CN113644691A - Micro-grid distributed power supply cooperative control method and device based on variable-structure topological network - Google Patents

Micro-grid distributed power supply cooperative control method and device based on variable-structure topological network Download PDF

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CN113644691A
CN113644691A CN202111019807.6A CN202111019807A CN113644691A CN 113644691 A CN113644691 A CN 113644691A CN 202111019807 A CN202111019807 A CN 202111019807A CN 113644691 A CN113644691 A CN 113644691A
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power supply
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CN113644691B (en
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冯强
熊师洵
吕沁
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Nanjing Huiqiang New Energy Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • Y02P80/14—District level solutions, i.e. local energy networks

Abstract

The method includes the steps of introducing a variable structure directed network topology idea into information interaction of distributed power supplies in a microgrid, constructing a second-order dynamical system model of the distributed power supplies to model the power supply state, further establishing a state error system equation of the microgrid system, analyzing the state error system equation by utilizing a Lyapunov stability theory, obtaining a stability condition, and solving the stability condition to obtain a control gain coefficient capable of achieving stability of the microgrid system.

Description

Micro-grid distributed power supply cooperative control method and device based on variable-structure topological network
Technical Field
The application relates to the technical field of micro-grids, in particular to a micro-grid distributed power supply cooperative control method and device based on a variable-structure topological network.
Background
With the continuous development of smart power grids and power electronic technologies, micro-grid systems gradually become a new direction for the development of related fields of power grids. The microgrid system is a power grid system formed by a plurality of distributed power sources and related loads according to a certain topological structure, wherein various power generation technologies such as wind power generation, photovoltaic power generation and the like can be integrated, and the microgrid system can be specifically formed by the distributed power sources, the loads, an energy storage device, an energy conversion device, a control device and the like.
Each distributed power supply in the micro-grid system has a plug-and-play function, and the distributed power supplies can be connected to or separated from the micro-grid system based on the plug-and-play function, so that the micro-grid system can be expanded or rebuilt. However, when the number of distributed power sources in the microgrid system changes, for example, when devices of a photovoltaic power station are expanded, the topological relation of the microgrid system changes, the power balance inside the system is also affected in real time, and finally the group consistency of the distributed power sources in the original microgrid cannot be guaranteed. In addition, in the operation process of the distributed power supplies, noise interference in the operation environment can be caused, so that unstable fluctuation of state parameters such as frequency and voltage in each distributed power supply is caused, and the overall consistency of the micro-grid is damaged.
Therefore, how to retain the plug-and-play function of each distributed power source in the microgrid system, and reduce the influence of noise interference in the operating environment on each distributed power source, so as to meet the real-time power balance requirement in the microgrid system, so that each distributed power source in the microgrid system can maintain a desired state, and thus ensuring the group consistency in the microgrid system is a problem to be considered urgently in the field.
Disclosure of Invention
In order to ensure group consistency in a microgrid system, the embodiment of the application provides a microgrid distributed power supply cooperative control method and device based on a variable-structure topology network. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a microgrid distributed power source cooperative control method based on a variable-structure topology network, where the method includes:
dividing all distributed power supplies in the micro-grid system into a leader power supply and a plurality of follower power supplies, and generating an information interaction relation among the distributed power supplies by using a variable-structure topology network;
constructing a control input equation u of each follower power supply according to the control input r (t) of the leader power supply, the output voltage Y (t) of each distributed power supply and the information interaction relationiSaid control input equation uiComprises a stand forThe solved control gain coefficient;
establishing a second-order dynamical system model of each distributed power supply, and establishing a state error system equation of the micro-grid system based on the second-order dynamical system model;
analyzing the state error system equation through a Lyapunov stability theory to generate a stability control condition of the micro-grid system;
control input equation u for each follower power source using stability control conditions of the microgrid systemiSolving to obtain a control gain coefficient corresponding to the follower power supply;
loading the control gain factor into a controller of each of the follower power supplies to cause the controller to control the follower power supplies in accordance with the control gain factor.
Based on the technical scheme, the distributed power supply state of the micro-grid system is modeled, the cooperative control gain coefficient among the distributed power supplies is obtained through mathematical analysis, and the distributed power supplies can be accurately controlled in real time by using the power controller, so that the group consistency of the micro-grid system can be effectively realized.
Optionally, the generating, by using the variable-structure topology network, an information interaction relationship between the distributed power supplies includes:
setting all the distributed power supplies as nodes in a variable structure topology network by using graph theory knowledge;
generating an adjacency matrix A ═ alpha of the variable-structure topology networkij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ij1 indicates that information interaction exists between the ith distributed power supply and the jth distributed power supply, and the element alphaij0 means that there is no information interaction between the ith distributed power source and the jth distributed power source.
Based on the technical scheme, the graph theory knowledge is utilized, the variable structure directed network topology idea is introduced into the information interaction of the distributed power supplies in the micro-grid system, the fact that any new power supply can be started in real time after being merged into the micro-grid is guaranteed, plug and play of the micro-grid system in the grid connection process is achieved, and the actual requirements of micro-grid system construction are met better.
Optionally, the control input equation u of each follower power supply is constructed according to the control input r (t) of the leader power supply, the output voltage y (t) of each distributed power supply and the information interaction relationiThe method comprises the following steps:
based on the adjacency matrix A ═ alphaij]An output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) and information interaction weights g for the leader power supply and each of the follower power suppliesiBuilding control input correlation coefficients
Figure BDA0003241000050000031
Figure BDA0003241000050000032
Based on the control input correlation coefficient eiAnd controlling the input dynamic coefficient ziConstructing a variation equation of the control input dynamic coefficient
Figure BDA0003241000050000033
Based on a control input r (t) of the leader power supply, the control input dynamic coefficient ziAn output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing a control input equation u for each of said follower power sourcesi＝K(Y0-Yi)+Mzi+ Fr, where N, G, K, M, F are the control gain coefficients to be solved.
Based on the technical scheme, the control input equation of the follower power supply is established by utilizing the information interaction relation between the follower power supply and other distributed power supplies and the output voltage of each distributed power supply, so that the control input equation can be matched with the real requirement of the follower power supply.
Optionally, the establishing a second-order dynamical system model of each distributed power supply includes:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000041
And a control input r (t) for establishing a second order dynamical system model of the leader power supply;
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000042
Bounded noise disturbance and the control input equation uiAnd establishing a second-order dynamic system model of each follower power supply.
Based on the technical scheme, the complex state inside the distributed power supply can be modeled by establishing a second-order dynamic system model, so that the subsequent state change of the distributed power supply can be quickly and effectively analyzed.
Optionally, the internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000046
And a control input r (t) for establishing a second order dynamical system model of the leader power supply, comprising:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000047
Constructing an internal state augmentation vector X for a leader Power supply0(t)；
An internal state augmentation vector X based on the leader power supply0(t) and control inputs r (t) establishing an equation of state change for the leader power supply
Figure BDA0003241000050000043
Wherein the content of the first and second substances,
Figure BDA0003241000050000044
the superscript T denotes transpose, and
Figure BDA0003241000050000045
e is a power supply capacitance value, S is a voltage load regulation effect coefficient, and R is maximum active power;
an internal state augmentation vector X based on the leader power supply0(t) establishing a voltage output equation Y for the leader power supply0(t)＝CX0(t), wherein C ═ 10]；
The internal state X based on each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000058
Bounded noise disturbance and the control input equation uiEstablishing a second-order dynamic system model of each follower power supply, comprising:
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000059
Constructing an internal state augmentation vector X for a follower power supplyi(t)；
Internal state augmentation vector X based on the follower power supplyi(t) bounded noise interference vector wi(t) and the control input equation ui(t) establishing an equation of state change of said follower power supply
Figure BDA0003241000050000051
Wherein the content of the first and second substances,
Figure BDA0003241000050000052
the superscript T denotes transpose, and
Figure BDA0003241000050000053
establishing a voltage output equation Y of the follower power supply based on a state change equation of the follower power supplyi(t)＝CXi(t), wherein C ═ 10]。
Based on the technical scheme, the state quantity and the state variation of the distributed power supply are fully considered, the state influence of input and noise interference on the distributed power supply is controlled, and the fact that a second-order dynamic system model is constructed to be more consistent with the actual state of the distributed power supply in the micro-grid system can be guaranteed.
Optionally, the establishing a state error system equation of the microgrid system based on the second-order dynamical system model includes:
taking state control augmented vector
Figure BDA0003241000050000054
Setting each of the follower power and the leader power state errors
Figure BDA0003241000050000055
Constructing state error system equations of all distributed power supplies in the microgrid system
Figure BDA0003241000050000056
Wherein the content of the first and second substances,
Figure BDA0003241000050000057
Figure BDA0003241000050000061
INis an N-dimensional unit matrix and is a matrix,
Figure BDA0003241000050000062
which represents the kronecker product of,
Figure BDA0003241000050000063
based on the technical scheme, the state error system equation of the micro-grid system is constructed by using the internal state augmentation vectors of the follower power supply and the leader power supply, so that the state error system equation can fully reflect the stability requirement of the micro-grid system.
Optionally, the stability control conditions are: if a constant λ existsm> 0, an adaptive matrix N, G, K, M, F and a positive definite matrix P1,P2Satisfy the following requirements
Figure BDA0003241000050000064
And if so, all distributed power supplies in the microgrid system can reach group consistency, wherein,
Figure BDA0003241000050000065
wherein the element represents an element R symmetrical to the element RT，Q1、Q2Are respectively P1、P2The inverse matrix of (c).
Optionally, the method further includes:
based on said constant λmSetting a residence time T of the variable-structure topology networkaSo that T isa≥lnμ/ln(1-λm) Wherein, mu>1；
When an adjusting instruction for the variable-structure topology network is received, judging whether the time interval between the current time and the last adjusting time is larger than the residence time;
if so, executing the adjusting instruction, otherwise, discarding the adjusting instruction.
Based on the technical scheme, the adjustment frequency of the topological structure of the micro-grid system is controlled by setting the residence time, so that the situation that the internal state of the micro-grid system jumps due to too frequent structural adjustment can be avoided, and the stability of the micro-grid system can be further ensured.
Optionally, the method further includes:
an output voltage Y for each of the follower power suppliesi(t) and bounded noise disturbance wi(t) introduction of l2-l∞Performance index function of
Figure BDA0003241000050000071
Performing the performance index function by Lyapunov stability theoryAnalyzing and generating disturbance suppression conditions, wherein the disturbance suppression conditions are as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure BDA0003241000050000072
If yes, all distributed power supplies in the micro-grid system have disturbance suppression performance;
solving a control input equation of each follower power supply by using the stability condition of the microgrid system to obtain a control gain coefficient corresponding to the follower power supply, wherein the control gain coefficient comprises:
and solving a control input equation of each follower power supply by using the stability condition of the micro-grid system and the interference suppression performance to obtain a control gain coefficient corresponding to the follower power supply.
Based on the technical scheme, l is introduced outside the stability control condition2-l∞The method sets disturbance suppression conditions of the distributed power supply and solves the control gain coefficient corresponding to the follower power supply by integrating the stability control conditions and the disturbance suppression conditions, so that the solved control gain coefficient can meet the stability requirement of the micro-grid system and can effectively suppress noise interference.
In a second aspect, an embodiment of the present application further provides a microgrid distributed power source cooperative control apparatus based on a variable-structure topology network, where the apparatus includes:
the topological structure analysis module is used for dividing all distributed power supplies in the micro-grid system into a leader power supply and a plurality of follower power supplies and generating information interaction relations among the distributed power supplies by utilizing a variable-structure topological network;
the control input analysis module is used for constructing a control input equation u of each follower power supply according to the control input r (t) of the leader power supply, the output voltage Y (t) of each distributed power supply and the information interaction relation generated by the topological structure analysis moduleiSaid control input equation uiInvolving control to be solvedA gain factor;
the system equation building module is used for building a second-order dynamic system model of each distributed power supply and building a state error system equation of the micro-grid system based on the second-order dynamic system model;
the stability analysis module is used for analyzing the state error system equation generated by the system equation building module through the Lyapunov stability theory to generate a stability control condition of the micro-grid system;
a control gain solving module for utilizing the stability control condition generated by the stability analyzing module to control the input equation u of each follower power supplyiSolving to obtain a control gain coefficient corresponding to the follower power supply;
and the control gain loading module is used for loading the control gain coefficient output by the control gain solving module into the controller of each follower power supply so as to enable the controller to control the follower power supply according to the control gain coefficient.
Optionally, the topology analysis module is specifically configured to:
setting all the distributed power supplies as nodes in a variable structure topology network by using graph theory knowledge;
generating an adjacency matrix A ═ alpha of the variable-structure topology networkij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ij1 indicates that information interaction exists between the ith distributed power supply and the jth distributed power supply, and the element alphaij0 means that there is no information interaction between the ith distributed power source and the jth distributed power source.
Optionally, the control input analysis module is specifically configured to:
based on the adjacency matrix A ═ alphaij]An output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) and information interaction weights g for the leader power supply and each of the follower power suppliesiBuilding control input correlation coefficients
Figure BDA0003241000050000091
Figure BDA0003241000050000092
Based on the control input correlation coefficient eiAnd controlling the input dynamic coefficient ziConstructing a variation equation of the control input dynamic coefficient
Figure BDA0003241000050000093
Based on a control input r (t) of the leader power supply, the control input dynamic coefficient ziAn output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing a control input equation u for each of said follower power sourcesi＝K(Y0-Yi)+Mzi+ Fr, where N, G, K, M, F are the control gain coefficients to be solved.
Optionally, the system equation building module is specifically configured to:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000094
And a control input r (t) for establishing a second order dynamical system model of the leader power supply;
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000095
Bounded noise disturbance and the control input equation uiAnd establishing a second-order dynamic system model of each follower power supply.
Optionally, the system equation building module is specifically configured to:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000096
Constructing an internal state augmentation vector X for a leader Power supply0(t)；
An internal state augmentation vector X based on the leader power supply0(t) and control inputs r (t) establishing an equation of state change for the leader power supply
Figure BDA0003241000050000101
Wherein the content of the first and second substances,
Figure BDA0003241000050000102
the superscript T denotes transpose, and
Figure BDA0003241000050000103
e is a power supply capacitance value, S is a voltage load regulation effect coefficient, and R is maximum active power;
an internal state augmentation vector X based on the leader power supply0(t) establishing a voltage output equation Y for the leader power supply0(t)＝CX0(t), wherein C ═ 10]；
Based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000104
Constructing an internal state augmentation vector X for a follower power supplyi(t)；
Internal state augmentation vector X based on the follower power supplyi(t) bounded noise interference vector wi(t) and the control input equation ui(t) establishing an equation of state change of said follower power supply
Figure BDA0003241000050000105
Wherein the content of the first and second substances,
Figure BDA0003241000050000106
the superscript T denotes transpose, and
Figure BDA0003241000050000107
establishing a voltage output equation Y of the follower power supply based on a state change equation of the follower power supplyi(t)＝CXi(t), wherein C ═ 10]。
Optionally, the system equation building module is specifically configured to:
taking state control augmented vector
Figure BDA0003241000050000108
Setting each of the follower power and the leader power state errors
Figure BDA0003241000050000109
Constructing state error system equations of all distributed power supplies in the microgrid system
Figure BDA00032410000500001010
Wherein the content of the first and second substances,
Figure BDA00032410000500001011
Figure BDA00032410000500001012
INis an N-dimensional unit matrix and is a matrix,
Figure BDA00032410000500001013
which represents the kronecker product of,
Figure BDA00032410000500001014
optionally, the stability control conditions are: if a constant λ existsm> 0, an adaptive matrix N, G, K, M, F and a positive definite matrix P1,P2Satisfy the following requirements
Figure BDA0003241000050000111
And if so, all distributed power supplies in the microgrid system can reach group consistency, wherein,
Figure BDA0003241000050000112
wherein the element represents an element R symmetrical to the element RT，Q1、Q2Are respectively P1、P2The inverse matrix of (c).
Optionally, the apparatus further includes a topology adjusting module, configured to:
based on said constant λmSetting a residence time T of the variable-structure topology networkaSo that T isa≥lnμ/ln(1-λm) Wherein, mu>1；
When an adjusting instruction for the variable-structure topology network is received, judging whether the time interval between the current time and the last adjusting time is larger than the residence time;
if so, executing the adjusting instruction, otherwise, discarding the adjusting instruction.
Optionally, the stability analysis module is further configured to:
an output voltage Y for each of the follower power suppliesi(t) and bounded noise disturbance wi(t) introduction of l2-l∞Performance index function of
Figure BDA0003241000050000113
Analyzing the performance index function through a Lyapunov stability theory to generate a disturbance suppression condition, wherein the disturbance suppression condition is as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure BDA0003241000050000121
If yes, all distributed power supplies in the micro-grid system have disturbance suppression performance;
the control gain solving module is specifically configured to:
and solving a control input equation of each follower power supply by using the stability condition of the micro-grid system and the interference suppression performance to obtain a control gain coefficient corresponding to the follower power supply.
In a third aspect, a central controller is provided, and the central controller includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for cooperative control of a microgrid distributed power supply based on a variable-structure topology network according to the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for cooperative control of a microgrid distributed power supply based on a variable-structure topology network according to the first aspect.
In summary, the present application has the following beneficial effects:
by adopting the cooperative control method for the distributed power supply of the micro-grid based on the variable-structure topological network, the variable-structure directed network topology idea is introduced into information interaction of the distributed power supply in the micro-grid, a second-order dynamic system model of the distributed power supply is constructed to model the power supply state, a state error system equation of the micro-grid system is further established, the state error system equation is analyzed by utilizing the Lyapunov stability theory, the stability condition is obtained, and therefore the control gain coefficient capable of realizing the stability of the micro-grid system can be obtained through solving the stability condition. Therefore, the distributed power supply state of the micro-grid system is modeled, the cooperative control gain coefficient among the distributed power supplies is obtained through mathematical analysis, and the distributed power supplies can be accurately controlled in real time by the power controller, so that the group consistency of the micro-grid system can be effectively realized.
Drawings
Fig. 1 is a schematic view of a scenario architecture of a microgrid system in an embodiment of the present application;
fig. 2 is a flowchart of a distributed power source cooperative control method in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a distributed power source cooperative control apparatus in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a distributed power source cooperative control apparatus in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to fig. 1-4 and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application provides a micro-grid distributed power supply cooperative control method based on a variable-structure topological network, which can be applied to a micro-grid system shown in fig. 1 and can be specifically executed by a central controller of the micro-grid system, wherein the micro-grid system can comprise the central controller and a plurality of distributed power supplies, and each distributed power supply corresponds to one controller. The central controller may perform data interaction with controllers (hereinafter, simply referred to as power controllers) of each distributed power source in the microgrid system, for example, obtain internal state data of the distributed power source from the power controllers, and send control gain coefficients to the power controllers. The central controller may further have a data processing and analyzing function, that is, the central controller may be configured to process and analyze the internal state data and the state change data of the distributed power source to obtain the control gain coefficient loaded in the controller of the distributed power source.
The process flow shown in fig. 2 will be described in detail below with reference to the specific embodiments, and the contents may be as follows:
step 201, dividing all distributed power supplies in the microgrid system into a leader power supply and a plurality of follower power supplies, and generating information interaction relations among the distributed power supplies by using a variable-structure topology network.
In an implementation, the central controller may record all online distributed power sources in the microgrid system, and select one of the distributed power sources as a leader power source and determine the remaining distributed power sources as follower power sources. In other words, there are n +1 distributed power sources in the microgrid, and 1 of the distributed power sources is set as a leader power source, and the remaining n distributed power sources are set as follower power sources. And then, the central controller can introduce a variable-structure topological network, and an undirected graph G is taken to describe the communication network topological relation between a leader and a follower in the microgrid, so that the information interaction relation between any two distributed power supplies is generated.
Alternatively, the information interaction relationship may be expressed by using an adjacency matrix, and accordingly, the processing of step 201 may be as follows: setting all distributed power supplies as nodes in a variable structure topology network by using graph theory knowledge; generating adjacency matrix A ═ alpha of variable structure topological networkij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ij1 indicates that information interaction exists between the ith distributed power supply and the jth distributed power supply, and the element alphaij0 means that there is no information interaction between the ith distributed power source and the jth distributed power source.
In implementation, the central controller may introduce graph theory knowledge to identify information interaction relationships between distributed power sources in the microgrid system using a variable topology network. Specifically, the distributed power source may be set as a node in the variable topology network, and then the adjacency matrix a ═ α of the variable topology network may be generatedij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ijThat is, the information interaction weight between the ith distributed power supply and the jth distributed power supply, when the element alphaij1 indicates that information interaction exists between the ith distributed power supply and the jth distributed power supply, and the element alphaij0 means that there is no information interaction between the ith and jth distributed power sources, e.g., α12And 0, no information interaction exists between the 1 st distributed power source and the 2 nd distributed power source. Further, α may be definediiAnd 0, namely, the information interaction weight value of each distributed power supply and the distributed power supply is 0. It is worth mentioning that in the present embodiment, the leader power source is set as the 0 th distributed power source, α, by defaultijThe information interaction weight between follower power supplies can be weighted.
Step 202, according to the control input r (t) of the leader power supply, the output voltage Y (t) of each distributed power supply and the information interaction relation, a control input equation u of each follower power supply is constructedi。
In implementation, considering that the micro-grid system tends to be stable, the association of the follower power supply with other distributed power supplies, particularly with the leader power supply, needs to be fully considered when setting the control input of each follower power supply controller. Therefore, after the central controller generates the information interaction relation among the distributed power supplies in the microgrid system, the central controller can acquire the control input r (t) of the leader power supply and the output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) of (d). Thereafter, for the ith follower power source, the information interaction relationship, r (t), Y, of the ith follower power source with other distributed power sources (including the leader power source and other follower power sources) in the microgrid system can be combined0(t) and Yi(t) constructing a control input equation u for the ith follower power supply including the control gain factor to be solvedi。
Optionally, a control input correlation coefficient and a control input dynamic coefficient may be introduced when constructing the control input equation, and accordingly, the process of step 202 may be as follows: based on the adjacency matrix A ═ alphaij]Output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t), and information interaction weight g of the leader power supply and each follower power supplyiBuilding control input correlation coefficients
Figure BDA0003241000050000151
Figure BDA0003241000050000152
Correlation coefficient e based on control inputiAnd controlling the input dynamic coefficient ziConstructing a variation equation for controlling the input dynamic coefficient
Figure BDA0003241000050000153
Leader power based controlInput r (t), control input dynamic coefficient ziOutput voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing a control input equation u for each follower power supplyi＝K(Y0-Yi)+Mzi+ Fr, where N, G, K, M, F are the control gain coefficients to be solved.
In implementation, when the central controller constructs the control input equation of the follower power supply, the central controller may first construct the control input correlation coefficient e of the follower power supply based on the information interaction relationship between the follower power supply and other distributed power suppliesi. Specifically, for the ith follower power supply, the slave adjacency matrix a ═ αij]The information interaction weight alpha of the ith follower power supply and other follower power supplies is obtainedijAnd acquiring the information interaction weight g of the ith follower power supply and the leader power supplyiThen, the output voltage Y (t) of each distributed power supply can be combined to construct a control input correlation coefficient
Figure BDA0003241000050000161
Figure BDA0003241000050000162
Meanwhile, considering that the control input is also influenced by system fluctuation and has dynamic change, a control input dynamic coefficient z can be introducediCombining the control input correlation coefficient, a change equation of the control input dynamic coefficient can be further constructed
Figure BDA0003241000050000163
Where N, G is the control gain factor to be solved for. Next, considering that the control input of the follower power supply needs to be associated with the control input of the leader power supply and needs to consider the output voltages of the follower power supply and the leader power supply at the present time in order for the microgrid system to achieve population stability, the control input dynamic coefficient z may be based on the control input r (t) of the leader power supplyiOutput voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing each heelControl input equation u of follower power supplyi＝K(Y0-Yi)+Mzi+ Fr, where K, M, F is the control gain factor to be solved for.
And step 203, establishing a second-order dynamical system model of each distributed power supply, and establishing a state error system equation of the microgrid system based on the second-order dynamical system model.
In implementation, when the distributed power supplies in the microgrid system run, state values and changes of the distributed power supplies can be fitted by a second-order dynamical system model, and further, stability among the distributed power supplies can be reflected by errors of the follower power supplies and the leader power supply. Therefore, aiming at each distributed power supply, the central controller can establish a second-order dynamic system model of the distributed power supply and construct an error system equation epsilon between the leader power supply and the follower power supply through the second-order dynamic system modeliFurther, the central controller can incorporate multiple error system equations ε between the leader power supply and each follower power supplyiAnd establishing a state error system equation of the micro-grid system.
Optionally, the second order dynamical system model of the distributed power source in step 203 may specifically include: the second-order dynamic system model of the leader power supply and the second-order dynamic model of the follower power supply can be constructed in the following corresponding processes: internal state X based on leader power0Amount of change in state
Figure BDA0003241000050000171
And a control input r (t) for establishing a second order dynamical system model of the leader power supply; internal state X based on each follower power supplyiAmount of change in state
Figure BDA0003241000050000172
Bounded noise disturbance and control input equation uiAnd establishing a second-order dynamic system model of each follower power supply.
In implementation, when constructing the second-order dynamical system model of the leader power supply, the state quantity and the state variation of the leader power supply and the control input of the power controller to the leader power supply can be considered, so that the second-order dynamical system model of the leader power supply can be established based on the internal state, the state variation and the control input of the leader power supply. For the follower power supply, the state quantity and the state variation of the follower power supply and the control input of the power supply controller to the follower power supply also need to be considered, and besides, the influence of the bounded noise interference on the follower power supply can be considered, so that a second-order dynamic system model of the follower power supply can be established based on the internal state, the state variation, the bounded noise interference and the control input equation of the follower power supply. It can be understood that when the leader power supply shares its own state with the follower power supply, in order to achieve the goal of output consistency, the leader itself can be regarded as an agent with anti-interference performance, so that noise interference can be not considered when constructing a second-order dynamic system model of the leader power supply.
Further, the process of establishing the second-order dynamical system model of the leader power supply may specifically be as follows: internal state X based on leader power0Amount of change in state
Figure BDA0003241000050000173
Constructing an internal state augmentation vector X for a leader Power supply0(t); leader power based internal state augmentation vector X0(t) and control inputs r (t) to establish a state change equation for the leader power supply
Figure BDA0003241000050000181
Wherein the content of the first and second substances,
Figure BDA0003241000050000182
the superscript T denotes transpose, and
Figure BDA0003241000050000183
e is the capacitance value of the power supply, S is the voltage load regulation effect coefficient, R is the maximum active power, S and R are both the values to be determined associated with the hardware parameters of the power supply, and before the power supply is put into use, the values can be determined by the technologyPersonnel determine corresponding specific numerical values according to power supply hardware parameters; leader power based internal state augmentation vector X0(t) establishing a voltage output equation Y for the leader power supply0(t)＝CX0(t), wherein C ═ 10]。
In implementation, for the leader power supply, its internal state augmentation vector is set to X0(t)，X0(t) may be comprised of an internal state X0Amount of change in state
Figure BDA0003241000050000184
An augmented vector of, i.e.
Figure BDA0003241000050000185
Wherein the superscript T denotes transpose, and
Figure BDA0003241000050000186
X0subscripts 1 and 2 of (a) are used for distinction only, and in combination with the control input r (t), the equation of state change of the leader power supply can be established
Figure BDA0003241000050000187
Wherein A and B are adaptive system matrix,
Figure BDA0003241000050000188
e is the power supply capacitance value, and S and R are both the to-be-determined values associated with the power supply hardware parameters, and before the power supply is put into use, a technician can determine the corresponding specific values according to the power supply hardware parameters, where S is the voltage load regulation effect coefficient, and R is the maximum active power. In addition, the voltage output equation Y for the leader power supply0(t) vector X may be augmented by the internal state of the leader power supply0(t) is established directly, i.e. Y0(t)＝CX0(t), wherein C is an adaptive system matrix, C ═ 10]。
Further, the establishment process of the second order dynamical system model of the follower power supply may specifically be as follows: internal state X based on each follower power supplyiAmount of change in state
Figure BDA00032410000500001811
Constructing an internal state augmentation vector X for a follower power supplyi(t); internal state augmentation vector X based on follower power supplyi(t) bounded noise interference vector wi(t) and control input equation ui(t) establishing a state change equation of the follower power supply
Figure BDA0003241000050000189
Wherein the content of the first and second substances,
Figure BDA00032410000500001810
the superscript T denotes transpose, and
Figure BDA0003241000050000191
s is the voltage load regulation effect coefficient, R is the maximum active power,
Figure BDA0003241000050000192
establishing voltage output equation Y of follower power supply based on state change equation of follower power supplyi(t)＝CXi(t), wherein C ═ 10]。
In practice, the follower power supply is set to have its internal state increment vector Xi(t)，Xi(t) may be comprised of an internal state XiAmount of change in state
Figure BDA0003241000050000193
An augmented vector of, i.e.
Figure BDA0003241000050000194
Wherein the superscript T denotes transpose, and
Figure BDA0003241000050000195
Xisubscripts 1 and 2 of (a) are used only for distinction, in combination with a bounded noise interference vector w that the follower power supply may be subjected toi(t), and corresponding control input equation ui(t) an equation of state change of the follower power supply may be established
Figure BDA0003241000050000196
Wherein A, B and D are adaptive system matrixes,
Figure BDA0003241000050000197
e is a power supply capacitance value, and S and R are both to-be-determined values associated with power supply hardware parameters, and before the power supply is put into use, a technician can determine corresponding specific values according to the power supply hardware parameters, where S is a voltage load regulation effect coefficient, and R is maximum active power, and values of E, R, S corresponding to different distributed power supplies of the microgrid system in this embodiment are all the same. Here, the possible bounded noise disturbance vector w of the follower power supplyi(t) can be obtained by sorting and analyzing a large amount of actually measured data, and specifically, the maximum value of actually measured noise interference can be obtained. In addition, voltage output equation Y of follower power supplyi(t) vector X may be augmented by the internal state of the follower power supplyi(t) is established directly, i.e. Yi(t)＝CXi(t), wherein C is an adaptive system matrix, C ═ 10]。
Further, based on the constructed second-order dynamical system model of the leader power supply and the follower power supply, the establishing process of the state error system equation can be specifically as follows: taking augmented vectors
Figure BDA0003241000050000198
Setting per follower and leader Power State errors
Figure BDA0003241000050000199
Constructing state error system equations for all distributed power supplies in a microgrid system
Figure BDA0003241000050000201
Wherein the content of the first and second substances,
Figure BDA0003241000050000202
Figure BDA0003241000050000203
INis an N-dimensional unit matrix and is a matrix,
Figure BDA0003241000050000204
which represents the kronecker product of,
Figure BDA0003241000050000205
in an implementation, the central controller may augment vector X based on the internal state of the follower power supplyi(t) control input dynamic coefficient ziAnd an internal state augmentation vector X of the leader power supply0(t) constructing a state control augmentation vector
Figure BDA0003241000050000206
And may further set the state error between the follower power supply and the leader power supply
Figure BDA0003241000050000207
Furthermore, the central controller can integrate the state errors corresponding to all follower power supplies to construct a state error system equation of all distributed power supplies in the micro-grid system
Figure BDA0003241000050000208
Wherein, A is an adjacent matrix,
Figure BDA0003241000050000209
col { } represents the column vector; i isNIs an N-dimensional unit matrix and is a matrix,
Figure BDA00032410000500002010
which represents the kronecker product of,
Figure BDA00032410000500002011
and 204, analyzing a state error system equation through a Lyapunov stability theory to generate a stability control condition of the micro-grid system.
In practice, the lyapunov stability theory is a theory for studying the stability of a system, that is, a balance state of the system is sought, and when the system is in the balance state, the system finally tends to return to the balance state no matter what external disturbance exists. Therefore, the state error system equation can be analyzed based on the Lyapunov stability theory to calculate the control condition to be met when the micro-grid system has the equilibrium state, namely the stability control condition of the micro-grid system is generated.
System equation based on the state error
Figure BDA00032410000500002012
If the micro-grid system finally achieves group consistency, the micro-grid system needs to be subjected to
Figure BDA00032410000500002013
Tending to 0. Therefore, the following stability control conditions can be obtained by analyzing the state error system equation by utilizing the Lyapunov stability theory:
if a constant λ existsm> 0, an adaptive matrix N, G, K, M, F and a positive definite matrix P1,P2Satisfy the following requirements
Figure BDA0003241000050000211
And if so, all distributed power supplies in the microgrid system can achieve population consistency, wherein,
Figure BDA0003241000050000212
wherein the element represents an element R symmetrical to the element RT，Q1、Q2Are respectively P1、P2The inverse matrix of (c).
Step 205, utilizing the stability control conditions of the microgrid system to input an equation u for the control of each follower power supplyiAnd solving to obtain a control gain coefficient corresponding to the follower power supply.
In implementation, after the stability control condition of the microgrid system is obtained through analysis, the central controller can reversely solve the control gain coefficient of each distributed power supply in the microgrid system by using the stability control condition. In particular, the central controller may be based on the above-mentioned stabilityControl input equation u for qualitative control condition to each follower power supplyiAnd uniformly solving, so that the control gain coefficient corresponding to the follower power supply can be obtained.
Step 206, load the control gain factor into the controller of each follower power supply, so that the controller controls the follower power supply according to the control gain factor.
In an implementation, the central controller may send the control gain factor to the power supply controller of each follower power supply after determining the control gain factor for the follower power supply. The power supply controller receives and loads the control gain coefficient, and substitutes the control gain coefficient into a control input equation u of the follower power supplyiTo obtain a specific control input for each follower power supply, so that the power supply controller can control the follower power supply via the specific control input.
Optionally, based on a constant λ in the stability control condition described abovemThe residence relationship of the variable-structure topology network can be set to control the structure change of the microgrid system, and the corresponding processing can be as follows: based on a constant lambdamSetting residence time T of variable structure topological networkaSo that T isa≥lnμ/ln(1-λm) Wherein, mu>1; when an adjusting instruction aiming at the variable structure topology network is received, judging whether the time interval between the current time and the last adjusting time is larger than the residence time or not; if so, executing the adjusting instruction, otherwise, discarding the adjusting instruction.
In implementation, the constant lambda can be obtained while the control gain coefficient of the power supply controller is solved through stability control condition analysismThe specific value of (a). The central controller may then base on the constant λmSetting residence time T of variable structure topological networkaI.e. Ta≥lnμ/ln(1-λm) In which μ>1. Therefore, when the central controller detects that the variable-structure topological network of the micro-grid system changes, the change time can be recorded, a new control gain system can be analyzed and solved based on the new variable-structure topological network, and meanwhile, new residence time can be set. Next, the central controller receives the needleWhen the adjustment instruction is given to the topology network with a variable structure, it can be determined whether the time interval between the current time and the last adjustment time (i.e. the previous change time) is greater than the set residence time. If the time interval is larger than the residence time, the central controller can execute the adjusting instruction, namely, adjust the variable structure topology network, otherwise, refuse to execute the adjusting operation and discard the adjusting instruction.
Alternatively,/, may be used2-l∞The control method suppresses the bounded noise interference, and accordingly, the following processing can be performed: output voltage Y for each follower power supplyi(t) and bounded noise disturbance wi(t) introduction of l2-l∞Performance index function of
Figure BDA0003241000050000221
Analyzing the performance index function through the Lyapunov stability theory to generate a disturbance inhibition condition, wherein the disturbance inhibition condition is as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure BDA0003241000050000231
And if so, all distributed power supplies in the microgrid system have disturbance suppression performance.
In implementation, the central controller may introduce l2-l∞Performance index function
Figure BDA0003241000050000232
Therefore, the voltage output of each distributed power supply in the micro-grid system is always kept within a boundary, so that the voltage and other state parameters of the distributed power supplies can be ensured not to exceed the instantaneous range, and the safety performance requirement of the micro-grid system is met. Then, the performance index function can be analyzed by using the lyapunov stability theory to obtain a disturbance suppression condition, wherein the disturbance suppression condition specifically can be as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure BDA0003241000050000233
If so, all distributed power sources in the microgrid system have disturbance rejection performance, where C ═ 10]And I is an identity matrix.
Further, the control gain coefficient may be solved by using the above-mentioned disturbance suppression condition, and accordingly, the processing of step 205 may be as follows: and solving the control input equation of each follower power supply by using the stability condition and the interference suppression performance of the micro-grid system to obtain the control gain coefficient corresponding to the follower power supply.
In implementation, after the stability control condition and the interference suppression condition of the microgrid system are obtained through analysis, the central controller can simultaneously use the stability control condition and the interference suppression condition to solve the control gain coefficient of each distributed power source in the microgrid system. Specifically, the central controller may apply the control input equation u for each follower power supply based on the stability control condition and the interference suppression condition described aboveiThe solution is carried out uniformly, so that the control gain coefficient corresponding to the follower power supply can be obtained, the solved control gain coefficient can meet the requirement on the stability of the micro-grid system, and the effective suppression on noise interference can be realized.
By adopting the cooperative control method for the distributed power supply of the micro-grid based on the variable-structure topological network, the variable-structure directed network topology idea is introduced into information interaction of the distributed power supply in the micro-grid, a second-order dynamic system model of the distributed power supply is constructed to model the power supply state, a state error system equation of the micro-grid system is further established, the state error system equation is analyzed by utilizing the Lyapunov stability theory, the stability condition is obtained, and therefore the control gain coefficient capable of realizing the stability of the micro-grid system can be obtained through solving the stability condition. Therefore, the distributed power supply state of the micro-grid system is modeled, the cooperative control gain coefficient among the distributed power supplies is obtained through mathematical analysis, and the distributed power supplies can be accurately controlled in real time by the power controller, so that the group consistency of the micro-grid system can be effectively realized.
An embodiment of the present application further provides a microgrid distributed power source cooperative control apparatus based on a variable-structure topology network, as shown in fig. 3, the apparatus includes:
the topological structure analysis module 301 is used for dividing all distributed power supplies in the microgrid system into a leader power supply and a plurality of follower power supplies and generating information interaction relations among the distributed power supplies by using a variable-structure topological network;
a control input analysis module 302, configured to construct a control input equation u for each follower power source according to the control input r (t) of the leader power source, the output voltage y (t) of each distributed power source, and the information interaction relationship generated by the topology analysis moduleiSaid control input equation uiIncluding the control gain coefficient to be solved;
the system equation building module 303 is configured to build a second-order dynamical system model of each distributed power supply, and build a state error system equation of the microgrid system based on the second-order dynamical system model;
the stability analysis module 304 is used for analyzing the state error system equation generated by the system equation building module through the Lyapunov stability theory to generate a stability control condition of the micro-grid system;
a control gain solving module 305 for solving the control input equation u of each follower power source by using the stability control condition generated by the stability analyzing moduleiSolving to obtain a control gain coefficient corresponding to the follower power supply;
a control gain loading module 306, configured to load the control gain coefficient output by the control gain solving module into the controller of each follower power supply, so that the controller controls the follower power supply according to the control gain coefficient.
Optionally, the topology analysis module 301 is specifically configured to:
setting all the distributed power supplies as nodes in a variable structure topology network by using graph theory knowledge;
generating an adjacency matrix A ═ alpha of the variable-structure topology networkij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ij1 indicates that information interaction exists between the ith distributed power supply and the jth distributed power supply, and the element alphaij0 means that there is no information interaction between the ith distributed power source and the jth distributed power source.
Optionally, the control input analysis module 302 is specifically configured to:
based on the adjacency matrix A ═ alphaij]An output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) and information interaction weights g for the leader power supply and each of the follower power suppliesiBuilding control input correlation coefficients
Figure BDA0003241000050000251
Figure BDA0003241000050000252
Based on the control input correlation coefficient eiAnd controlling the input dynamic coefficient ziConstructing a variation equation of the control input dynamic coefficient
Figure BDA0003241000050000261
Based on a control input r (t) of the leader power supply, the control input dynamic coefficient ziAn output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing a control input equation u for each of said follower power sourcesi＝K(Y0-Yi)+Mzi+ Fr, where N, G, K, M, F are the control gain coefficients to be solved.
Optionally, the system equation building module 303 is specifically configured to:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000262
And a control input r (t) for establishing a second order dynamical system model of the leader power supply;
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000263
Bounded noise disturbance and the control input equation uiAnd establishing a second-order dynamic system model of each follower power supply.
Optionally, the system equation building module 303 is specifically configured to:
internal state X based on the leader power supply0Amount of change in state
Figure BDA0003241000050000268
Constructing an internal state augmentation vector X for a leader Power supply0(t)；
An internal state augmentation vector X based on the leader power supply0(t) and control inputs r (t) establishing an equation of state change for the leader power supply
Figure BDA0003241000050000264
Wherein the content of the first and second substances,
Figure BDA0003241000050000265
the superscript T denotes transpose, and
Figure BDA0003241000050000266
e is a power supply capacitance value, S is a voltage load regulation effect coefficient, and R is maximum active power;
an internal state augmentation vector X based on the leader power supply0(t) establishing a voltage output equation Y for the leader power supply0(t)＝CX0(t), wherein C ═ 10]；
Based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure BDA0003241000050000267
Constructing an internal state augmentation vector X for a follower power supplyi(t)；
Internal state augmentation vector X based on the follower power supplyi(t) bounded noise interference vector wi(t) and the control input equation ui(t) establishing an equation of state change of said follower power supply
Figure BDA0003241000050000271
Wherein the content of the first and second substances,
Figure BDA0003241000050000272
the superscript T denotes transpose, and
Figure BDA0003241000050000273
establishing a voltage output equation Y of the follower power supply based on a state change equation of the follower power supplyi(t)＝CXi(t), wherein C ═ 10]。
Optionally, the system equation building module 303 is specifically configured to:
taking state control augmented vector
Figure BDA0003241000050000274
Setting each of the follower power and the leader power state errors
Figure BDA0003241000050000275
Constructing state error system equations of all distributed power supplies in the microgrid system
Figure BDA0003241000050000276
Wherein the content of the first and second substances,
Figure BDA0003241000050000277
Figure BDA0003241000050000278
INis an N-dimensional unit matrix and is a matrix,
Figure BDA0003241000050000279
which represents the kronecker product of,
Figure BDA00032410000500002710
optionally, the stability control conditions are: if a constant λ existsm> 0, an adaptive matrix N, G, K, M, F and a positive definite matrix P1,P2Satisfy the following requirements
Figure BDA00032410000500002711
And if so, all distributed power supplies in the microgrid system can reach group consistency, wherein,
Figure BDA00032410000500002712
wherein the element represents an element R symmetrical to the element RT，Q1、Q2Are respectively P1、P2The inverse matrix of (c).
Optionally, as shown in fig. 4, the apparatus further includes a topology adjusting module 307, configured to:
based on said constant λmSetting a residence time T of the variable-structure topology networkaSo that T isa≥lnμ/ln(1-λm) Wherein, mu>1；
When an adjusting instruction for the variable-structure topology network is received, judging whether the time interval between the current time and the last adjusting time is larger than the residence time;
if so, executing the adjusting instruction, otherwise, discarding the adjusting instruction.
Optionally, the stability analysis module 304 is further configured to:
an output voltage Y for each of the follower power suppliesi(t) and bounded noise disturbance wi(t) introduction of l2-l∞Performance index function of
Figure BDA0003241000050000281
Analyzing the performance index function through a Lyapunov stability theory to generate a disturbance suppression condition, wherein the disturbance suppression condition is as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure BDA0003241000050000282
If yes, all distributed power supplies in the micro-grid system have disturbance suppression performance;
the control gain solving module 305 is specifically configured to:
and solving a control input equation of each follower power supply by using the stability condition of the micro-grid system and the interference suppression performance to obtain a control gain coefficient corresponding to the follower power supply.
An embodiment of the present application further provides a central controller, where the central controller includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for cooperative control of a microgrid distributed power supply based on a variable-structure topology network according to the first aspect.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing is a preferred embodiment of the present application and is not intended to limit the scope of the application in any way, and any features disclosed in this specification (including the abstract and drawings) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.

Claims (10)

1. A micro-grid distributed power supply cooperative control method based on a variable-structure topological network is characterized by comprising the following steps:
dividing all distributed power supplies in the micro-grid system into a leader power supply and a plurality of follower power supplies, and generating an information interaction relation among the distributed power supplies by using a variable-structure topology network;
constructing a control input equation u of each follower power supply according to the control input r (t) of the leader power supply, the output voltage Y (t) of each distributed power supply and the information interaction relationiSaid control input equation uiIncluding the control gain coefficient to be solved;
establishing a second-order dynamical system model of each distributed power supply, and establishing a state error system equation of the micro-grid system based on the second-order dynamical system model;
analyzing the state error system equation through a Lyapunov stability theory to generate a stability control condition of the micro-grid system;
control input equation u for each follower power source using stability control conditions of the microgrid systemiSolving to obtain a control gain coefficient corresponding to the follower power supply;
loading the control gain factor into a controller of each of the follower power supplies to cause the controller to control the follower power supplies in accordance with the control gain factor.
2. The method of claim 1, wherein generating the information interaction relationship between the distributed power sources by using the variable topology network comprises:
setting all the distributed power supplies as nodes in a variable structure topology network by using graph theory knowledge;
generating an adjacency matrix A ═ alpha of the variable-structure topology networkij]Wherein i and j are adjacent distributed power sources, i, j ═ 1,2ij1 denotes the ith and jth distributed power sourcesThere is information interaction between power supplies, element αij0 means that there is no information interaction between the ith distributed power source and the jth distributed power source.
3. The method of claim 2, wherein the constructing of the control input equation u for each of the follower power supplies is based on the control input r (t) of the leader power supply, the output voltage Y (t) of each of the distributed power supplies, and the information interaction relationshipiThe method comprises the following steps:
based on the adjacency matrix A ═ alphaij]An output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) and information interaction weights g for the leader power supply and each of the follower power suppliesiBuilding control input correlation coefficients
Figure FDA0003241000040000021
Figure FDA0003241000040000022
Based on the control input correlation coefficient eiAnd controlling the input dynamic coefficient ziConstructing a variation equation of the control input dynamic coefficient
Figure FDA0003241000040000023
Based on a control input r (t) of the leader power supply, the control input dynamic coefficient ziAn output voltage Y of the leader power supply0(t) and the output voltage Y of the follower power supplyi(t) establishing a control input equation u for each of said follower power sourcesi＝K(Y0-Yi)+Mzi+ Fr, where N, G, K, M, F are the control gain coefficients to be solved.
4. The method of claim 1, wherein said modeling a second order dynamical system of each of said distributed power sources comprises:
internal state X based on the leader power supply0Amount of change in state
Figure FDA0003241000040000024
And a control input r (t) for establishing a second order dynamical system model of the leader power supply;
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure FDA0003241000040000025
Bounded noise disturbance and the control input equation uiAnd establishing a second-order dynamic system model of each follower power supply.
5. The method of claim 4, wherein the internal state X based on the leader power source0Amount of change in state
Figure FDA0003241000040000026
And a control input r (t) for establishing a second order dynamical system model of the leader power supply, comprising:
internal state X based on the leader power supply0Amount of change in state
Figure FDA0003241000040000039
Constructing an internal state augmentation vector X for a leader Power supply0(t)；
An internal state augmentation vector X based on the leader power supply0(t) and control inputs r (t) establishing an equation of state change for the leader power supply
Figure FDA0003241000040000031
Wherein the content of the first and second substances,
Figure FDA0003241000040000032
the superscript T denotes transpose, and
Figure FDA0003241000040000033
e is a power supply capacitance value, S is a voltage load regulation effect coefficient, and R is maximum active power;
an internal state augmentation vector X based on the leader power supply0(t) establishing a voltage output equation Y for the leader power supply0(t)＝CX0(t), wherein C ═ 10]；
The internal state X based on each of the follower power suppliesiAmount of change in state
Figure FDA0003241000040000034
Bounded noise disturbance and the control input equation uiEstablishing a second-order dynamic system model of each follower power supply, comprising:
based on an internal state X of each of the follower power suppliesiAmount of change in state
Figure FDA0003241000040000035
Constructing an internal state augmentation vector X for a follower power supplyi(t)；
Internal state augmentation vector X based on the follower power supplyi(t) bounded noise interference vector wi(t) and the control input equation ui(t) establishing an equation of state change of said follower power supply
Figure FDA0003241000040000036
Wherein the content of the first and second substances,
Figure FDA0003241000040000037
the superscript T denotes transpose, and
Figure FDA0003241000040000038
establishing a voltage output equation Y of the follower power supply based on a state change equation of the follower power supplyi(t)＝CXi(t), wherein C ═ 10]。
6. The method of claim 5, wherein establishing a state error system equation for the microgrid system based on the second order dynamical system model comprises:
taking state control augmented vector
Figure FDA0003241000040000041
Setting each of the follower power and the leader power state errors
Figure FDA0003241000040000042
Constructing state error system equations of all distributed power supplies in the microgrid system
Figure FDA0003241000040000043
Wherein the content of the first and second substances,
Figure FDA0003241000040000044
Figure FDA0003241000040000045
INis an N-dimensional unit matrix and is a matrix,
Figure FDA0003241000040000046
which represents the kronecker product of,
Figure FDA0003241000040000047
7. the method of claim 6, wherein the stability control condition is: if a constant λ existsm> 0, an adaptive matrix N, G, K, M, F and a positive definite matrix P1,P2Satisfy the following requirements
Figure FDA0003241000040000048
If so, all distributions in the microgrid systemGroup consistency can be achieved with a modular power supply, wherein,
Figure FDA0003241000040000049
wherein the element represents an element R symmetrical to the element RT，Q1、Q2Are respectively P1、P2The inverse matrix of (c).
8. The method of claim 7, further comprising:
based on said constant λmSetting a residence time T of the variable-structure topology networkaSo that T isa≥lnμ/ln(1-λm) Wherein, mu>1；
When an adjusting instruction for the variable-structure topology network is received, judging whether the time interval between the current time and the last adjusting time is larger than the residence time;
if so, executing the adjusting instruction, otherwise, discarding the adjusting instruction.
9. The method of claim 1, further comprising:
an output voltage Y for each of the follower power suppliesi(t) and bounded noise disturbance wi(t) introduction of l2-l∞Performance index function of
Figure FDA0003241000040000051
Analyzing the performance index function through a Lyapunov stability theory to generate a disturbance suppression condition, wherein the disturbance suppression condition is as follows: if an interference suppression parameter 0 < gamma < 1 and a positive definite matrix P are present1,P2Satisfy the following requirements
Figure FDA0003241000040000052
If yes, all distributed power supplies in the micro-grid system have disturbance suppression performance;
solving a control input equation of each follower power supply by using the stability condition of the microgrid system to obtain a control gain coefficient corresponding to the follower power supply, wherein the control gain coefficient comprises:
and solving a control input equation of each follower power supply by using the stability condition of the micro-grid system and the interference suppression performance to obtain a control gain coefficient corresponding to the follower power supply.
10. A microgrid distributed power supply cooperative control device based on a variable-structure topological network is characterized by comprising:
the topological structure analysis module is used for dividing all distributed power supplies in the micro-grid system into a leader power supply and a plurality of follower power supplies and generating information interaction relations among the distributed power supplies by utilizing a variable-structure topological network;
the control input analysis module is used for constructing a control input equation u of each follower power supply according to the control input r (t) of the leader power supply, the output voltage Y (t) of each distributed power supply and the information interaction relation generated by the topological structure analysis moduleiSaid control input equation uiIncluding the control gain coefficient to be solved;
the system equation building module is used for building a second-order dynamic system model of each distributed power supply and building a state error system equation of the micro-grid system based on the second-order dynamic system model;
the stability analysis module is used for analyzing the state error system equation generated by the system equation building module through the Lyapunov stability theory to generate a stability control condition of the micro-grid system;
a control gain solving module for utilizing the stability control condition generated by the stability analyzing module to control the input equation u of each follower power supplyiSolving to obtain a control gain coefficient corresponding to the follower power supply;
and the control gain loading module is used for loading the control gain coefficient output by the control gain solving module into the controller of each follower power supply so as to enable the controller to control the follower power supply according to the control gain coefficient.
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