CN113780759A - Comprehensive performance evaluation method for multi-energy complementary distributed energy system - Google Patents

Comprehensive performance evaluation method for multi-energy complementary distributed energy system Download PDF

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CN113780759A
CN113780759A CN202110972303.XA CN202110972303A CN113780759A CN 113780759 A CN113780759 A CN 113780759A CN 202110972303 A CN202110972303 A CN 202110972303A CN 113780759 A CN113780759 A CN 113780759A
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王进仕
郭彦君
严俊杰
薛凯
种道彤
刘明
刘继平
韩小渠
陈伟雄
邢秦安
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Abstract

The invention discloses a comprehensive performance evaluation method of a multi-energy complementary distributed energy system. Secondly, selecting the primary energy utilization rate,
Figure DDA0003226140620000011
Efficiency, net present value, initial investment, carbon dioxide emission and sulfur dioxide emission are used as specific secondary evaluation indexes to obtain each of the optimization schemesAnd after the numerical values of the secondary indexes are obtained, weighting is given to each secondary index by adopting a triangular fuzzy number analytic hierarchy process, the weighting is used in a good-bad solution distance process, and each optimization scheme is evaluated and sequenced by adopting the good-bad solution distance process. The triangular fuzzy analytic hierarchy process fully considers the fuzziness of human thinking, so that the weighting mode is more scientific and reasonable, the evaluation selection is carried out on the optimization scheme by combining the good and bad solution distance method, the scientificity and effectiveness of performance evaluation are embodied, and theoretical guidance is provided for the optimization research of the multi-energy complementary distributed energy system.

Description

Comprehensive performance evaluation method for multi-energy complementary distributed energy system
Technical Field
The invention relates to performance evaluation of a multi-energy complementary distributed energy system, in particular to a system performance evaluation method based on a triangular fuzzy number analytic hierarchy process and a good-bad solution distance method.
Background
The multi-energy complementary distributed energy system couples various energy types and energy technologies, and the wide application of the system is beneficial to adjusting the energy structure in China, improving the energy utilization efficiency, realizing energy conservation and consumption reduction, and greatly improving the environmental protection performance, thereby becoming one of the current research hotspots.
However, the multi-energy complementary distributed energy system is a complex energy system with various time-varying load requirements (cold, heat and electricity) and a plurality of optimization targets (energy efficiency, economy, environmental protection and the like), and the design process of the system needs to consider the mutual influence relationship among various links of energy generation, conversion, storage, use and the like in the system and simultaneously meet various time-varying load requirements of cold, heat, electricity and the like.
Therefore, in order to measure the performance advantages and disadvantages of the multi-energy complementary distributed energy system, the multi-aspect attributes of the energy supply system need to be considered comprehensively, and a proper evaluation analysis rule and an evaluation method are introduced to obtain a scientific and reasonable evaluation result, so that guidance is provided for the optimal design and the optimal operation of the distributed energy system, and a scientific basis is provided for selecting an optimal scheme.
Disclosure of Invention
The invention aims to provide a comprehensive performance evaluation method for a multi-energy complementary distributed energy system and provide a scientific theoretical basis for selecting an optimal optimization scheme of comprehensive performance.
In order to achieve the purpose, the invention adopts the following technical scheme:
a comprehensive performance evaluation method for a multi-energy complementary distributed energy system is realized based on a triangular fuzzy number analytic hierarchy process and a good-bad solution distance method, and specifically comprises the following steps:
the first step is as follows: establishing a comprehensive index system: comprehensively considering three primary performance indexes of energy efficiency, economy and environmental protection, and establishing primary energy utilization rate and primary energy utilization rate under the energy efficiency indexes
Figure BDA0003226140600000027
Two secondary indexes of efficiency; under the economic index, two secondary indexes of net present value and initial investment are established; under the environmental protection index, two secondary indexes of carbon dioxide emission and sulfur dioxide emission are established;
A. index of primary energy utilization
Figure BDA0003226140600000021
In the formula: PERDMESRepresenting the primary energy utilization of the system; e represents the electrical load demand, kW.h; qcIndicating the refrigeration load demand, kW.h; qhIndicating heating heat load demand and hot water load demand, kW · h; fDMESRepresents the primary energy consumption of the system;
B.
Figure BDA0003226140600000028
efficiency index
Figure BDA0003226140600000022
Ee＝E
Figure BDA0003226140600000023
Figure BDA0003226140600000024
In the formula:
Figure BDA0003226140600000025
presentation system
Figure BDA0003226140600000029
Efficiency; ee、Eh、EcRespectively representing the electric quantity output by the system
Figure BDA00032261406000000215
Heat quantity
Figure BDA00032261406000000213
Cold quantity
Figure BDA00032261406000000212
kW·h；
Figure BDA0003226140600000026
Fuel representing system input
Figure BDA00032261406000000214
kW·h；T0、Th、TcRespectively representing the ambient temperature, the heat source temperature and the cold source temperature;
C. index of net present value
Figure BDA0003226140600000031
In the formula: NPV represents the net present value of the system; CIt、COtRespectively showing the cash inflow and outflow in the t year; i.e. i0Representing a reference discount rate; n represents the life time of the project;
D. initial investment index
Figure BDA0003226140600000032
In the formula: ICC represents the initial investment of the system; n is a radical ofnRepresents the installation capacity, kW, of the nth equipment; pnRepresents the investment cost per unit capacity of the nth equipment, yuan/kW; m represents the total number of energy supply system equipment;
E. index of carbon dioxide emission
Figure BDA0003226140600000033
In the formula: CDEDMESRepresents the carbon dioxide emission of the system;
Figure BDA0003226140600000034
representing the power purchasing quantity of the power grid of the system, kW.h;
Figure BDA0003226140600000035
the primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure BDA0003226140600000036
the primary energy consumption of a system prime motor is expressed, kW.h; mu.scdeIndicating power grid purchasing CO2The emission conversion coefficient is the electric quantity marginal emission factor g/(kW & h); mu.scdfCO representing combustion of natural gas2The emission conversion factor.
F. Index of emission of sulfur dioxide
Figure BDA0003226140600000037
In the formula: SOEDMESRepresenting the emission of sulfur dioxide of the system;
Figure BDA0003226140600000038
representing the power purchasing quantity of the power grid of the system, kW.h;
Figure BDA0003226140600000039
the primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure BDA00032261406000000310
the primary energy consumption of the prime mover in the system is expressed, kW.h; mu.ssoeIndicating the power purchasing time of the grid SO2Emission conversion factor, g/(kW · h); mu.ssofSO representing combustion of natural gas2The emission conversion factor.
The second step is that: weighting each secondary index by adopting a triangular fuzzy number analytic hierarchy process;
A. and (3) quantitatively expressing the importance result of two indexes compared and judged by experts by adopting a triangular fuzzy number to obtain a fuzzy judgment matrix consisting of the triangular fuzzy numbers:
A＝(aij)n×n
wherein n is the number of second-level indexes, aij＝(lij,mij,uij) Is the number of triangular ambiguities, mijIs a triangular fuzzy number aijThe value of the judgment variable is 1-9 of the analytic hierarchy process; lij，uijLower and upper bounds of the triangular fuzzy number, respectively, when uij-lijThe smaller the judgment is, the clearer the judgment of an expert is, otherwise, the more fuzzy the judgment is;
B. and (3) carrying out consistency check on the fuzzy judgment matrix, and carrying out consistency check by adopting a median matrix, wherein the consistency check indexes are as follows:
Figure BDA0003226140600000041
in the formula: CI is a consistency check index, λmaxIs the maximum eigenvalue of the median matrix;
determining an average random consistency index RI according to the order of the fuzzy judgment matrix;
consistency ratio CR:
Figure BDA0003226140600000042
if the formula is satisfied, the consistency of the fuzzy judgment matrix is accepted;
C. after the fuzzy judgment matrix is judged to meet the consistency, calculating the weight of each secondary index based on the triangular fuzzy number:
firstly, constructing a fuzzy judgment factor matrix K:
Figure BDA0003226140600000051
in the formula:
Figure BDA0003226140600000052
is the standard deviation ratio, kijThe smaller the ambiguity, the greater the confidence;
then, calculating to obtain an adjustment judgment matrix O:
Figure BDA0003226140600000053
in the formula: m is a median matrix;
performing column transformation on the adjustment judgment matrix O to convert the adjustment judgment matrix O into a judgment matrix O' with a diagonal of 1;
d is obtained by calculating the n-th square root of each row element of the judgment matrix OiTo d is pairediCarrying out normalization processing to obtain the weight omegai；
Figure BDA0003226140600000054
Figure BDA0003226140600000055
The weight vector W ═ ω is thus calculated1,ω2,…ωi]T；
The third step: respectively taking n secondary evaluation indexes as optimization targets to obtain an optimization scheme, counting n schemes, and evaluating and deciding each scheme by adopting a good-bad solution distance method on the basis of each secondary evaluation index value in different optimization schemes:
A. acquiring each secondary index value under each optimization scheme to form an n X n dimensional matrix X;
Figure BDA0003226140600000061
in the formula: xijRepresenting the j index value under the ith scheme;
B. constructing an initial matrix
1) Adopting a standard 0-1 transformation method to make the evaluation indexes into homotrend and non-dimension
If xjThe larger the benefit index, i.e. index value, the better, then:
Figure BDA0003226140600000062
if xjThe lower the cost index, i.e. the index value, the better, then:
Figure BDA0003226140600000063
in the formula: bijIs the index value after homotrending, xijFor the jth index value in the ith scheme,
Figure BDA0003226140600000064
respectively representing the maximum value and the minimum value of the jth index in the n optimization schemes;
2) normalizing the matrix after the homotrenization, namely the matrix transformed in the step 1) of the step B, and establishing a corresponding matrix
Figure BDA0003226140600000065
In the formula: y isijThe index value is the jth index value in the ith scheme after normalization;
C. constructing a weighted normalization matrix
Figure BDA0003226140600000071
In the formula: omega is the weight of each secondary index, and the weight is determined by the triangular fuzzy number analytic hierarchy process in the second step; v is a weighted normalization matrix, VnnWeighting the normalized index for the nth optimization scheme;
D. determining positive and negative ideal solutions
Figure BDA0003226140600000072
Figure BDA0003226140600000073
In the formula: v+，V-Respectively positive and negative ideal solution sets; j. the design is a square+Is a benefit type index; j-is a cost-type index
E. Calculating distance
Figure BDA0003226140600000074
Figure BDA0003226140600000075
In the formula:
Figure BDA0003226140600000076
the distance between each optimized scheme and the ideal solution;
Figure BDA0003226140600000077
optimizing the distance between each scheme and the negative ideal solution;
F. calculating relative proximity and making a determination
Figure BDA0003226140600000078
In the formula: fiRelative proximity for the ith protocol;
the optimization scheme with the larger relative proximity is the optimal scheme.
And in the second step, index weight is calculated by adopting a triangular fuzzy number analytic hierarchy process.
In the third step, the index weight is used for constructing a weighted standardization matrix in the good-bad solution sorting method.
Compared with the prior art: the method is used for the optimal design or the optimal operation result evaluation of the multi-energy complementary distributed energy system, and establishes the method which takes the energy efficiency, the economy and the environmental protection as first-level indexes and takes the primary energy utilization rate,
Figure BDA0003226140600000081
The comprehensive evaluation index system takes efficiency, net present value, initial investment, carbon dioxide emission and sulfur dioxide emission as secondary indexes, the evaluation system comprehensively and reasonably shows the quality of system performance, and a triangular fuzzy number analytic hierarchy process is utilized to overcome the defects that when the index value is too much, the expert scores fuzziness and subjective randomness easily appear, so that the expert scores more scientifically and accurately; the weight is endowed with the advantages that the speciality of the expert in scoring can be reflected subjectively, and the importance degree of each evaluation index can be reflected objectively; the weight is used in the distance method of good and bad solutions, so that the selection of positive and negative ideal solutions and the scheme ordering are betterAnd (4) the method is reasonable. On the basis of the technical effects, the invention is convenient and simple, easy to realize and strong in practicability.
Drawings
The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a schematic diagram of an evaluation index system suitable for a multi-energy complementary distributed energy system
FIG. 2 is a flow chart of an evaluation method based on a triangular fuzzy number analytic hierarchy process and a good-bad solution sorting method
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The invention discloses a comprehensive performance evaluation method of a multi-energy complementary distributed energy system, which specifically comprises the following steps:
the first step is as follows: and establishing a comprehensive evaluation index system as shown in the attached figure 1. Comprehensively considering three primary performance indexes of energy efficiency, economy and environmental protection, and establishing primary energy utilization rate and primary energy utilization rate under the energy efficiency indexes
Figure BDA0003226140600000082
Two secondary indexes of efficiency; under the economic index, two secondary indexes of net present value and initial investment are established; under the environmental protection index, two secondary indexes of carbon dioxide emission and sulfur dioxide emission are established.
A primary energy utilization index
Figure BDA0003226140600000091
In the formula: PERDMESRepresenting the primary energy utilization of the system; e represents the electrical load demand (kW. h), QcDenotes the refrigeration load demand (kW. h), QhRepresenting heating and hot water load demand (kW. h), FDMESRepresenting the primary energy consumption of the system.
B
Figure BDA0003226140600000099
Efficiency index
Figure BDA0003226140600000092
Ee＝E
Figure BDA0003226140600000093
Figure BDA0003226140600000094
In the formula:
Figure BDA0003226140600000095
presentation system
Figure BDA00032261406000000910
Efficiency; ee、Eh、EcRespectively representing the electric quantity output by the system
Figure BDA00032261406000000917
Heat quantity
Figure BDA00032261406000000916
Cold quantity
Figure BDA00032261406000000915
kW·h；
Figure BDA0003226140600000096
Fuel representing system input
Figure BDA00032261406000000914
kW·h；T0、Th、TcRespectively representing the ambient temperature, the heat source temperature and the heat sink temperature.
C net present value index NPV
Figure BDA0003226140600000097
In the formula: CIt、COtRespectively showing the cash inflow and outflow in the t year; i.e. i0Representing a reference discount rate; n represents the project life time.
D initial investment index ICC
Figure BDA0003226140600000098
In the formula: n is a radical ofnRepresents the installation capacity, kW, of the nth equipment; pnRepresents the investment cost per unit capacity of the nth equipment, yuan/kW; and m represents the total number of energy supply system equipment.
E carbon dioxide emission index CDEDMES
Figure BDA0003226140600000101
In the formula:
Figure BDA0003226140600000102
representing the power purchasing quantity of the power grid of the system, kW.h;
Figure BDA0003226140600000103
the primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure BDA0003226140600000104
the primary energy consumption of a system prime motor is expressed, kW.h; mu.scdeIndicating power grid purchasing CO2The emission conversion coefficient is the electric quantity marginal emission factor g/(kW & h); mu.scdfCO representing combustion of natural gas2The emission conversion factor.
F sulfur dioxide emission index SOEDMES
Figure BDA0003226140600000105
In the formula:
Figure BDA0003226140600000106
representing the power purchasing quantity of the power grid of the system, kW.h;
Figure BDA0003226140600000107
the primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure BDA0003226140600000108
the primary energy consumption of the prime mover in the system is expressed, kW.h; mu.ssoeIndicating the power purchasing time of the grid SO2Emission conversion factor, g/(kW · h); mu.ssofSO representing combustion of natural gas2The emission conversion factor.
The second step is that: weighting each secondary index by adopting a triangular fuzzy number analytic hierarchy process, as shown in the attached figure 2:
A. the importance results of two indexes are compared and judged by experts through quantitative representation of triangular fuzzy numbers, and the results are given
Figure BDA0003226140600000109
After fuzzy judgment (n is 6), a fuzzy judgment matrix consisting of triangular fuzzy numbers is obtained:
A＝(aij)n×n
wherein n is the number of second-level indexes, aij＝(lij,mij,uij) Is the number of triangular ambiguities, mijIs a triangular fuzzy number aijThe value of the judgment variable is 1-9 scale method in the analytic hierarchy process, and the details are shown in table 1; lij，uijThe values of the lower bound and the upper bound of the triangular fuzzy number are shown in the table 2, and when u isij-lijSmaller means that the judgment of an expert is clear, and conversely, the judgment is more fuzzy. When a plurality of experts judge, the average value of the expert scores is taken as the comprehensive triangular fuzzy number.
Table 1: median value basis of triangular fuzzy number
Median value of triangular fuzzy number mij Means of
1 The front index i is as important as the rear index j
3 The front index i is more important than the rear index j, and the importance degree is slight
5 The front index i is more important than the rear index j, and the importance degree is obvious
7 The front index i is more important than the rear index j, and the importance degree is strong
9 The front index i is more important than the rear index j, and the importance degree is maximum
2,4,6,8 Median of the above-mentioned difference
Table 2: upper and lower bound value basis of triangular fuzzy number
Categories Value taking Means of
1 (max(m-1/2，1)，m，min(m+1/2，9) Score values are not ambiguous
2 (max(m-1，1)，m，min(m+1，9) Score values are more fuzzy
3 (max(m-3/2，1)，m，min(m+3/2，9) Score scores are very fuzzy
B. In order to avoid the judgment of contradiction and confusion made by experts, consistency check is carried out on the fuzzy judgment matrix, a median matrix is approximately adopted for consistency check, and the consistency check indexes are as follows:
Figure BDA0003226140600000111
in the formula: CI is a consistency check index, λmaxIs the maximum eigenvalue of the median matrix.
The average random consistency index RI determined from the rank of the fuzzy decision matrix and given a scale of the average random consistency index are shown in table 3.
Table 3: average random consistency index RI
Figure BDA0003226140600000112
Figure BDA0003226140600000121
RI is the average value of consistency indexes of the random judgment matrix of the same order, and the indexes are utilized to change absolute values into relative values, so that the problem that the consistency judgment indexes are increased along with the increase of the number of the indexes can be solved to a certain extent.
Consistency ratio CR.:
Figure BDA0003226140600000122
if the above formula is satisfied, the consistency of the fuzzy judgment matrix is acceptable.
C. After the fuzzy judgment matrix is judged to meet the consistency, calculating the weight of each secondary index based on the triangular fuzzy number:
firstly, constructing a fuzzy judgment factor matrix K:
Figure BDA0003226140600000123
in the formula
Figure BDA0003226140600000124
Is the standard deviation ratio kijThe smaller the blur, the greater the confidence.
Then, calculating to obtain an adjustment judgment matrix O:
Figure BDA0003226140600000125
in the formula: m is the median matrix.
Performing column transformation on the adjustment judgment matrix O to convert the adjustment judgment matrix O into a judgment matrix O' with a diagonal of 1;
d is obtained by calculating the n-th square root of each row element of the judgment matrix OiTo d is pairediCarrying out normalization processing to obtain the weight omegai；
Figure BDA0003226140600000131
Figure BDA0003226140600000132
The weight vector W ═ ω is thus calculated1,ω2,…ωi]T
The third step: and (3) performing sorting decision by using a good-bad solution sorting method, as shown in the attached figure 2.
A. And respectively optimizing the system by taking the six secondary indexes as optimization targets, and acquiring the numerical values of the secondary indexes under each scheme after six optimization schemes are acquired to form an n multiplied by n dimensional matrix X, wherein n is 6.
Figure BDA0003226140600000133
In the formula: xijRepresents the j index value under the ith scheme
B. Constructing an initial matrix
1) Homotrending and non-dimensionalizing evaluation indexes
When the good and bad solution distance method is used for evaluation, all secondary indexes are required to have consistent change directions (namely, homotrending), and a standard 0-1 transformation method is adopted
If xjThe larger the benefit index, i.e. index value, the better, then:
Figure BDA0003226140600000134
if xjThe lower the cost index, i.e. the index value, the better, then:
Figure BDA0003226140600000141
in the formula: bijIs the index value after homotrending, xijFor the jth index value in the ith scheme,
Figure BDA0003226140600000142
the maximum and minimum values of the j-th index in all schemes, respectively.
2) Normalizing the matrix after the homotrenization, namely the matrix transformed in the step 1) of the step B, and establishing a corresponding matrix
Figure BDA0003226140600000143
In the formula: y isijIs the j index value in the ith scheme after normalization.
C. Constructing a weighted normalization matrix
Figure BDA0003226140600000144
In the formula: omega is the weight of each secondary index, and the determination of the weight is obtained by a triangular fuzzy number analytic hierarchy process; v is a weighting index matrix, VnnThe n-th weighted normalization index in the n-th optimization scheme.
D. Determining positive and negative ideal solutions
Figure BDA0003226140600000145
Figure BDA0003226140600000146
In the formula: v+，V-Respectively positive and negative ideal solution sets; j. the design is a square+Is a benefit type index; j-is a cost-type index
E. Calculating distance
Figure BDA0003226140600000147
Figure BDA0003226140600000151
In the formula:
Figure BDA0003226140600000152
the distance between each optimized scheme and the ideal solution;
Figure BDA0003226140600000153
the distance of each solution from the negative ideal solution.
F. Calculating relative proximity and making a determination
Figure BDA0003226140600000154
In the formula: fiRelative proximity to the ith scheme.
The optimization scheme with the larger relative proximity is the optimal scheme.
It should be noted that: the method is convenient and simple, is easy to realize, is used for the optimal design or the optimal operation result evaluation of the multi-energy complementary distributed energy system, establishes the primary indexes of energy efficiency, economy and environmental protection, and uses the primary energy utilization rate,
Figure BDA0003226140600000155
The comprehensive evaluation index system takes efficiency, net present value, initial investment, carbon dioxide emission and sulfur dioxide emission as secondary indexes, the evaluation system comprehensively and reasonably shows the quality of system performance, and a triangular fuzzy number analytic hierarchy process is utilized to overcome the defects that when the index value is too much, the expert scores fuzziness and subjective randomness easily appear, so that the expert scores more scientifically and accurately; the weight is endowed with the advantages that the speciality of the expert in scoring can be reflected subjectively, and the importance degree of each evaluation index can be reflected objectively; the weight is used in a distance method of good and bad solutions, so that the selection of positive and negative ideal solutions and the scheme ordering are more reasonable.
The above description is not intended to limit the present invention, and modifications and equivalents made within the spirit of the present invention are within the scope of the present invention.

Claims (3)

1. A comprehensive performance evaluation method for a multi-energy complementary distributed energy system is characterized by being realized based on a triangular fuzzy number analytic hierarchy process and a good-bad solution distance method, and specifically comprising the following steps of:
the first step is as follows: establishing a comprehensive index system: comprehensively considering three primary performance indexes of energy efficiency, economy and environmental protection, and establishing primary energy utilization rate and primary energy utilization rate under the energy efficiency indexes
Figure FDA0003226140590000011
Two secondary indexes of efficiency; under the economic index, two secondary indexes of net present value and initial investment are established; under the environmental protection index, two secondary indexes of carbon dioxide emission and sulfur dioxide emission are established;
A. index of primary energy utilization
Figure FDA0003226140590000012
In the formula: PERDMESRepresenting the primary energy utilization of the system; e represents the electrical load demand, kW.h; qcIndicating the refrigeration load demand, kW.h; qhIndicating heating heat load demand and hot water load demand, kW · h; fDMESRepresents the primary energy consumption of the system;
B.
Figure FDA0003226140590000013
efficiency index
Figure FDA0003226140590000014
Ee＝E
Figure FDA0003226140590000015
Figure FDA0003226140590000016
In the formula:
Figure FDA0003226140590000017
presentation system
Figure FDA0003226140590000018
Efficiency; ee、Eh、EcRespectively representing the electric quantity output by the system
Figure FDA0003226140590000019
Heat quantity
Figure FDA00032261405900000110
Cold quantity
Figure FDA00032261405900000111
kW·h；
Figure FDA00032261405900000112
Fuel representing system input
Figure FDA00032261405900000113
kW·h；T0、Th、TcRespectively representing the ambient temperature, the heat source temperature and the cold source temperature;
C. index of net present value
Figure FDA0003226140590000021
In the formula: NPV represents the net present value of the system; CIt、COtRespectively represent cash flow of t yearThe inflow and outflow, Yuan; i.e. i0Representing a reference discount rate; n represents the life time of the project;
D. initial investment index
Figure FDA0003226140590000022
In the formula: ICC represents the initial investment of the system; n is a radical ofnRepresents the installation capacity, kW, of the nth equipment; pnRepresents the investment cost per unit capacity of the nth equipment, yuan/kW; m represents the total number of energy supply system equipment;
E. index of carbon dioxide emission
Figure FDA0003226140590000023
In the formula: CDEDMESRepresents the carbon dioxide emission of the system;
Figure FDA0003226140590000024
representing the power purchasing quantity of the power grid of the system, kW.h; fb DMESThe primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure FDA0003226140590000025
the primary energy consumption of a system prime motor is expressed, kW.h; mu.scdeIndicating power grid purchasing CO2The emission conversion coefficient is the electric quantity marginal emission factor g/(kW & h); mu.scdfCO representing combustion of natural gas2The emission conversion factor.
F. Index of emission of sulfur dioxide
Figure FDA0003226140590000026
In the formula: SOEDMESRepresenting the emission of sulfur dioxide of the system;
Figure FDA0003226140590000027
representing the power purchasing quantity of the power grid of the system, kW.h; fb DMESThe primary energy consumption of a peak boiler of the system is expressed, kW.h;
Figure FDA0003226140590000028
the primary energy consumption of the prime mover in the system is expressed, kW.h; mu.ssoeIndicating the power purchasing time of the grid SO2Emission conversion factor, g/(kW · h); mu.ssofSO representing combustion of natural gas2The emission conversion factor.
The second step is that: weighting each secondary index by adopting a triangular fuzzy number analytic hierarchy process;
A. and (3) quantitatively expressing the importance result of two indexes compared and judged by experts by adopting a triangular fuzzy number to obtain a fuzzy judgment matrix consisting of the triangular fuzzy numbers:
A＝(aij)n×n
wherein n is the number of second-level indexes, aij＝(lij,mij,uij) Is the number of triangular ambiguities, mijIs a triangular fuzzy number aijThe value of the judgment variable is 1-9 of the analytic hierarchy process; lij，uijLower and upper bounds of the triangular fuzzy number, respectively, when uij-lijThe smaller the judgment is, the clearer the judgment of an expert is, otherwise, the more fuzzy the judgment is;
B. and (3) carrying out consistency check on the fuzzy judgment matrix, and carrying out consistency check by adopting a median matrix, wherein the consistency check indexes are as follows:
Figure FDA0003226140590000031
in the formula: CI is a consistency check index, λmaxIs the maximum eigenvalue of the median matrix;
determining an average random consistency index RI according to the order of the fuzzy judgment matrix;
consistency ratio CR:
Figure FDA0003226140590000032
if the formula is satisfied, the consistency of the fuzzy judgment matrix is accepted;
C. after the fuzzy judgment matrix is judged to meet the consistency, calculating the weight of each secondary index based on the triangular fuzzy number:
firstly, constructing a fuzzy judgment factor matrix K:
Figure FDA0003226140590000041
in the formula:
Figure FDA0003226140590000042
is the standard deviation ratio, kijThe smaller the ambiguity, the greater the confidence;
then, calculating to obtain an adjustment judgment matrix O:
Figure FDA0003226140590000043
in the formula: m is a median matrix;
performing column transformation on the adjustment judgment matrix O to convert the adjustment judgment matrix O into a judgment matrix O' with a diagonal of 1;
d is obtained by calculating the n-th square root of each row element of the judgment matrix OiTo d is pairediCarrying out normalization processing to obtain the weight omegai；
Figure FDA0003226140590000044
Figure FDA0003226140590000045
The weight vector is obtained by calculationW＝[ω1,ω2,…ωi]T；
The third step: respectively taking n secondary evaluation indexes as optimization targets to obtain an optimization scheme, counting n schemes, and evaluating and deciding each scheme by adopting a good-bad solution distance method on the basis of each secondary evaluation index value in different optimization schemes:
A. acquiring each secondary index value under each optimization scheme to form an n X n dimensional matrix X;
Figure FDA0003226140590000051
in the formula: xijRepresenting the j index value under the ith scheme;
B. constructing an initial matrix
1) Adopting a standard 0-1 transformation method to make the evaluation indexes into homotrend and non-dimension
If xjThe larger the benefit index, i.e. index value, the better, then:
Figure FDA0003226140590000052
if xjThe lower the cost index, i.e. the index value, the better, then:
Figure FDA0003226140590000053
in the formula: bijIs the index value after homotrending, xijIs the jth index value, x, in the ith schemej max,xj minRespectively representing the maximum value and the minimum value of the jth index in the n optimization schemes;
2) normalizing the matrix after the homotrenization, namely the matrix transformed in the step 1) of the step B, and establishing a corresponding matrix
Figure FDA0003226140590000054
In the formula: y isijThe index value is the jth index value in the ith scheme after normalization;
C. constructing a weighted normalization matrix
Figure FDA0003226140590000061
In the formula: omega is the weight of each secondary index, and the weight is determined by the triangular fuzzy number analytic hierarchy process in the second step; v is a weighted normalization matrix, VnnWeighting the normalized index for the nth optimization scheme;
D. determining positive and negative ideal solutions
Figure FDA0003226140590000062
Figure FDA0003226140590000063
In the formula: v+，V-Respectively positive and negative ideal solution sets; j. the design is a square+Is a benefit type index; j. the design is a square-Is a cost-type index
E. Calculating distance
Figure FDA0003226140590000064
Figure FDA0003226140590000065
In the formula:
Figure FDA0003226140590000066
for each optimized scheme and theoryThe distance to be solved;
Figure FDA0003226140590000067
optimizing the distance between each scheme and the negative ideal solution;
F. calculating relative proximity and making a determination
Figure FDA0003226140590000068
In the formula: fiRelative proximity for the ith protocol;
the optimization scheme with the larger relative proximity is the optimal scheme.
2. The comprehensive performance evaluation method of the multi-energy complementary distributed energy system according to claim 1, wherein: and in the second step, index weight is calculated by adopting a triangular fuzzy number analytic hierarchy process.
3. The comprehensive performance evaluation method of the multi-energy complementary distributed energy system according to claim 1, wherein: in the third step, the index weight is used for constructing a weighted standardization matrix in the good-bad solution sorting method.
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