CN113554296A - Multi-index evaluation method for planning of park comprehensive energy system - Google Patents

Multi-index evaluation method for planning of park comprehensive energy system Download PDF

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CN113554296A
CN113554296A CN202110805231.XA CN202110805231A CN113554296A CN 113554296 A CN113554296 A CN 113554296A CN 202110805231 A CN202110805231 A CN 202110805231A CN 113554296 A CN113554296 A CN 113554296A
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index
park
planning
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scheme
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周洪伟
邹盛
宗炫君
郭莉
杨凯
沈高锋
张敏
吴晨
李志鹏
孙永辉
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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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 multi-index evaluation method for park comprehensive energy system planning. The method comprises the steps of firstly, considering indexes such as investment cost, operation and maintenance cost, insufficient energy supply rate, environmental income, carbon emission reduction amount and comprehensive energy utilization rate, establishing a comprehensive evaluation index system, and performing evaluation on a park from different dimensions; then, the indexes are subjected to combined weighting by adopting an analytic hierarchy process and an improved entropy weight method, and the subjectivity and complexity in weighting are effectively reduced by combining the advantages of the two methods; secondly, scoring and sequencing the evaluation of the calculation results of the indexes by using a compromise solution sequencing method, and sequencing the evaluation results in an ascending order according to the scores of the schemes to select an optimal planning scheme; finally, the effectiveness and the rationality of the model and the method in the aspect of planning and evaluating the park integrated energy system are verified through specific embodiments, and the method can provide references for planning, equipment capacity configuration, unit combination modes and the like of the park integrated energy system.

Description

Multi-index evaluation method for planning of park comprehensive energy system
Technical Field
The invention belongs to an energy system planning evaluation method, and particularly relates to a multi-index evaluation method for park comprehensive energy system planning.
Background
The traditional energy distribution mode can not meet social requirements due to less consumption of renewable energy, greater environmental pollution and low comprehensive utilization efficiency of energy. To solve the above problems, the concept of a campus integrated energy system is in force. Compared with the traditional energy supply system, the development trend of the park comprehensive energy system in the energy field is more important. Therefore, evaluation and analysis of the park integrated energy system planning are of great significance for promoting the conversion of energy structures. However, the currently widely adopted planning and evaluating method for the park integrated energy system usually only considers a single index of the system, such as economy, too single evaluation level, or too single index, and cannot make perfect and objective guidance opinions on the planning of the system. In addition, in the weighting of the conventional multi-index evaluation method, the adopted weighting method is either too subjective, such as an Analytic Hierarchy Process (AHP), or difficult to realize due to high requirement on the original data of the system to be evaluated, such as an entropy weight method. In the process of processing the index data, the currently adopted method is mostly to compare the result of the scheme to be evaluated with the positive ideal point, so that individual deviation is easy to occur, and the evaluation result of the planning of the comprehensive energy system of the park is seriously influenced.
Therefore, when the planning scheme of the park comprehensive energy system is evaluated, a plurality of indexes of the system are considered, combined empowerment is utilized, and an objective evaluation method is adopted to score and order each scheme, so that the method has important practical significance.
Disclosure of Invention
The invention aims to provide a multi-index evaluation method for planning of a park integrated energy system, which realizes the optimal selection in alternative planning schemes of a plurality of park integrated energy systems, thereby reducing the system planning and operating cost, improving the system operating reliability, reducing the pollution degree to the environment and increasing the utilization rate of energy supply equipment.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows.
A multi-index evaluation method for park comprehensive energy system planning comprises the following steps:
(1) collecting park comprehensive energy system information, including time-of-use electricity price of the park, natural gas price of the park, park pollutant emission parameters, parameters of equipment to be configured in the park and various load information in the park;
(2) determining the equipment capacity, the unit combination mode, the planning period and the operation mode of the park to be planned, and determining various alternative planning schemes of the system to be evaluated according to the equipment capacity, the unit combination mode, the planning period and the operation mode;
(3) respectively establishing a multi-index evaluation model including investment cost, operation and maintenance cost, insufficient energy supply rate, environmental income, carbon emission reduction and comprehensive energy utilization rate;
(4) calculating each evaluation index of the park comprehensive energy system planning according to each index model, and processing the result, including forward and standardization;
(5) performing combined weighting on each index obtained by calculation by using an Analytic Hierarchy Process (AHP) -improved entropy weight method;
(6) and evaluating and scoring each scheme by using a compromise solution ranking method (VIKOR), and arranging the schemes in an ascending order according to the scores of the schemes to obtain an optimal planning scheme.
The multi-index evaluation method for the planning of the park integrated energy system comprises a park integrated energy system evaluation index model in the step (3), an index result processing method in the step (4), an index combination empowerment method in the step (5) and a planning scheme evaluation scoring and sorting method in the step (6).
In the above method, further, in step (3), the investment cost mathematical model is as follows:
Figure BDA0003166093650000021
in the formula:
Figure BDA0003166093650000022
and PiThe unit investment cost and the configuration capacity of the ith equipment are respectively;
in step (3), the operation and maintenance cost is as follows:
Figure BDA0003166093650000023
Rk＝(1+r)-k
in the formula: rkRepresents the discount coefficient of the k year;
Figure BDA0003166093650000024
and
Figure BDA0003166093650000025
respectively representing the time-of-use voltage and the natural gas price for the time period t,
Figure BDA0003166093650000026
and
Figure BDA0003166093650000027
respectively the electric power and the natural gas power delivered to the park by the external power grid and the natural gas grid at the time t,
Figure BDA0003166093650000028
and Pk,iThe maintenance cost and the consumed power of the ith equipment in the k year,r is the discount rate.
In step (3), the energy supply shortage rate indicates that the system should meet the supply of cooling, heating and power loads in the campus after being disconnected from the external energy network, that is, after the system is in island operation until the system is in grid-connected operation, and may be represented as:
Figure BDA0003166093650000029
in the formula: Δ WE,T、ΔWH,T、ΔWC,TEnergy supply deviation amounts of electric loads, heat loads and cold loads in the park in the T period are respectively; wE,T、WH,T、WC,TThe energy consumed by the electric load, the heat load and the cold load in the park in the T period is respectively. They can be represented as follows:
Figure BDA0003166093650000031
Figure BDA0003166093650000032
Figure BDA0003166093650000033
Figure BDA0003166093650000034
Figure BDA0003166093650000035
Figure BDA0003166093650000036
in the formula:
Figure BDA0003166093650000037
the electric load, the heat load and the cold load at the time t are respectively;
Figure BDA0003166093650000038
Figure BDA0003166093650000039
the electric powers of the heat pump, the electric boiler, the electric refrigerator and the photovoltaic equipment at the moment t are respectively;
Figure BDA00031660936500000310
Figure BDA00031660936500000311
the thermal powers of the adsorption refrigerator, the heat pump and the electric boiler at the time t are respectively;
Figure BDA00031660936500000312
respectively the cold power of the adsorption refrigerator and the electric refrigerator at the moment t;
Figure BDA00031660936500000313
respectively representing the charging power of the electricity storage device, the heat storage device and the cold storage device at the moment t;
Figure BDA00031660936500000314
respectively showing the energy discharge power of the electricity storage device, the heat storage device and the cold storage device at the time t.
In the step (3), the environmental benefit represents that from the viewpoint of environmental protection, the consumption of renewable energy power generation in a park is considered, and the consumption of CO generated by coal combustion power generation is converted into equivalent standard power generation2、SO2And NOxThe emission amount of the gases can be expressed by the following formula:
Figure BDA00031660936500000315
in the formula: p represents the kind of contaminant;
Figure BDA00031660936500000316
and
Figure BDA00031660936500000317
respectively representing the average annual generated power and the average annual utilization hours of the photovoltaic power generation equipment;
Figure BDA00031660936500000318
and
Figure BDA00031660936500000319
respectively representing the annual average generating power, the heating power and the annual average utilization hours of the cogeneration unit; m isiRepresenting the mass of the i-th contaminant;
Figure BDA00031660936500000320
and
Figure BDA00031660936500000321
respectively representing the environmental value and the punishment cost of the ith pollutant.
In step (3), the carbon emission reduction amount represents CO concentration in the energy supply mode of the multi-energy coupling park compared with the traditional thermal power concentrated power generation mode2Emission reduction, which can be expressed as:
Figure BDA0003166093650000041
in the formula:
Figure BDA0003166093650000042
and
Figure BDA0003166093650000043
respectively representing the annual carbon emission of the traditional energy supply scheme and the planning scheme to be evaluated. Wherein:
Figure BDA0003166093650000044
in the formula: pt gridRepresenting the power purchasing at any time t in the year;
Figure BDA0003166093650000045
indicating the quality of the carbon dioxide emissions.
In step (3), the energy comprehensive utilization rate represents the ratio of load consumption energy to electric energy and natural gas input to the park in a certain period of time, and can be represented as:
Figure BDA0003166093650000046
in the formula: xi represents the loss rate of the network in the process of transmitting power to the park;
Figure BDA0003166093650000047
indicating the gas purchase power at time t.
In the step (4), since the indexes can be divided into two types, namely cost type and benefit type, the invention uniformly converts the indexes into benefit type, namely, the cost type indexes are forward-oriented, and the formula of the forward-oriented indexes is as follows:
Figure BDA0003166093650000048
in the formula (f)ijAnd
Figure BDA0003166093650000049
respectively representing the original calculated value and the normalized value of the index;
Figure BDA00031660936500000410
and the maximum value of a certain index in all planning schemes is shown, and m represents the number of the schemes.
In the step (4), in order to facilitate index evaluation, the settlement result of each index is standardized, and the index standardization formula is as follows:
Figure BDA00031660936500000411
in the formula: x is the number ofijIndicating the value of the index after normalization.
In step (5), the combined hierarchal analysis-entropy weight improvement weighting method comprises the following steps:
5.1 analytic hierarchy Process
1) The decision expert scores each index through the judgment of the importance of each index to obtain a judgment matrix:
Figure BDA0003166093650000051
in the formula: n represents the number of indexes;
2) and (3) carrying out consistency check on the judgment matrix A:
Figure BDA0003166093650000052
in the formula: CR represents the consistency ratio, if CR is less than 0.1, the consistency check is passed, otherwise, the judgment matrix needs to be corrected; CI represents the consistency index:
Figure BDA0003166093650000053
in the formula: lambda [ alpha ]maxRepresenting the maximum eigenvalue of the decision matrix a.
RI represents the average random consistency index and can be determined by the following table:
Figure BDA0003166093650000054
3) determining the maximum eigenvalue lambda of the decision matrix AmaxA corresponding feature vector;
4) normalizing the feature vector to obtain the weight vector solved by the analytic hierarchy process
Figure BDA0003166093650000055
5.2 improving entropy weight method
1) According to the calculation result of each index, the characteristic proportion p of each scheme corresponding to each index is obtainedijAnd its entropy value Hj：
Figure BDA0003166093650000056
In the formula: 1,2, …, m, j 1,2, …, n;
2) according to entropy value HjWeighting each index
Figure BDA0003166093650000057
Figure BDA0003166093650000058
3) The weight vector found by the modified entropy weight method is
Figure BDA0003166093650000061
5.3 Combined empowerment of analytic hierarchy Process-modified entropy weight Process
1) Taking coupling vector [ theta ] according to index weight obtained by analytic hierarchy process and improved entropy weight method1,θ2]Comprises the following steps:
Figure BDA0003166093650000062
in the formula:
Figure BDA0003166093650000063
and
Figure BDA0003166093650000064
respectively representing the index j against the weight coefficient
Figure BDA0003166093650000065
And
Figure BDA0003166093650000066
the coupling weights of (a) may be expressed as follows:
Figure BDA0003166093650000067
2) weighting factor of index j according to coupling weight
Figure BDA0003166093650000068
And
Figure BDA0003166093650000069
coupling is performed, and the weight after coupling is obtained:
Figure BDA00031660936500000610
3) for omegajAnd (3) carrying out normalization treatment:
Figure BDA00031660936500000611
4) the weight vector obtained by the combined weighting method is ω ═ ω1,ω2,…,ωn]T。
In step (6), the method of compromise ranking comprises the steps of:
6.1, forming a decision matrix X by the normalized index values:
Figure BDA00031660936500000612
in the formula: m represents the number of alternative solutions, and n represents the number of indexes;
6.2 calculating to obtain the ideal points of each scheme under specific indexes
Figure BDA00031660936500000613
And negative ideal point
Figure BDA00031660936500000614
Figure BDA00031660936500000615
6.3 determining the average weighted distance S between each solution and the positive ideal pointiDistance from maximum weight Ri：
Figure BDA0003166093650000071
6.4 determining the composite weighted distance Q between each solution and the positive ideal pointi：
Figure BDA0003166093650000072
In the formula: v is an element of [0,1 ]]Decision strategy coefficients for decision makers to represent the average weighted distance SiDistance from maximum weight RiThe proportion in the integrated weighted distance;
Figure BDA0003166093650000073
and
Figure BDA0003166093650000074
the meanings represented are respectively as follows:
Figure BDA0003166093650000075
6.5 average weighted distance S according to each scheme, respectivelyiMaximum weighted distance RiAnd a synthetic weighted distance QiArranging the schemes in an ascending order;
6.6 will weight the distance Q according to the synthesisiOrdering firstScheme is marked as alpha1The second scheme is denoted as α2If, if
Figure BDA0003166093650000076
Then call scheme α1Favorable conditions are met; if it is
Figure BDA0003166093650000077
Or
Figure BDA0003166093650000078
Scheme alpha is called when at least one is arranged in ascending order1The stability condition is satisfied. When scheme alpha1When both favorable condition and stable condition are satisfied, the scheme alpha1The final compromise solution, namely the optimal scheme, is obtained; if only favorable conditions are satisfied, then α1And alpha2Are all optimal schemes; if only the stability condition is satisfied, then α in the ascending order scheme1,α2,…,αMAre all the optimal solutions, wherein1And alphaMNeed to satisfy
Figure BDA0003166093650000079
Has the advantages that: compared with the prior art, the substantive progress and the remarkable effect of the multi-index evaluation method for the park comprehensive energy system planning provided by the invention comprise the following points:
(1) the economic cost of the park is reduced, and the configuration capacity and external energy purchase of equipment are reduced;
(2) the consumption of new energy is promoted, and the operation reliability of the system is improved;
(3) the pollution degree to the environment is reduced, and the carbon emission is reduced;
(4) the energy utilization rate of the system is improved.
(5) The mathematical model and the like in the method can provide reference for the selection of the planning scheme of the park comprehensive energy system, and the optimal configuration and adjustment of the energy system are facilitated.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a block diagram of an exemplary embodiment of a park energy system;
FIG. 3 is a graph of load versus predicted photovoltaic output for a typical day of the park;
FIG. 4 is a plot of real-time electricity prices for a campus;
fig. 5 is a graph of the variation of the integrated weighted distance with the decision strategy coefficients.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the drawings and the specific embodiments, but the scope of the present invention is not limited to the embodiments.
The invention provides a multi-index evaluation method for park comprehensive energy system planning, which comprises the steps of firstly, considering a plurality of indexes such as investment cost, operation and maintenance cost, insufficient energy supply rate, environmental income, carbon emission reduction amount, comprehensive energy utilization rate and the like, and performing comprehensive evaluation on parks from different dimensions; then, an AHP-improved entropy weight method is adopted to carry out combined weighting on each index, so that the subjectivity and complexity in weighting are effectively reduced; and finally, scoring and sorting the evaluation of the calculation results of the indexes by using a compromise solution sorting method (VIKOR) to select an optimal planning scheme. Compared with a single index evaluation method or a multi-index evaluation method without combined empowerment, the method can improve the effectiveness and the practicability of the park planning evaluation analysis, guides the planning of the park comprehensive energy system, and is an effective means for perfecting the planning and the operation of the park comprehensive energy system.
The multi-index evaluation method for the park comprehensive energy system planning comprises a data acquisition unit, an index model construction unit, an index result processing unit, an index result empowerment unit and a planning scheme evaluation scoring and sorting unit.
The system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit acquires time-of-use electricity price of a park comprehensive energy system to be evaluated, natural gas price of the park, pollutant emission parameters of the park, parameters of equipment to be configured in the park and various load information in the park; the index model building unit builds various evaluation index models of the park comprehensive energy system; the index result processing unit is used for determining a forward and standardized method of the index result; the index result weighting unit is used for determining the weight information of each index; the planning scheme evaluation scoring and sorting unit establishes a multi-index evaluation method of the park comprehensive energy system.
Further, the flow of the multi-index evaluation method for the park integrated energy system planning is shown in fig. 1, and the method comprises the following steps:
(1) the method comprises the steps of collecting time-of-use electricity price, natural gas price, park pollutant discharge parameters, parameters of equipment to be configured in the park and various load information in the park of a park to be evaluated.
(2) And determining alternative planning schemes to be evaluated, including selection of the capacity of the equipment in the park, combination modes of all units, planning period, operation modes and the like.
(3) Establishing a park comprehensive energy system evaluation index model, specifically comprising an investment cost model, an operation and maintenance cost model, an energy supply shortage rate model, an environmental income model, a carbon emission reduction model and an energy comprehensive utilization rate model, wherein specific mathematical expressions of the models are shown as follows.
A. Investment cost model
Figure BDA0003166093650000091
In the formula:
Figure BDA0003166093650000092
and PiThe unit investment cost and the configuration capacity of the ith equipment are respectively;
B. operation maintenance cost model
Figure BDA0003166093650000093
Rk＝(1+r)-k
In the formula: rkRepresents the discount coefficient of the k year;
Figure BDA0003166093650000094
and
Figure BDA0003166093650000095
respectively representing the time-of-use voltage and the natural gas price for the time period t,
Figure BDA0003166093650000096
and
Figure BDA0003166093650000097
respectively the electric power and the natural gas power delivered to the park by the external power grid and the natural gas grid at the time t,
Figure BDA0003166093650000098
and Pk,iThe maintenance cost and the consumed power of the ith equipment in the k year are respectively, and r is the discount rate.
C. Model of insufficient energy supply rate
The insufficient energy supply rate indicates that the system should meet the supply of cooling, heating and power loads in the park after being disconnected with an external energy network, namely after being operated in an island and before being operated in a grid-connected mode, and can be expressed as follows:
Figure BDA0003166093650000099
in the formula: Δ WE,T、ΔWH,T、ΔWC,TEnergy supply deviation amounts of electric loads, heat loads and cold loads in the park in the T period are respectively; wE,T、WH,T、WC,TThe energy consumed by the electric load, the heat load and the cold load in the park in the T period is respectively. They can be represented as follows:
Figure BDA0003166093650000101
Figure BDA0003166093650000102
Figure BDA0003166093650000103
Figure BDA0003166093650000104
Figure BDA0003166093650000105
Figure BDA0003166093650000106
in the formula:
Figure BDA0003166093650000107
the electric load, the heat load and the cold load at the time t are respectively;
Figure BDA0003166093650000108
Figure BDA0003166093650000109
the electric powers of the heat pump, the electric boiler, the electric refrigerator and the photovoltaic equipment at the moment t are respectively;
Figure BDA00031660936500001010
Figure BDA00031660936500001011
the thermal powers of the adsorption refrigerator, the heat pump and the electric boiler at the time t are respectively;
Figure BDA00031660936500001012
respectively the cold power of the adsorption refrigerator and the electric refrigerator at the moment t;
Figure BDA00031660936500001013
respectively representing the charging power of the electricity storage device, the heat storage device and the cold storage device at the moment t;
Figure BDA00031660936500001014
respectively showing the energy discharge power of the electricity storage device, the heat storage device and the cold storage device at the time t.
D. Environmental revenue model
Environmental benefit expression from the viewpoint of environmental protection, the consumption of CO generated by renewable energy power generation conversion equivalent standard coal combustion power generation in a park is considered2、SO2And NOxThe emission amount of the gases can be expressed by the following formula:
Figure BDA00031660936500001015
in the formula: p represents the kind of contaminant;
Figure BDA00031660936500001016
and
Figure BDA00031660936500001017
respectively representing the average annual generated power and the average annual utilization hours of the photovoltaic power generation equipment;
Figure BDA00031660936500001018
and
Figure BDA00031660936500001019
respectively representing the annual average generating power, the heating power and the annual average utilization hours of the cogeneration unit; m isiRepresenting the mass of the i-th contaminant;
Figure BDA00031660936500001020
and
Figure BDA00031660936500001021
respectively representing the environmental value and the punishment cost of the ith pollutant.
E. Carbon emission reduction model
The reduction of carbon emission represents the CO concentration power generation mode compared with the traditional thermal power concentrated power generation mode by the energy supply mode of a multi-energy coupling park2Emission reduction, which can be expressed as:
Figure BDA0003166093650000111
in the formula:
Figure BDA0003166093650000112
and
Figure BDA0003166093650000113
respectively representing the annual carbon emission of the traditional energy supply scheme and the planning scheme to be evaluated. Wherein:
Figure BDA0003166093650000114
in the formula: pt gridRepresenting the power purchasing at any time t in the year;
Figure BDA0003166093650000115
indicating the quality of the carbon dioxide emissions.
F. Energy comprehensive utilization rate model
The energy comprehensive utilization rate represents the ratio of load consumption energy in a certain period of time to electric energy and natural gas input into a park, and can be expressed as:
Figure BDA0003166093650000116
in the formula: xi represents the loss rate of the network in the process of transmitting power to the park;
Figure BDA0003166093650000117
indicating the gas purchase power at time t.
(4) And index calculation result processing, including forward processing and standardization processing.
(41) Orthogonalization of
Because the indexes can be divided into a cost type and a profit type, the method uniformly converts the indexes into the profit type, namely, the cost type indexes are forward-oriented, and the forward-oriented formula of the indexes is as follows:
Figure BDA0003166093650000118
in the formula (f)ijAnd
Figure BDA0003166093650000119
respectively representing the original calculated value and the normalized value of the index;
Figure BDA00031660936500001110
and the maximum value of a certain index in all planning schemes is shown, and m represents the number of the schemes.
(42) Standardization
In order to facilitate index evaluation, the settlement result of each index is standardized, and the index standardization formula is as follows:
Figure BDA00031660936500001111
in the formula: x is the number ofijIndicating the value of the index after normalization.
(5) The index weighting specifically comprises an analytic hierarchy process, an improved entropy weight process and a combined weighting process of the analytic hierarchy process and the improved entropy weight process, and specifically comprises the following steps:
(51) analytic hierarchy process
1) The decision expert scores each index through the judgment of the importance of each index to obtain a judgment matrix:
Figure BDA0003166093650000121
in the formula: n represents the number of indexes;
2) and (3) carrying out consistency check on the judgment matrix A:
Figure BDA0003166093650000122
in the formula: CR represents the consistency ratio, if CR is less than 0.1, the consistency check is passed, otherwise, the judgment matrix needs to be corrected; CI represents the consistency index:
Figure BDA0003166093650000123
in the formula: lambda [ alpha ]maxRepresenting the maximum eigenvalue of the decision matrix a. RI represents the average random consistency index, which can be found by looking up the table:
Figure BDA0003166093650000124
3) determining the maximum eigenvalue lambda of the decision matrix AmaxA corresponding feature vector;
4) normalizing the feature vector to obtain the weight vector solved by the analytic hierarchy process
Figure BDA0003166093650000125
(52) Method of improving entropy weight
1) According to the calculation result of each index, the characteristic proportion p of each scheme corresponding to each index is obtainedijAnd its entropy value Hj：
Figure BDA0003166093650000126
In the formula: 1,2, …, m, j 1,2, …, n;
2) according to entropy value HjWeighting each index
Figure BDA0003166093650000131
Figure BDA0003166093650000132
3) The weight vector found by the modified entropy weight method is
Figure BDA0003166093650000133
(53) Combined empowerment of analytic hierarchy process-improved entropy weight method
1) Taking coupling vector [ theta ] according to index weight obtained by analytic hierarchy process and improved entropy weight method1,θ2]Comprises the following steps:
Figure BDA0003166093650000134
in the formula:
Figure BDA0003166093650000135
and
Figure BDA0003166093650000136
respectively representing the index j against the weight coefficient
Figure BDA0003166093650000137
And
Figure BDA0003166093650000138
the coupling weights of (a) may be expressed as follows:
Figure BDA0003166093650000139
2) weighting factor of index j according to coupling weight
Figure BDA00031660936500001310
And
Figure BDA00031660936500001311
coupling is performed, and the weight after coupling is obtained:
Figure BDA00031660936500001312
3) for omegajAnd (3) carrying out normalization treatment:
Figure BDA00031660936500001313
4) the weight vector obtained by the combined weighting method is ω ═ ω1,ω2,…,ωn]T。
(6) The evaluation scoring and ranking of the alternative planning schemes specifically comprises the following calculation processes.
(61) And (3) forming a decision matrix X by the normalized index values:
Figure BDA00031660936500001314
in the formula: m represents the number of alternative solutions, and n represents the number of indexes;
(62) calculating to obtain the ideal points of each scheme under specific indexes
Figure BDA0003166093650000141
And negative ideal point
Figure BDA0003166093650000142
Figure BDA0003166093650000143
(63) Determining an average weighted distance S between each solution and a positive ideal pointiDistance from maximum weight Ri：
Figure BDA0003166093650000144
(64) Determining the respective solutions and positive ideal pointsThe overall weighted distance Q betweeni：
Figure BDA0003166093650000145
In the formula: v is an element of [0,1 ]]Decision strategy coefficients for decision makers to represent the average weighted distance SiDistance from maximum weight RiThe proportion in the integrated weighted distance;
Figure BDA0003166093650000146
and
Figure BDA0003166093650000147
the meanings represented are respectively as follows:
Figure BDA0003166093650000148
(65) average weighted distance S according to each scheme respectivelyiMaximum weighted distance RiAnd a synthetic weighted distance QiArranging the schemes in an ascending order;
(66) will be in accordance with the overall weighted distance QiThe first scheme of the ordering is denoted as α1The second scheme is denoted as α2If, if
Figure BDA0003166093650000149
Then call scheme α1Favorable conditions are met; if it is
Figure BDA00031660936500001410
Or
Figure BDA00031660936500001411
Scheme alpha is called when at least one is arranged in ascending order1The stability condition is satisfied. When scheme alpha1When both favorable condition and stable condition are satisfied, the scheme alpha1The final compromise solution, namely the optimal scheme, is obtained; if only favorable conditions are satisfied, then α1And alpha2Are all bestThe optimal scheme is adopted; if only the stability condition is satisfied, then α in the ascending order scheme1,α2,…,αMAre all the optimal solutions, wherein1And alphaMNeed to satisfy
Figure BDA00031660936500001412
Based on the above method and steps, an application example is given below to fully illustrate the implementation process of the present invention and the significant technical effects thereof.
In this embodiment, 3 campus integrated energy systems shown in fig. 2 are taken as an example, and a planning scheme thereof is evaluated. There are 4 alternative planning schemes, which are respectively:
scheme 1: 3, planning the parks separately, wherein the planning period is 15 years, and the maximum load at the final stage of planning is used as the basis for the capacity configuration of the system equipment;
scheme 2: planning 3 parks separately, wherein the planning period is 15 years, but the whole planning period is divided into 3 small periods according to the load growth condition, the periods are respectively 3 years, 5 years and 7 years, the capacity of the equipment required to be configured in the current small period is planned according to the maximum load at the end of each small period, and the capacity which is configured before is considered in the following small period when the equipment is configured, and the capacity is superposed on the capacity;
scheme 3: the 3 parks are planned in a unified mode and operated in an interconnected mode, the maximum load at the last stage of planning is used as the basis of system equipment capacity configuration, and the power exchange condition possibly existing among the 3 parks needs to be considered;
scheme 4: the method comprises the steps of uniformly planning 3 parks, performing interconnection operation, considering the power exchange condition possibly existing among the 3 parks, dividing the whole planning period into 3 small periods according to the load increase condition, wherein the periods are respectively 3 years, 5 years and 7 years, and planning the equipment capacity required to be configured in the current small period by the maximum load at the end of each small period.
In order to reflect the operation conditions of all the equipment in the garden, a typical day is selected for each garden in summer. The load and PV predicted output per unit value curve of the park is shown in the figure by taking one day as a scheduling cycle and dividing each day into 24 time intervals3, real-time electricity rate data of the park is shown in fig. 4, the maximum load information of each stage is shown in table 1, and the natural gas price is 2.71 yuan/m3The calorific value is 9.7kWh/m3. The discount rate r is 8%, the net loss rate xi is 5%, and the decision strategy coefficient v is 0.5. The relevant economic and technical parameters of each plant are shown in tables 2 and 3.
To evaluate the reliability of the system, i.e. calculate the energy supply shortage index, assume that the system is disconnected from the external energy network at 11:00 and is connected to it again at 12: 00. I.e. in islanding operation during the 12 th period, during which the system is maintained in operation only by the photovoltaic power generation inside the park.
TABLE 1 maximum load at each stage of the park
Figure BDA0003166093650000151
Figure BDA0003166093650000161
TABLE 2 energy conversion device parameters
Figure BDA0003166093650000162
TABLE 3 energy storage device parameters
Figure BDA0003166093650000163
B. Analysis of results
The embodiment of the invention writes a model and an algorithm program based on a LINGO18.0 and Matlab2018b software platform. And calling a global solver in the LINGO18.0 to solve the indexes of the embodiment, and performing index processing and index weighting in Matlab2018b and scoring and sequencing the planning schemes by a VIKOR method.
According to the original numerical value of the calculation result of the corresponding index under each scheme, a decision matrix F can be obtained:
Figure BDA0003166093650000171
in the formula: lines 1 to 4 represent schemes 1 to 4, columns 1 to 6 represent investment cost (ten thousand yuan), operation and maintenance cost (ten thousand yuan), energy supply shortage, environmental benefit (ten thousand yuan), carbon emission reduction (ton) and energy comprehensive utilization rate.
By carrying out forward and standardization processing on the calculation result of the index, a standardized decision matrix X can be obtained:
Figure BDA0003166093650000172
according to the index weighting method provided by the invention, the index weight vectors obtained by the analytic hierarchy process and the improved entropy weight process are respectively omega1＝[0.4082,0.2041,0.1020,0.6080,0.0816,0.1361]TAnd omega2＝[0.3770,0.2231,0.1370,0.1677,0.0957,0.0005]T. Then, according to the proposed combination weighting method, the weights after coupling are obtained as ω ═ 0.2681,0.1459,0.0832,0.3496,0.0608,0.0924]T. The weights are normalized.
And (3) evaluating and scoring the alternative planning schemes by using a VIKOR method, wherein the calculation results of the average weighted distance, the maximum weighted distance and the comprehensive weighted distance of each scheme are shown in a table 5.
S, R and Q calculation results for each protocol of Table 5
Figure BDA0003166093650000173
It can be seen that when the overall weighting distances are arranged in the order of small to large, the solution 4 is the best and the solution 1 is the worst. The result is also the same in the ordering of the average weighted distance and the maximum weighted distance, so the stability condition is checked; in addition, it is suboptimal in the ranking of the composite weighted distancesThe planning scheme of (1) is scheme 2, because
Figure BDA0003166093650000174
Again by checking for favorable conditions. Therefore, the scheme 4 is the optimal alternative planning scheme.
This result is also more realistic. In the scheme 4, because the load increase condition in the construction period is considered, the equipment configuration capacity is gradually accumulated by taking a small period as a unit during planning, the equipment in the early stage of planning can be prevented from being idle, the investment and operation and maintenance cost is further reduced, the utilization rate of the equipment is improved, and the emission of pollutants can also be reduced by reducing the equipment configuration capacity; meanwhile, the power exchange conditions among the 3 parks are considered, when one system has power shortage, the surplus parks can be supplemented with power according to the running conditions of other parks without purchasing from an external energy network, so that the operation and maintenance cost is saved, and the energy utilization rate is improved.
The result of the above-mentioned integrated weighted distance Q is calculated from the decision strategy coefficient ν being 0.5. When the decision strategy coefficient v changes, the curve of the change of the comprehensive weighting distance of the 4 schemes with v is shown in fig. 5. As can be seen from the figure, the integrated weighted distance of scheme 4 is always the smallest, always 0, regardless of the variation of v; correspondingly, the comprehensive weighting distance of the scheme 1 is always the largest and is always 1; and the comprehensive weighting distance of the scheme 2 and the scheme 3 is gradually reduced along with the increase of v. Comparing the details of scheme 2 with scheme 3: scheme 2 adopts 3 independent but multistage planning's of garden mode, and scheme 3 adopts 3 mutual connection of garden but single-stage planning's mode, and two schemes respectively have the advantage and the shortcoming, but on the whole, scheme 2 not only can improve economic nature, energy efficiency nature, can also improve the environmental protection nature in garden simultaneously. Furthermore, it can also be seen from the figure that the integrated weighting distance Q of scheme 2 decreases faster as the decision strategy coefficient v increases. In summary, it can be seen that scheme 2 is superior to scheme 3, and in the case that scheme 4 is not implemented, scheme 2 can be selected as a suboptimal alternative planning scheme.
Through the analysis, the multi-index evaluation method provided by the invention can make powerful, reasonable, real and objective evaluation for the planning of the park comprehensive energy system, and further make reference for the actual park planning to guide the actual production construction.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (6)

1. A multi-index evaluation method for planning of a park comprehensive energy system is characterized by comprising the following steps:
(1) collecting park comprehensive energy system information, including time-of-use electricity price of the park, natural gas price of the park, park pollutant emission parameters, parameters of equipment to be configured in the park and various load information in the park;
(2) determining the equipment capacity, the unit combination mode, the planning period and the operation mode of a park to be planned, and determining an alternative planning scheme of a system to be evaluated;
(3) respectively establishing evaluation index models including investment cost, operation and maintenance cost, insufficient energy supply rate, environmental income, carbon emission reduction and comprehensive energy utilization rate;
(4) calculating each evaluation index of the park comprehensive energy system planning according to each index model, and processing the result, including forward conversion and standardization;
(5) performing combined weighting on each index obtained by calculation by using an analytic hierarchy process-an improved entropy weight method;
(6) and evaluating and scoring each scheme by using a compromise solution sorting method, and arranging the schemes in an ascending order according to the scores of the schemes to obtain an optimal planning scheme.
2. The multi-index evaluation method for the campus integrated energy system planning of claim 1, wherein:
in step (3), the mathematical expression of the investment cost is as follows:
Figure FDA0003166093640000011
in the formula:
Figure FDA0003166093640000012
and PiThe unit investment cost and the configuration capacity of the ith equipment are respectively;
the mathematical expression of the operation and maintenance cost is as follows:
Figure FDA0003166093640000013
Rk＝(1+r)-k
in the formula: rkRepresents the discount coefficient of the k year;
Figure FDA0003166093640000014
and
Figure FDA0003166093640000015
respectively representing the time-of-use voltage and the natural gas price for the time period t,
Figure FDA0003166093640000016
and
Figure FDA0003166093640000017
respectively the electric power and the natural gas power delivered to the park by the external power grid and the natural gas grid at the time t,
Figure FDA0003166093640000018
and Pk,iThe maintenance cost and the consumed power of the ith equipment in the k year are respectively, and r is the discount rate;
the insufficient energy supply rate indicates that the system can meet the supply of cooling, heating and power loads in a park after being disconnected with an external energy network, namely after the system is in isolated island operation and before the system is in grid-connected operation, and the mathematical expression of the insufficient energy supply rate is as follows:
Figure FDA0003166093640000021
in the formula: Δ WE,T、ΔWH,T、ΔWC,TEnergy supply deviation amounts of electric loads, heat loads and cold loads in the park in the T period are respectively; wE,T、WH,T、WC,TEnergy consumed by electric load, heat load and cold load in the park in the T period is respectively; wherein the content of the first and second substances,
Figure FDA0003166093640000022
Figure FDA0003166093640000023
Figure FDA0003166093640000024
Figure FDA0003166093640000025
Figure FDA0003166093640000026
Figure FDA0003166093640000027
in the formula:
Figure FDA0003166093640000028
the electric load, the heat load and the cold load at the time t are respectively;
Figure FDA0003166093640000029
Figure FDA00031660936400000210
the electric powers of the heat pump, the electric boiler, the electric refrigerator and the photovoltaic equipment at the moment t are respectively;
Figure FDA00031660936400000211
Figure FDA00031660936400000212
the thermal powers of the adsorption refrigerator, the heat pump and the electric boiler at the time t are respectively;
Figure FDA00031660936400000213
respectively the cold power of the adsorption refrigerator and the electric refrigerator at the moment t;
Figure FDA00031660936400000214
respectively representing the charging power of the electricity storage device, the heat storage device and the cold storage device at the moment t;
Figure FDA00031660936400000215
respectively representing the energy discharge power of the electricity storage device, the heat storage device and the cold storage device at the moment t;
the environmental benefit is the emission of waste gas generated by the renewable energy power generation conversion standard coal combustion power generation in the park, and the waste gas comprises CO2、SO2And NOxThe exhaust emission calculation model expression is as follows:
Figure FDA00031660936400000216
in the formula: p represents the kind of contaminant;
Figure FDA00031660936400000217
and
Figure FDA00031660936400000218
respectively representing the average annual generated power and the average annual utilization hours of the photovoltaic power generation equipment;
Figure FDA00031660936400000219
and
Figure FDA00031660936400000220
respectively representing the annual average generating power, the heating power and the annual average utilization hours of the cogeneration unit; m isiRepresenting the mass of the i-th contaminant;
Figure FDA0003166093640000031
and
Figure FDA0003166093640000032
respectively representing the environmental value and the punishment cost of the ith pollutant;
the carbon emission reduction amount represents the CO concentration power generation mode compared with the traditional thermal power concentration power generation mode by the energy supply mode of a multi-energy coupling park2Emission reduction, the mathematical expression of which is as follows:
Figure FDA0003166093640000033
in the formula:
Figure FDA0003166093640000034
and
Figure FDA0003166093640000035
respectively representing the annual carbon emission of a traditional energy supply scheme and a planning scheme to be evaluated;
wherein the content of the first and second substances,
Figure FDA0003166093640000036
in the formula: pt gridRepresenting the power purchasing at any time t in the year;
Figure FDA0003166093640000037
represents the emission quality of carbon dioxide;
the comprehensive utilization rate of the energy represents the ratio of load consumption energy in a certain period of time to electric energy and natural gas input into a park, and the mathematical model expression is as follows:
Figure FDA0003166093640000038
in the formula: xi represents the loss rate of the network in the process of transmitting power to the park;
Figure FDA0003166093640000039
indicating the gas purchase power at time t.
3. The multi-index evaluation method for the campus integrated energy system planning of claim 1, wherein: the indexes in the step (4) of the method are divided into a cost type and a profit type, the method uniformly converts the indexes into the profit type, namely, the cost type indexes are forward-oriented, and the index forward-oriented formula is as follows:
Figure FDA00031660936400000310
in the formula: f. ofijAnd
Figure FDA00031660936400000311
respectively represent the original indexesCalculating the numerical value and the numerical value after the forward conversion;
Figure FDA00031660936400000312
and the maximum value of a certain index in all planning schemes is shown, and m represents the number of the schemes.
4. The multi-index evaluation method for the campus integrated energy system planning of claim 1 or 3, wherein: in the step (4), the settlement result of each index is standardized, and the index standardization formula is as follows:
Figure FDA00031660936400000313
in the formula: x is the number ofijIndicating the value of the index after normalization.
5. The multi-index evaluation method for the campus integrated energy system planning of claim 1, wherein: in step (5), the combined hierarchal analysis-entropy weight improvement weighting method comprises the following steps:
5.1 analytic hierarchy Process
1) The decision expert scores each index through the judgment of the importance of each index to obtain a judgment matrix:
Figure FDA0003166093640000041
in the formula: n represents the number of indexes;
2) and (3) carrying out consistency check on the judgment matrix A:
Figure FDA0003166093640000042
in the formula: CR represents the consistency ratio, if CR is less than 0.1, the consistency check is passed, otherwise, the judgment matrix needs to be corrected; CI represents the consistency index:
Figure FDA0003166093640000043
in the formula: lambda [ alpha ]maxThe maximum eigenvalue of the decision matrix a is represented, RI represents the average random consistency index, and is determined by the following table:
Figure FDA0003166093640000044
3) determining the maximum eigenvalue lambda of the decision matrix AmaxA corresponding feature vector;
4) normalizing the feature vector to obtain the weight vector solved by the analytic hierarchy process
Figure FDA0003166093640000045
5.2 improving entropy weight method
1) According to the calculation result of each index, the characteristic proportion p of each scheme corresponding to each index is obtainedijAnd its entropy value Hj：
Figure FDA0003166093640000046
In the formula: 1,2, …, m, j 1,2, …, n;
2) according to entropy value HjWeighting each index
Figure FDA0003166093640000051
Figure FDA0003166093640000052
3) The weight vector found by the modified entropy weight method is
Figure FDA0003166093640000053
5.3 Combined empowerment of analytic hierarchy Process-modified entropy weight Process
1) Taking coupling vector [ theta ] according to index weight obtained by analytic hierarchy process and improved entropy weight method1,θ2]Comprises the following steps:
Figure FDA0003166093640000054
in the formula:
Figure FDA0003166093640000055
and
Figure FDA0003166093640000056
respectively representing the index j against the weight coefficient
Figure FDA0003166093640000057
And
Figure FDA0003166093640000058
the coupling weights of (a) may be expressed as follows:
Figure FDA0003166093640000059
2) weighting factor of index j according to coupling weight
Figure FDA00031660936400000510
And
Figure FDA00031660936400000511
coupling is performed, and the weight after coupling is obtained:
Figure FDA00031660936400000512
3) for omegajAnd (3) carrying out normalization treatment:
Figure FDA00031660936400000513
4) the weight vector obtained by the combined weighting method is ω ═ ω1,ω2,…,ωn]T。
6. The multi-index evaluation method for the campus integrated energy system planning of claim 1, wherein: in step (6), the method of compromise ranking comprises the steps of:
6.1, forming a decision matrix X by the normalized index values:
Figure FDA00031660936400000514
in the formula: m represents the number of alternative solutions, and n represents the number of indexes;
6.2 calculating to obtain the ideal points of each scheme under specific indexes
Figure FDA0003166093640000061
And negative ideal point
Figure FDA0003166093640000062
Figure FDA0003166093640000063
6.3 determining the average weighted distance S between each solution and the positive ideal pointiDistance from maximum weight Ri：
Figure FDA0003166093640000064
6.4 determining the overall weighted distance Q between each solution and the positive ideal pointi：
Figure FDA0003166093640000065
In the formula: v is an element of [0,1 ]]Decision strategy coefficients for decision makers to represent the average weighted distance SiDistance from maximum weight RiThe proportion in the integrated weighted distance;
Figure FDA0003166093640000066
and
Figure FDA0003166093640000067
the meanings represented are respectively as follows:
Figure FDA0003166093640000068
6.5 average weighted distance S according to each scheme, respectivelyiMaximum weighted distance RiAnd a synthetic weighted distance QiArranging the schemes in an ascending order;
6.6 will weight the distance Q according to the synthesisiThe first scheme of the ordering is denoted as α1The second scheme is denoted as α2If, if
Figure FDA0003166093640000069
Then call scheme α1Favorable conditions are met; if it is
Figure FDA00031660936400000610
Or
Figure FDA00031660936400000611
Scheme alpha is called when at least one is arranged in ascending order1The stability condition is satisfied; when scheme alpha1When both favorable condition and stable condition are satisfied, the scheme alpha1The final compromise solution is the optimal scheme; if only favorable conditions are satisfied, then α1And alpha2Are all optimal schemes; if only the stability condition is satisfied, then α in the ascending order scheme1,α2,…,αMAre all the optimal solutions, wherein1And alphaMNeed to satisfy
Figure FDA00031660936400000612
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