Distributed optimization method and system for space-ground integrated microgrid cluster topology targeting network risks

Abstract

The present invention discloses a distributed optimization method and system for a space-ground integrated microgrid cluster topology targeting network risks. The method comprises: constructing a microgrid cluster model based on the topological structure information of the microgrid cluster, constructing a communication topology optimization model and constraint conditions based on the communication network topology information, solving the communication topology optimization model, obtaining an adjacency matrix and a Laplace matrix, generating a virtual signal according to the reference signal of each distributed power generation unit, generating a smooth reference trajectory based on the virtual signal, determining to construct a decentralized controller model for the microgrid cluster based on the smooth reference trajectory, applying the solution result of the topology optimization model and the decentralized controller model to the microgrid cluster model, optimizing the communication network topology structure of the microgrid cluster, improving 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 about by the space-ground integrated network.

Description

Technical Field

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

[0002] With the rapid development of renewable energy, microgrid clusters, through interconnected networks, are achieving energy complementarity and optimized scheduling, becoming an ideal solution for power supply in remote areas. Space-ground integrated information networks (combining satellite communications and terrestrial networks) are widely used for remote communication and coordinated control in microgrid clusters due to their wide-area coverage and strong resilience, enabling cross-regional energy coordination and optimized scheduling. However, distributed optimization in microgrid clusters relies on real-time information exchange. The open channels, dynamic topology, and long-distance transmission characteristics make them vulnerable to cyberattacks, particularly malicious cyber threat risk behaviors (such as malicious spoofing, denial of service, and replay), which can cause system imbalance or even paralysis. Furthermore, space-ground integrated microgrid clusters face significant communication latency challenges, including: Long-distance transmission latency: Due to the long propagation distances of satellite and drone communication links, data transmission latency is typically significant, potentially reaching tens to hundreds of milliseconds, impacting the real-time performance of microgrid control systems. Dynamic link latency fluctuation: The mobility of nodes in space-ground integrated networks (such as low-orbit satellites and drone base stations) results in constant changes in network topology and significant communication latency fluctuations, impacting system convergence speed and stability. Cumulative delay effect: In a multi-hop communication environment (such as the "satellite-drone-ground" link), data packets need to undergo multiple forwarding, further exacerbating the delay problem, which may cause control instructions to lag or be lost, thereby affecting the coordinated optimization and stable operation of the microgrid cluster.

[0003] Space-ground integrated microgrid clusters combine satellite, drone, and terrestrial communications. However, traditional optimization methods are mostly based on ground-based microgrid communication models and fail to fully account for the high latency, bandwidth limitations, and packet loss of space-ground links. This results in limited effectiveness of these optimization strategies in practical applications. Existing microgrid cluster communication networks often use fixed topologies. In the face of deliberate cyberattacks, attacks on key nodes can paralyze communications across the entire network, severely impacting coordinated control and power supply within the microgrid cluster. Summary of the Invention

[0004] The main purpose of the present invention is to provide a distributed optimization method and system for the topology of a space-ground integrated microgrid cluster targeting network risks, aiming to solve the technical problems that the existing technology does not fully consider the high latency, bandwidth limitation and data packet loss problems of the space-ground link, cannot effectively optimize the communication network topology of the microgrid cluster, resulting in instability of the microgrid cluster communication network and inability to effectively deal with malicious network behavior.

[0005] To achieve the above objectives, the present invention provides a distributed optimization method for a space-ground integrated microgrid cluster topology targeting network risks, the method comprising the following steps:

[0006] Constructing a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes a plurality of distributed power generation units;

[0007] Acquire communication network topology information of the microgrid cluster according to the weighted undirected graph, wherein the communication network topology information includes the number of topological edges, the number of nodes, and topological dependencies;

[0008] Constructing a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information;

[0009] Solving the communication topology optimization model based on the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix;

[0010] generating a virtual signal according to a reference signal of each distributed power generation unit, and generating a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the distributed power generation unit's own reference signal and adjacent reference signals of adjacent nodes;

[0011] Determining error variables of each distributed generation unit based on a smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variables;

[0012] The inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model are applied to the microgrid cluster model to optimize the communication network topology structure of each distributed power generation unit in the microgrid cluster.

[0013] Optionally, the constructing a microgrid cluster model based on the topology information of the microgrid cluster includes:

[0014] The coordinated secondary voltage control strategy of each distributed generation unit is determined based on the topological structure information of the microgrid cluster:

[0015]

[0016]

[0017] in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value;

[0018] A microgrid cluster model is constructed according to the collaborative secondary voltage control strategy.

[0019] Optionally, constructing the communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information includes:

[0020] Constructing an initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information;

[0021] Optimizing the initial optimization model according to the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain a communication topology optimization model;

[0022] The initial optimization model includes:

[0023]

[0024]

[0025] in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, represents the objective function;

[0026] The constraints include:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] in, represents the adjacency matrix, and They represent nodes in the weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent;

[0035] The communication topology optimization model includes:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] in, Indicates the weighted undirected graph The degree of a node, represents a constant, express and Auxiliary variable with the minimum value.

[0043] Optionally, the virtual signal is generated based on the following formula:

[0044]

[0045]

[0046] in, and Represents nodes respectively and nodes Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, represents the time-varying weight;

[0047] The smooth reference trajectory is generated based on the following formula:

[0048]

[0049] in, represents the smooth reference trajectory, represents the time node of interpolation, represents the coefficients of the interpolation polynomial, Indicates the order.

[0050] Optionally, the error variable includes a voltage output error variable and a controller model output error variable;

[0051] The step of determining the error variables of each distributed power generation unit based on the smooth reference trajectory and constructing a decentralized controller model of the microgrid cluster based on the error variables includes:

[0052] Determine the voltage output error variable of each distributed generation unit based on the smooth reference trajectory:

[0053]

[0054] in, represents the voltage output error variable, Distributed Generation Unit The output voltage, A reference signal representing a smooth reference trajectory;

[0055] Generate a first virtual controller model according to the voltage output error variable:

[0056]

[0057] in, represents the first virtual controller model, represents the derivative of the smooth reference trajectory, represents a positive gain, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, Indicates the predicted value of reactive power;

[0058] Determine a controller model output error variable based on the first virtual controller model:

[0059]

[0060] in, represents the controller model output error variable, Represents the secondary voltage regulation term;

[0061] generating a second virtual controller model according to the voltage output variable, the controller model output error variable, and the first virtual controller model;

[0062] Constructing a decentralized controller model of the microgrid cluster based on the second virtual controller model:

[0063]

[0064]

[0065] in, 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.

[0066] Optionally, 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:

[0067] Generate a first adaptive adjustment signal according to the voltage output error variable:

[0068]

[0069] in, represents a first adaptive adjustment signal;

[0070] generating a second adaptive adjustment signal based on the first adaptive adjustment signal, the controller output error variable, and the first virtual controller:

[0071]

[0072] in, represents a second adaptive adjustment signal;

[0073] generating 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;

[0074]

[0075] in, represents a positive gain, represents the positive definite adaptive gain matrix, represents the second derivative of the smoothed reference trajectory.

[0076] In addition, to achieve the above-mentioned purpose, the present invention also proposes a space-ground integrated microgrid cluster topology distributed optimization system for network risks, the system comprising:

[0077] A microgrid cluster model construction module is used to construct a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units;

[0078] A communication network topology analysis module, configured to obtain communication network topology information of the microgrid cluster according to the weighted undirected graph, wherein the communication network topology information includes the number of topological edges, the number of nodes, and topological dependencies;

[0079] 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;

[0080] A communication network optimization solving module, configured to solve the communication topology optimization model based on the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix;

[0081] a virtual signal design module, configured to generate a virtual signal according to a reference signal of each distributed generation unit, and generate a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the distributed generation unit's own reference signal and adjacent reference signals of adjacent nodes;

[0082] a controller design module, configured to determine an error variable of each distributed generation unit based on a smooth reference trajectory, and construct a decentralized controller model of the microgrid cluster based on the error variable;

[0083] A communication network topology optimization module is used to apply the inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed power generation unit in the microgrid cluster.

[0084] Optionally, the microgrid cluster model building module is further configured to determine a collaborative secondary voltage control strategy for each distributed generation unit based on topological structure information of the microgrid cluster:

[0085]

[0086]

[0087] in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value;

[0088] A microgrid cluster model is constructed according to the collaborative secondary voltage control strategy.

[0089] Optionally, the optimization model construction module 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 infinity matrix of each node in the weighted undirected graph to obtain a communication topology optimization model;

[0090] The initial optimization model includes:

[0091]

[0092]

[0093] in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, represents the objective function;

[0094] The constraints include:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] in, represents the adjacency matrix, and They represent nodes in a weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent;

[0103] The communication topology optimization model includes:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] in, Indicates the weighted undirected graph The degree of a node, represents a constant, express and Auxiliary variable with the minimum value.

[0111] Optionally, the virtual signal is generated based on the following formula:

[0112]

[0113]

[0114] in, and Represents nodes respectively and nodes Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, represents the time-varying weight;

[0115] The smooth reference trajectory is generated based on the following formula:

[0116]

[0117] in, represents the smooth reference trajectory, represents the time node of interpolation, represents the coefficients of the interpolation polynomial, Indicates the order.

[0118] In addition, to achieve the above-mentioned purpose, the present application also proposes a distributed optimization device for the topology of a space-ground integrated microgrid cluster targeting 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 distributed optimization method for the topology of a space-ground integrated microgrid cluster targeting network risks as described above.

[0119] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the distributed optimization method of the space-ground integrated microgrid cluster topology for network risks as described above are implemented.

[0120] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the distributed optimization method of the space-ground integrated microgrid cluster topology for network risks as described above.

[0121] The present invention constructs a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units; obtains 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; constructs a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; solves the communication topology optimization model based on the constraint conditions and the inner degree matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix; generates a virtual signal according to the reference signal of each distributed power generation unit, and generates a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the self-parameters of the distributed power generation unit. reference signals and adjacent reference signals of adjacent nodes; determine the error variables of each distributed power generation unit based on a smooth reference trajectory, and construct a decentralized controller model of the microgrid cluster based on the error variables; apply the inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology of the microgrid cluster; because the present invention improves the robustness of the communication delay of the microgrid cluster communication network topology by optimizing the controller design, ensuring that efficient and stable energy scheduling and collaborative control can still be achieved in high delay and network fluctuation environments, effectively improving 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 about by the integrated space-ground network. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0123] Figure 1 It is a structural diagram of a distributed optimization device for a space-ground integrated microgrid cluster topology targeting network risks in a hardware operating environment involved in an embodiment of the present invention;

[0124] Figure 2 This is a flow chart of a first embodiment of a distributed optimization method for a space-ground integrated microgrid cluster topology targeting network risks according to the present invention;

[0125] Figure 3 A schematic diagram of a communication network topology optimization process for a microgrid cluster in accordance with an embodiment of a distributed optimization method for a space-ground integrated microgrid cluster topology targeting network risks of the present invention;

[0126] Figure 4 This is a structural block diagram of the first embodiment of the space-ground integrated microgrid cluster topology distributed optimization system for network risks of the present invention.

[0127] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0128] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0129] Reference Figure 1 , Figure 1 This is a schematic diagram of the distributed optimization device structure for a space-ground integrated microgrid cluster topology targeting network risks in the hardware operating environment involved in the embodiment of the present invention.

[0130] like Figure 1 As shown, the space-ground integrated microgrid cluster topology distributed optimization device for cyber 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. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may be a storage system independent of the processor 1001.

[0131] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the distributed optimization device of the integrated space-ground microgrid cluster topology for network risks, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0132] like Figure 1 As 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.

[0133] exist Figure 1 In the distributed optimization device for the topology of a space-ground integrated microgrid cluster 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 distributed optimization device for the topology of a space-ground integrated microgrid cluster for network risks of the present invention can be set in the distributed optimization device for the topology of a space-ground integrated microgrid cluster for network risks, and the distributed optimization device for the topology of a space-ground integrated microgrid cluster for network risks calls the microgrid cluster optimization program stored in the memory 1005 through the processor 1001, and executes the distributed optimization method for the topology of a space-ground integrated microgrid cluster for network risks provided in an embodiment of the present invention.

[0134] The embodiment of the present invention provides a distributed optimization method for the topology of a space-ground integrated microgrid cluster targeting network risks, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the distributed optimization method for the space-ground integrated microgrid cluster topology targeting network risks of the present invention.

[0135] In this embodiment, the distributed optimization method for the space-ground integrated microgrid cluster topology for network risks includes the following steps:

[0136] Step S10: constructing a microgrid cluster model based on the topological structure information of the microgrid cluster.

[0137] It should be noted that this embodiment is applied to the optimization of the integrated satellite terrestrial network (ISTN) topology of a space-ground integrated microgrid cluster. This topology can be a heterogeneous network architecture that integrates satellite communication systems and terrestrial mobile communication systems. ISTN stands for Integrated Satellite and Terrestrial Network. It is a heterogeneous network architecture that integrates satellite communication systems and terrestrial mobile communication systems.

[0138] It should be understood that the execution entity of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, or mobile phone, or a terminal electronic device capable of performing the aforementioned functions. This embodiment and the following embodiments will be described below using a distributed optimization device for a space-ground integrated microgrid cluster topology (hereinafter referred to as the optimization device) to address network risks.

[0139] It should be noted that the microgrid cluster model is a weighted undirected graph, and the microgrid cluster (MG) includes multiple distributed generation units (DGs). DGs can be small power generation devices near the load, typically distributed across multiple nodes in the microgrid system. A microgrid cluster can be an autonomous power system that includes distributed energy resources, energy storage systems, loads, and control systems. The microgrid cluster model can be a physical model of the microgrid cluster.

[0140] Furthermore, in order to accurately construct a network communication topology model that is identical to the microgrid cluster structure, the above step S10 may include:

[0141] Step S101: determining a collaborative secondary voltage control strategy for each distributed generation unit based on the topological structure information of the microgrid cluster;

[0142] Step S102: constructing a microgrid cluster model according to the collaborative secondary voltage control strategy.

[0143] It should be noted that the cooperative secondary voltage control strategy of each distributed generation unit in the microgrid cluster can be described as follows:

[0144]

[0145]

[0146] in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value.

[0147] In some embodiments, the optimization device can establish a space-ground integrated microgrid cluster model, constructing a The AC microgrid model consists of distributed generators, where A 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 a microgrid cluster are usually considered to be bidirectional, that is, all edges are undirected. and power generation units The communication link between Therefore, the communication network in a microgrid cluster can be described as a weighted undirected graph . is a finite set of nodes, is a collection of node labels. express The weighted adjacency matrix of yes and Since self-loops are not considered, all 、 ,for >0, . The degree of a is the number of edges connected to it, which can be expressed as Represents the local characteristics of the node. Represents a weighted undirected graph The inner degree matrix of .also, The Laplace matrix satisfies the relationship .

[0148] Step S20: Acquire communication network topology information of the microgrid cluster according to the weighted undirected graph.

[0149] It should be noted that the communication network topology information includes the number of topological edges, the number of nodes and topological dependencies.

[0150] Step S30: constructing a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information.

[0151] It should be noted that in order to reduce the impact of malicious network attacks on the integrated space-ground microgrid cluster and improve the structural survivability of the communication topology, this embodiment requires optimizing the communication topology and establishing a topology optimization model.

[0152] Furthermore, in order to effectively optimize the communication network of the microgrid cluster, the above step S30 may include:

[0153] Step S301: constructing an initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information;

[0154] Step S302: Optimizing the initial optimization model according to the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain a communication topology optimization model;

[0155] Understandably, the network and physical layers of an AC microgrid are tightly coupled. Once the network system is paralyzed by a malicious cyberattack, the operation of the physical system will be severely impacted. Node degree is an important indicator of the topological structure, reflecting the importance of a node in the communication network. Therefore, to improve the resilience of the communication network to multiple deliberate cyberattacks, the node degree of each distributed generation unit should be as uniform as possible. In graph theory, the entropy of the degree distribution is often used to measure the uniformity of node degrees. The entropy of the degree distribution is calculated using the following formula:

[0156]

[0157] in, is the distribution of node degrees, which is the The ratio of nodes with different degrees can be calculated as follows.

[0158]

[0159] in, It is the The number of nodes, However, if you use As the goal of the network design problem, it is necessary to introduce additional binary variables to connect 、 and decision variables , and the optimization model objective is nonlinear. In addition, the function Defined on (0, +∞), this means that when Inspired by the above definition of degree distribution, a new structural survivability index based on the degree deviation between communication nodes is proposed. , used for communication topology optimization. On the contrary, the index is the decision variable This will greatly reduce the computational cost of network design problems. Therefore, this embodiment can use the variance of node degree to measure the uniformity of node degree, referring to the following formula:

[0160]

[0161] in, The smaller the value of , the stronger the structural survivability of the communication topology under multiple intentional network attacks. Therefore, in order to improve the structural survivability of the communication topology, the communication topology is optimized and the initial optimization model is established as follows:

[0162]

[0163]

[0164] in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, Denotes the objective function. The objective function of the initial optimization model is to minimize the variance of node degrees.

[0165] The constraints include:

[0166] The first constraint is:

[0167]

[0168] Second constraint:

[0169]

[0170] The third constraint:

[0171]

[0172] The fourth constraint:

[0173]

[0174] Fifth constraint:

[0175]

[0176] The sixth constraint:

[0177]

[0178] Seventh constraint:

[0179]

[0180] in, represents the adjacency matrix, and They represent nodes in the weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent;

[0181] The first constraint mentioned above refers to the adjacent matrix The second constraint defines the relationship between node degree and edge number; the third constraint defines the degree matrix and the adjacency matrix The relationship between and The Laplace matrix is defined L ; The fifth constraint defines 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 Laplace matrix based on graph theory The seventh constraint is used to constrain the identity matrix With the Laplace matrix dimension.

[0182] The problem consisting of the first to seventh constraints is a mixed integral nonlinear programming model because the eigenvalue Cannot be expressed as Therefore, the fifth constraint can be relaxed to the following semipositive definition constraint.

[0183] It should be noted that the above initial optimization model is still a MISDP model that is difficult to solve. Therefore, this embodiment can decompose the initial optimization model into an IQP problem to optimize and , and the auxiliary feasibility problem to find a connected graph, i.e., given the in-degree matrix Value-optimized adjacency matrix and the Laplacian matrix , the communication topology optimization model includes:

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] in, Indicates the weighted undirected graph The degree of each node; represents a constant; express and Auxiliary variable with the minimum value of ; Represents the objective function, used to measure the degree matrix With the reference matrix differences; Represents the total number of nodes in the weighted undirected graph, that is, the total number of generators; Denotes the degree matrix , adjacency matrix , the degree of each node and auxiliary variables Optimize and find the minimum value; Represents the traversal index, The role of is to impose detailed constraints on the distribution of node degrees by traversing the node subset to meet the specific requirements of the graph structure and optimization problem.

[0191] Although there are (or ), but The solution of is not unique in this model and cannot produce a disconnected graph. Therefore, the following auxiliary feasibility problem is proposed to solve the problem based on the given Find the connected graph, refer to the following formula:

[0192]

[0193] The above formula is also a MISDP problem. However, compared with the model established in step 2, it can be solved more efficiently for two reasons: (1) the number of integer variables is greatly reduced; and (2) it only needs to find a feasible solution, not an optimal solution. Numerical simulations also verify that it can be solved efficiently.

[0194] Step S40: Solving the communication topology optimization model based on the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix.

[0195] Step S50: generating a virtual signal according to the reference signal of each distributed power generation unit, and generating a smooth reference trajectory based on the virtual signal.

[0196] It should be noted that the smooth reference trajectory may be a smooth trajectory reference signal. The reference signal of the distributed power generation unit includes the distributed power generation unit's own reference signal and adjacent reference signals of adjacent nodes.

[0197] Furthermore, in order to solve the communication delay problem caused by the integrated space-ground network communication structure, in some embodiments, the optimization device can design a distributed optimization algorithm for generating virtual signals. The algorithm can minimize the optimization function under the influence of intermittent communication delay. Distributed Generation Unit Nodes , virtual signals can be introduced , these signals are updated by the following distributed optimization algorithm:

[0198]

[0199]

[0200] in, and Represents nodes respectively i and nodes j Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, Represents the time-varying weight, the time-varying weight used here It is caused by intermittent communication network;

[0201] However, due to intermittent communication delays, The second-order derivatives of do not exist. However, this property is crucial when using the recursive inversion design process. In order to ensure that the higher-order derivatives of the reference trajectory exist, the new Order smooth reference trajectory The design is as follows:

[0202]

[0203] in, represents the smooth reference trajectory, Indicates the time node of interpolation , is a small positive constant, represents the coefficients of the interpolation polynomial, Represents the order, when hour, ; , represents the coefficients of the interpolation polynomial, where yes No. OK:

[0204]

[0205]

[0206]

[0207] Among them, for :

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214] Step S60: determining the error variables of each distributed power generation unit based on the smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variables.

[0215] In some embodiments, the optimization device may design the decentralized controller based on a recursive inversion process.

[0216] Furthermore, in order to improve the stability and timeliness of the communication network of the microgrid cluster, in some embodiments, the error variable includes a voltage output error variable and a controller model output error variable; the above step S60 may include:

[0217] Step S601: determining the voltage output error variable of each distributed power generation unit based on a smooth reference trajectory.

[0218] It should be noted that for Distributed Generation Units , we define the following error variables:

[0219]

[0220] in, represents the voltage output error variable, Distributed Generation Unit The output voltage, A reference signal representing a smooth reference trajectory;

[0221] Step S602: Generate a first virtual controller model according to the voltage output error variable.

[0222] It should be noted that the first virtual controller model is designed based on the following formula:

[0223]

[0224] in, represents the first virtual controller model, represents the derivative of the smoothed reference trajectory, represents a positive gain, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, Indicates the predicted value of reactive power.

[0225] Step S603: Determine a controller model output error variable based on the first virtual controller model.

[0226] It should be noted that the The controller model output error variable of each distributed generation unit is calculated according to the following formula:

[0227]

[0228] in, represents the controller model output error variable, Represents the secondary voltage regulation term;

[0229] Step S604: generating a second virtual controller model according to the voltage output variable, the controller model output error variable and the first virtual controller model.

[0230] Furthermore, in order to accurately construct the second virtual controller model to improve communication stability, the above step S604 may include:

[0231] Step S6041: Generate a first adaptive adjustment signal according to the voltage output error variable:

[0232]

[0233] in, represents a first adaptive adjustment signal;

[0234] 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:

[0235]

[0236] in, represents a second adaptive adjustment signal;

[0237] Step S6043: generating 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:

[0238]

[0239] in, represents a positive gain, represents the positive definite adaptive gain matrix, represents the second derivative of the smoothed reference trajectory.

[0240] Step S605: constructing a decentralized controller model of the microgrid cluster based on the second virtual controller model.

[0241] It should be noted that the decentralized controller model is designed based on the following formula:

[0242]

[0243]

[0244] in, 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:

[0245]

[0246] in, represents the adaptive law gain; in addition, Updated to:

[0247]

[0248] in, represents the positive definite adaptive gain matrix.

[0249] Step S70: applying the inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology of each distributed power generation unit in the microgrid cluster.

[0250] It can be understood that this embodiment inputs the adjacency matrix and Laplace matrix obtained by solving the communication topology optimization model and the inner degree matrix of each node in the weighted undirected graph into the decentralized controller model, and applies the decentralized controller model to the microgrid cluster model to achieve communication optimization of the communication network topology structure of the microgrid cluster.

[0251] It should be noted that this embodiment employs a two-stage optimization strategy. First, it optimizes the communication topology to enhance the survivability and resilience of the microgrid cluster against malicious attacks (such as spurious data injection and denial of service), ensuring stable operation even when some communication nodes are compromised. Furthermore, this embodiment proposes a layered design scheme combining a distributed optimization algorithm with a decentralized tracking controller to overcome communication latency issues associated with integrated space-ground networks. Because integrated space-ground systems involve multiple layers of links, including satellites, drones, and ground communications, long-distance transmission, dynamic link changes, and data loss can lead to delays in control signal transmission within the microgrid cluster, impacting the real-time and accuracy of distributed optimization. To address this issue, this embodiment optimizes the controller design to improve the system's robustness to communication latency, ensuring efficient and stable energy scheduling and coordinated control even in high-latency and network-fluctuating environments. This approach effectively enhances the security, stability, and dynamic control performance of integrated space-ground microgrid clusters, providing key technical support for the reliable operation and efficient optimized scheduling of future smart grids.

[0252] In some embodiments, the optimization device may be based on Figure 3 Optimize the communication network topology of the microgrid cluster, referring to Figure 3 , Figure 3 The topology optimization process of the communication network of the microgrid cluster is shown in FIG. The topology optimization process may include communication topology optimization and controller design. The optimization device may set the nodes in the communication network of the microgrid cluster to , the number of edges is set to , solve the communication topology optimization model, obtain the inner degree matrix, and determine the Laplace matrix The eigenvalue of Is it greater than 0? If so, directly design the controller; if not, then based on The adjacency matrix and Laplace matrix are solved, virtual signals are generated based on a distributed optimization algorithm, smooth reference trajectories are designed by using the Hermite interpolation method, and a decentralized controller is designed based on a recursive inversion process to optimize the communication network topology of the microgrid cluster.

[0253] This embodiment constructs a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units; obtains 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; constructs a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; solves the communication topology optimization model based on the constraint conditions and the inner degree matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix; generates a virtual signal according to the reference signal of each distributed power generation unit, and generates a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the self-parameters of the distributed power generation unit. reference signals and adjacent reference signals of adjacent nodes; determine the error variables of each distributed power 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 inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology of the microgrid cluster; because this embodiment improves the robustness of the communication delay of the microgrid cluster communication network topology by optimizing the controller design, ensuring that efficient and stable energy scheduling and collaborative control can still be achieved in high delay and network fluctuation environments, effectively improving 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 about by the integrated space-ground network.

[0254] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, which stores a microgrid cluster optimization program. When the microgrid cluster optimization program is executed by a processor, it implements the steps of the distributed optimization method of the space-ground integrated microgrid cluster topology for network risks as described above.

[0255] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0256] The above-mentioned computer-readable storage medium can be included in the space-ground integrated microgrid cluster topology distributed optimization device for network risks; or it can exist independently without being assembled into the space-ground integrated microgrid cluster topology distributed optimization device for network risks.

[0257] In addition, an embodiment of the present invention also proposes a computer program product, including a microgrid cluster optimization program, which, when executed by a processor, implements the steps of the above-mentioned distributed optimization method for the topology of a space-ground integrated microgrid cluster targeting network risks.

[0258] The specific implementation methods of the computer program product of the present invention are basically the same as the embodiments of the above-mentioned distributed optimization method for the topology of the integrated space-ground microgrid cluster for network risks, and will not be repeated here.

[0259] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the space-ground integrated microgrid cluster topology distributed optimization system for network risks of the present invention.

[0260] like Figure 4 As shown, the embodiment of the present invention proposes a distributed optimization system for space-ground integrated microgrid cluster topology for network risks, including:

[0261] A microgrid cluster model construction module 10 is used to construct a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units;

[0262] A communication network topology analysis module 20 is configured to obtain communication network topology information of the microgrid cluster according to the weighted undirected graph, wherein the communication network topology information includes the number of topological edges, the number of nodes, and topological dependencies;

[0263] An optimization model construction module 30 is used to construct a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information;

[0264] A communication network optimization solving module 40 is configured to solve the communication topology optimization model based on the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix;

[0265] a virtual signal design module 50, configured to generate a virtual signal according to a reference signal of each distributed power generation unit, and generate a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the distributed power generation unit's own reference signal and adjacent reference signals of adjacent nodes;

[0266] a controller design module 60 for determining an error variable of each distributed generation unit based on a smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variable;

[0267] The communication network topology optimization module 70 is used to apply the inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed power generation unit in the microgrid cluster.

[0268] Furthermore, the microgrid cluster model building module 10 is further configured to determine a collaborative secondary voltage control strategy for each distributed generation unit based on the topological structure information of the microgrid cluster:

[0269]

[0270]

[0271] in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value;

[0272] A microgrid cluster model is constructed according to the collaborative secondary voltage control strategy.

[0273] 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 infinity matrix of each node in the weighted undirected graph to obtain a communication topology optimization model;

[0274] The initial optimization model includes:

[0275]

[0276]

[0277] in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, represents the objective function;

[0278] The constraints include:

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285]

[0286] in, represents the adjacency matrix, and They represent nodes in a weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent;

[0287] The communication topology optimization model includes:

[0288]

[0289]

[0290]

[0291]

[0292]

[0293]

[0294] in, Indicates the weighted undirected graph The degree of a node, represents a constant, express and Auxiliary variable with the minimum value.

[0295] Furthermore, the virtual signal is generated based on the following formula:

[0296]

[0297]

[0298] in, and Represents nodes respectively and nodes Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, represents the time-varying weight;

[0299] The smooth reference trajectory is generated based on the following formula:

[0300]

[0301] in, represents the smooth reference trajectory, represents the time node of interpolation, represents the coefficients of the interpolation polynomial, Indicates the order.

[0302] This embodiment constructs a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units; obtains 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; constructs a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; solves the communication topology optimization model based on the constraint conditions and the inner degree matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix; generates a virtual signal according to the reference signal of each distributed power generation unit, and generates a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the self-parameters of the distributed power generation unit. reference signals and adjacent reference signals of adjacent nodes; determine the error variables of each distributed power 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 inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology of the microgrid cluster; because this embodiment improves the robustness of the communication delay of the microgrid cluster communication network topology by optimizing the controller design, ensuring that efficient and stable energy scheduling and collaborative control can still be achieved in high delay and network fluctuation environments, effectively improving 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 about by the integrated space-ground network.

[0303] The distributed optimization system for the topology of a space-ground integrated microgrid cluster for network risks provided by this application adopts the distributed optimization method for the topology of a space-ground integrated microgrid cluster for network risks in the above-mentioned embodiment, which can solve the technical problem of the distributed optimization of the topology of a space-ground integrated microgrid cluster for network risks. Compared with the existing technology, the beneficial effects of the distributed optimization system for the topology of a space-ground integrated microgrid cluster for network risks provided by this application are the same as the beneficial effects of the distributed optimization method for the topology of a space-ground integrated microgrid cluster for network risks provided by the above-mentioned embodiment, and the other technical features of the distributed optimization system for the topology of a space-ground integrated microgrid cluster for network risks are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0304] 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 make settings as needed, and the present invention does not impose any limitation on this.

[0305] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0306] In addition, for technical details not fully described in this embodiment, please refer to the distributed optimization method for the topology of the space-ground integrated microgrid cluster for network risks provided in any embodiment of the present invention, which will not be repeated here.

[0307] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0308] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0309] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by means of software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0310] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distributed optimization method for space-ground integrated microgrid cluster topology targeting network risks, characterized by: The method comprises: Constructing a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes a plurality of distributed power generation units; Acquire communication network topology information of the microgrid cluster according to the weighted undirected graph, wherein the communication network topology information includes the number of topological edges, the number of nodes, and topological dependencies; Constructing a communication topology optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; Solving the communication topology optimization model based on the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix; generating a virtual signal according to a reference signal of each distributed power generation unit, and generating a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the distributed power generation unit's own reference signal and adjacent reference signals of adjacent nodes; Determining error variables of each distributed generation unit based on a smooth reference trajectory, and constructing a decentralized controller model of the microgrid cluster based on the error variables; The inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model are applied to the microgrid cluster model to optimize the communication network topology structure of each distributed power generation unit in the microgrid cluster.

2. The distributed optimization method for space-ground integrated microgrid cluster topology targeting network risks according to claim 1, characterized in that: The constructing of a microgrid cluster model based on the topological structure information of the microgrid cluster includes: The coordinated secondary voltage control strategy of each distributed generation unit is determined based on the topological structure information of the microgrid cluster: in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value; A microgrid cluster model is constructed according to the collaborative secondary voltage control strategy.

3. The distributed optimization method for space-ground integrated microgrid cluster topology targeting network risks according to claim 2, characterized in that: The communication topology optimization model and constraint conditions of the microgrid cluster are constructed based on the communication network topology information, including: Constructing an initial optimization model and constraint conditions of the microgrid cluster based on the communication network topology information; Optimizing the initial optimization model according to the constraint conditions and the infinity matrix of each node in the weighted undirected graph to obtain a communication topology optimization model; The initial optimization model includes: in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, represents the objective function; The constraints include: in, represents the adjacency matrix, and They represent nodes in the weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent; The communication topology optimization model includes: in, Indicates the weighted undirected graph The degree of a node, represents a constant, express and Auxiliary variable with the minimum value.

4. The distributed optimization method for space-ground integrated microgrid cluster topology targeting network risks according to claim 3 is characterized in that: The virtual signal is generated based on the following formula: in, and Represents nodes respectively and nodes Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: in, represents the smooth reference trajectory, represents the time node of interpolation, represents the coefficients of the interpolation polynomial, Indicates the order.

5. The distributed optimization method for space-ground integrated microgrid cluster topology targeting 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 step of determining the error variables of each distributed power generation unit based on the smooth reference trajectory and constructing a decentralized controller model of the microgrid cluster based on the error variables includes: Determine the voltage output error variable of each distributed generation unit based on the smooth reference trajectory: in, represents the voltage output error variable, Distributed Generation Unit The output voltage, A reference signal representing a smooth reference trajectory; Generate a first virtual controller model according to the voltage output error variable: in, represents the first virtual controller model, represents the derivative of the smoothed reference trajectory, represents a positive gain, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, Indicates the predicted value of reactive power; Determine a controller model output error variable based on the first virtual controller model: in, 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 of the microgrid cluster based on the second virtual controller model: in, 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 distributed optimization method for space-ground integrated microgrid cluster topology targeting network risks according to claim 5, characterized in that: The generating of the second virtual controller model according to the voltage output variable, the controller model output error variable and the first virtual controller model comprises: Generate a first adaptive adjustment signal according to the voltage output error variable: in, represents a first adaptive adjustment signal; generating a second adaptive adjustment signal based on the first adaptive adjustment signal, the controller output error variable, and the first virtual controller: in, represents a second adaptive adjustment signal; generating 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; in, represents a positive gain, represents the positive definite adaptive gain matrix, represents the second derivative of the smoothed reference trajectory.

7. A space-ground integrated microgrid cluster topology distributed optimization system for network risks, characterized by: The system comprises: A microgrid cluster model construction module is used to construct a microgrid cluster model based on the topological structure information of the microgrid cluster, wherein the microgrid cluster model is a weighted undirected graph, and the microgrid cluster includes multiple distributed power generation units; A communication network topology analysis module, configured to obtain communication network topology information of the microgrid cluster according to the weighted undirected graph, wherein the communication network topology information includes the number of topological edges, the number of nodes, and topological dependencies; 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 infinity matrix of each node in the weighted undirected graph to obtain an adjacency matrix and a Laplace matrix; a virtual signal design module, configured to generate a virtual signal according to a reference signal of each distributed generation unit, and generate a smooth reference trajectory based on the virtual signal, wherein the reference signal includes the distributed generation unit's own reference signal and adjacent reference signals of adjacent nodes; a controller design module, configured to determine an error variable of each distributed generation unit based on a smooth reference trajectory, and construct a decentralized controller model of the microgrid cluster based on the error variable; A communication network topology optimization module is used to apply the inner degree matrix, the adjacency matrix, the Laplace matrix and the decentralized controller model to the microgrid cluster model to optimize the communication network topology structure of each distributed power generation unit in the microgrid cluster.

8. The space-ground integrated microgrid cluster topology distributed optimization system for network risks according to claim 7, characterized in that: The microgrid cluster model building module is further used to determine the collaborative secondary voltage control strategy of each distributed generation unit based on the topological structure information of the microgrid cluster: in, represents the voltage control coefficient, Distributed Generation Unit Output voltage; represents the derivative of the output voltage, Indicates the nominal voltage amplitude, represents the voltage droop coefficient, Represents reactive power, represents the secondary voltage regulation term, is the controller gain coefficient, Expressed as the weight coefficient of reactive power distribution and voltage recovery, represents the output of the decentralized controller model, Indicates the rated voltage reference value of the system, Indicates the expected average voltage value; A microgrid cluster model is constructed according to the collaborative secondary voltage control strategy.

9. The space-ground integrated microgrid cluster topology distributed optimization system for network risks according to claim 8, characterized in that: The optimization model construction module is further used 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 inlier matrix of each node in the weighted undirected graph to obtain a communication topology optimization model; The initial optimization model includes: in, 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 a node in a weighted undirected graph, represents the identity matrix, represents the average degree of nodes, represents the objective function; The constraints include: in, represents the adjacency matrix, and They represent nodes in the weighted undirected graph, represents transpose, represents the Laplace matrix, It means extracting the diagonal elements in the matrix. represents the Laplacian matrix The eigenvalues of Represents eigenvalues The lower bound constant of represents auxiliary variables used to construct semidefinite constraints, Represents the upper limit constant of the eigenvalue, the identity matrix The dimensions and Laplacian matrix The dimensions are consistent; The communication topology optimization model includes: in, Indicates the weighted undirected graph The degree of a node, represents a constant, express and Auxiliary variable with the minimum value.

10. The space-ground integrated 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: in, and Represents nodes respectively and nodes Virtual signal, represents the derivative of the virtual signal, represents auxiliary variables, represents the derivative of the auxiliary variable, and represents the control gain, represents the local objective function, represents the gradient of the local objective function, Indicates a time node, represents the time-varying weight; The smooth reference trajectory is generated based on the following formula: in, represents the smooth reference trajectory, represents the time node of interpolation, represents the coefficients of the interpolation polynomial, Indicates the order.

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