Method, Device and Medium for Voltage Control of Distribution Network under Cyber-Physical Coupling

Abstract

The present invention discloses a method, device and medium for voltage control of a distribution network under cyber-physical coupling. By collecting the node status information of the transmission network and the distribution network, as well as the base station communication resource information, an information network data transmission model is established, which solves the influence of time delay on voltage control. In addition, the voltage control model is transformed into a binary variable problem, and the quantum approximate optimization algorithm (QAOA) is used to solve the Ising model. This innovative method can significantly improve the solution efficiency compared with the traditional classical optimization method. Especially when dealing with large-scale and complex voltage control problems, the advantages of quantum computing are more prominent, and it can effectively overcome the computational challenges of traditional optimization methods. Therefore, this method not only improves the accuracy and stability of voltage control, but also has strong scalability and adaptability, and can cope with more complex distribution network environments.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid voltage control, and particularly to a method, device and medium for distribution network voltage control under cyber-physical coupling. Background Art

[0002] Cyber-Physical Coupling refers to the close interaction and integration between the cyber space and the physical space. The cyber space includes computing, communication and control, while the physical space covers physical entities and their dynamic behaviors. The two achieve real-time interaction through sensors, actuators and communication networks, forming a closed-loop system. The application of cyber-physical coupling in power grid voltage control realizes the real-time monitoring, analysis and control of power grid voltage through the deep integration of the cyber space and the physical space, improving the stability, reliability and efficiency of the power grid.

[0003] How to conduct cyber-physical coupling modeling and evaluation for the application in the field of power grid voltage control has become an urgent research topic under the new situation of the power system. The power flow interaction describes the continuous process of the physical network, while the information flow interaction is based on the discrete data structure in computer science. This heterogeneity brings great challenges to the analysis and application of the power CPS system. The traditional power system analysis and control methods basically separate the two, and there is an urgent need for an accurate model to describe the interaction behavior between the information system and the physical world, and accurately reflect the impact of communication performance on the security and stability in the power CPS. At present, the vast majority of research on the modeling of power cyber-physical coupling systems (power CPS) focuses on reflecting the mapping relationship of information. In fact, the accurate description of communication performance is what is really concerned about when studying the impact of communication networks on power cyber-physical integration systems, but there is a lack of relevant research and mature technical solutions. Summary of the Invention

[0004] A method, device and medium for distribution network voltage control under cyber-physical coupling proposed by the present invention can solve at least one of the technical problems in the background art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for distribution network voltage control under cyber-physical coupling includes the following steps:

[0007] S100. Collect the node status information of the transmission network, the node status information of the distribution network, and the base station communication resource information;

[0008] S200. Establish an information network data transmission model based on the node status information of the transmission network, the node status information of the distribution network, and the base station communication resource information;

[0009] S300. Build a voltage control model based on the information network data transmission model;

[0010] S400. Optimize the voltage control model to obtain the optimized voltage control model for the distribution network;

[0011] S500. Convert the optimized voltage control model for the distribution network into a binary variable problem;

[0012] S600. Approximately optimize the binary variable problem through the Ising model to determine the final voltage control method for the distribution network.

[0013] Furthermore, the node status node information and base station communication resource information of the distribution network in step S100 of the present invention include:

[0014] The node status information includes:

[0015] Voltage status: The voltage value of each node, including the voltage amplitude and phase information of the node;

[0016] Power information: The active power and reactive power of each node, including the change amount and sensitivity coefficient of the power;

[0017] Node location and topology: The geographical location, network topology of each node, and the connection relationship with other nodes;

[0018] Load information: The type, size, and change trend of the node load;

[0019] Status information of the photovoltaic inverter: For the nodes participating in voltage control, the working status and reactive power output information of the photovoltaic inverter;

[0020] The base station communication resource information includes:

[0021] The maximum transmission power of the base station: The maximum wireless transmission power available for each base station;

[0022] The power gain of the wireless link: The signal transmission gain between each node and the base station;

[0023] Uplink and downlink transmission power: The power and bandwidth of the uplink and downlink communications between nodes.

[0024] Furthermore, the method for establishing the information network data transmission model of the present invention includes:

[0025] S210. Assume that the maximum transmission power of base station b is , and the maximum transmission power of the node is ;

[0026] The power gain of the wireless link channel of the nth node is expressed as , is the channel vector, which is a random variable. Assume that the channel vector is known;

[0027] The uplink transmission power of the wireless link channel of the nth node is , and the downlink transmission power is ; The constraint of its transmission power is expressed as:

[0028]

[0029]

[0030] Then the uplink transmission rate of the wireless link channel of the nth node and the downlink transmission rate are respectively expressed as:

[0031] )

[0032] )

[0033] is the channel bandwidth for the nth node's uplink transmission, is the channel bandwidth for the nth node's downlink transmission, is the transmission power for the nth node's uplink transmission, , is the channel gain during the nth node's uplink and downlink transmissions;

[0034] Assume that each control cycle, the node needs to fix the uplink transmission data packet DS. Then the size of each uplink transmission data packet is:

[0035]

[0036] The total data packets to be transmitted by n nodes in k regulation cycles, is the total data packets to be transmitted by n nodes in k + 1 regulation cycles; represents the transmission speed when n nodes transmit uplink data packets in k regulation cycles; DT is the regulation time interval, and T is the time slot;

[0037] Combined with the size of each uplink transmission data packet, the uplink transmission delay is expressed as:

[0038]

[0039]

[0040] is the uplink transmission delay of the nth node in the kth regulation period. is the maximum value of the uplink transmission delays of all nodes in a partition in the kth regulation period;

[0041] Since the amount of data transmitted in the downlink is smaller than that in the uplink, it is considered that there is no remainder in a single transmission, so the downlink transmission delay is directly expressed as:

[0042]

[0043] The data packets that the base station needs to transmit to the nth node in the kth regulation period, so the delay caused by the information network in the kth regulation period is expressed as:

[0044]

[0045] is the processing delay.

[0046] Furthermore, the method for building the voltage control model in step S300 of the present invention includes:

[0047] Using a photovoltaic inverter for reactive voltage control to establish a state - space equation for the voltage control of the distribution network:

[0048] The linear model of the distribution network is developed by evaluating the sensitivity matrix of the node with respect to the active input and reactive input at a given operating point. On this basis, the linear modeling of the network node voltage is:

[0049]

[0050] are the voltage states of the node in the kth and k + 1th regulation periods, are the voltage sensitivity coefficients of the node voltage with respect to reactive power and active power respectively, is the change in reactive power of the node in the kth regulation period, is the change in reactive power of the node in the kth regulation period considering the communication network delay.

[0051] Furthermore, the method for obtaining the voltage control optimization model of the distribution network of the present invention includes:

[0052] S410. Set the objective function F:

[0053]

[0054] is the set value of the ith node;

[0055] S420. In the distribution network, the nodes need to satisfy the following power flow constraints:

[0056]

[0057]

[0058]

[0059] and are the active power and reactive power of node i respectively, is the voltage amplitude of node i, is the voltage amplitude of node j, are the conductance component and susceptance component in the admittance matrix between node i and node j respectively, is the voltage phase difference between node i and node j, is the impedance between node i and node j, is the complex conjugate of the voltage of node j.

[0060] Furthermore, the method for transforming the distribution network voltage control optimization model into a binary variable problem in step S500 of the present invention includes:

[0061] S510. Based on the Lagrange multiplier method, the constraint conditions of the optimization problem can be transformed into the objective function, changing the constrained problem into an unconstrained one:

[0062]

[0063] is a constant coefficient vector; is a constant coefficient matrix; is a Lagrange multiplier; is the reactive power output of the i-th PV;

[0064] S520. Discretization of continuous variables:

[0065]

[0066] In the formula: is the floor value; is the expansion precision;

[0067] Introduce continuous Lagrange multipliers and perform discretization by imitating the above formula. Then the discretized unconstrained 0-1 variable optimization problem is:

[0068]

[0069] In the formula and are constant coefficient matrices is a constant coefficient vector; and are respectively and vectors composed of 0-1 variables after discretization and are respectively and the number of 0-1 variables in λ;

[0070] After expanding each vector matrix, all 0-1 variables can be uniformly represented by , and further, the 0-1 variable is converted into a spin state . After strict proof and satisfy:

[0071]

[0072] Substituting it in, the objective function represented by is as follows:

[0073]

[0074] In the formula: and are respectively the coefficient of the term and the coefficient of the term is a constant term, and are respectively the coefficients of the quadratic term and the linear term.

[0075] Furthermore, the method for determining the final distribution network voltage control in step S600 of the present invention includes:

[0076] S610. Prepare the initial state, and use the Hadamard gate to convert it into a uniformly superposed qubit ;

[0077]

[0078] S620. Introduce two unitary operators;

[0079]

[0080] Among them, , , in the formula: and are respectively the Pauli matrix acting on the th qubit and the Pauli matrix acting on the Matrix;

[0081] S630. Build a quantum circuit;

[0082] Apply and and separately to the input , specifically executed by the gate and the gate; finally obtain the evolved quantum state , as shown in the following formula:

[0083]

[0084] Perform multiple measurements on to obtain the expected value of the loss function . The formula of the loss function is as follows:

[0085]

[0086] S640. It is necessary to use the gradient descent algorithm to optimize the algorithm to tune the parameters and , and then input the optimized parameters and into the quantum computer for the next quantum state preparation;

[0087] In the formula: is the number of optimization steps is the optimization step size is the gradient solving operator;

[0088] S650. After multiple iterations of the parameters and , when the Hamiltonian of is equal to the minimum energy eigenstate, the expected value reaches the minimum. At this time, measure the final quantum state to obtain the decision command for the distribution network voltage control.

[0089] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the above method.

[0090] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to execute the steps of the above method.

[0091] As can be seen from the above technical solutions, the cyber-physical coupled distribution network voltage control method of the present invention, compared with the traditional voltage control method, combines the innovative designs of cyber-physical coupling and quantum computing. This method not only optimizes the voltage control model but also takes into account the time delay of the communication network and the complexity of power transmission, thus more accurately reflecting the dynamic changes in the actual distribution network. By collecting the node status information of the transmission network and the distribution network, as well as the base station communication resource information, this method establishes an information network data transmission model to solve the impact of time delay on voltage control. In addition, during the optimization process, the voltage control model is transformed into a binary variable problem, and the quantum approximate optimization algorithm (QAOA) is used to solve the Ising model. This innovative method can significantly improve the solution efficiency compared with the traditional classical optimization method. Especially when dealing with large-scale and complex voltage control problems, the advantages of quantum computing are more prominent, and it can effectively overcome the computational challenges of traditional optimization methods. Therefore, this method not only improves the accuracy and stability of voltage control but also has strong scalability and adaptability, and can cope with more complex distribution network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0093] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention.

[0094] As Figure 1 shown, the cyber-physical coupled distribution network voltage control method described in this embodiment includes the following steps:

[0095] S100. Collect the node status information of the transmission network, the node status information of the distribution network, and the base station communication resource information;

[0096] S200. Based on the node status information of the transmission network, the node status information of the distribution network, and the base station communication resource information, establish an information network data transmission model;

[0097] S300. Based on the information network data transmission model, build a voltage control model;

[0098] S400. Optimize the voltage control model to obtain an optimized distribution network voltage control model;

[0099] S500. Transform the optimized distribution network voltage control model into a binary variable problem;

[0100] S600. Approximately optimize the binary variable problem through the Ising model to determine the optimal distribution network voltage control method.

[0101] The following provides a detailed description of each step:

[0102] S100. Collect the node status information of the transmission network and distribution network, and the base station communication resource information;

[0103] The node status information includes:

[0104] Voltage status: The voltage value of each node, including the voltage amplitude and phase information of the node.

[0105] Power information: The active power and reactive power of each node, including the change amount and sensitivity coefficient of the power.

[0106] Node location and topology: The geographical location, network topology of each node, and the connection relationship with other nodes.

[0107] Load information: The type, size, and change trend of the node load.

[0108] Status information of the photovoltaic inverter: For the nodes participating in voltage control, the working status and reactive power output information of the photovoltaic inverter.

[0109] The base station communication resource information includes:

[0110] Maximum transmission power of the base station: The maximum wireless transmission power available for each base station.

[0111] Power gain of the wireless link: The signal transmission gain between each node and the base station.

[0112] Uplink and downlink transmission power: The power and bandwidth of the uplink and downlink communications between nodes.

[0113] Packet transmission size: The size of the data packets transmitted by each node within the control period.

[0114] These data provide the necessary support for the subsequent optimal control and are the basis for the entire control process.

[0115] S200. Based on the node status information of the distribution network and the base station communication resource information, establish an information network data transmission model;

[0116] The information network data transmission model considers the data transmission process between nodes, including transmission power, channel gain, uplink and downlink rates of the wireless link, etc., and also considers the delay of uplink and downlink transmissions. The delay of data transmission will affect the subsequent voltage control decision.

[0117] Suppose the distribution network has E partitions, and each partition has a base station responsible for communication, and there are n nodes distributed. The communication method adopts time-division duplex transmission mode;

[0118] The method for establishing an information network data transmission model includes:

[0119] S210. Let the maximum transmission power of base station b be , and the maximum transmission power of the node be ;

[0120] The power gain of the wireless link channel of the nth node can be expressed as , is the channel vector, which is a random variable. Assume that the channel vector is known.

[0121] The uplink transmission power of the wireless link channel of the nth node is , and the downlink transmission power is . The constraint of its transmission power is expressed as:

[0122]

[0123]

[0124] Then the uplink transmission rate and the downlink transmission rate of the wireless link channel of the nth node are respectively expressed as:

[0125] )

[0126] )

[0127] is the channel bandwidth of the nth node's uplink transmission, is the channel bandwidth of the nth node's downlink transmission, is the transmission power of the nth node's uplink transmission, , is the channel gain of the nth node's uplink and downlink transmissions.

[0128] Suppose each control cycle the node needs to fixedly transmit an uplink data packet DS, then the size of each uplink-transmitted data packet is:

[0129]

[0130] The total data packets required to be transmitted by n nodes in k regulation cycles, is The total number of data packets to be transmitted by n nodes in k + 1 control cycles; denote The data packets transmitted upstream by n nodes in k control cycles when the transmission speed; DT is the control time interval, and T is the time slot.

[0131] Combined with the size of the data packets transmitted each time upstream, the upstream transmission delay can be expressed as:

[0132]

[0133]

[0134] is the upstream transmission delay of the nth node in the kth control cycle, is the maximum value of the upstream transmission delays of all nodes in the kth control cycle within a partition, that is, the actual upstream transmission delay.

[0135] Since the amount of data transmitted downstream is smaller compared to the amount of data during upstream transmission, it can be considered that there is no remainder for a single transmission. Then the downstream transmission delay can be directly expressed as

[0136]

[0137] The data packets that the base station needs to transmit to the nth node in k control cycles. Then the delay caused by the information network in the kth control cycle can be expressed as:

[0138]

[0139] is the processing delay.

[0140] S300. Based on the information network data transmission model, build a voltage control model;

[0141] Use a photovoltaic inverter for reactive voltage control and establish a state - space equation for the voltage control of the distribution network.

[0142] For the linear model of the distribution network, at a given operating point, it can be developed by evaluating its sensitivity matrix with respect to active input and reactive input. On this basis, the network node voltage can be linearly modeled as:

[0143]

[0144] are the voltage states of the node in the kth and k + 1th control cycles, are the voltage sensitivity coefficients of the node voltage to reactive power and active power respectively, is the reactive power variation of the node in the k-th regulation cycle, is the reactive power variation of the node in the k-th regulation cycle considering the communication network delay.

[0145] By considering the impact of data transmission delay and combining with photovoltaic inverters, the voltage control model performs reactive power and voltage control, and adds the sensitivity of the distribution network voltage to the active power and reactive power variations in the model, ensuring that the voltage of the distribution network can still be effectively controlled even in the presence of communication delay.

[0146] S400. Optimize the voltage control model to obtain the optimized model for distribution network voltage control;

[0147] Based on the previous voltage control model of the information-physical coupling considering time delay, further establish the optimized model for grid voltage control. The optimized model for grid voltage control defines the objective function and constraint conditions, aiming to optimize the voltage control of each node in the distribution network to ensure meeting the power flow constraints and other operating conditions. The specific method is as follows:

[0148] S410. Set the objective function F:

[0149]

[0150] is the set value of the i-th node.

[0151] S420. In the distribution network, the nodes need to satisfy the following power flow constraints:

[0152]

[0153]

[0154]

[0155] and are the active power and reactive power of node i respectively, is the voltage amplitude of node i, is the voltage amplitude of node j, are the conductance component and susceptance component in the admittance matrix between node i and node j respectively, is the voltage phase difference between node i and node j. is the impedance between node i and node j, is the complex conjugate of the voltage of node j.

[0156] S500. Convert the optimized model for distribution network voltage control into a binary variable problem;

[0157] The energy function of the Ising model consists of only two parts: the interaction between qubits and the action of an external magnetic field on qubits. Therefore, the optimization model suitable for solution by a quantum computer should meet the following conditions: the model has no constraints, the decision variables are integer variables with a value range of {-1, 1}, and the objective function only allows two forms: a single decision variable or the product of two distinct decision variables.

[0158] S510. Based on the Lagrange multiplier method, the constraint conditions of the optimization problem can be transformed into the objective function, turning the constrained problem into an unconstrained one:

[0159]

[0160] is a constant coefficient vector; is a constant coefficient matrix; is a Lagrange multiplier; is the reactive power output of the i-th PV.

[0161] S520. Discretization of continuous variables:

[0162]

[0163] where: is the floor value; is the expansion accuracy.

[0164] Since continuous Lagrange multipliers are introduced, discretization needs to be carried out following the above formula. Then, the unconstrained 0-1 variable optimization problem after discretization is:

[0165]

[0166] where and are constant coefficient matrices is a constant coefficient vector; and are respectively and vectors composed of discretized 0-1 variables and are respectively and the number of 0-1 variables in λ.

[0167] After expanding each vector matrix, all 0-1 variables can be uniformly represented by Furthermore, converting the 0-1 variable to the spin state , after strict proof and satisfy the following:

[0168]

[0169] Substituting into it, the objective function expressed by is as follows:

[0170]

[0171] In the formula: and are respectively the coefficient of the term and the coefficient of the term is the constant term, and are respectively the coefficients of the quadratic term and the linear term.

[0172] To improve the optimization efficiency, the Ising model of quantum computing is introduced. At this step, the voltage control optimization problem is transformed into a form suitable for quantum computers to process. By transforming the optimization problem into a problem of binary variables, the Ising model enables the problem to be solved by leveraging the advantages of quantum computing, especially using quantum annealing technology to overcome the computational challenges of traditional optimization methods.

[0173] S600. Solve the Ising model based on the quantum approximate optimization algorithm to determine the final distribution network voltage control method;

[0174] Apply the quantum approximate optimization algorithm (QAOA) to solve the Ising model. By initializing the quantum state, using quantum gates (such as the Hadamard gate and Pauli matrices) to act on qubits, and iterating through multiple optimization steps. After multiple iterations, the quantum algorithm will converge to an optimal voltage control strategy, and the final quantum state represents the voltage control decision of the distribution network. The method is as follows:

[0175] S610. Prepare the initial state, and use the Hadamard gate to transform it into qubits with uniform superposition

[0176]

[0177] S620. Introduce two unitary operators:

[0178]

[0179] where , , in the formula: and are respectively the Pauli matrix acting on the -th qubit and the Pauli matrix acting on the -th qubit.

[0180] S630. Quantum circuit construction: Apply and to the input respectively, which are specifically executed by the gate and the gate; finally, obtain the evolved quantum state , as shown in the following formula. As follows:

[0181]

[0182] Perform multiple measurements on to obtain the expected value of the loss function . The formula of the loss function is as follows:

[0183]

[0184] S640. The gradient descent algorithm optimization algorithm is used to optimize the parameters and , and then the optimized parameters and are input into the quantum computer for the next quantum state preparation.

[0185] In the formula: is the number of optimization steps is the optimization step size is the gradient solving operator.

[0186] S650. After multiple iterations of the parameters and , when the Hamiltonian of is equal to the minimum energy eigenstate, the expected value of reaches the minimum. At this time, measure the final quantum state to obtain the distribution network voltage control decision command.

[0187] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor is caused to execute the steps of the above method.

[0188] On yet another hand, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the above method.

[0189] In yet another embodiment provided by the present application, there is also provided a computer program product containing instructions, and when it runs on a computer, the computer is caused to execute any of the cyber-physical coupling-based distribution network voltage control methods in the above embodiments.

[0190] It is understandable that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention. For the explanations, examples, and beneficial effects of the relevant content, reference can be made to the corresponding parts in the above methods.

[0191] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center via wired (such as coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

[0192] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article, or device. 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 device including the element.

[0193] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the related parts, reference can be made to the partial description of the method embodiment.

[0194] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling voltage in a distribution network under cyber-physical coupling, characterized in that: The following steps are involved: S100, collecting node status information of the transmission network, node status information of the distribution network, and base station communication resource information; S200, establishing an information network data transmission model based on the node status information of the transmission network, the node status information of the distribution network, and the base station communication resource information; The method for establishing an information network data transmission model includes: S210, assuming that the maximum transmission power of base station b is , the maximum transmission power of the node is ; The power gain of the wireless link channel of the nth node is expressed as , is the channel vector, is a random variable, assuming The channel vector is known; The uplink transmission power of the wireless link channel of the nth node is , the downlink transmission power is ; The constraint of its transmission power is expressed as: Then the uplink transmission rate of the wireless link channel of the nth node is , Downlink transmission rate Respectively expressed as: ） ） is the channel bandwidth for uplink transmission of the nth node, is the channel bandwidth for downlink transmission of the nth node, is the uplink transmission power of the nth node, , is the channel gain of the nth node’s uplink and downlink transmission; Assuming that each control period node needs to have a fixed uplink transmission data packet DS, the size of each uplink transmission data packet is: The total number of data packets that n nodes need to transmit in k control cycles, for The total number of data packets that n nodes need to transmit in k+1 control cycles; express k control cycles n nodes transmit data packets upstream The transmission speed at that time; DT is the control time interval, T is the time slot; Combined with the size of each uplink transmission packet, the uplink transmission delay is expressed as: is the uplink transmission delay of the kth control cycle of the nth node, is the maximum value of the uplink transmission delay of all nodes in a partition in the kth control cycle; Compared with the data volume of uplink transmission, the amount of data transmitted in the downlink is small, and it is assumed that there will be no surplus in a single transmission. The downlink transmission delay is directly expressed as: The base station needs to transmit a data packet to the nth node in k control cycles, so the delay caused by the kth control cycle of the information network is It is expressed as: To handle delay; S300, build a voltage control model based on the information network data transmission model; S400, optimizing the voltage control model to obtain a distribution network voltage control optimization model; S500, converting the distribution network voltage control optimization model into a binary variable problem; S600. Approximate optimization of the binary variable problem is performed through the Ising model to determine the final distribution network voltage control method.

2. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 1, characterized in that: The node status node information and base station communication resource information of the distribution network in step S100 include: Node status information includes: Voltage status: the voltage value of each node, including the voltage amplitude and phase information of the node; Power information: active power and reactive power of each node, including power change and sensitivity coefficient; Node location and topology: the geographical location of each node, network topology, and connection relationship with other nodes; Load information: type, size and change trend of node load; PV inverter status information: For nodes involved in voltage control, PV inverter working status and reactive power output information; Base station communication resource information includes: Maximum transmit power of base station: the maximum wireless transmission power available for each base station; Power gain of wireless link: the signal transmission gain between each node and the base station; Uplink and downlink transmission power: The power and bandwidth of uplink and downlink communications between nodes.

3. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 1, characterized in that: The voltage control model building method in step S300 includes: Use photovoltaic inverters for reactive voltage control and establish the distribution network voltage control state space equation: The linear model of the distribution network is developed by evaluating the sensitivity matrix of the nodes with respect to the active and reactive inputs at a given operating point, based on which the network node voltages are linearly modeled as: is the voltage state of the node in the kth and k+1th regulation cycles, are the voltage sensitivity coefficients of node voltage to reactive power and active power, respectively. is the reactive power change of the node in the kth regulation cycle, The reactive power change of node k in the control cycle considering the communication network delay.

4. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 1, characterized in that: The methods for obtaining the distribution network voltage control optimization model include: S410, setting the objective function F: St is the setting value of the i-th node; S420. In the power distribution network, nodes need to satisfy the following power flow constraints: and are the active power and reactive power of node i respectively, is the voltage amplitude at node i, is the voltage amplitude at node j, are the admittance component and susceptance component in the admittance matrix between node i and node j, respectively. is the voltage phase difference between node i and node j, is the impedance between node i and node j, is the complex conjugate of the voltage at node j.

5. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 4, characterized in that: The method of converting the distribution network voltage control optimization model into a binary variable problem in step S500 includes: S510. Based on the Lagrange multiplier method, the constraints of the optimization problem can be transformed into the objective function, turning the constrained into the unconstrained: is a constant coefficient vector; is a constant coefficient matrix; is the Lagrange multiplier; is the reactive output of the i-th PV; S520, Discretization of continuous variables: Where: Round down the value; For the accuracy of the expansion; Introducing continuous Lagrange multipliers and discretizing according to the above formula, the unconstrained 0-1 variable optimization problem after discretization is: In the formula and is a constant coefficient matrix is a constant coefficient vector; and They are and A vector of discretized 0-1 variables and They are and the number of 0-1 variables in λ; Expand each vector matrix, and all 0-1 variables can be unified using Indicates that, further, the 0-1 variable Convert to spin state , strictly proved and Satisfy between: Substituting into The objective function is expressed as follows: Where: and They are Item and The coefficient of the term is a constant term, and are the coefficients of the quadratic and linear terms respectively.

6. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 1, characterized in that: The method for determining the final distribution network voltage control in step S600 includes: S610, prepare the initial state, use Hadamard gatekeeper Transformed into a uniform superposition of quantum bits ; S620, introduce two unitary operators; in, , , where: and They act on Pauli The matrix and the effect are Pauli matrix; S630, build quantum circuits; Will indivual and Act on input separately , specifically by Door and The gate is executed; finally the evolved quantum state is obtained , as shown below: right After multiple measurements, we can get the loss function The expected value of the loss function The formula is as follows: S640, use the gradient descent algorithm to optimize the parameters and Perform optimization and then use the optimized parameters and Input into the quantum computer for the next quantum state preparation; Where: To prevent optimization steps To optimize the step size is the gradient solving operator; S650, after the parameters and When multiple iterations When the Hamiltonian of is equal to the minimum energy eigenstate, The expected value of reaches the minimum, at this time the final quantum state is measured to obtain the distribution network voltage control decision command.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 6.

8. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 6.

Images