CN116070952A - Multi-dimensional energy utilization efficiency evaluation method, system, equipment and medium - Google Patents
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Abstract
The invention provides a multi-dimensional energy utilization efficiency evaluation method, a system, equipment and a medium, wherein the method comprises the following steps: constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; according to an analytic hierarchy process, determining a subjective weight vector of a multi-dimensional energy utilization efficiency index, and according to an entropy weight process, determining an objective weight vector of the multi-dimensional energy utilization efficiency index; establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combined weight vector of the multi-dimensional energy utilization efficiency index; and according to the combined weight vector, carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation degree analysis method to obtain an energy efficiency evaluation result. The invention can ensure scientificity, rationality and comprehensiveness of the comprehensive energy efficiency evaluation result, and provides important basis for realizing planning design, regulation and control optimization of the comprehensive energy system.
Description
Technical Field
The invention relates to the technical field of power engineering, in particular to a multi-dimensional energy utilization efficiency evaluation method, a system, equipment and a medium.
Background
The comprehensive energy system is a physical carrier of the energy internet, realizes coordination planning, optimized operation, collaborative management, interactive response and complementary interaction among various heterogeneous energy subsystems, and can effectively improve the energy utilization efficiency and promote the sustainable development of energy while meeting the diversified energy utilization requirements in the system. In order to ensure the effective construction of the comprehensive energy system, the establishment of a reasonable and effective comprehensive energy system evaluation index system has important significance.
The current evaluation method for the comprehensive energy system mainly comprises the following steps: considering a large number of components in the system, realizing effective evaluation of operation equipment in the comprehensive energy system; according to different energy network flow models, the system state is specifically analyzed, and the equipment energy saving potential of the comprehensive energy system is researched; the comprehensive energy system is researched from the angles of planning method, model construction, benefit evaluation and the like in the system planning and running angles; and analyzing different factors influencing the efficiency of the comprehensive energy system by a parameter analysis method, and researching the problem of energy utilization efficiency in the comprehensive energy system. However, although the prior art can realize application evaluation of the comprehensive energy system to a certain extent, the following disadvantages and shortcomings still exist: 1) Only the equipment conversion problem is considered, unified analysis is not performed on the energy supply subsystem, most of evaluation research work is concentrated on a certain characteristic independent system, and the refinement of evaluation indexes leads to the lack of multidirectional and integrity of evaluation contents; 2) The dimension considered is mainly aimed at the energy transmission efficiency and the energy conversion efficiency, the research dimension is relatively single, the support for multi-dimensional comprehensive evaluation of the comprehensive energy system is lacking, and the effective support for the energy utilization efficiency evaluation of the future comprehensive energy system development taking electricity as a subject is difficult to meet; 3) The method for evaluating the comprehensive energy system in the designated area tends to be personalized and customized, has poor applicability and is difficult to popularize and popularize.
Disclosure of Invention
The invention aims to provide a multi-dimensional energy utilization efficiency assessment method, which is used for constructing a multi-dimensional index system for assessing the energy efficiency of a comprehensive energy system from six dimensions such as energy transmission efficiency, energy conversion efficiency, energy transmission reliability, energy transmission quality, economic benefit and social benefit by taking the running state of an assessment object and the essential energy efficiency attribute of a system as directions, calculating and analyzing each assessment index weight through principal and objective weights and combination weights, carrying out energy efficiency assessment by adopting a TOPSIS method and a gray correlation analysis method, solving the application defect of the comprehensive energy system assessment method, avoiding the one-sidedness and limitation of the comprehensive energy system assessment result, ensuring the scientificity, rationality and comprehensiveness of the comprehensive energy efficiency assessment result, and providing important basis for realizing planning design, regulation and optimization of the comprehensive energy system.
In order to achieve the above objective, it is necessary to provide a multi-dimensional energy utilization efficiency evaluation method and system for solving the above technical problems.
In a first aspect, an embodiment of the present invention provides a method for evaluating multi-dimensional energy utilization efficiency, the method including the steps of:
Constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
according to an analytic hierarchy process, determining a subjective weight vector of the multi-dimensional energy utilization efficiency index, and according to an entropy weight process, determining an objective weight vector of the multi-dimensional energy utilization efficiency index;
establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combined weight vector of the multi-dimensional energy utilization efficiency index;
and according to the combination weight vector, carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation degree analysis method to obtain an energy efficiency evaluation result.
In a second aspect, embodiments of the present invention provide a multi-dimensional energy utilization efficiency assessment system, the system comprising:
the index construction module is used for constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
The weight calculation module is used for determining subjective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an analytic hierarchy process and determining objective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an entropy weight process;
the weight combination module is used for establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combination weight vector of the multi-dimensional energy utilization efficiency index;
and the energy efficiency evaluation module is used for carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation analysis method according to the combination weight vector to obtain an energy efficiency evaluation result.
In a third aspect, embodiments of the present invention further provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.
The method is used for constructing a multi-dimensional energy utilization efficiency index of a comprehensive energy system to be evaluated, determining a subjective weight vector of the multi-dimensional energy utilization efficiency index according to a hierarchical analysis method, determining an objective weight vector of the multi-dimensional energy utilization efficiency index according to an entropy weight method, establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, solving the multi-objective optimization model to obtain a combined weight vector of the multi-dimensional energy utilization efficiency index, and performing comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated according to the combined weight vector by adopting a TOPSIS method and a gray correlation analysis method to obtain an energy efficiency evaluation result. Compared with the prior art, the multidimensional energy utilization efficiency evaluation method effectively avoids the unilateral performance and limitation of the comprehensive energy system evaluation result, ensures the scientificity, rationality and comprehensiveness of the comprehensive energy efficiency evaluation result, and provides an important basis for realizing the planning design, regulation and control optimization of the comprehensive energy system.
Drawings
FIG. 1 is a schematic diagram of an application scenario of a multi-dimensional energy utilization efficiency evaluation method in an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-dimensional energy utilization efficiency evaluation method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a multi-dimensional energy utilization efficiency evaluation system according to an embodiment of the present invention;
fig. 4 is an internal structural view of a computer device in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantageous effects of the present application more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples, and it should be understood that the examples described below are only illustrative of the present invention and are not intended to limit the scope of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The multi-dimensional energy utilization efficiency evaluation method provided by the invention can be applied to the terminal and the server shown in fig. 1. The terminal may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers and portable wearable devices, and the server may be implemented by a separate server or a server cluster formed by a plurality of servers. The server can construct a multi-dimensional energy utilization efficiency index based on the to-be-evaluated data acquired from six dimensions of energy transmission efficiency, energy conversion efficiency, energy transmission reliability, energy transmission quality, economic benefit, social benefit and the like, and the multi-dimensional energy utilization efficiency evaluation method provided by the invention is adopted to carry out scientific, effective and comprehensive energy efficiency evaluation on the to-be-evaluated comprehensive energy system, and the obtained energy efficiency evaluation result is used for subsequent research of the server or is sent to the terminal for the user of the terminal to check and analyze. The following examples will explain the multi-dimensional energy utilization efficiency evaluation method of the present invention in detail.
In one embodiment, as shown in fig. 2, a multi-dimensional energy utilization efficiency evaluation method is provided, which includes the following steps:
s11, constructing a multidimensional energy utilization efficiency index of a comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
specifically, the energy transmission efficiency index mainly considers the loss in a transmission network and storage equipment in the comprehensive energy system; the embodiment mainly considers the transmission efficiency of electric, cold, hot and gas systems, including electric energy transmission efficiency, cold energy transmission efficiency and heat energy transmission efficiency;
the electric energy transmission in the comprehensive energy system mainly comprises an upper power grid, a distributed power supply, an energy storage system and an energy conversion device, and the electric energyThe consumption object is mainly pure electric load, electric storage device, equipment for realizing heat (cold), gas and other conversion, etc.; based on this, in the embodiment, when considering the power transmission efficiency, the links of delivering, distributing and storing the power are mainly considered, and the difference of the power input from the outside by the integrated energy system and the voltage level provided by the triple power supply unit is considered, and the specific study is performed by selecting the input voltage level of the integrated energy system and the voltage level of the triple power supply unit as a representative; let the electric energy transmission capacity of the comprehensive energy system be W D The following steps are:
W D ＝[P D γ transformer +DG D γ Inverter with a power supply +D LRD +α D D D ]θ line
Wherein P is D Representing the amount of electricity input from the outside; gamma ray Transformer Representing the operation efficiency of the distribution transformer; DG (differential g) D Representing the power generation capacity of a distributed power supply of the comprehensive energy system; gamma ray Inverter with a power supply Representing inverter operating efficiency; d (D) LRD Representing the electric quantity generated by the triple co-generation system; alpha D A conversion factor representing the electrical load; d (D) D Representing the amount of electricity released by the electricity storage device; θ line Representing the transmission efficiency of the distribution line;
on the basis of the obtained electric energy transmission electric quantity of the comprehensive energy system, the electric energy transmission efficiency theta of the system can be calculated D I.e. the power transfer efficiency is expressed as:
wherein θ D Representing power transmission efficiency; θ sd And theta dd Respectively representing the storage/discharge efficiency of the electricity storage device; d (D) E real Representing the actual stored energy after accounting for the energy storage loss of the electric storage device; w (W) D Representing the electric energy transmission quantity of the comprehensive energy system;
the cold supply in the comprehensive energy system mainly comprises input cold outside the system, cold provided by a distributed energy source and cold storage system, cold provided by an energy conversion device and the like. In a cold energy transfer system, there must be some dissipation of energy. Let the cold energy transmission quantity be W L Then:
W L ＝[L DL +L RL +DG L +α L D L ]×(1-0.01l L a L )
wherein L is DL And L RL Respectively representing cold energy obtained by electric energy and heat energy through an energy conversion device; DG (differential g) L Representing cold energy generated by the distributed new energy; alpha L A conversion factor representing the cooling load; d (D) L Representing the heat energy released by the cold storage device; l (L) L Representing the length of the cold pipe network; a, a L Representing the dissipation ratio of the cold pipe network per 1 km;
based on the comprehensive energy system cold energy supply quantity, the system cold energy supply efficiency can be calculated, namely the cold energy transmission efficiency is expressed as:
wherein θ L Representing the cold energy transmission efficiency; w (W) L Representing the amount of cold feed; l (L) DL And L RL Respectively representing cold energy obtained by electric energy and heat energy through an energy conversion device; θ sl And theta dl Respectively representing the heat storage/release efficiency of the heat storage device; d (D) L Representing the heat energy released by the cold/hot storage device; alpha L A conversion factor representing the cold/hot load; d (D) L real Representing the actual stored energy after accounting for the energy storage loss of the cold storage device;
similarly, the heat supply in the integrated energy system is mainly composed of input heat outside the system, heat generated by distributed energy sources, heat provided by a heat storage system, heat provided by an energy conversion device and the like. In a thermal energy transfer system there must be some dissipation of energy. Let the heat transfer quantity be W R Then:
W R ＝[R TR +R DR +DG R +R LRD +α R D R ]×(1-0.01l R a R )
wherein, the liquid crystal display device comprises a liquid crystal display device,R TR and R is DR Respectively representing the heat energy obtained by conversion of the natural gas and the electric energy conversion device; DG (differential g) R Representing heat energy generated by the distributed new energy; alpha R A conversion factor representing the thermal load; r is R LRD Representing heat energy generated by the triple co-generation system; d (D) R Representing the heat energy released by the heat storage device; l (L) R Representing the length of the heat pipe network; a, a R Representing the dissipation ratio of the heat pipe network per 1 km;
on the basis of the heat energy supply quantity of the comprehensive energy system, the heat energy supply efficiency of the system can be calculated, namely, the heat energy transmission efficiency is expressed as:
wherein θ R Representing heat energy transfer efficiency; w (W) R Representing the heat supply transmission quantity; alpha R A conversion factor representing the thermal load; θ sr And theta dr Respectively representing the heat storage/release efficiency of the heat storage device; d (D) R real Representing the actual stored energy after accounting for the energy storage loss of the cold/hot storage device; p (P) R Indicating the heat input by an external heat supply network; r is R LRD The heat energy is generated by a triple co-generation system; r is R DR Representing the thermal energy converted by the electrical energy conversion means; d (D) R Representing the thermal energy released by the thermal storage device.
Specifically, in the integrated energy system, energy conversion mainly includes electric heat/cold conversion, air heat conversion, heat-to-cold conversion and the like; the coefficient indexes of conversion between different energies mainly considered in the embodiment comprise electric-to-cold energy conversion efficiency, electric-to-heat energy conversion efficiency and heat-to-cold energy conversion efficiency;
the conversion efficiency of electric energy to cold energy can be understood as the efficiency of converting electric energy into cold energy in the process of electric energy to cold energy, and is specifically expressed as:
Wherein delta DL Representing conversion of electric power to cold energyEfficiency is improved; c (C) DL The heating coefficient of electric conversion cooling is represented; lambda (lambda) L And lambda (lambda) D Respectively representing the cold and electric energy conversion coefficients, which can be understood as being obtained by converting the energy source and the standard coal conversion coefficient based on the standard coal conversion coefficient standard;
the conversion efficiency of electric energy to heat energy is understood as the efficiency of converting electric energy to heat energy in the electric heat conversion process, and is specifically expressed as:
wherein delta DR Representing the conversion efficiency of electric heat energy; c (C) DR A refrigeration coefficient representing the electric heat transfer; lambda (lambda) R And lambda (lambda) D Respectively representing heat and electric energy conversion coefficients, and equally can be understood as being obtained by converting energy sources and standard coal conversion coefficients based on standard coal conversion coefficient standards;
the conversion efficiency of heat to cold energy is understood to be the conversion efficiency of heat energy to cold energy, expressed as:
wherein delta RL The conversion efficiency of heat to cold energy is represented; c (C) RL Representing the refrigeration coefficient of the refrigerator; lambda (lambda) L And lambda (lambda) R The conversion coefficients respectively representing the cold energy and the heat energy can be understood as being obtained by converting the energy and the standard coal conversion coefficient based on the standard coal conversion coefficient standard;
specifically, the energy supply reliability index is reliably understood to be an index which is mainly constructed by taking the degree of reliability of cold, hot and electric energy supply into consideration, and comprises an average power supply reliability rate, a heat supply equipment failure rate and a cold supply equipment failure rate;
The average power supply reliability is understood as the reliability of a power supply system, and is expressed by the power supply reliability, specifically expressed as:
wherein delta AIDI-1 Representing an average power supply reliability; MTTR represents the theoretical average repair time of the power supply system; note that, 8760 in the formula is the number of hours of 1 year, i.e., 365 days×24 hours=8760 hours, and can be basically adjusted according to the evaluation period of the integrated energy system;
the failure rate of the heat supply equipment can be understood as representing the heat supply reliability of the comprehensive energy system by the failure rate of the related equipment, and is expressed as follows:
wherein delta R Representing the failure rate of the heating equipment; t is t R-n And t R-m Respectively representing the normal operation time and the fault stop supply time of the heating equipment;
the failure rate of the cooling equipment can be understood as that the cooling reliability of the comprehensive energy system is characterized by the failure rate of the related equipment, and is expressed as follows:
wherein delta L Representing the failure rate of the cooling equipment; t is t L-n And t L-m Respectively representing normal operation time and fault stop supply time of the cooling equipment;
specifically, the energy supply quality index can be understood as an index which mainly considers the supply quality of electric, cold, hot and gas energy in the comprehensive energy system and is constructed according to the energy supply quality index, and comprises the comprehensive voltage qualification rate, the comprehensive heat supply qualification rate and the comprehensive cold supply qualification rate;
The comprehensive voltage qualification rate can be understood as the power supply quality in a power supply network of the comprehensive energy system, and mainly considers the voltage qualification rate of a low-voltage transformer area, and is expressed as follows:
wherein t is up And t low Respectively representing the voltage exceeding upper limit time and the voltage exceeding lower limit time;
the comprehensive cooling qualification rate can be understood as the cooling quality of a comprehensive energy system, represented by the fluctuation qualification rate of the outlet temperature of a cooling network, and is expressed as:
wherein t is L-MAX And t L-MIN Respectively represent the highest and the lowest value t of the measured temperature of the outlet of the cold net L-N A temperature value representing the demand of a cold user;
the comprehensive heat supply qualification rate can be understood as the heat supply quality of the comprehensive energy system, and is represented by the heat supply network outlet temperature fluctuation qualification rate, and is expressed as follows:
wherein t is R-MAX And t R-MIN Respectively representing the highest and lowest measured temperatures of the outlet of the heat supply network; t is t R-N Representing a temperature value required by a hot user;
in particular, economic benefit index may be understood as an index reflecting the level of economy of the integrated energy system, including unit investment energy costs and financial internal profitability;
wherein the unit investment energy supply cost is expressed as:
wherein I is 0 Representing an initial investment; v (V) R Representing a fixed asset residual; n represents the operational year of the project; a is that r Representing the running cost of the r year; d (D) r Representing the depreciation of the r year; p (P) r Representing interest in the r year；Y r Represents energy in the r year; i.e r Representing the discount rate;
the financial internal yield is expressed as:
wherein NC (numerical control) t Representing a new net cash flow for the t-th year in the calculation period; FIRR represents financial internal rate of return;
specifically, the social benefit index may be understood as a social benefit mainly analyzed from the perspective of the influence of the comprehensive energy system on the environment, including CO 2 Annual emission reduction; the CO 2 Annual emission reduction is expressed as:
wherein F is CO2 Representing CO 2 Annual emission reduction; f (F) C Represents the energy conservation amount of carbon-containing energy (converted into standard coal); c (C) SC Represents CO generated by power generation per ton of standard coal 2 Discharge amount.
After the multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated is obtained by the construction method, the energy efficiency evaluation can be carried out by adopting the existing evaluation method based on the index system; however, in view of the application drawbacks of the TOPSIS method and the gray correlation analysis method commonly used for the existing energy efficiency evaluation, the present embodiment preferably combines the two methods of the TOPSIS method and the gray correlation analysis, and constructs a new gray ideal value approximation model for performing the energy efficiency evaluation according to the effective multi-dimension by the following method.
S12, determining a subjective weight vector of the multi-dimensional energy utilization efficiency index according to an analytic hierarchy process, and determining an objective weight vector of the multi-dimensional energy utilization efficiency index according to an entropy weight process;
analytic Hierarchy Process (AHP) is a decision method of decomposing elements related to decision always into layers of targets, criteria, schemes and the like, and performing qualitative and quantitative analysis on the basis of the decomposition; in the embodiment, the method is preferably adopted to calculate subjective weight of each multidimensional energy utilization efficiency index; specifically, the step of determining the subjective weight vector of the multi-dimensional energy utilization efficiency index according to the analytic hierarchy process includes:
establishing a corresponding analytic hierarchy process model according to the multi-dimensional energy utilization efficiency index and the primary index and the secondary index; wherein, the analytic hierarchy model is shown in Table 1:
TABLE 1 analytic hierarchy model
Obtaining a corresponding judgment matrix according to the relative importance degree of each level of index in the chromatographic analysis model;
calculating the maximum feature root and the corresponding feature vector of the judgment matrix, and carrying out normalization processing on the feature vector to obtain the subjective weight vector; wherein, the subjective weight vector can be understood as a feature vector obtained by normalizing the maximum feature root vector of the judgment matrix, and the maximum feature root of the judgment matrix P is assumed to be lambda max And the corresponding eigenvector is ω, then the matrix eigenvalue formula p=λ max Omega, the feature vector omega is easy to obtain, and then the weight distribution of each index is obtained by normalizing the feature vector omega;
in addition, in order to further ensure the rationality and reliability of the subjective weight vector obtained by the method, the embodiment preferably also uses a consistency test method to verify the rationality of the weight, so that effective adjustment is convenient; the method comprises the following specific steps: determining whether there is a significant difference between the averages or variances at a level of significance, and if there is no significant difference, the f-averages or variances are consistent using a consistency index of ci=λ max (k-1), wherein k is the number of influencing factors in each layer, and the calculation result interval of the consistency index CI is-1 to 1; if CI is<0.2, the consistency is extremely weak; if CI is 0.2 or less<0.4, sayThe consistency is weaker; if CI is 0.4 or less<0.6, then the description consistency is moderate; if CI is 0.6 or less<0.8, the consistency of the description is stronger; if CI is more than or equal to 0.8 and less than or equal to 1.0, the consistency is extremely strong. In principle, whether the subjective weight vector is reasonably set can be directly judged according to the obtained consistency index CI, and in order to improve the accuracy of analysis, the consistency ratio can be further calculated through a formula cr=ci/RI (RI is a random consistency index) to assist in judgment and adjustment by setting a random consistency index.
Meanwhile, the objective weight vector can be understood as a weight vector obtained by determining an entropy value by utilizing the information entropy of each index and then correcting the weight through entropy weight calculation; specifically, the step of determining the objective weight vector of the multi-dimensional energy utilization efficiency index according to the entropy weight method includes:
constructing a corresponding index matrix according to the multidimensional energy utilization efficiency index, and normalizing the index matrix to obtain a normalized matrix; wherein the index matrix is understood as a matrix V determined according to the number of samples p of the object to be evaluated, the total number of multi-dimensional energy utilization efficiency index values q, and the various multi-dimensional energy utilization efficiency index values pq The corresponding element is v ij (i＝1,2,…,p；j＝1,2,…,q)；
Normalization of the index matrix can be understood as normalizing each index element in the index matrix by the following formula to obtain a normalized matrix x= (X) ij ) pq The process of (1):
wherein x is ij Element values representing the ith row and jth column in the normalized matrix;
obtaining information entropy of each multidimensional energy utilization efficiency index according to the normalized matrix; wherein, the information entropy is expressed as:
in the method, in the process of the invention,
wherein k is j Information entropy representing the jth multi-dimensional energy utilization efficiency index; h is a ij Representing the probability of occurrence of the index j;
obtaining the objective weight vector according to each information entropy; wherein, the objective weight vector is expressed as:
S＝(s 1 ,s 2 ,…,s q ) T
wherein s is q Objective weights representing the q-th index.
After the subjective weight vector and the objective weight vector are obtained through the calculation, the expected values of the subjective/objective weights of different indexes are calculated according to the moment estimation thought, the relative important coefficients of the single-index subjective/objective weight vector are obtained through calculation, and then the relative important coefficients of the whole subjective/objective weight vector are calculated according to the evaluation indexes in the multi-decision matrix, so that the final combined weight is obtained.
S13, establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combined weight vector of the multi-dimensional energy utilization efficiency index;
specifically, the step of establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain the combination weight vector of the multi-dimensional energy utilization efficiency index includes:
According to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, taking the minimized combination weight and the subjective weight deviation as optimization targets, and establishing a multi-target optimization model; the multi-objective optimization model is expressed as:
in the method, in the process of the invention,
wherein H (omega) j ) A deviation value of the combination weight and the subjective weight of the j-th multidimensional energy utilization efficiency index; omega j A combination weight representing a j-th multidimensional energy utilization efficiency index; alpha and beta respectively represent subjective weights t j And objective weight s j The relative importance of the combining weights; q represents the number of multidimensional energy utilization efficiency indicators;
converting the multi-objective optimization model into a single-objective optimization model, and solving the single-objective optimization model to obtain the combination weight vector; the single-objective optimization model can be understood as a model obtained by converting each index with the minimum deviation between the combined weight and the subjective weight, and the specific conversion process is as follows:
first, for each deviation of the index, the formula (2) is obtained based on the formula (1):
wherein H (omega) j ) Representing the deviation of the combination weight and the subjective weight corresponding to the j-th multidimensional energy utilization efficiency index;
Based on the formula (2), converting the multi-objective optimization model into a single-objective optimization model shown in the formula (3), and solving, wherein the single-objective optimization model is expressed as:
wherein H represents the sum of the deviation of the combination weight and the subjective weight of all the multidimensional energy utilization efficiency indexes.
It should be noted that, the solution of the above formula (3) may be any conventional optimization solution method, and is not limited herein;
s14, according to the combined weight vector, performing comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation degree analysis method to obtain an energy efficiency evaluation result;
specifically, according to the combined weight vector, the comprehensive energy efficiency evaluation is performed on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation analysis method, and the step of obtaining an energy efficiency evaluation result comprises the following steps:
obtaining a correction index value to be evaluated according to the combination weight vector; wherein the correction index value to be evaluated can be understood as a combination weight vector ω to be solved for by equation (3),
ω＝(ω 1 ,ω 2 ,…,ω j ,…,ω q )
substitution into (1) to obtain the deviation H (omega) j ) Based on the deviation H (omega) j ) And the original index value, the obtained correction value
Obtaining an initial matrix to be evaluated according to the correction index value to be evaluated, the preset ideal index value and the preset worst index value, normalizing the initial matrix to be evaluated, and obtaining a corresponding normalized decision matrix; wherein, the preset ideal index value can be understood as the index value of the positive ideal scheme reasonably set according to the evaluation requirement 
Similarly, the preset worst index value can be understood as the index value of the negative ideal scheme +.>
The initial matrix to be evaluated can be obtained as:
the above canonical decision matrix can be understood as a matrix C ' = (C ' obtained by normalizing the initial matrix to be evaluated shown in equation (4) ' mn ) 3×q Wherein C' mn The nth column element of the mth row representing the canonical decision matrix is expressed as:
wherein m=1, 2,3; n=1, 2, …, q; c (C) mn An nth column element of an mth row representing an initial matrix to be evaluated;
obtaining an ideal distance and gray correlation coefficient matrix according to the canonical decision matrix; wherein the ideal distance includes a positive ideal distance and a negative ideal distance, expressed as:
wherein d + And d - Respectively representing a positive ideal distance and a negative ideal distance; c'. m To-be-evaluated correction index value C representing canonical decision matrix C + And C - The index values of the positive ideal scheme and the index values of the negative ideal scheme of the decision matrix C' are respectively represented;
the gray correlation coefficient matrix can be understood as to beEvaluating gray correlation coefficient matrixes between the comprehensive energy system and the positive ideal scheme and the negative ideal scheme respectively, wherein the gray correlation coefficient matrixes comprise the positive ideal correlation matrix
And negative ideal correlation matrix->
Wherein, the liquid crystal display device comprises a liquid crystal display device,
wherein C is min Representing that the minimum value is firstly taken for each row of the standard decision matrix C', and then the minimum value of each row is taken; c (C) max Firstly, taking the maximum value of each row of the standard decision matrix C', and then taking the maximum value of each row of the maximum value; then there are:
wherein, the liquid crystal display device comprises a liquid crystal display device,
and->
Respectively represent the positive ideal scheme in C min Index value in corresponding matrix column and negative ideal scheme at C min Corresponding to the index value in the matrix array; />
And->
Representing the positive ideal scheme at C max Index value in corresponding matrix column and negative ideal scheme at C max Corresponding to the index value in the matrix array; θ ε (0, 1) represents the resolution factor, preferably a value of 0.5;
obtaining corresponding gray correlation degree according to the gray correlation coefficient matrix, and respectively obtaining corresponding first relative closeness degree and second relative closeness degree according to the ideal distance and the gray correlation degree; the gray association degree comprises a positive ideal association degree and a negative ideal association degree, and is specifically expressed as follows:
wherein l + And l - Respectively representing positive ideal association degree and negative ideal association degree;
based on the calculated positive ideal distance and negative ideal distance, and the positive ideal correlation and negative ideal correlation, a first relative proximity and a second relative proximity can be obtained according to the following formulas (11) and (12):
wherein P is + And U + Respectively representing a first relative closeness and a second relative closeness;
Weighting the first relative closeness and the second relative closeness according to corresponding preset weightsAnd obtaining the energy efficiency evaluation result; the first relative closeness and the second relative closeness can be set according to actual application requirements or experience according to corresponding preset weights, and the preference degree of a decision maker is reflected to a certain extent; assuming that the preset weights of the first relative closeness and the second relative closeness are v respectively 1 And v 2 And v 1 +v 2 =1, the energy efficiency evaluation result can be calculated as:
Q + ＝v 1 P + +v 2 U +
wherein Q is + And the energy efficiency evaluation result is represented, and the larger the value is, the better the corresponding comprehensive energy system is.
According to the embodiment of the application, the multi-dimensional energy utilization efficiency index of the comprehensive energy system to be evaluated is constructed from six dimensions of energy transmission efficiency, energy conversion efficiency, energy transmission reliability, energy transmission quality, economic benefit, social benefit and the like by taking the intrinsic energy efficiency attribute reflecting the running state of the evaluation object and the system as directions, the subjective weight vector of the multi-dimensional energy utilization efficiency index is determined according to a hierarchical analysis method, the objective weight vector of the multi-dimensional energy utilization efficiency index is determined according to an entropy weight method, a multi-objective optimization model is established according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, the multi-objective optimization model is solved to obtain the combined weight vector of the multi-dimensional energy utilization efficiency index, the comprehensive energy efficiency evaluation is carried out on the comprehensive energy system to be evaluated according to the combined weight vector by adopting a TOPSIS method and a gray correlation analysis method, the scientific, reasonable and comprehensive energy efficiency of the comprehensive energy efficiency evaluation result are ensured while the aspects and limitations of the comprehensive energy system evaluation result are effectively avoided, and an important planning basis is provided for realizing the comprehensive design and regulation and control of the comprehensive energy system.
In order to further provide reasonable effectiveness of the multi-dimensional energy utilization efficiency evaluation method of the present invention, the present embodiment also uses an industrial park integrated energy system formed by an industrial production area and a living area as an example for calculation and analysis. The electric load in the area is supplied by commercial power and distributed photovoltaic, the cold load is supplied by a conventional chiller and the like (heating/cooling load estimation is performed based on a heating load index and land planning conditions, wherein 180 days are selected for accumulating heating and cooling days all year round, and the hot water load requirement is converted according to the hot water temperature of 50 ℃ and the cold water temperature of 16 ℃).
When the multi-dimensional energy efficiency evaluation is carried out on the typical scene of the two comprehensive energy systems of the industrial production area and the living area of the area, the optimal capacity configuration of the source-network-load-storage configuration can ensure that the system can realize the lowest cost investment under the condition of meeting the reliability and safety constraint and ensure that the system operates under the optimal condition. And selecting proper distributed energy equipment according to the regional resource condition, and carrying out capacity configuration with the aim of lowest annual cost. Annual costs include annual values such as initial investment in equipment, annual energy costs, annual equipment maintenance costs. The optimization of the operational level targets the hourly economics, minimizing the operational cost of the system. Under the optimal energy supply mode of multi-target benefits, the resource condition of the industrial production area selects proper distributed energy equipment, and three configuration conditions are set: 1) 40MW of the photovoltaic power generation system, 64MW of the absorption refrigerator, 70MW of the waste heat boiler, 1.5MW of the water source heat pump and the rest energy are provided by a power grid; 2) 20MW of a photovoltaic power generation system, 32MW of an absorption refrigerator, 35MW of a waste heat boiler, 0.8MW of a water source heat pump and the rest of energy are provided by a power grid; 3) Are provided by the power grid.
The index calculation is performed according to the multi-dimensional energy utilization efficiency index calculation method, and the results shown in table 2 can be obtained:
table 2 example of multi-dimensional energy efficiency index calculation results
By evaluating the multidimensional energy utilization efficiency in the energy supply mode of the industrial park, the energy efficiency evaluation results shown in table 3 can be obtained: case one scores 0.87932, case two scores 0.88613, case three scores 0.60231. The grading result shows that the energy utilization efficiency of the distributed energy equipment condition of the second condition is higher than that of the other two conditions, and the evaluation result also obviously accords with objective actual conditions, so that the method can effectively avoid the one-sided and limitation of the evaluation result of the comprehensive energy system, ensure the scientificity, rationality and comprehensiveness of the evaluation result of the comprehensive energy system, provide important basis for realizing planning design, regulation and control optimization of the comprehensive energy system, and assist enterprises to make investment decisions and investment plans.
Table 3 energy efficiency evaluation results based on the multidimensional energy use efficiency index shown in table 2
In one embodiment, as shown in FIG. 3, a multi-dimensional energy utilization efficiency evaluation system is provided, the system comprising:
The index construction module 1 is used for constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
the weight calculation module 2 is used for determining subjective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an analytic hierarchy process and determining objective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an entropy weight process;
the weight combination module 3 is used for establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combination weight vector of the multi-dimensional energy utilization efficiency index;
and the energy efficiency evaluation module 4 is used for carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation analysis method according to the combination weight vector to obtain an energy efficiency evaluation result.
For a specific limitation of the multi-dimensional energy utilization efficiency evaluation system, reference may be made to the limitation of the multi-dimensional energy utilization efficiency evaluation method hereinabove, and the description thereof will not be repeated here. Each of the modules in the multi-dimensional energy utilization efficiency evaluation system may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 4 shows an internal structural diagram of a computer device, which may be a terminal or a server in particular, in one embodiment. As shown in fig. 4, the computer device includes a processor, a memory, a network interface, a display, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a multi-dimensional energy utilization efficiency assessment method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those of ordinary skill in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present application and is not intended to limit the computer device on which the present application may be implemented, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have the same arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when the computer program is executed.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the above method.
In summary, the multi-dimensional energy utilization efficiency evaluation method and system provided by the embodiment of the invention realize that the multi-dimensional energy utilization efficiency index of the comprehensive energy system to be evaluated is constructed from six dimensions of energy transmission efficiency, energy conversion efficiency, energy transmission reliability, energy transmission quality, economic benefit and social benefit by taking the running state reflecting an evaluation object and the intrinsic energy efficiency attribute of the system as directions, the subjective weight vector of the multi-dimensional energy utilization efficiency index is determined according to a hierarchical analysis method, a multi-objective optimization model is established according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, the multi-objective optimization model is solved to obtain the combined weight vector of the multi-dimensional energy utilization efficiency index, and the comprehensive energy efficiency evaluation is carried out on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation analysis method according to the combined weight vector.
In this specification, each embodiment is described in a progressive manner, and all the embodiments are directly the same or similar parts referring to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. It should be noted that, any combination of the technical features of the foregoing embodiments may be used, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few preferred embodiments of the present application, which are described in more detail and are not thereby to be construed as limiting the scope of the invention. It should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and substitutions should also be considered to be within the scope of the present application. Therefore, the protection scope of the patent application is subject to the protection scope of the claims.
Claims (10)
1. A multi-dimensional energy utilization efficiency evaluation method, characterized in that the method comprises the steps of:
constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
according to an analytic hierarchy process, determining a subjective weight vector of the multi-dimensional energy utilization efficiency index, and according to an entropy weight process, determining an objective weight vector of the multi-dimensional energy utilization efficiency index;
establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combined weight vector of the multi-dimensional energy utilization efficiency index;
and according to the combination weight vector, carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation degree analysis method to obtain an energy efficiency evaluation result.
2. The multi-dimensional energy utilization efficiency assessment method according to claim 1, wherein the energy transmission efficiency index includes electric energy transmission efficiency, cold energy transmission efficiency and heat energy transmission efficiency;
The energy conversion coefficient indexes comprise electric-to-cold energy conversion efficiency, electric-to-heat energy conversion efficiency and heat-to-cold energy conversion efficiency;
the energy supply reliability indexes comprise average power supply reliability rate, heat supply equipment failure rate and cold supply equipment failure rate;
the energy supply quality indexes comprise comprehensive voltage qualification rate, comprehensive heat supply qualification rate and comprehensive cold supply qualification rate;
the economic benefit index comprises unit investment energy supply cost and financial internal yield;
the social benefit index comprises CO 2 Annual emission reduction.
3. The multi-dimensional energy utilization efficiency evaluation method according to claim 2, wherein the electric energy transmission efficiency is expressed as:
wherein θ D Representing power transmission efficiency; θ sd And theta dd Respectively representing the storage/discharge efficiency of the electricity storage device; d (D) E real Representing the actual stored energy after accounting for the energy storage loss of the electric storage device; w (W) D Representing the electric energy transmission quantity of the comprehensive energy system; p (P) D Representing the amount of electricity input from the outside; DG (differential g) D Representing the power generation capacity of a distributed power supply of the comprehensive energy system; d (D) LRD Representing the electric quantity generated by the triple co-generation system; alpha D A conversion factor representing the electrical load; d (D) D Representing the amount of electricity released by the electricity storage device; θ line Representing the transmission efficiency of the distribution line;
the cold energy transmission efficiency is expressed as:
wherein θ L Representing the cold energy transmission efficiency; w (W) L Representing the amount of cold feed; l (L) DL And L RL Respectively representing cold energy obtained by electric energy and heat energy through an energy conversion device; θ sl And theta dl Respectively representing the heat storage/release efficiency of the heat storage device; d (D) L Representing the heat energy released by the cold/hot storage device; alpha L A conversion factor representing the cold/hot load; d (D) L real Representing the actual stored energy after accounting for the energy storage loss of the cold storage device;
the heat energy transfer efficiency is expressed as:
wherein θ R Representing heat energy transfer efficiency; w (W) R Representing the heat supply transmission quantity; alpha R A conversion factor representing the thermal load; θ sr And theta dr Respectively representing the heat storage/release efficiency of the heat storage device; d (D) R real Representing the actual stored energy after accounting for the energy storage loss of the cold/hot storage device; p (P) R Indicating the heat input by an external heat supply network; r is R LRD The heat energy is generated by a triple co-generation system; r is R DR Representing the thermal energy converted by the electrical energy conversion means; d (D) R Representing the heat energy released by the heat storage device;
the conversion efficiency of the electric conversion to cold energy is expressed as follows:
wherein delta DL Representing the conversion efficiency of electric conversion into cold energy; c (C) DL The heating coefficient of electric conversion cooling is represented; lambda (lambda) L And lambda (lambda) D Respectively representing the cold and electric energy conversion coefficients;
The electric-to-thermal energy conversion efficiency is expressed as:
wherein delta DR Representing the conversion efficiency of electric heat energy; c (C) DR A refrigeration coefficient representing the electric heat transfer; lambda (lambda) R And lambda (lambda) D Respectively representing heat and electric energy conversion coefficients;
the heat-to-cold energy conversion efficiency is expressed as:
wherein delta RL The conversion efficiency of heat to cold energy is represented; c (C) RL Representing the refrigeration coefficient of the refrigerator; lambda (lambda) L And lambda (lambda) R Respectively representing the conversion coefficients of cold energy and heat energy;
the average power supply reliability is expressed as:
wherein delta AIDI-1 Representing an average power supply reliability; MTTR represents the theoretical average repair time of the power supply system;
the failure rate of the heating equipment is expressed as follows:
wherein delta R Representing the failure rate of the heating equipment; t is t R-n And t R-m Respectively representing the normal operation time and the fault stop supply time of the heating equipment;
the failure rate of the cooling equipment is expressed as follows:
wherein delta L Representing the failure rate of the cooling equipment; t is t L-n And t L-m Respectively representing normal operation time and fault stop supply time of the cooling equipment;
the integrated voltage yield is expressed as:
wherein t is up And t low Respectively representing the voltage exceeding upper limit time and the voltage exceeding lower limit time;
the comprehensive cold supply qualification rate is expressed as follows:
wherein t is L-MAX And t L-MIN Respectively represent the highest and the lowest value t of the measured temperature of the outlet of the cold net L-N A temperature value representing the demand of a cold user;
the comprehensive heat supply qualification rate is expressed as follows:
wherein t is R-MAX And t R-MIN Respectively representing the highest and lowest measured temperatures of the outlet of the heat supply network; t is t R-N Representing a temperature value required by a hot user;
the unit investment energy supply cost is expressed as:
wherein I is 0 Representing an initial investment; v (V) R Representing a fixed asset residual; n represents the operational year of the project; a is that r Representing the running cost of the r year; d (D) r Representing the depreciation of the r year; p (P) r Interest representing the r year; y is Y r Represents energy in the r year; i.e r Representing the discount rate;
the financial internal yield is expressed as:
wherein NC (numerical control) t Representing a new net cash flow for the t-th year in the calculation period; FIRR represents financial internal rate of return;
the CO 2 Annual emission reduction is expressed as:
wherein F is CO2 Representing CO 2 Annual emission reduction; f (F) C Representing the energy conservation amount of carbon-containing; c (C) SC Represents CO generated by power generation per ton of standard coal 2 Discharge amount.
4. The multi-dimensional energy utilization efficiency evaluation method according to claim 2, wherein the step of determining the subjective weight vector of the multi-dimensional energy utilization efficiency index according to the hierarchical analysis method comprises:
establishing a corresponding analytic hierarchy process model according to the multi-dimensional energy utilization efficiency index and the primary index and the secondary index;
Obtaining a corresponding judgment matrix according to the relative importance degree of each level of index in the chromatographic analysis model;
and calculating the maximum characteristic root and the corresponding characteristic vector of the judgment matrix, and carrying out normalization processing on the characteristic vector to obtain the subjective weight vector.
5. The method of evaluating the utilization efficiency of a multi-dimensional energy according to claim 1, wherein the step of determining an objective weight vector of the utilization efficiency index of the multi-dimensional energy according to an entropy weight method comprises:
constructing a corresponding index matrix according to the multidimensional energy utilization efficiency index, and normalizing the index matrix to obtain a normalized matrix;
obtaining information entropy of each multidimensional energy utilization efficiency index according to the normalized matrix;
and obtaining the objective weight vector according to each information entropy.
6. The method of evaluating the utilization efficiency of a multi-dimensional energy according to claim 1, wherein the step of creating a multi-objective optimization model from the subjective weight vector and the objective weight vector of the utilization efficiency index of the multi-dimensional energy and solving the multi-objective optimization model to obtain the combined weight vector of the utilization efficiency index of the multi-dimensional energy comprises:
According to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, taking the minimized combination weight and the subjective weight deviation as optimization targets, and establishing a multi-target optimization model; the multi-objective optimization model is expressed as:
in the method, in the process of the invention,
wherein H (omega) j ) A deviation value of the combination weight and the subjective weight of the j-th multidimensional energy utilization efficiency index; omega j A combination weight representing a j-th multidimensional energy utilization efficiency index; alpha and beta respectively represent subjective weights t j And objective weight s j The relative importance of the combining weights; q represents the number of multidimensional energy utilization efficiency indicators;
converting the multi-objective optimization model into a single-objective optimization model, and solving the single-objective optimization model to obtain the combination weight vector; the single-objective optimization model is expressed as:
wherein H represents the sum of the deviation of the combination weight and the subjective weight of all the multidimensional energy utilization efficiency indexes.
7. The method for estimating multi-dimensional energy utilization efficiency according to claim 1, wherein the step of estimating the comprehensive energy efficiency of the comprehensive energy system to be estimated by using TOPSIS method and gray correlation analysis method according to the combined weight vector, and obtaining the energy efficiency estimation result comprises:
Obtaining a correction index value to be evaluated according to the combination weight vector;
obtaining an initial matrix to be evaluated according to the correction index value to be evaluated, the preset ideal index value and the preset worst index value, normalizing the initial matrix to be evaluated, and obtaining a corresponding normalized decision matrix;
obtaining an ideal distance and gray correlation coefficient matrix according to the canonical decision matrix; the ideal distance includes a positive ideal distance and a negative ideal distance; the gray correlation coefficient matrix comprises a positive ideal correlation matrix and a negative ideal correlation matrix;
obtaining corresponding gray correlation degree according to the gray correlation coefficient matrix, and respectively obtaining corresponding first relative closeness degree and second relative closeness degree according to the ideal distance and the gray correlation degree; the gray correlation degree comprises a positive ideal correlation degree and a negative ideal correlation degree;
and carrying out weighted summation on the first relative closeness and the second relative closeness according to corresponding preset weights to obtain the energy efficiency evaluation result.
8. A multi-dimensional energy efficiency assessment system, the system comprising:
the index construction module is used for constructing a multidimensional energy utilization efficiency index of the comprehensive energy system to be evaluated; the multi-dimensional energy utilization efficiency index comprises an energy transmission efficiency index, an energy conversion coefficient index, an energy supply reliability index, an energy supply quality index, an economic benefit index and a social benefit index;
The weight calculation module is used for determining subjective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an analytic hierarchy process and determining objective weight vectors of the multi-dimensional energy utilization efficiency indexes according to an entropy weight process;
the weight combination module is used for establishing a multi-objective optimization model according to the subjective weight vector and the objective weight vector of the multi-dimensional energy utilization efficiency index, and solving the multi-objective optimization model to obtain a combination weight vector of the multi-dimensional energy utilization efficiency index;
and the energy efficiency evaluation module is used for carrying out comprehensive energy efficiency evaluation on the comprehensive energy system to be evaluated by adopting a TOPSIS method and a gray correlation analysis method according to the combination weight vector to obtain an energy efficiency evaluation result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN117610981A (en) * | 2023-10-20 | 2024-02-27 | 国网上海市电力公司 | Comprehensive energy system energy efficiency assessment method |
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