CN114935205A - Double-model optimized cooperative control method for multi-variable-frequency air conditioner - Google Patents

Double-model optimized cooperative control method for multi-variable-frequency air conditioner Download PDF

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CN114935205A
CN114935205A CN202210894239.2A CN202210894239A CN114935205A CN 114935205 A CN114935205 A CN 114935205A CN 202210894239 A CN202210894239 A CN 202210894239A CN 114935205 A CN114935205 A CN 114935205A
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air conditioner
frequency air
variable frequency
variable
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CN114935205B (en
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彭涛
樊立攀
田瑞
傅晨
刘鸣
周世祺
刘喆成
余鹤
汪应春
魏伟
王文
叶睿文
赵煜东
王璟韬
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Metering Center of State Grid Hubei Electric Power Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The application relates to a double-model optimized cooperative control method of a multi-variable-frequency air conditioner, which comprises the following steps of: constructing a system framework aiming at the problem of supply and demand balance in the resource scheduling of the demand side of the micro power grid with the renewable new energy consumption capability; performing double-model mathematical modeling on a single controllable and adjustable load, namely the variable frequency air conditioner; on the premise that the indoor temperature meets the constraint target of the comfortable temperature range, setting a tracking target for a controllable regulation load, namely the variable frequency air conditioner; establishing an information exchange topological relation among multiple controllable adjusting loads, namely multiple variable-frequency air conditioners; designing a cooperative control strategy of iterative optimization according to the double models and the control target; providing related data, and performing feasibility analysis and theoretical verification according to MATLAB; the problem of supply and demand balance in power grid demand side resource scheduling with renewable new energy consumption is solved on the premise of meeting indoor temperature comfort degree constraint. The method and the device solve the problem of the coupled complex network of the cooperative tracking target with temperature comfort degree constraint.

Description

Double-model optimized cooperative control method of multi-variable-frequency air conditioner
Technical Field
The application relates to a double-model optimized cooperative control method, in particular to an optimized control method for processing constraint conditions of a novel multi-variable-frequency air conditioner for power grid demand side resource scheduling with renewable new energy consumption.
Background
With the rapid development of modern renewable new energy, resource scheduling on the demand side of a power grid becomes a hot research problem. Due to environmental and economic considerations, research into new smart grids that can consume and utilize new renewable energy sources has received great attention from the scientific community. While many techniques for harvesting solar and solar energy have been developed, integration of distributed generation with conventional generation remains a significant challenge because wind and solar generated power fluctuates and has a high degree of uncertainty, thus resulting in the generated power not being matched to the load consumed power, resulting in a supply and demand imbalance problem. In order to solve the problem of scheduling of resources on the demand side of a power grid with renewable new energy consumption, currently, a method for managing supply and demand balance generated by distributed renewable power generation by using storage batteries, hydrogen-electricity conversion and other storage devices is provided, but the method is not suitable for large-scale operation due to high cost.
Another approach is to allow Energy Management System (EMS) deployments to control the load in real time so that fluctuations in power generated by the distributed power supply can be absorbed by controllably adjusting load changes. This is the treatment method referred to in this application. The basic idea is to operate a series of constant Temperature Control Loads (TCLs), such as variable frequency air conditioners and refrigeration systems, to satisfy the supply and demand balance between the total power consumption of the TCLs and the power generated by the distributed power generation equipment. More specifically, the power consumption area of each TCL (variable frequency air conditioner) is changed by adjusting the temperature set point within the user comfort range so that the number of TCLs (variable frequency air conditioners) can make an aggregated power demand response on an hourly or minute time scale to track the power of the renewable new energy distributed generator.
Disclosure of Invention
The embodiment of the application aims to provide a double-model optimized cooperative control method for a multi-variable-frequency air conditioner, which meets the requirement of supply and demand balance of energy consumption of the multi-variable-frequency air conditioner and the capacity of a renewable new energy distributed generator by selecting appropriate control parameters.
In order to achieve the above purpose, the present application provides the following technical solutions:
the embodiment of the application provides a double-model optimized cooperative control method for a multi-variable-frequency air conditioner, which comprises the following specific steps of:
s1, constructing a system framework aiming at a supply and demand balance problem in the resource scheduling of a demand side of a micro power grid with renewable new energy consumption capacity;
s2, carrying out double-model mathematical modeling on a single controllable and adjustable load, namely the variable frequency air conditioner;
s3, setting a tracking target for a controllable regulation load, namely the variable frequency air conditioner, on the premise that the indoor temperature meets the constraint target of the comfortable temperature range;
s4, establishing an information exchange topological relation among multiple controllable adjusting loads, namely multiple variable-frequency air conditioners;
s5, designing a cooperative control strategy of iterative optimization according to the double models and the control target;
s6, providing relevant data, and performing feasibility analysis and theoretical verification according to MATLAB;
and S7, realizing the supply and demand balance problem in the power grid demand side resource scheduling with renewable new energy consumption on the premise of meeting the indoor temperature comfort degree constraint.
In step S1, in order to satisfy the problem of balance between supply and demand in the resource scheduling at the demand side of the power grid, the sum of the electric energy generated by the novel power grid with renewable new energy consumption and the electric energy consumed by the N multi-variable frequency air conditioning loads should satisfy the following relationship:
Figure 885602DEST_PATH_IMAGE001
(1)
wherein the content of the first and second substances,
Figure 777334DEST_PATH_IMAGE002
represents the first
Figure 367716DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 65413DEST_PATH_IMAGE004
Energy consumed at a time;
Figure 956009DEST_PATH_IMAGE005
represents the sum of the electrical energy generated by a new electrical network with new renewable energy consumption;
since the multi-inverter air conditioning system is limited by hardware equipment, the consumed power of the inverter air conditioner should meet the following constraints:
Figure 80960DEST_PATH_IMAGE006
（2）
wherein the content of the first and second substances,
Figure 158637DEST_PATH_IMAGE007
representing the rated power of the multi-variable frequency air conditioning system.
In step S2, the relationship between the generated temperature and the consumed power for the multi-variable frequency air conditioning system is as follows:
Figure 394446DEST_PATH_IMAGE008
（3）
wherein, the first and the second end of the pipe are connected with each other,
Figure 998603DEST_PATH_IMAGE009
representing a variable frequency air conditioner
Figure 435401DEST_PATH_IMAGE010
The temperature generated at the moment;
Figure 921746DEST_PATH_IMAGE011
represents the indoor ambient temperature;
Figure 102191DEST_PATH_IMAGE012
perturbation factors representing various uncertainties;
Figure 826434DEST_PATH_IMAGE013
represents a sampling interval;
Figure 168553DEST_PATH_IMAGE014
represents the thermal capacitance coefficient;
Figure 345457DEST_PATH_IMAGE015
represents the thermal impedance coefficient;
Figure 329593DEST_PATH_IMAGE016
represents a controllable regulated load efficiency;
due to the fact that in practical application
Figure 314867DEST_PATH_IMAGE017
Are all constants that are obtained directly, and therefore the above equation (3) is subjected to the following processing:
first order
Figure 218101DEST_PATH_IMAGE018
Representing the fluctuation difference value of the temperature generated by the inverter air conditioner and the indoor environment temperature, the equation (3) becomes the following model:
Figure 757666DEST_PATH_IMAGE019
（4）
then in the second place
Figure 404548DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 713170DEST_PATH_IMAGE010
Energy consumed at a moment
Figure 787305DEST_PATH_IMAGE020
Derivation and discretization processing to obtain the following model:
Figure 814167DEST_PATH_IMAGE021
（5）
wherein
Figure 999161DEST_PATH_IMAGE022
Which represents a control input that is to be controlled,
Figure 162289DEST_PATH_IMAGE013
represents a sampling interval;
thereby obtaining a double-model mathematical modeling of a single controllable adjusting load, namely the variable frequency air conditioner, namely formulas (4) and (5); in order to ensure that the indoor temperature is always kept in a comfortable environment, the fluctuation difference value of the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 876167DEST_PATH_IMAGE023
The following constraints must be satisfied:
Figure 249379DEST_PATH_IMAGE024
（6）
wherein
Figure 113430DEST_PATH_IMAGE025
Representing the minimum and maximum values of the temperature fluctuation, respectively.
Specifically, in the step S3, the tracking target is set for the controllable adjustment load, i.e. the variable frequency air conditioner,
the following processing is performed according to equation (1):
Figure 521278DEST_PATH_IMAGE026
（7）
it is only necessary to ensure that one of the controllable and adjustable loads, namely the inverter air conditioner, consumes power which tracks one-N times of the total power generated by the renewable energy sources, namely
Figure 812582DEST_PATH_IMAGE027
And the rest controllable adjusting loads, namely the variable frequency air conditioner, track the adjacent neighbor nodes, namely:
Figure 345194DEST_PATH_IMAGE028
（8）。
in the step S5, according to the dual model and the control objective, a collaborative control strategy for iterative optimization is specifically designed,
make disturbance
Figure 137570DEST_PATH_IMAGE029
That is, uncertainty factors such as external disturbance are not considered, so that the following space vector models are integrated by the bimodal equations (4) and (5):
Figure 275290DEST_PATH_IMAGE030
（9）
wherein the content of the first and second substances,
Figure 862129DEST_PATH_IMAGE031
then, aiming at the equation (9), combining the indoor temperature comfort degree constraint equation (6) and the controllable regulation load, namely the variable frequency air conditioner tracking target equation (8), obtaining the following iterative optimization coordination control strategy:
Figure 85300DEST_PATH_IMAGE032
（10）
wherein
Figure 415787DEST_PATH_IMAGE033
Is represented in
Figure 408014DEST_PATH_IMAGE010
Iterate backwards in time
Figure 165755DEST_PATH_IMAGE034
The state of the step(s) is,
Figure 876222DEST_PATH_IMAGE035
is represented in
Figure 744821DEST_PATH_IMAGE010
Iterate backwards in time
Figure 857133DEST_PATH_IMAGE034
The control input of the step(s) is,
Figure 785775DEST_PATH_IMAGE036
represents the number of iteration steps and the number of the iteration steps,
Figure 983538DEST_PATH_IMAGE037
representing the tracking target(s),
Figure 327932DEST_PATH_IMAGE038
and
Figure 419384DEST_PATH_IMAGE039
representing a weight matrix;
and solving the partial derivative of the iterative optimization equation (10) to obtain an optimal control sequence, thereby realizing a coordination control target.
The tracking target (8) in the step S3 is further refined to obtain the following tracking target
Figure 987769DEST_PATH_IMAGE040
：
Figure 407249DEST_PATH_IMAGE041
Wherein the first inverter air conditioner consumes power
Figure 555334DEST_PATH_IMAGE042
Tracking total bus power generated by new power grid with new renewable energy consumption
Figure 501293DEST_PATH_IMAGE043
I.e. by
Figure 912683DEST_PATH_IMAGE027
The residual multi-frequency-conversion air conditioner obtains the state information of the neighbor nodes through the information exchange topological relation, and therefore the average value of the self state and the neighbor states is tracked
Figure 678513DEST_PATH_IMAGE044
Finally when the first one
Figure 567972DEST_PATH_IMAGE042
Track to
Figure 634017DEST_PATH_IMAGE027
Average value of self and neighbor states of other multi-frequency-conversion air conditioners
Figure 216308DEST_PATH_IMAGE045
And further all the variable frequency air conditioners can realize the tracking target.
The specific step of the step S5 is,
firstly, the following iterative calculation is carried out by using a space vector model (9):
Figure 735014DEST_PATH_IMAGE046
the above iterative calculations are then written in the form of a matrix as follows:
Figure 428164DEST_PATH_IMAGE047
（13）
wherein the content of the first and second substances,
Figure 83136DEST_PATH_IMAGE048
，
Figure 836328DEST_PATH_IMAGE049
，
Figure 514434DEST_PATH_IMAGE050
，
Figure 135908DEST_PATH_IMAGE051
the iterative optimization function that is obtained by substituting the above iteratively calculated equation (13) into equation (10) is as follows:
Figure 786333DEST_PATH_IMAGE052
(14)
wherein the content of the first and second substances,
Figure 569481DEST_PATH_IMAGE053
not only representing the fluctuation difference between the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 938145DEST_PATH_IMAGE054
I.e. to satisfy the temperature comfort constraint and to express letting
Figure 97731DEST_PATH_IMAGE055
A variable frequency air conditioner
Figure 602662DEST_PATH_IMAGE010
Energy consumed at a moment
Figure 37272DEST_PATH_IMAGE056
Track to target tracking
Figure 17866DEST_PATH_IMAGE057
Then, the iterative optimization function (14) is subjected to partial derivation, and an optimal control sequence can be obtained as follows:
Figure 856509DEST_PATH_IMAGE058
（15）
then the first item of the optimal control sequence is taken as the control input to (9) to realize closed-loop control.
Compared with the prior art, the invention has the beneficial effects that: providing a new algorithm of cooperative control based on dual-model iterative optimization; the method can be suitable for large power systems and small micro-grid systems; the problem of supply and demand balance of power grid demand side resource scheduling with renewable new energy consumption is solved; the problem of a coupled complex network of cooperative tracking targets with temperature comfort constraints can be solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a control flow chart of a control method according to an embodiment of the present invention;
fig. 2 is a communication topology diagram of a multi-variable-frequency air conditioner according to an embodiment of the present invention;
FIG. 3 is a structural diagram of a micro power system with new renewable energy consumption capability according to an embodiment of the present invention;
fig. 4 is a graph of indoor temperatures generated by a multi-variable frequency air conditioner according to an embodiment of the present invention;
fig. 5 is a diagram illustrating an error between the indoor temperature and the ambient temperature generated by the multi-variable-frequency air conditioner according to the embodiment of the present invention;
fig. 6 is a power diagram of the multi-variable-frequency air conditioner according to an embodiment of the present invention;
FIG. 7 is a graph of the total power generated by distributed generation and the total power consumed by multi-inverter air conditioners provided by an embodiment of the present invention;
fig. 8 is a diagram illustrating an error between the total power generated by distributed power generation and the total power consumed by a multi-variable-frequency air conditioner according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present application provides a dual-model optimized cooperative control method for a multi-variable frequency air conditioner, including the following specific steps:
s1, constructing a system framework aiming at a supply and demand balance problem in resource scheduling of a demand side of a micro power grid with renewable new energy consumption capacity;
s2, carrying out double-model mathematical modeling on a single controllable and adjustable load, namely the variable frequency air conditioner;
s3, setting a tracking target for a controllable regulation load, namely the variable frequency air conditioner, on the premise that the indoor temperature meets the constraint target of the comfortable temperature range;
s4, establishing an information exchange topological relation among multiple controllable adjusting loads, namely multiple multi-variable-frequency air conditioners, and establishing a communication topological graph between a total bus of electric energy generated by a novel power grid with renewable new energy consumption and N multi-variable-frequency air conditioner loads according to graph theory knowledge;
s5, designing a cooperative control strategy of iterative optimization according to the double models and the control target;
s6, providing relevant data, and performing feasibility analysis and theoretical verification according to MATLAB;
and S7, realizing the supply and demand balance problem in the power grid demand side resource scheduling with renewable new energy consumption on the premise of meeting the indoor temperature comfort degree constraint.
Firstly, the electric energy generated by four renewable new energy power generation devices is gathered to the same bus power grid through a direct current/alternating current converter by considering two distributed wind power generation devices and two photovoltaic power generation devices, so that a micro power system with renewable new energy consumption is formed. The load part of the system consists of 6 controllable adjusting loads (a multi-variable-frequency air conditioning system). In step S1, in order to satisfy the problem of balance between supply and demand in the resource scheduling at the demand side of the power grid, the sum of the electric energy generated by the novel power grid with renewable new energy consumption and the electric energy consumed by the N multi-variable frequency air conditioning loads should satisfy the following relationship:
Figure 340580DEST_PATH_IMAGE001
(1)
wherein the content of the first and second substances,
Figure 606476DEST_PATH_IMAGE002
represents the first
Figure 808787DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 716701DEST_PATH_IMAGE004
Energy consumed at a time;
Figure 320857DEST_PATH_IMAGE005
represents the sum of the electrical energy generated by a new electrical network with new renewable energy consumption;
since the multi-inverter air conditioning system is limited by hardware devices, the consumed power of the inverter air conditioner should satisfy the following constraints:
Figure 492076DEST_PATH_IMAGE006
（2）
wherein the content of the first and second substances,
Figure 181683DEST_PATH_IMAGE007
representing the rated power of the multi-variable frequency air conditioning system.
In step S2, the relationship between the generated temperature and the consumed power for the multi-variable frequency air conditioning system is as follows:
Figure 362128DEST_PATH_IMAGE008
（3）
wherein the content of the first and second substances,
Figure 820792DEST_PATH_IMAGE009
representing a variable frequency air conditioner
Figure 428491DEST_PATH_IMAGE010
The temperature generated at the moment;
Figure 605394DEST_PATH_IMAGE011
represents the indoor ambient temperature;
Figure 323951DEST_PATH_IMAGE012
perturbation factors representing various uncertainties;
Figure 574804DEST_PATH_IMAGE013
represents a sampling interval;
Figure 478038DEST_PATH_IMAGE014
represents the thermal capacitance coefficient;
Figure 611079DEST_PATH_IMAGE015
represents the thermal impedance coefficient;
Figure 133327DEST_PATH_IMAGE016
represents a controllable regulated load efficiency;
due to the practical application
Figure 566583DEST_PATH_IMAGE017
Are all constants that are obtained directly, and therefore the above equation (3) is subjected to the following processing:
first order
Figure 516084DEST_PATH_IMAGE018
Representing the fluctuation difference value of the temperature generated by the inverter air conditioner and the indoor environment temperature, the equation (3) becomes the following model:
Figure 402000DEST_PATH_IMAGE019
（4）
then in the second place
Figure 462360DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 750122DEST_PATH_IMAGE010
Energy consumed at a moment
Figure 870525DEST_PATH_IMAGE020
Derivation and discretization processing to obtain the following model:
Figure 774896DEST_PATH_IMAGE021
（5）
wherein
Figure 638947DEST_PATH_IMAGE022
Which represents a control input that is to be controlled,
Figure 781215DEST_PATH_IMAGE013
represents a sampling interval;
thereby obtaining a double-model mathematical modeling of a single controllable adjusting load, namely the variable frequency air conditioner, namely formulas (4) and (5); in order to ensure that the indoor temperature is always kept in a comfortable environment, the fluctuation difference value of the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 72519DEST_PATH_IMAGE023
The following constraints must be satisfied:
Figure 933028DEST_PATH_IMAGE024
（6）
wherein
Figure 663086DEST_PATH_IMAGE025
Representing the minimum and maximum values of the temperature fluctuation, respectively.
In step S3, the set tracking target for the controllable adjustment load, that is, the variable frequency air conditioner, is specifically,
the following processing is performed according to equation (1):
Figure 331965DEST_PATH_IMAGE026
（7）
it is only necessary to ensure that one of the controllable and adjustable loads, namely the inverter air conditioner, consumes power which tracks one-N times of the total power generated by the renewable energy sources, namely
Figure 184384DEST_PATH_IMAGE027
And the rest controllable adjusting loads, namely the variable frequency air conditioner, track the adjacent neighbor nodes, namely:
Figure 407554DEST_PATH_IMAGE028
（8）。
in the step S5, according to the dual model and the control objective, a collaborative control strategy for iterative optimization is specifically designed,
make the disturbance
Figure 941304DEST_PATH_IMAGE029
That is, uncertainty factors such as external disturbance are not considered, so the dual-model equations (4) and (5) are integrated into the following space vector model:
Figure 58165DEST_PATH_IMAGE030
（9）
wherein the content of the first and second substances,
Figure 956850DEST_PATH_IMAGE031
then, aiming at the formula (9), combining an indoor temperature comfort degree constraint formula (6) and a controllable regulation load, namely a variable frequency air conditioner tracking target formula (8), obtaining the following iterative optimization coordination control strategy:
Figure 526372DEST_PATH_IMAGE032
（10）
wherein
Figure 535916DEST_PATH_IMAGE033
Is represented in
Figure 772863DEST_PATH_IMAGE010
Iterate backwards in time
Figure 576871DEST_PATH_IMAGE034
The state of the step(s) is,
Figure 899267DEST_PATH_IMAGE035
is represented in
Figure 243661DEST_PATH_IMAGE010
Iterate backwards in time
Figure 210480DEST_PATH_IMAGE034
The control input of the step(s) is,
Figure 310023DEST_PATH_IMAGE036
represents the number of iteration steps and represents the number of iteration steps,
Figure 995082DEST_PATH_IMAGE037
representing the tracking target(s),
Figure 471063DEST_PATH_IMAGE038
and
Figure 292389DEST_PATH_IMAGE039
representing a weight matrix;
and solving the partial derivative of the iterative optimization equation (10) to obtain an optimal control sequence, thereby realizing a coordination control target.
The multi-variable-frequency air conditioner load system comprises N controllable adjusting loads (variable-frequency air conditioners). We use directed graphs
Figure 828412DEST_PATH_IMAGE059
To represent information communication between the inverter air conditioners. Wherein, set
Figure 735188DEST_PATH_IMAGE060
Respectively representing a point set (multi-variable frequency air conditioning load system), an edge set and an adjacency matrix. Edge
Figure 749281DEST_PATH_IMAGE061
Is shown as
Figure 690692DEST_PATH_IMAGE062
The inverter air conditioner can receive
Figure 132038DEST_PATH_IMAGE063
Status information of individual inverter air conditioners, i.e. second
Figure 260531DEST_PATH_IMAGE063
A variable frequency air conditioner is
Figure 78314DEST_PATH_IMAGE062
A neighbor of the inverter air conditioner. Collection
Figure 139811DEST_PATH_IMAGE064
Is shown as
Figure 17637DEST_PATH_IMAGE062
The set of all neighbors of the inverter air conditioner.
The adjacency matrix of the directed graph is specifically defined as: if frequency conversion air conditioner
Figure 899005DEST_PATH_IMAGE062
Variable frequency air conditioner
Figure 254900DEST_PATH_IMAGE065
There is information exchange between them, then
Figure 233221DEST_PATH_IMAGE066
(ii) a Otherwise, if there is no information exchange, then
Figure 762508DEST_PATH_IMAGE067
. Definition of the inverter air conditioner without connectivity, i.e.
Figure 131173DEST_PATH_IMAGE067
，
Figure 290759DEST_PATH_IMAGE068
。
Therefore, according to the knowledge of the graph theory, an information exchange topological relation between multiple controllable regulation loads (multiple variable frequency air conditioners) can be established.
The tracking target (8) in the step S3 is further refined to obtain the following tracking target
Figure 795689DEST_PATH_IMAGE069
：
Figure 15318DEST_PATH_IMAGE070
Wherein the first inverter air conditioner consumes power
Figure 871279DEST_PATH_IMAGE071
Tracking total bus power generated by new power grid with new renewable energy consumption
Figure 568976DEST_PATH_IMAGE072
I.e. by
Figure 193993DEST_PATH_IMAGE073
The residual multi-frequency-conversion air conditioner obtains the state information of the neighbor nodes through the information exchange topological relation, and therefore the average value of the states of the residual multi-frequency-conversion air conditioner and the neighbor nodes is tracked
Figure 584523DEST_PATH_IMAGE074
Finally when the first one
Figure 662200DEST_PATH_IMAGE071
Track to
Figure 101272DEST_PATH_IMAGE073
Average value of self and neighbor states of other multi-variable-frequency air conditioners
Figure 908691DEST_PATH_IMAGE075
And further all the variable frequency air conditioners can realize the tracking target.
The specific step of the step S5 is,
firstly, the following iterative calculation is carried out by using a space vector model (9):
Figure 142226DEST_PATH_IMAGE046
the above iterative calculations are then written in the form of a matrix as follows:
Figure 831833DEST_PATH_IMAGE076
（13）
wherein the content of the first and second substances,
Figure 746700DEST_PATH_IMAGE077
，
Figure 470942DEST_PATH_IMAGE078
，
Figure 78641DEST_PATH_IMAGE079
，
Figure 255544DEST_PATH_IMAGE080
the iterative optimization function that is obtained by substituting the above iteratively calculated equation (13) into equation (10) is as follows:
Figure 974102DEST_PATH_IMAGE081
(14)
wherein the content of the first and second substances,
Figure 552850DEST_PATH_IMAGE082
not only representing the fluctuation difference between the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 331451DEST_PATH_IMAGE083
I.e. to satisfy the temperature comfort constraint and to express letting
Figure 995650DEST_PATH_IMAGE084
A variable frequency air conditioner
Figure 252319DEST_PATH_IMAGE085
Energy consumed at a moment
Figure 685574DEST_PATH_IMAGE086
Track to target tracking
Figure 635076DEST_PATH_IMAGE087
Then, the iterative optimization function (14) is subjected to partial derivation, and an optimal control sequence can be obtained as follows:
Figure 193096DEST_PATH_IMAGE088
（15）
then the first item of the optimal control sequence is taken as the control input to (9), and the closed-loop control can be realized.
The present application is further described below by referring to the figures and MATLAB simulation examples.
In MATLAB simulation verification, as shown in fig. 3, a micro power system with renewable new energy consumption capability, which is composed of 2 wind power generation devices, 2 photovoltaic power generation devices and 6 variable frequency air conditioners, is considered, wherein a total electric energy model generated by the 2 wind power generation devices and the 2 photovoltaic power generation devices is as follows:
Figure 643669DEST_PATH_IMAGE089
relevant parameters of the 6 variable frequency air conditioners are shown in a table 1, and parameters of the dual-model iterative optimization coordination control are shown in a table 2:
TABLE 1. related parameters in multi-variable frequency air conditioner load system
Figure 806797DEST_PATH_IMAGE090
TABLE 2 Dual model iterative optimization coordination control parameters
Figure 51834DEST_PATH_IMAGE091
Fig. 2 is a communication topology diagram of a multi-variable frequency air conditioner, which represents information exchange between the multi-variable frequency air conditioners.
Fig. 4 is a graph showing indoor temperature generated by the inverter air conditioner, and it can be seen from simulation results that the temperature generated by the inverter air conditioner is always kept within the temperature comfort degree constraint range (i.e. between 21 ℃ and 29 ℃) within 24 h.
Fig. 5 shows the difference between the indoor temperature and the ambient temperature generated by the multi-variable-frequency air conditioner, and it can be seen that the temperature fluctuation difference is always kept between-4 ℃ and 4 ℃ within 24h, thereby ensuring that the indoor temperature difference is controlled within the temperature comfort range.
Fig. 6 is a diagram showing the power consumed by the multiple inverter air conditioners, and it can be seen that after a short adjustment, the power consumed by all the inverter air conditioners can be substantially stabilized to one sixth of the total power generated by the distributed power generation, and the existing error is very small and can be substantially ignored.
FIG. 7 is a graph showing a total power trace for the total power generated by distributed generation and the total power consumed by multi-inverter air conditioners. It can be obviously seen that the total power consumed by the multi-inverter air conditioner can be well tracked to the total power generated by the distributed power generation basically, and as can be seen from fig. 8, the error between the two is very small, as long as the error is a few tenths of a day.
In conclusion, the cooperative control method of the dual-model iterative optimization provided by the application is finally proved to be capable of well realizing the supply and demand balance problem in the power grid demand side resource scheduling with renewable new energy consumption on the premise of meeting the indoor temperature comfort degree constraint.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (7)

1. A double-model optimized cooperative control method of a multi-variable-frequency air conditioner is characterized by comprising the following specific steps of:
s1, constructing a system framework aiming at a supply and demand balance problem in the resource scheduling of a demand side of a micro power grid with renewable new energy consumption capacity;
s2, carrying out double-model mathematical modeling on a single controllable and adjustable load, namely the variable frequency air conditioner;
s3, setting a tracking target for a controllable regulation load, namely the variable frequency air conditioner, on the premise that the indoor temperature meets the constraint target of the comfortable temperature range;
s4, establishing an information exchange topological relation among multiple controllable adjusting loads, namely multiple variable-frequency air conditioners;
s5, designing a cooperative control strategy of iterative optimization according to the double models and the control target;
s6, providing relevant data, and performing feasibility analysis and theoretical verification according to MATLAB;
and S7, realizing the supply and demand balance problem in the power grid demand side resource scheduling with new renewable energy consumption on the premise of meeting the indoor temperature comfort degree constraint.
2. The cooperative control method for the dual-model optimization of the multi-variable-frequency air conditioner according to claim 1, wherein in step S1, in order to satisfy the problem of supply and demand balance in the resource scheduling of the demand side of the power grid, the sum of the electric energy generated by the novel power grid with renewable new energy consumption and the electric energy consumed by the N multi-variable-frequency air conditioner loads should satisfy the following relationship:
Figure 907983DEST_PATH_IMAGE001
(1)
wherein the content of the first and second substances,
Figure 956711DEST_PATH_IMAGE002
represents the first
Figure 122113DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 157065DEST_PATH_IMAGE004
Energy consumed at a time;
Figure 786629DEST_PATH_IMAGE005
represents the sum of the electrical energy generated by a new electrical network with new renewable energy consumption;
since the multi-inverter air conditioning system is limited by hardware devices, the consumed power of the inverter air conditioner should satisfy the following constraints:
Figure 881624DEST_PATH_IMAGE006
（2）
wherein the content of the first and second substances,
Figure 799902DEST_PATH_IMAGE007
representing the rated power of the multi-variable frequency air conditioning system.
3. The cooperative control method for dual model optimization of multi-variable frequency air conditioners according to claim 2, wherein the relationship between the generated temperature and the consumed power of the multi-variable frequency air conditioner system in the step S2 is as follows:
Figure 435282DEST_PATH_IMAGE008
（3）
wherein the content of the first and second substances,
Figure 388195DEST_PATH_IMAGE009
representing a variable frequency air conditioner
Figure 450829DEST_PATH_IMAGE010
The temperature generated at the moment;
Figure 653140DEST_PATH_IMAGE011
represents the indoor ambient temperature;
Figure 29895DEST_PATH_IMAGE012
perturbation factors representing various uncertainties;
Figure 634052DEST_PATH_IMAGE013
represents a sampling interval;
Figure 70849DEST_PATH_IMAGE014
represents the thermal capacitance coefficient;
Figure 963719DEST_PATH_IMAGE015
represents the thermal impedance coefficient;
Figure 268798DEST_PATH_IMAGE016
represents a controllable regulated load efficiency;
due to the practical application
Figure 868407DEST_PATH_IMAGE017
Are all constants that are directly obtained, and therefore the following is performed on the above equation (3):
first order
Figure 272843DEST_PATH_IMAGE018
Representing the fluctuation difference value of the temperature generated by the inverter air conditioner and the indoor environment temperature, the equation (3) becomes the following model:
Figure 184167DEST_PATH_IMAGE019
（4）
then in the second place
Figure 168304DEST_PATH_IMAGE003
A variable frequency air conditioner
Figure 747053DEST_PATH_IMAGE010
Energy consumed at a moment
Figure 525653DEST_PATH_IMAGE020
Derivation and discretization processing to obtain the following model:
Figure 924273DEST_PATH_IMAGE021
（5）
wherein
Figure 446522DEST_PATH_IMAGE022
Which represents a control input, is provided,
Figure 879777DEST_PATH_IMAGE013
represents a sampling interval;
thereby obtaining a double-model mathematical modeling of a single controllable adjusting load, namely the variable frequency air conditioner, namely formulas (4) and (5); in order to ensure that the indoor temperature is always kept in a comfortable environment, the fluctuation difference value of the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 829278DEST_PATH_IMAGE023
The following constraints must be satisfied:
Figure 980774DEST_PATH_IMAGE024
（6）
wherein
Figure 41134DEST_PATH_IMAGE025
Representing the minimum and maximum values of the temperature fluctuation, respectively.
4. The cooperative control method for dual model optimization of multi-inverter air conditioners according to claim 3, wherein the step S3 is to set a tracking target for the controllable load (inverter air conditioner),
the following processing is performed according to equation (1):
Figure 594475DEST_PATH_IMAGE026
（7）
it is only necessary to ensure that one of the controllable and adjustable loads, namely the inverter air conditioner, consumes power which tracks one-N times of the total power generated by the renewable energy sources, namely
Figure 714878DEST_PATH_IMAGE027
And the rest controllable adjusting loads, namely the variable frequency air conditioner, track the adjacent neighbor nodes, namely:
Figure 88090DEST_PATH_IMAGE028
（8）。
5. the cooperative control method for dual model optimization of multi-variable frequency air conditioner according to claim 4, wherein in step S5, based on the dual models and the control objective, an iterative optimization cooperative control strategy is designed specifically,
make disturbance
Figure 686562DEST_PATH_IMAGE029
That is, uncertainty factors such as external disturbance are not considered, so the dual-model equations (4) and (5) are integrated into the following space vector model:
Figure 94409DEST_PATH_IMAGE030
（9）
wherein the content of the first and second substances,
Figure 385713DEST_PATH_IMAGE031
then, aiming at the formula (9), combining an indoor temperature comfort degree constraint formula (6) and a controllable regulation load, namely a variable frequency air conditioner tracking target formula (8), obtaining the following iterative optimization coordination control strategy:
Figure 511801DEST_PATH_IMAGE032
（10）
wherein
Figure 179543DEST_PATH_IMAGE033
Is represented in
Figure 176318DEST_PATH_IMAGE010
Iterate backwards in time
Figure 638523DEST_PATH_IMAGE034
The state of the step(s) is,
Figure 658432DEST_PATH_IMAGE035
is represented in
Figure 254498DEST_PATH_IMAGE010
Iterate backwards in time
Figure 246725DEST_PATH_IMAGE034
The control input of the step(s) is,
Figure 4465DEST_PATH_IMAGE036
represents the number of iteration steps and represents the number of iteration steps,
Figure 714933DEST_PATH_IMAGE037
representing the tracking target(s),
Figure 849111DEST_PATH_IMAGE038
and
Figure 23740DEST_PATH_IMAGE039
representing a weight matrix;
and solving the partial derivative of the iterative optimization equation (10) to obtain an optimal control sequence, thereby realizing a coordination control target.
6. The cooperative control method for dual model optimization of multi-variable frequency air conditioner according to claim 5, wherein the tracking target (8) in step S3 is further refined to obtain the following tracking target
Figure 890065DEST_PATH_IMAGE040
：
Figure 822249DEST_PATH_IMAGE041
Wherein the first inverter air conditioner consumes power
Figure 760118DEST_PATH_IMAGE042
Tracking total bus power generated by new power grid with new renewable energy consumption
Figure 461358DEST_PATH_IMAGE043
I.e. by
Figure 826480DEST_PATH_IMAGE027
The residual multi-frequency-conversion air conditioner obtains the state information of the neighbor nodes through the information exchange topological relation, and therefore the average value of the states of the residual multi-frequency-conversion air conditioner and the neighbor nodes is tracked
Figure 245960DEST_PATH_IMAGE044
Finally when the first one
Figure 721941DEST_PATH_IMAGE042
Track to
Figure 543266DEST_PATH_IMAGE027
Average value of self and neighbor states of other multi-frequency-conversion air conditioners
Figure 79290DEST_PATH_IMAGE045
And further, all the variable frequency air conditioners can achieve the tracking target.
7. The cooperative control method for dual model optimization of multi-variable frequency air conditioners according to claim 6, wherein the step S5 includes the specific steps of,
firstly, the following iterative calculation is carried out by using a space vector model (9):
Figure 986066DEST_PATH_IMAGE046
the above iterative calculations are then written in the form of a matrix as follows:
Figure 277456DEST_PATH_IMAGE047
（13）
wherein the content of the first and second substances,
Figure 953288DEST_PATH_IMAGE048
，
Figure 660213DEST_PATH_IMAGE049
，
Figure 54285DEST_PATH_IMAGE050
，
Figure 872068DEST_PATH_IMAGE051
the iterative optimization function that is obtained by substituting the above iteratively calculated equation (13) into equation (10) is as follows:
Figure 667986DEST_PATH_IMAGE052
(14)
wherein the content of the first and second substances,
Figure 14654DEST_PATH_IMAGE053
not only representing the fluctuation difference between the temperature generated by the variable frequency air conditioner and the indoor environment temperature
Figure 551814DEST_PATH_IMAGE054
I.e. to satisfy the temperature comfort constraint and to express letting
Figure 314234DEST_PATH_IMAGE055
A variable frequency air conditioner
Figure 89292DEST_PATH_IMAGE010
Energy consumed at a moment
Figure 13386DEST_PATH_IMAGE056
Track to target tracking
Figure 241105DEST_PATH_IMAGE057
Then, the iterative optimization function (14) is subjected to partial derivation, and an optimal control sequence can be obtained as follows:
Figure 276057DEST_PATH_IMAGE058
（15）
then the first item of the optimal control sequence is taken as the control input to (9), and the closed-loop control can be realized.
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