Space-ground integrated micro-grid cluster topology distributed optimization method and system for network risk

Abstract

The invention discloses a space-ground integrated micro-grid cluster topology distributed optimization method and system for network risks, and the method comprises the steps: constructing a micro-grid cluster model based on the topological structure information of a micro-grid cluster, and constructing a communication topological optimization model and constraint conditions based on the communication network topological information, the communication topology optimization model is solved, an adjacent matrix and a Laplacian matrix are obtained, a virtual signal is generated according to the reference signal of each distributed power generation unit, a smooth reference trajectory is generated based on the virtual signal, and a decentralized controller model for constructing the micro-grid cluster is determined based on the smooth reference trajectory; a solution result based on the topology optimization model and the decentralized controller model are applied to the microgrid cluster model, so that the communication network topology structure of the microgrid cluster is optimized, the coping capacity of the microgrid cluster to network risk behaviors is improved, the stability of network communication is greatly improved, and the network communication efficiency is improved. And the problem of communication delay caused by a space-ground integrated network is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and particularly to a space-ground integrated microgrid cluster topology distributed optimization method and system for network risks. Background Art

[0002] With the rapid development of renewable energy, microgrid clusters achieve energy complementarity and optimal scheduling through interconnection, becoming an ideal solution for power supply in remote areas. The space-ground integrated information network (combining satellite communication and ground network), due to its wide-area coverage and strong anti-destruction characteristics, is widely used in the remote communication and coordinated control of microgrid clusters to achieve cross-regional energy coordination and optimal scheduling. However, the distributed optimization of microgrid clusters relies on real-time information interaction, and the characteristics of open channels, dynamic topology, and long-distance transmission make it face network attack threats, especially malicious network threat risk behaviors (such as false data injection malicious behaviors, denial-of-service malicious behaviors, replay malicious behaviors, etc.), which may lead to system operation imbalance or even paralysis. In addition, the space-ground integrated microgrid cluster faces significant communication delay problems, including: long-distance transmission delay: due to the long propagation distance of satellite and drone communication links, the data transmission delay is usually large, possibly reaching dozens to hundreds of milliseconds, affecting the real-time performance of the microgrid control system. Dynamic link delay fluctuation: The nodes in the space-ground integrated network (such as low-earth orbit satellites, drone base stations) have mobility, resulting in continuous changes in the network topology and large communication delay fluctuations, affecting the system convergence speed and stability. Delay accumulation effect: In a multi-hop communication environment (such as "satellite-drone-ground" link), data packets need to go through multiple forwards, further exacerbating the delay problem, which may lead to control instructions lagging or being lost, thus affecting the collaborative optimization and stable operation of the microgrid cluster.

[0003] The space-ground integrated microgrid cluster combines satellite, drone, and ground communication, but most traditional optimization methods are based on the ground microgrid communication model and do not fully consider the problems of high delay, bandwidth limitation, and data packet loss of the space-ground link, resulting in limited effects of the optimization strategy in practical applications. The communication networks of existing microgrid clusters mostly adopt a fixed topology structure. When facing deliberate network malicious behaviors, the attack of key nodes may lead to the paralysis of the whole network communication, seriously affecting the coordinated control and power supply of the microgrid cluster. Summary of the Invention

[0004] The main purpose of the present invention is to provide a space-ground integrated microgrid cluster topology distributed optimization method and system for network risks, aiming to solve the technical problems that the prior art does not fully consider the problems of high delay, bandwidth limitation, and data packet loss of the space-ground link, cannot effectively optimize the communication network topology result of the microgrid cluster, resulting in unstable communication network of the microgrid cluster, and cannot effectively cope with network malicious behaviors.

[0005] To achieve the above object, the present invention provides a space-earth integrated microgrid cluster topology distributed optimization method for network risks, and the method includes the following steps: Construct a microgrid cluster model based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes a plurality of distributed generation units; Obtain the communication network topology information of the microgrid cluster according to the weighted undirected graph. The communication network topology information includes the number of topological edges, the number of nodes, and the topological dependency; Construct the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; Solve the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix; Generate virtual signals according to the reference signals of each distributed generation unit, and generate a smooth reference trajectory based on the virtual signals. The reference signals include the own reference signals of the distributed generation units and the adjacent reference signals of adjacent nodes; Determine the error variables of each distributed generation unit based on the smooth reference trajectory, and construct a decentralized controller model of the microgrid cluster based on the error variables; Apply the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed generation unit in the microgrid cluster.

[0006] Optionally, the constructing a microgrid cluster model based on the topological structure information of the microgrid cluster includes: Determine the cooperative secondary voltage control strategy of each distributed generation unit based on the topological structure information of the microgrid cluster: Wherein, represents the voltage control coefficient, represents the distributed generation unit output voltage; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage recovery, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the expected average voltage value; Construct a microgrid cluster model according to the collaborative secondary voltage control strategy.

[0007] Optionally, constructing the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information includes: Construct the initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; Optimize the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the communication topology optimization model; The initial optimization model includes: Wherein, represents the number of edges of the communication network topology of the microgrid cluster, represents the number of nodes of the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, represents the identity matrix, represents the average node degree, represents the objective function; The constraint conditions include: Wherein, represents the adjacency matrix, and respectively represent the nodes in the weighted undirected graph, represents the transpose, represents the Laplacian matrix, represents extracting the diagonal elements from the matrix, represents the Laplacian matrix of the eigenvalues, represents the eigenvalue of the lower bound constant of the constraint, represents the auxiliary variable used to construct the positive semi-definite constraint, represents the upper bound constant of the eigenvalue, identity matrix has the same dimension as the Laplacian matrix ; The communication topology optimization model includes: wherein, represents the degree of the -th node in the weighted undirected graph, represents a constant, represents and auxiliary variables of the minimum value of

[0008] Optionally, the virtual signal is generated based on the following formula: wherein, and respectively represent the virtual signals of node and node , represents the derivative of the virtual signal, represents an auxiliary variable, represents the derivative of the auxiliary variable, and represent control gains, represents the local objective function, represents the gradient of the local objective function, represents the time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: wherein, represents the smooth reference trajectory, represents the interpolated time node, represents the coefficient of the interpolation polynomial, represents the order.

[0009] Optionally, the error variables include voltage output error variables and controller model output error variables; Determining the error variables of each distributed generation unit based on the smooth reference trajectory, and constructing a decentralized controller model for the microgrid cluster based on the error variables, including: Determining the voltage output error variables of each distributed generation unit based on the smooth reference trajectory: Wherein, represents the voltage output error variable, represents the distributed generation unit 's output voltage, represents the reference signal of the smooth reference trajectory; Generating a first virtual controller model according to the voltage output error variable: Wherein, represents the first virtual controller model, represents the derivative of the smooth reference trajectory, represents a positive gain, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the predicted value of the reactive power; Determining the controller model output error variable based on the first virtual controller model: Wherein, represents the controller model output error variable, represents the secondary voltage regulation term; Generating a second virtual controller model according to the voltage output variable, the controller model output error variable, and the first virtual controller model; Constructing a decentralized controller model for the microgrid cluster based on the second virtual controller model: Wherein, represents the output of the decentralized controller model, represents the controller gain coefficient, represents the second virtual controller model, represents the actual value of the control gain, represents the predicted value of the control gain.

[0010] Optionally, the generating a second virtual controller model according to the voltage output variable, the controller model output error variable, and the first virtual controller model includes: Generate a first adaptive adjustment signal based on the voltage output error variable: Wherein, represents the first adaptive adjustment signal; Generate a second adaptive adjustment signal based on the first adaptive adjustment signal, the controller output error variable, and the first virtual controller: Wherein, represents the second adaptive adjustment signal; Generate a second virtual controller model based on the voltage output error variable, the controller model output error variable, the first virtual controller model, and the second adaptive adjustment signal: Wherein, represents a positive gain, represents a positive definite adaptive gain matrix, represents the second derivative of the smooth reference trajectory.

[0011] In addition, to achieve the above object, the present invention also proposes a space-ground integrated microgrid cluster topology distributed optimization system for network risks, and the system includes: A microgrid cluster model construction module, configured to construct a microgrid cluster model based on the topological structure information of the microgrid cluster, the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes a plurality of distributed generation units; A communication network topology analysis module, configured to obtain the communication network topology information of the microgrid cluster according to the weighted undirected graph, and the communication network topology information includes the number of topological edges, the number of nodes, and the topological dependency relationship; An optimization model construction module, configured to construct a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; A communication network optimization solving module, configured to solve the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplacian matrix; A virtual signal design module, configured to generate a virtual signal according to the reference signal of each distributed generation unit, and generate a smooth reference trajectory based on the virtual signal, and the reference signal includes the own reference signal of the distributed generation unit and the adjacent reference signal of the adjacent node; A controller design module, configured to determine the error variable of each distributed generation unit based on the smooth reference trajectory, and construct a decentralized controller model of the microgrid cluster based on the error variable; A communication network topology optimization module, which is used to apply the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed generation unit in the microgrid cluster.

[0012] Optionally, the microgrid cluster model construction module is further used to determine the cooperative secondary voltage control strategy of each distributed generation unit based on the topology structure information of the microgrid cluster: where, represents the voltage control coefficient, represents the distributed generation unit 's output voltage; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage restoration, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the expected average voltage value; Construct a microgrid cluster model according to the cooperative secondary voltage control strategy.

[0013] Optionally, the optimization model construction module is further used to construct the initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; optimize the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain a communication topology optimization model; The initial optimization model includes: where, represents the number of communication network topology edges of the microgrid cluster, represents the number of nodes in the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, represents the identity matrix, represents the average node degree, represents the objective function; The constraint conditions include: Among them, represents the adjacency matrix, and respectively represent the nodes in the weighted undirected graph, represents the transpose, represents the Laplacian matrix, represents extracting the diagonal elements of the matrix, represents the Laplacian matrix 's eigenvalues, represents the eigenvalue 's lower bound constraint constant, represents the auxiliary variable used to construct the positive semi - definite constraint, represents the upper bound constant of the eigenvalue, the identity matrix 's dimension is consistent with that of the Laplacian matrix ; The communication topology optimization model includes: Among them, represents the degree of the th node in the weighted undirected graph, represents a constant, represents and 's minimum value of the auxiliary variable.

[0014] Optionally, the virtual signal is generated based on the following formula: Among them, and respectively represent the virtual signals of node and node , represents the derivative of the virtual signal, represents an auxiliary variable, represents the derivative of the auxiliary variable, and represents a control gain, represents a local objective function, represents the gradient of the local objective function, represents a time node, represents a time-varying weight; The smooth reference trajectory is generated based on the following formula: wherein, represents the smooth reference trajectory, represents the interpolated time node, represents the coefficient of the interpolation polynomial, represents the order.

[0015] In addition, to achieve the above object, the present application also proposes a space-ground integrated microgrid cluster topology distributed optimization device for network risks, the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program is configured to implement the steps of the space-ground integrated microgrid cluster topology distributed optimization method for network risks as described above.

[0016] In addition, to achieve the above object, the present application also proposes a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the space-ground integrated microgrid cluster topology distributed optimization method for network risks as described above.

[0017] In addition, to achieve the above object, the present application also provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the space-ground integrated microgrid cluster topology distributed optimization method for network risks as described above.

[0018] The present invention constructs a microgrid cluster model based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units. The communication network topological information of the microgrid cluster is obtained according to the weighted undirected graph, and the communication network topological information includes the number of topological edges, the number of nodes, and the topological dependency relationship. A communication topology optimization model and constraint conditions of the microgrid cluster are constructed based on the communication network topological information. The communication topology optimization model is solved based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix. A virtual signal is generated according to the reference signals of each distributed generation unit, and a smooth reference trajectory is generated based on the virtual signal. The reference signals include the self-reference signal of the distributed generation unit and the adjacent reference signals of adjacent nodes. The error variables of each distributed generation unit are determined based on the smooth reference trajectory, and a decentralized controller model of the microgrid cluster is constructed based on the error variables. The in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model are applied to the microgrid cluster model to optimize the communication network topological structure of the microgrid cluster. Since the present invention improves the robustness of the communication delay of the communication network topological structure of the microgrid cluster through optimizing the controller design, it ensures that efficient and stable energy scheduling and cooperative control can still be achieved under high-delay and network fluctuation environments, effectively improves the ability of the microgrid cluster to cope with network risk behaviors, greatly improves the stability of network communication, and effectively overcomes the communication delay problem brought by the space-ground integrated network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic structural diagram of a space-ground integrated microgrid cluster topology distributed optimization device for network risks in the hardware operating environment related to the embodiment solution of the present invention; Figure 2 It is a schematic flowchart of the first embodiment of the space-ground integrated microgrid cluster topology distributed optimization method for network risks of the present invention; Figure 3 It is a schematic flowchart of the optimization of the communication network topological structure of the microgrid cluster in an embodiment of the space-ground integrated microgrid cluster topology distributed optimization method for network risks of the present invention; Figure 4This is a structural block diagram of the first embodiment of the integrated space-ground microgrid cluster topology distributed optimization system for network risks according to the present invention.

[0021] The realization of the object, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] Referring to Figure 1 , Figure 1 This is a schematic structural diagram of the integrated space-ground microgrid cluster topology distributed optimization device for network risks in the hardware operating environment involved in the embodiment solution of the present invention.

[0024] As Figure 1 shown, the integrated space-ground microgrid cluster topology distributed optimization device for network risks may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0025] Those skilled in the art can understand that Figure 1 the structure shown in

[0026] does not constitute a limitation on the integrated space-ground microgrid cluster topology distributed optimization device for network risks, and may include more or fewer components than shown, or combine some components, or have different component arrangements. Figure 1 As

[0027] shown, the memory 1005, as a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a microgrid cluster optimization program. Figure 1In the integrated satellite-terrestrial microgrid cluster topology distributed optimization device for network risks shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the integrated satellite-terrestrial microgrid cluster topology distributed optimization device for network risks of the present invention can be arranged in the integrated satellite-terrestrial microgrid cluster topology distributed optimization device for network risks. The integrated satellite-terrestrial microgrid cluster topology distributed optimization device for network risks calls the microgrid cluster optimization program stored in the memory 1005 through the processor 1001 and executes the integrated satellite-terrestrial microgrid cluster topology distributed optimization method provided by the embodiments of the present invention.

[0028] Embodiments of the present invention provide an integrated satellite-terrestrial microgrid cluster topology distributed optimization method for network risks. Referring to Figure 2 , Figure 2 is a schematic flowchart of the first embodiment of the integrated satellite-terrestrial microgrid cluster topology distributed optimization method for network risks of the present invention.

[0029] In this embodiment, the integrated satellite-terrestrial microgrid cluster topology distributed optimization method for network risks includes the following steps: Step S10: Construct a microgrid cluster model based on the topological structure information of the microgrid cluster.

[0030] It should be noted that this embodiment is applied to the optimization of the communication network topology structure (Integrated Satellite Terrestrial Network, ISTN) of the integrated satellite-terrestrial microgrid cluster. The above communication network topology structure can be a heterogeneous network architecture integrating a satellite communication system and a terrestrial mobile communication system. Among them, ISTN refers to an integrated satellite and terrestrial network. It is a heterogeneous network architecture integrating a satellite communication system and a terrestrial mobile communication system.

[0031] It should be understood that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a terminal electronic device capable of implementing the above functions. Hereinafter, the integrated satellite-terrestrial microgrid cluster topology distributed optimization device (abbreviated as the optimization device) for network risks is taken as an example to illustrate this embodiment and the following embodiments.

[0032] It should be noted that the microgrid cluster model is a weighted undirected graph, and the microgrid cluster (Microgrid, MG) includes multiple distributed generation units (Distributed Generation, DG). The distributed generation units can be small-scale power generation devices near the load, usually distributed on multiple nodes in the microgrid system. The microgrid cluster can be an autonomous power system including distributed energy, energy storage systems, loads, and control systems. The above microgrid cluster model can be a physical model of the microgrid cluster.

[0033] Further, in order to accurately construct a network communication topology structure model identical to the microgrid cluster structure, step S10 above may include: Step S101: Determine the cooperative secondary voltage control strategy for each distributed generation unit based on the topology structure information of the microgrid cluster; Step S102: Construct a microgrid cluster model according to the cooperative secondary voltage control strategy.

[0034] It should be noted that the cooperative secondary voltage control strategy for each distributed generation unit in the microgrid cluster can be described as follows: Among them, represents the voltage control coefficient, represents the distributed generation unit 's output voltage; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage restoration, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the expected average voltage value.

[0035] In some embodiments, the optimization device can establish a space-ground integrated microgrid cluster model and construct an AC microgrid model composed of distributed generators, where the th distributed generator corresponds to a node . According to graph theory, the communication links between distributed generation units can be regarded as edges. The communication links in the microgrid cluster are usually considered to be bidirectional, that is, all edges are undirected. The power generation unit and the power generation unit The communication link between them is regarded as . Therefore, the communication network in the microgrid cluster can be described as a weighted undirected graph . is a finite set of nodes, is the set of node labels. denotes the weighted adjacency matrix of, where and is the weight of the edge between them. Since self-loops are not considered, all , , for >0, . The degree of is the number of edges connected to it, which can be represented by indicating the local characteristics of the node. denotes the in-degree matrix of in the weighted undirected graph, where . In addition, the Laplacian matrix of satisfies the relation .

[0036] Step S20: Obtain the communication network topology information of the microgrid cluster according to the weighted undirected graph.

[0037] It should be noted that the communication network topology information includes the number of topological edges, the number of nodes, and the topological dependence relationship.

[0038] Step S30: Construct the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information.

[0039] It should be noted that in this embodiment, to reduce the impact of malicious network attacks on the space-ground integrated microgrid cluster and improve the structural survivability of the communication topology, it is necessary to optimize the communication topology and establish a topology optimization model.

[0040] Furthermore, in order to effectively optimize the communication network of the microgrid cluster, the above step S30 may include: Step S301: Construct the initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; Step S302: Optimize the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the communication topology optimization model; ​It is understandable that the network layer and the physical layer of the AC microgrid are tightly coupled. Once the network system is paralyzed due to malicious cyberattacks, the operation of the physical system will be severely affected. The node degree is an important indicator of the topological structure, reflecting the importance of a node in the communication network. Therefore, in order to improve the attack resilience of the communication network under multiple deliberate cyberattacks, the node degrees of each distributed generation unit should be as uniform as possible. In graph theory, the entropy of the degree distribution is usually used to measure the uniformity of node degrees, and the entropy of the degree distribution is calculated with reference to the following formula: where, is the distribution of node degrees, which is the ratio of the number of nodes with degrees in the network and can be calculated as follows.

[0041] where, is the number of nodes of order , . However, if is used as the objective of the network design problem, additional binary variables need to be introduced to connect , and the decision variable , and the objective of the optimization model is non-linear. In addition, the function is defined on (0, +∞), which means that numerical problems will occur when . Inspired by the above definition of degree distribution, a new structural survivability index based on the degree deviation between communication nodes is proposed for communication topology optimization. Contrary to , the exponent is a quadratic function of the decision variable , which will greatly reduce the computational cost of the network design problem. Therefore, in this embodiment, the variance of node degrees can be used to measure the uniformity of node degrees, with reference to the following formula: where, The smaller the value of, the stronger the structural survivability of the communication topology under multiple deliberate cyberattacks. Therefore, to improve the structural survivability of the communication topology, the communication topology is optimized to establish an initial optimization model as follows: where, represents the number of edges in the communication network topology of the microgrid cluster, represents the number of nodes in the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, denotes the identity matrix, denotes the average node degree, denotes the objective function, and the objective function of the initial optimization model is to minimize the variance of the node degrees; The constraint conditions include: The first constraint condition: The second constraint condition: The third constraint condition: The fourth constraint condition: The fifth constraint condition: The sixth constraint condition: The seventh constraint condition: Among them, denotes the adjacency matrix, and respectively denote the nodes in the weighted undirected graph, denotes the transpose, denotes the Laplacian matrix, denotes extracting the diagonal elements from the matrix, denotes the Laplacian matrix 's eigenvalues, denotes the eigenvalue 's lower bound constraint constant, denotes the auxiliary variable used to construct the positive semi-definite constraint, denotes the upper bound constant of the eigenvalue, and the dimension of the identity matrix is the same as the dimension of the Laplacian matrix ; The above first constraint condition refers to the property of the adjacent matrix ; the second constraint condition defines the relationship between the node degree and the number of edges; the third constraint condition defines the relationship between the degree matrix and the adjacent matrix ; the fourth constraint condition defines the Laplacian matrix and through L ; the fifth constraint condition defines that the eigenvalue of should be greater than 0 to ensure that the graph is connected, where is a small constant; the sixth constraint defines the Laplacian matrix based on graph theory constraint; the seventh constraint is used to constrain the identity matrix and the Laplacian matrix dimension.

[0042] The problem composed of the first to seventh constraints is a mixed-integer nonlinear programming model because the eigenvalue cannot be expressed as analytical expression. Therefore, the fifth constraint can be relaxed to the following semi-positive definite constraint.

[0043] It should be noted that the above initial optimization model is still a difficult-to-solve MISDP model. Therefore, in this embodiment, the initial optimization model can be decomposed into an IQP problem to optimize and , and an auxiliary feasibility problem to find a connected graph, that is, to optimize the adjacency matrix value and the Laplacian matrix and the Laplacian matrix based on the given in-degree matrix Among them, represents the degree of the th node in the weighted undirected graph; represents a constant; represents and auxiliary variable of the minimum value; represents the objective function, which is used to measure the difference between the degree matrix and the reference degree matrix ; represents the total number of nodes in the weighted undirected graph, that is, the total number of generators; represents the optimization and solution of the minimum value of the degree matrix , the adjacency matrix , the degree of each node and the auxiliary variable ; represents the traversal index, acts to carefully constrain the distribution of node degrees by traversing the node subset to meet the specific requirements of the graph structure and the optimization problem.

[0044] Although there is a connected graph implementation obtained from the communication topology optimization model (or ), the solution in this model is not unique and cannot generate a non-connected graph. Therefore, the following auxiliary feasibility problem is proposed to find a connected graph based on the given , referring to the following formula: Find a connected graph, with reference to the following formula: where the above formula is also an MISDP problem. However, compared with the model established in step 2, it can be solved more effectively for two reasons: (1) the integer variables are greatly reduced; (2) it only needs to find a feasible solution instead of an optimal solution. Numerical simulations also verify that it can be solved effectively.

[0045] Step S40: Solve the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph, and obtain the adjacency matrix and the Laplacian matrix.

[0046] Step S50: Generate virtual signals according to the reference signals of each distributed generation unit, and generate a smooth reference trajectory based on the virtual signals.

[0047] It should be noted that the above smooth reference trajectory can be a smooth trajectory reference signal. The reference signals of the above distributed generation units include the self-reference signals of the distributed generation units and the adjacent reference signals of adjacent nodes.

[0048] Furthermore, to solve the communication delay problem brought by the space-ground integrated network communication structure, in some embodiments, the optimization device can design a distributed optimization algorithm for generating virtual signals, and this algorithm can minimize the optimization function under the influence of intermittent communication delay. For the th distributed generation unit node , virtual signals can be introduced, and these signals are updated by the following distributed optimization algorithm: where and respectively represent the virtual signals of node i and node j , represents the derivative of the virtual signal, represents the auxiliary variable, represents the derivative of the auxiliary variable, and represent the control gains, represents the local objective function, represents the gradient of the local objective function, represents the time node, represents the time-varying weight, and the time-varying weight used here is caused by the intermittent communication network; However, due to the intermittent communication delay, the second derivative of does not exist. However, this property is crucial when using the recursive inversion design process. To ensure the existence of the high-order derivative of the reference trajectory, by using the Hermite interpolation method, the new order smoothed reference trajectory is designed as follows: where, represents the smoothed reference trajectory, represents the interpolated time node , is a small positive constant, represents the coefficient of the interpolation polynomial, represents the order, when time, ; , represents the coefficient of the interpolation polynomial, where is the th row of: where, for : Step S60: Determine the error variables of each distributed generation unit based on the smoothed reference trajectory, and construct a decentralized controller model for the microgrid cluster based on the error variables.

[0049] In some embodiments, the optimization device can design a decentralized controller based on a recursive inversion process.

[0050] Furthermore, to improve the stability and communication timeliness of the communication network of the microgrid cluster, in some embodiments, the error variables include voltage output error variables and controller model output error variables; the above step S60 may include: Step S601: Determine the voltage output error variables of each distributed generation unit based on the smooth reference trajectory.

[0051] It should be noted that for the th distributed generation unit , we define the following error variables: Among them, represents the voltage output error variable, represents the output voltage of the distributed generation unit , represents the reference signal of the smooth reference trajectory; Step S602: Generate a first virtual controller model according to the voltage output error variable.

[0052] It should be noted that the first virtual controller model is designed based on the following formula: Among them, represents the first virtual controller model, represents the derivative of the smooth reference trajectory, represents a positive gain, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the predicted value of the reactive power.

[0053] Step S603: Determine the controller model output error variable based on the first virtual controller model.

[0054] It should be noted that the controller model output error variable of the th distributed generation unit is calculated with reference to the following formula: Among them, represents the controller model output error variable, represents the secondary voltage regulation term; Step S604: Generate a second virtual controller model according to the voltage output variable, the controller model output error variable, and the first virtual controller model.

[0055] Furthermore, in order to accurately construct the second virtual controller model and improve communication stability, the above step S604 may include: Step S6041: Generate a first adaptive adjustment signal according to the voltage output error variable: Among them, represents the first adaptive adjustment signal; Step S6042: Generate a second adaptive adjustment signal based on the first adaptive adjustment signal, the controller output error variable, and the first virtual controller: Among them, represents the second adaptive adjustment signal; Step S6043: Generate a second virtual controller model based on the voltage output error variable, the controller model output error variable, the first virtual controller model, and the second adaptive adjustment signal: Among them, represents a positive gain, represents a positive definite adaptive gain matrix, represents the second derivative of the smooth reference trajectory.

[0056] Step S605: Construct a decentralized controller model for the microgrid cluster based on the second virtual controller model.

[0057] It should be noted that the decentralized controller model is designed based on the following formula: Among them, represents the output of the decentralized controller model, represents the controller gain coefficient, represents the second virtual controller model, represents the actual value of the control gain, represents the predicted value of the control gain, which is updated based on the following formula: Among them, represents the adaptive law gain; in addition, is updated to: Among them, represents a positive definite adaptive gain matrix.

[0058] Step S70: Apply the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed generation unit in the microgrid cluster.

[0059] It can be understood that in this embodiment, the adjacency matrix, Laplacian matrix, and in-degree matrix of each node in the weighted undirected graph obtained by solving the communication topology optimization model are input into the decentralized controller model, and the decentralized controller model is applied to the microgrid cluster model to achieve communication optimization of the communication network topology structure of the microgrid cluster.

[0060] It should be noted that this embodiment adopts a two-stage optimization strategy. First, the communication topology structure is optimized to improve the survivability and anti-destruction ability of the microgrid cluster under malicious behaviors (such as false data injection behavior, denial of service behavior, etc.), and to ensure that the microgrid cluster can still operate stably when some communication nodes are damaged. On this basis, this embodiment further proposes a hierarchical design scheme combining a distributed optimization algorithm and a decentralized tracking controller, aiming to overcome the communication delay problem brought by the space-ground integrated network. Since the space-ground integrated system involves multi-level links such as satellites, drones, and ground communications, its long-distance transmission, dynamic link changes, and data packet loss may cause delays in the transmission of control signals of the microgrid cluster, affecting the real-time performance and accuracy of distributed optimization. To address this problem, this embodiment improves the robustness of the system to communication delays by optimizing the controller design, ensuring efficient and stable energy scheduling and cooperative control in high-delay and network fluctuation environments. This method can effectively improve the security, stability, and dynamic control performance of the space-ground integrated microgrid cluster, providing key technical support for the reliable operation and efficient optimal scheduling of future smart grids.

[0061] In some embodiments, the optimization device can be based on Figure 3 to optimize the communication network topology structure of the microgrid cluster. Referring to Figure 3 , Figure 3 is a schematic diagram of the optimization process of the communication network topology structure of the microgrid cluster. The topology structure optimization process can include communication topology optimization and controller design. Among them, the optimization device can set the nodes in the communication network of the microgrid cluster as , set the number of edges as , solve the communication topology optimization model to obtain the in-degree matrix, and judge whether the eigenvalues of the Laplacian matrix are greater than 0. If so, directly proceed with controller design; if not, solve the adjacency matrix and Laplacian matrix based on , generate virtual signals based on the distributed optimization algorithm, design a smooth reference trajectory by using the Hermite interpolation method, and design a decentralized controller based on the recursive inversion process to achieve the optimization of the communication network topology structure of the microgrid cluster.

[0062] In this embodiment, a microgrid cluster model is constructed based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units. The communication network topological information of the microgrid cluster is obtained according to the weighted undirected graph, and the communication network topological information includes the number of topological edges, the number of nodes, and the topological dependence relationship. A communication topology optimization model and constraint conditions of the microgrid cluster are constructed based on the communication network topological information. The communication topology optimization model is solved based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix. Virtual signals are generated according to the reference signals of each distributed generation unit, and a smooth reference trajectory is generated based on the virtual signals. The reference signals include the self-reference signals of the distributed generation units and the adjacent reference signals of adjacent nodes. The error variables of each distributed generation unit are determined based on the smooth reference trajectory, and a decentralized controller model of the microgrid cluster is constructed based on the error variables. The in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model are applied to the microgrid cluster model to optimize the communication network topological structure of the microgrid cluster. Since, in this embodiment, the communication delay robustness of the communication network topological structure of the microgrid cluster is improved by optimizing the controller design, it is ensured that efficient and stable energy scheduling and cooperative control can still be achieved in a high-delay and network fluctuation environment, effectively enhancing the microgrid cluster's ability to respond to network risk behaviors, greatly improving the stability of network communication, and effectively overcoming the communication delay problem brought by the space-ground integrated network.

[0063] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, on which a microgrid cluster optimization program is stored. When the microgrid cluster optimization program is executed by a processor, the steps of the above-mentioned space-ground integrated microgrid cluster topology distributed optimization method for network risks are implemented.

[0064] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0065] The above computer-readable storage medium can be included in the integrated space-earth microgrid cluster topology distributed optimization device for network risks; it can also exist independently without being assembled into the integrated space-earth microgrid cluster topology distributed optimization device for network risks.

[0066] In addition, an embodiment of the present invention also proposes a computer program product, including a microgrid cluster optimization program. When the microgrid cluster optimization program is executed by a processor, it implements the steps of the integrated space-earth microgrid cluster topology distributed optimization method for network risks as described above.

[0067] The specific implementation manner of the computer program product of the present invention is basically the same as that of each embodiment of the above integrated space-earth microgrid cluster topology distributed optimization method for network risks, and will not be elaborated here.

[0068] Refer to Figure 4 , Figure 4 which is the structural block diagram of the first embodiment of the integrated space-earth microgrid cluster topology distributed optimization system for network risks of the present invention.

[0069] As Figure 4 shown, the integrated space-earth microgrid cluster topology distributed optimization system for network risks proposed by the embodiment of the present invention includes: The microgrid cluster model construction module 10 is used to construct a microgrid cluster model based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units; The communication network topology analysis module 20 is used to obtain the communication network topology information of the microgrid cluster according to the weighted undirected graph. The communication network topology information includes the number of topological edges, the number of nodes, and the topological dependency; The optimization model construction module 30 is used to construct the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; The communication network optimization solving module 40 is used to solve the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix; The virtual signal design module 50 is used to generate virtual signals according to the reference signals of each distributed generation unit and generate a smooth reference trajectory based on the virtual signals. The reference signals include the self-reference signal of the distributed generation unit and the adjacent reference signals of adjacent nodes; The controller design module 60 is used to determine the error variables of each distributed generation unit based on the smooth reference trajectory and construct a decentralized controller model of the microgrid cluster based on the error variables; The communication network topology optimization module 70 is used to apply the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed generation unit in the microgrid cluster.

[0070] Furthermore, the microgrid cluster model construction module 10 is also used to determine the cooperative secondary voltage control strategy of each distributed generation unit based on the topological structure information of the microgrid cluster: where, represents the voltage control coefficient, represents the distributed generation unit 's output voltage; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage restoration, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the expected average voltage value; Construct a microgrid cluster model according to the collaborative secondary voltage control strategy.

[0071] Furthermore, the optimization model construction module 30 is further configured to construct an initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; optimize the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain a communication topology optimization model; The initial optimization model includes: where, represents the number of edges in the communication network topology of the microgrid cluster, represents the number of nodes in the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, represents the identity matrix, represents the average node degree, represents the objective function; The constraint conditions include: where, represents the adjacency matrix, and respectively represent nodes in the weighted undirected graph, represents the transpose, represents the Laplacian matrix, represents extracting the diagonal elements from the matrix, represents the Laplacian matrix of the eigenvalues, represents the eigenvalue of the constraint lower bound constant, represents an auxiliary variable used to construct a positive semi-definite constraint, represents the upper bound constant of the eigenvalue, the identity matrix whose dimension is the same as that of the Laplacian matrix ; The communication topology optimization model includes: Among them, represents the degree of the -th node in the weighted undirected graph, represents a constant, represents and auxiliary variables of the minimum value of.

[0072] Furthermore, the virtual signal is generated based on the following formula: Among them, and respectively represent the virtual signals of node and node , represents the derivative of the virtual signal, represents an auxiliary variable, represents the derivative of the auxiliary variable, and represent the control gain, represents the local objective function, represents the gradient of the local objective function, represents the time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: Among them, represents the smooth reference trajectory, represents the interpolation time node, represents the coefficient of the interpolation polynomial, represents the order.

[0073] In this embodiment, a microgrid cluster model is constructed based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units. The communication network topological information of the microgrid cluster is obtained according to the weighted undirected graph, and the communication network topological information includes the number of topological edges, the number of nodes, and the topological dependency relationship. A communication topology optimization model and constraint conditions of the microgrid cluster are constructed based on the communication network topological information. The communication topology optimization model is solved based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix. Virtual signals are generated according to the reference signals of each distributed generation unit, and a smooth reference trajectory is generated based on the virtual signals. The reference signals include the self-reference signals of the distributed generation units and the adjacent reference signals of adjacent nodes. Error variables of each distributed generation unit are determined based on the smooth reference trajectory, and a decentralized controller model of the microgrid cluster is constructed based on the error variables. The in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model are applied to the microgrid cluster model to optimize the communication network topological structure of the microgrid cluster. Since this embodiment improves the robustness of the communication delay of the communication network topological structure of the microgrid cluster through optimized controller design, it ensures that efficient and stable energy scheduling and cooperative control can still be achieved under high-delay and network fluctuation environments, effectively improving the ability of the microgrid cluster to respond to network risk behaviors, greatly enhancing the stability of network communication, and effectively overcoming the communication delay problem brought by the space-ground integrated network.

[0074] The space-ground integrated microgrid cluster topology distributed optimization system for network risks provided by this application adopts the space-ground integrated microgrid cluster topology distributed optimization method in the above embodiment, and can solve the technical problems of the space-ground integrated microgrid cluster topology distributed optimization for network risks. Compared with the prior art, the beneficial effects of the space-ground integrated microgrid cluster topology distributed optimization system for network risks provided by this application are the same as those of the space-ground integrated microgrid cluster topology distributed optimization method provided by the above embodiment, and other technical features in the space-ground integrated microgrid cluster topology distributed optimization system are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0075] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not limit this.

[0076] It should be noted that the workflow described above is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is imposed here.

[0077] In addition, for the technical details not described in detail in this embodiment, reference can be made to the integrated space-earth microgrid cluster topology distributed optimization method for network risks provided in any embodiment of the present invention, which will not be elaborated here.

[0078] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or system including the element.

[0079] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0080] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory / random access memory, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0081] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A space-earth integrated microgrid cluster topology distributed optimization method for network risks, characterized in that The method includes: Constructing a microgrid cluster model based on the topological structure information of the microgrid cluster, where the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units; Obtaining the communication network topological information of the microgrid cluster according to the weighted undirected graph, where the communication network topological information includes the number of topological edges, the number of nodes, and the topological dependency relationship; Constructing a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topological information; Solving the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplacian matrix; Generating virtual signals according to the reference signals of each distributed generation unit, and generating a smooth reference trajectory based on the virtual signals, where the reference signals include the self-reference signals of the distributed generation units and the adjacent reference signals of adjacent nodes; Determining the error variables of each distributed generation unit based on the smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variables; Applying the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topological structure of each distributed generation unit in the microgrid cluster.

2. The integrated space-earth microgrid cluster topology distributed optimization method for network risks according to claim 1, characterized in that The constructing a microgrid cluster model based on the topological structure information of the microgrid cluster includes: Determining the cooperative secondary voltage control strategy of each distributed generation unit based on the topological structure information of the microgrid cluster: Among them, represents the voltage control coefficient, represents the distributed generation unit output voltage; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage restoration, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the predicted average voltage value; Constructing a microgrid cluster model according to the cooperative secondary voltage control strategy.

3. The integrated space-ground microgrid cluster topology distributed optimization method for network risks according to claim 2, characterized in that, The constructing a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topological information includes: Constructing an initial optimization model and constraint conditions of the microgrid cluster based on the communication network topological information; Optimizing the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain a communication topology optimization model; The initial optimization model includes: Among them, represents the number of edges in the communication network topology of the microgrid cluster, represents the number of nodes in the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, represents the identity matrix, represents the average node degree, represents the objective function; The constraint conditions include: Among them, represents the adjacency matrix, and respectively represent the nodes in the weighted undirected graph, represents the transpose, represents the Laplacian matrix, represents extracting the diagonal elements in the matrix, represents the Laplacian matrix 's eigenvalues, represents the eigenvalue 's lower bound constraint constant, represents the auxiliary variable used to construct the positive semi - definite constraint, represents the upper bound constant of the eigenvalue, and the identity matrix 's dimension is consistent with that of the Laplacian matrix ; The communication topology optimization model includes: Among them, represents the degree of the -th node in the weighted undirected graph, represents a constant, represents and is an auxiliary variable representing the minimum value of 4. The integrated space-ground microgrid cluster topology distributed optimization method for network risks according to claim 3, characterized in that The virtual signal is generated based on the following formula: Among them, and respectively represent the node and the node virtual signal, represents the derivative of the virtual signal, represents the auxiliary variable, represents the derivative of the auxiliary variable, and represent the control gain, represents the local objective function, represents the gradient of the local objective function, represents the time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: Among them, represents the smooth reference trajectory, represents the interpolation time nodes, represents the coefficients of the interpolation polynomial, represents the order.

5. The integrated space-ground microgrid cluster topology distributed optimization method for network risks according to any one of claims 1 to 4, characterized in that, The error variables include voltage output error variables and controller model output error variables; The determining the error variables of each distributed generation unit based on the smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variables includes: Determining the voltage output error variables of each distributed generation unit based on the smooth reference trajectory: Among them, represents the voltage output error variable, represents the distributed generation unit output voltage, represents the reference signal for smoothing the reference trajectory; Generating a first virtual controller model according to the voltage output error variables: Among them, represents the first virtual controller model, represents the derivative of the smooth reference trajectory, represents a positive gain, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the predicted value of the reactive power; Determining the controller model output error variables based on the first virtual controller model: Among them, represents the output error variable of the controller model, represents the secondary voltage regulation term; Generating a second virtual controller model according to the voltage output variables, the controller model output error variables, and the first virtual controller model; Constructing a decentralized controller model of the microgrid cluster based on the second virtual controller model: Among them, represents the output of the decentralized controller model, represents the controller gain coefficient, represents the second virtual controller model, represents the actual value of the control gain, represents the predicted value of the control gain.

6. The integrated space-ground microgrid cluster topology distributed optimization method for network risks according to claim 5, wherein The generating a second virtual controller model according to the voltage output variables, the controller model output error variables, and the first virtual controller model includes: Generate a first adaptive adjustment signal according to the voltage output error variable: Among them, represents the first adaptive adjustment signal; Generate a second adaptive adjustment signal based on the first adaptive adjustment signal, the controller output error variable, and the first virtual controller: Among them, represents the second adaptive adjustment signal; Generate a second virtual controller model based on the voltage output error variable, the controller model output error variable, the first virtual controller model, and the second adaptive adjustment signal: Among them, represents positive gain, represents a positive definite adaptive gain matrix, represents the second derivative of the smooth reference trajectory.

7. An integrated space-ground microgrid cluster topology distributed optimization system for network risks, characterized in that, The system includes: A microgrid cluster model construction module, configured to construct a microgrid cluster model based on the topological structure information of the microgrid cluster. The microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed generation units; A communication network topology analysis module, configured to obtain the communication network topology information of the microgrid cluster according to the weighted undirected graph. The communication network topology information includes the number of topological edges, the number of nodes, and the topological dependency relationship; An optimization model construction module, configured to construct the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; A communication network optimization solving module, configured to solve the communication topology optimization model based on the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the adjacency matrix and the Laplacian matrix; A virtual signal design module, configured to generate a virtual signal according to the reference signal of each distributed generation unit and generate a smooth reference trajectory based on the virtual signal. The reference signal includes the self-reference signal of the distributed generation unit and the adjacent reference signal of the adjacent node; A controller design module, configured to determine the error variable of each distributed generation unit based on the smooth reference trajectory and construct a decentralized controller model of the microgrid cluster based on the error variable; A communication network topology optimization module, configured to apply the in-degree matrix, the adjacency matrix, the Laplacian matrix, and the decentralized controller model to the microgrid cluster model to optimize the communication network topological structure of each distributed generation unit in the microgrid cluster.

8. The integrated space-ground microgrid cluster topology distributed optimization system for network risks according to claim 7, characterized in that The microgrid cluster model construction module is further configured to determine the cooperative secondary voltage control strategy of each distributed generation unit based on the topological structure information of the microgrid cluster: Among them, represents the voltage control coefficient, represents the output voltage of the distributed generation unit ; represents the derivative of the output voltage, represents the nominal voltage amplitude, represents the voltage droop coefficient, represents the reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, represents the weight coefficient for reactive power distribution and voltage restoration, represents the output of the decentralized controller model, represents the rated voltage reference value of the system, represents the expected average voltage value; Construct a microgrid cluster model according to the cooperative secondary voltage control strategy.

9. The integrated space-ground microgrid cluster topology distributed optimization system for network risks according to claim 8, characterized in that, The optimization model construction module is further configured to construct the initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; optimize the initial optimization model according to the constraint conditions and the in-degree matrix of each node in the weighted undirected graph to obtain the communication topology optimization model; The initial optimization model includes: Among them, represents the number of edges in the communication network topology of the microgrid cluster, represents the number of nodes in the communication network, represents the in-degree matrix of nodes in the weighted undirected graph, represents the identity matrix, represents the average node degree, represents the objective function; The constraint conditions include: Among them, represents the adjacency matrix, and respectively represent the nodes in the weighted undirected graph, represents the transpose, represents the Laplacian matrix, represents extracting the diagonal elements in the matrix, represents the Laplacian matrix 's eigenvalues, represents the eigenvalue 's lower bound constraint constant, represents the auxiliary variable used to construct the positive semi - definite constraint, represents the upper bound constant of the eigenvalue, and the identity matrix 's dimension is consistent with that of the Laplacian matrix ; The communication topology optimization model includes: wherein, represents the degree of the th node in the weighted undirected graph, represents a constant, represents and is an auxiliary variable representing the minimum value of 10. The integrated space-ground microgrid cluster topology distributed optimization system for network risks according to claim 9, characterized in that The virtual signal is generated based on the following formula: Among them, and respectively represent the node and the node virtual signals, represents the derivative of the virtual signal, represents the auxiliary variable, represents the derivative of the auxiliary variable, and represent the control gain, represents the local objective function, represents the gradient of the local objective function, represents the time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: Among them, represents the smoothed reference trajectory, represents the interpolation time nodes, represents the coefficients of the interpolation polynomial, represents the order.

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