CN116611610A - Park comprehensive energy system scheduling evaluation method considering multi-attribute group decision - Google Patents
Park comprehensive energy system scheduling evaluation method considering multi-attribute group decision Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention discloses a park comprehensive energy system scheduling evaluation method considering multi-attribute group decision, which comprises the following steps: firstly, collecting information of a park comprehensive energy system, establishing a robust random low-carbon optimal scheduling model of the park comprehensive energy system, and establishing a comprehensive evaluation index system; secondly, constructing a PDHFSs decision information matrix, determining each index weight based on an entropy method, calculating a consensus index set of decision specialists, updating different decision specialist weights, calculating the group polarization degree, and measuring the group polarization effect; and finally, calculating the score of each scheme by using an improved score function, sequencing and optimizing the schemes based on an improved TODIM method, and outputting a final decision result. The method can reduce the evaluation error of decision-making specialists caused by the self-limitation and the environment complexity, thereby comprehensively, scientifically and objectively evaluating the comprehensive performance of the park comprehensive energy system and providing reference for the selection of the optimal scheduling scheme of the park comprehensive energy system.
Description
Technical Field
The invention relates to an energy system scheduling evaluation method, in particular to a park comprehensive energy system scheduling evaluation method considering multi-attribute group decision.
Background
Energy is the basis of the power of social development and human survival, but the energy faces the double dilemma of rapid consumption of traditional fossil energy and increasingly serious environmental pollution in the development process. Under the background, the energy Internet strain taking the electric power system as the core is generated, the advantage complementation and the sharing and the high-efficiency utilization of new energy in a wide range are realized through the systems such as coupled electric power, natural gas, heating power, cooling power and the like, and the high-quality energy service is provided. As an important component of the energy Internet and an important bearing form of social energy, the core of the park comprehensive energy system is to realize energy conversion utilization, collaborative optimization and coupling complementation. The park comprehensive energy system is faced with the selection of different schemes in the process of optimizing and regulating. In order to reasonably evaluate the benefits of the comprehensive energy system in the aspects of economy, environment, safety and the like so as to further guide the production activities, the invention establishes a complete and applicable park comprehensive energy system benefit evaluation system, simultaneously provides a park comprehensive energy system scheduling evaluation method for taking multi-attribute group decisions, solves the problems of strong subjectivity of weight setting, easy influence by artificial factors and the like in the evaluation process, reduces the evaluation errors caused by self limitations and environmental complexity of experts, comprehensively, scientifically and objectively evaluates the comprehensive performance of a park comprehensive energy system optimizing scheduling scheme, enables the evaluation result to have more authenticity and generality, and provides guiding opinion for the scheduling scheme selection of the park comprehensive energy system.
Disclosure of Invention
The invention aims to provide a dispatching evaluation method for a park comprehensive energy system considering multi-attribute group decision, which realizes the optimal selection among alternative dispatching schemes of a plurality of park comprehensive energy systems, thereby reducing the running cost of the system, improving the running reliability of the system and reducing the pollution degree to the environment.
In order to achieve the above object, the present invention adopts the following technical scheme.
A park comprehensive energy system scheduling evaluation method considering multi-attribute group decision comprises the following steps:
(1) Collecting information of a park comprehensive energy system;
(2) Establishing a park comprehensive energy system robust random low-carbon optimal scheduling model considering source load uncertainty;
(3) Establishing a multi-index evaluation model, wherein the multi-index evaluation model comprises an operation cost model, a carbon transaction cost model, a carbon emission quantity model and an energy supply rate model;
(4) Constructing a PDHFSs decision information matrix, and determining each index weight based on an entropy method; the method specifically comprises the following steps:
(4a) Constructing a PDHFSs decision information matrix;
decision expert E k According to the evaluation criterion c= { C j J=1, 2,..n } versus alternative decision scheme a= { a i I=1, 2,..m } is evaluated; n represents the number of decision attributes, m represents the number of decision schemes; the evaluation result is constructed as a PDHFSs decision information matrix:
wherein r represents the number of rounds of decision making; pd (pd) ij(k) Representing decision expert E k The decision information is given;
according to the decision weight of the decision specialists, integrating the decision information matrixes given by the decision specialists into a consensus matrix:
PD * ＝(pd i j *) m×n ；
in the formula, pd ij * ＝PDHFWA(pd ij (1) ,pd ij (2) ,...,pd ij (t) ) PDHFWA represents a probability dual hesitation fuzzy weighted average operator;
(4b) Determining each index weight based on an entropy weight method;
calculating entropy of the j decision attribute according to the decision information of the decision expert as follows:
the objective weight of the j decision attribute is calculated as follows:
subjective and objective weight combination of decision attributes is performed:
in the method, in the process of the invention,representing the subjective weight of the decision expert, delta reflects whether the decision expert prefers to trust self experience, and delta satisfies delta epsilon 0,1]；
(5) Calculating a consensus index set of decision-making experts, judging whether each decision-making expert achieves consensus according to a given consensus threshold value, if so, jumping to the step (7), and if not, entering the step (6);
the method comprises the following steps:
(5a) Calculating a consensus index set of decision making experts;
defining consensus index as:
in the method, in the process of the invention,expert E respectively k And expert E l Is a decision information matrix of (1); consensus index->For judging the degree of proximity between the decision expert and the judgment of the group consensus; GD (graphics device) ij An equiprobable distance measure representing a probability dual hesitation blur element.
(5b) Judging whether consensus is achieved;
given a consensus threshold ε.gtoreq.0, ifAll specialists E k If the decision expert has reached consensus, outputting a consensus matrix to the step (7) for judging the group polarization degree, and if the decision expert has not reached consensus in decision, analyzing an 'dissonance' decision maker through the step (6);
(6) Decision expert E for finding out 'least harmony' through group consensus h If decision expert E h Accepting the group suggestion, updating the decision information matrix to beAnd jump to step (5), if other decision specialists E k Agree E h Updating the decision information matrix to +.>If decision expert E h Insisting on self-decision, expert E will make the decision h Defined as polarization expert, the weight of the polarization expert is updated as follows:
in the method, in the process of the invention,is a weight adjustment parameter and satisfies +.>If->The smaller the impact of the polarization expert's decision on the overall decision;
rest of decision expert E k The weight correspondence of (2) is:
(7) Calculating the polarization degree of the group, measuring the polarization effect of the group, outputting a result if the polarization degree does not exceed a threshold value, and sending out a warning if the polarization degree exceeds the threshold value;
(8) And calculating the score of each scheme by using the improved score function, sequencing and optimizing the schemes based on an improved TODIM method, and outputting a final decision result.
Preferably, in the step (1), the information of the park comprehensive energy system includes park load size, park electricity purchase and selling price, park carbon dioxide emission parameters, parameter information of a ladder-type carbon transaction mechanism, installation capacity and operation parameters of equipment in the park, carbon emission right quota parameters of unit power supply, and carbon emission right quota parameters of unit heat supply.
In the step (2), the park comprehensive energy system robust random low-carbon optimization scheduling model considering the uncertainty of the source load comprises a photovoltaic and load scene generation model, and the expression is as follows:
the photovoltaic output deviates from the predicted value of the cold, hot, electric and gas loads, the actual value of the photovoltaic output and the load is regarded as the sum of the predicted value and the predicted deviation, and the expression is as follows:
wherein P is PV,a,t 、The actual output and the predicted value of the photovoltaic at the t period under the a-th scene are obtained; delta PV,a,t Predicting a deviation for the photovoltaic output; p (P) Load,a,t 、/>The actual value and the predicted value of the load in the scene a at the t period are shown as the following; delta Load,a,t Predicting a deviation for the load;
assuming that the photovoltaic output and load obey normal distribution; the mean value and standard deviation of the photovoltaic and load are respectively as follows:
wherein mu is PV,a,t 、μ Load,a,t The average value of the photovoltaic output and the load in the t period under the a scene is; mu (mu) PV,a,t 、μ Load,a,t The standard deviation of the load output and the load in the t period under the scene a; p (P) PVN Rated capacity of the photovoltaic unit;
generating corresponding samples by random sampling according to probability distribution aiming at uncertainty of photovoltaic output and load; the scene set obtained by hypercube sampling of each uncertain variable Latin is normalized and then combined to obtain a system sampling scene set in an improved centralized reduction mode, and then the improved centralized reduction is carried out on the system sampling scene set by adopting a heuristic synchronous substitution method to obtain a system typical scene set and probability:
wherein m is 1 The number of the sampling scenes; k (k) 1 The number of the set reduction target scenes is set; s is S IC A scene set is sampled for the system under the improved centralized reduction mode; p (P) PV,max 、P PV,min 、P load,e,max 、P load,e,min 、P load,h,max 、P load,h,min 、P load,g,max 、P load,g,min 、P load,c,max 、P load,c,min The maximum value and the minimum value of the power in the sampling scene set are generated for the photovoltaic and the load respectively;to improve the a-th typical scene and the probability thereof obtained in the centralized clipping mode.
Further, in the step (2), the park comprehensive energy system robust random low-carbon optimization scheduling model considering the uncertainty of the source load comprises a two-stage robust optimization model; the uncertainty of wind power output is processed by using a two-stage robust min-max-min optimization algorithm, and an optimal scheduling scheme under the worst wind power scene is solved; the outermost layer min is a first stage, a weighted average of multiple objective functions of comprehensive operation cost, carbon emission and energy supply rate under all photovoltaic and load scenes is taken as a target, a fuzzy optimization algorithm of an improved membership function is adopted to solve the multiple objective functions to obtain an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, an inner layer max-min model is a second stage, and on the basis of the first stage, the worst scene of wind power output and an optimal scheduling scheme with optimal energy purchasing cost under the scene are searched;
the two-stage robust optimization model is described as:
s.t.H buy,s (x,u,y)＝0,G buy,s (x,u,y)≤0
H op,s (x)＝0,G op,s (x)≤0
H E,s (x)＝0,G E,s (x)≤0
H su,s (x)＝0,G su,s (x)≤0
wherein N is a typical scene number; ρ s Probability of occurrence for a typical scene s; c (C) buy,s 、C op,s 、F CO2,s 、E s 、C su,s Respectively the purchase energy cost, the operation maintenance cost, the carbon transaction cost, the carbon emission and the energy supply rate under the typical scene s; lambda is multi-objective fuzzy optimization satisfaction; x is a first-stage optimization variable, and comprises an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan; y is a second-stage optimization variable, including unit output and power grid interaction quantity; u is the uncertainty set of wind power output;
further, in step (3), the multi-index evaluation model includes an operation cost model, and the expression is as follows:
F＝C buy +C op
wherein F is the running cost of the system; c (C) buy 、C op The system purchase cost and the operation maintenance cost are respectively;
wherein, c buy,e,t 、c buy,g,t Electricity and gas prices at time t, respectively; p (P) buy,e,t To purchase electric quantity upwards in the period t; p (P) buy,g,t The air purchasing amount is the upward air purchasing amount in the period t; p (P) PV,t Is the actual output of the photovoltaic in the period t; p (P) CHP,g,t Natural gas power for CHP at time t; p (P) GB,g,t To input GB natural gas power at time t; p (P) EL,e,t To input electric power of the EL in t period;for inputting hydrogen energy of MR in t period; />Inputting hydrogen energy of HFC in t period; p (P) EC,e,t Representing the electrical power input to EC during period t; p (P) AC,h,t Representing the thermal power of the input AC for the period t; />Respectively the nth 1 The charging and discharging power of the energy storage device in the period t.
Further, in step (3), the multi-index evaluation model includes a carbon trade cost model, and the expression is as follows:
in the method, in the process of the invention,cost for carbon trade; x is X w Carbon emission for participating in carbon transaction in the park comprehensive energy system; lambda is the carbon trade base price; kappa is the price increase rate; d is the interval length.
Further, in the step (3), the multi-index evaluation model includes a carbon dioxide emission amount model, and the expression thereof is as follows:
E＝E buy +E CHP_GB +E gload -E MR
in E, E buy 、E gload The actual carbon emission of the system, the superior electricity purchasing and the gas load are respectively; e (E) CHP_GB For CHP, GB total actual carbon emissionsAmount, E MR CO absorbed for MR hydrogen to natural gas process 2 An amount of;
wherein P is CHP,e,t 、P CHP,h,t The electric power and the thermal power output by the CHP in the t period are respectively; p (P) GB,h,t The thermal power outputted at the t period GB; p (P) total,t Is the sum of the output powers of CHP, GB at time t; a, a 1 、b 1 、c 1 And a 2 、b 2 、c 2 The carbon emission coefficients of the coal-fired unit, CHP and GB are respectively; τ MR Absorption of CO for conversion of hydrogen to natural gas in MR devices 2 Parameters; p (P) MR,g,t Is the natural gas power output by the MR during period t.
Further, in the step (3), the multi-index evaluation model includes an energy supply rate model, and the expression is as follows:
wherein C is us For the energy supply rate of the system, the load is suddenly increased to the original valueWhen the time is doubled, the scheduling standby condition of outsourcing energy is +.>P load,e,t An electrical load at time t; p (P) load,h,t The thermal load at time t; p (P) load,g,t The gas load at time t; p (P) load,c,t The cold load at time t is shown.
Further, the step (7) specifically includes the following:
the degree of population polarization is expressed as:
PD *k and PD *k+r Respectively representing the group consensus after the kth and the k+r iterations;
setting a threshold value of group polarization, outputting a result if the polarization degree does not exceed the threshold value, and reminding a decision expert if the polarization degree exceeds the threshold value.
Further, the step (8) specifically includes the following:
determining evaluation criterion C j Evaluating criterion C against a reference r Relative weights of (2)
Wherein C is r The evaluation criterion with the greatest weight is adopted;
calculation alternative A i Pair A k Is the dominance of (3):
in the formula, scheme A i Scheme A k In evaluation criterion C j The following dominance is:
in the formula, EPD (pd) ij ,pd kj ) Is an equiprobable distance measure; rs (pd) ij ) A score function is improved; the parameter tau is a positive number, and represents the attenuation coefficient of decision specialists for loss avoidance, and the smaller the value tau is, the higher the avoidance degree of the loss is;
determining the actual utility value of the probability dual hesitation fuzzy information through the improved scoring function:
in the method, in the process of the invention,representing the comprehensive distance between the corresponding probability dual hesitation fuzzy element and the scoring function for the degree of deviation; s' pdhfe (pd) is a scoring function that accounts for expert sensitivity, representing the supportability of a decision-making scheme, with a value greater than 0 representing that the scheme is supported to a greater extent than the objection, and a value less than 0 representing that the scheme is supported to a greater extent than the supportability; />The method is used for representing the influence of the hesitation degree on the scheme, and the denominator value is always positive; the smaller the hesitation, the larger the term, the higher the scoring function, the better the solution and vice versa;
the expected values for each alternative are calculated, expressed as:
the higher the expected value, the better the scheme is, ordered by expected value for each alternative.
The beneficial effects are that:
compared with the prior art, the invention has the following substantial characteristics and remarkable progress:
(1) The reliability of the operation of the park comprehensive energy system is improved;
(2) The evaluation error of decision-making specialists caused by self-limitation and environmental complexity is reduced, so that the comprehensive performance of the comprehensive energy system of the park is comprehensively, scientifically and objectively evaluated, and the evaluation result is more general;
(3) The evaluation result can provide reference for the selection of the operation scheme of the park comprehensive energy system.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the present invention;
FIG. 2 is a diagram of an example architecture of a campus integrated energy system;
FIG. 3 is a graph of the electrical, thermal, gas, and cold loads of the integrated energy system for the campus;
FIG. 4 is a graph of wind and photovoltaic output of a park integrated energy system;
FIG. 5 is a plot of the price of electricity purchased and sold for a campus integrated energy system;
FIG. 6 is a graph of utility value distribution trend for each scheme under four algorithms.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
As shown in fig. 1, a method for scheduling and evaluating a park comprehensive energy system considering multi-attribute group decision includes the following steps:
(1) And collecting information of a park comprehensive energy system.
Preferably, the information of the park comprehensive energy system comprises park load size, park electricity purchasing and selling price, park carbon dioxide emission parameters, parameter information of a ladder-type carbon transaction mechanism, installation capacity and operation parameters of equipment in the park, carbon emission allowance parameters of unit power supply and carbon emission allowance parameters of unit heat supply.
(2) And establishing a park comprehensive energy system robust random low-carbon optimal scheduling model considering the uncertainty of the source load.
Preferably, the park comprehensive energy system robust random low-carbon optimization scheduling model comprises a photovoltaic and load scene generation model and a two-stage robust optimization model.
(2a) Photovoltaic and load scene generation model
The photovoltaic output deviates from the predicted value of the cold, hot, electric and gas loads, the actual value of the photovoltaic output and the load is regarded as the sum of the predicted value and the predicted deviation, and the expression is as follows:
wherein: p (P) PV,a,t 、The actual output and the predicted value of the photovoltaic at the t period under the a-th scene are obtained; delta PV,a,t Predicting a deviation for the photovoltaic output; p (P) Load,a,t 、/>The actual value and the predicted value of the load in the scene a at the t period are shown as the following; delta Load,a,t Deviations are predicted for the load.
It is assumed that the photovoltaic output and load follow a normal distribution. The mean value and standard deviation of the photovoltaic and load are respectively as follows:
wherein: mu (mu) PV,a,t 、μ Load,a,t The average value of the photovoltaic output and the load in the t period under the a scene is; mu (mu) PV,a,t 、μ Load,a,t The standard deviation of the load output and the load in the t period under the scene a; p (P) PVN Is the rated capacity of the photovoltaic unit.
For uncertainty in photovoltaic output and load, corresponding samples can be generated by random sampling according to probability distribution. The scene set obtained by hypercube sampling of each uncertain variable Latin is normalized and then combined to obtain a system sampling scene set in an improved centralized reduction mode, and then the improved centralized reduction is carried out on the system sampling scene set by adopting a heuristic synchronous substitution method to obtain a system typical scene set and probability:
wherein: m is m 1 The number of the sampling scenes; k (k) 1 The number of the set reduction target scenes is set; s is S IC A scene set is sampled for the system under the improved centralized reduction mode; p (P) PV,max 、P PV,min 、P load,e,max 、P load,e,min 、P load,h,max 、P load,h,min 、P load,g,max 、P load,g,min 、P load,c,max 、P load,c,min The maximum value and the minimum value of the power in the sampling scene set are generated for the photovoltaic and the load respectively;to improve the a-th typical scene and the probability thereof obtained in the centralized clipping mode.
(2b) Two-stage robust optimization model
And (3) processing the uncertainty of wind power output by using a two-stage robust min-max-min optimization algorithm, and solving an optimal scheduling scheme under the worst wind power scene. The outermost layer min is a first stage, the weighted average of the multi-objective function of comprehensive operation cost, carbon emission and energy supply rate under all photovoltaic and load scenes is minimized, the multi-objective function is solved by adopting an improved membership function fuzzy optimization algorithm, an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan are obtained, an inner layer max-min model is a second stage, a worst wind power output scene is found on the basis of the first stage, and an optimal scheduling scheme with optimal energy purchasing cost under the scene is obtained, and the two-stage robust optimization model can be described as follows:
s.t.H buy,s (x,u,y)＝0,G buy,s (x,u,y)≤0
H op,s (x)＝0,G op,s (x)≤0
H E,s (x)＝0,G E,s (x)≤0
H su,s (x)＝0,G su,s (x)≤0
wherein: n is the typical scene number; ρ s Probability of occurrence for a typical scene s; c (C) buy,s 、C op,s 、F CO2,s 、E s 、C su,s Respectively the purchase energy cost, the operation maintenance cost, the carbon transaction cost, the carbon emission and the energy supply rate under the typical scene s; lambda is multi-objective fuzzy optimization satisfaction; x is a first-stage optimization variable, and comprises an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan; y is a second-stage optimization variable, including unit output and power grid interaction quantity; u is the uncertainty set of wind power output.
(3) And establishing a multi-dimensional index evaluation model.
Preferably, the index evaluation model comprises a plurality of running cost models, carbon transaction cost models, carbon emission quantity models and energy supply rate models, and the scheduling results of the park comprehensive energy system are unfolded and evaluated from different dimensions of running cost, energy supply rate, carbon transaction cost, carbon emission quantity and the like.
(3a) Running cost model
F＝C buy +C op
Wherein: f is the running cost of the system; c (C) buy 、C op The system purchase cost and the operation maintenance cost are respectively;
wherein: c buy,e,t 、c buy,g,t Electricity and gas prices at time t, respectively; p (P) buy,e,t To purchase electric quantity upwards in the period t; p (P) buy,g,t The air purchasing amount is the upward air purchasing amount in the period t; p (P) PV,t Is the actual output of the photovoltaic in the period t; p (P) CHP,g,t Natural gas power for CHP at time t; p (P) GB,g,t To input GB natural gas power at time t; p (P) EL,e,t To input electric power of the EL in t period;for inputting hydrogen energy of MR in t period; />Inputting hydrogen energy of HFC in t period; p (P) EC,e,t Representing the electrical power input to EC during period t; p (P) AC,h,t Representing the thermal power of the input AC for the period t; />Respectively the nth 1 The charging and discharging power of the energy storage device in the period t.
(3b) Carbon trade cost model
Wherein:cost for carbon trade; x is X w Carbon emission for participating in carbon transaction in the park comprehensive energy system; lambda is the carbon trade base price; kappa is the price increase rate; d is the interval length.
(3c) Carbon dioxide emission model
E＝E buy +E CHP_GB +E gload -E MR
Wherein: E. e (E) buy 、E gload The actual carbon emission of the system, the superior electricity purchasing and the gas load are respectively; e (E) CHP_GB Is the total actual carbon emission of CHP and GB, E MR CO absorbed for MR hydrogen to natural gas process 2 An amount of;
wherein: p (P) CHP,e,t 、P CHP,h,t The electric power and the thermal power output by the CHP in the t period are respectively; p (P) GB,h,t The thermal power outputted at the t period GB; p (P) total,t Is the sum of the output powers of CHP, GB at time t; a, a 1 、b 1 、c 1 And a 2 、b 2 、c 2 The carbon emission coefficients of the coal-fired unit, CHP and GB are respectively; τ MR Absorption of CO for conversion of hydrogen to natural gas in MR devices 2 Parameters; p (P) MR,g,t Natural gas power output by MR in period t; t is the scheduling period.
(3d) Energy supply rate model
Wherein: c (C) us For the energy supply rate of the system, the sudden increase of the load to original is reflectedWhen the time is doubled, the energy source is outsourced for standby; p (P) load,e,t An electrical load at time t; p (P) load,h,t The thermal load at time t; p (P) load,g,t The gas load at time t; p (P) load,c,t The cold load at time t is shown.
(4) And constructing a PDHFSs decision information matrix, and determining each index weight based on an entropy method.
(4a) PDHFSs decision information matrix
Decision expert E k According to the evaluation criterion c= { C j J=1, 2,..n } versus alternative decision scheme a= { a i I=1, 2,..m } is evaluated; n represents the number of evaluation criteria; m represents the number of decision schemes.
The evaluation result is constructed as a PDHFSs decision information matrix:
wherein: r represents the number of rounds of decision making. Then, according to the decision weight of the decision expert, the decision information matrix given by each decision expert can be integrated into a consensus matrix by using, but not limited to, PDHFWA operator:
PD * ＝(pd ij * ) m×n
wherein: pd (pd) ij * ＝PDHFWA(pd ij (1) ,pd ij (2) ,...,pd ij (t) ) PDHFWA represents a probabilistic dual hesitation fuzzy weighted average operator.
(4b) Determining each index weight based on entropy weight method
The entropy method is an objective assignment method, and its principle is to determine the attribute weight according to the information content of attribute index, and to measure the uncertainty of probability dual hesitation fuzzy element by using improved PDHFS information entropy, and to set a certain probability dual hesitation fuzzy elementExpressed as the evaluation value of the ith scheme on the jth decision attribute (decision attribute is the evaluation criterion above), the corresponding information entropy is:
wherein: ζ and ζ are respectively pd ij The average degree of difference and average hesitation of (c) is specifically expressed as:
wherein: p is p τijl Is τ ijl Is a probability of association;is->Is a probability of association;
based on the theory, calculating the entropy value of the j decision attribute according to the decision information of the decision expert as follows:
then, the objective weight of the j decision attribute is calculated as follows:
considering that decision specialists have different decision preferences on decision attributes according to different decision problems, the invention also introduces subjective weights of decision specialistsAfter the subjective weight is given by the decision expert, subjective and objective weight combination of the decision attribute is carried out:
wherein: delta reflects whether the decision expert prefers to trust itself and delta satisfies delta e 0, 1.
(5) Calculating a consensus index set of decision-making experts, judging whether each decision-making expert achieves consensus according to a given consensus threshold value, if so, jumping to the step (7), and if not, entering the step (6).
(5a) Computing consensus index set for decision making expert
The consensus index measures the distance that the decision expert deviates from the consensus. Defining and comparing the equal probability distance measure of two probability dual hesitation fuzzy elements as follows:
GD(pd 1 ,pd 2 )＝EPD(pd 1 ,pd 2 )
based on the above theory, the consensus index is defined as follows:
wherein:expert E respectively k And expert E l Is a decision information matrix of (a). Consensus index->The degree of closeness between the decision making expert and the judgment of the group consensus can be judged; GD (graphics device) ij An equiprobable distance measure representing a probability dual hesitation blur element.
(5b) Judging whether to reach consensus
Given a consensus threshold ε.gtoreq.0, ifAll specialists E k If the decision expert has reached the consensus, outputting the consensus matrix to the step (7) to judge the group polarization degree, and if the decision expert has not reached the consensus during decision, developing deep analysis on the 'dissonant' decision maker, namely, entering the step (6).
(6) Decision maker E for finding out least harmony through group consensus h ("least harmony" means that the decision result of a decision expert is most different from that of other experts, and the standard of measurement is the magnitude of consensus index). If decision expert E h Accepting the group suggestion, and updating the decision information matrix asAnd skipping to step (5). If other decision maker E k Quilt E h Is convinced of the point of view of (E) h Updating its decision information matrix to +.>If decision expert E h Still persisting with self-decision, expert E will make decision h The weight of (2) is reduced, avoiding affecting the overall decision.
Specifically, expert E will be decision h The weight update of (2) is:
wherein:is a weight adjustment parameter and satisfies +.>If->The smaller the impact of the polarization expert's decision on the overall decision.
In the case of reducing polarization expert E h After the specific gravity of (C), the rest of decision making expert E k Corresponding to an increase in weight:
(7) Calculating the polarization degree of the group, measuring the polarization effect of the group, outputting a result if the polarization degree does not exceed a threshold value, and sending out a warning if the polarization degree exceeds the threshold value.
After decision-making expert makes multiple rounds of group decision, the group polarization phenomenon is gradually developed and continuously increased along with the increase of the consensus degree, and the average tendency of group members can be expressed by using a consensus matrix, so that the group polarization effect can be measured by comparing the distances of the consensus matrix before and after iteration, and the method is expressed by using Hadamard product in modeling. With PD *k And PD *k+r Respectively represent the group consensus after the kth and the k+r iterations, the group polarization pathThe degree is expressed as:
the threshold setting of population extreme is preferably 1.20, if the polarization degree does not exceed the threshold, a result can be output, and if the polarization degree exceeds the threshold, a decision expert is reminded.
(8) And calculating the score of each scheme by using the improved score function, sequencing and optimizing the schemes based on an improved TODIM method, and outputting a final decision result.
(8a) Determining evaluation criterion C j Evaluating criterion C against a reference r Relative weights of (2)
Wherein: c (C) r The evaluation criterion with the greatest weight is adopted;
(8b) Calculation alternative A i Pair A k Is the dominance of (3):
wherein: scheme A i Scheme A k In evaluation criterion C j The following dominance is:
wherein: EPD (pd) ij ,pd kj ) Is an equiprobable distance measure; rs (pd) ij ) A score function is improved; the parameter tau is a positive number, represents the attenuation coefficient of decision specialist for loss avoidance, and the smaller the value tau is, the higher the avoidance degree of loss is, and the decision specialist can adjust the value tau according to the actual decision situation。
The improved scoring function can accurately and simply determine the actual utility value of the probability dual hesitation fuzzy information:
wherein:for the degree of deviation, the combined distance of the corresponding probability dual hesitation fuzzy element and the scoring function is represented. s' pdhfe (pd) is a scoring function that accounts for expert sensitivity, and represents the supportability of a decision-making scheme, with a value greater than 0 representing that the scheme is supported to a greater extent than the objection, and a value less than 0 representing that the scheme is supported to a greater extent than the supportability. />The influence of hesitation on the scheme is reflected, and the denominator value is always positive. The smaller the hesitation, the larger the term, the higher the scoring function, the more excellent the solution, and vice versa. Compared with the traditional scoring function, the improved scoring function considers the influence of hesitation on the scheme, and can describe the actual information expressed by the dual hesitation fuzzy elements more accurately.
(8c) The expected values for each alternative are calculated, expressed as:
(8d) The higher the expected value, the better the scheme is, ordered by expected value for each alternative.
Based on the method, an application example is specifically given below, and the effectiveness and rationality of the method on the aspect of evaluation of the dispatching result of the park comprehensive energy system and the obvious technical effect are verified through examples and comparative examples.
Park comprehensive energy system establishment aiming at considering source load uncertaintyThe corresponding evaluation index system is shown in Table 1. Decision expert E k The fuzzy decision information matrices made are shown in tables 1,2 and 3, respectively. The initial expert weight is recorded as set asThe consensus matrix is calculated using the PDHFWA operator as shown in table 4.
Table 1 shows a park comprehensive energy system evaluation index system with uncertainty of source load
The 4 schemes for optimizing and scheduling the park comprehensive energy system are designed as follows:
scheme A 1 : the uncertainty of the source load is not considered in the optimization scheduling of the park comprehensive energy system, and the carbon transaction is not considered. Scheme A 2 : and (3) the uncertainty of the source load is not considered in the optimization scheduling of the park comprehensive energy system, and the carbon transaction is considered. Scheme A 3 : the uncertainty of the source load is considered in the optimization scheduling of the park comprehensive energy system, and the carbon transaction is not considered. Scheme A 4 : and (5) taking the uncertainty of the source load into consideration in the optimization scheduling of the park comprehensive energy system, and taking carbon transaction into consideration.
Table 2 evaluation value decision matrix given by decision expert 1
Table 3 evaluation value decision matrix given by decision expert 2
Table 4 evaluation value decision matrix given by decision expert 3
TABLE 5 consensus matrix partial results
Calculating entropy values through the consensus matrix, and obtaining objective weights of the attributes as follows: expert gives attribute subjective weight +.>Let λ=0.5, finally obtain attribute weights, respectively: />Setting the consensus index threshold epsilon=0.1375, then mxn×epsilon=2.2. Calculating a consensus index, and obtaining the following results: /> Thus decision expert E 3 Exceeding a threshold, deviating from the population consensus. Asking three experts if they accept the advice of the other party. At this point, three experts have chosen to adhere to their own decisions. The decision weights of the three experts are adjusted accordingly. Setting upThe adjusted expert weights are: omega 1 ＝ω 2 ＝7/18，ω 3 =2/9. A population polarization degree threshold δ=1.1 is set. The degree of Polarization D (PD) at this time was calculated *k ,PD *k+r ) =0.34, the degree of polarization does not exceed the threshold, meeting the requirements.
Taking the decay coefficient τ=1, improvementExpert sensitivity θ=0.5 in the scoring function, and expected values for each alternative are calculated according to equations (21) and (22). The calculation result is as follows: phi (A) 1 )＝0.32，Φ(A 2 )＝0.41，Φ(A 3 )＝0.63，Φ(A 4 ) =0.81. Thus, a scheme ranking of A is obtained 4 ＞A 3 ＞A 2 ＞A 1 . So it can be determined that the optimal evaluation scheme is A 4 The method considers the uncertainty of the source load and the carbon transaction mechanism in the park comprehensive energy system, can consider the economic, low-carbon and reliable performance of the system in multiple aspects, and is the optimal selection scheme.
To illustrate the feasibility and effectiveness of the method of the present invention, the proposed algorithm is compared to several existing multi-attribute decision algorithms. The TOPSIS decision algorithm based on the prospect theory is realized by calculating the comprehensive damage-benefit ratio tau i And optimizing and sequencing the implementation scheme. In the improved VIKOR decision algorithm, a defined comprehensive weighted distance Q is calculated depending on the policy coefficients v=0.5, k=0.8, and p=2. The PDHFSs decision algorithm can also calculate corresponding score results to evaluate the system scheme.
Finally, the distribution trend of the decision utility values calculated according to each scheme is shown in fig. 6, and the specific utility values are shown in table 6. From the calculation result, the four algorithms all consider that the optimal scheme is A 4 The worst scheme is A 1 The validity of the decision result is proved. But the results calculated by different decision algorithms have some differences, for example, all three other decision algorithms consider A 4 ＞A 3 ＞A 2 ＞A 1 Modified VIKOR alone to be a 4 ＞A 3 ＞A 1 ＞A 2 . The reason for the difference is that: (1) The compromise theory in the VIKOR decision algorithm has computational limitations, ignoring the impact of the departure function; (2) The improved VIKOR decision algorithm has drawbacks for the definition of distance measures. When the number of membership information or non-membership information in the dual hesitation fuzzy elements to be compared is different, larger errors can be generated. For example for two dual hesitation blurring elements<(0.2,0.3),(0.4,0.5)>And<(0.2),(0.4)>intuitively, the distance between two dual hesitation blur elements is notBut since the former has a larger amount of information than the latter, the calculated distance measure is significantly too large. The equal probability distance measure adopted by the invention avoids the defect, and can better determine the distance between two probability dual hesitation fuzzy elements with different information amounts, so that the calculated consensus index is more accurate.
TABLE 6 population pressure impact values
From the trend graph, other algorithms except the PDHFs decision algorithm have a certain degree of differentiation. Wherein the PDHFSs-TODIM decision algorithm has the highest discrimination, and the modified TOPIS algorithm and the modified VIKOR algorithm are in scheme A 1 And A 2 The differentiation therebetween is insufficient. This is caused by a number of reasons: firstly, the opinion of an 'dissonance' decision maker is mixed in an initial decision information matrix, so that the preference of the decision maker cannot be expressed completely, and the influence of the 'dissonance' decision maker on the whole decision is effectively reduced by dynamically reducing the decision weight of the 'dissonance' decision maker, and the degree of distinction of the whole decision is improved. Secondly, most expert decisions have a tendency to converge towards group consensus, affected by group pressure. The invention effectively improves the decision distinguishing degree between the experts through correcting the group pressure, and can express the true ideas of the experts.
In addition, compared with other methods at home and abroad, the invention has the further innovation point that the decision model provided by the invention allows all experts to correct own decision information. In actual decision making, there is a phenomenon that "true grasp on the hands of a few people", and therefore, other experts may be convinced by "discordant" decision makers, and correct their decisions. In the decision model proposed by the present invention, the opinion of "discordant" decision makers can also become consensus through iteration, as long as other decision makers are approved in the discussion.
In summary, the algorithm provided by the invention has feasibility and higher accuracy in the aspects of deciding the optimal scheme, evaluating the comprehensive performance and the like. The algorithm can solve the problem of multiple attribute information of multiple different types, obtain accurate decision evaluation results and has reliability and effectiveness. Through the analysis, the multi-index evaluation method provided by the invention can be used for making powerful, reasonable, real and objective evaluation on the optimization scheme of the park comprehensive energy system, further making reference for actual park scheduling and guiding actual production and construction.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A park comprehensive energy system scheduling evaluation method considering multi-attribute group decision is characterized by comprising the following steps:
(1) Collecting information of a park comprehensive energy system;
(2) Establishing a park comprehensive energy system robust random low-carbon optimal scheduling model considering source load uncertainty;
(3) Establishing a multi-index evaluation model, wherein the multi-index evaluation model comprises an operation cost model, a carbon transaction cost model, a carbon emission quantity model and an energy supply rate model;
(4) Constructing a PDHFSs decision information matrix, and determining each index weight based on an entropy method; the method specifically comprises the following steps:
(4a) Constructing a PDHFSs decision information matrix;
decision expert E k According to the evaluation criterion c= { C j J=1, 2,..n } versus alternative decision scheme a= { a i I=1, 2,..m } is evaluated; n represents the number of evaluation criteria, m represents the number of decision schemes; the evaluation result is constructed as a PDHFSs decision information matrix:
wherein r represents the number of rounds of decision making; pd (pd) ij(k) Representing decision expert E k The decision information is given;
according to the decision weight of the decision specialists, integrating the decision information matrixes given by the decision specialists into a consensus matrix:
PD * ＝(pd ij * ) m×n ；
in the formula, pd ij * ＝PDHFWA(pd ij (1) ,pd ij (2) ,...,pd ij (t) ) PDHFWA represents a probability dual hesitation fuzzy weighted average operator;
(4b) Determining each index weight based on an entropy weight method;
calculating entropy of the j decision attribute according to the decision information of the decision expert as follows:
the objective weight of the j decision attribute is calculated as follows:
subjective and objective weight combination of decision attributes is performed:
in the method, in the process of the invention,representing the subjective weight of the decision expert, delta reflects whether the decision expert prefers to trust self experience, and delta satisfies delta epsilon 0,1]；
(5) Calculating a consensus index set of decision-making experts, judging whether each decision-making expert achieves consensus according to a given consensus threshold value, if so, jumping to the step (7), and if not, entering the step (6);
the method comprises the following steps:
(5a) Calculating a consensus index set of decision making experts;
defining consensus index as:
in the method, in the process of the invention,expert E respectively k And expert E l Is a decision information matrix of (1); consensus index->For judging the degree of proximity between the decision expert and the judgment of the group consensus; GD (graphics device) ij An equiprobable distance measure representing a probability dual hesitation blur element.
(5b) Judging whether consensus is achieved;
given a consensus threshold ε.gtoreq.0, ifAll specialists E k If the decision expert has reached consensus, outputting a consensus matrix to the step (7) for judging the group polarization degree, and if the decision expert has not reached consensus in decision, analyzing an 'dissonance' decision maker through the step (6);
(6) Decision expert E for finding out 'least harmony' through group consensus h If decision expert E h Accepting the group suggestion, updating the decision information matrix to beAnd jump to step (5), if other decision specialists E k Agree E h Updating the decision information matrix to +.>If decision expert E h Insisting on self-decision, expert E will make the decision h Defined as polarization expert, the weight of the polarization expert is updated as follows:
ω h r+1 ＝θ r ω h r +(1-θ r )ω h r ω h r
in θ r Is a weight adjustment parameter and satisfies theta r E (0, 1); if theta is r The smaller the impact of the polarization expert's decision on the overall decision;
rest of decision expert E k The weight correspondence of (2) is:
ω k r+1 ＝ω k r +(1-θ r )ω k r ω k r
(7) Calculating the polarization degree of the group, measuring the polarization effect of the group, outputting a result if the polarization degree does not exceed a threshold value, and sending out a warning if the polarization degree exceeds the threshold value;
(8) And calculating the score of each scheme by using the improved score function, sequencing and optimizing the schemes based on an improved TODIM method, and outputting a final decision result.
2. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (1), the information of the park comprehensive energy system comprises park load size, park electricity purchase and selling price, park carbon dioxide emission parameters, parameter information of a ladder-type carbon transaction mechanism, installation capacity and operation parameters of equipment in the park, carbon emission allowance parameters of unit power supply and carbon emission allowance parameters of unit heat supply.
3. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (2), the park comprehensive energy system robust random low-carbon optimization scheduling model considering the uncertainty of the source load comprises a photovoltaic and load scene generation model, and the expression is as follows:
the photovoltaic output deviates from the predicted value of the cold, hot, electric and gas loads, the actual value of the photovoltaic output and the load is regarded as the sum of the predicted value and the predicted deviation, and the expression is as follows:
wherein P is PV,a,t 、The actual output and the predicted value of the photovoltaic at the t period under the a-th scene are obtained; delta PV,a,t Predicting a deviation for the photovoltaic output; p (P) Load,a,t 、/>The actual value and the predicted value of the load in the scene a at the t period are shown as the following; delta Load,a,t Predicting a deviation for the load;
assuming that the photovoltaic output and load obey normal distribution; the mean value and standard deviation of the photovoltaic and load are respectively as follows:
wherein mu is PV,a,t 、μ Load,a,t The average value of the photovoltaic output and the load in the t period under the a scene is; mu (mu) PV,a,t 、μ Load,a,t The standard deviation of the load output and the load in the t period under the scene a; p (P) PVN Rated capacity of the photovoltaic unit;
generating corresponding samples by random sampling according to probability distribution aiming at uncertainty of photovoltaic output and load; the scene set obtained by hypercube sampling of each uncertain variable Latin is normalized and then combined to obtain a system sampling scene set in an improved centralized reduction mode, and then the improved centralized reduction is carried out on the system sampling scene set by adopting a heuristic synchronous substitution method to obtain a system typical scene set and probability:
wherein m is 1 The number of the sampling scenes; k (k) 1 The number of the set reduction target scenes is set; s is S IC A scene set is sampled for the system under the improved centralized reduction mode; p (P) PV,max 、P PV,min 、P load,e,max 、P load,e,min 、P load,h,max 、P load,h,min 、P load,g,max 、P load,g,min 、P load,c,max 、P load,c,min The maximum value and the minimum value of the power in the sampling scene set are generated for the photovoltaic and the load respectively;to improve the a-th typical scene and the probability thereof obtained in the centralized clipping mode.
4. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (2), the park comprehensive energy system robust random low-carbon optimization scheduling model considering the uncertainty of the source load comprises a two-stage robust optimization model; the uncertainty of wind power output is processed by using a two-stage robust min-max-min optimization algorithm, and an optimal scheduling scheme under the worst wind power scene is solved; the outermost layer min is a first stage, a weighted average of multiple objective functions of comprehensive operation cost, carbon emission and energy supply rate under all photovoltaic and load scenes is taken as a target, a fuzzy optimization algorithm of an improved membership function is adopted to solve the multiple objective functions to obtain an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, an inner layer max-min model is a second stage, and on the basis of the first stage, the worst scene of wind power output and an optimal scheduling scheme with optimal energy purchasing cost under the scene are searched;
the two-stage robust optimization model is described as:
s.t.H buy,s (x,u,y)＝0,G buy,s (x,u,y)≤0
H op,s (x)＝0,G op,s (x)≤0
H E,s (x)＝0,G E,s (x)≤0
H su,s (x)＝0,G su,s (x)≤0
wherein N is a typical scene number; ρ s Probability of occurrence for a typical scene s; c (C) buy,s 、C op,s 、F CO2,s 、E s 、C su,s Respectively the purchase energy cost, the operation maintenance cost, the carbon transaction cost, the carbon emission and the energy supply rate under the typical scene s; lambda is multi-objective fuzzy optimization satisfaction; x is a first-stage optimization variable, and comprises an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan; y is a second-stage optimization variable, including unit output and power grid interaction quantity; u is the uncertainty set of wind power output.
5. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in step (3), the multi-index evaluation model includes an operation cost model, and the expression is as follows:
F＝C buy +C op
wherein F is the running cost of the system; c (C) buy 、C op The system purchase cost and the operation maintenance cost are respectively;
wherein, c buy,e,t 、c buy,g,t Electricity and gas prices at time t, respectively; p (P) buy,e,t To purchase electric quantity upwards in the period t; p (P) buy,g,t The air purchasing amount is the upward air purchasing amount in the period t; p (P) PV,t Is the actual output of the photovoltaic in the period t; p (P) CHP,g,t Natural gas power for CHP at time t; p (P) GB,g,t To input GB natural gas power at time t; p (P) EL,e,t To input electric power of the EL in t period;for inputting hydrogen energy of MR in t period; />Inputting hydrogen energy of HFC in t period; p (P) EC,e,t Representing the electrical power input to EC during period t; p (P) AC,h,t Representing the thermal power of the input AC for the period t; />Respectively the nth 1 The charging and discharging power of the energy storage device in the period t.
6. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (3), the multi-index evaluation model includes a carbon trade cost model, and the expression is as follows:
in the method, in the process of the invention,cost for carbon trade; x is X w Carbon emission for participating in carbon transaction in the park comprehensive energy system; lambda is the carbon trade base price; kappa is the price increase rate; d is the interval length.
7. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (3), the multi-index evaluation model includes a carbon dioxide emission amount model, and the expression is as follows:
E＝E buy +E CHP_GB +E gload -E MR
in E, E buy 、E gload The actual carbon emission of the system, the superior electricity purchasing and the gas load are respectively; e (E) CHP_GB Is the total actual carbon emission of CHP and GB, E MR CO absorbed for MR hydrogen to natural gas process 2 An amount of;
wherein P is CHP,e,t 、P CHP,h,t The electric power and the thermal power output by the CHP in the t period are respectively; p (P) GB,h,t The thermal power outputted at the t period GB; p (P) total,t Is the sum of the output powers of CHP, GB at time t; a, a 1 、b 1 、c 1 And a 2 、b 2 、c 2 The carbon emission coefficients of the coal-fired unit, CHP and GB are respectively; τ MR Absorption of CO for conversion of hydrogen to natural gas in MR devices 2 Parameters; p (P) MR,g,t Is the natural gas power output by the MR during period t.
8. The campus integrated energy system scheduling evaluation method considering multi-attribute group decisions according to claim 1, wherein: in the step (3), the multi-index evaluation model includes an energy supply rate model, and the expression is as follows:
wherein C is us For the energy supply rate of the system, the load is suddenly increased to the original valueWhen the time is doubled, the scheduling standby condition of outsourcing energy is +.>P load,e,t An electrical load at time t; p (P) load,h,t The thermal load at time t; p (P) load,g,t The gas load at time t; p (P) load,c,t The cold load at time t is shown.
9. The method for scheduling and evaluating a campus integrated energy system taking into account multi-attribute group decisions according to claim 1, wherein the step (7) specifically comprises the following steps:
the degree of population polarization is expressed as:
PD *k and PD *k+r Respectively representing the group consensus after the kth and the k+r iterations;
setting a threshold value of group polarization, outputting a result if the polarization degree does not exceed the threshold value, and reminding a decision expert if the polarization degree exceeds the threshold value.
10. The method for scheduling and evaluating a campus integrated energy system taking into account multi-attribute group decisions according to claim 1, wherein step (8) specifically comprises the following:
determining evaluation criterion C j Evaluating criterion C against a reference r Relative weights of (2)
Wherein C is r The evaluation criterion with the greatest weight is adopted;
calculation alternative A i Pair A k Is the dominance of (3):
in the formula, scheme A i Scheme A k In evaluation criterion C j The following dominance is:
in the formula, EPD (pd) ij ,pd kj ) Is an equiprobable distance measure; rs (pd) ij ) A score function is improved; the parameter tau is a positive number, and represents the attenuation coefficient of decision specialists for loss avoidance, and the smaller the value tau is, the higher the avoidance degree of the loss is;
determining the actual utility value of the probability dual hesitation fuzzy information through the improved scoring function:
in the method, in the process of the invention,representing the comprehensive distance between the corresponding probability dual hesitation fuzzy element and the scoring function for the degree of deviation; s' pdhfe (pd) is a scoring function that accounts for expert sensitivity, representing the supportability of a decision-making scheme, with a value greater than 0 representing that the scheme is supported to a greater extent than the objection, and a value less than 0 representing that the scheme is supported to a greater extent than the supportability; />The method is used for representing the influence of the hesitation degree on the scheme, and the denominator value is always positive; the smaller the hesitation, the larger the term, the higher the scoring function, the better the solution and vice versa;
the expected values for each alternative are calculated, expressed as:
the higher the expected value, the better the scheme is, ordered by expected value for each alternative.
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