CN114418160A - Park multi-energy system optimal scheduling method based on comprehensive evaluation system - Google Patents

Park multi-energy system optimal scheduling method based on comprehensive evaluation system Download PDF

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CN114418160A
CN114418160A CN202111350876.5A CN202111350876A CN114418160A CN 114418160 A CN114418160 A CN 114418160A CN 202111350876 A CN202111350876 A CN 202111350876A CN 114418160 A CN114418160 A CN 114418160A
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崔颢骞
解飞
张国庆
卜洪亮
李璐
于浩
周夕然
王丹
张阳
于田
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State Grid Fuxin Electric Power Supply Co
State Grid Corp of China SGCC
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Abstract

A park multi-energy system optimal scheduling method based on a comprehensive evaluation system is characterized in that each unit model is established by analyzing the structure and composition of a multi-energy system and the charging and discharging behaviors of electric vehicles, a multi-target optimal scheduling model containing the multi-energy system of the electric vehicles is further provided in consideration of combination weight, so that the economic cost is lowest, the carbon emission is lowest, the utilization rate of clean energy is highest, and the low carbon of the multi-energy system is realized by a large-scale access system of the electric vehicles; the comprehensive evaluation index of the multi-energy system is selected, the comprehensive evaluation system of the multi-energy system is established, the system is evaluated according to the operation mode of the multi-energy system, and the operation mode of the multi-energy system is further optimized by taking the evaluation result as input.

Description

Park multi-energy system optimal scheduling method based on comprehensive evaluation system
Technical Field
The invention relates to a park multi-energy system optimal scheduling method based on a comprehensive evaluation system.
Background
At present, the economy in the world is developed at a high speed, and fossil energy is used in a large amount, so that not only is the energy crisis caused, but also a large amount of harmful gas is generated, and environmental problems such as climate warming are caused. Therefore, clean energy such as wind power, photovoltaic and the like is vigorously developed, and the problems of energy and environment can be effectively solved. However, due to the insufficient flexibility of the power system and the strong randomness and fluctuation of the output of clean energy, serious wind and light abandoning phenomena are caused. The multi-energy system strengthens the relation among the electric, gas, hot and cold systems, realizes energy complementation and efficient utilization, is beneficial to enhancing the flexibility of the system and promotes the energy system to transform to diversification, cleanness and low carbon. Therefore, performing the multi-energy system optimization scheduling research is a main approach to solve the above problems. The planning and construction of the multi-energy system can drive the development and the transformation of the energy industry, and the whole process from investment construction to production operation of related projects can bring remarkable influence on national economy, energy production and utilization modes, environment and the like. In view of relatively few researches on comprehensive evaluation of multi-energy projects at the present stage, a multi-energy profit evaluation mechanism is further perfected, an economic and technical evaluation system is built, an evaluation method is perfected, evaluation indexes are refined, positive guiding significance is provided for feasibility analysis and landing construction of the multi-energy projects, and wide research space is provided.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a campus multi-energy system optimization scheduling method based on a comprehensive evaluation system, which can improve the renewable energy consumption capability of the multi-energy system, reduce the operation cost and improve the adjustment capability of the multi-energy system.
The technical scheme of the invention is as follows:
a campus multi-energy system optimization scheduling method based on a comprehensive evaluation system is carried out according to the following steps:
step 1, establishing a multi-energy system architecture, modeling each unit in the architecture, analyzing the operation mechanism of a unit, determining the energy flow direction and different forms of energy coupling modes, and forming an electric-heat-gas-cold network;
step 2, establishing a multi-objective function with the lowest economic cost, the lowest carbon dioxide emission and the highest wind energy and light energy utilization rate of the multi-energy system, combining an analytic hierarchy process and an entropy weight method, formulating combined weight for the multi-objective function, and optimizing a scheduling result;
step 3, establishing an evaluation index and evaluation quantitative grading for each multi-target function, determining the weight of the evaluation index by adopting an analytic hierarchy process, inputting the optimized scheduling result of the step 2 into an evaluation system of the comprehensive energy system, evaluating the multi-target function according to the evaluation index weight and evaluation index evaluation data by utilizing a fuzzy comprehensive evaluation method, meeting the requirement of evaluation quantitative grading, and completing weight distribution; otherwise, returning to the step 2 for circulation.
Further, the electricity, gas and heat supply networks are coupled through a cogeneration unit, and the renewable energy generator set is accessed into the system; the combined heat and power generation unit of the coupling node comprises a gas turbine, a gas boiler, an electric refrigerator and an absorption refrigerator, and the renewable energy generator set comprises a wind generating set and a photovoltaic generator set; the energy storage device is accessed to the power grid for bidirectional communication;
(1) combined heat and power generation unit model
Figure RE-GDA0003537573710000021
In the formula, Pe,CHP(t)、Ph,CHP(t) respectively outputting electric power and thermal power (kW) for the CHP unit at the moment t; etaCHPThe generating efficiency of the CHP unit is obtained; alpha (t) is the proportion of the natural gas injected into the CHP unit at the moment t; qlIs the low heating value of natural gas; q. q.sgas(t) is the amount of natural gas used by the CHP unit at the moment t; gamma rayh,CHPThe thermoelectric conversion rate of the CHP unit;
(2) absorption type refrigerator model
The absorption refrigerator is a main source of cold load, absorbs the waste heat of the CHP unit and the gas boiler to obtain cold energy, and outputs cold power as follows:
Pc,AR(t)＝COPARPh,AR(t)
in the formula, Pc,AR(t) is the absorption chiller output cold power (kW), COPARFor absorption refrigeration machine refrigeration efficiency (kW), Ph,AR(t) is the absorption thermal power (kW) of the absorption refrigerator;
(3) wind power generation model
Figure RE-GDA0003537573710000022
In the formula, PW(t) fan output power (kW) at time t, PWNRated power (kW) of the fan, V is actual wind speed, and V isciFor cutting into wind speed, VNRated wind speed, VcoCutting out the wind speed;
(4) photovoltaic power generation model
Figure RE-GDA0003537573710000023
In the formula, PPV(t) photovoltaic array output power (kW), η at time tPVConversion of solar energy into electrical energy efficiency for photovoltaic arrays, SPVFor photovoltaic array solar panel area (m)3)，
Figure RE-GDA0003537573710000024
The unit area illumination intensity (lx) of the photovoltaic array at the time t;
(5) electric refrigerator model
The electric refrigerator mainly converts electric power into cold power to supply to cold load, and the output cold power is as follows:
Pc,EC(t)＝COPECPe,EC(t)
in the formula, Pc,EC(t) the output cold power (kW) of the electric refrigerator at time t, COPECFor the refrigeration efficiency of electric refrigerators, Pe,EC(t) the electric power (kW) consumed by the electric refrigerator at the moment t;
(6) energy storage device model
When the power grid load is in low tide, the energy storage device can be used for charging the power grid load; when the battery of the energy storage device is fully charged, the energy storage device can reversely transmit the electric energy back to the power grid; the energy storage device charging and discharging model and the energy constraint formula are as follows;
Figure RE-GDA0003537573710000031
Emin≤Ei(t)≤Emax
in the formula, Ei(t) is the electric quantity (kW) of the ith energy storage device at the moment t, mueIs the self-discharge rate, eta, of the battery of the energy storage devicein、ηoutCharging and discharging efficiencies, P, of the energy storage device, respectivelyi EV,in(t)、Pi EV,out(t) is the charging and discharging power (kW) of the ith energy storage device at the moment t, Emin、EmaxThe minimum and maximum battery capacity (kW) of the energy storage device are respectively.
Further, establishing a multi-objective function and constraint conditions:
(1) multi-energy system operating cost in multi-objective function
The operating economy cost of the multi-energy system is divided into equipment power generation cost, energy storage device charging and discharging cost, microgrid-grid interaction cost and microgrid-gas grid interaction cost;
minCJ＝Cc+Ce+Cg+CEV
in the formula, CJFor economic comprehensive cost (yuan/kW), CcFor unit operating costs (yuan/kW), CeCost (yuan/kW) for interaction between microgrid and power grid, CgCost (yuan/kW), C, for interaction between microgrid and gas networkEVThe cost (yuan/kW) for charging and discharging the energy storage device;
Figure RE-GDA0003537573710000032
in the formula, CGTFor the unit operating cost (yuan/kW), C of the gas turbineGBIs the unit operation cost (yuan/kW), C of the gas boilerECThe unit operation cost (yuan/kW) of the electric refrigerator is CARFor the unit operating cost (yuan/kW), C of the absorption refrigeratorWIs unit operating cost (yuan/kW), C of the fanPVFor the operating cost (yuan/kW), P, of the photovoltaic power generation unite,GTConsuming electric power (kW), P, for a gas turbinee,GBConsuming electrical power (kW) for the gas boiler;
Figure RE-GDA0003537573710000033
in the formula, ce,tCost (yuan/kW) of electricity purchasing and selling unit from micro grid to power grid, Pe,tPower for interaction of the microgrid and the power grid;
Figure RE-GDA0003537573710000034
in the formula, cg,tCost of gas purchase (yuan/kW), P, from microgrid to gas gridg,tPower for microgrid and gas grid interaction;
Figure RE-GDA0003537573710000041
in the formula, Cin、CoutThe unit charging and discharging costs (yuan/kW), P, of the energy storage device are respectivelyEV,in(t)、PEV,out(t) total charging and discharging power (kW) for the energy storage device;
(2) minimum carbon dioxide emission in multi-objective function
The formula of the emission amount of carbon dioxide is as follows:
Figure RE-GDA0003537573710000042
in the formula, λe、λgRespectively is the carbon dioxide emission coefficient under unit electric power and the carbon dioxide emission coefficient of unit volume of natural gas; pg,GT(t)、Pg,GB(t) gas power (kW) consumed at the moment t of the gas turbine and the gas boiler respectively;
(3) wind energy utilization in multi-objective functions
The wind power utilization rate formula is as follows:
Figure RE-GDA0003537573710000043
in the formula etaWIs wind power utilization rate, P'WOutputting electric power (kW) for maximum wind power generation at the moment t;
(4) light energy utilization in multi-objective functions
The photovoltaic power generation utilization rate formula is as follows:
Figure RE-GDA0003537573710000044
in the formula etaPVIs photovoltaic power generation utilization rate, P'PVOutputting electric power (kW) for maximum photovoltaic power generation at the moment t;
the integrated objective function formula is as follows:
Figure RE-GDA0003537573710000045
wherein Z is the overall target value, QiIs a combined weight value;
the multi-energy system constraint comprises an electric load constraint, a heat load constraint, a cold load constraint and a gas quantity constraint, and the electric load constraint, the heat load constraint, the cold load constraint and the gas quantity constraint conditions are as follows:
PW+PPV+Pe,t+Pe,GT-Pe,EC-Le＝0
Ph,GB+Ph,GT-Ph,AR-Lh＝0
Pg,t-Pg,GT-Pg,GB-Lg＝0
PC,EC+PC,AR-Lc＝0
in the formula, Ph,GBGenerating thermal power (kW), P for gas fired boilersh,GTGenerating thermal power (kW), L for a gas turbinee、Lh、Lg、 LcRespectively an electric load, a heat load, a gas load and a cold load (kW) of the multi-energy system.
Further, in step 2, the multi-energy system multi-target function weight is formulated by comprehensively formulating subjective weight obtained by an analytic hierarchy process and objective weight obtained by an entropy weight method, and the multi-energy system multi-target function weight QiThe formula is as follows:
Qi＝α×vi+(1-α)wi
labeling the objective weight of the ith factor as viMarking the subjective weight of the ith factor as wiα is a coefficient of the combining weight;
the analytical hierarchy method comprises the following steps:
(1) constructing an expert judgment matrix, and scaling by a pairwise comparison method;
Figure RE-GDA0003537573710000051
a1representing the economic cost correspondence scale, a2Represents a scale corresponding to carbon dioxide emission, a3Representing the wind energy utilisation corresponding scale, a4Representing a photovoltaic utilization rate correspondence scale;
(2) calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000052
(3) Consistency check
1) Calculating a consistency index CI
Figure RE-GDA0003537573710000053
Wherein λ ismaxIs the maximum eigenvalue of the matrix A;
2) the corresponding average random consistency index RI is 0.9;
3) calculating the consistency ratio CR
Figure RE-GDA0003537573710000061
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the entropy weight method comprises the following steps:
(1) normalization processing of indexes:
homogenizing measurement units of the multi-target function, and adopting different algorithms to perform data standardization processing on positive and negative target functions:
forward objective function:
Figure RE-GDA0003537573710000062
negative objective function:
Figure RE-GDA0003537573710000063
for convenience, normalized data x'tiIs still marked as xti；
(2) Calculating the proportion of the t sample value in the index under the ith index:
Figure RE-GDA0003537573710000064
(3) calculating the entropy value of the i index:
Figure RE-GDA0003537573710000065
wherein k is 1/ln (24) > 0; satisfies ei≥0；
(4) Computing information entropy redundancy (difference):
di＝1-ei,i＝1,2,3,4
(5) calculating the weight of each index:
Figure RE-GDA0003537573710000066
further, an evaluation index weight is formulated by utilizing an analytic hierarchy process;
(1) and constructing an expert judgment matrix, and performing calibration by a pairwise comparison method.
Figure RE-GDA0003537573710000071
(2) Calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000072
(3) Consistency check
1) Calculating a consistency index CI
Figure RE-GDA0003537573710000073
2) Searching corresponding average random consistency index n is 1, and RI is 0; n is 2, RI is 0; n is 3, RI is 0.58; n is 4, RI is 0.90; n is 5, RI is 1.12; n is 6, RI is 1.24; n is 7, RI is 1.32; n is 8, RI is 1.41; n is 9, RI is 1.45;
3) calculating the consistency ratio CR
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the fuzzy comprehensive evaluation method comprises the following steps:
(1) determining factor domains of evaluation objects
Let N be evaluation indexes, X ═ X1,X2,...Xn)；
(2) Determining comment level discourse domain
Let A ═ W1,W2…), each level may correspond to a fuzzy subset, i.e., a set of levels;
(3) establishing a fuzzy relationship matrix
After the rank fuzzy subset is constructed, the evaluated objects are subjected to one-by-one treatment according to each factor XiThe quantification is carried out, namely the membership (R | X) of the evaluated object to the grade fuzzy subset from the single factor is determinedi) And then obtaining a fuzzy relation matrix
Figure RE-GDA0003537573710000074
Wherein, the ith row and the jth column element represent a certain evaluated object XiFrom the aspect of factor to WjMembership of the rank-fuzzy subset;
(4) determining weight vectors for evaluation factors
Determining a weight vector of an evaluation factor according to the evaluation index weight determined by the analytic hierarchy process and the evaluation index data: u ═ U1,u2,…)；
(5) Fuzzy comprehensive evaluation result vector
And synthesizing the U and the R of the evaluation index to obtain a fuzzy comprehensive evaluation result vector B of the evaluation index, namely:
Figure RE-GDA0003537573710000081
wherein, biRepresents that the evaluated object is on W as a wholejDegree of membership of the rank-fuzzy subset;
(6) ranking the composite score value
And carrying out comprehensive scoring according to the fuzzy comprehensive evaluation result vector, and judging whether the evaluation quantitative grading requirement is met.
The invention has the beneficial effects that:
the invention provides a park multi-energy system optimization scheduling method based on a comprehensive evaluation system, which breaks the barriers among energy subsystems by establishing a multi-energy system optimization scheduling model, realizes the complementation and collaborative optimization of various energy sources, can promote the consumption of clean energy sources, reduce the environmental pollution, realize the peak clipping and valley filling of the energy sources and improve the regulation capacity of the multi-energy system by adjusting the weight of a multi-objective function.
Drawings
FIG. 1 is a flow chart of optimal scheduling of a park multi-energy system based on a comprehensive evaluation system according to the present invention;
FIG. 2 is a flow chart of the comprehensive energy system evaluation combining the analytic hierarchy process and the fuzzy comprehensive evaluation of the present invention;
FIG. 3 is a schematic diagram of an optimized dispatching model of a multi-energy system;
FIG. 4 is a graph of 24 hour output for a conventionally scheduled gas turbine and gas boiler;
FIG. 5 is a graph of the 24 hour output of the optimally scheduled gas turbine and gas boiler of the present invention.
Detailed Description
As shown in fig. 1 to 3, the optimal scheduling method for the campus multi-energy system based on the comprehensive evaluation system is performed according to the following steps:
step 1, establishing a multi-energy system architecture, modeling each unit in the architecture, analyzing the operation mechanism of a unit, determining the energy flow direction and different forms of energy coupling modes, and forming an electric-heat-gas-cold network;
step 2, establishing a multi-objective function with the lowest economic cost, the lowest carbon dioxide emission and the highest wind energy and light energy utilization rate of the multi-energy system, combining an analytic hierarchy process and an entropy weight method, formulating combined weight for the multi-objective function, and optimizing a scheduling result;
step 3, establishing an evaluation index and evaluation quantitative grading for each multi-target function, determining the weight of the evaluation index by adopting an analytic hierarchy process, inputting the optimized scheduling result of the step 2 into an evaluation system of the comprehensive energy system, evaluating the multi-target function according to the evaluation index weight and evaluation index evaluation data by utilizing a fuzzy comprehensive evaluation method, meeting the requirement of evaluation quantitative grading, and completing weight distribution; otherwise, returning to the step 2 for circulation.
The electricity, gas and heat supply networks are coupled through a cogeneration unit, and a renewable energy generator set is connected into the system; the combined heat and power generation unit of the coupling node comprises a gas turbine, a gas boiler, an electric refrigerator and an absorption refrigerator, and the renewable energy generator set comprises a wind generating set and a photovoltaic generator set; the energy storage device is accessed to the power grid for bidirectional communication;
(1) combined heat and power generation unit model
Figure RE-GDA0003537573710000091
In the formula, Pe,CHP(t)、Ph,CHP(t) respectively outputting electric power and thermal power (kW) for the CHP unit at the moment t; etaCHPThe generating efficiency of the CHP unit is obtained; alpha (t) is the proportion of the natural gas injected into the CHP unit at the moment t; qlIs the low heating value of natural gas; q. q.sgas(t) is the day used by the CHP unit at the moment tA natural gas amount (t); gamma rayh,CHPThe thermoelectric conversion rate of the CHP unit;
(2) absorption type refrigerator model
The absorption refrigerator is a main source of cold load, absorbs the waste heat of the CHP unit and the gas boiler to obtain cold energy, and outputs cold power as follows:
Pc,AR(t)＝COPARPh,AR(t)
in the formula, Pc,AR(t) is the absorption chiller output cold power (kW), COPARFor absorption refrigeration machine refrigeration efficiency (kW), Ph,AR(t) is the absorption thermal power (kW) of the absorption refrigerator;
(3) wind power generation model
Figure RE-GDA0003537573710000092
In the formula, PW(t) fan output power (kW) at time t, PWNRated power (kW) of the fan, V is actual wind speed, and V isciFor cutting into wind speed, VNRated wind speed, VcoCutting out the wind speed;
(4) photovoltaic power generation model
Figure RE-GDA0003537573710000093
In the formula, PPV(t) photovoltaic array output power (kW), η at time tPVConversion of solar energy into electrical energy efficiency for photovoltaic arrays, SPVFor photovoltaic array solar panel area (m)3)，
Figure RE-GDA0003537573710000094
The unit area illumination intensity (lx) of the photovoltaic array at the time t;
(5) electric refrigerator model
The electric refrigerator mainly converts electric power into cold power to supply to cold load, and the output cold power is as follows:
Pc,EC(t)＝COPECPe,EC(t)
in the formula, Pc,EC(t) the output cold power (kW) of the electric refrigerator at time t, COPECFor the refrigeration efficiency of electric refrigerators, Pe,EC(t) the electric power (kW) consumed by the electric refrigerator at the moment t;
(6) energy storage device model
When the power grid load is in low tide, the energy storage device can be used for charging the power grid load; when the battery of the energy storage device is fully charged, the energy storage device can reversely transmit the electric energy back to the power grid; the energy storage device charging and discharging model and the energy constraint formula are as follows;
Figure RE-GDA0003537573710000101
Emin≤Ei(t)≤Emax
in the formula, Ei(t) is the electric quantity (kW) of the ith energy storage device at the moment t, mueIs the self-discharge rate, eta, of the battery of the energy storage devicein、ηoutCharging and discharging efficiencies, P, of the energy storage device, respectivelyi EV,in(t)、Pi EV,out(t) is the charging and discharging power (kW) of the ith energy storage device at the moment t, Emin、EmaxThe minimum and maximum battery capacity (kW) of the energy storage device are respectively.
(6) Multi-energy system operating cost in multi-objective function
The operating economy cost of the multi-energy system is divided into equipment power generation cost, energy storage device charging and discharging cost, microgrid-grid interaction cost and microgrid-gas grid interaction cost;
minCJ＝Cc+Ce+Cg+CEV
in the formula, CJFor economic comprehensive cost (yuan/kW), CcFor unit operating costs (yuan/kW), CeCost (yuan/kW) for interaction between microgrid and power grid, CgCost (yuan/kW), C, for interaction between microgrid and gas networkEVThe cost (yuan/kW) for charging and discharging the energy storage device;
Figure RE-GDA0003537573710000102
in the formula, CGTFor the unit operating cost (yuan/kW), C of the gas turbineGBIs the unit operation cost (yuan/kW), C of the gas boilerECThe unit operation cost (yuan/kW) of the electric refrigerator is CARFor the unit operating cost (yuan/kW), C of the absorption refrigeratorWIs unit operating cost (yuan/kW), C of the fanPVFor the operating cost (yuan/kW), P, of the photovoltaic power generation unite,GTConsuming electric power (kW), P, for a gas turbinee,GBConsuming electrical power (kW) for the gas boiler;
Figure RE-GDA0003537573710000103
in the formula, ce,tCost (yuan/kW) of electricity purchasing and selling unit from micro grid to power grid, Pe,tPower for interaction of the microgrid and the power grid;
Figure RE-GDA0003537573710000111
in the formula, cg,tCost of gas purchase (yuan/kW), P, from microgrid to gas gridg,tPower for microgrid and gas grid interaction;
Figure RE-GDA0003537573710000112
in the formula, Cin、CoutThe unit charging and discharging costs (yuan/kW), P, of the energy storage device are respectivelyEV,in(t)、PEV,out(t) total charging and discharging power (kW) for the energy storage device;
(7) minimum carbon dioxide emission in multi-objective function
The formula of the emission amount of carbon dioxide is as follows:
Figure RE-GDA0003537573710000113
in the formula, λe、λgRespectively is the carbon dioxide emission coefficient under unit electric power and the carbon dioxide emission coefficient of unit volume of natural gas; pg,GT(t)、Pg,GB(t) gas power (kW) consumed at the moment t of the gas turbine and the gas boiler respectively;
(8) wind energy utilization in multi-objective functions
The wind power utilization rate formula is as follows:
Figure RE-GDA0003537573710000114
in the formula etaWIs wind power utilization rate, P'WOutputting electric power (kW) for maximum wind power generation at the moment t;
(9) light energy utilization in multi-objective functions
The photovoltaic power generation utilization rate formula is as follows:
Figure RE-GDA0003537573710000115
in the formula etaPVIs photovoltaic power generation utilization rate, P'PVOutputting electric power (kW) for maximum photovoltaic power generation at the moment t;
the integrated objective function formula is as follows:
Figure RE-GDA0003537573710000116
wherein Z is the overall target value, QiIs a combined weight value;
the multi-energy system constraint comprises an electric load constraint, a heat load constraint, a cold load constraint and a gas quantity constraint, and the electric load constraint, the heat load constraint, the cold load constraint and the gas quantity constraint conditions are as follows:
PW+PPV+Pe,t+Pe,GT-Pe,EC-Le＝0
Ph,GB+Ph,GT-Ph,AR-Lh＝0
Pg,t-Pg,GT-Pg,GB-Lg＝0
PC,EC+PC,AR-Lc＝0
in the formula, Ph,GBGenerating thermal power (kW), P for gas fired boilersh,GTGenerating thermal power (kW), L for a gas turbinee、Lh、Lg、 LcRespectively an electric load, a heat load, a gas load and a cold load (kW) of the multi-energy system.
The multi-energy system multi-target function weight formulation is a comprehensive formulation of subjective weight obtained by an analytic hierarchy process and objective weight obtained by an entropy weight method, and the multi-energy system multi-target function weight QiThe formula is as follows:
Qi＝α×vi+(1-α)wi
labeling the objective weight of the ith factor as viMarking the subjective weight of the ith factor as wiα is a coefficient of the combining weight;
the analytical hierarchy method comprises the following steps:
firstly, constructing an expert judgment matrix, and carrying out calibration by a pairwise comparison method;
Figure RE-GDA0003537573710000121
a1representing the economic cost correspondence scale, a2Represents a scale corresponding to carbon dioxide emission, a3Representing the wind energy utilisation corresponding scale, a4Representing a photovoltaic utilization rate correspondence scale;
second, calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000122
Checking consistency
1) Calculating a consistency index CI
Figure RE-GDA0003537573710000123
Wherein λ ismaxIs the maximum eigenvalue of the matrix A;
2) the corresponding average random consistency index RI is 0.9;
3) calculating the consistency ratio CR
Figure RE-GDA0003537573710000131
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the entropy weight method comprises the following steps:
normalization processing of indexes:
homogenizing measurement units of the multi-target function, and adopting different algorithms to perform data standardization processing on positive and negative target functions:
forward objective function:
Figure RE-GDA0003537573710000132
negative objective function:
Figure RE-GDA0003537573710000133
for convenience, normalized data x'tiIs still marked as xti；
Secondly, calculating the proportion of the t sample value in the index under the ith index:
Figure RE-GDA0003537573710000134
calculating entropy of the ith index:
Figure RE-GDA0003537573710000135
wherein k is 1/ln (24) > 0; satisfies ei≥0；
Fourthly, calculating the information entropy redundancy (difference):
di＝1-ei,i＝1,2,3,4
calculating the weight of each index:
Figure RE-GDA0003537573710000136
establishing evaluation index weight by using an analytic hierarchy process;
firstly, constructing an expert judgment matrix, and carrying out calibration by a pairwise comparison method.
Figure RE-GDA0003537573710000141
Second, calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000142
Checking consistency
1) Calculating a consistency index CI
Figure RE-GDA0003537573710000143
2) Searching corresponding average random consistency index n is 1, and RI is 0; n is 2, RI is 0; n is 3, RI is 0.58; n is 4, RI is 0.90; n is 5, RI is 1.12; n is 6, RI is 1.24; n is 7, RI is 1.32; n is 8, RI is 1.41; n is 9, RI is 1.45;
TABLE 1 index Table of average random consistency
3) Calculating the consistency ratio CR
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the fuzzy comprehensive evaluation method comprises the following steps:
determining factor domain of evaluation object
Let N be evaluation indexes, X ═ X1,X2,...Xn)；
② determining comment grade domain
Let A ═ W1,W2…), each level may correspond to a fuzzy subset, i.e., a set of levels;
establishing fuzzy relation matrix
After the rank fuzzy subset is constructed, the evaluated objects are subjected to one-by-one treatment according to each factor XiThe quantification is carried out, namely the membership (R | X) of the evaluated object to the grade fuzzy subset from the single factor is determinedi) And then obtaining a fuzzy relation matrix
Figure RE-GDA0003537573710000144
Wherein, the ith row and the jth column element represent a certain evaluated object XiFrom the aspect of factor to WjMembership of the rank-fuzzy subset;
determining weight vector of evaluation factor
Determining a weight vector of an evaluation factor according to the evaluation index weight determined by the analytic hierarchy process and the evaluation index data: u ═ U1,u2,…)；
Fifthly, fuzzy comprehensive evaluation result vector
And synthesizing the U and the R of the evaluation index to obtain a fuzzy comprehensive evaluation result vector B of the evaluation index, namely:
Figure RE-GDA0003537573710000151
wherein, biShowing the things to be evaluatedView on the whole to WjDegree of membership of the rank-fuzzy subset;
sixthly, grading the comprehensive scoring value
And carrying out comprehensive scoring according to the fuzzy comprehensive evaluation result vector, and judging whether the evaluation quantitative grading requirement is met.
The optimal scheduling method for the multi-energy system comprises the following steps:
step 1, establishing a multi-energy system architecture, and modeling the behaviors of each unit of the multi-energy system and the electric automobile V2G. The multi-energy system comprises an electricity network, a gas network and a heat network, each unit plays an important role in energy transmission and coupling, and a mathematical model and upper and lower output limit constraints of each unit are established according to the working principle, the working characteristics and the energy transmission rule of each unit;
determining the constitution of multi-energy system, the number of unit sets and parameters
The electricity, gas and heat supply networks are coupled through a cogeneration unit, and a wind generating set and a photovoltaic generating set can be accessed into the system through renewable energy sources. The combined heat and power generation unit with the coupling node comprises a gas turbine, a gas boiler, an electric refrigerator and an absorption refrigerator, and the renewable energy generator set mainly comprises a wind generating set and a photovoltaic generator set. The electric automobile is connected to a power grid through V2G.
Specifically, in the present embodiment, 2 gas turbines, 1 gas boiler, 1 wind turbine, 1 photovoltaic power plant, 1 absorption chiller, 1 electric chiller, and 40 electric vehicles are selected.
(1) Cogeneration plant (CHP plant) model.
Figure RE-GDA0003537573710000152
In the formula, Pe,CHP(t)、Ph,CHP(t) respectively outputting electric power at the moment t of the CHP unit and outputting thermal power (kW) at the moment t of the CHP unit; etaCHPThe generating efficiency of the CHP unit is obtained; alpha (t) is the proportion of the natural gas injected into the CHP unit at the moment t; qlIs the low heating value of natural gas; q. q.sgas(t) isthe amount of the natural gas used by the CHP unit at the moment t (t); gamma rayh,CHPIs the thermoelectric conversion rate of the CHP unit.
(2) Absorption type refrigerator model
The absorption refrigerator is a main source of cold load, absorbs the waste heat of the CHP unit and the gas boiler to obtain cold energy, and outputs cold power as follows:
Pc,AR(t)＝COPARPh,AR(t) (2)
in the formula, Pc,AR(t) is the absorption chiller output cold power (kW), COPARFor the refrigerating efficiency of absorption refrigerators, Ph,ARAnd (t) is the absorption heat power (kW) of the absorption refrigerator.
(3) Wind power generation model
Figure RE-GDA0003537573710000161
In the formula, PW(t) fan output power (kW) at time t, PWNRated power (kW) of the fan, V is actual wind speed, and V isciFor cutting into the wind speed (V)ci＝3m/s)，VNIs rated wind speed (V)N＝12m/s)，VcoTo cut out the wind speed (V)co＝30m/s)。
(4) Photovoltaic power generation model
Figure RE-GDA0003537573710000162
In the formula, PPV(t) photovoltaic array output power (kW), η at time tPVConversion of solar energy into electrical energy efficiency for photovoltaic arrays, SPVFor photovoltaic array solar panel area (m)3)，
Figure RE-GDA0003537573710000163
And the unit area illumination intensity (lx) of the photovoltaic array at the time t.
(5) Electric refrigerator
The electric refrigerator mainly converts electric power into cold power to supply to cold load, and the output cold power is as follows:
Pc,EC(t)＝COPECPe,EC(t) (5)
in the formula, Pc,EC(t) the output cold power (kW) of the electric refrigerator at time t, COPECFor the refrigeration efficiency of electric refrigerators, Pe,ECAnd (t) is the electric power (kW) consumed by the electric refrigerator at the time t.
(6) Electric vehicle V2G behavior modeling
The V2G realizes bidirectional interaction between energy flow and information flow by using bidirectional communication between the electric automobile and a power grid and regarding the electric automobile in a stop state as a mobile energy storage device. When the power grid load is in low tide, the battery of the electric automobile can be used as the power grid load to charge. When the battery of the electric automobile is fully charged, the electric automobile can be used as an energy storage device or a standby power supply to reversely transmit the electric energy back to the power grid. The charging and discharging model and the energy constraint of the electric automobile are shown as (6) and (7).
Figure RE-GDA0003537573710000164
Emin≤Ei(t)≤Emax (7)
In the formula, Ei(t) electric quantity (kW) of the ith electric vehicle at time t, μeIs the self-discharge rate of the battery of the electric automobile etain、ηoutRespectively charging efficiency and discharging efficiency of the electric automobile, Pi EV,in(t)、Pi EV,out(t) is charging power of the ith electric vehicle at time t, discharging power (kW) of the ith electric vehicle at time t, Emin、EmaxThe battery capacity of the electric automobile is the minimum value and the maximum value (kW) of the battery capacity of the electric automobile.
(II) setting scheduling period and inputting initial conditions
In the embodiment, 24 hours is used as a scheduling period, and the time interval is 1 hour. Inputting wind power generation, photovoltaic power generation prediction situations, 24-hour electricity, heat, gas and cold load prediction situations and other known conditions of a multi-energy system.
And 2, providing a multi-objective model comprehensively considering economic cost, carbon dioxide emission and renewable energy (wind power and photovoltaic power generation) utilization rate, and establishing weights for multiple objectives by a combined weight method combining subjective and objective weights to realize optimal scheduling of the multi-energy system.
The objective function is divided into four parts.
(1) The first part is economic cost which is divided into equipment power generation cost, electric vehicle charging and discharging cost, microgrid-power grid interaction cost and microgrid-gas grid interaction cost.
minCJ＝Cc+Ce+Cg+CEV (8)
In the formula, CJFor economic comprehensive cost (yuan/kW), CcFor unit operating costs (yuan/kW), CeCost (yuan/kW) for interaction between microgrid and power grid, CgCost (yuan/kW), C, for interaction between microgrid and gas networkEVThe cost (yuan/kW) of charging and discharging of the electric automobile is reduced.
Figure RE-GDA0003537573710000171
In the formula, CGTFor the unit operating cost (yuan/kW), C of the gas turbineGBIs the unit operation cost (yuan/kW), C of the gas boilerECThe unit operation cost (yuan/kW) of the electric refrigerator is CARFor the unit operating cost (yuan/kW), C of the absorption refrigeratorWIs unit operating cost (yuan/kW), C of the fanPVFor the operating cost (yuan/kW), P, of the photovoltaic power generation unite,GTConsuming electric power (kW), P, for a gas turbinee,GBConsuming electrical power (kW) for the gas boiler.
Figure RE-GDA0003537573710000172
In the formula, ce,tCost (yuan/kW) of electricity purchasing and selling unit from micro grid to power grid, Pe,tThe power (kW) for interaction of the microgrid and the power grid.
Figure RE-GDA0003537573710000173
In the formula, cg,tCost of gas purchase (yuan/kW), P, from microgrid to gas gridg,tThe power (kW) for interaction of the micro-grid and the gas grid.
Figure RE-GDA0003537573710000174
In the formula, Cin、CoutRespectively charging cost per electric vehicle, discharging cost per electric vehicle (yuan/kW), PEV,in(t)、 PEV,outAnd (t) is the total charging electric power of the electric automobile and the total discharging power (kW) of the electric automobile.
(2) The second part is carbon dioxide emissions.
Figure RE-GDA0003537573710000181
In the formula, λe、λgThe carbon dioxide emission coefficient per unit electric power and the carbon dioxide emission coefficient per unit volume of natural gas are respectively. Pg,GT(t)、Pg,GB(t) gas power consumed at time t of the gas turbine and gas power (kW) consumed at time t of the gas boiler, respectively.
(3) The third part is the wind power utilization rate.
Figure RE-GDA0003537573710000182
In the formula etaWIs wind power utilization rate, P'WAnd outputting electric power (kW) for the maximum wind power generation at the moment t.
(4) The fourth part is the photovoltaic power generation utilization rate.
Figure RE-GDA0003537573710000183
In the formula etaPVIs photovoltaic power generation utilization rate, P'PVAnd outputting the maximum photovoltaic power generation output electric power (kW) at the moment t.
(5) According to (8), (12), (13), and (14), the integrated objective function is shown as (15).
Z＝Q1min{CJ}+Q2min{qco2}+Q3max{ηW}+Q4max{ηPV} (16)
Wherein Z is the overall target value, QiAre combined weight values.
(6) The multi-energy system constraint comprises an electric load constraint, a heat load constraint, a cold load constraint and a gas quantity constraint.
PW+PPV+Pe,t+Pe,GT-Pe,EC-Le＝0 (17)
Ph,GB+Ph,GT-Ph,AR-Lh＝0 (18)
Pg,t-Pg,GT-Pg,GB-Lg＝0 (19)
PC,EC+PC,AR-Lc＝0 (20)
In the formula, Ph,GBGenerating thermal power (kW), P for gas fired boilersh,GTGenerating thermal power (kW), L for a gas turbinee、Lh、Lg、 LcRespectively an electric load, a heat load, a gas load and a cold load (kW) of the multi-energy system. And after the multi-target function is obtained and the constraint is carried out, determining the weight for the multi-target function by using a mode of combining an analytic hierarchy process and an entropy weight method.
(7) And after the multi-target function is obtained and the constraint is carried out, determining the weight for the multi-target function by using a mode of combining an analytic hierarchy process and an entropy weight method.
1) The analytic hierarchy process factorizes and stratifies the problem according to the target requirement and property, and digitally expresses the difference degree between the factors through the subjective judgment of experts. The mathematical model of the analytic hierarchy process comprises the following steps:
firstly, constructing an expert judgment matrix, and carrying out calibration by a pairwise comparison method.
Figure RE-GDA0003537573710000191
In this example, a1Representing the economic cost correspondence scale, a2Represents a scale corresponding to carbon dioxide emission, a3Representing the wind energy utilisation corresponding scale, a4And representing a photovoltaic utilization rate corresponding scale.
Second, calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000192
Checking consistency
a. Calculating a consistency index CI
Figure RE-GDA0003537573710000193
Wherein λ ismaxIs the largest eigenvalue of matrix a.
b. The average random consistency index RI is 0.9.
c. Calculating the consistency ratio CR
Figure RE-GDA0003537573710000194
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
2) the entropy weight method is an objective weighting method because it relies only on the discreteness of the data itself. According to the characteristics of entropy, the randomness and the disorder degree of an event can be judged by calculating the entropy, or the dispersion degree of a certain index can be judged by using the entropy, and the larger the dispersion degree of the index is, the larger the influence (weight) of the index on comprehensive evaluation is.
The entropy weight method of the objective function comprises the following steps:
normalization processing of indexes: heterogeneous objective function homogenization
Because the measurement units of the objective functions are not uniform, before the objective functions are used for calculating the comprehensive objective function, standardization treatment is carried out, namely the absolute value of the objective function is converted into a relative value, so that the homogenization problem of different objective function values is solved.
In addition, the positive objective function and the negative objective function have different meanings (the higher the positive objective function is, the better the negative objective function is), so that different algorithms are required for the positive and negative objective functions to perform data normalization:
forward objective function:
Figure RE-GDA0003537573710000201
negative objective function:
Figure RE-GDA0003537573710000202
x in equations (24) and (25)ijRepresenting the ith sample value under the j item standard function;
in this example, j is 1,2,3, 4. Wherein, { x11，x21，…，xi1Is the i samples of the objective function of economic cost, { x12， x22，…，xi2I samples of the target function of carbon dioxide emission, { x13，x23，…，xi3I samples with wind power utilization as the objective function, { x14，x24，…，xi4I samples with the photovoltaic power generation utilization rate as an objective function;
for convenience, normalized data x'tiIs still marked as xti
In the embodiment, the economic cost and the carbon dioxide emission are negative objective functions, and the wind power utilization rate and the photovoltaic power generation utilization rate are positive objective functions;
calculating the proportion of the t-th sample value in the target function under the ith project standard function:
Figure RE-GDA0003537573710000203
calculating entropy of the ith objective function:
Figure RE-GDA0003537573710000204
wherein k is 1/ln (24) > 0. Satisfies ei≥0；
Fourthly, calculating the information entropy redundancy (difference):
di＝1-ei,i＝1,2,3,4 (29)
calculating the weight of each item of objective function:
Figure RE-GDA0003537573710000205
sixthly, calculating the comprehensive score of each sample:
Figure RE-GDA0003537573710000211
wherein x istiIs normalized data.
Combining the subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the entropy weight process, and marking the objective weight of the ith factor as v in the combined calculationiMarking the subjective weight of the ith factor as wiThen the constant weight coefficient represented by the ith factor combining weight is:
Qi＝α×vi+(1-α)wi (32)
where α is a coefficient of the combining weight, α in this embodiment takes 0.5.
The weight values of the four objective functions are thus calculated according to the above procedure.
And solving the model by using a Gurobi solver in Matlab to obtain an optimized scheduling result. The flow of optimizing and scheduling the multi-energy system from step 1 to step 2 is specifically shown in fig. 3.
Step 3, analyzing influence on operation of the multi-energy system
TABLE 1
Figure RE-GDA0003537573710000212
Establishing evaluation index weight by using an analytic hierarchy process;
(1) and constructing an expert judgment matrix, and performing calibration by a pairwise comparison method.
Figure RE-GDA0003537573710000213
(2) Calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-GDA0003537573710000221
(3) Consistency check
1) Calculating a consistency index CI
Figure RE-GDA0003537573710000222
2) Searching corresponding average random consistency index n is 1, and RI is 0; n is 2, RI is 0; n is 3, RI is 0.58; n is 4, RI is 0.90; n is 5, RI is 1.12; n is 6, RI is 1.24; n is 7, RI is 1.32; n is 8, RI is 1.41; n is 9, RI is 1.45;
3) calculating the consistency ratio CR
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the fuzzy comprehensive evaluation method comprises the following steps:
the fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. The comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to the membership theory of fuzzy mathematics, namely, fuzzy mathematics is used for making overall evaluation on objects or objects restricted by various factors. The method has the characteristics of clear result and strong systematicness, can better solve the problems of fuzziness and difficult quantization, and is suitable for solving various non-determinacy problems. The basic steps can be summarized as follows:
(1) determining factor domains of evaluation objects
5 evaluation indexes (n is 5) are set for the wind energy/light energy utilization rate, 1 evaluation index is set for the environmental protection performance, and 2 evaluation indexes are set for the system operation economy; x ═ X (X)1,X2,...Xn)；
(2) Determining comment level
Let A ═ W1,W2) Each level may correspond to a fuzzy subset, i.e., a set of levels, with the two comment levels being the respective qualifiers and qualifiers.
(3) Establishing a fuzzy relationship matrix
After the rank fuzzy subset is constructed, the evaluated objects are subjected to one-by-one treatment according to each factor XiThe quantification is carried out, namely the membership (R | X) of the evaluated object to the grade fuzzy subset from the single factor is determinedi) And then obtaining a fuzzy relation matrix, wherein m is 2 in the embodiment;
Figure RE-GDA0003537573710000223
(4) determining weight vectors for evaluation factors
Determining a weight vector of an evaluation factor according to the evaluation index weight determined by the analytic hierarchy process and the evaluation index data: u ═ U1,u2,…)；
(5) Fuzzy comprehensive evaluation result vector
And synthesizing the U and the R of the evaluation index to obtain a fuzzy comprehensive evaluation result vector B of the evaluation index, namely:
Figure RE-GDA0003537573710000231
wherein, biRepresents that the evaluated object is on W as a wholejDegree of membership of the rank-fuzzy subset;
(6) ranking the composite score value
And carrying out comprehensive scoring according to the fuzzy comprehensive evaluation result vector, and judging whether the evaluation quantitative grading requirement is met.
Step 4, feeding back the evaluation result of the multi-energy system to the multi-energy system optimization scheduling model, and adjusting the weight value according to the evaluation result of the evaluation system;
and 5, further performing optimized scheduling by using a Gurobi solver in Matlab according to the adjusted multi-energy optimized scheduling model, returning to the step 2, circulating until all indexes of the multi-energy system are within a qualified range, and outputting a final multi-energy system operation result.
The technical effect of the energy optimization scheduling implementation method of the campus-type multi-energy system of the present invention is examined below by specific embodiments. Specifically, in the multi-energy system of this embodiment, 2 gas turbines, 1 gas boiler, 1 wind turbine, 1 photovoltaic power plant, 1 absorption chiller, 1 electric chiller, and 40 electric vehicles are selected for use, the scheduling period is 24 hours, and the scheduling time period is 1 hour. The 24-hour electrical load demand is [2800, 2700, 3000, 3800, 4600, 4600, 5200, 5400, 5800, 6300, 7400, 8700, 9700, 10000, 10100, 10300, 9000, 7000, 6700, 5900, 4500, 3000, 2700, 2800] kW; the 24 hour thermal load requirements are [1300, 1400, 1360, 1700, 1600, 2000, 3100, 3450, 3900, 4400, 4300, 4500, 5400, 5000, 5100, 4950, 4600, 4710, 4400, 4000, 3650, 3570, 2900, 2420] kW, respectively; the 24-hour cooling load requirements are [2350, 2800, 2850, 3400, 3800, 4000, 4700, 4350, 5100, 7500, 7600, 8450, 8700, 8050, 7700, 7450, 7200, 6650, 7300, 5700, 5100, 4450, 3600, 3080] kW respectively; the purchase price is [0.182, 0.182, 0.182, 0.182, 0.518, 0.518, 0.882, 0.882, 0.882, 0.882, 0.518, 0.182, 0.182] yuan/KW respectively, and the sale price is [0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.406, 0.406, 0.7, 0.7, 0.7, 0.7, 0.406, 0.406, 0.14, 0.14] yuan/KW respectively; the wind power output prediction is [11160, 12410, 12140, 12590, 12410, 11320, 10040, 10536, 8230, 9004, 8050, 8320, 8878, 8500, 8230, 8680, 9482, 9500, 11770, 11518, 11068, 11860, 11140, 8700] kW respectively; the photovoltaic output is predicted to be [0, 0, 0, 0, 0, 1650, 2450, 3250, 3350, 3400, 3750, 3450, 3250, 3200, 2400, 2100, 1300, 0, 0, 0, 0, 0] kW, respectively. The embodiment provides that the membership degree of the three secondary indexes is more than 0.6, namely the qualified secondary indexes are qualified, and the membership degree range can be adjusted according to the actual engineering requirements. The result of the 24-hour optimal scheduling of the multi-energy system by using the method of the invention is shown in table 2 and fig. 4 and 5.
TABLE 2
Figure RE-GDA0003537573710000241
Example tests show that the optimal scheduling method for the park multi-energy system based on the comprehensive evaluation system can effectively realize the optimal scheduling of the multi-energy system and optimize the operation of the system. Simulation results show that after power generation equipment such as a gas turbine, a gas boiler and the like is optimally scheduled to operate, on the basis of guaranteeing electric energy balance, the output of a traditional unit is reduced, the output of a new energy unit is increased, the cost of a multi-energy system is reduced, the utilization rate of renewable energy is effectively improved, and carbon emission is further reduced.
The above description is only exemplary of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A campus multi-energy system optimization scheduling method based on a comprehensive evaluation system is characterized by comprising the following steps: the optimized scheduling method comprises the following steps:
step 1, establishing a multi-energy system architecture, modeling each unit in the architecture, analyzing the operation mechanism of a unit, determining the energy flow direction and different forms of energy coupling modes, and forming an electric-heat-gas-cold network;
step 2, establishing a multi-objective function with the lowest economic cost, the lowest carbon dioxide emission and the highest wind energy and light energy utilization rate of the multi-energy system, combining an analytic hierarchy process and an entropy weight method, formulating combined weight for the multi-objective function, and optimizing a scheduling result;
step 3, establishing an evaluation index and evaluation quantitative grading for each multi-target function, determining the weight of the evaluation index by adopting an analytic hierarchy process, inputting the optimized scheduling result of the step 2 into an evaluation system of the comprehensive energy system, evaluating the multi-target function according to the evaluation index weight and evaluation index evaluation data by utilizing a fuzzy comprehensive evaluation method, meeting the requirement of evaluation quantitative grading, and completing weight distribution; otherwise, returning to the step 2 for circulation.
2. The optimal scheduling method of the campus multi-energy system based on the comprehensive evaluation system according to claim 1, wherein: the electricity, gas and heat supply networks are coupled through a cogeneration unit, and a renewable energy generator set is connected into the system; the combined heat and power generation unit of the coupling node comprises a gas turbine, a gas boiler, an electric refrigerator and an absorption refrigerator, and the renewable energy generator set comprises a wind generating set and a photovoltaic generator set; the energy storage device is accessed to the power grid for bidirectional communication;
(1) combined heat and power generation unit model
Figure RE-FDA0003537573700000011
In the formula, Pe,CHP(t)、Ph,CHP(t) respectively outputting electric power and thermal power (kW) for the CHP unit at the moment t; etaCHPThe generating efficiency of the CHP unit is obtained; alpha (t) is the proportion of the natural gas injected into the CHP unit at the moment t; qlIs the low heating value of natural gas; q. q.sgas(t) is the amount of natural gas used by the CHP unit at the moment t; gamma rayh,CHPThe thermoelectric conversion rate of the CHP unit;
(2) absorption type refrigerator model
The absorption refrigerator is a main source of cold load, absorbs the waste heat of the CHP unit and the gas boiler to obtain cold energy, and outputs cold power as follows:
Pc,AR(t)＝COPARPh,AR(t)
in the formula, Pc,AR(t) is the absorption chiller output cold power (kW), COPARFor absorption refrigeration machine refrigeration efficiency (kW), Ph,AR(t) is the absorption thermal power (kW) of the absorption refrigerator;
(3) wind power generation model
Figure RE-FDA0003537573700000021
In the formula, PW(t) fan output power (kW) at time t, PWNRated power (kW) of the fan, V is actual wind speed, and V isciFor cutting into wind speed, VNRated wind speed, VcoCutting out the wind speed;
(4) photovoltaic power generation model
Figure RE-FDA0003537573700000022
In the formula, PPV(t) photovoltaic array output power (kW), η at time tPVConversion of solar energy into electrical energy efficiency for photovoltaic arrays, SPVFor photovoltaic array solar panel area (m)3)，
Figure RE-FDA0003537573700000023
The unit area illumination intensity (lx) of the photovoltaic array at the time t;
(5) electric refrigerator model
The electric refrigerator mainly converts electric power into cold power to supply to cold load, and the output cold power is as follows:
Pc,EC(t)＝COPECPe,EC(t)
in the formula, Pc,EC(t) the output cold power (kW) of the electric refrigerator at time t, COPECFor the refrigeration efficiency of electric refrigerators, Pe,EC(t) the electric power (kW) consumed by the electric refrigerator at the moment t;
(6) energy storage device model
When the power grid load is in low tide, the energy storage device can be used for charging the power grid load; when the battery of the energy storage device is fully charged, the energy storage device can reversely transmit the electric energy back to the power grid; the energy storage device charging and discharging model and the energy constraint formula are as follows;
Figure RE-FDA0003537573700000024
Emin≤Ei(t)≤Emax
in the formula, Ei(t) is the electric quantity (kW) of the ith energy storage device at the moment t, mueIs the self-discharge rate, eta, of the battery of the energy storage devicein、ηoutCharging and discharging efficiencies, P, of the energy storage device, respectivelyi EV,in(t)、Pi EV,out(t) is the charging and discharging power (kW) of the ith energy storage device at the moment t, Emin、EmaxThe minimum and maximum battery capacity (kW) of the energy storage device are respectively.
3. The optimal scheduling method of the campus multi-energy system based on the comprehensive evaluation system according to claim 1, wherein:
(1) multi-energy system operating cost in multi-objective function
The operating economy cost of the multi-energy system is divided into equipment power generation cost, energy storage device charging and discharging cost, microgrid-grid interaction cost and microgrid-gas grid interaction cost;
min CJ＝Cc+Ce+Cg+CEV
in the formula, CJFor economic comprehensive cost (yuan/kW), CcFor unit operating costs (yuan/kW), CeCost (yuan/kW) for interaction between microgrid and power grid, CgCost (yuan/kW), C, for interaction between microgrid and gas networkEVThe cost (yuan/kW) for charging and discharging the energy storage device;
Figure RE-FDA0003537573700000031
in the formula, CGTFor the unit operating cost (yuan/kW), C of the gas turbineGBIs the unit operation cost (yuan/kW), C of the gas boilerECThe unit operation cost (yuan/kW) of the electric refrigerator is CARFor the unit operating cost (yuan/kW), C of the absorption refrigeratorWIs unit operating cost (yuan/kW), C of the fanPVFor the operating cost (yuan/kW), P, of the photovoltaic power generation unite,GTConsuming electric power (kW), P, for a gas turbinee,GBConsuming electrical power (kW) for the gas boiler;
Figure RE-FDA0003537573700000032
in the formula, ce,tCost (yuan/kW) of electricity purchasing and selling unit from micro grid to power grid, Pe,tPower for interaction of the microgrid and the power grid;
Figure RE-FDA0003537573700000033
in the formula, cg,tCost of gas purchase (yuan/kW), P, from microgrid to gas gridg,tPower for microgrid and gas grid interaction;
Figure RE-FDA0003537573700000034
in the formula, Cin、CoutThe unit charging and discharging costs (yuan/kW), P, of the energy storage device are respectivelyEV,in(t)、PEV,out(t) total charging and discharging power (kW) for the energy storage device;
(2) minimum carbon dioxide emission in multi-objective function
The formula of the emission amount of carbon dioxide is as follows:
Figure RE-FDA0003537573700000035
in the formula, λe、λgRespectively is the carbon dioxide emission coefficient under unit electric power and the carbon dioxide emission coefficient of unit volume of natural gas; pg,GT(t)、Pg,GB(t) gas power (kW) consumed at the moment t of the gas turbine and the gas boiler respectively;
(3) wind energy utilization in multi-objective functions
The wind power utilization rate formula is as follows:
Figure RE-FDA0003537573700000041
in the formula etaWIs wind power utilization rate, P'WOutputting electric power (kW) for maximum wind power generation at the moment t;
(4) light energy utilization in multi-objective functions
The photovoltaic power generation utilization rate formula is as follows:
Figure RE-FDA0003537573700000042
in the formula etaPVIs photovoltaic power generation utilization rate, P'PVOutputting electric power (kW) for maximum photovoltaic power generation at the moment t;
the integrated objective function formula is as follows:
Figure RE-FDA0003537573700000043
wherein Z is the overall target value, QiIs a combined weight value;
the multi-energy system constraint comprises an electric load constraint, a heat load constraint, a cold load constraint and a gas quantity constraint, and the electric load constraint, the heat load constraint, the cold load constraint and the gas quantity constraint conditions are as follows:
PW+PPV+Pe,t+Pe,GT-Pe,EC-Le＝0
Ph,GB+Ph,GT-Ph,AR-Lh＝0
Pg,t-Pg,GT-Pg,GB-Lg＝0
PC,EC+PC,AR-Lc＝0
in the formula, Ph,GBGenerating thermal power (kW), P for gas fired boilersh,GTGenerating thermal power (kW), L for a gas turbinee、Lh、Lg、LcRespectively an electric load, a heat load, a gas load and a cold load (kW) of the multi-energy system.
4. The optimal scheduling method of the campus multi-energy system based on the comprehensive evaluation system according to claim 1, wherein:
in step 2, the multi-energy system multi-target function weight is formulated by comprehensively formulating subjective weight obtained by an analytic hierarchy process and objective weight obtained by an entropy weight method, and the multi-energy system multi-target function weight QiThe formula is as follows:
Qi＝α×vi+(1-α)wi
labeling the objective weight of the ith factor as viMarking the subjective weight of the ith factor as wiα is a coefficient of the combining weight;
the analytical hierarchy method comprises the following steps:
(1) constructing an expert judgment matrix, and scaling by a pairwise comparison method;
Figure RE-FDA0003537573700000051
a1representing the economic cost correspondence scale, a2Represents a scale corresponding to carbon dioxide emission, a3Representing the wind energy utilisation corresponding scale, a4Representing a photovoltaic utilization rate correspondence scale;
(2) calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-FDA0003537573700000052
(3) Consistency check
1) Calculating a consistency index CI
Figure RE-FDA0003537573700000053
Wherein λ ismaxIs the maximum eigenvalue of the matrix A;
2) the corresponding average random consistency index RI is 0.9;
3) calculating the consistency ratio CR
Figure RE-FDA0003537573700000054
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the entropy weight method comprises the following steps:
(1) normalization processing of indexes:
homogenizing measurement units of the multi-target function, and adopting different algorithms to perform data standardization processing on positive and negative target functions:
forward objective function:
Figure RE-FDA0003537573700000055
negative objective function:
Figure RE-FDA0003537573700000061
for convenience, normalized data x'tiIs still marked as xti；
(2) Calculating the proportion of the t sample value in the index under the ith index:
Figure RE-FDA0003537573700000062
(3) calculating the entropy value of the i index:
Figure RE-FDA0003537573700000063
wherein k is 1/ln (24) > 0; satisfies ei≥0；
(4) Computing information entropy redundancy (difference):
di＝1-ei,i＝1,2,3,4
(5) calculating the weight of each index:
Figure RE-FDA0003537573700000064
5. the optimal scheduling method of the campus multi-energy system based on the comprehensive evaluation system according to claim 1, wherein: establishing evaluation index weight by using an analytic hierarchy process;
(1) and constructing an expert judgment matrix, and performing calibration by a pairwise comparison method.
Figure RE-FDA0003537573700000065
(2) Calculating the weight of the judgment matrix by arithmetic mean method
Figure RE-FDA0003537573700000066
(3) Consistency check
1) Calculating a consistency index CI
Figure RE-FDA0003537573700000067
2) Searching corresponding average random consistency index n is 1, and RI is 0; n is 2, RI is 0; n is 3, RI is 0.58; n is 4, RI is 0.90; n is 5, RI is 1.12; n is 6, RI is 1.24; n is 7, RI is 1.32; n is 8, RI is 1.41; n is 9, RI is 1.45;
3) calculating the consistency ratio CR
When CR is less than 0.1, the inconsistency degree of A is considered to be in an allowable range, and the A passes one-time inspection; otherwise, reconstructing a judgment matrix A;
the fuzzy comprehensive evaluation method comprises the following steps:
(1) determining factor domains of evaluation objects
Let N be evaluation indexes, X ═ X1,X2,...Xn)；
(2) Determining comment level discourse domain
Let A ═ W1,W2…), each level may correspond to a fuzzy subset, i.e., a set of levels;
(3) establishing a fuzzy relationship matrix
After the rank fuzzy subset is constructed, the evaluated objects are subjected to one-by-one treatment according to each factor XiThe quantification is carried out, namely the membership (R | X) of the evaluated object to the grade fuzzy subset from the single factor is determinedi) And then obtaining a fuzzy relation matrix
Figure RE-FDA0003537573700000071
Wherein, the ith row and the jth column element represent a certain evaluated object XiFrom the aspect of factor to WjMembership of the rank-fuzzy subset;
(4) determining weight vectors for evaluation factors
Determining a weight vector of an evaluation factor according to the evaluation index weight determined by the analytic hierarchy process and the evaluation index data: u ═ U1,u2,…)；
(5) Fuzzy comprehensive evaluation result vector
And synthesizing the U and the R of the evaluation index to obtain a fuzzy comprehensive evaluation result vector B of the evaluation index, namely:
Figure RE-FDA0003537573700000072
wherein, biRepresents that the evaluated object is on W as a wholejDegree of membership of the rank-fuzzy subset;
(6) ranking the composite score value
And carrying out comprehensive scoring according to the fuzzy comprehensive evaluation result vector, and judging whether the evaluation quantitative grading requirement is met.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619288A (en) * 2022-11-18 2023-01-17 北京国网电力技术有限公司 Method and system for evaluating utilization of distributed comprehensive energy
CN116227167A (en) * 2023-01-17 2023-06-06 国网山东省电力公司德州供电公司 Multi-target optimization method and system for multi-park comprehensive energy system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619288A (en) * 2022-11-18 2023-01-17 北京国网电力技术有限公司 Method and system for evaluating utilization of distributed comprehensive energy
CN115619288B (en) * 2022-11-18 2023-02-28 北京国网电力技术有限公司 Method and system for evaluating utilization of distributed comprehensive energy
CN116227167A (en) * 2023-01-17 2023-06-06 国网山东省电力公司德州供电公司 Multi-target optimization method and system for multi-park comprehensive energy system

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