Comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired unit

Abstract

The invention discloses a multi-dimensional dynamic equilibrium comprehensive evaluation method for flexible operation of a coal-fired unit, and the method comprises the steps: determining a comprehensive evaluation index system of the coal-fired unit, and calculating each three-stage evaluation index value; calculating the objective weight of the secondary evaluation index by using an entropy weight method, introducing a dynamic group decision-making method on the basis of an analytic hierarchy process to obtain a subjective dynamic group decision-making model, and further determining the dynamic subjective weight of the secondary evaluation index; performing combined weighting on the subjective and objective weights to calculate a dynamic subjective and objective combined weight of each secondary evaluation index; and based on the dynamic subjective and objective combination weight of the second-level evaluation index, calculating the closeness of the first-level evaluation index and the positive and negative ideal solutions by using an approximate ideal solution sorting method and a gray correlation analysis method, and taking the closeness as a final evaluation result of the first-level evaluation index, thereby realizing the multi-dimensional dynamic equilibrium comprehensive evaluation of the second-level scale operation state of the coal-fired unit. According to the method, second-scale multi-dimensional dynamic equilibrium comprehensive evaluation can be realized during deep peak regulation and frequency modulation flexible operation of the coal-fired unit.

Description

Technical Field

[0001] The present invention belongs to the field of comprehensive evaluation of flexible operation of coal-fired units, and in particular relates to a comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of deep peak-shaving and frequency-modulating coal-fired units. Background Art

[0002] With the continuous increase in installed capacity of renewable energy power generation such as wind and solar power, the safety risks brought by the grid absorbing a high proportion of new energy electricity have increased significantly. The positioning of coal-fired power as the "pillar" and "ballast stone" to ensure the absorption of new energy and the stability of the power grid has become increasingly clear. Now and in the future, coal-fired power units will mainly undertake deep peak regulation and rapid frequency regulation tasks. Their operation process has been in dynamic change for a long time. The traditional evaluation method of using steady-state data to calculate performance indicators is no longer applicable. It is urgent to establish a multi-dimensional dynamic equilibrium evaluation method and system for the deep and flexible operation performance of coal-fired units to promote the clean, efficient, flexible, low-carbon and intelligent high-quality development of coal-fired power.

[0003] The comprehensive performance evaluation of coal-fired units includes three levels: indicator modeling, weight determination, and multi-dimensional decision-making. First, it is necessary to establish a quantitative indicator calculation model for multiple dimensions, mainly including economic, environmental, safety, energy efficiency, stability, and flexibility. The evaluation of existing coal-fired units is mainly carried out on a single performance, especially the evaluation research on thermal economy alone. In addition, the indicator calculation in current research is mostly based on steady-state data, lacking performance evaluation of dynamic processes.

[0004] In addition, most of the current comprehensive evaluations of coal-fired units are aimed at the normal operating conditions of the units, or the conditions of small-scale variable loads. There is a relative lack of multi-dimensional dynamic equilibrium comprehensive evaluation of coal-fired units specifically for deep peak-shaving and frequency-regulation conditions. There is a large difference between the operating conditions of coal-fired units with deep peak-shaving and frequency-regulation and normal operating conditions. Since the deep flexible peak-shaving and frequency-regulation operation mode will aggravate the damage of high-temperature and high-pressure components of coal-fired units and greatly reduce the design life, it is also necessary to establish safety indicators for evaluating their damage failure and remaining life, and incorporate them into the comprehensive evaluation system. However, the current evaluation system lacks consideration of safety indicators, which urgently needs to be studied and improved. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a comprehensive evaluation method for the multi-dimensional dynamic balance of the flexible operation of deep peak-shaving and frequency-regulating coal-fired units. The method takes into account the time series of the deep peak-shaving and frequency-regulating operation process of the coal-fired units, and realizes the comprehensive evaluation of the multi-dimensional dynamic balance of the operation process of the coal-fired units through the collected second-level operation data.

[0006] Technical solution: The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units of the present invention comprises the following steps:

[0007] Conduct multi-dimensional analysis on coal-fired units with deep peak load and frequency regulation, and use the Delphi method to screen and determine the comprehensive evaluation index system of coal-fired units based on expert experience. The comprehensive evaluation index system includes first-level evaluation index, second-level evaluation index and third-level evaluation index. Collect the second-level operation data of coal-fired units with deep peak load and frequency regulation according to the comprehensive evaluation index system, and calculate the values ​​of each third-level evaluation index.

[0008] Based on the values ​​of the three-level evaluation indicators, the entropy weight method is used to calculate the objective weights of the second-level evaluation indicators. The dynamic group decision-making method is introduced on the basis of the hierarchical analysis method AHP to obtain a subjective dynamic group decision-making model extended to the time series, and then determine the dynamic subjective weights of the second-level evaluation indicators; the subjective and objective weights are combined and weighted to calculate the dynamic subjective and objective combined weights of each second-level evaluation indicator;

[0009] Based on the dynamic subjective and objective combined weights of the secondary evaluation indicators, the ideal relative closeness of the evaluation scheme is calculated using the approximate ideal solution ranking method TOPSIS. The grey correlation between the positive ideal solution and the evaluation scheme is calculated using the fusion grey correlation analysis method GRA. 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. According to the final evaluation result, a multi-dimensional dynamic balanced comprehensive evaluation of the operating status of the coal-fired unit 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: power supply coal consumption rate, plant power consumption rate and power supply comprehensive water consumption rate; the environmental protection indicators include: SO 2 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: climbing rate, adjustment accuracy and auxiliary frequency regulation Kp; the indicators included in each secondary evaluation indicator are tertiary evaluation indicators.

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

[0013] Furthermore, the entropy weight method is used to calculate the objective weights of the secondary evaluation indicators, including:

[0014] The initial indicator decision matrix X is constructed by n evaluation schemes and m secondary evaluation indicators. ij ) n×m, x ij is the jth secondary evaluation index of the ith 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 seconds, and the standardized index decision matrix Y = (y ij ) n×m ,y ij is the jth secondary evaluation index of the i-th evaluation scheme after standardization;

[0015] The standardized indicator decision matrix is ​​normalized:

[0016]

[0017] Among them, p ij is the jth secondary evaluation index of the i-th evaluation scheme after normalization;

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

[0019]

[0020] Determine the objective weight w of each secondary evaluation index j :

[0021]

[0022] The calculation method of dynamic subjective weight of secondary evaluation indicators is:

[0023] Select x experts to evaluate m secondary evaluation indicators and introduce dynamic group decision-making method based on the analytic hierarchy process AHP to obtain dynamic subjective weight w 1 , w 2 ,…,w x , where w 1 , w 2 ,…,w x are the dynamic subjective weights of the m secondary evaluation indicators given by the 1st, 2nd, ..., xth experts, is the dynamic subjective weight vector of m secondary evaluation indicators, represents the dynamic subjective weight of the mth secondary evaluation index given by the xth expert; at time t, the objective weight vector w of the secondary evaluation index is obtained by using the entropy weight method j (t)=(w 1 (t),w 2 (t),…,w n (t)), the objective weight vector of the secondary evaluation index w j(t) is a time series feature; the vector angle cosine formula is used to express 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. The calculation formula is as follows:

[0024]

[0025] in, is 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;

[0026] The objective weight vector w of the secondary evaluation index at time t j (t) and the dynamic subjective weight vector w of the secondary evaluation index calculated by the xth expert x Deviation DEV x (t) is:

[0027]

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

[0029]

[0030] DEV x (t) represents the deviation of the xth expert, It represents the sum of the deviations from the 1st to the xth expert;

[0031] When DEV x When (t) = 0, δ x (t) = 1 / x, that is, in the evaluation process, all experts have the same importance. x When (t)≠0, it means that the importance of experts is inconsistent.

[0032] Furthermore, the subjective and objective weights are combined and weighted to calculate the dynamic subjective and objective combined weights of each secondary evaluation index, including:

[0033] According to the secondary evaluation index value at time t, the objective weight vector of the secondary evaluation index is obtained by using the entropy weight method, and the dynamic subjective weight vector of the secondary evaluation index is obtained by introducing the dynamic group decision-making method based on the hierarchical analysis method AHP. The dynamic subjective and objective combined weights of each secondary evaluation index at time t+△t are calculated. The calculation formula is as follows:

[0034]

[0035] Among them, w(t+△t) is the dynamic subjective and objective combined weight of each secondary evaluation index at time t+△t, δ1 (t+△t) is the weight of the first expert at time t+△t, δ 2 (t+△t) is the weight of the second expert at time t+△t, δ x (t+△t) is the weight of the x-th expert at time t+△t.

[0036] Furthermore, the ideal relative closeness of the evaluation scheme is calculated using the approximate ideal solution ranking method TOPSIS, including:

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

[0038] Z=(z ij ) n×m =(w j y ij ) n×m

[0039] Among them, Z is the weighted standardized indicator matrix, z ij is the element determined by the i-th row and j-th column of the weighted normalized indicator matrix, w j is the objective weight of the secondary evaluation index, y ij is the jth secondary evaluation index of the i-th evaluation scheme after standardization;

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

[0041]

[0042] in, are the maximum values ​​of the 1st, 2nd, …, mth columns of the weighted normalized indicator matrix Z, are the minimum values ​​of the 1st, 2nd, …, mth columns of the weighted normalized indicator 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 + is the standard distance between each evaluation scheme and the positive ideal solution, is the square of the objective weight of the secondary evaluation index, The standardized index decision matrix Y = (y ij ) n×m The maximum value of the jth column of is the maximum value of the jth column of the weighted normalized indicator matrix Z, i.e., the positive ideal solution, D st,i - is the standard distance between each evaluation scheme and the negative ideal solution, The standardized index decision matrix Y = (y ij ) n×m The minimum value of the jth column of is the minimum value of the jth column of the weighted normalized indicator matrix Z, i.e., the negative ideal solution;

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

[0047]

[0048] Among them, C i is the ideal relative closeness of the i-th evaluation scheme;

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

[0050] Determine the reference sequence and the comparison sequence, the reference sequence is the determined positive ideal solution Z + ,The comparison sequence is the three-level evaluation index values ​​of different second-level operation data, that is, each evaluation scheme;

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

[0052]

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

[0054] Calculate the grey correlation degree. Each reference sequence and comparison sequence has several grey correlation coefficients. Find their average value as the grey correlation degree between the reference sequence and the comparison sequence:

[0055]

[0056] ε i is the grey correlation degree between the ith reference sequence and the corresponding ith comparison sequence.

[0057] Furthermore, the closeness between the first-level evaluation index and the positive and negative ideal solutions is further calculated as the final evaluation result of the first-level evaluation index, including:

[0058] The grey correlation degree ζ between each evaluation scheme and the positive and negative ideal solutions is obtained by using the grey correlation analysis method GRA using the secondary evaluation index weight standardization index matrix. st,i + and st,i - At the same time, the standard distance D between each evaluation scheme and the positive and negative ideal solutions is obtained by calculating the approximate ideal solution ranking method TOPSIS st,i + and D st,i - , D st,i - and st,i + The larger the value, the closer the evaluation scheme is to the positive ideal solution. st,i + and st,i - The larger the value, the closer the evaluation scheme is to the negative ideal solution. The preference calculation is performed on the grey relational analysis method GRA and the approximate ideal solution ranking method TOPSIS:

[0059]

[0060] Among them, r and s are preference coefficients, which respectively represent the preference degree for the grey relational analysis method GRA and the approximate ideal solution ranking method TOPSIS during calculation, r+s=1; T i + is the relative closeness between the ith evaluation scheme and the positive ideal solution, T i - is the relative closeness between the i-th evaluation scheme and the negative ideal solution, T i + The larger the evaluation scheme is, the closer it is to the positive ideal solution. i - The larger the evaluation scheme is, the closer it is to the negative ideal solution;

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

[0062]

[0063] The first-level evaluation index is combined with the positive and negative ideal solutions to achieve the progress T i As the final evaluation result of the first-level evaluation indicator.

[0064] The system corresponding to the method includes:

[0065] The index calculation unit is used to conduct multi-dimensional analysis on the coal-fired units with deep peak load regulation and frequency modulation, and to screen and determine the comprehensive evaluation index system of the coal-fired units by applying the Delphi method in combination with expert experience. The comprehensive evaluation index system includes first-level evaluation index, second-level evaluation index and third-level evaluation index. The second-level operation data of the coal-fired units with deep peak load regulation and frequency modulation are collected according to the comprehensive evaluation index system, and the values ​​of the three-level evaluation indexes are calculated;

[0066] The combined weight calculation unit is used to calculate the objective weight of the secondary evaluation index based on the values ​​of each third-level evaluation index using the entropy weight method, introduce the dynamic group decision-making method based on the hierarchical analysis method AHP, and obtain the subjective dynamic group decision-making model extended to the time series, and then determine the dynamic subjective weight of the secondary evaluation index; combine and weight the subjective and objective weights to calculate the dynamic subjective and objective combined weights of each secondary evaluation index;

[0067] The evaluation unit is used to calculate the ideal relative closeness of the evaluation scheme based on the dynamic subjective and objective combined weights of the secondary evaluation indicators, using the approximate ideal solution ranking method TOPSIS, and using the fusion grey correlation analysis method GRA to calculate the grey correlation between the positive ideal solution and the evaluation scheme, and further calculate the closeness of the primary evaluation indicators to the positive and negative ideal solutions as the final evaluation results of the primary evaluation indicators. According to the final evaluation results, a multi-dimensional dynamic balanced comprehensive evaluation of the operating status of the coal-fired unit at the second level is achieved.

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

[0069] A memory storing 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 multi-dimensional dynamic balance of flexible operation of coal-fired units.

[0072] A computer-readable storage medium for storing and executing the method, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the steps of the comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units.

[0073] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are: the comprehensive evaluation index system can be screened and determined according to the collected second-level operation data of the deep peak-shaving and frequency-modulating flexible operation coal-fired units; the second-level operation data is processed to calculate the three-level evaluation index value; the dynamic group decision method can be introduced on the basis of the analytic hierarchy process (AHP) to calculate the dynamic subjective weight of the secondary evaluation index, the entropy weight method is used to calculate the objective weight of the secondary evaluation index, the dynamic subjective weight and the objective weight are combined and weighted to obtain the dynamic subjective and objective combined weight; and according to the comprehensive evaluation index of the flexible operation of the coal-fired unit The evaluation index system, the evaluation index value and the dynamic subjective and objective combined weights are integrated with the grey relational analysis method (GRA) by the method of ranking by approximation to ideal solutions (TOPSIS) to calculate the closeness between the first-level evaluation index and the positive and negative ideal solutions, which is used as the final evaluation result of the first-level evaluation index. According to the final evaluation result, a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of the coal-fired unit at the second level is achieved; the multi-dimensional dynamic equilibrium comprehensive evaluation of the deep peak-shaving and frequency-regulating flexible operating coal-fired units during operation can be solved, and method support is provided for the deep peak-shaving and frequency-regulating coal-fired units to participate in auxiliary services in the power market and enhance the stability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic flow chart of the method of the present invention;

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

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

[0077] Figure 4 It is a comprehensive evaluation index system determined by a comprehensive evaluation method for realizing multi-dimensional dynamic balance of flexible operation of deep peak-shaving and frequency-regulating coal-fired units in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

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

[0079] Figure 1 The general process of a comprehensive evaluation method for multi-dimensional dynamic balance of deep peak-shaving and frequency-shaving coal-fired units of the present invention is shown, and the process includes:

[0080] Step 1: Conduct a multi-dimensional analysis of the deep peak-shaving and frequency-modulating flexible coal-fired units, and use the Delphi method in combination with expert experience to screen and determine the comprehensive evaluation index system of the coal-fired units. The comprehensive evaluation index system includes first-level evaluation indicators, second-level evaluation indicators, and third-level evaluation indicators. Collect the second-level operation data of the deep peak-shaving and frequency-modulating flexible coal-fired units according to the comprehensive evaluation index system, and calculate the values ​​of each third-level evaluation index;

[0081] The collected second-level operating data of the deep peak-shaving and frequency-regulating flexible coal-fired units are dimensionlessly processed, and the three-level evaluation index values ​​are positively or reversely normalized and standardized.

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

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

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

[0085] Step 2: Based on the values ​​of the three-level evaluation indicators calculated in step 1, the objective weights of the second-level evaluation indicators are calculated using the entropy weight method, and 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 a time series, thereby determining the dynamic subjective weights of the second-level evaluation indicators; the subjective and objective weights are combined and weighted to calculate the dynamic subjective and objective combined weights of each second-level evaluation indicator;

[0086] The specific calculation steps of the entropy weight method are as follows:

[0087] The initial indicator decision matrix X is constructed by n evaluation schemes and m secondary evaluation indicators. ij ) n×m , x ijis the jth secondary evaluation index of the i-th evaluation scheme, that is, the element determined by the i-th row and j-th column of the initial index decision matrix, 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 seconds. Based on the initial index decision matrix X, the standardized index decision matrix Y = (y ij ) n×m ,y ij is the jth 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 ​​normalized:

[0089]

[0090] Among them, p ij is the normalized j-th secondary evaluation index of the ith evaluation scheme, that is, the standardized index decision matrix Y = (y ij ) n×m The corresponding value obtained after the normalization of the elements in the i-th row and j-th column.

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

[0092]

[0093] Determine the objective weight w of each secondary evaluation index j :

[0094]

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

[0096] Select x experts to evaluate m secondary evaluation indicators using the analytic hierarchy process (AHP) and introduce a dynamic group decision-making method to obtain the dynamic subjective weight w. 1 , w 2 ,…,w x , where w 1 , w 2 ,…,w x are the dynamic subjective weights of the m secondary evaluation indicators given by the 1st, 2nd, ..., xth experts, is the dynamic subjective weight vector of m secondary evaluation indicators, represents the dynamic subjective weight of the mth secondary evaluation index given by the xth expert. At time t, the objective weight vector w of the secondary evaluation index is obtained by using the entropy weight method. j(t)=(w 1 (t),w 2 (t),…,w n (t)), where the objective weight vector of the secondary evaluation index w j (t) has time series characteristics. The vector angle cosine formula is used to express 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. The calculation formula is as follows:

[0097]

[0098] in, is 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;

[0099] The objective weight vector w of the secondary evaluation index at time t j (t) and the dynamic subjective weight vector w of the secondary evaluation index calculated by the xth expert x Deviation DEV x (t) is:

[0100]

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

[0102]

[0103] DEV x (t) represents the deviation of the xth expert, It represents the sum of the deviations of the experts from the 1st to the xth position.

[0104] When DEV x When (t) = 0, δ x (t) = 1 / x, that is, in the evaluation process, all experts have the same importance. x When (t)≠0, it means that the importance of experts is inconsistent.

[0105] According to the secondary evaluation index value at time t, the entropy weight method is used to obtain the objective weight vector of the secondary evaluation index, and the dynamic group decision-making method is introduced on the basis of the analytic hierarchy process (AHP) to obtain the dynamic subjective weight vector of the secondary evaluation index. The dynamic subjective and objective combined weights of each secondary evaluation index at time t+△t are calculated. The calculation formula is as follows:

[0106]

[0107] Among them, w(t+△t) is the dynamic subjective and objective combined weight of each secondary evaluation index at time t+△t, δ 1 (t+△t) is the weight of the first expert at time t+△t, δ 2 (t+△t) is the weight of the second expert at time t+△t, δ x (t+△t) is the weight of the x-th expert at time t+△t.

[0108] Combining the above formulas, the dynamic subjective and objective combined weights of the secondary evaluation indicators at each moment in the dynamic change process of the evaluation indicator data of the coal-fired unit are obtained.

[0109] Step 3: Based on the dynamic subjective and objective combined weights of the secondary evaluation indicators obtained in step 2, the TOPSIS method combined with the grey relational analysis method (GRA) is used to calculate the closeness of the primary evaluation indicators to the positive and negative ideal solutions, which 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 second-level operation status of the coal-fired unit is achieved. The specific calculation steps are as follows:

[0110] First, according to the TOPSIS method, the optimal solution of each evaluation scheme (positive ideal solution Z + ) and the worst solution (negative ideal solution Z - ), further calculate the standard distance between each evaluation scheme and the positive and negative ideal solutions, and the ideal relative closeness of each evaluation scheme; including:

[0111] Based on the standardized indicator decision matrix Y obtained in step 2, ij ) n×m , construct a weighted standardized indicator matrix:

[0112] Z=(z ij ) n×m =(w j y ij ) n×m

[0113] Among them, Z is the weighted standardized indicator matrix, z ij is the element determined by the i-th row and j-th column of the weighted normalized indicator matrix, w j is the objective weight of the secondary evaluation index. This operation is to give the standardized index decision matrix Y = (y ij ) n×m Objective weighting.

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

[0115]

[0116] in, are the maximum values ​​of the 1st, 2nd, …, mth columns of the weighted normalized indicator matrix Z, are the minimum values ​​of the 1st, 2nd, …, mth columns of the weighted standardized indicator matrix Z respectively. The meanings of the subscript letters in the formula are consistent with the above meanings.

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

[0118]

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

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

[0121]

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

[0123] Secondly, according to the grey relational analysis method GRA, the grey relational coefficient is calculated, and the positive ideal solution Z is further calculated. + Grey correlation between each evaluation scheme; including:

[0124] Determine the reference sequence and the comparison sequence, 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 operation data, that is, each evaluation scheme;

[0125] Based on the standardized indicator decision matrix Y=(y ij ) n×m , calculate the grey correlation coefficient:

[0126]

[0127] Among them, β ij The standardized index decision matrix Y = (y ij ) n×m The grey correlation coefficient of the element corresponding to the i-th row and j-th column, y i,max is the maximum value of the index in the jth column, ρ is an adjustable coefficient, and its value is in the range of [0,1]. The purpose of this item is to adjust the difference in the output results. In this embodiment, ρ is set to 0.5;

[0128] Calculate the grey correlation degree. Each reference sequence and comparison sequence has several grey correlation coefficients. Find their average value as the grey correlation degree between the reference sequence and the comparison sequence:

[0129]

[0130] ε i is the grey correlation degree between the ith reference sequence and the corresponding ith 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 first-level evaluation index and the positive and negative ideal solutions is further calculated. i As the final evaluation result of the first-level evaluation index, it realizes the multi-dimensional dynamic equilibrium comprehensive evaluation of the operation status of the coal-fired unit at the second level. Including:

[0132] The grey relational analysis (GRA) method is used to obtain the grey relational degree ζ between each evaluation scheme and the positive and negative ideal solutions by using the secondary index weight standardized index matrix. st,i + and st,i - At the same time, the standard distance D between each evaluation scheme and the positive and negative ideal solutions is calculated by TOPSIS st,i + and D st,i - , D st,i - and st,i + The larger the value, the closer the evaluation scheme is to the positive ideal solution. st,i + and st,i - The larger the value, the closer the evaluation scheme is to the negative ideal solution, and the preference calculation is performed on the grey relational analysis (GRA) and the topology of ideal solutions (TOPSIS):

[0133]

[0134] Among them, r and s are preference coefficients, which respectively represent the preference degree for grey relational analysis (GRA) and topology of best solutions (TOPSIS) in calculation, r+s=1. i + is the relative closeness between the ith evaluation scheme and the positive ideal solution, T i - is the relative closeness between the i-th evaluation scheme and the negative ideal solution, T i + The larger the evaluation scheme is, the closer it is to the positive ideal solution. i - The larger the evaluation solution is, the closer it is to the negative ideal solution.

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

[0136]

[0137] The first-level evaluation index is combined with the positive and negative ideal solutions to achieve the progress T i As the final evaluation result of the first-level evaluation indicator.

[0138] According to the first-level evaluation index and the positive and negative ideal solution pasting progress T i The size of the coal-fired units is evaluated by a multi-dimensional dynamic equilibrium comprehensive evaluation on their operating status at the second level.

[0139] Figure 3 A detailed principle block diagram of an exemplary embodiment of a comprehensive evaluation method for flexible and intelligent operation of a deep peak-shaving and frequency-shaving coal-fired unit according to the present invention is shown. Figure 1 A more detailed description of the specific steps of the three general processes is provided to enhance the understanding and application of the comprehensive evaluation method for the flexible operation of multi-dimensional dynamic balance of deep peak-shaving and frequency-regulating coal-fired units by relevant technical personnel in the same field.

[0140] Figure 4 The comprehensive evaluation system of an exemplary embodiment of a comprehensive evaluation method for flexible operation of multi-dimensional dynamic balance of deep peak-shaving and frequency-regulating coal-fired units of the present invention is shown. In this embodiment, three of the six dimensions in the general process description are selected for implementation examples to further explain the present invention. Figure 2 Not all indicators shown in the table are necessary under different working conditions. All indicators can be dynamically increased or decreased in different application scenarios and for different evaluation purposes.

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

Claims

1. A comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units, characterized in that: The following steps are involved: Conduct multi-dimensional analysis on coal-fired units with deep peak load and frequency regulation, and use the Delphi method to screen and determine the comprehensive evaluation index system of coal-fired units based on expert experience. The comprehensive evaluation index system includes first-level evaluation index, second-level evaluation index and third-level evaluation index. Collect the second-level operation data of coal-fired units with deep peak load and frequency regulation according to the comprehensive evaluation index system, and calculate the values ​​of each third-level evaluation index. Based on the values ​​of the three-level evaluation indicators, the entropy weight method is used to calculate the objective weights of the second-level evaluation indicators. The dynamic group decision-making method is introduced on the basis of the hierarchical analysis method AHP to obtain a subjective dynamic group decision-making model extended to the time series, and then determine the dynamic subjective weights of the second-level evaluation indicators; the subjective and objective weights are combined and weighted to calculate the dynamic subjective and objective combined weights of each second-level evaluation indicator; Based on the dynamic subjective and objective combined weights of the secondary evaluation indicators, the ideal relative closeness of the evaluation scheme is calculated using the approximate ideal solution ranking method TOPSIS. The grey correlation analysis method GRA is used to calculate the grey correlation 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. According to the final evaluation result, a multi-dimensional dynamic balanced comprehensive evaluation of the operating status of the coal-fired unit at the second level is achieved.

2. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 1 is 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: power supply coal consumption rate, plant power consumption rate and power supply comprehensive water consumption rate; 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: climbing rate, adjustment accuracy and auxiliary frequency regulation Kp; the indicators included in each secondary evaluation indicator are tertiary evaluation indicators.

3. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 1 is characterized in that: Also includes: The second-level operation data of the coal-fired unit is dimensionlessly processed, and the three-level evaluation index values ​​are positively or reversely normalized and standardized.

4. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 1 is characterized in that: The entropy weight method is used to calculate the objective weights of the secondary evaluation indicators, including: The initial indicator decision matrix X is constructed by n evaluation schemes and m secondary evaluation indicators. ij ) n×m , x ij is the jth secondary evaluation index of the ith 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 seconds, and the standardized index decision matrix Y = (y ij ) n×m ,y ij is the jth secondary evaluation index of the i-th evaluation scheme after standardization; The standardized indicator decision matrix is ​​normalized: Among them, p ij is the jth secondary evaluation index of the i-th evaluation scheme after normalization; Calculate the information entropy e of each secondary evaluation index j : Determine the objective weight w of each secondary evaluation index j : The calculation method of dynamic subjective weight of secondary evaluation indicators is: Select x experts to evaluate m secondary evaluation indicators and introduce dynamic group decision-making method based on the analytic hierarchy process AHP to obtain dynamic subjective weight w 1 , w 2 ,…,w x , where w 1 , w 2 ,…,w x are the dynamic subjective weights of the m secondary evaluation indicators given by the 1st, 2nd, ..., xth experts, is the dynamic subjective weight vector of m secondary evaluation indicators, represents the dynamic subjective weight of the mth secondary evaluation index given by the xth expert; at time t, the objective weight vector w of the secondary evaluation index is obtained by using the entropy weight method j (t)=(w1(t),w2(t),…,w n (t)), the objective weight vector of the secondary evaluation index w j (t) is a time series feature; the vector angle cosine formula is used to express 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. The calculation formula is as follows: in, is 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; The objective weight vector w of the secondary evaluation index at time t j (t) and the dynamic subjective weight vector w of the secondary evaluation index calculated by the xth expert x Deviation DEV x (t) is: When DEV x (t)≠0, then the weight δ of the expert x x (t) is: DEV x (t) represents the deviation of the xth expert, It represents the sum of the deviations from the 1st to the xth expert; When DEV x When (t) = 0, δ x (t) = 1 / x, that is, in the evaluation process, all experts have the same importance. x When (t)≠0, it means that the importance of experts is inconsistent.

5. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 4 is characterized in that: The subjective and objective weights are combined and weighted to calculate the dynamic subjective and objective combined weights of each secondary evaluation index, including: According to the secondary evaluation index value at time t, the objective weight vector of the secondary evaluation index is obtained by using the entropy weight method, and the dynamic subjective weight vector of the secondary evaluation index is obtained by introducing the dynamic group decision-making method based on the hierarchical analysis method AHP. The dynamic subjective and objective combined weights of each secondary evaluation index at time t+△t are calculated. The calculation formula is as follows: Among them, w(t+△t) is the dynamic subjective and objective combined weight of each secondary evaluation index at time t+△t, δ 1 (t+△t) is the weight of the first expert at time t+△t, δ 2 (t+△t) is the weight of the second expert at time t+△t, δ x (t+△t) is the weight of the x-th expert at time t+△t.

6. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 1 is characterized in that: The ideal relative closeness of the evaluation scheme is calculated using the approximate ideal solution ranking method TOPSIS, including: Based on the standardized indicator decision matrix Y=(y ij ) n×m , construct a weighted standardized indicator matrix: From=(from ij ) n×m =(in j and ij ) n×m Among them, Z is the weighted standardized indicator matrix, z ij is the element determined by the i-th row and j-th column of the weighted normalized indicator matrix, w j is the objective weight of the secondary evaluation index, y ij is the jth secondary evaluation index of the i-th evaluation scheme after standardization; Calculate the positive ideal solution Z of each evaluation scheme + and negative ideal solution Z - : in, are the maximum values ​​of the 1st, 2nd, …, mth columns of the weighted normalized indicator matrix Z, are the minimum values ​​of the 1st, 2nd, …, mth columns of the weighted normalized indicator matrix Z, respectively; Calculate the standard distance between each evaluation scheme and the positive and negative ideal solutions: Among them, D st,i + is the standard distance between each evaluation scheme and the positive ideal solution, is the square of the objective weight of the secondary evaluation index, The standardized index decision matrix Y = (y ij ) n×m The maximum value of the jth column of is the maximum value of the jth column of the weighted normalized indicator matrix Z, i.e., the positive ideal solution, D st,i - is the standard distance between each evaluation scheme and the negative ideal solution, The standardized index decision matrix Y = (y ij ) n×m The minimum value of the jth column of is the minimum value of the jth column of the weighted normalized indicator matrix Z, i.e., the negative ideal solution; Calculate the ideal relative closeness of each evaluation scheme: Among them, C i is the ideal relative closeness of the i-th evaluation scheme; The grey correlation analysis method GRA is used to calculate the grey correlation between the positive ideal solution and the evaluation scheme, including: Determine the reference sequence and the comparison sequence, the reference sequence is the determined positive ideal solution Z + ,The comparison sequence is the three-level evaluation index values ​​of different second-level operation data, that is, each evaluation scheme; Based on the standardized indicator decision matrix Y=(y ij ) n×m , calculate the grey correlation coefficient: Among them, β ij The standardized index decision matrix Y = (y ij ) n×m The grey correlation coefficient of the element corresponding to the i-th row and j-th column, y i,max is the maximum value of the index in the jth column, and ρ is an adjustable coefficient; Calculate the grey correlation degree. Each reference sequence and comparison sequence has several grey correlation coefficients. Find their average value as the grey correlation degree between the reference sequence and the comparison sequence: ε i is the grey correlation degree between the ith reference sequence and the corresponding ith comparison sequence.

7. The comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of coal-fired units according to claim 1 is characterized in that: The closeness between the first-level evaluation index and the positive and negative ideal solutions is further calculated as the final evaluation result of the first-level evaluation index, including: The grey correlation degree ζ between each evaluation scheme and the positive and negative ideal solutions is obtained by using the grey correlation analysis method GRA using the secondary evaluation index weight standardization index matrix. st,i + and st,i - At the same time, the standard distance D between each evaluation scheme and the positive and negative ideal solutions is obtained by calculating the approximate ideal solution ranking method TOPSIS st,i + and D st,i - , D st,i - and st,i + The larger the value, the closer the evaluation scheme is to the positive ideal solution. st,i + and st,i - The larger the value, the closer the evaluation scheme is to the negative ideal solution. The preference calculation is performed on the grey relational analysis method GRA and the approximate ideal solution ranking method TOPSIS: Among them, r and s are preference coefficients, which respectively represent the preference degree for the grey relational analysis method GRA and the approximate ideal solution ranking method TOPSIS during calculation, r+s=1; T i + is the relative closeness between the ith evaluation scheme and the positive ideal solution, T i - is the relative closeness between the i-th evaluation scheme and the negative ideal solution, T i + The larger the evaluation scheme is, the closer it is to the positive ideal solution. i - The larger the evaluation scheme is, the closer it is to the negative ideal solution; According to T i + and T i - Calculate the progress T of the first-level evaluation index and the positive and negative ideal solutions i : The first-level evaluation index is combined with the positive and negative ideal solutions to achieve the progress T i As the final evaluation result of the first-level evaluation indicator.

8. A comprehensive evaluation system for multi-dimensional dynamic balance of flexible operation of coal-fired units, characterized in that: include: The index calculation unit is used to conduct multi-dimensional analysis on the coal-fired units with deep peak load regulation and frequency modulation, and to screen and determine the comprehensive evaluation index system of the coal-fired units by applying the Delphi method in combination with expert experience. The comprehensive evaluation index system includes first-level evaluation index, second-level evaluation index and third-level evaluation index. The second-level operation data of the coal-fired units with deep peak load regulation and frequency modulation are collected according to the comprehensive evaluation index system, and the values ​​of the three-level evaluation indexes are calculated; The combined weight calculation unit is used to calculate the objective weight of the secondary evaluation index based on the values ​​of each third-level evaluation index using the entropy weight method, introduce the dynamic group decision-making method based on the hierarchical analysis method AHP, and obtain the subjective dynamic group decision-making model extended to the time series, and then determine the dynamic subjective weight of the secondary evaluation index; combine and weight the subjective and objective weights to calculate the dynamic subjective and objective combined weights of each secondary evaluation index; The evaluation unit is used to calculate the ideal relative closeness of the evaluation scheme based on the dynamic subjective and objective combined weights of the secondary evaluation indicators, using the approximate ideal solution ranking method TOPSIS, and using the fusion grey correlation analysis method GRA to calculate the grey correlation between the positive ideal solution and the evaluation scheme, and further calculate the closeness of the primary evaluation indicators to the positive and negative ideal solutions as the final evaluation results of the primary evaluation indicators. According to the final evaluation results, a multi-dimensional dynamic balanced comprehensive evaluation of the operating status of the coal-fired unit at the second level is achieved.

9. An electronic device, characterized in that: The device comprises: A memory storing 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 multi-dimensional dynamic balance of flexible operation of a coal-fired unit as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the steps of the comprehensive evaluation method for multi-dimensional dynamic balance of flexible operation of a coal-fired unit as described in any one of claims 1-7.

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