A comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units

Abstract

This invention discloses a comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units, comprising: determining a comprehensive evaluation index system for coal-fired power units and calculating the values ​​of each tertiary evaluation index; calculating the objective weights of secondary evaluation indicators using the entropy weight method, and introducing a dynamic group decision-making method based on the analytic hierarchy process to obtain a subjective dynamic group decision-making model, thereby determining the dynamic subjective weights of the secondary evaluation indicators; calculating the dynamic subjective-objective combined weights of each secondary evaluation indicator by combining and weighting the subjective and objective weights; and calculating the closeness between the primary evaluation indicators and the positive and negative ideal solutions using the approximation ideal solution ranking method and the grey relational analysis method based on the dynamic subjective-objective combined weights of the secondary evaluation indicators, as the final evaluation result of the primary evaluation indicators, thereby achieving a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of coal-fired power units at the second-level scale. This invention can realize a multi-dimensional dynamic equilibrium comprehensive evaluation of coal-fired power units at the second-level scale during flexible operation with deep peak shaving and frequency regulation.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive evaluation of flexible operation of coal-fired power units, and specifically relates to a comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of deep peak-shaving and frequency-regulating coal-fired power units. Background Technology

[0002] With the continuous increase in installed capacity of renewable energy power generation such as wind and solar, the security risks brought about by the grid absorbing a high proportion of new energy power have increased significantly. The role of coal-fired power as the "backbone" and "ballast" for ensuring the absorption of new energy and the stability of the grid has become increasingly clear. Now and in the future, coal-fired power units mainly undertake the tasks of deep peak shaving and rapid frequency regulation. Their operation process is in a state of dynamic change for a long time. The traditional evaluation method of calculating performance indicators using steady-state data is no longer applicable. There is an urgent need to establish a method and system for multi-dimensional dynamic equilibrium evaluation of the deep flexible operation performance of coal-fired power units in order to promote the high-quality development of coal-fired power in terms of cleanliness, efficiency, flexibility, low carbon, and intelligence.

[0003] The comprehensive performance evaluation of coal-fired power units comprises three levels: index modeling, weight determination, and multi-dimensional decision-making. First, it is necessary to establish a multi-dimensional quantitative index calculation model, mainly including six dimensions: economic, environmental, safety, energy efficiency, stability, and flexibility. Existing evaluations of coal-fired power units primarily focus on individual performance aspects, especially thermal economics, and current research often bases index calculations on steady-state data, lacking performance evaluation of dynamic processes.

[0004] Furthermore, current comprehensive evaluations of coal-fired power units mostly focus on normal operating conditions or small-scale load variations, lacking a multi-dimensional dynamic equilibrium comprehensive evaluation specifically for coal-fired power units operating under deep peak-shaving and frequency-regulation conditions. The operating conditions of coal-fired power units operating under deep peak-shaving and frequency-regulation differ significantly from normal operating conditions. Because this mode exacerbates damage to high-temperature and high-pressure components, greatly reducing design life, it is necessary to establish safety indicators to assess damage, failure, and remaining life, and incorporate them into the comprehensive evaluation system. However, the current evaluation system lacks consideration for safety indicators and urgently needs further research and improvement. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a comprehensive evaluation method for the multi-dimensional dynamic equilibrium of flexible operation of deep peak shaving and frequency regulation coal-fired power units. This method considers the time series of the deep peak shaving and frequency regulation operation process of coal-fired power units and realizes a comprehensive evaluation of the multi-dimensional dynamic equilibrium of the operation process of coal-fired power units through the collection of second-level operation data.

[0006] Technical solution: The comprehensive evaluation method for flexible operation and multi-dimensional dynamic equilibrium of coal-fired power units described in this invention includes the following steps:

[0007] A multi-dimensional analysis was conducted on the flexible operation of coal-fired power units with deep peak shaving and frequency regulation. Based on expert experience, the Delphi method was used to screen and determine the comprehensive evaluation index system for coal-fired power units. The comprehensive evaluation index system includes primary evaluation index, secondary evaluation index and tertiary evaluation index. Second-level operating data of the flexible operation of coal-fired power units with deep peak shaving and frequency regulation were collected according to the comprehensive evaluation index system, and the values ​​of each tertiary evaluation index were calculated.

[0008] Based on the values ​​of each tertiary evaluation index, the objective weights of the secondary evaluation indexes are calculated using the entropy weight method. A dynamic group decision-making method is introduced on the basis of the analytic hierarchy process (AHP) to obtain a subjective dynamic group decision-making model extended to time series, thereby determining the dynamic subjective weights of the secondary evaluation indexes. The dynamic subjective-objective combined weights of each secondary evaluation index are calculated by combining and assigning weights to the subjective and objective weights.

[0009] Based on the dynamic subjective and objective combination weights of the secondary evaluation indicators, the TOPSIS (Topological Solution Ranking) method is used to calculate the relative closeness of the evaluation scheme to the ideal solution. The grey relational analysis method (GRA) is used to calculate the grey relational degree between the positive ideal solution and the evaluation scheme. The closeness of the primary evaluation indicators to the positive and negative ideal solutions is further calculated as the final evaluation result of the primary evaluation indicators. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of coal-fired power units at the second scale is achieved.

[0010] Furthermore, the secondary evaluation indicators include: economic indicators, environmental indicators, safety indicators, energy efficiency indicators, stability indicators, and flexibility indicators.

[0011] The economic indicators include: coal consumption rate for power supply, plant power consumption rate, and comprehensive water consumption rate for power supply; the environmental indicators include: SO2 emission concentration, NO... x Emission concentration and dust emission concentration; the safety indicators include: primary stress and creep failure; the energy efficiency indicators include: turbine unit consumption, boiler unit consumption and auxiliary machine unit consumption; the stability indicators include: adjustment time, attenuation rate and adjustment sensitivity; the flexibility indicators include: ramp rate, adjustment accuracy and auxiliary frequency regulation Kp; each secondary evaluation indicator includes indicators that are tertiary evaluation indicators.

[0012] Furthermore, it also includes: performing dimensionless processing on the second-level operating data of the coal-fired unit, and performing forward or reverse normalization and standardization processing on the values ​​of each of the three-level evaluation indicators.

[0013] Furthermore, the objective weights of the secondary evaluation indicators are calculated using the entropy weight method, including:

[0014] An initial indicator decision matrix X = (x_i, x ... ij ) n×m x ijLet j be the j-th secondary evaluation index of the i-th evaluation scheme, i = 1, 2, ..., n, j = 1, 2, ..., m; the evaluation scheme is the operating status of the coal-fired unit corresponding to different number of seconds. Based on the initial index decision matrix X, the standardized index decision matrix Y = (y ij ) n×m y ij This refers to the j-th secondary evaluation indicator of the i-th standardized evaluation scheme.

[0015] The standardized indicator decision matrix is ​​then normalized.

[0016]

[0017] Where, p ij For the j-th secondary evaluation index of the i-th evaluation scheme after normalization;

[0018] Calculate the information entropy e of each of the secondary evaluation indicators. j :

[0019]

[0020] Determine the objective weights w of each of the secondary evaluation indicators. j :

[0021]

[0022] The dynamic subjective weight calculation method for secondary evaluation indicators is as follows:

[0023] By selecting x experts and applying the Analytic Hierarchy Process (AHP) to m secondary evaluation indicators, a dynamic group decision-making method is introduced to obtain the dynamic subjective weights w. 1 w 2 ,…,w x , where w 1 w 2 ,…,w x These are the dynamic subjective weights of the m secondary evaluation indicators given by the 1st, 2nd, ..., xth experts. Let be the dynamic subjective weight vector of m secondary evaluation indicators. This represents the dynamic subjective weight of the m-th secondary evaluation indicator given by the x-th expert; at time t, the objective weight vector w of the secondary evaluation indicator is obtained using the aforementioned entropy weight method. j (t)=(w1(t),w2(t),…,w n (t)), the objective weight vector of the secondary evaluation index w j(t) has time series characteristics; the similarity between the objective weight vector of the secondary evaluation index at time t and the dynamic subjective weight vector of the secondary evaluation index obtained by the xth expert is expressed by the cosine formula of the vector angle, and the calculation formula is as follows:

[0024]

[0025] in, Let t be the similarity between the objective weight vector of the secondary evaluation index at time t and the dynamic subjective weight vector of the secondary evaluation index obtained by the xth expert.

[0026] At time t, the objective weight vector w of the secondary evaluation index j (t) and the dynamic subjective weight vector w of the secondary evaluation index calculated based on the xth expert. x Deviation degree DEV x (t) is:

[0027]

[0028] When DEV x (t)≠0, then the weight δ of the expert x is... x (t) is:

[0029]

[0030] DEV x (t) represents the bias of the x-th expert. This represents the sum of the biases of experts from the 1st to the xth expert.

[0031] When DEV x When (t) = 0, δ x (t) = 1 / x, meaning that all experts are of equal importance during the evaluation process, if DEV x When (t)≠0, the importance of the experts is inconsistent.

[0032] Furthermore, the dynamic combined subjective and objective weights of each secondary evaluation indicator are calculated by combining and assigning weights to both subjective and objective factors, including:

[0033] Based on the objective weight vector of the secondary evaluation indicators obtained by the entropy weight method using the secondary evaluation indicator values ​​at time t, and the dynamic subjective weight vector of the secondary evaluation indicators obtained by introducing the dynamic group decision-making method based on the analytic hierarchy process (AHP), the dynamic subjective-objective combined weight of each secondary evaluation indicator at time t+Δt is calculated. The calculation formula is as follows:

[0034]

[0035] Where w(t+△t) is the dynamic subjective-objective combination weight of each secondary evaluation indicator at time t+△t, δ1 (t+△t) represents the weight of the first expert at time t+△t, δ 2 (t+△t) represents the weight of the second expert at time t+△t, δ x (t+△t) represents the weight of the x-th expert at time t+△t.

[0036] Furthermore, the Top-Ideal Solution Ranking Method (TOPSIS) is used to calculate the ideal relative closeness of the evaluation schemes, including:

[0037] Based on the standardized indicator decision matrix Y=(y ij ) n×m Construct a weighted standardized index matrix:

[0038] Z = (z ij ) n×m =(w j y ij ) n×m

[0039] Where Z is the weighted standardized index matrix, z ij The element determined by the i-th row and j-th column of the weighted standardized index matrix, w j For the objective weights of the secondary evaluation indicators, y ij This refers to the j-th secondary evaluation indicator of the i-th standardized evaluation scheme.

[0040] Calculate the positive ideal solution Z for each evaluation scheme. + and negative ideal solution Z - :

[0041]

[0042] in, These represent the maximum values ​​of columns 1, 2, ..., m of the weighted standardized index matrix Z. These are the minimum values ​​of the 1st, 2nd, ..., mth columns of the weighted standardized index matrix Z, respectively.

[0043] Calculate the standard distance between each evaluation scheme and the positive and negative ideal solutions:

[0044]

[0045] Among them, D st,i + The standard distance between each evaluation scheme and the positive ideal solution is... The square of the objective weight of the secondary evaluation indicator. The standardized indicator decision matrix Y = (y ij ) n×m The maximum value in the j-th column, D is the maximum value of the j-th column of the weighted standardized index matrix Z, i.e., the positive ideal solution. st,i - Let the standard distance between each evaluation scheme and the negative ideal solution be denoted as . The standardized indicator decision matrix Y = (y ij ) n×m The minimum value of the j-th column. The minimum value of the j-th column of the weighted standardized index matrix Z is the negative ideal solution.

[0046] Calculate the ideal relative closeness of each evaluation scheme:

[0047]

[0048] Among them, C i Let be the ideal relative fit of the i-th evaluation scheme;

[0049] The grey relational analysis method (GRA) is used to calculate the grey relational degree between the positive ideal solution and the evaluation scheme, including:

[0050] Determine the reference sequence and the comparison sequence, where the reference sequence is the determined positive ideal solution Z. + The comparison sequence consists of the three-level evaluation index values ​​of different second-level running data, which are the evaluation schemes.

[0051] Based on the standardized indicator decision matrix Y=(y ij ) n×m Calculate the grey relational coefficient:

[0052]

[0053] Where, β ij The standardized indicator decision matrix Y = (y ij ) n×m The grey relational coefficient y of the element corresponding to the i-th row and j-th column. i,max ρ represents the maximum value in the j-th column index, and ρ is an adjustable coefficient.

[0054] To calculate the grey relational degree, each reference sequence and comparison sequence has several grey relational coefficients. The average of these coefficients is used as the grey relational degree between the reference and comparison sequences.

[0055]

[0056] ε i Let represent the grey relational degree between the i-th reference sequence and the corresponding i-th comparison sequence.

[0057] Furthermore, the degree of closeness between the primary evaluation index and the positive and negative ideal solutions is calculated, and this is used as the final evaluation result of the primary evaluation index, including:

[0058] Using the standardized index matrix with secondary evaluation index weights, the grey relational analysis (GRA) method was employed to obtain the grey relational degree ζ between each evaluation scheme and the positive and negative ideal solutions. st,i + and ζ st,i - Simultaneously, the standard distance D between each evaluation scheme and the positive and negative ideal solutions is calculated using the TOPSIS (Topological Solution Approximation System) method. st,i + and D st,i - D st,i - and ζ st,i + The larger the value of D, the closer the evaluation scheme is to the positive ideal solution. st,i + and ζ st,i - The larger the value, the closer the evaluation scheme is to the negative ideal solution. Preference calculations are performed using Grey Relational Analysis (GRA) and the Top-Ideal Solution Ranking Method (TOPSIS).

[0059]

[0060] Where r and s are preference coefficients, representing the degree of preference for Grey Relational Analysis (GRA) and Topology-Based Solution Ranking (TOPSIS) during the calculation, respectively, with r + s = 1; T i + T represents the relative closeness of the i-th evaluation scheme to the positive ideal solution. i - T represents the relative closeness between the i-th evaluation scheme and the negative ideal solution. i + The larger the value of T, the closer the evaluation scheme is to the positive ideal solution. i - The larger the value, the closer the evaluation scheme is to the negative ideal solution;

[0061] According to T i + and T i - Calculate the application progress T of the primary evaluation index and the positive and negative ideal solutions. i :

[0062]

[0063] The primary evaluation indicators are correlated with the positive and negative ideal solutions in the progress T. i The final evaluation result serves as the primary evaluation indicator.

[0064] The system corresponding to the method includes:

[0065] The index calculation unit is used to conduct multi-dimensional analysis of flexible coal-fired power units with deep peak shaving and frequency regulation. It combines expert experience and uses the Delphi method to screen and determine the comprehensive evaluation index system of coal-fired power units. The comprehensive evaluation index system includes primary evaluation index, secondary evaluation index and tertiary evaluation index. Based on the comprehensive evaluation index system, it collects second-level operating data of flexible coal-fired power units with deep peak shaving and frequency regulation and calculates the values ​​of each tertiary evaluation index.

[0066] The combined weight calculation unit is used to calculate the objective weights of the secondary evaluation indicators based on the values ​​of each tertiary evaluation indicator using the entropy weight method. Based on the analytic hierarchy process (AHP), a dynamic group decision-making method is introduced to obtain a subjective dynamic group decision-making model extended to time series, thereby determining the dynamic subjective weights of the secondary evaluation indicators. The unit then performs combined weighting on the subjective and objective weights to calculate the dynamic subjective and objective combined weights of each secondary evaluation indicator.

[0067] The evaluation unit is used to calculate the relative closeness of the evaluation scheme to the ideal solution by using the TOPSIS method (approaching the ideal solution ranking method) based on the dynamic subjective and objective combination weights of the secondary evaluation indicators. It also uses the fusion grey relational analysis method (GRA) to calculate the grey relational degree between the positive ideal solution and the evaluation scheme. Furthermore, it calculates the closeness between the primary evaluation indicators and the positive and negative ideal solutions, which serves as the final evaluation result of the primary evaluation indicators. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of the coal-fired power unit at the second scale is achieved.

[0068] An electronic device for storing and executing the method, the device comprising:

[0069] Memory containing executable program code;

[0070] A processor coupled to the memory;

[0071] The processor calls the executable program code stored in the memory to execute the steps of the comprehensive evaluation method for flexible operation of coal-fired power units with multi-dimensional dynamic equilibrium.

[0072] A computer-readable storage medium for storing and executing the method, the computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the steps of the comprehensive evaluation method for flexible operation multidimensional dynamic equilibrium of coal-fired power units.

[0073] Beneficial Effects: Compared with existing technologies, the significant technical effects of this invention are as follows: A comprehensive evaluation index system can be selected and determined based on the second-level operating data of the coal-fired power unit with flexible operation of deep peak shaving and frequency regulation; the second-level operating data is processed to calculate the three-level evaluation index values; the dynamic subjective weights of the second-level evaluation indexes can be calculated by introducing a dynamic group decision-making method based on the Analytic Hierarchy Process (AHP), and the objective weights of the second-level evaluation indexes can be calculated using the entropy weight method; the dynamic subjective and objective weights are combined and weighted to obtain a dynamic subjective-objective combined weight; and the comprehensive evaluation index system for flexible operation of the coal-fired power unit can be determined based on the collected second-level operating data of the coal-fired power unit. The evaluation system, the evaluation index values, and the dynamic subjective-objective combination weights are used to calculate the closeness between the primary evaluation index and the positive and negative ideal solutions by combining the Top-Order Ideal Solution Ranking Method (TOPSIS) with the Grey Relational Analysis Method (GRA). This closeness serves as the final evaluation result for the primary evaluation index. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of coal-fired power units at the second-level scale is achieved. This approach can solve the multi-dimensional dynamic equilibrium comprehensive evaluation of coal-fired power units with flexible operation during deep peak-shaving and frequency regulation, providing methodological support for coal-fired power units with deep peak-shaving and frequency regulation to participate in ancillary services in the power market and enhance the stability of power grid operation. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0075] Figure 2 This is a schematic diagram of the comprehensive evaluation index system determined in the method of this invention;

[0076] Figure 3 This is a principle block diagram of an exemplary embodiment of the present invention for realizing a comprehensive evaluation of multi-dimensional dynamic equilibrium of flexible operation of deep peak-shaving and frequency-regulating coal-fired power units;

[0077] Figure 4 This is a comprehensive evaluation index system determined by a comprehensive evaluation method for achieving multi-dimensional dynamic equilibrium of flexible operation of deep peak-shaving and frequency-regulating coal-fired power units, as described in an exemplary embodiment of the present invention. Detailed Implementation

[0078] The following description and accompanying drawings fully explain the comprehensive evaluation method proposed in this invention, so that those skilled in the art can apply the invention.

[0079] Figure 1 The present invention illustrates a general process for a comprehensive evaluation method of multi-dimensional dynamic equilibrium for flexible operation of deep peak-shaving and frequency-regulating coal-fired power units, comprising:

[0080] Step 1: Conduct a multi-dimensional analysis of the flexible coal-fired power units with deep peak shaving and frequency regulation. Combine expert experience with the Delphi method to screen and determine the comprehensive evaluation index system of the coal-fired power units. The comprehensive evaluation index system includes primary evaluation index, secondary evaluation index and tertiary evaluation index. Collect second-level operating data of the flexible coal-fired power units with deep peak shaving and frequency regulation according to the comprehensive evaluation index system, and calculate the values ​​of each tertiary evaluation index.

[0081] The second-level operating data of the collected coal-fired power units with flexible operation of deep peak shaving and frequency regulation are processed into dimensionless data, and the values ​​of each third-level evaluation index are normalized or standardized in either a positive or negative direction.

[0082] The comprehensive evaluation index system includes six dimensions, defined as secondary indicators, including: economic indicators, environmental protection indicators, safety indicators, energy efficiency indicators, stability indicators, and flexibility indicators.

[0083] The economic indicators include: coal consumption rate for power supply, plant power consumption rate, and comprehensive water consumption rate for power supply; the environmental indicators include: SO2 emission concentration, NOx emission concentration, and dust emission concentration; the safety indicators include: primary stress and creep failure; the energy efficiency indicators include: turbine unit consumption, boiler unit consumption, and auxiliary equipment unit consumption; the stability indicators include: adjustment time, attenuation rate, and adjustment sensitivity; the flexibility indicators include: ramp rate, adjustment accuracy, and auxiliary frequency regulation Kp; the indicators included in the secondary indicators are defined as tertiary indicators.

[0084] Figure 2 This invention illustrates the comprehensive evaluation index system determined in a comprehensive evaluation method for flexible operation and multi-dimensional dynamic equilibrium of deep peak-shaving and frequency-regulating coal-fired power units. It should be understood that this invention... Figure 2 Not all of the indicators shown are necessary under different working conditions. All indicators can be dynamically increased or decreased under different application scenarios and different evaluation purposes.

[0085] Step 2: Based on the values ​​of each tertiary evaluation index calculated in Step 1, the objective weights of the secondary evaluation indexes are calculated using the entropy weight method. A dynamic group decision-making method is introduced on the basis of the Analytic Hierarchy Process (AHP) to obtain a subjective dynamic group decision-making model extended to time series, thereby determining the dynamic subjective weights of the secondary evaluation indexes. The dynamic subjective-objective combined weights of each secondary evaluation index are calculated by combining and assigning weights to the subjective and objective weights.

[0086] The entropy weight method described above has the following specific calculation steps:

[0087] An initial indicator decision matrix X = (x_i, x ... ij ) n×m x ijLet be the j-th secondary evaluation index of the i-th evaluation scheme, i.e., the element determined by the i-th row and j-th column of the initial index decision matrix, where i = 1, 2, ..., n, j = 1, 2, ..., m. The evaluation scheme can be understood as the operating status of the coal-fired unit corresponding to different numbers of seconds. Based on the initial index decision matrix X, a standardized index decision matrix Y = (y... ij ) n×m y ij For the j-th secondary evaluation index of the i-th evaluation scheme after standardization, that is, the element determined by the i-th row and j-th column of the standardized index decision matrix;

[0088] The standardized indicator decision matrix is ​​then normalized.

[0089]

[0090] Where, p ij For the j-th secondary evaluation index of the i-th evaluation scheme after normalization, i.e., the standardized index decision matrix Y = (y ij ) n×m The corresponding value obtained after normalizing the element in the i-th row and j-th column of the array.

[0091] Calculate the information entropy e of each of the secondary evaluation indicators. j :

[0092]

[0093] Determine the objective weights w of each of the secondary evaluation indicators. j :

[0094]

[0095] The detailed calculation steps of the dynamic subjective-objective combined weighting method are as follows:

[0096] Select x experts to calculate the dynamic subjective weights w for m secondary evaluation indicators using the analytic hierarchy process (AHP) and a dynamic group decision-making method. 1 w 2 ,…,w x , where w 1 w 2 ,…,w x These are the dynamic subjective weights of the m secondary evaluation indicators given by the 1st, 2nd, ..., xth experts. Let be the dynamic subjective weight vector of m secondary evaluation indicators. Let w represent the dynamic subjective weight of the m-th secondary evaluation indicator given by the x-th expert. At time t, the objective weight vector w of the secondary evaluation indicators is obtained using the aforementioned entropy weight method. j(t)=(w1(t),w2(t),…,w n (t)), where the objective weight vector of the secondary evaluation index w is... j (t) has time series characteristics. The similarity between the objective weight vector of the secondary evaluation index at time t and the dynamic subjective weight vector of the secondary evaluation index obtained by the x-th expert is expressed using the cosine formula of the vector angle. The calculation formula is as follows:

[0097]

[0098] in, Let t be the similarity between the objective weight vector of the secondary evaluation index at time t and the dynamic subjective weight vector of the secondary evaluation index obtained by the xth expert.

[0099] At time t, the objective weight vector w of the secondary evaluation index j (t) and the dynamic subjective weight vector w of the secondary evaluation index calculated based on the xth expert. x Deviation degree DEV x (t) is:

[0100]

[0101] When DEV x (t)≠0, then the weight δ of the expert x is... x (t) is:

[0102]

[0103] DEV x (t) represents the bias of the x-th expert. This represents the sum of the biases of experts from the 1st to the xth expert.

[0104] When DEV x When (t) = 0, δ x (t) = 1 / x, meaning that all experts are of equal importance during the evaluation process, if DEV x When (t)≠0, the importance of the experts is inconsistent.

[0105] Based on the secondary evaluation index values ​​at time t, the objective weight vector of the secondary evaluation index is obtained using the entropy weight method, and the dynamic subjective weight vector of the secondary evaluation index is obtained by introducing a dynamic group decision-making method based on the analytic hierarchy process (AHP). The dynamic subjective-objective combined weight of each secondary evaluation index at time t+Δt is calculated, and the calculation formula is as follows:

[0106]

[0107] Where w(t+△t) is the dynamic subjective-objective combination weight of each secondary evaluation indicator at time t+△t, δ1 (t+△t) represents the weight of the first expert at time t+△t, δ 2 (t+△t) represents the weight of the second expert at time t+△t, δ x (t+△t) represents the weight of the x-th expert at time t+△t.

[0108] By combining the above formulas, the dynamic subjective and objective combined weights of the secondary evaluation indicators at each moment are obtained under the dynamic change process of the evaluation index data of coal-fired power units.

[0109] Step 3: Based on the dynamic subjective and objective combination weights of the secondary evaluation indicators obtained in Step 2, the Top-Ideal Solution Ranking Method (TOPSIS) combined with Grey Relational Analysis (GRA) is used to calculate the closeness between the primary evaluation indicators and the positive and negative ideal solutions. This closeness is used as the final evaluation result of the primary evaluation indicators. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of the coal-fired power unit at the second-level scale is achieved. The specific calculation steps are as follows:

[0110] First, based on the TOPSIS (Topological Solution Approximation Method), the optimal solution (positive ideal solution Z) of each evaluation scheme is calculated. + ) and worst solution (negative ideal solution Z) - Further calculations are performed to determine the standard distance between each evaluation scheme and the positive and negative ideal solutions, as well as the relative closeness of each evaluation scheme to the ideal solution; including:

[0111] Based on the standardized index decision matrix Y = (y) obtained in step 2 ij ) n×m Construct a weighted standardized index matrix:

[0112] Z = (z ij ) n×m =(w j y ij ) n×m

[0113] Where Z is the weighted standardized index matrix, z ij The element determined by the i-th row and j-th column of the weighted standardized index matrix, w j This operation, which assigns objective weights to the secondary evaluation indicators, is to provide the standardized indicator decision matrix Y = (y ij ) n×m Perform objective weighting.

[0114] Calculate the optimal solution (positive ideal solution Z) for each evaluation scheme. + ) and worst solution (negative ideal solution Z) - ):

[0115]

[0116] in, These represent the maximum values ​​of columns 1, 2, ..., m of the weighted standardized index matrix Z. These are the minimum values ​​of columns 1, 2, ..., m of the weighted standardized index matrix Z, respectively. The meanings of the subscript letters in the formula are consistent with those mentioned above.

[0117] Calculate the standard distance between each evaluation scheme and the positive and negative ideal solutions:

[0118]

[0119] Among them, D st,i + The standard distance between each evaluation scheme and the positive ideal solution is... The square of the objective weight of the secondary evaluation indicator. The standardized indicator decision matrix Y = (y ij ) n×m The maximum value in the j-th column, Let D be the maximum value of the j-th column of the weighted standardized index matrix Z, which is the positive ideal solution referred to in this invention. st,i - Let the standard distance between each evaluation scheme and the negative ideal solution be denoted as . The standardized indicator decision matrix Y = (y ij ) n×m The minimum value of the j-th column. The minimum value of the j-th column of the weighted standardized index matrix Z is the negative ideal solution referred to in this invention.

[0120] Calculate the ideal relative closeness of each evaluation scheme:

[0121]

[0122] Among them, C i Let be the ideal relative closeness of the i-th evaluation scheme.

[0123] Secondly, based on the grey relational analysis method (GRA), the grey relational coefficient is calculated, and then the positive ideal solution Z is further calculated. + Grey relational degree between various evaluation schemes; including:

[0124] The reference sequence and comparison sequence are determined. The reference sequence refers to the positive ideal solution Z determined above. + The comparison sequence refers to the three-level evaluation index values ​​of different second-level running data, which are the evaluation schemes.

[0125] Based on the standardized index decision matrix Y = (y ij ) n×m Calculate the grey relational coefficient:

[0126]

[0127] Where, β ij The standardized indicator decision matrix Y = (y ij ) n×m The grey relational coefficient y of the element corresponding to the i-th row and j-th column. i,max ρ is the maximum value in the j-th column index. ρ is an adjustable coefficient that takes a value in the range [0,1]. The purpose of this term is to adjust the difference in the output results. In this embodiment, ρ is set to 0.5.

[0128] To calculate the grey relational degree, each reference sequence and comparison sequence has several grey relational coefficients. The average of these coefficients is used as the grey relational degree between the reference and comparison sequences.

[0129]

[0130] ε i Let represent the grey relational degree between the i-th reference sequence and the corresponding i-th comparison sequence.

[0131] Then, the relative closeness between the evaluation scheme and the positive and negative ideal solutions is calculated, and the closeness T between the primary evaluation index and the positive and negative ideal solutions is further calculated. i As the final evaluation result of the primary evaluation indicators, it enables a multi-dimensional, dynamic, and comprehensive evaluation of the operating status of coal-fired power units at the second-level scale. This includes:

[0132] Using the standardized index matrix with secondary index weights, grey relational analysis (GRA) was employed to obtain the grey relational degree ζ between each evaluation scheme and the positive and negative ideal solutions. st,i + and ζ st,i - Simultaneously, the standard distance D between each evaluation scheme and the positive and negative ideal solutions is calculated using the Top-Order Ideal Solution Approximation Method (TOPSIS). st,i + and D st,i - D st,i - and ζ st,i + The larger the value of D, the closer the evaluation scheme is to the positive ideal solution. st,i + and ζ st,i - The larger the value, the closer the evaluation scheme is to the negative ideal solution. Preference calculations are performed using Grey Relational Analysis (GRA) and the Top-Order Solution Ranking Method (TOPSIS).

[0133]

[0134] Where r and s are preference coefficients, representing the degree of preference for Grey Relational Analysis (GRA) and Top-Ranking Method for Approximating Ideal Solutions (TOPSIS) during the calculation, respectively, and r + s = 1. i + T represents the relative closeness of the i-th evaluation scheme to the positive ideal solution. i - T represents the relative closeness between the i-th evaluation scheme and the negative ideal solution. i + The larger the value of T, the closer the evaluation scheme is to the positive ideal solution. i - The larger the value, the closer the evaluation scheme is to the negative ideal solution.

[0135] According to T i + and T i - Calculate the application progress T of the primary evaluation index and the positive and negative ideal solutions. i :

[0136]

[0137] The primary evaluation indicators are correlated with the positive and negative ideal solutions in the progress T. i The final evaluation result serves as the primary evaluation indicator.

[0138] Based on the aforementioned primary evaluation index and the matching progress T of the positive and negative ideal solutions. i The size of the coal-fired power unit is used to conduct a multi-dimensional dynamic equilibrium comprehensive evaluation of its operating status on a second-level scale.

[0139] Figure 3 This diagram illustrates a detailed principle block diagram of an exemplary embodiment of the comprehensive evaluation method for flexible and intelligent multi-dimensional dynamic equilibrium of deep peak-shaving and frequency-regulating coal-fired power units according to the present invention. It also serves as a... Figure 1 The three general processes are described in more detail to enhance the understanding and application of the comprehensive evaluation method for the flexible operation and multi-dimensional dynamic equilibrium of deep peak-shaving and frequency-regulating coal-fired power units by technical personnel in the same field.

[0140] Figure 4 This paper illustrates an exemplary embodiment of the comprehensive evaluation system for a multi-dimensional dynamic equilibrium comprehensive evaluation method for flexible operation of deep peak-shaving and frequency-regulating coal-fired power units according to the present invention. This embodiment selects three of the six dimensions from the general process description for implementation examples to further explain the present invention. Figure 2 Not all of the indicators shown are necessary under different working conditions. All indicators can be dynamically increased or decreased under different application scenarios and different evaluation purposes.

[0141] This invention is not limited to the processes and structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units, characterized in that, Includes the following steps: A multi-dimensional analysis was conducted on the flexible operation of coal-fired power units with deep peak shaving and frequency regulation. Based on expert experience, the Delphi method was used to screen and determine the comprehensive evaluation index system for coal-fired power units. The comprehensive evaluation index system includes primary evaluation index, secondary evaluation index and tertiary evaluation index. Second-level operating data of the flexible operation of coal-fired power units with deep peak shaving and frequency regulation were collected according to the comprehensive evaluation index system, and the values ​​of each tertiary evaluation index were calculated. Based on the values ​​of each tertiary evaluation index, the objective weights of the secondary evaluation indexes are calculated using the entropy weight method. A dynamic group decision-making method is introduced on the basis of the analytic hierarchy process (AHP) to obtain a subjective dynamic group decision-making model extended to time series, thereby determining the dynamic subjective weights of the secondary evaluation indexes. The dynamic subjective-objective combined weights of each secondary evaluation index are calculated by combining and assigning weights to the subjective and objective weights. The dynamic subjective weight calculation method for secondary evaluation indicators is as follows: Select x experts to The dynamic subjective weights of each secondary evaluation indicator are obtained by introducing a dynamic group decision-making method based on the Analytic Hierarchy Process (AHP). , , ..., ,in, , , ..., These are the answers given by the 1st, 2nd, ..., xth experts, respectively. The dynamic subjective weights of each secondary evaluation indicator for The dynamic subjective weight vector of each secondary evaluation indicator. Indicates the first The first expert gave the first The dynamic subjective weights of each secondary evaluation indicator; The objective weight vector of the secondary evaluation index is obtained at each step using the entropy weight method described above. Objective weight vector of secondary evaluation indicators It exhibits time-series characteristics; it is represented using the cosine formula of the angle between vectors. The objective weight vector of the second-level evaluation index at time step and the first The similarity of the dynamic subjective weight vectors of the secondary evaluation indicators derived by the experts is calculated using the following formula: ； in, for The objective weight vector of the second-level evaluation index at time step and the first The similarity of the dynamic subjective weight vectors of the secondary evaluation indicators derived by the experts; Time-based secondary evaluation index objective weight vector According to the Dynamic subjective weight vector of secondary evaluation indicators derived by experts deviation for: ； when The experts mentioned above weight for: ； Representing the The degree of bias of the experts Indicates the first to the second position. The sum of the biases of each expert; when hour, That is, in the evaluation process, all experts are of equal importance, if At that time, the level of importance of the experts represented varied. The dynamic combined subjective and objective weights of each secondary evaluation indicator are calculated by combining and assigning weights to both subjective and objective factors, including: according to The objective weight vector of the secondary evaluation index at time t is obtained using the entropy weight method, and the dynamic subjective weight vector of the secondary evaluation index is obtained by introducing a dynamic group decision-making method based on the analytic hierarchy process (AHP). The dynamic subjective and objective combined weights of each secondary evaluation indicator at any given time are calculated using the following formula: ； in, for The dynamic subjective and objective combination weights of each secondary evaluation indicator at any given time. for The weight of the first expert at any given moment. for The weight of the second expert at any given moment. for Time of the first The weight of each expert; Based on the dynamic subjective and objective combination weights of the secondary evaluation indicators, the TOPSIS (Topology of Ideal Solution Ranking) method is used to calculate the relative closeness of the evaluation scheme to the ideal solution. The Grey Relational Analysis (GRA) method is used to calculate the grey relational degree between the positive ideal solution and the evaluation scheme. Furthermore, the closeness between the primary evaluation indicators and the positive and negative ideal solutions is calculated as the final evaluation result of the primary evaluation indicators. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of coal-fired power units at the second scale is achieved.

2. The comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units according to claim 1, characterized in that, The secondary evaluation indicators include: economic indicators, environmental indicators, safety indicators, energy efficiency indicators, stability indicators, and flexibility indicators; The economic indicators include: coal consumption rate for power supply, plant power consumption rate, and comprehensive water consumption rate for power supply; the environmental indicators include: SO2 emission concentration, NO... x Emission concentration and dust emission concentration; the safety indicators include: primary stress and creep failure; the energy efficiency indicators include: turbine unit consumption, boiler unit consumption and auxiliary machine unit consumption; the stability indicators include: adjustment time, attenuation rate and adjustment sensitivity; the flexibility indicators include: ramp rate, adjustment accuracy and auxiliary frequency regulation Kp; each secondary evaluation indicator includes indicators that are tertiary evaluation indicators.

3. The comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units according to claim 1, characterized in that, Also includes: The second-level operating data of the coal-fired unit is processed to be dimensionless, and the values ​​of each of the three-level evaluation indicators are processed to be normalized or standardized in either a positive or negative direction.

4. The comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units according to claim 1, characterized in that, The objective weights of secondary evaluation indicators are calculated using the entropy weight method, including: Depend on One evaluation scheme and Construct an initial indicator decision matrix using two secondary evaluation indicators. , For the first The first evaluation scheme Two secondary evaluation indicators, , The evaluation scheme assesses the operating status of coal-fired power units for different durations of time, based on the aforementioned initial indicator decision matrix. Standardization is performed to obtain the standardized indicator decision matrix. , For the standardized first The first evaluation scheme One secondary evaluation indicator; The standardized indicator decision matrix is ​​then normalized. ； in, For the normalized first The first evaluation scheme One secondary evaluation indicator; Calculate the information entropy of each of the secondary evaluation indicators. : ； Determine the objective weights of each of the secondary evaluation indicators. : 。 5. The comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units according to claim 1, characterized in that, The TOPSIS (Topological Approximation Solution Ranking) method is used to calculate the relative approximation degree of the evaluation schemes, including: Decision matrix based on standardized indicators Construct a weighted standardized index matrix: ； in, For the weighted standardized index matrix, The first weighted standardized index matrix Okay, number The elements determined by the column, For the objective weights of the secondary evaluation indicators, For the standardized first The first evaluation scheme One secondary evaluation indicator; Calculate the positive ideal solution for each evaluation scheme. and negative ideal solution : ； in, These are the weighted standardized index matrices. The The maximum value of the column. These are the weighted standardized index matrices. The The minimum value in the column; Calculate the standard distance between each evaluation scheme and the positive and negative ideal solutions: ； in, The standard distance between each evaluation scheme and the positive ideal solution is... The square of the objective weight of the secondary evaluation indicator. Standardized indicator decision matrix The The maximum value of the column. Standardized index matrix for weights The The maximum value of the column, i.e., the positive ideal solution. Let the standard distance between each evaluation scheme and the negative ideal solution be denoted as . Standardized indicator decision matrix The The minimum value of the column. The first weighted standardized index matrix Z is the... The minimum value of the column, i.e., the negative ideal solution; Calculate the ideal relative closeness of each evaluation scheme: ； in, For the first The ideal relative closeness of each evaluation scheme; The grey relational analysis method (GRA) is used to calculate the grey relational degree between the positive ideal solution and the evaluation scheme, including: A reference sequence and a comparison sequence are determined, where the reference sequence is the determined positive ideal solution. The comparison sequence consists of the three-level evaluation index values ​​of different second-level running data, which are the evaluation schemes. Decision matrix based on standardized indicators Calculate the grey relational coefficient: ； in, Standardized indicator decision matrix The Okay, number The grey relational coefficient of the elements corresponding to the column. For the first The maximum value in the column indicators, It is an adjustable coefficient; To calculate the grey relational degree, each reference sequence and comparison sequence has several grey relational coefficients. The average of these coefficients is used as the grey relational degree between the reference and comparison sequences. ； For the first The reference sequence and its corresponding first reference sequence Grey relational degree of a comparison sequence.

6. The comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units according to claim 1, characterized in that, Further calculations are made to determine the degree of closeness between the primary evaluation indicators and the positive and negative ideal solutions, which serves as the final evaluation result for the primary evaluation indicators. This includes: Using the standardized index matrix with secondary evaluation index weights, the grey relational analysis (GRA) method was employed to obtain the grey relational degree between each evaluation scheme and the positive and negative ideal solutions. and Meanwhile, the standard distances between each evaluation scheme and the positive and negative ideal solutions are calculated using the TOPSIS (Topological Solution Approximation System) method. and , and The larger the value, the closer the evaluation scheme is to the ideal solution. and The larger the value, the closer the evaluation scheme is to the negative ideal solution. Preference calculations are performed using Grey Relational Analysis (GRA) and the Top-Ideal Solution Ranking Method (TOPSIS). ； in, and Here, are preference coefficients, representing the degree of preference for the Grey Relational Analysis (GRA) method and the Top-Order Solution (TOPSIS) method during the calculation. ; For the first The relative closeness of each evaluation scheme to the ideal solution. For the first The relative closeness of each evaluation scheme to the negative ideal solution. The larger the value, the closer the evaluation scheme is to the ideal solution. The larger the value, the closer the evaluation scheme is to the negative ideal solution; according to and Calculate the progress of the primary evaluation index and the positive and negative ideal solutions. : ； The progress of aligning the primary evaluation indicators with the positive and negative ideal solutions. The final evaluation result serves as the primary evaluation indicator.

7. A system for a comprehensive evaluation method of multi-dimensional dynamic equilibrium for flexible operation of coal-fired power units as described in any one of claims 1 to 6, characterized in that, include: The index calculation unit is used to conduct multi-dimensional analysis of flexible coal-fired power units with deep peak shaving and frequency regulation. It combines expert experience and uses the Delphi method to screen and determine the comprehensive evaluation index system of coal-fired power units. The comprehensive evaluation index system includes primary evaluation index, secondary evaluation index and tertiary evaluation index. Based on the comprehensive evaluation index system, it collects second-level operating data of flexible coal-fired power units with deep peak shaving and frequency regulation and calculates the values ​​of each tertiary evaluation index. The combined weight calculation unit is used to calculate the objective weights of the secondary evaluation indicators based on the values ​​of each tertiary evaluation indicator using the entropy weight method. Based on the analytic hierarchy process (AHP), a dynamic group decision-making method is introduced to obtain a subjective dynamic group decision-making model extended to time series, thereby determining the dynamic subjective weights of the secondary evaluation indicators. The unit then performs combined weighting on the subjective and objective weights to calculate the dynamic subjective and objective combined weights of each secondary evaluation indicator. The evaluation unit is used to calculate the relative closeness of the evaluation scheme to the ideal solution by using the TOPSIS method (approaching the ideal solution ranking method) based on the dynamic subjective and objective combination weights of the secondary evaluation indicators. It also uses the fusion grey relational analysis method (GRA) to calculate the grey relational degree between the positive ideal solution and the evaluation scheme. Furthermore, it calculates the closeness between the primary evaluation indicators and the positive and negative ideal solutions, which serves as the final evaluation result of the primary evaluation indicators. Based on the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of the coal-fired power unit at the second scale is achieved.

8. An electronic device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the steps of the comprehensive evaluation method for flexible operation multi-dimensional dynamic equilibrium of coal-fired power units as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the steps of the comprehensive evaluation method for flexible operation multi-dimensional dynamic equilibrium of coal-fired power units as described in any one of claims 1-6.

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