Power distribution network voltage control method and device under information physical coupling and medium

Abstract

The invention discloses a power distribution network voltage control method and device under information physical coupling and a medium, and the method comprises the steps: building an information network data transmission model through collecting node state information of a power transmission network and a power distribution network and base station communication resource information, and solving the influence of time delay on voltage control. In addition, a voltage control model is converted into a binary variable problem, and a quantum approximate optimization algorithm (QAOA) is adopted to solve an Isin model, so that compared with a traditional classical optimization method, the innovative method can remarkably improve the solving efficiency, and particularly, the advantages of quantum calculation are more prominent when the large-scale and complex voltage control problem is processed, and the method is suitable for large-scale and complex voltage control. And the calculation challenge of the traditional optimization method can be effectively overcome. Therefore, the method not only improves the precision and stability of voltage control, but also has strong expansibility and adaptability, and can cope with a more complex power distribution network environment.

Description

Technical Field

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

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

[0003] How to model and evaluate cyber-physical coupling for applications in the field of grid voltage control has become a topic that needs to be studied urgently under the new situation of power systems. Power flow interaction is a description of the continuous process of the physical network, while information flow interaction is a computer science based on discrete data structures. This heterogeneity brings huge challenges to the analysis and application of power CPS systems. Traditional power system analysis and control methods basically separate the two. There is an urgent need for an accurate model to describe the interactive behavior between the information system and the physical world, and accurately reflect the impact of communication performance on the safety and stability of power CPS. At present, most of the research on modeling of power cyber-physical coupling systems (power CPS) focuses on the reflection of the mapping relationship of information. In fact, the accurate description of communication performance is what we really care about when studying the impact of communication networks on power cyber-physical fusion systems, but there is a lack of relevant research and mature technical solutions. Summary of the invention

[0004] The present invention proposes a method, device and medium for controlling voltage in a distribution network under cyber-physical coupling, which can solve at least one of the technical problems in the background technology.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for controlling voltage of a distribution network under cyber-physical coupling comprises the following steps: 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; 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. Approximately optimize the binary variable problem through the Ising model and determine the optimal distribution network voltage control method.

[0006] Furthermore, the node status node information and base station communication resource information of the distribution network in step S100 of the present invention 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.

[0007] Furthermore, the method for establishing an information network data transmission model of the present invention 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:

[0008]

[0009]

[0010] 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 transmission power of the nth node uplink transmission, , 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:

[0011] 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:

[0012]

[0013] 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:

[0014] 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:

[0015] To process delay.

[0016] Furthermore, the voltage control model building method in step S300 of the present invention 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:

[0017] 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.

[0018] Furthermore, the method for obtaining a distribution network voltage control optimization model of the present invention includes: S410, setting the objective function F:

[0019] is the setting value of the i-th node; S420. In the power distribution network, nodes need to satisfy the following power flow constraints:

[0020]

[0021]

[0022] 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.

[0023] Furthermore, in step S500 of the present invention, the method for converting the distribution network voltage control optimization model into a binary variable problem 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:

[0024] 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:

[0025] 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:

[0026] 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:

[0027] Substituting into The objective function is expressed as follows:

[0028] 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.

[0029] Furthermore, the method for determining the final distribution network voltage control in step S600 of the present invention includes: S610, prepare the initial state, use Hadamard gatekeeper Transformed into a uniform superposition of quantum bits ;

[0030] S620, introduce two unitary operators;

[0031] 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:

[0032] right After multiple measurements, we can get the loss function The expected value of the loss function The formula is as follows:

[0033] 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;

[0034] Where: To prevent optimization steps To optimize the step size is the gradient solver; 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.

[0035] In yet another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0036] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0037] It can be seen from the above technical scheme that the voltage control method of the distribution network under information-physical coupling of the present invention, compared with the traditional voltage control method, combines the innovative design of information-physical coupling and quantum computing. This method not only optimizes the voltage control model, but also takes into account the delay of the communication network and the complexity of power transmission, thereby 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 communication resource information of the base station, this method establishes an information network data transmission model and solves the influence of delay on voltage control. In addition, during the optimization process, the voltage control model is converted into a binary variable problem, and the quantum approximate optimization algorithm (QAOA) is used to solve the Ising model. Compared with the traditional classical optimization method, this innovative method can significantly improve the solution efficiency, especially when dealing with large-scale and complex voltage control problems. The advantages of quantum computing are more prominent, and 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

[0038] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0040] like Figure 1 As shown, the method for controlling the voltage of a distribution network under cyber-physical coupling described in this embodiment includes the following steps: 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; 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. Approximately optimize the binary variable problem through the Ising model and determine the optimal distribution network voltage control method.

[0041] The following is a detailed description of each step: S100, collecting node status information of the transmission network and the distribution network, and base station communication resource information; Node status information includes: Voltage status: The voltage value of each node, including the voltage amplitude and phase information of the node.

[0042] Power information: active power and reactive power of each node, including power change and sensitivity coefficient.

[0043] Node location and topology: The geographical location of each node, network topology, and connection relationships with other nodes.

[0044] Load information: type, size and change trend of node load.

[0045] PV inverter status information: For nodes involved in voltage control, the working status and reactive power output information of the PV inverter.

[0046] Base station communication resource information includes: Maximum transmit power of base station: the maximum wireless transmission power available for each base station.

[0047] Power gain of wireless link: the signal transmission gain between each node and the base station.

[0048] Uplink and downlink transmission power: The power and bandwidth of uplink and downlink communications between nodes.

[0049] Packet transmission size: The size of the packet transmitted by each node during the control period.

[0050] These data provide necessary support for subsequent optimization control and are the basis of the entire control process.

[0051] S200, establishing an information network data transmission model based on node status information of the distribution network and base station communication resource information; The information network data transmission model takes into account the data transmission process between nodes, including transmission power, channel gain, uplink and downlink rates of the wireless link, etc. It also takes into account the delay of uplink and downlink transmission. The delay in data transmission will affect the subsequent voltage control decision.

[0052] Assume that the distribution network has E partitions, each partition has a base station responsible for communication, and there are n nodes distributed, and the communication mode adopts time division duplex transmission; 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 can be expressed as , is the channel vector, is a random variable, assuming The channel vector is known.

[0053] The uplink transmission power of the wireless link channel of the nth node is , the downlink transmission power is The transmission power constraint is expressed as:

[0054]

[0055]

[0056] 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 transmission power of the nth node uplink transmission, , is the channel gain for uplink and downlink transmission of the nth node.

[0057] 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:

[0058] 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 DT is the transmission speed at that time; DT is the control time interval and T is the time slot.

[0059] Combined with the size of each uplink transmission packet, the uplink transmission delay can be expressed as:

[0060]

[0061] is the uplink transmission delay of the kth control cycle of the nth node, It is the maximum value of the uplink transmission delay of all nodes in a partition in the kth regulation cycle, that is, the actual uplink transmission delay.

[0062] Since the amount of data transmitted in the downlink is smaller than that in the uplink, it can be assumed that there will be no surplus in a single transmission, so the downlink transmission delay can be directly expressed as

[0063] 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 can be expressed as:

[0064] To process delay.

[0065] S300, build a voltage control model based on the information network data transmission model; Photovoltaic inverters are used for reactive voltage control, and the state space equation for voltage control of the distribution network is established.

[0066] The linear model of the distribution network, at a given operating point, can be developed by evaluating its sensitivity matrix with respect to active and reactive inputs. On this basis, the network node voltages can be linearly modeled as:

[0067] 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.

[0068] The voltage control model takes into account the impact of data transmission delay and combines photovoltaic inverters to perform reactive voltage control. The sensitivity of the distribution network voltage to changes in active power and reactive power is also incorporated into the model, ensuring that the voltage of the distribution network can be effectively controlled even in the presence of communication delays.

[0069] S400, optimizing the voltage control model to obtain a distribution network voltage control optimization model; Based on the previous time-delay-connected cyber-physical coupled voltage control model, a grid voltage control optimization model is further established. The grid voltage control optimization model defines the objective function and constraints, aiming to optimize the voltage control of each node in the distribution network to ensure that the power flow constraints and other operating conditions are met. The specific method is as follows: S410, setting the objective function F:

[0070] is the setting value of the i-th node.

[0071] S420. In the power distribution network, nodes need to satisfy the following power flow constraints:

[0072]

[0073]

[0074] 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.

[0075] S500, converting the distribution network voltage control optimization model into a binary variable problem; The energy function of the Ising model consists of only two parts: the interaction between quanta and the effect of the external magnetic field on quanta. Therefore, the optimization model suitable for quantum computers 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 a single decision variable or the multiplication of two different decision variables.

[0076] 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:

[0077] is a constant coefficient vector; is a constant coefficient matrix; is the Lagrange multiplier; is the reactive power output of the i-th PV.

[0078] S520, Discretization of continuous variables:

[0079] Where: Round down the value; The precision of the expansion.

[0080] Due to the introduction of continuous Lagrange multipliers, it is necessary to discretize according to the above formula, so the unconstrained 0-1 variable optimization problem after discretization is:

[0081] 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.

[0082] After expanding each vector matrix, all 0-1 variables can be uniformly used Indicates that, further, the 0-1 variable Convert to spin state , strictly proved and Satisfy between:

[0083] Generations can get The objective function is expressed as follows:

[0084] 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.

[0085] In order to improve the optimization efficiency, the Ising model of quantum computing is introduced. In this step, the voltage control optimization problem is converted into a form suitable for quantum computer processing. By converting the optimization problem into a binary variable problem, the Ising model enables the problem to be solved through the advantages of quantum computing, especially using quantum annealing technology to overcome the computational challenges of traditional optimization methods.

[0086] S600, solve the Ising model based on the quantum approximate optimization algorithm and determine the final distribution network voltage control method; The quantum approximate optimization algorithm (QAOA) is applied to solve the Ising model by initializing the quantum state, using quantum gates (such as Hadamard gates and Pauli matrices) to act on the quantum bits, 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: S610, prepare the initial state, use Hadamard gatekeeper Transformed into a uniform superposition of quantum bits

[0087]

[0088] S620, introduce two unitary operators:

[0089] in , , where: and They act on Pauli The matrix and the effect are Pauli matrix.

[0090] S630, build quantum circuit: indivual and Act on input separately , specifically by Door and The gate is executed; finally the evolved quantum state is obtained , as shown below.

[0091]

[0092] right After multiple measurements, we can get the loss function The expected value of the loss function The formula is as follows:

[0093] 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.

[0094] Where: To prevent optimization steps To optimize the step size is the gradient solver operator.

[0095] 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.

[0096] In yet another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0097] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0098] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the methods for controlling voltage in a distribution network under information-physical coupling in the above embodiments.

[0099] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0100] 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0101] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0102] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and 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 the relevant parts can be referred to the partial description of the method embodiment.

[0103] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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; 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. Approximately optimize the binary variable problem through the Ising model and determine the optimal 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 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 process delay.

4. 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.

5. 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: 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.

6. The method for controlling voltage of a distribution network under cyber-physical coupling according to claim 5, 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.

7. The method for controlling voltage in a power 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 S200 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.

8. 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 7.

9. 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 7.

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