensionless processing on the indicators to eliminate the influence of differences in units, amplitudes, and trends between indicators on the evaluation results. The efficacy coefficient method is calculated as follows: For positive indicators: In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,max s and s represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1, c is used to shift the dimensionless value of the indicator, and d is used to scale the dimensionless value of the indicator. For contrarian indicators: In the formula, This represents the j-th index of the i-th hydropower station at time t after dimensionless transformation; u j,max and u j,min Let c and d represent the maximum and minimum values ​​of the j-th indicator, respectively; c and d are constants, c = d = 1. The function of c is to shift the dimensionless value of the indicator, and the function of d is to scale the dimensionless value of the indicator.

3. The method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations, as described in claim 2, is characterized in that... The detailed steps for determining the combined weights of hydropower station dispatch benefit indicators in step 3 are as follows: Step 3.1: Based on the strength value of the benefit indicators, obtain the amount of useful information carried and transmitted by each benefit indicator. Then, calculate the weight of each benefit indicator using the entropy weight method, as follows: In the formula, α ej e represents the weight of the j-th benefit indicator obtained based on the entropy weight method; j Let J represent the entropy value of the j-th benefit indicator; J represents the total number of benefit indicators. Step 3.2: Based on the strength values ​​of the benefit indicators, obtain the degree of conflict and volatility of each benefit indicator data. Then, calculate the weight of each benefit indicator using the CRITIC method, as follows: In the formula, α cj σ represents the weight of the j-th benefit indicator obtained based on the CRITIC method; j R represents the degree of fluctuation of the j-th benefit indicator; j S represents the degree of conflict for the j-th benefit indicator; j This represents the information content of the j-th benefit indicator; J represents the total number of benefit indicators. Step 3.3 minimizes the heterogeneity of the weight results from the entropy weight method and the CRITIC method to obtain a combined weight that fully considers the disorder and conflict of the indicator data. The calculation method is as follows: In the formula, α j α represents the combined weight of the j-th benefit indicator; ej α represents the weight of the j-th benefit indicator obtained based on the entropy weight method; cj represents the weight of the j-th benefit indicator obtained based on the CRITIC method; J represents the total number of benefit indicators.

4. The method for benchmarking and evaluating the scheduling benefits of a cascade hydropower station group considering the characteristics of hydropower stations, as described in claim 3, is characterized in that... The detailed steps for calculating the hydropower station dispatch efficiency level and schedule in step 4 are as follows: Step 4.1: Select J indicators, I hydropower stations or river basin entities, and T time periods to construct the original evaluation matrix: In the formula, R represents the original evaluation matrix; Let J represent the intensity value of the j-th benefit indicator of the i-th hydropower enterprise at time t; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period. Step 4.2: The original evaluation matrix is ​​dimensionless to obtain the standardized evaluation matrix R. * The weighted evaluation matrix is ​​obtained by combining the standardized evaluation matrix with the weighted combination of benefit indicators, as shown below: In the formula, Z represents the weighted evaluation matrix; express The value after dimensionless processing; α j This represents the weight of the j-th benefit indicator; This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; J represents the number of benefit indicators; I represents the total number of hydropower enterprises; and T represents the calculation period. Step 4.3: Obtain the positive and negative ideal solutions based on the maximum and minimum values ​​corresponding to each indicator in the weighted evaluation matrix. In the formula, Z + and Z - These represent the positive ideal solution and the negative ideal solution, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators. Step 4.4, calculate the Euclidean distance and grey relational degree; the Euclidean distance is calculated as follows: In the formula, and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. This represents the value of the j-th benefit indicator of the i-th hydropower enterprise at time t in the weighted evaluation matrix; This represents the maximum value of the j-th benefit indicator; This represents the minimum value of the j-th benefit indicator; J represents the number of benefit indicators. The grey relational degree is calculated as follows: In the formula, and These are the grey relational degrees between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively. and It is the grey correlation coefficient; J represents the number of benefit indicators; Step 4.5: Construct an improved distance metric using grey relational analysis and Euclidean distance. The calculation method is as follows: In the formula, and These are the new distance metrics of the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions; and These are the Euclidean distances between the i-th hydropower enterprise and the positive and negative ideal solutions at time t, respectively. and λ represents the grey relational degree between the i-th hydropower enterprise at time t and the positive and negative ideal solutions, respectively; λ represents the degree of emphasis on the two distances. Step 4.6: Calculate the relative tracking progress using the new distance metric. A larger relative tracking progress value indicates that the benefit of the evaluated object i at time t is closer to the positive ideal solution, and the better the benefit. The calculation method for relative tracking progress is as follows: Where D i,t This represents the relative progress value of the i-th hydropower enterprise at time t. and These are the new distance metrics for the i-th hydropower enterprise at time t relative to the positive and negative ideal solutions.

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