Node voltage value adjusting method and optimizing method of power grid simulation model and related device

Abstract

The embodiment of the invention provides a node voltage value adjusting method of a power grid simulation model. The power grid simulation model comprises a plurality of power grid nodes. The method is applied to a quantum computing system and comprises the following steps: constructing a first quantum operator representing a power deviation matrix of a plurality of power grid nodes and a second quantum operator representing a coefficient matrix of node voltage correction of the plurality of power grid nodes; based on a l-infinity norm chromatography sampling algorithm, sampling operation results obtained by performing linear operation on the first quantum operator and the second quantum operator by using a specified quantum linear system solver to obtain node voltage correction values of the plurality of power grid nodes; and adjusting the node voltage values of the plurality of power grid nodes according to the node voltage correction amounts of the plurality of power grid nodes. Through the implementation mode of the specification, in the Newton-Raphson method load flow calculation process, the calculation amount of the basic calculation unit is reduced, and the requirement for the operation performance of the basic calculation unit is lowered.

Description

Technical Field

[0001] The implementation methods in this specification relate to the field of power network flow calculation, and specifically to a method for adjusting and optimizing node voltage values ​​in a power grid simulation model and related devices. Background Art

[0002] Power flow calculation is of great significance to the stable operation of power grids. By using models to simulate the operation of power grids during the planning and actual operation stages of power grids, and adjusting and optimizing the parameters of each node in the power grid simulation model, it is possible to optimize the power grid power supply scheme, eliminate power grid operation faults, and improve the stability of power grid operation.

[0003] In the related art, classical computers are usually used to perform power flow calculations with the help of the Newton-Raphson (NR) method. Specifically, the node admittance matrix representing the relationship between node voltage and node current can be used as a power grid simulation model, and the node voltage value can be adjusted with the help of a given node voltage initial value and node power balance relationship. However, due to the high computational complexity of the NR method power flow calculation, it also has high requirements on the computing performance of the computer. With the continuous expansion of the scale of the power grid, the number of nodes in the power grid simulation model continues to increase, and the computational complexity increases exponentially with the increase in the number of nodes. The computing performance of classical computers is difficult to meet the computing performance requirements of the NR method power flow calculation. Summary of the invention

[0004] In view of this, multiple embodiments of this specification are dedicated to providing a node voltage value adjustment method, optimization method and related devices for a power grid simulation model to reduce the computing performance requirements of the NR method power flow calculation on classical computers.

[0005] One embodiment of the present specification provides a method for adjusting node voltage values ​​of a power grid simulation model, the method being applied to a quantum computing system; the power grid simulation model comprising a plurality of power grid nodes; the method comprising: constructing a first quantum operator representing a power deviation matrix of the plurality of power grid nodes, and a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of power grid nodes; based on l ∞ A norm tomography sampling algorithm is used to sample the operation results of performing linear operations on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections of the multiple power grid nodes; and the node voltage values ​​of the multiple power grid nodes are adjusted according to the node voltage corrections of the multiple power grid nodes.

[0006] One embodiment of the present specification provides a node voltage value optimization method for a power grid simulation model, the method being applied to a quantum computing system; the power grid simulation model comprising a plurality of power grid nodes; the method comprising: receiving initial node voltage values ​​of the plurality of power grid nodes; adjusting the node voltage values ​​of the plurality of power grid nodes according to the method described in the above embodiment based on the initial node voltage values; iteratively executing the node voltage value adjustment method described in the above embodiment based on the adjusted node voltage values ​​when the node voltage values ​​of the plurality of power grid nodes do not satisfy specified conditions, until the node voltage values ​​of the plurality of power grid nodes satisfy the specified conditions, and using the node voltage values ​​of the plurality of power grid nodes satisfying the specified conditions as target node voltage values.

[0007] One embodiment of the present specification provides a node voltage value adjustment method for a power grid simulation model, the method being applied to a basic computing unit; the power grid simulation model comprising a plurality of power grid nodes; the method comprising: constructing a first quantum operator representing a power deviation matrix of the plurality of power grid nodes, and a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of power grid nodes; generating first quantum state data according to the first quantum operator, and generating second quantum state data according to the second quantum operator; and sending the first quantum state data and the second quantum state data to a quantum computing unit, so that the quantum computing unit uses a specified quantum linear system solver to perform linear operations on the first quantum state data and the second quantum state data to obtain operation results; receiving the operation results of the specified quantum linear system solver sent by the quantum computing unit; and, based on l ∞ The norm tomography sampling algorithm performs sampling processing on the operation results to obtain node voltage correction amounts of the multiple power grid nodes; and adjusts the node voltage values ​​of the multiple power grid nodes according to the node voltage correction amounts of the multiple power grid nodes.

[0008] One embodiment of the present specification provides a node voltage value adjustment method for a power grid simulation model, the method being applied to a quantum computing unit; the power grid simulation model comprising a plurality of power grid nodes; the method comprising: receiving first quantum state data and second quantum state data sent by a basic computing unit; wherein the first quantum state data is generated according to a first quantum operator representing a power deviation matrix of the plurality of power grid nodes; the second quantum state data is generated according to a second quantum operator representing a coefficient matrix of node voltage corrections of the plurality of power grid nodes; storing the first quantum state data and the second quantum state data in a quantum random access memory according to a specified quantum data structure; extracting the first quantum state data and the second quantum state data from the quantum random access memory according to a specified extraction rule; and inputting the first quantum state data and the second quantum state data into a specified quantum linear system solver, performing linear operations on the first quantum state data and the second quantum state data, and obtaining operation results; and sending the operation results to a basic computing unit for the basic computing unit to calculate the node voltage value adjustment method based on the first quantum state data structure. ∞ The norm tomography sampling algorithm performs sampling processing on the operation results to obtain node voltage correction amounts of the multiple power grid nodes, and adjusts the node voltage values ​​of the multiple power grid nodes according to the node voltage correction amounts of the multiple power grid nodes.

[0009] One embodiment of the present specification provides a node voltage value adjustment device for a power grid simulation model, the device being applied to a quantum computing system; the power grid simulation model comprising a plurality of power grid nodes; the device comprising: a construction module for constructing a first quantum operator representing a power deviation matrix of the plurality of power grid nodes, and a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of power grid nodes; a sampling module for ∞ A norm tomography sampling algorithm is used to sample the operation results of performing linear operations on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections of the multiple power grid nodes; an adjustment module is used to adjust the node voltage values ​​of the multiple power grid nodes according to the node voltage corrections of the multiple power grid nodes.

[0010] The embodiments of this specification provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the above embodiments when executing the computer program.

[0011] The embodiments of this specification provide a computer-readable storage medium having computer program instructions stored thereon. When the program is executed by a processor, the method described in the above embodiments is implemented.

[0012] In multiple implementations provided in this specification, for multiple grid nodes included in the grid simulation model, by constructing a first quantum operator representing a node power deviation matrix and a second quantum operator representing a coefficient matrix of a node voltage correction amount, the first quantum operator and the second quantum operator are input into a specified quantum linear system solver to perform linear operations, and based on l ∞ The norm tomography sampling algorithm samples the results of linear operations to obtain node voltage corrections, and adjusts the node voltage values ​​according to the node voltage corrections, thereby reducing the amount of calculation of the basic calculation unit and the calculation performance requirements of the basic calculation unit during the NR method power flow calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A schematic diagram of a flow chart of using a quantum computing system to perform NR method power flow calculations according to one embodiment of the present specification.

[0015] Figure 2 A schematic diagram of a quantum computing system provided for one embodiment of the present specification.

[0016] Figure 3 A schematic flow chart of a method for adjusting node voltage values ​​of a power grid simulation model provided in accordance with one embodiment of the present specification.

[0017] Figure 4 A schematic flow chart of a method for optimizing node voltage values ​​of a power grid simulation model provided in accordance with one embodiment of the present specification.

[0018] Figure 5 A schematic flow chart of a method for adjusting node voltage values ​​of a power grid simulation model provided in another embodiment of the present specification.

[0019] Figure 6 A schematic flow chart of a method for adjusting node voltage values ​​of a power grid simulation model provided in yet another embodiment of the present specification.

[0020] Figure 7 A schematic diagram of a node voltage value adjustment device for a power grid simulation model provided in one embodiment of the present specification.

[0021] Figure 8 A schematic diagram of a computer device is provided for one embodiment of the present specification. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0023] In the description of the embodiments of the present application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0024] In the related art, using a classical computer to perform NR method power flow calculation may include the following steps.

[0025] Please refer to Formula 1. First, a node admittance matrix for a power grid simulation model can be formed according to the given initial data of the power grid. The node admittance matrix can reflect the connection relationship between the nodes of the power grid, and the matrix elements of the node admittance matrix can represent the mutual admittance between two nodes. If the two nodes are not directly connected, the mutual admittance between the two nodes is 0. Since any node is directly connected to only a limited number of nodes, the node admittance matrix is ​​a highly sparse matrix.

[0026]

[0027] Where n represents the number of grid nodes, each node has a unique node identifier, and Y 1n Indicates the mutual admittance between the node with node ID 1 and the node with node ID n.

[0028] Please refer to Formula 2 to Formula 4. Then, the active power imbalance and reactive power imbalance of each node in the power grid simulation model can be calculated based on the preset mapping relationship between the initial voltage values ​​and power deviations of multiple nodes. Taking the node identified as node i as an example, please refer to Formula 3. The active power and reactive power of the node can be calculated based on the mapping relationship between the active power and reactive power of the node and the node voltage and the conductance, susceptance, and phase angle difference between the node and other nodes. Please refer to Formula 4. The active power imbalance of the node can be calculated based on the mapping relationship between the active power imbalance of the node and the injected active power and active power of the node, and the reactive power imbalance of the node can be calculated based on the mapping relationship between the reactive power imbalance of the node and the injected reactive power and reactive power of the node.

[0029]

[0030] Where i represents the node ID, Represents the node voltage in polar coordinates, V i Represents the node voltage value, ∠θ i Represents the node voltage phase angle.

[0031]

[0032] Among them, i and j represent node identifiers, n represents the number of nodes, and P i and Q i Represent the active power and reactive power of the node respectively, V i and V j represents the node voltage, θ ij Represents the phase angle difference between nodes, C ij , B ij represent the conductance and susceptance between nodes respectively.

[0033]

[0034] Where i and j represent node identifiers, ΔP i and ΔQ i They represent the active power imbalance and reactive power imbalance of the node, P iS , Q iS They represent the injected active power and injected reactive power of the node respectively.

[0035] Please refer to Formula 5 to Formula 7. After obtaining the active power imbalance and reactive power imbalance of the node, the nonlinear power deviation mapping relationship can be corrected according to the specified correction rule to obtain the linear mapping relationship between the active power imbalance and reactive power imbalance and the node voltage correction amount, and the active power imbalance and reactive power imbalance are substituted into the linear mapping relationship to calculate the node voltage correction amount.

[0036]

[0037] The node voltage correction coefficient matrix is ​​a Jacobian matrix, and its matrix elements can be calculated based on the node voltage of any two nodes and the conductance, susceptance, and phase difference between the two nodes. Taking the nodes with node identifiers i and j as an example, please refer to Formula 6. When the node identifier j≠i, the matrix elements of the Jacobian matrix can be calculated based on the node voltage of the nodes with node identifiers i and j, the conductance, susceptance, and phase difference between the nodes. Please refer to Formula 7. When j=i, the matrix elements of the Jacobian matrix can be calculated based on the node voltage of the nodes with node identifiers i and k, the conductance, susceptance, and phase difference between the nodes. Among them, the node with node identifier k is any node in the power grid simulation model that is different from the node with node identifier i. It represents the sum of the node voltages of all nodes except the node identified as i in the power grid simulation model.

[0038]

[0039]

[0040] Please refer to Formula 8 and Formula 9. After the node voltage correction amount is calculated, the node voltage value may be adjusted according to the node voltage correction amount.

[0041]

[0042]

[0043] Among them, i represents the node identifier, V i (1) Represents the adjusted node voltage value, V i (0) represents the initial node voltage value, Indicates the node voltage value correction, represents the adjusted node voltage phase angle value, represents the initial node voltage phase angle value, Indicates the node voltage phase angle correction value of the node.

[0044] The above process of calculating and adjusting the node voltage value is iterated until the active power imbalance and reactive power imbalance of all nodes included in the power grid simulation model meet the specified numerical condition. The specified numerical condition can be a preset value, for example, a preset accuracy.

[0045] From the above calculation process, it can be seen that in the process of using classical computers to perform NR method power flow calculation, for any node in the power grid simulation model, the classical computer needs to perform relatively complex calculations to obtain the node voltage correction of the node. As the actual power grid scale continues to expand, the number of nodes in the power grid simulation model continues to increase, and the computational complexity of NR method power flow calculation increases at an exponential rate of the number of nodes, making it difficult for the computing performance of classical computers to meet the computing performance requirements of NR method power flow calculation.

[0046] Therefore, please refer to Figure 1 It is necessary to provide a node voltage value adjustment method for a power grid simulation model. For multiple power grid nodes included in the power grid simulation model, a first quantum operator representing a node power deviation matrix and a second quantum operator representing a coefficient matrix of a node voltage correction are constructed, and the first quantum operator and the second quantum operator are input into a specified quantum linear system solver to perform linear operations. Based on l ∞ The norm tomography sampling algorithm samples the results of linear operations to obtain node voltage corrections, and adjusts the node voltage values ​​based on the node voltage corrections. The quantum computing unit completes linear operations with large computational complexity, while the basic computing unit completes iterative operations with small computational complexity. The NR method power flow calculation is completed collaboratively by the quantum computing unit and the basic computing unit, which reduces the computational complexity of the basic computing unit and reduces the computing performance requirements for the basic computing unit.

[0047] See also Figure 2 One embodiment of the present specification provides a node voltage value adjustment system for a power grid simulation model. The node voltage value adjustment system for the power grid simulation model can be implemented by a quantum computing system. The quantum computing system can include a basic computing unit and a quantum computing unit.

[0048] The basic computing unit may be an electronic device with certain computing and display capabilities. Specifically, for example, the basic computing unit may include a desktop computer, a tablet computer, a laptop computer, a smart phone, a smart TV, and a smart wearable device equipped with a display screen. The basic computing unit may have a network communication module, a processor, and a memory.

[0049] In some embodiments, the basic computing unit can be used as a client. Alternatively, the basic computing unit can be deployed with a client software program. The basic computing unit can send specified data or data computing tasks to the quantum computing unit for computing, receive quantum computing results fed back by the quantum computing unit, and process and display the quantum computing results fed back by the quantum computing unit.

[0050] Based on the existing computer architecture, the basic computing unit may also include a server or a workstation. The server may be a distributed server, which may be a system with multiple processors, memories, network communication modules, etc. operating in coordination. Alternatively, the server may also be a server cluster formed by several servers. The basic computing unit may process the quantum computing results fed back by the quantum computing unit through a server or a workstation, and then send the processing results to the client for display.

[0051] A quantum computing unit can be a device that uses the characteristics of quantum mechanics to realize quantum computing. Specifically, the quantum computing unit can use the quantum state of the quantum bit as a data carrier, and drive the evolution of the quantum bit based on the linear superposition principle of the quantum state to realize a specified operation process. For example, the quantum computing unit can be a superconducting quantum bit control circuit based on ultra-low temperature technology. Alternatively, the quantum computing unit can be a quantum bit control circuit built using quantum well technology. Of course, the quantum computing unit can also be an optical quantum integrated chip, etc.

[0052] The basic computing unit and the quantum computing unit can communicate with each other. For example, the basic computing unit and the quantum computing unit can communicate with each other through a wired connection. Alternatively, the basic computing unit and the quantum computing unit can communicate wirelessly through a network communication module.

[0053] See also Figure 3 One embodiment of the present specification provides a method for adjusting node voltage values ​​of a power grid simulation model. The method for adjusting node voltage values ​​of a power grid simulation model can be applied to a quantum computing system. The method for adjusting node voltage values ​​of a power grid simulation model can include the following steps.

[0054] Step S110: constructing a first quantum operator representing a power deviation matrix of the plurality of grid nodes and a second quantum operator representing a coefficient matrix of node voltage corrections of the plurality of grid nodes.

[0055] When using a classical computer to perform NR method power flow calculation, the amount of calculation required to perform linear operations on the corrected power deviation mapping relationship is large and the calculation complexity is high. Limited by the computing performance of classical computers, the speed of classical computers performing linear operations is slow. When the number of nodes included in the power grid simulation model is large, the computing performance of classical computers is difficult to meet the computing performance requirements for performing linear operations. Therefore, a quantum operator that meets the processing requirements of a quantum computing unit can be constructed according to the computing rules of the NR method to perform linear operations using a quantum computing unit with higher computing performance.

[0056] In this embodiment, the power deviation matrix can be used to represent the active power imbalance and reactive power imbalance of the power grid node. Specifically, for example, the power deviation matrix of multiple power grid nodes may include the same number of matrix elements as the number of power grid nodes. Each matrix element may be a data pair consisting of the active power imbalance and reactive power imbalance of any power grid node.

[0057] In this embodiment, the first quantum operator may represent a quantum state preparation process of exciting a quantum bit in the |0> state to a quantum state capable of characterizing a power deviation matrix of multiple power grid nodes. Specifically, for example, the power deviation matrices of multiple power grid nodes may be constructed based on the power deviation mapping relationship of multiple power grid nodes, and then the quantum bit in the |0> state may be operated using a specified quantum logic gate, so that the quantum state of the quantum bit after the operation can characterize the power deviation matrix of multiple power grid nodes.

[0058] O b |0>=|b> Formula 10

[0059] See Equation 5 and Equation 10. b represents the first quantum operator, |0> represents a quantum bit in the |0> state, and |b> represents a quantum state that can characterize the power deviation matrix of multiple power grid nodes.

[0060] In this embodiment, the node voltage correction amount may be a correction amount of the node voltage of the power grid node in polar coordinates. Specifically, for example, for any power grid node, the node voltage correction amount may include a node voltage value correction amount and a node voltage phase angle value correction amount. Among them, the node voltage value correction amount may be equal to the product of the node voltage value normalization operation result and the node voltage value normalization coefficient. The node voltage phase angle value correction amount may be equal to the product of the node voltage phase angle value normalization operation result and the node voltage phase angle value normalization coefficient.

[0061] In this embodiment, the coefficient matrix of the node voltage correction amount can be a Jacobian matrix, see Formula 6 and Formula 7, whose matrix elements can be calculated based on the node voltage of any two nodes in the power grid simulation model, the conductance, susceptance and phase difference between the nodes.

[0062] In this embodiment, the second quantum operator may represent a quantum state preparation process of exciting a quantum bit in a specified quantum state to a quantum state of a coefficient matrix capable of representing node voltage corrections for multiple power grid nodes. Specifically, for example, the quantum bit in the |0> state may be prepared to a specified quantum state according to the position and value of the non-zero matrix elements in the coefficient matrix, and then the quantum bit in the specified quantum state may be operated using a specified quantum logic gate, so that the quantum state of the quantum bit after the operation can represent the coefficient matrix of node voltage corrections for multiple power grid nodes.

[0063] In this embodiment, a first quantum operator characterizing the power deviation matrices of the plurality of power grid nodes is constructed, and a quantum program executable by a quantum computing unit can be directly written according to the power deviation matrices of the plurality of power grid nodes, and the quantum program is sent to the quantum computing unit for execution. Specifically, for example, the number of quantum bits required to characterize the power deviation matrices of the plurality of power grid nodes, the type, number, and logic gate parameters of the quantum logic gates operating on the quantum bits, and the initial values ​​of the power deviation matrices of the plurality of power grid nodes can be used as initial parameters of the quantum program to write the quantum program.

[0064] In this embodiment, the process of constructing the second quantum operator is substantially the same as the process of constructing the first quantum operator, and will not be described in detail herein.

[0065] Step S120: Based on l ∞ The norm tomography sampling algorithm samples the operation results of linear operations performed on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections for the multiple power grid nodes.

[0066] In this embodiment, after the construction of the first quantum operator and the second quantum operator is completed, the first quantum operator and the second quantum operator can be input into a specified quantum linear system solver to perform linear operations, and based on l ∞ The norm tomography sampling algorithm samples the results of linear operations to improve the accuracy of node voltage correction.

[0067] In this embodiment, the specified quantum linear system solver (Quantum Linear System Solver, QLSS) can be an algorithm model for performing linear operations on input quantum operators. Specifically, for example, the specified QLSS can include an HHL (Harrow-Hassidim-Lloyd) algorithm model, a CKS (Cao-Kieferová-Swaddle) algorithm model, a variational quantum eigensolver (VQE) algorithm model, etc.

[0068] In this embodiment, l ∞ The norm tomographic sampling algorithm can be used to ∞ The quantum state tomography sampling algorithm for estimating the quantum state norm. Specifically, for example, l ∞ The norm tomographic sampling algorithm can be a given unitary transformation U, so that U|0>=|x> and can be completed in T(U) time, the time complexity is The quantum state tomography algorithm makes it possible to make in Represents the sampled unit vector, ∈ s Indicates the sampling accuracy.

[0069] In this embodiment, a designated quantum linear system solver is used to perform linear operations on the first quantum operator and the second quantum operator. The first quantum operator and the second quantum operator can be simultaneously input into the designated quantum linear system solver, linear operations are performed to obtain normalized operation results, and then the normalized operation results and the normalized coefficients are multiplied to obtain the linear operation results.

[0070] In this embodiment, based on l ∞ The norm tomographic sampling algorithm samples the results of linear operations. Multiple linear operations can be performed by specifying QLSS to obtain multiple operation results. ∞ The norm tomography sampling algorithm samples multiple operation results to obtain an estimated value of the node voltage correction amount, and then calculates the node voltage correction amount according to the estimated value of the node voltage correction amount.

[0071] Step S130: adjusting the node voltage values ​​of the plurality of grid nodes according to the node voltage correction amounts of the plurality of grid nodes.

[0072] In this embodiment, based on l ∞ After the norm tomography sampling algorithm obtains the node voltage correction amounts of multiple power grid nodes, the node voltage values ​​of the multiple power grid nodes can be adjusted according to the node voltage correction amounts.

[0073] In this implementation, the node voltage value may include a node voltage numerical value and a node voltage phase angle value.

[0074] In this embodiment, the node voltage values ​​of multiple power grid nodes are adjusted according to the node voltage correction amounts of multiple power grid nodes. The initial node voltage value of each power grid node and the corresponding node voltage correction amount are summed, and the summed result is used as the adjusted node voltage value of each node. Please refer to Formula 11 and Formula 12. Specifically, taking the node with node identification i as an example, the initial node voltage value of the node can be a preset value. The adjusted node voltage value of the node can be the sum of the initial node voltage value and the node voltage correction amount. Among them, the adjusted node voltage value of the node can be the sum of the initial node voltage value of the node and the node voltage value correction amount of the node, and the adjusted node voltage phase angle value of the node can be the sum of the initial node voltage phase angle value of the node and the node voltage phase angle value correction amount of the node.

[0075]

[0076]

[0077] Among them, i represents the node identifier, V i (1) Represents the adjusted node voltage value, V i (0) represents the initial node voltage value, C ΔV represents the node voltage normalization coefficient, Represents the node voltage value correction, θ i (1) represents the adjusted node voltage phase angle value, represents the initial node voltage phase angle value, C Δθ represents the normalization coefficient of the node voltage phase angle value, Indicates the node voltage phase angle correction value.

[0078] In the implementation manner of the present specification, for a plurality of grid nodes included in the grid simulation model, by constructing a first quantum operator representing a node power deviation matrix and a second quantum operator representing a coefficient matrix of a node voltage correction amount, the first quantum operator and the second quantum operator are input into a designated quantum linear system solver to perform linear operations, based on l ∞ The norm tomography sampling algorithm samples the results of linear operations to obtain node voltage corrections, and adjusts the node voltage values ​​based on the node voltage corrections. The quantum computing unit completes linear operations with large computational complexity, while the basic computing unit completes iterative operations with small computational complexity. The NR method power flow calculation is completed collaboratively by the quantum computing unit and the basic computing unit, which reduces the computational complexity of the basic computing unit and reduces the computing performance requirements for the basic computing unit.

[0079] In some embodiments, constructing a first quantum operator representing a power deviation matrix of the multiple power grid nodes and a second quantum operator representing a coefficient matrix of node voltage corrections of the multiple power grid nodes may include: constructing the first quantum operator according to multiple power deviation mapping relationships of the multiple power grid nodes; correcting the multiple power deviation mapping relationships according to a specified correction rule to obtain a linear mapping relationship between active power imbalance and reactive power imbalance of the multiple power grid nodes and node voltage corrections of the multiple power grid nodes; and constructing the second quantum operator according to the linear mapping relationship.

[0080] Since the quantum linear system solver can only perform linear operations, and the power deviation mapping relationship of any power grid node is a nonlinear mapping relationship, in order to use the quantum linear system solver to perform linear operations with large amount of calculation and high computational complexity in the NR method power flow calculation process, it is necessary to correct the nonlinear power deviation mapping relationship to obtain the linear mapping relationship between the power imbalance amount and the node voltage correction amount of any power grid node.

[0081] Please refer to Formula 3 and Formula 4. In this embodiment, the power deviation mapping relationship of any grid node among the multiple grid nodes may include an active power deviation mapping relationship and a reactive power mapping relationship. Among them, the active power deviation mapping relationship can represent the mapping relationship between the active power imbalance of any grid node and the injected active power value, node voltage value, node conductance value and node susceptance value of any grid node. The reactive power deviation mapping relationship can represent the mapping relationship between the reactive power imbalance of any grid node and the injected reactive power value, node voltage value, node conductance value and node susceptance value of any grid node.

[0082] In this embodiment, the designated correction rule can be determined based on the basic principle of the NR method.

[0083] Please refer to formula 13. In this embodiment, the linear mapping relationship between the power imbalance amount and the node voltage correction amount of multiple grid nodes obtained after correcting the multiple power deviation mapping relationships is as follows:

[0084] AΔx=-|b> Formula 13

[0085] Wherein, matrix A represents the coefficient matrix of node voltage correction, Δx represents the node voltage correction, and |b> represents the node power deviation matrix. Please refer to formula 14 and formula 15. |b> and A can be calculated by the following formula:

[0086]

[0087] Among them, C brepresents the norm of the node power imbalance, i and n represent the node identifier, and f i (x) represents the mapping relationship between the power imbalance amount of the node with the node identifier being i, the injected power of the node, and the power of the node.

[0088]

[0089] Where F′(x) represents the Jacobian matrix of node power imbalance, ‖F′(x)‖ max It can be estimated by the following formula 16. Since the expression of each element of the Jacobian matrix and the domain of the independent variable are known, the upper bound of the maximum value can be determined based on the expression.

[0090] ‖F′(x)‖ max =max j,k |F′(x) ik | Formula 16

[0091] In some embodiments, the second quantum operator includes a third quantum operator determined by non-zero matrix elements in the coefficient matrix of the node voltage correction amount and a fourth quantum operator for performing a bitwise exclusive-OR operation on a specified quantum state.

[0092] Since the mapping relationships between different matrix elements in the coefficient matrix of the node voltage correction and the node voltage, conductance, susceptance and phase difference between any two nodes in the power grid simulation model are different, it is relatively complicated to construct the second quantum operator based on the linear mapping relationship between the corrected node power imbalance and the node voltage correction. In order to simplify the construction process of the second quantum operator and reduce the computational complexity of constructing the second quantum operator, the second quantum operator can be decomposed into the third quantum operator and the fourth quantum operator and the third quantum operator and the fourth quantum operator can be constructed respectively.

[0093] Please refer to Formula 17. In this embodiment, the third quantum operator O A1 It can represent the quantum state preparation process of exciting two quantum bits in the |j,l> quantum state to the |j,h(j,l)> quantum state.

[0094] O A1 |j,l>=|j,h(j,l)> Formula 17

[0095] Where h(j,l) represents the column subscript of the lth non-zero matrix element in the jth row of the node voltage correction coefficient matrix. Since the quantum states |j,l> and |j,h(j,l)> are determined by the non-zero matrix elements of the node voltage correction coefficient matrix, O A1 The construction of is determined by the specific form of the node voltage correction coefficient matrix.

[0096] Please refer to Formula 18. In this embodiment, the fourth quantum operator O A2 It can be expressed as operating the three quantum bits in the quantum state |j,k,z>, so that the quantum bits after the operation are in The quantum state preparation process of quantum state.

[0097]

[0098] Among them, A jk represents a quantum logic gate controlled by two qubits j and k, Indicates the bitwise XOR operation, that is, when the corresponding bits are different, the operation result is 1, and when the corresponding bits are the same, the operation result is 0.

[0099] In some embodiments, based on l ∞ The norm tomography sampling algorithm is used to sample the operation results of the linear operation performed on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain the node voltage correction amount of the plurality of power grid nodes, which may include: inputting the first quantum operator, the third quantum operator and the fourth quantum operator into the specified quantum linear system solver; performing a specified number of linear operations to obtain the specified number of linear operation results; using l ∞ The norm tomography sampling algorithm samples the specified number of linear operation results to obtain the distribution of the linear operation results; and calculates the node voltage correction amounts of the multiple power grid nodes according to the distribution of the linear operation results.

[0100] In some cases, in order to obtain the operation result of QLSS, it is necessary to measure the quantum bits involved in the linear operation. In the quantum computing unit, the operation and measurement of quantum bits are probabilistic, that is, the operation results and measurement results of quantum bits may be probability distributions. Therefore, when a linear operation is performed and measured using a specified QLSS, the measurement result obtained is difficult to reflect the linear operation result. In order to improve the accuracy of the linear operation result and thus improve the accuracy of the node voltage correction amount, it is possible to use a specified QLSS to perform multiple linear operations and use l ∞ The norm tomography sampling algorithm samples multiple linear operation results and calculates the node voltage correction amount according to the distribution of the sampled linear operation results.

[0101] In this implementation, the specified number of times may be a preset number of times determined according to historical linear operation execution conditions.

[0102] In this embodiment, the linear operation result can be a normalized node voltage correction amount |Δx> of multiple power grid nodes. The distribution of the linear operation result can reflect the frequency distribution of the values ​​of multiple linear operation results at different values. The normalized node voltage correction amount of multiple power grid nodes can be estimated according to the distribution of the linear operation result to obtain an estimated amount of the normalized node voltage correction amount

[0103] Please refer to Formula 19. In this embodiment, after the first quantum operator, the third quantum operator and the fourth quantum operator are input into the designated QLSS, the designated QLSS has a certain probability of successfully obtaining the linear operation result when performing the linear operation, and the execution success probability is as follows:

[0104]

[0105] Among them, A -1 represents the inverse matrix of the node voltage correction coefficient matrix, |b> represents the node power deviation matrix, and a can be calculated by the following formulas 20 to 22.

[0106]

[0107] Wherein, s represents the condition number of the node voltage correction coefficient matrix, i and j represent the row subscript and column subscript of the node voltage correction coefficient matrix respectively.

[0108]

[0109] b1=κ 2 log(κ / ε) Formula 22 Among them, κ represents the matrix condition number and ε represents the preset accuracy.

[0110] Please refer to formula 23. In this embodiment, after obtaining the estimated amount of normalized node voltage correction amount of multiple grid nodes, After that, the estimated amount and the normalization coefficient can be multiplied, and the result of the multiplication operation can be used as the node voltage correction amount of multiple power grid nodes. Among them, the normalization coefficient can be calculated by the following formula:

[0111]

[0112] See also Figure 4 One embodiment of the present specification provides a method for optimizing node voltage values ​​of a power grid simulation model. The method for optimizing node voltage values ​​of a power grid simulation model can be applied to a quantum computing system. The method for optimizing node voltage values ​​of a power grid simulation model can include the following steps.

[0113] Step S210: receiving initial node voltage values ​​of the plurality of grid nodes.

[0114] In this embodiment, the initial node voltage values ​​of multiple power grid nodes are received by directly receiving the initial node voltage setting values ​​input by a user or forwarded by other clients or servers. In some embodiments, the initial node voltage values ​​of multiple power grid nodes are received by receiving the node voltage data generated during the actual operation of the power grid, and using the node voltage data as the initial node voltage value.

[0115] Step S220: Based on the initial node voltage value, adjust the node voltage values ​​of the plurality of grid nodes according to the method described in any one of the above embodiments.

[0116] Step S230: When the node voltage values ​​of the multiple power grid nodes do not meet the specified conditions, the node voltage value adjustment method described in any of the above-mentioned embodiments is iteratively executed based on the adjusted node voltage values ​​until the node voltage values ​​of the multiple power grid nodes meet the specified conditions, and the node voltage values ​​of the multiple power grid nodes that meet the specified conditions are used as target node voltage values.

[0117] In this embodiment, the specified condition may be a specified numerical condition satisfied by a specified physical quantity. Specifically, for example, the specified condition may be a specified accuracy condition satisfied by the norm of the power imbalance amount of multiple power grid nodes. The norm of the power imbalance amount of multiple power grid nodes may be calculated based on the node voltage values ​​of the multiple power grid nodes.

[0118] In some embodiments, in order to save computing resources of the quantum computing system, the number of iterations of the iterative execution of the node voltage value adjustment method can be limited. Specifically, for example, when the number of iterative executions of the node voltage value adjustment method reaches a specified number, regardless of whether the node voltage values ​​of the multiple power grid nodes meet the specified conditions, the node voltage values ​​of the multiple power grid nodes obtained by executing the node voltage value adjustment method for the last time are used as the target node voltage values. The specified number can be a preset number determined according to the historical iterative execution situation.

[0119] In the implementation manner of this specification, based on the received initial node voltage values ​​of the multiple power grid nodes, the node voltage values ​​are adjusted according to any of the above implementation manners, and when the node voltage values ​​do not meet the specified conditions, the node voltage values ​​are iteratively adjusted according to any of the above implementation manners until the node voltage values ​​of the multiple power grid nodes meet the specified conditions, and the node voltage values ​​that meet the specified conditions are used as the target node voltage values. ∞ The norm tomography sampling algorithm samples the linear operation results of the specified QLSS, which improves the iteration speed and accuracy in the process of iterative adjustment of node voltage values.

[0120] See also Figure 5 One embodiment of the present specification provides a method for adjusting node voltage values ​​of a power grid simulation model. The method for adjusting node voltage values ​​of a power grid simulation model can be applied to a basic calculation unit. The method for adjusting node voltage values ​​of a power grid simulation model can include the following steps.

[0121] Step S310: constructing a first quantum operator representing a power deviation matrix of the plurality of grid nodes and a second quantum operator representing a coefficient matrix of node voltage corrections of the plurality of grid nodes.

[0122] Step S320: Generate first quantum state data according to the first quantum operator, and generate second quantum state data according to the second quantum operator; and send the first quantum state data and the second quantum state data to a quantum computing unit, so that the quantum computing unit uses a specified quantum linear system solver to perform linear operations on the first quantum state data and the second quantum state data to obtain operation results.

[0123] In this embodiment, the first quantum state data may include quantum logic gate data corresponding to the quantum state preparation process represented by the first quantum operator and quantum state data capable of characterizing the power deviation matrix of multiple power grid nodes.

[0124] In this embodiment, the second quantum state data may include quantum logic gate data corresponding to the quantum state preparation process represented by the second quantum operator and quantum state data of a coefficient matrix capable of representing node voltage correction amounts of multiple power grid nodes.

[0125] Step S330: receiving the operation result of the specified quantum linear system solver sent by the quantum computing unit; and based on l ∞ The norm tomography sampling algorithm performs sampling processing on the operation results to obtain node voltage correction values ​​of the multiple power grid nodes.

[0126] Step S340: adjusting the node voltage values ​​of the plurality of grid nodes according to the node voltage correction amounts of the plurality of grid nodes.

[0127] In the implementation manner of this specification, the basic computing unit generates first quantum state data according to the first quantum operator, generates second quantum state data according to the second quantum operator, and sends the first quantum state data and the second quantum state data to the quantum computing unit for the quantum computing unit to perform linear operations, and then receives the linear operation results sent by the quantum computing unit and generates the first quantum state data according to the second quantum operator. ∞The norm tomography sampling algorithm samples the results of linear operations to obtain node voltage corrections of multiple power grid nodes and adjusts the node voltage values ​​of multiple power grid nodes according to the node voltage corrections, thereby realizing that linear operations with large computational amount and high computational complexity are completed by the quantum computing unit, and iterative operations with small computational amount and low computational complexity are completed by the basic computing unit. The NR method power flow calculation is completed through the collaboration of the quantum computing unit and the basic computing unit, which reduces the computational amount of the basic computing unit and reduces the computing performance requirements of the NR method power flow calculation on the basic computing unit.

[0128] See also Figure 6 One embodiment of the present specification provides a method for adjusting node voltage values ​​of a power grid simulation model. The method for adjusting node voltage values ​​of a power grid simulation model can be applied to a quantum computing unit. The method for adjusting node voltage values ​​of a power grid simulation model can include the following steps.

[0129] Step S410: Receive first quantum state data and second quantum state data sent by a basic computing unit; wherein the first quantum state data is generated based on a first quantum operator representing a power deviation matrix of the plurality of power grid nodes; and the second quantum state data is generated based on a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of power grid nodes.

[0130] Step S420: storing the first quantum state data and the second quantum state data into a quantum random access memory (QRAM) according to a specified quantum data structure.

[0131] In this embodiment, the designated quantum data structure may be a tree structure. Each leaf node of the tree structure may store a mapping relationship between a node voltage correction amount of any power grid node and a power deviation of any power grid node; the root node of the tree structure may store the norm of the node voltage correction amount of multiple power grid nodes and the norm of the power deviation matrix of multiple power grid nodes; the middle node of the tree structure may store a d determined according to the node voltage correction amount of a power grid node. ijx and d determined according to the power deviation mapping relationship of a grid node ijy Among them, d ijx and d ijy Please refer to Formula 24 and Formula 25 for the calculation method of .

[0132]

[0133]

[0134] Among them, d ijx and d ijyThe subscript represents the jth node of the i-th layer in the tree structure. The value ranges of i and j are i=0,...,n-1, j=0,...,2 n-i -1. n is the number of nodes in the tree structure.

[0135] Step S430: extracting the first quantum state data and the second quantum state data from the quantum random access memory according to a specified extraction rule; and inputting the first quantum state data and the second quantum state data into a specified quantum linear system solver, performing linear operations on the first quantum state data and the second quantum state data, and obtaining operation results.

[0136] In this embodiment, the specified extraction rule may include the time complexity of taking the specified quantum state data out of the QRAM.

[0137] Step S440: Send the calculation result to the basic calculation unit for the basic calculation unit to calculate the result based on l ∞ The norm tomography sampling algorithm performs sampling processing on the operation results to obtain node voltage correction amounts of the multiple power grid nodes, and adjusts the node voltage values ​​of the multiple power grid nodes according to the node voltage correction amounts of the multiple power grid nodes.

[0138] In the implementation manner of the present specification, the quantum computing unit receives the first quantum state data and the second quantum state data sent by the basic computing unit, stores the first quantum state data and the second quantum state data in QRAM according to the specified quantum data structure, extracts the first quantum state data and the second quantum state data from the QRAM according to the specified extraction rule and inputs the specified QLSS to perform linear operation, and then sends the operation result of the linear operation to the basic computing unit for subsequent sampling and node voltage value adjustment. The first quantum state data and the second quantum state data are stored by specifying the quantum data structure, and the first quantum state data and the second quantum state data are extracted according to the specified extraction rule, thereby reducing the time complexity of obtaining the first quantum state data and the second quantum state data, accelerating the calculation speed of the quantum computing unit to perform linear calculations, and improving the calculation efficiency of the quantum computing unit.

[0139] See also Figure 7 One embodiment of the present specification provides a node voltage value adjustment device for a power grid simulation model. The device may include: a construction module, a sampling module, and an adjustment module.

[0140] The construction module can be used to construct a first quantum operator representing a power deviation matrix of the plurality of grid nodes and a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of grid nodes.

[0141] Sampling module, can be used based on l∞ The norm tomography sampling algorithm samples the operation results of linear operations performed on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections for the multiple power grid nodes.

[0142] The adjustment module can be used to adjust the node voltage values ​​of the multiple grid nodes according to the node voltage correction amounts of the multiple grid nodes.

[0143] Regarding the specific functions and effects achieved by the node voltage value adjustment device of the power grid simulation model, you can refer to other embodiments of this specification for comparison and explanation, and will not repeat them here. The various modules in the processing device of the data processing task can be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0144] See also Figure 8 The present specification also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the node voltage value adjustment method and optimization method of the power grid simulation model in any of the above-mentioned embodiments when executing the computer program. The computer device may be a classical computer. The computer device may also be a quantum computer.

[0145] The computer device may include a processor, a non-volatile storage medium, an internal memory, a communication interface and an input device connected by a system bus. The non-volatile storage medium may store an operating system and related computer program instructions.

[0146] The embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer, the computer executes the node voltage value adjustment method and optimization method of the power grid simulation model in any of the above embodiments.

[0147] The embodiments of this specification also provide a computer program product including instructions, which, when executed by a computer, enables the computer to execute the node voltage value adjustment method and optimization method of the power grid simulation model in any of the above embodiments.

[0148] It should be understood that the specific examples in this specification are only intended to help those skilled in the art to better understand the implementation methods of this specification, rather than to limit the scope of the present invention.

[0149] It can be understood that in the various implementations of this specification, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of this specification.

[0150] It can be understood that the various embodiments described in this specification can be implemented individually or in combination, and the embodiments of this specification are not limited to this.

[0151] Unless otherwise specified, all technical and scientific terms used in the embodiments of this specification have the same meaning as those generally understood by those skilled in the art of the technical field of this specification. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the scope of this specification. The term "and / or" used in this specification includes any and all combinations of one or more related listed items. The singular forms of "a", "above", and "the" used in the embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0152] It can be understood that the processor of the embodiment of this specification can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method implementation can be completed by the hardware integrated logic circuit or software instructions in the processor. The above processor can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiment of this specification can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of this specification can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor are combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0153] It is understood that the memory in the embodiments of this specification may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (programmable ROM, PROM), an erasable programmable read-only memory (erasablePROM, EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0156] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0158] In addition, each functional unit in each embodiment of the present specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0159] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0160] The above is only a specific implementation of this specification, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this specification, which should be included in the protection scope of this specification. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for adjusting node voltage values ​​of a power grid simulation model, characterized in that: Application to quantum computing systems; The power grid simulation model includes a plurality of power grid nodes; the method includes: Constructing a first quantum operator representing a power deviation matrix of the plurality of grid nodes and a second quantum operator representing a coefficient matrix of node voltage corrections of the plurality of grid nodes; Based on l ∞ A norm tomographic sampling algorithm is used to sample and process the operation results of performing linear operations on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections of the multiple power grid nodes; The node voltage values ​​of the multiple grid nodes are adjusted according to the node voltage correction amounts of the multiple grid nodes.

2. The method according to claim 1, characterized in that The step of constructing a first quantum operator characterizing a power deviation matrix of the plurality of grid nodes and a second quantum operator characterizing a coefficient matrix of node voltage corrections of the plurality of grid nodes comprises: The first quantum operator is constructed according to multiple power deviation mapping relationships of the multiple power grid nodes; wherein the power deviation mapping relationship of any power grid node among the multiple power grid nodes includes an active power deviation mapping relationship and a reactive power mapping relationship; the active power deviation mapping relationship represents a mapping relationship between the active power imbalance of any power grid node and the injected active power value, node voltage value, node conductance value and node susceptance value of any power grid node; the reactive power deviation mapping relationship represents a mapping relationship between the reactive power imbalance of any power grid node and the injected reactive power value, node voltage value, node conductance value and node susceptance value of any power grid node; Correcting the multiple power deviation mapping relationships according to a specified correction rule to obtain a linear mapping relationship between the active power imbalance amount and the reactive power imbalance amount of the multiple power grid nodes and the node voltage correction amount of the multiple power grid nodes; The second quantum operator is constructed according to the linear mapping relationship.

3. The method according to claim 1, characterized in that The second quantum operator includes a third quantum operator determined by non-zero matrix elements in the coefficient matrix of the node voltage correction amount and a fourth quantum operator for performing a bitwise exclusive-OR operation on a designated quantum state.

4. The method according to claim 3, characterized in that Based on l ∞ The norm tomography sampling algorithm is a step of sampling the operation results of performing linear operations on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage correction values ​​of the plurality of power grid nodes, including: inputting the first quantum operator, the third quantum operator, and the fourth quantum operator into the specified quantum linear system solver; Perform a specified number of linear operations to obtain the specified number of linear operation results; Utilize l ∞ The norm tomographic sampling algorithm samples the specified number of linear operation results to obtain the distribution of the linear operation results; The node voltage correction amounts of the plurality of grid nodes are calculated according to the distribution of the linear operation results.

5. A method for optimizing node voltage values ​​of a power grid simulation model, characterized in that: Application to quantum computing systems; The power grid simulation model includes a plurality of power grid nodes; the method includes: Receiving initial node voltage values ​​of the plurality of grid nodes; Based on the initial node voltage value, adjusting the node voltage values ​​of the plurality of grid nodes according to the method of any one of claims 1 to 4; When the node voltage values ​​of the multiple power grid nodes do not satisfy specified conditions, the node voltage value adjustment method described in any one of claims 1 to 4 is iteratively executed based on the adjusted node voltage values ​​until the node voltage values ​​of the multiple power grid nodes satisfy the specified conditions, and the node voltage values ​​of the multiple power grid nodes satisfying the specified conditions are used as target node voltage values.

6. A method for adjusting node voltage values ​​of a power grid simulation model, characterized in that: Applied to basic computing units; The power grid simulation model includes a plurality of power grid nodes; the method includes: Constructing a first quantum operator representing a power deviation matrix of the plurality of grid nodes and a second quantum operator representing a coefficient matrix of node voltage corrections of the plurality of grid nodes; Generate first quantum state data according to the first quantum operator, and generate second quantum state data according to the second quantum operator; and send the first quantum state data and the second quantum state data to a quantum computing unit, so that the quantum computing unit uses a specified quantum linear system solver to perform linear operations on the first quantum state data and the second quantum state data to obtain an operation result; receiving a calculation result of a specified quantum linear system solver sent by the quantum computing unit; and, based on l ∞ A norm tomography sampling algorithm is used to sample the operation results to obtain node voltage correction values ​​of the plurality of power grid nodes; The node voltage values ​​of the multiple grid nodes are adjusted according to the node voltage correction amounts of the multiple grid nodes.

7. A method for adjusting node voltage values ​​of a power grid simulation model, characterized in that: Application to quantum computing units; The power grid simulation model includes a plurality of power grid nodes; the method includes: Receive first quantum state data and second quantum state data sent by a basic computing unit; wherein the first quantum state data is generated according to a first quantum operator representing a power deviation matrix of the plurality of grid nodes; and the second quantum state data is generated according to a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of grid nodes; Storing the first quantum state data and the second quantum state data in a quantum random access memory according to a specified quantum data structure; Extracting the first quantum state data and the second quantum state data from the quantum random access memory according to a specified extraction rule; and inputting the first quantum state data and the second quantum state data into a specified quantum linear system solver, performing a linear operation on the first quantum state data and the second quantum state data, and obtaining an operation result; The operation result is sent to the basic computing unit so that the basic computing unit can calculate the result based on l ∞ The norm tomography sampling algorithm performs sampling processing on the operation results to obtain node voltage correction amounts of the multiple power grid nodes, and adjusts the node voltage values ​​of the multiple power grid nodes according to the node voltage correction amounts of the multiple power grid nodes.

8. A node voltage value adjustment device for a power grid simulation model, characterized in that: Application to quantum computing systems; The power grid simulation model includes a plurality of power grid nodes; the device includes: A construction module, configured to construct a first quantum operator representing a power deviation matrix of the plurality of grid nodes, and a second quantum operator representing a coefficient matrix of node voltage correction amounts of the plurality of grid nodes; Sampling module for l-based ∞ A norm tomographic sampling algorithm is used to sample and process the operation results of performing linear operations on the first quantum operator and the second quantum operator using a specified quantum linear system solver to obtain node voltage corrections of the multiple power grid nodes; The adjustment module is used to adjust the node voltage values ​​of the multiple power grid nodes according to the node voltage correction amounts of the multiple power grid nodes.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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