CN112348276A - Comprehensive energy system planning optimization method based on multiple elements and three levels - Google Patents

Comprehensive energy system planning optimization method based on multiple elements and three levels Download PDF

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CN112348276A
CN112348276A CN202011293869.1A CN202011293869A CN112348276A CN 112348276 A CN112348276 A CN 112348276A CN 202011293869 A CN202011293869 A CN 202011293869A CN 112348276 A CN112348276 A CN 112348276A
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energy system
planning
weight
comprehensive
equipment
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钱康
袁越
吕桐
孙纯军
苏麟
朱俊澎
晏阳
朱东升
袁简
王欣怡
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Hohai University HHU
China Energy Engineering Group Jiangsu Power Design Institute Co Ltd
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China Energy Engineering Group Jiangsu Power Design Institute Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a comprehensive energy system planning optimization method based on multiple elements and three layers, which comprises the following steps: firstly, performing primary planning and energy flow analysis on the comprehensive energy system based on an energy hub model, and establishing a comprehensive energy system planning optimization model; then solving the planning optimization model based on a multi-target particle swarm algorithm to obtain a Potore optimal solution set, and forming a planning scheme set to be selected according to the optimal solution set; and finally, establishing a comprehensive evaluation index system according to engineering requirements, and evaluating and scoring the optimal planning scheme set by adopting an analytic hierarchy process-entropy weight inversion method to obtain an optimal planning scheme. The comprehensive energy system optimization planning method carries out three-layer planning and optimization solution on the comprehensive energy system, and meets multi-element requirements by setting multiple optimization targets and multiple evaluation indexes, so that an optimal planning scheme of the comprehensive energy system is designed.

Description

Comprehensive energy system planning optimization method based on multiple elements and three levels
Technical Field
The invention relates to a comprehensive energy system planning optimization method based on multiple elements and three layers, and belongs to the technical field of comprehensive energy system planning optimization.
Background
Energy is the basis for human survival and development and is the life line of economic society. Because the traditional fossil energy such as coal, petroleum and the like can not be regenerated, the utilization efficiency of the traditional fossil energy is improved, and the comprehensive utilization of renewable energy is enhanced, so that the method becomes an inevitable choice for solving the contradiction between the increase of energy demand and energy shortage, and between energy utilization and environmental protection. The comprehensive energy system is a novel regional energy supply system which takes an electric power system as a core, breaks the existing modes of independent planning, independent design and independent operation of various energy supply systems such as power supply, gas supply, cold supply, heat supply and the like, organically coordinates and optimizes links such as distribution, conversion, storage, consumption and the like of various energy sources in the planning, design, construction and operation processes, and fully utilizes renewable energy sources. For regional comprehensive energy system planning, the industry barrier needs to be broken through, the past power, gas, heat and cold production and supply and independent planning mode turns to multi-form energy combined planning, technical breakthrough is realized, the boundaries of policies, regions and the like are broken, comprehensive utilization and cascade utilization of various energy sources are realized, the absorption capacity of clean energy sources such as wind energy, solar energy and the like is improved, the advantage complementation among the energy sources is realized, the requirements of various energy sources at a user side are met, and the firmness and reliability of an energy network are enhanced.
Disclosure of Invention
The invention aims to overcome the technical defects in the prior art, solve the technical problems and provide a comprehensive energy system planning optimization method based on multi-element three-level, and the comprehensive energy system is planned in a layered mode by combining multi-element requirements, so that an optimal planning scheme of the comprehensive energy system is designed.
The invention specifically adopts the following technical scheme: the comprehensive energy system planning optimization method based on the multi-element three-level comprises the following steps:
analyzing equipment composition of the comprehensive energy system by adopting an energy hub model, combing an energy flow relation, primarily planning each unit equipment, establishing a multi-objective function according to engineering requirements, and establishing constraint conditions according to the energy flow relation and equipment performance to form a comprehensive energy system planning optimization model;
solving the comprehensive energy system planning optimization model by adopting a multi-objective particle swarm algorithm to obtain a Potore optimal solution set, and forming an optimal planning scheme set according to the optimal solution set;
and establishing a comprehensive evaluation index system according to engineering requirements, and evaluating and scoring the optimal planning scheme set by adopting an analytic hierarchy process-entropy weight inversion method to obtain an optimal planning scheme.
As a preferred embodiment, the objective function includes an annual total cost function, which specifically includes:
the total annual cost is the annual equal investment cost CinvAnnual running maintenance cost CopeSum of (a):
Ctotal＝Cinv+Cope (1)
annual equal investment cost CinvThe total investment cost of the system is calculated by equally distributing cost values of each year in the operation periodThe formula is as follows:
Figure BDA0002784621520000021
wherein I is the total number of devices; pi ratedIs the rated capacity of the equipment; c. CiIs the unit investment cost of the equipment; f. ofiMaintaining a cost factor for a fixed operation of the equipment; alpha is alphaiThe conversion coefficient for annual equal investment of equipment; wherein:
Figure BDA0002784621520000022
in the formula: m is annual interest rate; and N is the service life of the equipment.
The operating costs include operating maintenance costs C of the plantomAnd fuel cost Cfuel：
Figure BDA0002784621520000023
In the formula: h is the total annual operating hours of the equipment; a isiThe unit variable operation maintenance cost of the equipment; pi hThe output value of the device i in the h hour is obtained; j is the total number of types of input energy;
Figure BDA0002784621520000031
the price of the jth energy source at the h hour;
Figure BDA0002784621520000032
is the amount of the jth energy source used in the h hour.
As a preferred embodiment, the objective function includes an annual pollutant emission function, which specifically includes:
the emission of atmospheric pollutants is taken as an index for measuring the environmental benefit:
Figure BDA0002784621520000033
in the formula (I), the compound is shown in the specification,
Figure BDA0002784621520000034
the input emission coefficient of the j energy source;
Figure BDA0002784621520000035
the internal conversion emission coefficient for the j-th energy source.
As a preferred embodiment, the objective function further includes once-a-year energy consumption, specifically including:
the annual energy consumption is used as an index for measuring the energy benefit:
Figure BDA0002784621520000036
in the formula, λjThe standard coal consumption conversion coefficient of the jth energy source.
As a preferred embodiment, the constraint specifically includes:
device configuration capacity constraints:
the capacity of the device must be selected by considering the influence of the resource size, the size of the installable site, the maximum capacity which can be manufactured by the current technology, and the like, so that the following constraints exist:
0≤Ni≤Nmax (7)
Figure BDA0002784621520000037
in the formula: n is a radical ofmaxThe maximum number of devices that can be installed;
Figure BDA0002784621520000038
and
Figure BDA0002784621520000039
respectively, the minimum value and the maximum value which can be selected by the rated capacity of the equipment;
operation state constraint:
Figure BDA0002784621520000041
in the formula
Figure BDA0002784621520000042
The operation state of the equipment i in the t hour;
output power constraint:
Figure BDA0002784621520000043
in the formula:
Figure BDA0002784621520000044
and
Figure BDA0002784621520000045
respectively, a minimum boundary value and a maximum boundary value of the output power of the equipment;
energy balance constraint:
at each moment, its electricity, heat and cold need to satisfy the following constraints:
Figure BDA0002784621520000046
in the formula:
Figure BDA0002784621520000047
and
Figure BDA0002784621520000048
input electric power, output electric power of the device, and electric load demand of the user, respectively;
Figure BDA0002784621520000049
and
Figure BDA00027846215200000410
the input thermal power, the output thermal power of the device and the thermal load demand of the user are respectively;
Figure BDA00027846215200000411
and
Figure BDA00027846215200000412
respectively, the input cold power, the output cold power of the device and the cold load demand of the user.
As a preferred embodiment, the solving the comprehensive energy system planning and optimizing model by using the multi-objective particle swarm algorithm to obtain a patotol optimal solution set, and the forming of the optimal planning scheme set according to the optimal solution set specifically includes:
inputting various parameters of a comprehensive energy system;
initializing particle members of the multi-target particle swarm algorithm;
calculating the fitness of each particle;
comparing the advantages and disadvantages of the fitness values of all members, and updating the front edge of the paletor;
fifthly, updating population members and starting to calculate from the step II until the maximum iteration number is reached.
As a preferred embodiment, the establishing a comprehensive evaluation index system according to engineering requirements specifically includes: and establishing a comprehensive evaluation index system of the comprehensive energy system from three aspects of technical indexes, economic indexes and environmental indexes.
As a preferred embodiment, the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight reduction method specifically includes: the chromatographic analysis determines the index weight, namely:
firstly, establishing a relative importance matrix by pairwise comparison of each level, solving the maximum eigenvalue and eigenvector of the matrix, and obtaining index weight through consistency judgment;
secondly, calculating the index weight of each expert in sequence through the previous step, and setting the weight vector of m experts as xmForming a weightThe matrix C is shown as the formula (12);
Figure BDA0002784621520000051
thirdly, calculating a correlation coefficient matrix according to the formula (12);
Figure BDA0002784621520000052
from dijForming a correlation coefficient matrix D of the index weight vector;
Figure BDA0002784621520000053
fourthly, eliminating the weight of the expert with larger deviation degree according to the proportion:
Figure BDA0002784621520000061
in the formula: diIs the sum of the similarity degree of the expert i weighted opinions and the rest of the expert weighted opinions, diSmaller means greater degree of weight deviation for expert i's assessment;
fifthly, calculating the average value of the weight matrix column vector after screening
Figure BDA0002784621520000066
Is the weighted value of the evaluation index, and
Figure BDA0002784621520000062
as a preferred embodiment, the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight reduction method specifically includes: the anti-entropy weight method determines the index weight, namely:
normalizing indexes, converting the indexes into relative quantity data, and constructing a matrix X ═ Xij]y×nN is the index number, and y is the scheme number;
② standardizing matrix X to get B ═ Bij]y×n，
Figure BDA0002784621520000063
Calculating an evaluation index entropy value;
Figure BDA0002784621520000064
fourthly, calculating the weight value of the evaluation index:
Figure BDA0002784621520000065
as a preferred embodiment, the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight reduction method specifically includes: the subjective and objective weights are combined, namely:
introducing an entropy value variable, combining the index weight determined by the chromatography analysis method with the index weight determined by the anti-entropy weight method to obtain a comprehensive weight:
Figure BDA0002784621520000071
the invention achieves the following beneficial effects: aiming at solving the problem that the project of the regional comprehensive energy system needs to break the industry barrier, the prior power, gas, heat and cold production and supply and independent planning mode turns to the multi-form energy combined project, not only the technical breakthrough is realized, but also the policy, region and other boundaries are broken, the comprehensive utilization of various energy sources is realized, the method has the advantages that the method is used in a gradient mode, the consumption capacity of clean energy such as wind energy and solar energy is improved, the advantage complementation among energy sources is realized, the technical requirements of multiple energy requirements on a user side are met, the planning optimization of a comprehensive energy system is divided into three layers, the influence of multiple elements is comprehensively considered, the economic performance, the reliability, the environmental protection performance and the like of the comprehensive energy system are comprehensively optimized through a multi-objective function, more specific technical requirements, environment indexes and the like are introduced into the planning process through the establishment of a comprehensive evaluation index system, and therefore the planning scheme is more specific and reliable. The comprehensive energy system planning optimization model and the comprehensive evaluation index system in the patent can also be deleted, reduced and supplemented according to the requirements of different engineering designs, are flexible and reliable, and are easy to popularize.
Drawings
FIG. 1 is a topological schematic diagram of a preferred embodiment of the multi-element three-level based integrated energy system planning optimization method of the present invention;
FIG. 2 is a flow chart of the energy hub based integrated energy system planning of the present invention;
FIG. 3 is a flow chart of the multi-target particle swarm algorithm of the present invention;
FIG. 4 is a flow chart of the analytic hierarchy process-inverse entropy weight method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1: as shown in fig. 1, the invention provides a comprehensive energy system planning optimization method based on multiple elements and three levels, which comprises the following steps: firstly, performing primary planning and energy flow analysis on the comprehensive energy system based on an energy hub model, and establishing a comprehensive energy system planning optimization model; then solving the planning optimization model based on a multi-target particle swarm algorithm to obtain a Potore optimal solution set, and selecting and arranging to form an optimal planning scheme set; and finally, establishing a comprehensive evaluation index system according to engineering requirements, and evaluating and scoring the optimal planning scheme set by adopting an analytic hierarchy process-entropy weight inversion method to obtain an optimal planning scheme.
Example 2: the invention provides a comprehensive energy system planning optimization method based on multiple elements and three layers, which specifically comprises the following steps:
firstly, performing primary planning and energy flow analysis on the comprehensive energy system based on the energy hub model, and establishing a comprehensive energy system planning optimization model. As shown in fig. 2.
1. Objective function
Total cost of year
The total annual cost is the annual equal investment cost CinvAnnual running maintenance cost CopeSum of (a):
Ctotal＝Cinv+Cope (1)
annual equal investment cost CinvIs the value of the cost of the total investment cost of the system allocated to each year in the operating cycle by an equal amount, which is calculated as follows:
Figure BDA0002784621520000081
wherein I is the total number of devices; pi ratedIs the rated capacity of the equipment; c. CiIs the unit investment cost of the equipment; f. ofiMaintaining a cost factor for a fixed operation of the equipment; alpha is alphaiThe conversion coefficient of annual equal investment of equipment. Wherein:
Figure BDA0002784621520000082
in the formula: m is annual interest rate; and N is the service life of the equipment.
The operating costs include operating maintenance costs C of the plantomAnd fuel cost Cfuel：
Figure BDA0002784621520000091
In the formula: h is the total annual operating hours of the equipment; a isiThe unit variable operation maintenance cost of the equipment; pi hThe output value of the device i in the h hour is obtained; j is the total number of types of input energy;
Figure BDA0002784621520000092
the price of the jth energy source at the h hour;
Figure BDA0002784621520000093
is the amount of the jth energy source used in the h hour.
② annual pollution emission.
The emission of atmospheric pollutants is taken as an index for measuring the environmental benefit:
Figure BDA0002784621520000094
in the formula (I), the compound is shown in the specification,
Figure BDA0002784621520000095
the input emission coefficient of the j energy source;
Figure BDA0002784621520000096
the internal conversion emission coefficient for the j-th energy source. The pollutant types mainly comprise CO2、CO、SO2NOx, etc.
Consumption of energy once a year.
The annual energy consumption is used as an index for measuring the energy benefit:
Figure BDA0002784621520000097
in the formula, λjThe standard coal consumption conversion coefficient of the jth energy source.
2. Constraint conditions
Device configuration capacity constraint
The capacity of the device must be selected by considering the influence of the resource size, the size of the installable site, the maximum capacity which can be manufactured by the current technology, and the like, so that the following constraints exist:
0≤Ni≤Nmax (7)
Figure BDA0002784621520000098
in the formula: n is a radical ofmaxIs mountable to equipmentThe maximum number of cells; pi rated,minAnd Pi rated,maxRespectively, a selectable minimum and maximum value for the rated capacity of the device.
Operation state constraint:
Figure BDA0002784621520000101
in the formula
Figure BDA0002784621520000102
The operation state of the device i at the t hour.
Output power constraint:
Figure BDA0002784621520000103
in the formula: pi minAnd Pi maxRespectively, a minimum boundary value and a maximum boundary value of the output power of the device.
Energy balance constraint:
at each moment, its electricity, heat and cold need to satisfy the following constraints:
Figure BDA0002784621520000104
in the formula:
Figure BDA0002784621520000105
and
Figure BDA0002784621520000106
input electric power, output electric power of the device, and electric load demand of the user, respectively;
Figure BDA0002784621520000107
and
Figure BDA0002784621520000108
are respectivelyThe input thermal power, the output thermal power of the device and the thermal load demand of the user;
Figure BDA0002784621520000109
and
Figure BDA00027846215200001010
respectively, the input cold power, the output cold power of the device and the cold load demand of the user.
And step two, solving the planning optimization model based on the multi-target particle swarm algorithm to obtain a Potore optimal solution set, and forming a planning scheme set to be selected according to the optimal solution set, as shown in FIG. 3.
Inputting various parameters of a comprehensive energy system;
initializing particle members of the multi-target particle swarm algorithm;
calculating the fitness of each particle;
comparing the fitness values of all members and updating the front edge of the paletor;
fifthly, updating population members and starting to calculate from the step II until the maximum iteration number is reached.
And step three, establishing a comprehensive evaluation index system of the comprehensive energy system from three aspects of technology, economy and environment, as shown in table 1.
TABLE 1 comprehensive evaluation index system for comprehensive energy system
Figure BDA0002784621520000111
And step four, calculating the subjective and objective comprehensive weight of the index by adopting an analytic hierarchy process-entropy weight resisting method, and carrying out comprehensive scoring on each scheme, as shown in figure 4.
1. Determination of index weights by chromatography
Firstly, each level is compared pairwise to establish a relative importance matrix, the maximum eigenvalue and eigenvector of the matrix are solved, and index weight is obtained through consistency judgment.
Secondly, the fingers of each expert are obtained through the previous step and are calculated in sequenceWeighting, setting the weight vector of m experts as xiThe weight matrix C is formed as shown in equation (12).
Figure BDA0002784621520000121
And thirdly, calculating a correlation coefficient matrix according to the formula (12).
Figure BDA0002784621520000122
From dijAnd forming a correlation coefficient matrix D of the index weight vector.
Figure BDA0002784621520000123
Fourthly, eliminating the weight of the expert with larger deviation degree according to the proportion:
Figure BDA0002784621520000124
in the formula: diIs the sum of the similarity degree of the expert i weighted opinions and the rest of the expert weighted opinions, diSmaller weights indicate greater weight deviation from the expert i rating.
Fifthly, calculating the average value of the weight matrix column vector after screening
Figure BDA0002784621520000125
Is the weighted value of the evaluation index, and
Figure BDA0002784621520000126
2. determining index weight by inverse entropy weight method
Normalizing indexes, converting the indexes into relative quantity data, and constructing a matrix X ═ Xij]y×nN is the index number, and y is the scheme number.
② standardizing matrix X to get B ═ Bij]y×n，
Figure BDA0002784621520000131
And thirdly, calculating index entropy.
Figure BDA0002784621520000132
And fourthly, calculating the weight.
Figure BDA0002784621520000133
3. Combining subjective and objective weights
Introducing an entropy variable, combining subjective weighting and objective weighting together to obtain an evaluation index comprehensive weight:
Figure BDA0002784621520000134
the present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. The comprehensive energy system planning optimization method based on the multi-element three-level is characterized by comprising the following steps of:
analyzing equipment composition of the comprehensive energy system by adopting an energy hub model, combing an energy flow relation, primarily planning each unit equipment, establishing a multi-objective function according to engineering requirements, and establishing constraint conditions according to the energy flow relation and equipment performance to form a comprehensive energy system planning optimization model;
solving the comprehensive energy system planning optimization model by adopting a multi-objective particle swarm algorithm to obtain a Potore optimal solution set, and forming an optimal planning scheme set according to the optimal solution set;
and establishing a comprehensive evaluation index system according to engineering requirements, and evaluating and scoring the optimal planning scheme set by adopting an analytic hierarchy process-entropy weight inversion method to obtain an optimal planning scheme.
2. The method for optimizing a multi-element three-level-based integrated energy system planning according to claim 1, wherein the objective function comprises an annual total cost function, and specifically comprises:
the total annual cost is the annual equal investment cost CinvAnnual running maintenance cost CopeSum of (a):
Ctotal＝Cinv+Cope (1)
annual equal investment cost CinvThe total investment cost of the system is a cost value which is distributed to each year in the operation period by equal amount, and the calculation formula is as follows:
Figure FDA0002784621510000011
wherein I is the total number of devices; pi ratedIs the rated capacity of the equipment; c. CiIs the unit investment cost of the equipment; f. ofiMaintaining a cost factor for a fixed operation of the equipment; alpha is alphaiThe conversion coefficient for annual equal investment of equipment; wherein:
Figure FDA0002784621510000012
in the formula: m is annual interest rate; and N is the service life of the equipment.
The operating costs include operating maintenance costs C of the plantomAnd fuel cost Cfuel：
Figure FDA0002784621510000021
In the formula: h is the total annual operating hours of the equipment; a isiThe unit variable operation maintenance cost of the equipment; pi hThe output value of the device i in the h hour is obtained; j is the total number of types of input energy;
Figure FDA0002784621510000022
the price of the jth energy source at the h hour;
Figure FDA0002784621510000023
is the amount of the jth energy source used in the h hour.
3. The multi-element three-level-based integrated energy system planning and optimizing method according to claim 1, wherein the objective function includes an annual pollutant emission function, and specifically includes:
the emission of atmospheric pollutants is taken as an index for measuring the environmental benefit:
Figure FDA0002784621510000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002784621510000025
the input emission coefficient of the j energy source;
Figure FDA0002784621510000026
the internal conversion emission coefficient for the j-th energy source.
4. The multi-element three-layer based integrated energy system planning optimization method according to claim 1, wherein the objective function further includes once-a-year energy consumption, and specifically includes:
the annual energy consumption is used as an index for measuring the energy benefit:
Figure FDA0002784621510000027
in the formula, λjThe standard coal consumption conversion coefficient of the jth energy source.
5. The multi-element three-layer-based integrated energy system planning optimization method according to claim 1, wherein the constraint condition specifically includes:
device configuration capacity constraints:
the capacity of the device must be selected by considering the influence of the resource size, the size of the installable site, the maximum capacity which can be manufactured by the current technology, and the like, so that the following constraints exist:
0≤Ni≤Nmax (7)
Pi rated,min≤Pi rated≤Pi rated,max (8)
in the formula: n is a radical ofmaxThe maximum number of devices that can be installed; pi rated,minAnd Pi rated,maxRespectively, the minimum value and the maximum value which can be selected by the rated capacity of the equipment;
operation state constraint:
Figure FDA0002784621510000031
in the formula
Figure FDA0002784621510000032
The operation state of the equipment i in the t hour;
output power constraint:
Pi min≤Pi≤Pi max (10)
in the formula: pi minAnd Pi maxRespectively, a minimum boundary value and a maximum boundary value of the output power of the equipment;
energy balance constraint:
at each moment, its electricity, heat and cold need to satisfy the following constraints:
Figure FDA0002784621510000033
in the formula:
Figure FDA0002784621510000034
and
Figure FDA0002784621510000035
input electric power, output electric power of the device, and electric load demand of the user, respectively;
Figure FDA0002784621510000036
and
Figure FDA0002784621510000037
the input thermal power, the output thermal power of the device and the thermal load demand of the user are respectively;
Figure FDA0002784621510000038
and
Figure FDA0002784621510000039
respectively, the input cold power, the output cold power of the device and the cold load demand of the user.
6. The multi-element three-level-based integrated energy system planning optimization method according to claim 1, wherein the solving of the integrated energy system planning optimization model by using a multi-objective particle swarm algorithm to obtain a patorin optimal solution set, and the forming of the optimal planning scheme set according to the optimal solution set specifically comprises:
inputting various parameters of a comprehensive energy system;
initializing particle members of the multi-target particle swarm algorithm;
calculating the fitness of each particle;
comparing the advantages and disadvantages of the fitness values of all members, and updating the front edge of the paletor;
fifthly, updating population members and starting to calculate from the step II until the maximum iteration number is reached.
7. The multi-element three-level-based integrated energy system planning optimization method according to claim 1, wherein the establishing of the integrated evaluation index system according to the engineering requirements specifically comprises: and establishing a comprehensive evaluation index system of the comprehensive energy system from three aspects of technical indexes, economic indexes and environmental indexes.
8. The multi-element three-level-based integrated energy system planning optimization method according to claim 1, wherein the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight inversion method specifically comprises: the chromatographic analysis determines the index weight, namely:
firstly, establishing a relative importance matrix by pairwise comparison of each level, solving the maximum eigenvalue and eigenvector of the matrix, and obtaining index weight through consistency judgment;
secondly, calculating the index weight of each expert in sequence through the previous step, and setting the weight vector of m experts as xmForming a weight matrix C as shown in formula (12);
Figure FDA0002784621510000041
thirdly, calculating a correlation coefficient matrix according to the formula (12);
Figure FDA0002784621510000042
from dijForming a correlation coefficient matrix D of the index weight vector;
Figure FDA0002784621510000051
fourthly, eliminating the weight of the expert with larger deviation degree according to the proportion:
Figure FDA0002784621510000052
in the formula: diIs the sum of the similarity degree of the expert i weighted opinions and the rest of the expert weighted opinions, diSmaller presentation of expertsi the greater the degree of weight deviation of the rating;
fifthly, calculating the average value of the weight matrix column vector after screening
Figure FDA0002784621510000053
Is the weighted value of the evaluation index, and
Figure FDA0002784621510000054
9. the multi-element three-level-based integrated energy system planning optimization method according to claim 8, wherein the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight inversion method specifically comprises: the anti-entropy weight method determines the index weight, namely:
normalizing indexes, converting the indexes into relative quantity data, and constructing a matrix X ═ Xij]y×nN is the index number, and y is the scheme number;
② standardizing matrix X to get B ═ Bij]y×n，
Figure FDA0002784621510000055
Calculating an evaluation index entropy value;
Figure FDA0002784621510000056
fourthly, calculating the weight value of the evaluation index:
Figure FDA0002784621510000061
10. the multi-element three-level-based integrated energy system planning optimization method of claim 9, wherein the evaluating and scoring the preferred planning scheme set by using an analytic hierarchy process-entropy weight inversion method specifically comprises: the subjective and objective weights are combined, namely:
introducing an entropy value variable, combining the index weight determined by the chromatography analysis method with the index weight determined by the anti-entropy weight method to obtain a comprehensive weight:
Figure FDA0002784621510000062
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