A Health Status Assessment Method for a Distributed Low-Carbon Energy Station

Abstract

The present invention discloses a method for evaluating the health status of a distributed low-carbon energy station; first, collect the health status data of the distributed low-carbon energy station and construct an evaluation index for the health status of the distributed low-carbon energy station; secondly, classify the evaluation indexes, and perform standardization processing on each evaluation index according to the classification to obtain a standardized index matrix; secondly, solve the objective weight of the evaluation index, transform the solution of the objective weight of the evaluation index into a non-linear programming problem, and use the particle swarm algorithm to solve the objective weight of the evaluation index; secondly, use the group G1 method for subjective weighting and fuse the subjective and objective weights by using the game theory principle; finally, calculate the generalized grey absolute correlation degree to obtain the evaluation result. The method for evaluating the health status of the distributed low-carbon energy station proposed by the present invention helps to accurately evaluate the weak points of the health status of the distributed low-carbon energy station and has good practical significance.

Description

Technical Field

[0001] The present invention belongs to the field of energy system assessment, and particularly relates to a method for assessing the health status of a distributed low-carbon energy station. Background Art

[0002] With the continuous development of China's industrial system and the continuous improvement of people's living standards, the demand for energy is also increasing continuously. At the same time, the utilization of fossil energy has brought environmental crises, and the contradiction between the pollution caused by energy utilization and environmental protection has become increasingly deep. Under the promotion of environmental protection and energy utilization, distributed low-carbon energy stations have become an important direction to solve this problem. Accurately evaluating the health status of distributed low-carbon energy stations can timely and effectively feedback the weak links in the health status of distributed low-carbon energy stations, which is of great significance for the normal and efficient operation of low-carbon energy stations.

[0003] In terms of health status assessment, CN115080645A discloses a method for assessing the health status of railway tracks to realize the self-judgment of the health status of the tracks. CN114976130A discloses a method for assessing the health status of fuel cells and various auxiliary devices thereof to realize the understanding of the health status of vehicle fuel cell systems. CN114936657A publishes an evaluation system and method for the health status of transformers, and realizes the quantitative evaluation of the health status of transformers by analyzing the change rates of various indicators. CN114167730A publishes an evaluation of the health status related to the forced draft fan of a thermal power unit to ensure reliable and safe operation. Most of the existing health status evaluations are based on the equipment and systems of a single energy network, and there is little research on the integrated energy system under multi-energy coupling. At the same time, the research objects are mainly certain specific equipment, and there is little research on distributed low-carbon energy stations containing multiple devices and systems. In terms of evaluation methods, traditional methods cannot perform grade division, are difficult to conduct refined evaluation, and lack scientific and effective evaluation results. There is currently no research on the health status evaluation of distributed low-carbon energy stations. Summary of the Invention

[0004] In order to solve the problem of assessing the health status of distributed low-carbon energy stations, the present invention proposes a method for assessing the health status of distributed low-carbon energy stations.

[0005] The method for assessing the health status of distributed low-carbon energy stations proposed by the present invention includes the following steps:

[0006] S1. Collect the health status data of the distributed low-carbon energy station and construct an evaluation index for the health status of the distributed low-carbon energy station;

[0007] S2. Classify the evaluation indexes, and perform standardization processing on each evaluation index according to the classification to obtain a standardized index matrix;

[0008] S3. Solve the objective weights of the evaluation indicators, transform the solution of the objective weights of the evaluation indicators into a non-linear programming problem, and use the particle swarm optimization algorithm to solve the objective weights of the evaluation indicators;

[0009] S4. Use the group G1 method for subjective weighting and fuse the subjective and objective weights using game theory principles;

[0010] S5. Calculate the generalized grey absolute correlation degree to obtain the evaluation result;

[0011] S6. If the health status of the distributed low-carbon energy station meets the expected expectations, end; otherwise, after adjusting the distributed low-carbon energy station, return to step S2.

[0012] Optionally, in step S1, the health status evaluation index system of the distributed low-carbon energy station includes an energy supply reliability index, a low-carbon operation index, and a system health index; among them,

[0013] The low-carbon operation index further includes the renewable energy utilization rate, carbon dioxide emission reduction, and comprehensive energy efficiency;

[0014] The energy supply reliability index further includes the energy supply quality qualification rate, the average energy loss rate of the energy station, and the energy supply reliability rate;

[0015] The system health index further includes the power grid health index of the power supply system, the heat network health index of the heating system, and the cold network health index of the cooling system.

[0016] Optionally, step S2 further includes classifying the evaluation indicators into benefit-type indicators and cost-type indicators; among them, the benefit-type indicators include: the renewable energy utilization rate, the comprehensive energy efficiency, the energy supply quality qualification rate, the energy supply reliability rate, the power grid health index of the power supply system, the heat network health index of the heating system, and the cold network health index of the cooling system;

[0017] The cost-type indicators include: carbon dioxide emission reduction and the average energy loss rate of the energy station;

[0018] Assume that each distributed low-carbon energy station has m original index values, and standardize the original evaluation index values of n distributed low-carbon energy stations to obtain the standard index matrix A n×m .

[0019] Optionally, the standardization calculation formula for the benefit-type indicators is:

[0020]

[0021] The standardization calculation formula for the cost-type indicators is:

[0022]

[0023] Where: aij , x ij respectively represent the standardized index value and the original evaluation index value of the j-th index of the i-th distributed low-carbon energy station.

[0024] Optionally, step S3 further includes determining benchmark data, determining constraint conditions and objective functions;

[0025] For n distributed low-carbon energy stations, the benchmark data of their cost-type indicators is The benchmark data of their benefit-type indicators is Their benchmark data is denoted as x0 = {x 01 , x 02 , …, x 0m};

[0026] For each distributed low-carbon energy station, let W0 = {w1, w2, …, w m} be the objective weights of its various indicators, and the objective weight of the j-th evaluation indicator is w j , and

[0027] w j x ij represents the weighted comprehensive performance value of the j-th indicator of the i-th distributed low-carbon energy station; w j x 0j represents the weighted comprehensive performance value of the j-th indicator of the benchmark data; during the evaluation process, it is expected that the weighted comprehensive performance values w j x ij and the weighted comprehensive value w j x 0j of the benchmark scheme change relatively greatly to maximize the difference between different energy stations with the same indicator. Therefore, the solution of the objective weights of the evaluation indicators can be transformed into a nonlinear programming problem, and its objective function and constraint conditions are expressed as:

[0028]

[0029]

[0030] In the formula: F(W) is the objective function; s·t represents the constraint conditions; w j represents the objective weight of the j-th evaluation indicator of the distributed low-carbon energy station; represents the average value of the j-th evaluation indicator of n distributed low-carbon energy stations; according to the objective function and constraint conditions, the objective weight matrix W = [w1, w2, …, w m of the evaluation indicators is obtained.

[0031] Optionally, the nonlinear programming problem is solved by a particle swarm algorithm to obtain the objective weights of the evaluation indicators.

[0032] Optionally, step S4 includes subjectively assigning weights using the group G1 method based on the experience of each expert, with the following steps:

[0033] 1) The experts rank the importance of each evaluation index to obtain the ranking of each expert's relationship represents the ranking result of the jth index by the ath expert;

[0034] 2) Determine the importance between adjacent indexes; each expert compares the importance between adjacent indexes according to their own experience, and the importance scale R j The calculation formula is:

[0035]

[0036] where ω′ j-1 represents the subjective weight of the (j - 1)th evaluation index in the index ranking relationship, and ω′ j represents the subjective weight of the jth evaluation index in the index ranking relationship;

[0037] When occurs, R j needs to be corrected by multiplying the original value by a proportionality coefficient to ensure The calculation formula for the proportionality coefficient ρ is:

[0038]

[0039] R′ j = R j * ρ (17)

[0040] where: R′ j represents the corrected importance scale, and ρ represents the proportionality coefficient.

[0041] 3) Calculate the subjective weights of each evaluation index; after each expert gives the order relationship and importance scale R j between the indexes, calculate the subjective weights of each evaluation index according to the following formula:

[0042]

[0043] where: represents the subjective weight of the mth evaluation index of the ath expert, that is, the subjective weight of the last-ranked evaluation index; represents the subjective weight of the (j - 1)th evaluation index of the ath expert. The weights of each index need to be combined with the self-weights of each expert. Denote the self-weight of each expert as d a , d a satisfies 0 < d a < 1, Let \(l\) be the total number of experts, then we have:

[0044]

[0045] In the formula: \(\omega\) j represents the comprehensive subjective weight of the \(j\)-th evaluation index, and is expressed as the subjective weight of the \(j\)-th evaluation index of the \(a\)-th expert.

[0046] Optionally, step S4 further includes fusing the subjective and objective weights based on the game theory principle;

[0047] Taking the minimum deviation between the objective and subjective weights as the goal, by optimizing the objective and subjective weight vector coefficients, the optimal weight for the corresponding index can be obtained. The specific goal is:

[0048] \(\min(\|\theta - w\|^2+\|\theta-\omega\|^2)\ (20)\)

[0049] Transform the above optimization function into a system of linear equations through the properties of matrix differentiation:

[0050]

[0051] After normalizing formula (21), the comprehensive optimal weight \(\theta\) based on game theory is obtained:

[0052] \(\theta = b_1w\) T \(+b_2\omega\) T \((22)\)

[0053] In the formula: Both \(g_1\) and \(g_2\) are linear combination coefficients.

[0054] Optionally, the specific calculation steps of the generalized grey absolute correlation degree evaluation method in step S5 are as follows:

[0055] 1) "Reward the good and punish the bad" index transformation processing; give rewards to the index transformation values for those higher than the expected value, and impose penalties on the index transformation values for those lower than the expected value. The specific calculation formula is as follows:

[0056] For benefit-type indicators:

[0057]

[0058] For cost-type indicators:

[0059]

[0060] In the formula: represents the maximum value of the \(j\)-th index; represents the minimum value of the \(j\)-th index; represents the average value of the \(j\)-th index;

[0061] 2) Weighted calculation; standardize the index value matrix A n×m Multiply it by the combined weight matrix θ to obtain the weighted standard matrix F. The calculation formula is:

[0062] F = A n×m ·θ = (f ij ) n×m (25)

[0063] Where: f ij represents the weighted standard value of the jth index of the ith energy station, (f ij ) n×m indicates that there are n energy stations and m indicators in total.

[0064] 3) Set the absolute ideal solution; fix the absolute positive ideal solution as F + = [1, 1,..., 1] 1×m and the absolute negative ideal solution as F - = [-1, -1,...,-1] 1×m ;

[0065] 4) Obtain the evaluation result; according to the principle of the generalized grey absolute correlation degree, obtain the positive ideal solution closeness and the negative ideal solution closeness, and obtain the evaluation result. The calculation formula is:

[0066]

[0067]

[0068] Where: β i + and β i - respectively represent the generalized grey absolute correlation degrees of the ith energy station with the positive ideal solution and the negative ideal solution; F i represents the weighted standard matrix of the ith energy station; finally, obtain the grey absolute closeness C i :

[0069]

[0070] After obtaining the grey absolute closeness of each energy station, sort the energy stations according to the size of the grey absolute closeness of the energy stations; the larger the grey absolute closeness of the energy station, the better the energy station.

[0071] Compared with the prior art, the health status evaluation method of the distributed low-carbon energy station provided by the present invention has the following advantages or beneficial effects:

[0072] (1) The present invention constructs an evaluation system including dimensions of reliable energy supply, low-carbon operation, and system health, comprehensively reflecting the health status of the distributed low-carbon energy station;

[0073] (2) By applying the idea of non-linear programming and the group G1 method, the subjective and objective weights are obtained, and the weights are fused using the idea of game theory to make the weight results more accurate;

[0074] (3) Introducing the idea of approaching the absolute ideal solution, setting the absolute ideal solution, improving the traditional grey correlation degree, and forming a generalized grey absolute correlation degree evaluation method to make the evaluation results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of the method for evaluating the health status of the distributed low-carbon energy station provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The present invention proposes a method for evaluating the health status of a distributed low-carbon energy station. First, an evaluation system for the health status of the distributed low-carbon energy station is constructed. By applying the idea of non-linear programming and the group G1 method, the subjective and objective weights are obtained, and the weights are fused using the idea of game theory. Introducing the idea of approaching the absolute ideal solution, setting the absolute ideal solution, improving the traditional grey correlation degree, and forming a generalized grey absolute correlation degree evaluation method to make the evaluation results more accurate, fully understanding the weak points of the health status of the distributed low-carbon energy station, and promoting the development of the distributed low-carbon energy station.

[0077] The following further introduces the present invention with reference to the drawings through detailed description of preferred specific embodiments.

[0078] Figure 1 It is a flow chart of the method for evaluating the health status of the distributed low-carbon energy station provided by the present invention; the specific steps of the method for evaluating the health status of the distributed low-carbon energy station according to the present invention are as follows.

[0079] Step S1: Collect the health status data of the distributed low-carbon energy station and construct the evaluation index for the health status of the distributed low-carbon energy station.

[0080] According to the characteristics of the distributed low-carbon energy station, the present invention considers the relationship between multiple influencing factors and multi-party coordination, and constructs an evaluation index system for the health status of the distributed low-carbon energy station. The evaluation index system for the health status of the distributed low-carbon energy station mainly includes three dimensions: reliable energy supply index, low-carbon operation index, and system health index; among them, the low-carbon operation index further includes the renewable energy utilization rate, carbon dioxide emission reduction amount, and comprehensive energy efficiency.

[0081] 1) Renewable energy utilization rate

[0082] The renewable energy utilization rate of a distributed low-carbon energy station refers to the ratio of the total amount of renewable energy consumed by the energy station to the total amount of all energy consumed by the energy station; the calculation formula for the renewable energy utilization rate A1 of the distributed low-carbon energy station is:

[0083]

[0084] In the formula: S1 represents the total amount of renewable energy consumed by the energy station; S * represents the total amount of all energy consumed by the energy station.

[0085] 2) Carbon dioxide emission reduction

[0086] The carbon dioxide emission reduction of a distributed low-carbon energy station represents the sum of the products of the total amount of energy saved in various energy forms and the carbon dioxide emission coefficients corresponding to the energy forms; the calculation formula for the carbon dioxide emission reduction B1 of the distributed low-carbon energy station is:

[0087]

[0088] In the formula: k represents different energy forms such as electricity, heat, and cold; L k is the total amount of energy saved in the kth energy form per unit time, and f k represents the carbon dioxide emission coefficient of the kth energy form.

[0089] 3) Comprehensive energy efficiency

[0090] The comprehensive energy efficiency characterizes the utilization level of primary energy by a distributed low-carbon energy station, reflects the differences in the energy grades of different forms of energy through the energy quality coefficient, and obtains the comprehensive utilization efficiency of different forms of energy; the calculation formula for the comprehensive energy efficiency A2 of the distributed low-carbon energy station is:

[0091]

[0092] In the formula: c, h, and e represent cold energy, heat energy, and electrical energy respectively; E c , E h , E e represent the annual cooling capacity, heating capacity, and power generation of the energy station respectively; λ c , λ h , λ e represent the energy quality coefficients corresponding to the respective energy forms; W μ represents the consumption of the μth primary energy by the energy station, and λ μ represents the energy quality coefficient corresponding to the μth energy. The energy quality coefficient (energy quality coefficient, EQC) is the ratio of the value of the energy to the quantity of the energy.

[0093] The reliable energy supply index further includes the qualified rate of energy supply quality, the average energy loss rate of the energy station, and the reliable energy supply rate.

[0094] 1) Qualified rate of energy supply quality

[0095] The qualified rate of energy supply quality can intuitively reflect the good degree of energy supply quality of the distributed low-carbon energy station, including the qualified rates of cooling, heating, and power supply. The higher the qualified rate of energy supply quality, the better the health conditions of each link and equipment of the distributed low-carbon energy station. The present invention adopts the idea of averaging and uses the average qualified rate of energy supply quality of each energy form; the qualified rate of energy supply quality refers to the ratio of the sum of the qualified time of cooling, heating, and power supply of the energy station throughout the year to the sum of the cooling, heating, and power supply time of the energy station throughout the year; the calculation formula for the qualified rate of energy supply quality A3 of the distributed low-carbon energy station is:

[0096]

[0097] In the formula: c, h, and e respectively represent cold energy, heat energy, and electric energy; t c , t h , t e respectively represent the qualified time of cooling, heating, and power supply of the energy station throughout the year; ξ c , ξ h , ξ e respectively represent the cooling, heating, and power supply time of the energy station throughout the year.

[0098] 2) Average energy loss rate of the energy station

[0099] The average energy loss rate of the energy station refers to the ratio of the total time of the cooling, heat, and electric energy supply interruption due to failures of each user group throughout the year to the total energy supply time of the cooling, heat, and electric energy of each energy-consuming group; the calculation formula for the average energy loss rate B2 of the distributed low-carbon energy station is:

[0100]

[0101] In the formula: represents the time of the cooling, heat, and electric energy supply interruption due to failures of the z-th user group; N all represents the total number of user groups; 8760×3 is the total energy supply time of the cooling, heat, and electric energy of each user throughout the year.

[0102] 3) Reliable energy supply rate

[0103] The reliable energy supply rate has a strong correlation with the health status of the distributed low-carbon energy station; the reliable energy supply rate refers to the ratio of the average energy interruption time to the average supply time of the cooling, heat, and electric energy of the energy station throughout the year; the calculation formula for the reliable energy supply rate A4 of the distributed low-carbon energy station is:

[0104]

[0105] In the formula: t0 represents the average energy loss time of cold energy, heat energy, and electric energy throughout the year; 8760 is the average supply time of cold energy, heat energy, and electric energy throughout the year.

[0106] The system health index further includes the power grid health index of the power supply system, the heat network health index of the heating system, and the cold network health index of the cooling system.

[0107] 1) Power grid health index of the power supply system

[0108] The power grid health index of the power supply system refers to the average value of the health indexes of each power supply device in the power supply system, and the health index of the device is measured by its service life; the calculation formula for the power grid health index A5 of the power supply system is:

[0109]

[0110] In the formula: e represents electric energy respectively; T e is the specified service life of the nth e device in the power supply system; t e is the service life of the nth e device in the power supply system; N e is the total number of devices in the power supply system; 10 is the amplification factor of the power grid health index A5 of the power supply system.

[0111] 2) Heat network health index of the heating system

[0112] The heat network health index of the heating system refers to the average value of the health indexes of each heating device in the heating system, and the health index of the device is measured by its service life; the calculation formula for the heat network health index A6 of the heating system is:

[0113]

[0114] In the formula: h represents heat energy; T h is the specified service life of the nth h device in the heating system; t h is the service life of the nth h device in the heating system; N h is the total number of devices in the heating system; 10 is the amplification factor of the heat network health index A6 of the heating system.

[0115] 3) Cold network health index of the cooling system

[0116] The cold network health index of the cooling system refers to the average value of the health indexes of each cooling device in the cooling system, and the health index of the device is measured by its service life; the calculation formula for the cold network health index A7 of the cooling system is:

[0117]

[0118] Where: c represents cold energy; T c is the specified service life of the nth c equipment item in the cooling system; t c is the service life of the nth c equipment item in the cooling system; N c is the total number of equipment in the cooling system; 10 is the amplification factor of the cold network health index A7 of the cooling system.

[0119] Step S2: Classify the evaluation indicators, and perform standardization processing on each evaluation indicator according to the classification to obtain a standardized indicator matrix.

[0120] Since the magnitudes of each evaluation indicator are different, their data cannot be directly analyzed and calculated; for the convenience of subsequent operation and to make each evaluation indicator highly feasible, it is necessary to perform standardization processing on each evaluation indicator to obtain a standardized indicator matrix. The present invention analyzes the influence of evaluation indicators on the health status of a distributed low-carbon energy station, classifies the evaluation indicators into benefit-type indicators and cost-type indicators, and performs standardization processing on each evaluation indicator according to the classification.

[0121] Among them, the evaluation indicators A1, A2, A3, A4, A5, A6, and A7 are classified as benefit-type indicators, and the standardization calculation formula is:

[0122]

[0123] The evaluation indicators B1 and B2 are classified as cost-type indicators, and the standardization calculation formula is:

[0124]

[0125] Where: a ij , x ij respectively represent the standardized indicator value and the original evaluation indicator value of the jth indicator of the ith distributed low-carbon energy station; the original evaluation indicator value corresponds to the evaluation indicators A1, A2, A3, A4, A5, A6, A7, and B1 and B2 obtained in step one.

[0126] Assume that each distributed low-carbon energy station has m original indicator values. Standardize the original evaluation indicator values of n distributed low-carbon energy stations to obtain a standard indicator matrix A n×m .

[0127] Step S3: Solve the objective weights of the evaluation indicators, transform the solution of the objective weights of the evaluation indicators into a non-linear programming problem, and use the particle swarm algorithm to solve the objective weights of the evaluation indicators.

[0128] The solution of the objective weights of evaluation indicators is transformed into a non - linear programming problem. First, the benchmark data should be determined and the benchmark data scheme should be determined.

[0129] S31. Determine the benchmark data, and determine the constraint conditions and the objective function

[0130] For n distributed low - carbon energy stations, the benchmark data of their cost - type indicators are The benchmark data of their benefit - type indicators are The benchmark data is denoted as x0 = {x 01 , x 02 , …, x 0m}; For each distributed low - carbon energy station, let W0 = {w1, w2, …, w m} be the objective weights of its various indicators. The objective weight of the j - th evaluation indicator is w j , and w j x ij represents the weighted comprehensive performance value of the j - th indicator of the i - th distributed low - carbon energy station; w j x 0j represents the weighted comprehensive performance value of the j - th indicator of the benchmark data. During the evaluation process, it is expected that the weighted comprehensive performance values w j x ij and the weighted comprehensive value w j x 0j of the benchmark scheme change relatively greatly, that is, the deviation between them is the largest, so as to maximize the difference between different energy stations with the same indicators. From the above, the solution of the objective weights of evaluation indicators can be transformed into a non - linear programming problem, and its objective function and constraint conditions can be expressed as:

[0131]

[0132]

[0133] In the formula: F(W) is the objective function; s·t represents the constraint conditions; w j represents the objective weight of the j - th evaluation indicator of the distributed low - carbon energy station; represents the average value of the j - th evaluation indicator of n distributed low - carbon energy stations. According to the above - mentioned objective function and constraint conditions, the objective weight matrix W = [w1, w2, …, w m of the evaluation indicators can be obtained.

[0134] S32. Use the particle swarm algorithm to solve the non - linear programming problem

[0135] To solve the objective weights of each evaluation index, the present invention uses the particle swarm optimization algorithm for solution. The particle swarm optimization algorithm focuses on two attributes of particles: position and velocity. Each particle searches independently in space. They remember the optimal solutions they have found and also know the current optimal solution found by the entire particle swarm. Where to go next depends on the current direction of the particle, the direction of the optimal solution it has found, and the direction of the current optimal solution of the entire particle swarm. The main steps of the particle swarm optimization algorithm are

[0136] 1. Initialize a particle swarm of size N, with the velocity and position of each particle being random;

[0137] 2. Evaluate the fitness value of each particle;

[0138] 3. If the current fitness value of a certain particle is better than the previously recorded optimal solution of this particle, then update the optimal solution of this particle;

[0139] 4. If the current fitness value of a certain particle is better than the previously recorded global optimal solution, then update the global optimal solution;

[0140] 5. If the global optimal solution meets the requirements, then end; if the global optimal solution does not meet the requirements, the particle updates its velocity and new position according to the following formula:

[0141] v′=v + c1×rand()×(p ibest - x)+c2×rand()×(g ibest - x) (13)

[0142] x′=x + v′ (14)

[0143] In the formula: v represents the current velocity of the particle; v′ represents the next velocity of the particle; rand() represents a random number between (0, 1); x represents the current position of the particle; x′ represents the next position of the particle; c1 and c2 represent learning factors.

[0144] Step S4: Use the group G1 method for subjective weighting and fuse the subjective and objective weights using game theory principles.

[0145] S41. According to the experience of each expert, use the group G1 method to conduct subjective weight assignment

[0146] The present invention introduces the idea of group decision-making. The main steps are as follows: 1) Invite experts to rank the importance of each evaluation index based on their many years of experience, and the ranking results of each expert can be obtained represents the ranking result of the a-th expert for the j-th index.

[0147] 2) Determine the importance between adjacent indicators; each expert compares the importance between adjacent indicators according to their own experience, and the importance scale R jThe calculation formula is as follows:

[0148]

[0149] In the formula, ω′ j-1 represents the subjective weight of the (j - 1)-th evaluation index in the index sorting relationship, and ω′ j represents the subjective weight of the j-th evaluation index in the index sorting relationship. To prevent the cumulative importance from exceeding the extreme importance, resulting in unscientific setting of index weights, when occurs, R j needs to be corrected by multiplying the original value by a proportionality coefficient to ensure The calculation formula of the proportionality coefficient ρ is as follows:

[0150]

[0151] R′ j = R j *ρ (17)

[0152] In the formula: R′ j represents the importance scale after correction, and ρ represents the proportionality coefficient.

[0153] 3) Calculate the subjective weights of each evaluation index; after each expert gives the order relationship and importance scale R j between the indexes, calculate the subjective weights of each evaluation index according to the following formula:

[0154]

[0155] In the formula: represents the subjective weight of the m-th evaluation index of the a-th expert, that is, the subjective weight of the last-ranked evaluation index; represents the subjective weight of the (j - 1)-th evaluation index of the a-th expert. The weights of each index need to comprehensively consider the self-weights of each expert. Denote the self-weight of each expert as d a , d a satisfies 0 < d a < 1, l is the total number of experts, then there is:

[0156]

[0157] In the formula: ω j represents the comprehensive subjective weight of the j-th evaluation index, represents the subjective weight of the j-th evaluation index of the a-th expert.

[0158] S42. Integrate the subjective and objective weights based on the game theory principle

[0159] The objective and subjective weights obtained in steps S3 and S41 of the present invention are fused using game theory. With the goal of minimizing the deviation between the objective and subjective weights, the objective and subjective weight vector coefficients are optimized, and thus the optimal weight for the corresponding index can be obtained. The specific objective is as follows:

[0160] min(||θ - w||2 + ||θ - ω||2) (20)

[0161] The above optimization function is transformed into a system of linear equations through the properties of matrix differentiation:

[0162]

[0163] After normalizing the above formula, the comprehensive optimal weight θ based on game theory can be finally obtained:

[0164] θ = b1w T + b2ω T (22)

[0165] Where: Both g1 and g2 are linear combination coefficients.

[0166] Step S5: Calculate the generalized grey absolute correlation degree to obtain the evaluation result.

[0167] The present invention introduces the idea of approaching the absolute ideal solution, sets the absolute ideal solution, improves the traditional grey correlation degree, and forms a generalized grey absolute correlation degree evaluation method; the evaluation calculation is carried out by calculating the correlation degree relationship between the energy station and the positive ideal solution and the negative ideal solution. The specific calculation steps of the generalized grey absolute correlation degree evaluation method are as follows:

[0168] 1) "Rewarding the excellent and punishing the inferior" index transformation processing; the idea of "rewarding the excellent and punishing the inferior", that is, giving rewards to the index transformation values higher than the expected value and imposing penalties on the index transformation values lower than the expected value; the present invention makes use of this idea, and the specific calculation formula is as follows:

[0169] For benefit-type indicators:

[0170]

[0171] For cost-type indicators:

[0172]

[0173] Where: represents the maximum value of the j-th index; represents the minimum value of the j-th index; represents the average value of the j-th index.

[0174] 2) Weighted calculation; for the convenience of subsequent calculations, the standardized matrix A of the index values n×m is multiplied by the combined weight matrix θ to obtain the weighted standard matrix F, and the calculation formula is:

[0175] F = A n×m ·θ = (f ij ) n×m (25)

[0176] In the formula: f ij represents the weighted standard value of the jth index of the ith energy station, (f ij ) n×m indicates that there are n energy stations and m indicators in total.

[0177] 3) Set the absolute ideal solution; the traditional method of setting the ideal solution often uses the maximum and minimum values, which has certain limitations. To solve this problem, the present invention fixes the absolute positive ideal solution as F + = [1, 1,..., 1] 1×m and the absolute negative ideal solution as F - = [-1, -1,...,-1] 1×m , so that the evaluation will not have the problem of inverse sorting.

[0178] 4) Obtain the evaluation result; according to the principle of the generalized grey absolute correlation degree, the closeness degree of the positive ideal solution and the closeness degree of the negative ideal solution are obtained, and then the evaluation result is obtained. The calculation formula is:

[0179]

[0180]

[0181] In the formula: β i + and β i - respectively represent the generalized grey absolute correlation degrees of the ith energy station with the positive ideal solution and the negative ideal solution; F i represents the weighted standard matrix of the ith energy station; finally, the grey absolute closeness degree C i can be obtained:

[0182]

[0183] After obtaining the grey absolute closeness degrees of each energy station, the energy stations are sorted according to the magnitudes of the grey absolute closeness degrees of the energy stations; the larger the grey absolute closeness degree of an energy station, the better the energy station.

[0184] Step S6: If the health status of the distributed low-carbon energy station meets the expected expectation, end; otherwise, after adjusting the distributed low-carbon energy station, return to Step S2.

[0185] By collecting multi-objective data, applying the idea of non-linear programming and the group G1 method, the subjective and objective weights are obtained, and the weights are fused using the idea of game theory. The generalized grey absolute correlation degree evaluation method is used to obtain the positive and negative absolute correlation degrees, thereby obtaining the evaluation result. Analyze the gap between the health status of the distributed low-carbon energy station and the expected expectation. If the evaluation is unqualified, it is necessary to dynamically adjust the distributed low-carbon energy station, and repeat steps S2, S3, S4 and step S5 until the expected level is reached.

[0186] The health status evaluation method of the distributed low-carbon energy station proposed by the present invention constructs an evaluation system including dimensions of energy supply reliability, low-carbon operation, and system health, comprehensively reflecting the health status of the distributed low-carbon energy station; by applying the idea of non-linear programming and the group G1 method, the subjective and objective weights are obtained, and the weights are fused using the idea of game theory to make the weight result more accurate; the idea of approaching the absolute ideal solution is introduced, the absolute ideal solution is set, and the traditional grey correlation degree is improved to form the generalized grey absolute correlation degree evaluation method, making the evaluation result more accurate.

[0187] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description should not be construed as a limitation of the present invention. After those skilled in the art have read the above content, various modifications and substitutions to the present invention will be obvious. Therefore, the protection scope of the present invention should be defined by the appended claims.

Claims

1. A method for evaluating the health status of a distributed low-carbon energy station, characterized in that, It includes the following steps: S1. Collect the health status data of the distributed low-carbon energy station and construct the health status evaluation index of the distributed low-carbon energy station; S2. Classify the evaluation indexes, and perform standardization processing on each evaluation index according to the classification to obtain the standardized index matrix; S3. Solve the objective weight of the evaluation index, transform the solution of the objective weight of the evaluation index into a non-linear programming problem, and use the particle swarm optimization algorithm to solve the objective weight of the evaluation index; S4. Use the group G1 method for subjective weighting and fuse the subjective and objective weights by using the game theory principle; S5. Calculate the generalized grey absolute correlation degree to obtain the evaluation result; S6. If the health status of the distributed low-carbon energy station meets the expected expectation, end; otherwise, after adjusting the distributed low-carbon energy station, return to step S2; Among them, step S2 further includes classifying the evaluation indexes into benefit-type indexes and cost-type indexes; the benefit-type indexes include: renewable energy utilization rate, comprehensive energy efficiency, qualified rate of energy supply quality, energy supply reliability rate, power supply system grid health index, heat supply system heat network health index, and cooling supply system cold network health index; The cost-type indexes include: carbon dioxide emission reduction amount and average energy loss rate of the energy station; Suppose each distributed low-carbon energy station has m original index values, and the original evaluation index values of n distributed low-carbon energy stations are standardized to obtain the standard index matrix A n×m ; The standardized calculation formula for the benefit-type indexes is: The standardized calculation formula for the cost-type indexes is: Where: a ij 、x ij They represent the standardized index value and the original evaluation index value of the jth indicator of the i-th distributed low-carbon energy station respectively; Step S3 further includes determining the reference data, determining the constraint conditions and the objective function; For n distributed low-carbon energy stations, the benchmark data of the cost indicator is The benchmark data for its benefit indicators are Its benchmark data is recorded as x0={x 01 ,x 02 ,…,x 0m }; For each distributed low-carbon energy station, let \(W_0 = \{w_1, w_2, \ldots, w\) m \} be the objective weights of its various indicators. The objective weight of the \(j\)th evaluation indicator is \(w\) j , and w j x ij represents the weighted comprehensive performance value of the \(j\)th indicator of the \(i\)th distributed low-carbon energy station; \(w\) j x 0j represents the weighted comprehensive performance value of the \(j\)th indicator of the benchmark data; during the evaluation process, it is expected that the weighted comprehensive performance values \(w\) j x ij and the weighted comprehensive value \(w\) j x 0j of the benchmark scheme change relatively greatly to maximize the difference between different energy stations with the same indicators. Therefore, the solution of the objective weights of the evaluation indicators can be transformed into a nonlinear programming problem, and its objective function and constraints are expressed as: Where: F(W) is the objective function; s·t represents the constraint condition; w j represents the objective weight of the j-th evaluation index of the distributed low-carbon energy station; represents the average value of the j-th evaluation index of n distributed low-carbon energy stations; The objective weight matrix W = [w1, w2,..., w m is obtained according to the objective function and the constraint condition.

2. The health state assessment method of the distributed low-carbon energy station according to claim 1, wherein In step S1, the health status evaluation index system of the distributed low-carbon energy station includes energy supply reliability indexes, low-carbon operation indexes, and system health indexes; among them, The low-carbon operation indexes further include renewable energy utilization rate, carbon dioxide emission reduction amount, and comprehensive energy efficiency; The energy supply reliability indexes further include qualified rate of energy supply quality, average energy loss rate of the energy station, and energy supply reliability rate; The system health indexes further include power supply system grid health index, heat supply system heat network health index, and cooling supply system cold network health index.

3. The health status assessment method of the distributed low-carbon energy station according to claim 2, wherein, Solve the non-linear programming problem through the particle swarm optimization algorithm to obtain the objective weight of each evaluation index.

4. The method for evaluating the health state of the distributed low-carbon energy station according to claim 2, wherein Step S4 includes performing subjective weight assignment by using the group G1 method according to the experience of each expert, and has the following steps: 1) The experts rank the importance of each evaluation index to obtain the ranking of each expert relationship represents the ranking result of the ath expert for the jth index; 2) Determine the importance between adjacent indicators; each expert compares the importance between adjacent indicators according to their own experience, and the importance scale is R j The calculation formula is as follows: where ω′ j-1 represents the subjective weight of the (j - 1)-th evaluation index in the index ranking relationship, and ω′ j represents the subjective weight of the j-th evaluation index in the index ranking relationship; When occurs, R j needs to be corrected by multiplying the original value by a proportionality coefficient to ensure The calculation formula for the proportionality coefficient ρ is: R′ j = R j *ρ (17) where: R' j represents the importance scale after calibration, and ρ represents the proportionality coefficient; 3) Calculate the subjective weights of each evaluation index; after each expert gives the order relationship and importance scale R between the indexes j , calculate the subjective weights of each evaluation index according to the following formula: Wherein: represents the subjective weight of the m-th evaluation index of the a-th expert, that is, the subjective weight of the last-ranked evaluation index; represents the subjective weight of the (j - 1)-th evaluation index of the a-th expert; the weights of each index need to comprehensively consider the self-weights of each expert. Denote the self-weight of each expert as d a , d a satisfies 0 < d a < 1, l is the total number of experts, then there is: Where: ω j represents the comprehensive subjective weight of the j-th evaluation index, It is expressed as the subjective weight of the j-th evaluation indicator of the a-th expert.

5. The method for evaluating the health status of the distributed low-carbon energy station according to claim 4, wherein Step S4 also includes fusing the subjective and objective weights based on the game theory principle; Taking the minimum deviation between the objective and subjective weights as the goal, by optimizing the objective and subjective weight vector coefficients, the optimal weight of the corresponding index can be obtained. The specific goal is: min(||θ - w||2 + ||θ - ω||2)(20) Transform the optimization function into a linear equation set through the matrix differential property: Perform normalization processing on formula (21) to obtain the comprehensive optimal weight θ based on the game theory: θ = b1w T + b2ω T (22) In the formula: Both g1 and g2 are linear combination coefficients.

6. The health status assessment method of a distributed low-carbon energy station according to claim 5, characterized in that: The specific calculation steps of the generalized grey absolute correlation degree evaluation method in step S5 are as follows: 1) "Reward the excellent and punish the inferior" index transformation processing; give rewards to the index transformation values for those higher than the expected value, Give punishments to the index transformation values for those lower than the expected value. The specific calculation formula is as follows: For the benefit-type indexes: For the cost-type indexes: Where: represents the maximum value of the j-th index; represents the minimum value of the j-th indicator; represents the average value of the j-th indicator; 2) Weighted calculation; normalize the matrix A of index values n×m Multiply it by the combined weight matrix θ to obtain the weighted standard matrix F. The calculation formula is as follows: F = A n×m ·θ = (f ij ) n×m (25) Where: f ij represents the weighted standard value of the jth indicator of the i-th energy station, (f ij ) n×m It means there are n energy stations and m indicators; 3) Set the absolute ideal solution; fix the absolute positive ideal solution to F + =[1,1,...,1] 1×m 、The absolute negative ideal solution is F - =[-1,-1,...,-1] 1×m ; 4) Obtain the evaluation result; according to the generalized grey absolute correlation degree principle, obtain the positive ideal solution closeness and the negative ideal solution closeness, and obtain the evaluation result. The calculation formula is: Where: β i + and β i - respectively represent the generalized grey absolute correlation degrees of the i-th energy station with the positive ideal solution and the negative ideal solution; F i represents the weighted standard matrix of the i-th energy station; finally, the grey absolute closeness C is obtained i : After obtaining the grey absolute closeness degrees of each energy station, sort the energy stations according to the magnitudes of their grey absolute closeness degrees; The larger the grey absolute closeness degree of an energy station, the better the energy station.

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