CN114091917A - Dynamic environment-friendly economic dispatching method and system for cold-heat-power combined supply type micro-grid - Google Patents

Dynamic environment-friendly economic dispatching method and system for cold-heat-power combined supply type micro-grid Download PDF

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CN114091917A
CN114091917A CN202111400915.8A CN202111400915A CN114091917A CN 114091917 A CN114091917 A CN 114091917A CN 202111400915 A CN202111400915 A CN 202111400915A CN 114091917 A CN114091917 A CN 114091917A
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翟俊义
韩俊
李琥
王鑫
王琳媛
葛毅
朱星阳
殷俊平
孟诗语
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State Grid Suzhou Urban Energy Research Institute Co ltd
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention relates to a dynamic environment-friendly economic dispatching method and system for a combined cooling heating and power type microgrid, which comprises the steps of constructing a combined cooling heating and power type microgrid environment-friendly economic dispatching model, wherein the combined cooling heating and power type microgrid environment-friendly economic dispatching model comprises a target function and constraint conditions, and the constraint conditions constrain the target function; the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply; the constraints include a cold power meter balance constraint, a thermal power meter balance constraint, an electrical power balance constraint, and an electrical power spinning standby constraint. The micro-grid can meet the requirements of various types of loads, is low in energy consumption and high in stability, and provides a strategic reference for the economic operation of a combined cooling, heating and power micro-grid.

Description

Dynamic environment-friendly economic dispatching method and system for cold-heat-power combined supply type micro-grid
Technical Field
The invention relates to the technical field of micro-grids, in particular to a dynamic environment-friendly economic dispatching method and system for a combined cooling heating and power micro-grid.
Background
With the increasing prominence of energy and environment problems, the micro-grid technology gets more and more attention. The appearance of the micro-grid technology provides a new way for comprehensive and efficient utilization of distributed energy, and is effective supplement of a large power grid. The combined cooling heating and power type micro-grid can realize cascade utilization of energy, the energy utilization efficiency can reach 70% -90%, the energy-saving and environment-friendly benefits are high, and the operation pressure of the power grid is effectively relieved. However, compared with the traditional microgrid, the combined cooling heating and power type microgrid can simultaneously supply electric energy, heat energy and cold energy, and has greater scheduling operation, management and maintenance difficulties, so that the research on the environmental-friendly and economic scheduling problem of the combined cooling heating and power type microgrid has important significance.
At present, the existing research at home and abroad mainly aims at the traditional micro-grid and the combined heat and power micro-grid, but the research on the combined cold and heat and power micro-grid is less. The existing combined cooling heating and power micro-grid cannot meet the requirements of various types of loads, the energy consumption is high, and the influence of the uncertainty of renewable output of distributed wind, light and the like on the combined cooling heating and power micro-grid is large.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the technical defects that the existing cooling-heating-power combined supply type microgrid in the prior art cannot meet the requirements of various types of loads, the energy consumption is high, and the influence of the uncertainty of the renewable output of distributed wind, light and the like on the cooling-heating-power combined supply type microgrid is large.
In order to solve the technical problem, the invention provides a dynamic environment-friendly economic dispatching method for a combined cooling heating and power micro-grid, which comprises the following steps:
s1, constructing a combined cooling heating and power type microgrid environment-friendly economic dispatching model, wherein the combined cooling heating and power type microgrid environment-friendly economic dispatching model comprises a target function and constraint conditions, and the constraint conditions constrain the target function;
the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply;
the constraint conditions comprise a cold power meter balance constraint, a thermal power meter balance constraint, an electric power balance constraint and an electric power rotation standby constraint;
and S2, scheduling the microgrid based on the combined cooling heating and power supply type microgrid environment-friendly economic scheduling model.
Preferably, the constraint conditions further include a tie-line transmission power constraint, an active power constraint of the micro-combustion engine and the fuel cell, a climbing rate constraint of the micro-combustion engine and an energy storage device operation constraint.
Preferably, the constraint conditions specifically include:
electric power balance constraint:
Figure BDA0003371028400000021
thermal power balance constraint:
Figure BDA0003371028400000022
cold power balance constraint:
Figure BDA0003371028400000023
electric power rotation standby constraint:
Figure BDA0003371028400000024
tie line transmission power constraints:
Figure BDA0003371028400000025
active power constraints of micro-combustion engines and fuel cells:
Figure BDA0003371028400000026
and (3) restricting the climbing rate of the micro-combustion engine:
Figure BDA0003371028400000027
the energy storage device operation constraint comprises an electric power storage device, a heat storage device and a cold storage device, and the operation constraint conditions are as follows:
Figure BDA0003371028400000031
wherein Q isWH,tThe thermal output of the waste heat boiler at the moment t; qAC,tThe cold output of the refrigerator at the moment t; qHS,tThe heat output of the heat storage device at the moment t is achieved, the heat supply is positive, and the heat storage is negative; qCS,tThe cold output of the cold accumulation device at the moment t, the cold supply is positive, and the cold storage is negative; pES,tThe charging and discharging power of the storage battery at the time t is positive, and the charging is negative; pgrid,tThe interaction electric energy with the main network at the time t is positive, and the electricity purchasing is negative;
Figure BDA0003371028400000032
the upper limit and the lower limit of the interaction electric quantity of the micro-grid and the main grid are set; alpha is a confidence coefficient;
Figure BDA0003371028400000033
fuzzy parameters of wind power, photovoltaic, electric load value, heat load value and cold load value at the moment t; pi,tThe output of the micro source i at the time t; pi min、Pi maxThe upper and lower output limits of the micro source i are set;
Figure BDA0003371028400000034
the maximum landslide and climbing rate of the micro gas turbine; qjThe charging and discharging power of the energy storage device j is obtained;
Figure BDA0003371028400000035
the upper and lower capacity limits of the energy storage device j;
Figure BDA0003371028400000036
the maximum energy charging and discharging power of the energy storage device j is obtained;
Figure BDA0003371028400000037
the initial capacity of the energy storage device j is represented by j 1,2, and 3, which respectively represent a storage battery, a heat storage device, and a cold storage device.
Preferably, the pollutant gas emissions comprise SO emitted by micro gas turbines in the microgrid2Emission of pollutant gases and NOxAnd (4) discharging the polluted gas.
Preferably, the target function of the amount of the pollutant gas emission is as follows:
Figure BDA0003371028400000038
Figure BDA0003371028400000039
wherein the content of the first and second substances,
Figure BDA00033710284000000310
is NOxAnd SO2The emission coefficient of (a) is in g/(kWh), PMT,tThe output of the micro-combustion engine is the t period.
Preferably, between S1 and S2, there is further included:
normalizing the operation cost target function and the pollutant gas emission target function to obtain a normalized target function;
and solving the normalized objective function by the particle swarm algorithm based on the fuzzy satisfaction index, and optimizing and converting the multi-objective function into the particles with the highest satisfaction index.
Preferably, the normalizing the operation cost objective function and the pollutant gas emission objective function to obtain a normalized objective function includes:
setting the number of particles in the particle group as N, respectively obtaining three objective function values corresponding to each particle to obtain a 3 XN order objective function value matrix:
Figure BDA0003371028400000041
wherein, the row vector of F' represents a certain objective function value of all particles, and the column vector represents different objective function values of single particle, wherein, the three objective function values are respectively the running cost objective function and SO2Emission of pollutant gases and NOxEmission of pollution gas;
normalizing the objective function value matrix of 3 XN order to obtain a normalized matrix Z:
Figure BDA0003371028400000042
Figure BDA0003371028400000043
in the formula: f. ofmnIs the m-th objective function value of the n-th particle, zmnIs the normalized value of the mth objective function value of the nth particle, m is 1,2,3, N is 1,2, N.
Preferably, the particle swarm algorithm based on the fuzzy satisfaction index solves an objective function of normalization processing, and optimally converts a multi-objective function into a particle with the highest satisfaction index, and the method comprises the following steps:
the satisfaction index of each particle is:
S＝WZ＝[S1S2···SN]wherein, W is a weight coefficient conversion matrix;
the particle is the optimal solution of the multi-objective optimization problem, namely:
Smax＝max(S1,S2,···,SN)。
preferably, the operating cost objective function F is expressed as follows:
Figure BDA0003371028400000051
wherein T is the total time period number in the micro-grid dispatching cycle, Cf(t) Fuel cost for Fuel cell and micro gas turbine consumption, Com(t) operating maintenance costs, the invention only considers fuel cells, micro gas turbines and batteries, Cgd(t) Power interaction cost with Main network, Power purchase is Positive, Power sale is negative, Ccq(t) refrigeration benefit of the microgrid, CchAnd (t) the heating benefit of the microgrid.
The invention discloses a combined cooling heating and power type micro-grid dynamic environment-friendly economic dispatching system, which comprises:
the scheduling model building module is used for building a combined cooling heating and power type microgrid environment-friendly economic scheduling model, the combined cooling heating and power type microgrid environment-friendly economic scheduling model comprises a target function and a constraint condition, and the constraint condition is used for constraining the target function; the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply; the constraint conditions comprise a cold power meter balance constraint, a thermal power meter balance constraint, an electric power balance constraint and an electric power rotation standby constraint;
and the scheduling module schedules the microgrid based on the combined cooling, heating and power supply type microgrid environment-friendly economic scheduling model.
Compared with the prior art, the technical scheme of the invention has the following advantages:
1. the invention mainly aims at a combined cooling heating and power supply type micro-grid capable of supplying power, cooling and heat simultaneously, which comprises a micro gas turbine, a waste heat boiler, a photovoltaic device, a fan, an absorption refrigerator, an electric storage device, a cold accumulation device, a heat storage device, a fuel cell and a cooling, heating and power load.
2. The invention considers the economy, the environmental protection and the refrigerating and heating benefits of the dispatching, utilizes a multi-target processing method based on the satisfaction index to process the economic target and the environmental protection target, and provides a strategic reference for the economic operation of the combined cooling heating and power micro-grid.
3. The scheduling method can meet the requirements of various types of loads, and is low in energy consumption and high in stability.
Drawings
Fig. 1 is an energy flow diagram of a combined cooling, heating and power micro-grid according to the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1, the invention discloses a dynamic environment-friendly economic dispatching method for a combined cooling heating and power micro-grid, which comprises the following steps:
s1, constructing a combined cooling heating and power type microgrid environment-friendly economic dispatching model, wherein the combined cooling heating and power type microgrid environment-friendly economic dispatching model comprises a target function and constraint conditions, and the constraint conditions constrain the target function;
the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply;
the constraint conditions comprise cold power meter balance constraint, heat power meter balance constraint, electric power balance constraint and electric power rotation standby constraint, and the constraint conditions further comprise tie line transmission power constraint, active power constraint of the micro-combustion engine and the fuel cell, climbing rate constraint of the micro-combustion engine and energy storage device operation constraint.
And S2, scheduling the microgrid based on the combined cooling heating and power supply type microgrid environment-friendly economic scheduling model.
Between S1 and S2 further include: normalizing the operation cost target function and the pollutant gas emission target function to obtain a normalized target function; and solving the normalized objective function by the particle swarm algorithm based on the fuzzy satisfaction index, and optimizing and converting the multi-objective function into the particles with the highest satisfaction index.
The technical solution of the present invention is further explained and explained with reference to the specific embodiments.
The specific form of fuzzy chance constraint planning based on credibility measure is as follows:
Figure BDA0003371028400000071
in the formula: alpha is alphai、βjIs a confidence level, Cr {. cndot } represents the credibility of the event in {. cndot.,
Figure BDA0003371028400000072
is an objective function fi(x, xi) is not less than alpha at the confidence leveliMinimum value of (g)j(x, xi) are constraint conditions, x is an n-dimensional decision vector, and xi is a fuzzy parameter vector.
1. Energy flow of combined cooling heating and power supply type micro-grid
A typical energy flow of a cogeneration microgrid is shown in fig. 1. The cold-hot electricity connected micro-grid comprises a fuel cell, a micro gas turbine, a waste heat boiler and an absorption refrigerator. The natural gas supplies energy to the fuel cell, the micro gas turbine, the waste heat boiler and the absorption refrigerator respectively. The combined cooling, heating and power micro-grid comprises an electric load, a heat load and a cold load, the wind power and photovoltaic external grid, the fuel cell, the micro gas turbine and the storage battery need the electric load, the waste heat boiler and the heat storage device generate the heat load, and the absorption refrigerator and the cold storage device generate the cold load.
2. Waste heat boiler and absorption type refrigerator model
The waste heat boiler and the refrigerator are used for heating and refrigerating by using high-temperature flue gas discharged by the micro gas turbine, and heating and refrigerating formulas are shown as follows.
Figure BDA0003371028400000081
Figure BDA0003371028400000082
Figure BDA0003371028400000083
Figure BDA0003371028400000084
In the formula: pMT,tIs the active power, eta, of the micro gas turbine during the period tMTIs the power generation efficiency of the micro gas turbine, etaLFor the heat loss efficiency of the micro gas turbine, ρtThe flow ratio rho of the flue gas introduced into the waste heat boiler in the period of tt1 represents that the flue gas completely enters the waste heat boiler, rhot0 denotes that all the flue gas is introduced into the refrigerator, QAC,t、ηAC、γACThe refrigerating capacity, the waste heat recovery efficiency and the refrigerating coefficient of the refrigerating machine in the period of t, QWH,t、ηWH、γWHThe heating capacity, the waste heat recovery efficiency and the heating coefficient of the waste heat boiler in the period of T, Ta1、Ta2Is the flue gas inlet and outlet temperature (unit is K) alpha of the waste heat boileri，Tb1、Tb2Is the temperature T of the flue gas inlet and outlet of the refrigeratorwIs ambient temperature.
3-type environmental-friendly economic dispatching model of cooling, heating and power cogeneration type micro-grid
(1) Objective function
1) Operating cost objective
The operation cost targets comprise the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of each micro power supply, and the operation cost targets comprise the following steps:
Figure BDA0003371028400000085
in the formula: t is the total time period number in the micro-grid dispatching cycle, Cf(t) Fuel cost for Fuel cell and micro gas turbine consumption, Com(t) operating maintenance costs, the invention only considers fuel cells, micro gas turbines and batteries, Cgd(t) Power interaction cost with Main network, Power purchase is Positive, Power sale is negative, Ccq(t) refrigeration benefit of the microgrid, CchAnd (t) the heating benefit of the microgrid.
2) Emission target of pollutant gas
Micro gas turbine in microgrid may exhaust SO2And NOxAnd waiting for the pollutant gas, wherein the emission target of the pollutant gas is as follows:
Figure BDA0003371028400000091
Figure BDA0003371028400000092
in the formula:
Figure BDA0003371028400000093
is NOxAnd SO2The emission coefficient of (a) is in g/(kWh), PMT,tThe output of the micro-combustion engine is in a period t.
(2) Constraint conditions
Because the wind, light, energy and cold, heat and electricity load predicted values have certain prediction errors, cold, heat and electric power balance constraints and electric power rotation standby constraints containing uncertain variables are expressed in the form of opportunity constraints, and the probability of the establishment of the constraints is ensured to meet certain confidence coefficient. It should be noted that, the traditional heating capacity and cooling capacity of the combined cooling heating and power supply type microgrid at least can meet the requirements of the heat load and the cold load in the microgrid system, and the surplus heat energy and cold energy will generate extra heating benefit and refrigeration benefit. The specific constraints are as follows:
1) electrical power balance constraints.
Figure BDA0003371028400000094
2) And (4) heat power balance constraint.
Figure BDA0003371028400000095
3) Cold power balance constraints.
Figure BDA0003371028400000101
4) The electric power rotation standby constraint adopts fuzzy variables to represent the predicted values of uncertain variables, so that no additional standby power constant item is needed in the standby constraint.
Figure BDA0003371028400000102
5) The tie transmits a power constraint.
Figure BDA0003371028400000103
6) The active power constraints of the micro-combustion engine and the fuel cell.
Pi min≤Pi,t≤Pi max (14)
7) And (4) restricting the climbing rate of the micro-combustion engine.
Figure BDA0003371028400000104
8) And (5) restraining the operation of the energy storage device. Including an electric storage device, a heat storage device, and a cold storage device, the operating constraints of which are similar.
Figure BDA0003371028400000105
Figure BDA0003371028400000106
In the formula: qWH,tThe thermal output of the waste heat boiler at the moment t; qAC,tThe cold output of the refrigerator at the moment t; qHS,tThe heat output of the heat storage device at the moment t is achieved, the heat supply is positive, and the heat storage is negative; qCS,tThe cold output of the cold accumulation device at the moment t, the cold supply is positive, and the cold storage is negative; pES,tThe charging and discharging power of the storage battery at the time t is positive, and the charging is negative; pgrid,tThe interaction electric energy with the main network at the time t is positive, and the electricity purchasing is negative;
Figure BDA0003371028400000107
the upper limit and the lower limit of the interaction electric quantity of the micro-grid and the main grid are set; alpha is a confidence coefficient;
Figure BDA0003371028400000111
Figure BDA0003371028400000112
fuzzy parameters of wind power, photovoltaic, electric load value, heat load value and cold load value at the moment t; pi,tThe output of the micro source i at the time t; pi min、Pi maxThe upper and lower output limits of the micro source i are set;
Figure BDA0003371028400000113
the maximum landslide and climbing rate of the micro gas turbine; qjFor storing energySetting j charging and discharging power;
Figure BDA0003371028400000114
the upper and lower capacity limits of the energy storage device j;
Figure BDA0003371028400000115
the maximum energy charging and discharging power of the energy storage device j is obtained;
Figure BDA0003371028400000116
the initial capacity of the energy storage device j is represented by j 1,2, and 3, which respectively represent a storage battery, a heat storage device, and a cold storage device.
4. Model solution
(1) Clear equivalence classes for opportunistic constraints
The chance constraints (9) - (12) are expressed as clear equivalence classes using the trapezoidal function method as follows:
clear equivalence classes for electric power balance constraints:
(2-2α)(PE,t3-PWT,t2-PPV,t2)-PMT,t-PFC,t+(2α-1)(PE,t4-PWT,t1-PPV,t1)-Pgrid,t-PES,t＝0 (18)
clear equivalence classes for thermal power balance constraints:
(2-2α)QH,t3+(2α-1)QH,t4-QWH,t-QHS,t＝0 (19)
clear equivalence classes for cold power balance constraints:
(2-2α)QC,t3+(2α-1)QC,t4-QAC,t-QCS,t＝0 (20)
clear equivalence classes for electric power spinning reserve constraints:
Figure BDA0003371028400000117
in the formula: pE,t1-PE,t4、QC,t3-QC,t4QH,t3-QH,t4、PWT,t1-PWT,t4、PPV,t1-PPV,t4And the parameters are membership parameters of predicted values of electric load, cold load, heat load, wind power and photovoltaic.
(2) Satisfaction-based multi-target processing method
The scheduling object of the invention simultaneously considers economic cost and SO2Emission of gases and NOxThe three targets of the gas emission amount cannot be simply converted into a single target solution by adopting a linear weighting method due to the fact that the dimensions and the magnitude of each target function are different. The invention adopts a fuzzy satisfaction degree-based index [14 ]]The particle swarm optimization solves the multi-objective optimization problem, and three objectives are solved after normalization processing.
And (4) respectively obtaining three objective function values corresponding to each particle by setting the number of the particles of the particle group as N, and obtaining a 3 XN order objective function value matrix.
Figure BDA0003371028400000121
In the formula: the row vector of F' represents a certain objective function value for all particles and the column vector represents a different objective function value for a single particle.
Normalizing the objective function value matrix of 3 XN order to obtain a normalized matrix Z:
Figure BDA0003371028400000122
Figure BDA0003371028400000123
in the formula: f. ofmnIs the m-th objective function value of the n-th particle, zmnIs the normalized value of the mth objective function value of the nth particle, m is 1,2,3, N is 1,2, N.
Assume that the weight coefficient transformation matrix is:
Figure BDA0003371028400000124
the satisfaction index of each particle is:
S＝WZ＝[S1 S2 ··· SN] (26)
the multi-objective optimization problem of the present invention is converted into a particle with the highest satisfaction index, which is the optimal solution of the multi-objective optimization problem, that is:
Smax＝max(S1,S2,···,SN) (27)
the dynamic environment-friendly economic dispatching model of the combined cooling heating and power micro-grid provided by the invention is mainly characterized in that:
1) the model models the cold and heat energy output characteristics of the waste heat boiler, the absorption refrigerator and the cold and heat storage device.
2) The wind-solar output and the uncertainty of the predicted values of the cold, heat and electric loads are considered, the cold, heat and electric power balance constraint and the electric power rotation standby constraint containing uncertain variables are represented into an opportunity constraint form, and the opportunity constraint form are converted into corresponding clear equivalence class solving.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (10)

1. A dynamic environment-friendly economic dispatching method for a combined cooling heating and power micro-grid is characterized by comprising the following steps:
s1, constructing a combined cooling heating and power type microgrid environment-friendly economic dispatching model, wherein the combined cooling heating and power type microgrid environment-friendly economic dispatching model comprises a target function and constraint conditions, and the constraint conditions constrain the target function;
the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply;
the constraint conditions comprise a cold power meter balance constraint, a thermal power meter balance constraint, an electric power balance constraint and an electric power rotation standby constraint;
and S2, scheduling the microgrid based on the combined cooling heating and power supply type microgrid environment-friendly economic scheduling model.
2. The dynamic environment-friendly economic dispatching method for the combined cooling heating and power type microgrid according to claim 1, characterized in that the constraint conditions further comprise a tie line transmission power constraint, an active power output constraint of a micro gas turbine and a fuel cell, a climbing rate constraint of the micro gas turbine and an energy storage device operation constraint.
3. The dynamic, environment-friendly and economic dispatching method for the combined cooling heating and power micro-grid according to claim 1, wherein the constraint conditions specifically comprise:
electric power balance constraint:
Figure FDA0003371028390000011
and thermal power balance constraint:
Figure FDA0003371028390000012
cold power balance constraint:
Figure FDA0003371028390000013
electric power rotation standby constraint:
Figure FDA0003371028390000021
tie line transmission power constraint:
Figure FDA0003371028390000022
active power constraints of micro-combustion engines and fuel cells:
Figure FDA0003371028390000023
and (3) limiting the climbing rate of the micro-combustion engine:
Figure FDA0003371028390000024
the energy storage device operation constraint comprises an electric power storage device, a heat storage device and a cold storage device, and the operation constraint conditions are as follows:
Figure FDA0003371028390000025
wherein Q isWH,tThe thermal output of the waste heat boiler at the moment t; qAC,tThe cold output of the refrigerator at the moment t; qHS,tThe heat output of the heat storage device at the moment t is achieved, the heat supply is positive, and the heat storage is negative; qCS,tThe cold output of the cold accumulation device at the moment t, the cold supply is positive, and the cold storage is negative; pES,tThe charging and discharging power of the storage battery at the time t is positive, and the charging is negative; pgrid,tThe interaction electric energy with the main network at the time t is positive, and the electricity purchasing is negative;
Figure FDA0003371028390000026
the upper limit and the lower limit of the interaction electric quantity of the micro-grid and the main grid are set; alpha is a confidence coefficient;
Figure FDA0003371028390000027
fuzzy parameters of wind power, photovoltaic, electric load value, heat load value and cold load value at the moment t; pi,tThe output of the micro source i at the time t;
Figure FDA0003371028390000028
the upper and lower output limits of the micro source i are set;
Figure FDA0003371028390000029
the maximum landslide and climbing rate of the micro gas turbine; qjThe charging and discharging power of the energy storage device j is obtained;
Figure FDA00033710283900000210
the upper and lower capacity limits of the energy storage device j;
Figure FDA00033710283900000211
the maximum energy charging and discharging power of the energy storage device j is obtained;
Figure FDA00033710283900000212
in the initial capacity of the energy storage device j, j is 1,2, and 3 denote a battery, a heat storage device, and a cold storage device, respectively.
4. The dynamic, environment-friendly and economic dispatching method for the combined cooling, heating and power microgrid according to claim 1, characterized in that the emission of the polluted gas comprises SO emitted by a micro gas turbine in the microgrid2Emission of pollutant gases and NOxAnd (4) discharging the polluted gas.
5. The dynamic, environment-friendly and economic dispatching method for the combined cooling heating and power micro-grid according to claim 4, wherein the objective function of the emission of the polluted gas is as follows:
Figure FDA0003371028390000031
Figure FDA0003371028390000032
wherein the content of the first and second substances,
Figure FDA0003371028390000033
is NOxAnd SO2The emission coefficient of (a) is in g/(kWh), PMT,tThe output of the micro-combustion engine is in a period t.
6. The dynamic, environment-friendly and economic dispatching method for a combined cooling, heating and power microgrid according to claim 1, characterized in that the dispatching method between S1 and S2 further comprises:
normalizing the operation cost target function and the pollutant gas emission target function to obtain a normalized target function;
and solving the normalized objective function by the particle swarm algorithm based on the fuzzy satisfaction index, and optimizing and converting the multi-objective function into the particles with the highest satisfaction index.
7. The dynamic environment-friendly economic dispatching method for the combined cooling heating and power microgrid according to claim 6, characterized in that the normalization processing of the running cost objective function and the pollutant gas emission objective function to obtain the normalized objective function comprises:
setting the number of particles in the particle group as N, respectively obtaining three objective function values corresponding to each particle to obtain a 3 XN order objective function value matrix:
Figure FDA0003371028390000034
wherein, the row vector of F' represents a certain objective function value of all particles, and the column vector represents different objective function values of single particle, wherein, the three objective function values are respectively the running cost objective function and SO2Emission of pollutant gases and NOxEmission of pollution gas;
normalizing the objective function value matrix of 3 XN order to obtain a normalized matrix Z:
Figure FDA0003371028390000041
Figure FDA0003371028390000042
wherein f ismnIs the m-th objective function value of the n-th particle, zmnIs the normalized value of the mth objective function value of the nth particle, m is 1,2,3, N is 1,2, N.
8. The dynamic, environment-friendly and economic dispatching method for the combined cooling, heating and power micro-grid according to claim 7, wherein the fuzzy satisfaction index-based particle swarm algorithm solves an objective function of normalization processing, and the multi-objective function is optimized and converted into particles with the highest solving satisfaction index, and the method comprises the following steps:
the satisfaction index of each particle is:
S＝WZ＝[S1 S2 ··· SN]wherein, W is a weight coefficient conversion matrix;
the particle is the optimal solution of the multi-objective optimization problem, namely:
Smax＝max(S1,S2,···,SN)。
9. the dynamic environment-friendly economic dispatching method for the combined cooling heating and power micro-grid according to claim 1, wherein the running cost objective function F is expressed as follows:
Figure FDA0003371028390000043
wherein T is the total time period number in the micro-grid dispatching cycle, Cf(t) Fuel cost for Fuel cell and micro gas turbine consumption, Com(t) operating maintenance costs, the invention only considers fuel cells, micro gas turbines and batteries, Cgd(t) Power interaction cost with Main network, Power purchase is Positive, Power sale is negative, Ccq(t) refrigeration benefit of the microgrid, CchAnd (t) the heating benefit of the microgrid.
10. The utility model provides a little electric wire netting developments environmental protection economic dispatch system of combined cooling heating and power supply type which characterized in that includes:
the scheduling model building module is used for building a combined cooling heating and power type microgrid environment-friendly economic scheduling model, the combined cooling heating and power type microgrid environment-friendly economic scheduling model comprises a target function and a constraint condition, and the constraint condition is used for constraining the target function; the objective function comprises an operation cost objective function and a pollutant gas emission objective function, wherein the operation cost objective comprises the operation cost, the operation maintenance cost, the electricity purchasing cost to the main network, the refrigeration and heating income of the combined cooling, heating and power generation and the electricity selling income to the main network of the micro power supply; the constraint conditions comprise a cold power meter balance constraint, a thermal power meter balance constraint, an electric power balance constraint and an electric power rotation standby constraint;
and the scheduling module schedules the microgrid based on the combined cooling, heating and power supply type microgrid environment-friendly economic scheduling model.
CN202111400915.8A 2021-10-14 2021-11-24 Dynamic environment-friendly economic dispatching method and system for cold-heat-power combined supply type micro-grid Pending CN114091917A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115239032A (en) * 2022-09-26 2022-10-25 华北电力大学 Highway service area microgrid planning method and system considering energy self-consistency rate

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115239032A (en) * 2022-09-26 2022-10-25 华北电力大学 Highway service area microgrid planning method and system considering energy self-consistency rate

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