Active distribution network distributed operation optimization method, device, equipment, medium and product based on privacy protection

Abstract

The present application relates to a method, device, equipment, medium and product for distributed operation optimization of an active distribution network based on privacy protection, and relates to the field of power grid technology. The method includes: in response to an operation optimization instruction of a target active distribution network, obtaining interaction variables between a first power grid cluster and a second power grid cluster that have an adjacent relationship; performing operation optimization analysis based on the interaction variables and basic operation data of the target active distribution network to obtain variable values ​​of the interaction variables and operation optimization data; homomorphically encrypting the variable values ​​through the first power grid cluster and sending the encrypted results to the second power grid cluster; performing error analysis on the interaction variables based on the decrypted results obtained by homomorphically decrypting the encrypted results by the second power grid cluster to obtain variable errors; and operating the target active distribution network based on the operation optimization data when the variable error is less than an error threshold. The use of this method can ensure the data security of the active distribution network.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, equipment, medium and product for optimizing distributed operation of an active distribution network based on privacy protection. Background Art

[0002] Active distribution networks (ADGs) are those that integrate a large number of distributed power sources and allow for bidirectional power flow. With the rapid development of power grid technology, optimizing the operation of ADGs is a critical issue that needs to be addressed.

[0003] Traditional distribution network operation optimization usually adopts a centralized collection method, that is, centrally integrating all data of the active distribution network to achieve operation optimization, but this method cannot guarantee the privacy and security of the data. Summary of the Invention

[0004] Based on this, it is necessary to provide a privacy-protected active distribution network distributed operation optimization method, device, computer equipment, computer-readable storage medium and computer program product that can ensure the data security of the active distribution network in response to the above technical problems.

[0005] In a first aspect, the present application provides a distributed operation optimization method for an active distribution network based on privacy protection, comprising: in response to an operation optimization instruction for a target active distribution network, determining a first grid cluster in the target active distribution network and a second grid cluster that is adjacent to the first grid cluster, and obtaining interaction variables between the first grid cluster and the second grid cluster; performing operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; performing homomorphic encryption on the variable values ​​through the first grid cluster to obtain an encrypted result of the variable value, and sending the encrypted result to the second grid cluster; performing homomorphic decryption on the encrypted result based on the second grid cluster, performing error analysis on the interaction variables to obtain a variable error of the interaction variables; when the variable error is less than an error threshold, operating the target active distribution network according to the operation optimization data.

[0006] In one embodiment, an operation optimization analysis is performed on the target active distribution network based on the interaction variables and the basic operation data of the target active distribution network to obtain the operation optimization result of the target active distribution network, including: constructing a variable position matrix based on the attribute data of the interaction variables; constructing a constraint matrix based on the basic operation data of the target active distribution network; and performing an operation optimization analysis on the target active distribution network according to the variable position matrix and the constraint matrix to obtain the operation optimization result of the target active distribution network.

[0007] In one embodiment, the constraint matrix includes an equality constraint matrix and an inequality constraint matrix; based on the variable position matrix and the constraint matrix, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: constructing an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function; based on the objective function and the inequality constraint matrix, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0008] In one embodiment, the method further includes: obtaining internal variables of the first power grid cluster; internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; based on the interactive variables and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0009] In one embodiment, homomorphically encrypting a variable value to obtain an encrypted result of the variable value includes: performing data format transformation on the variable value to obtain a transformed value of the variable value; and homomorphically encrypting the transformed value based on an encryption function to obtain an encrypted result of the variable value.

[0010] In one embodiment, homomorphic decryption is performed on the encryption result based on the second power grid cluster, and the decryption result obtained is used to perform error analysis on the interaction variable to obtain the variable error of the interaction variable, including: performing vector analysis on the decryption result obtained by homomorphic decryption of the encryption result by the second power grid cluster to obtain a target vector of the decryption result; and taking the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0011] In a second aspect, the present application also provides a distributed operation optimization device for an active distribution network based on privacy protection, including: a variable acquisition module, which is used to respond to an operation optimization instruction for a target active distribution network, determine a first power grid cluster in the target active distribution network, and a second power grid cluster that is adjacent to the first power grid cluster, and obtain the interaction variables between the first power grid cluster and the second power grid cluster; an optimization analysis module, which is used to perform operation optimization analysis on the target active distribution network based on the interaction variables and the basic operation data of the target active distribution network, and obtain the operation optimization results of the target active distribution network; the operation optimization results include the variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; a homomorphic encryption module, which is used to perform homomorphic encryption on the variable values ​​through the first power grid cluster, obtain the encrypted results of the variable values, and send the encrypted results to the second power grid cluster; an error analysis module, which is used to perform homomorphic decryption on the encrypted results based on the second power grid cluster, and perform error analysis on the interaction variables obtained by the decryption results to obtain the variable errors of the interaction variables; and an operation module, which is used to operate the target active distribution network according to the operation optimization data when the variable error is less than the error threshold.

[0012] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program: in response to an operation optimization instruction for a target active distribution network, determining a first power grid cluster in the target active distribution network and a second power grid cluster that is adjacent to the first power grid cluster, and obtaining interaction variables between the first power grid cluster and the second power grid cluster; performing operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; performing homomorphic encryption on the variable values ​​through the first power grid cluster to obtain an encrypted result of the variable value, and sending the encrypted result to the second power grid cluster; performing homomorphic decryption on the encrypted result based on the second power grid cluster, performing error analysis on the interaction variables to obtain a variable error of the interaction variables; when the variable error is less than an error threshold, operating the target active distribution network according to the operation optimization data.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when executed by a processor: in response to an operation optimization instruction for a target active distribution network, determining a first power grid cluster in the target active distribution network and a second power grid cluster that is adjacent to the first power grid cluster, and obtaining interaction variables between the first power grid cluster and the second power grid cluster; performing operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; performing homomorphic encryption on the variable values ​​through the first power grid cluster to obtain an encrypted result of the variable value, and sending the encrypted result to the second power grid cluster; performing homomorphic decryption on the encrypted result based on the second power grid cluster, and performing error analysis on the interaction variables to obtain a variable error of the interaction variables; when the variable error is less than an error threshold, operating the target active distribution network according to the operation optimization data.

[0014] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps: in response to an operation optimization instruction for a target active distribution network, determines a first power grid cluster in the target active distribution network, and a second power grid cluster that is adjacent to the first power grid cluster, and obtains interaction variables between the first power grid cluster and the second power grid cluster; based on the interaction variables and basic operation data of the target active distribution network, performs operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; homomorphically encrypts the variable values ​​through the first power grid cluster to obtain an encrypted result of the variable value, and sends the encrypted result to the second power grid cluster; homomorphically decrypts the encrypted result based on the second power grid cluster, obtains the decrypted result, performs error analysis on the interaction variables, and obtains the variable error of the interaction variables; when the variable error is less than the error threshold, operates the target active distribution network according to the operation optimization data.

[0015] The above-mentioned privacy-preserving distributed operation optimization method, apparatus, computer device, computer-readable storage medium, and computer program product for an active distribution network, upon receiving an operation optimization instruction for a target active distribution network, first determines a first grid cluster in the target active distribution network and a second grid cluster that is adjacent to the first grid cluster. It should be noted that an active distribution network often includes multiple grid clusters, and data is exchanged between adjacent grid clusters. This interactive data is one of the important data for optimizing the distributed operation of the active distribution network. Therefore, the present application needs to obtain the interaction variables between the first grid cluster and the second grid cluster, and then perform an operation optimization analysis on the target active distribution network based on the interaction variables and the basic operation data of the target active distribution network to obtain the operation optimization results of the target active distribution network. The operation optimization results include the variable values ​​of the interaction variables and the operation optimization data of the target active distribution network. Since the interaction variables are variables that interact between the first grid cluster and the second grid cluster, to ensure data security, the present application introduces homomorphic encryption technology, that is, the variable values ​​are homomorphically encrypted by the first grid cluster, an encrypted result of the variable value is obtained, and the encrypted result is sent to the second grid cluster. The second power grid cluster decrypts the data and obtains the corresponding decryption result. Based on the decryption result, an error analysis is performed on the interaction variable to obtain the variable error of the interaction variable. If the variable error is less than the error threshold, the target active distribution network is operated according to the operation optimization data. This completes the distributed operation optimization process of the entire active distribution network. In this way, the present application utilizes homomorphic encryption technology to ensure the security of data exchanged between power grid clusters, thereby ensuring the data security of the entire distributed operation optimization process of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 FIG1 is an application environment diagram of a distributed operation optimization method for an active power distribution network based on privacy protection in one embodiment;

[0018] Figure 2 1 is a flow chart of a method for optimizing distributed operation of an active power distribution network based on privacy protection in one embodiment;

[0019] Figure 3 Schematic diagram of the construction process of the variable position matrix and the constraint matrix in one embodiment;

[0020] Figure 4 Schematic diagram of the process of constructing an objective function in one embodiment;

[0021] Figure 5 A schematic diagram of a homomorphic encryption process in one embodiment;

[0022] Figure 6 Schematic diagram of the error analysis process of interactive variables in one embodiment;

[0023] Figure 7 is a schematic diagram of the topological structure and partition structure of an active power distribution network in a specific embodiment;

[0024] Figure 8 1 is a flow chart of a method for optimizing distributed operation of an active power distribution network based on privacy protection in a specific embodiment;

[0025] Figure 9 is a graph showing a change in the coefficient of fluctuation at various moments in a specific embodiment;

[0026] Figure 10 is a curve diagram of the network loss change rate in a specific embodiment;

[0027] Figure 11 is a graph of convergence error of interaction variables in a specific embodiment;

[0028] Figure 12 is a graph showing a convergence error of an equality constraint in a specific embodiment;

[0029] Figure 13 A convergence error curve diagram of the interaction variables of Scheme 4 in a specific embodiment;

[0030] Figure 14 A convergence error curve diagram of the equality constraint of solution 4 in a specific embodiment;

[0031] Figure 15 1 is a structural block diagram of an active power distribution network distributed operation optimization device based on privacy protection in one embodiment;

[0032] Figure 16 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0034] The distributed operation optimization method of active distribution network based on privacy protection provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on a cloud or other network server. In response to the operation optimization instruction for the target active distribution network initiated by the terminal 102, the server 104 first determines a first grid cluster in the target active distribution network and a second grid cluster that is adjacent to the first grid cluster, and obtains the interaction variables between the first grid cluster and the second grid cluster. Based on the interaction variables and the basic operation data of the target active distribution network, the server 104 performs an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network. The operation optimization result includes the variable values ​​of the interaction variables and the operation optimization data of the target active distribution network. The variable values ​​are homomorphically encrypted through the first grid cluster to obtain an encrypted result of the variable value, and the encrypted result is sent to the second grid cluster. The encrypted result is homomorphically decrypted based on the second grid cluster, and the decrypted result is used to perform error analysis on the interaction variables to obtain the variable error of the interaction variables. If the variable error is less than the error threshold, the target active distribution network is operated according to the operation optimization data. The terminal 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and IoT devices. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0035] In an exemplary embodiment, Figure 2 As shown in the figure, a distributed operation optimization method of active distribution network based on privacy protection is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0036] Step S202 : In response to an operation optimization instruction for a target active distribution network, a first power grid cluster and a second power grid cluster adjacent to the first power grid cluster in the target active distribution network are determined, and interaction variables between the first power grid cluster and the second power grid cluster are obtained.

[0037] An active distribution network, also known as an active distribution network, refers to a distribution network that connects to a large number of distributed power sources and enables bidirectional power flow. The main differences between an active distribution network and a conventional distribution network lie in the access to distributed power sources and the direction of power flow. Active distribution networks connect to a large number of distributed power sources, enabling bidirectional power flow, while conventional distribution networks rely primarily on power from a higher-level power grid, with unidirectional power flow. A target active distribution network can refer to an active distribution network that requires operational optimization, and an operational optimization instruction can refer to an instruction to optimize the operation of a target active distribution network. Understandably, to address data ownership issues, clustering has gradually become an emerging distribution network operation control structure, strengthening data barriers between distribution network clusters. Therefore, there may be multiple grid clusters in the target active distribution network. The first grid cluster can be any grid cluster in the target active distribution network, and can also refer to multiple grid clusters. The second grid cluster is a cluster that is adjacent to the first grid cluster. The adjacent relationship indicates that the first and second grid clusters are adjacent. In one example, the second grid cluster may include an upstream cluster and a downstream cluster of the first grid cluster. The upstream cluster can be understood as the cluster that sends active power to the first grid cluster, and the downstream cluster can be understood as the cluster that absorbs active power from the first grid cluster. Data interaction occurs between adjacent grid clusters, thus generating interaction variables. Interaction variables can refer to variables that interact between the first and second grid clusters, including but not limited to the voltage between the first grid cluster and the downstream cluster, the active power of the line connecting the first grid cluster and the upstream cluster, and the reactive power of the line connecting the first grid cluster and the upstream cluster. Active power refers to the actual amount of AC energy generated or consumed per unit time, and is the average power within a cycle. Reactive power refers to the fact that in an AC circuit with reactance, the electric or magnetic field absorbs energy from the power source during part of the cycle and releases energy during another part. Over the entire cycle, the average power is zero, but energy is constantly exchanged between the power source and the reactive elements (capacitors and inductors). The maximum value of this exchange rate is the reactive power.

[0038] For example, upon receiving an operation optimization instruction for a target active distribution network initiated by a terminal, the server may first select any one or more clusters in the target active distribution network as a first grid cluster, thereby determining a second grid cluster that is adjacent to the first grid cluster, including an upstream cluster and a downstream cluster of the first grid cluster. Furthermore, variables related to interaction between the first grid cluster and the upstream and downstream clusters are obtained, namely, the voltage between the first grid cluster and the downstream cluster, the active power of the line connecting the first grid cluster and the upstream cluster, and the reactive power of the line connecting the first grid cluster and the upstream cluster.

[0039] Step S204: Based on the interaction variables and the basic operation data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes the variable values ​​of the interaction variables and the operation optimization data of the target active distribution network.

[0040] Among them, basic operation data may refer to the basic data of the target active distribution network during operation, which may include but is not limited to the topological structure of the target active distribution network, the cluster partitioning method and the access location, type, capacity, etc. of the distributed power source. The cluster partitioning method may include but is not limited to range partitioning, client partitioning, load balancing partitioning, etc. Distributed power sources may include but are not limited to solar energy, natural gas, biomass energy, wind energy, etc. The operation optimization result may refer to the result obtained after the operation optimization analysis of the target active distribution network, which may include the variable value of the interactive variable and the operation optimization data of the target active distribution network. It is understandable that the variable value may refer to the solution value of the interactive variable. The operation optimization data includes but is not limited to distributed power output data, node voltage data and network loss data.

[0041] For example, after obtaining the interaction variables between the first and second power grid clusters, the server can simultaneously obtain basic data such as the target active distribution network's topology, cluster partitioning, and the access locations, types, and capacities of distributed power sources. Based on the interaction variables and this basic data, an operational optimization analysis model for the target active distribution network can be constructed. Solving the operational optimization analysis model can yield the values ​​of the interaction variables, as well as the target active distribution network's distributed power source output data, node voltage data, and network loss data.

[0042] In one example, running an optimization analysis model may include a variable position matrix, a constraint matrix, and an objective function.

[0043] In one example, in addition to interaction variables and basic operating data, the server can also combine the fluctuation coefficients of both distributed power generation (DG) and load output to construct an operational optimization analysis model. Load output can refer to the total power consumed by all electrical devices in the target active distribution network. The fluctuation coefficient can be used to characterize the time-varying power of the DG or load output. Furthermore, the server can pre-assign initial values ​​to the interaction variables to facilitate model solving.

[0044] In one example, an optimization solver can be used to solve and run an optimization analysis model. An optimization solver is a software tool or library used to solve mathematical optimization problems. The core task of an optimization solver is to find the optimal solution based on a user-provided mathematical model, that is, to find the minimum or maximum value of an objective function under given constraints.

[0045] Step S206: Perform homomorphic encryption on the variable value through the first power grid cluster to obtain an encryption result of the variable value, and send the encryption result to the second power grid cluster.

[0046] It should be noted that the variable data interacting between power grid clusters are all in plain text, and the power system field has high requirements for data privacy. Therefore, homomorphic encryption technology is introduced in this embodiment. Homomorphic encryption is a special encryption method that allows calculations to be performed directly on encrypted data without decrypting the data first. After the calculation is completed, the result is still in an encrypted state, and only the object with the decryption key can see the true value of the calculation result. Among them, the encrypted result can refer to the result obtained after homomorphic encryption of the variable value of the interactive variable.

[0047] For example, after the server calculates the variable value of the interaction variable, the first power grid cluster can synchronously obtain the variable value, perform homomorphic encryption on the variable value to ensure the security of the variable value, and send the encrypted result to the second power grid cluster.

[0048] Step S208 : performing homomorphic decryption on the encryption result based on the second power grid cluster, performing error analysis on the interaction variable based on the obtained decryption result, and obtaining the variable error of the interaction variable.

[0049] Based on the above explanation of homomorphic encryption, it can be understood that homomorphic decryption refers to the process of using a specific decryption method to restore the encrypted result to plaintext, i.e., the variable value, after homomorphic encryption of the encrypted data. The decrypted result can refer to the result obtained after homomorphic decryption of the encrypted result, and the decrypted result can be the variable value. In order to ensure the accuracy of the optimization operation, error analysis of the interactive variables is also required in this embodiment. The variable error can be understood as the iterative error of the interactive variables.

[0050] For example, after receiving the encrypted result for the interaction variable from the first grid cluster, the second grid cluster can homomorphically decrypt the encrypted result using a characteristic decryption method, thereby obtaining the decrypted result, which is the variable value. The server can then synchronously obtain the variable value and, based on it, calculate the iterative error of the interaction variable.

[0051] Step S210 : When the variable error is less than the error threshold, the target active power distribution network is operated according to the operation optimization data.

[0052] The error threshold may refer to a preset error standard value.

[0053] For example, after calculating the variable error of the interaction variable, the server may compare the variable error with a pre-set error threshold. If the variable error is less than the error threshold, it indicates that the interaction variable has reached a convergence state, and the server may directly output operation optimization data. Based on the operation optimization data, a corresponding operation optimization strategy may be formulated to control the operation of the target active distribution network. If the variable error is not less than the error threshold, it indicates that the interaction variable has not reached a convergence state, and the initialization value of the interaction variable may be updated to the variable value, and step S204 may be executed.

[0054] In this embodiment, upon receiving an operation optimization instruction for a target active distribution network, the server first determines a first power grid cluster within the target active distribution network and a second power grid cluster adjacent to the first power grid cluster. It should be noted that an active distribution network often includes multiple power grid clusters, and adjacent power grid clusters exchange data. This exchanged data is crucial for optimizing the distributed operation of the active distribution network. Therefore, this embodiment requires obtaining interaction variables between the first and second power grid clusters. Based on these interaction variables and the basic operating data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result for the target active distribution network. The operation optimization result includes the values ​​of the interaction variables and the operation optimization data for the target active distribution network. Since interaction variables are variables that interact between the first and second power grid clusters, to ensure data security, this application introduces homomorphic encryption technology. Specifically, the first power grid cluster homomorphically encrypts the variable values, obtaining an encrypted result of the variable values, and transmits the encrypted result to the second power grid cluster. The second power grid cluster then decrypts the variable values ​​to obtain a corresponding decrypted result. Based on the decryption results, an error analysis is performed on the interaction variables to obtain the variable error of the interaction variables. If the variable error is less than the error threshold, the target active distribution network is operated based on the operation optimization data. This completes the distributed operation optimization process of the entire active distribution network. In this way, this application utilizes homomorphic encryption technology to ensure the security of data exchanged between power grid clusters, thereby ensuring data security throughout the distributed operation optimization process of the entire active distribution network.

[0055] In an exemplary embodiment, Figure 3 As shown in the figure, based on the interaction variables and the basic operation data of the target active distribution network, the operation optimization analysis of the target active distribution network is performed to obtain the operation optimization results of the target active distribution network, including:

[0056] Step S302: construct a variable position matrix based on the attribute data of the interaction variables.

[0057] The attribute data may refer to attribute data related to the interaction variables, including but not limited to the number, type, and function of the interaction variables. In this embodiment, the variable position matrix is ​​primarily constructed based on the number of interaction variables. The variable position matrix may be a matrix used to describe the position information of the interaction variables.

[0058] In one example, the variable position matrix can be expressed as:

[0059]

[0060] in, Represents the element in row Y, column Z of the variable position matrix, where Y represents the number of interaction variables and Z represents the number of internal variables. When the variable in row Y, column Z is an internal variable, the value in the matrix is ​​-1; when the variable in row Y, column Z is an interaction variable, the value in the matrix is ​​1; and when the variable in row Y, column Z is neither an internal variable nor an interaction variable, the value in the matrix is ​​0.

[0061] It should be noted that internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster, and can also be understood as variables of the first power grid cluster itself. These variables may include, but are not limited to, the active power, reactive power, and current of all lines within the first power grid cluster, and the active injected power, reactive injected power, and node voltage of all nodes within the first power grid cluster. Therefore, in one embodiment, the steps further include: obtaining internal variables of the first power grid cluster, where internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster.

[0062] Based on this, in one embodiment, based on the interactive variables and the basic operating data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0063] Exemplarily, after the server obtains the interaction variables between the first power grid cluster and the second power grid cluster, it can simultaneously obtain the internal variables of the first power grid cluster, thereby constructing a variable position matrix based on the interaction variables and the internal variables. The variable position matrix can be used to subsequently construct the objective function.

[0064] Step S304: constructing a constraint matrix based on the basic operating data of the target active power distribution network.

[0065] Constraint matrices can include equality constraint matrices and inequality constraint matrices. An equality constraint matrix is ​​a matrix used to represent equality constraints in an optimization problem. Equality constraint matrices are typically used to restrict the solution to an optimization problem to a set that satisfies specific equality conditions. An inequality constraint matrix is ​​a matrix used to represent inequality constraints in a matrix optimization problem. Inequality constraint matrices are typically used to restrict the solution to an optimization problem to a set that satisfies specific inequality conditions.

[0066] In one example, the expression of the inequality constraint matrix can be:

[0067]

[0068] Where B represents the inequality constraint matrix of the active distribution network, n represents the cluster number of the first power grid cluster, represents the inequality constraint matrix of the first power grid cluster n.

[0069] In one example, the expression of the equality constraint matrix can be:

[0070]

[0071] Where D represents the equality constraint matrix of the active distribution network, n represents the cluster number of the first power grid cluster, represents the equality constraint matrix of the first power grid cluster n.

[0072] For example, the server can formulate corresponding inequality constraints and equality constraints for the first power grid cluster based on the basic operating data of the target active distribution network, thereby establishing corresponding inequality constraint matrices and equality constraint matrices based on the inequality constraints and equality constraints. It should be noted that specific inequality constraints and equality constraints can be flexibly formulated based on actual conditions, such as power balance constraints, voltage and current constraints, load constraints, etc., and this embodiment does not limit the specific constraints.

[0073] Step S306 : performing an operation optimization analysis on the target active distribution network according to the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

[0074] Exemplarily, the server can construct an objective function based on the equality constraint matrix, and thus generate an operation optimization analysis model based on the objective function and the inequality constraint matrix, and then perform operation optimization analysis on the target active distribution network to obtain the operation optimization results of the target active distribution network.

[0075] In one example, the expression for running the optimization analysis model can be:

[0076]

[0077]

[0078] Wherein, n represents the cluster number of the first power grid cluster, and N represents the set of all clusters. represents the objective function of the k+1th iteration of the first grid cluster n. B represents the inequality constraint matrix of the active distribution network, b represents the constant vector of inequality constraints, and x represents the vector of all variables of the active distribution network, including interaction variables and internal variables.

[0079] In this embodiment, a variable position matrix and a constraint matrix are constructed to form an operation optimization analysis model, thereby performing operation optimization analysis on the target active distribution network, thereby improving the accuracy and efficiency of the active distribution network operation optimization.

[0080] In an exemplary embodiment, Figure 4 As shown in Figure 1, based on the variable position matrix and the constraint matrix, the target active distribution network is subjected to operation optimization analysis to obtain the operation optimization results of the target active distribution network, including:

[0081] Step S402: constructing an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function.

[0082] Step S404: performing an operation optimization analysis on the target active distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0083] The Lagrangian function describes the dynamic state of a physical system under the influence of only conservative forces. In this embodiment, the Lagrangian function is introduced to transform the constrained optimization problem into an unconstrained one, thereby simplifying the solution process and improving the efficiency of the model solution. The objective function aims to minimize the variable error.

[0084] In one example, the objective function can be expressed as:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] Where, represents the objective function of the k+1th iteration of cluster n, represents all variables of the first power grid cluster n, namely interaction variables and internal variables, represents the variable solution value of cluster n after the kth iteration, that is, the variable value, and x[k] represents the variable solution value of all clusters after the kth iteration. represents the Lagrangian function of the first grid cluster n, and denotes the Lagrange multiplier and Lagrange dual multiplier of the equality constraint matrix of the first grid cluster n at the kth iteration, and denotes the Lagrange multiplier and Lagrange dual multiplier of the variable position matrix of the first grid cluster n at the kth iteration, Indicates the first power grid cluster The network loss function is represents the equality constraint matrix of the first grid cluster n, represents the column corresponding to the interaction variable of the first grid cluster n in the variable position matrix, represents the rows corresponding to all variables of the first grid cluster n in the variable position matrix, represents the iteration step size, and represents the over-relaxation coefficient.

[0092] Exemplarily, the server constructs an objective function based on the equality constraint matrix, the variable position matrix, and the Lagrangian function to minimize the variable error. Based on the objective function and the inequality constraint matrix, an operation optimization analysis of the target active distribution network is performed to obtain an operation optimization result for the target active distribution network.

[0093] In this embodiment, the objective function is constructed according to the equality constraint matrix, the variable position matrix and the Lagrangian function, which improves the efficiency of model solution and further improves the efficiency of active distribution network operation optimization.

[0094] In an exemplary embodiment, Figure 5 As shown, the variable value is homomorphically encrypted to obtain the encrypted result of the variable value, including:

[0095] Step S502: performing data format conversion on the variable value to obtain a converted value of the variable value.

[0096] The transformed value may refer to a value obtained after performing data format transformation on the variable value.

[0097] Exemplarily, before homomorphically encrypting the variable value, the first power grid cluster may first perform data format conversion on the variable value. In this embodiment, it is set to perform non-negative integer conversion on the variable value to obtain the corresponding conversion value.

[0098] In one example, the calculation formula for data format conversion may be:

[0099]

[0100] in, represents the variable value of the interaction variable of the first grid cluster n, represents the value of the interaction variable of the first power grid cluster n after the non-negative integer transformation, C() represents the non-negative integer transformation function, Represents the set of non-negative integers.

[0101] Step S504: homomorphically encrypt the transformed value based on the encryption function to obtain an encrypted result of the variable value.

[0102] Among them, the encryption function can be understood as a specific mathematical function for homomorphic encryption.

[0103] In one example, the computational expression for homomorphic encryption can be:

[0104]

[0105] in, Represents the encryption result, E() represents the encryption function, g and r represent two random numbers, represents the product of p and q, where p and q are two pre-selected large prime numbers and the greatest common divisor of p*q, p-1, and q-1 must be 1. mod represents the remainder function.

[0106] It should be noted that and r can be understood as public keys, and p and q can be understood as private keys.

[0107] In one embodiment, the expression for homomorphically decrypting the encryption result by the second power grid cluster may be:

[0108]

[0109]

[0110]

[0111]

[0112] Wherein, D() represents the decryption function, R() represents the intermediate function, and the intermediate function is used to execute the decryption function. In one example, the intermediate function can be the Evaluate algorithm. x represents the variable of the intermediate function. represents the least common multiple of p-1 and q-1, mod represents the remainder function, Represents the inverse non-negative integer conversion function.

[0113] In this embodiment, homomorphic encryption technology is used to ensure the security of data interacting between power grid clusters, thereby ensuring the data security of the entire active distribution network distributed operation optimization process.

[0114] In an exemplary embodiment, Figure 6 As shown, the encrypted result is homomorphically decrypted based on the second power grid cluster, and the decrypted result is obtained. Error analysis is performed on the interactive variable to obtain the variable error of the interactive variable, including:

[0115] Step S602 : performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result for the second power grid cluster to obtain a target vector of the decryption result.

[0116] Step S604: The product of the target vector and the variable position matrix is ​​used as the variable error of the interaction variable.

[0117] Vector analysis can be understood as the process of calculating the vector of the decryption result, and the target vector can refer to the vector of the decryption result.

[0118] In one example, the calculation expression of the variable error can be:

[0119]

[0120] in, represents the row corresponding to all variables of the first grid cluster n in the variable position matrix, x represents the target vector, Indicates the error threshold.

[0121] For example, after obtaining the decryption result obtained by homomorphically decrypting the encryption result by the second power grid cluster, the server can perform error analysis on the interaction variables. First, the vector of the encryption result, namely the target vector, can be calculated. Then, the target vector is multiplied by the variable position matrix to obtain the variable error of the interaction variable. This variable error is compared with the error threshold to determine whether the interaction variable has converged during the iteration process, thereby determining whether to output the operation optimization data.

[0122] In this embodiment, by obtaining the target vector of the decryption result and using the product of the target vector and the variable position matrix as the variable error, the accuracy of the error analysis of the interactive variables is improved, thereby improving the accuracy of the operation optimization analysis of the active distribution network.

[0123] In a specific embodiment, taking an 80-node active distribution network as an example, its topology and partition structure are as follows: Figure 7 As shown, the network is divided into four clusters, with black circles representing nodes within the clusters. This active distribution network is connected to 16 photovoltaic power sources. The specific connection locations and capacity information are shown in Table 1. Active capacity refers to the electrical power required for the normal operation of the photovoltaic power source, while reactive capacity involves the exchange and maintenance of energy but does not involve energy loss or gain. Apparent capacity refers to the total power that the photovoltaic power source can provide.

[0124] Table 1 Distributed power generation capacity and access location

[0125]

[0126] Figure 8 The specific implementation steps are shown, including:

[0127] S1: Obtain basic operating data, interactive variables, and internal variables of the active distribution network. Specifically, in response to an operational optimization instruction for the active distribution network, the server selects, from each cluster, a first power grid cluster and a second power grid cluster adjacent to the first power grid cluster, including an upstream cluster and a downstream cluster. The server then obtains interactive variables between the first and second power grid clusters, including the voltage between the first power grid cluster and the downstream cluster, the active power of the line connecting the first power grid cluster to the upstream cluster, and the reactive power of the line connecting the first power grid cluster to the upstream cluster. Furthermore, the server obtains internal variables of the first power grid cluster (i.e., variables that do not interact with the second power grid cluster), including the active power, reactive power, and current of all lines within the first power grid cluster, the active injected power, reactive injected power, and node voltage of all nodes within the first power grid cluster. Furthermore, the server obtains basic operating data of the active distribution network, including the topology of the active distribution network, the cluster partitioning method, and the connection location, type, and capacity of distributed power sources. Furthermore, the server may assign initial values ​​to the interactive variables.

[0128] S2: Construct an operation optimization model. The operation optimization model includes a variable position matrix, a constraint matrix, and an objective function. The constraint matrix includes an equality constraint matrix and an inequality constraint matrix. Specifically, based on the number of variables of the interaction variables and the internal variables, a variable position matrix is ​​constructed. Based on the basic operation data, an equality constraint matrix and an inequality constraint matrix are constructed. Based on the equality constraint matrix, the variable position matrix, and the Lagrangian function, an objective function is constructed. The server can also input the fluctuation coefficients of the distributed power supply and the load output into the operation optimization analysis model, such as Figure 9 As shown in Figure 2, the fluctuation coefficient changes of different types of distributed power sources at different times.

[0129] S3: Solve the operation optimization model to obtain the operation optimization results of the active distribution network. Specifically, the optimization solver is used to solve the objective function based on the inequality constraint matrix to obtain the variable values ​​of the interaction variables and the operation optimization data of the active distribution network.

[0130] S4: Homomorphically encrypt the variable value of the interactive variable. Specifically, the first power grid cluster performs a non-negative integer transformation on the variable value to obtain a corresponding transformed value. The transformed value is homomorphically encrypted based on the encryption function to obtain an encrypted result of the variable value, and the encrypted result is sent to the second power grid cluster;

[0131] S5: Homomorphically decrypt the encrypted result. Homomorphically decrypt the encrypted result through the second power grid cluster to obtain the decrypted result. Perform vector calculations on the decrypted result to obtain the target vector of the decrypted result. The product of the target vector and the variable position matrix is ​​used as the variable error of the interaction variable.

[0132] S6: Determine whether the variable error is less than the error threshold.

[0133] S7: If the variable error is less than the error threshold, output the optimization data. If the variable error is not less than the error threshold, update the initialization value of the interaction variable and return to step S3.

[0134] In order to fully verify the effect of this embodiment, the scheme of this embodiment is compared and analyzed with the other four schemes. Among them, Scheme 1: Set the reactive output of the distributed power source to 0 to obtain the operation result of the active distribution network. Scheme 2: Use the adjacent cluster coordination method to obtain the optimization result of the active distribution network. The adjacent cluster coordination algorithm refers to each cluster of the active distribution network solving the optimization problem within its own cluster. Scheme 3: Centralized optimization method, that is, all data of the active distribution network are centrally integrated to achieve operation optimization. Scheme 4: Use the adjacent cluster coordination method and tamper with the interactive variables to obtain the optimization result of the active distribution network. Define the mean of the time period network loss deviation and the time period network loss voltage deviation as:

[0135]

[0136]

[0137] in, Indicates the network loss deviation during the time period, represents the mean deviation of network loss in a time period, H represents the total number of time periods, It represents the centralized optimization network loss value in the h period, Indicates the optimized network loss value in time period h.

[0138] The comparison results of each scheme are shown in Table 2.

[0139] Table 2 Optimization results of each scheme

[0140]

[0141] Figure 10 The network loss change rate curve of this embodiment is shown. Figure 11 The convergence error curve of the interaction variables of this embodiment is shown. Figure 12 The figure shows the convergence error curve of the equality constraint of the solution of this embodiment. Figure 13 The convergence error curve of the interaction variable of scenario four is shown. Figure 14 The convergence error curve of the equality constraint of scheme 4 is shown.

[0142] It can be seen that the second solution has good convergence performance compared to the present embodiment. On the basis of being able to achieve good operation optimization effect, the present embodiment can also ensure the security of power grid data.

[0143] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0144] Based on the same inventive concept, embodiments of the present application also provide a privacy-preserving active distribution network distributed operation optimization device for implementing the aforementioned privacy-preserving active distribution network distributed operation optimization method. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the privacy-preserving active distribution network distributed operation optimization device provided below can be found in the limitations of the privacy-preserving active distribution network distributed operation optimization method described above, and will not be repeated here.

[0145] In an exemplary embodiment, Figure 15 As shown, a distributed operation optimization device for an active distribution network based on privacy protection is provided, including: a variable acquisition module 1502, for determining a first power grid cluster and a second power grid cluster adjacent to the first power grid cluster in the target active distribution network in response to an operation optimization instruction for the target active distribution network, and obtaining interaction variables between the first power grid cluster and the second power grid cluster; an optimization analysis module 1504, for performing operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network, to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and operation optimization data of the target active distribution network; a homomorphic encryption module 1506, for performing homomorphic encryption on the variable values ​​through the first power grid cluster, obtaining an encrypted result of the variable values, and sending the encrypted result to the second power grid cluster; an error analysis module 1508, for performing homomorphic decryption on the encrypted result based on the second power grid cluster, performing error analysis on the interaction variables using the obtained decryption result, and obtaining a variable error of the interaction variables; and an operation module 1510, for operating the target active distribution network according to the operation optimization data when the variable error is less than an error threshold.

[0146] In one embodiment, the optimization analysis module 1504 is further used to: construct a variable position matrix based on the attribute data of the interactive variables; construct a constraint matrix based on the basic operation data of the target active distribution network; and perform an operation optimization analysis on the target active distribution network based on the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

[0147] In one embodiment, the constraint matrix includes an equality constraint matrix and an inequality constraint matrix; the optimization analysis module 1504 is further used to: construct an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function; perform an operation optimization analysis on the target active distribution network based on the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0148] In one embodiment, the device is further used to: obtain internal variables of the first power grid cluster; internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; the optimization analysis module 1504 is further used to: based on the internal variables, interactive variables, and basic operating data of the target active distribution network, perform operation optimization analysis on the target active distribution network to obtain operation optimization results of the target active distribution network.

[0149] In one embodiment, the homomorphic encryption module 1506 is further used to: perform data format transformation on the variable value to obtain the transformed value of the variable value; and perform homomorphic encryption on the transformed value based on the encryption function to obtain the encryption result of the variable value.

[0150] In one embodiment, the error analysis module 1508 is further used to: perform vector analysis on a decryption result obtained by homomorphically decrypting the encryption result of the second power grid cluster to obtain a target vector of the decryption result; and use the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0151] Each module in the aforementioned privacy-preserving distributed operation optimization device for active power distribution networks can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0152] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 16 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store distributed operation optimization data of an active distribution network based on privacy protection. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distributed operation optimization method of an active distribution network based on privacy protection is implemented.

[0153] Those skilled in the art will understand that Figure 16The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0154] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: in response to an operation optimization instruction for a target active distribution network, a first power grid cluster and a second power grid cluster that is adjacent to the first power grid cluster in the target active distribution network are determined, and interaction variables between the first power grid cluster and the second power grid cluster are obtained; based on the interaction variables and basic operation data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; the variable values ​​are homomorphically encrypted through the first power grid cluster to obtain an encrypted result of the variable value, and the encrypted result is sent to the second power grid cluster; based on the encrypted result, the encryption result is homomorphically decrypted by the second power grid cluster, and an error analysis is performed on the interaction variables to obtain a variable error of the interaction variable; when the variable error is less than an error threshold, the target active distribution network is operated according to the operation optimization data.

[0155] In one embodiment, when the processor executes the computer program, it further implements the following steps: constructing a variable position matrix based on the attribute data of the interactive variables; constructing a constraint matrix based on the basic operating data of the target active distribution network; and performing an operation optimization analysis on the target active distribution network based on the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

[0156] In one embodiment, when the processor executes the computer program, it further implements the following steps: constructing an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function; performing an operation optimization analysis on the target active distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0157] In one embodiment, when the processor executes the computer program, it further implements the following steps: obtaining internal variables of the first power grid cluster; internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; based on the interactive variables and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0158] In one embodiment, when the processor executes the computer program, it further implements the following steps: performing data format conversion on the variable value to obtain a transformed value of the variable value; performing homomorphic encryption on the transformed value based on an encryption function to obtain an encrypted result of the variable value.

[0159] In one embodiment, when executing the computer program, the processor further implements the following steps: performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result for the second power grid cluster to obtain a target vector of the decryption result; and using the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0160] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the following steps: in response to an operation optimization instruction for a target active distribution network, determining a first power grid cluster and a second power grid cluster that is adjacent to the first power grid cluster in the target active distribution network, and obtaining interaction variables between the first power grid cluster and the second power grid cluster; performing operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; homomorphically encrypting the variable values ​​through the first power grid cluster to obtain an encrypted result of the variable values, and sending the encrypted result to the second power grid cluster; homomorphically decrypting the encrypted result based on the second power grid cluster, performing error analysis on the interaction variables based on the obtained decrypted result to obtain a variable error of the interaction variable; and operating the target active distribution network according to the operation optimization data when the variable error is less than an error threshold.

[0161] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: constructing a variable position matrix based on the attribute data of the interactive variables; constructing a constraint matrix based on the basic operating data of the target active distribution network; and performing an operation optimization analysis on the target active distribution network based on the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

[0162] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: constructing an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function; performing an operation optimization analysis on the target active distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0163] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining internal variables of the first power grid cluster; internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; based on the interactive variables and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0164] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: performing data format conversion on the variable value to obtain the transformed value of the variable value; performing homomorphic encryption on the transformed value based on the encryption function to obtain the encrypted result of the variable value.

[0165] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result for the second power grid cluster to obtain a target vector of the decryption result; and taking the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0166] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the following steps: in response to an operation optimization instruction for a target active distribution network, determining a first power grid cluster and a second power grid cluster that is adjacent to the first power grid cluster in the target active distribution network, and obtaining interaction variables between the first power grid cluster and the second power grid cluster; performing an operation optimization analysis on the target active distribution network based on the interaction variables and basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; homomorphically encrypting the variable values ​​through the first power grid cluster to obtain an encrypted result of the variable values, and sending the encrypted result to the second power grid cluster; homomorphically decrypting the encrypted result based on the second power grid cluster, performing error analysis on the interaction variables based on the obtained decrypted result to obtain a variable error of the interaction variable; and operating the target active distribution network according to the operation optimization data when the variable error is less than an error threshold.

[0167] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: constructing a variable position matrix based on the attribute data of the interactive variables; constructing a constraint matrix based on the basic operating data of the target active distribution network; and performing an operation optimization analysis on the target active distribution network based on the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

[0168] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: constructing an objective function based on the equality constraint matrix, the variable position matrix and the Lagrangian function; performing an operation optimization analysis on the target active distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0169] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining internal variables of the first power grid cluster; internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; based on the interactive variables and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network, including: based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0170] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: performing data format conversion on the variable value to obtain the transformed value of the variable value; performing homomorphic encryption on the transformed value based on the encryption function to obtain the encrypted result of the variable value.

[0171] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result for the second power grid cluster to obtain a target vector of the decryption result; and taking the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0172] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0173] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of a non-volatile memory and a volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0174] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0175] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A distributed operation optimization method for active distribution network based on privacy protection, characterized in that: The method comprises: In response to an operation optimization instruction for a target active power distribution network, determining a first power grid cluster and a second power grid cluster that is adjacent to the first power grid cluster in the target active power distribution network, and obtaining an interaction variable between the first power grid cluster and the second power grid cluster; Based on the interactive variables and the basic operating data of the target active distribution network, an operation optimization analysis is performed on the target active distribution network to obtain an operation optimization result of the target active distribution network; the operation optimization result includes the variable value of the interactive variable and the operation optimization data of the target active distribution network; performing data format conversion on the variable value through the first power grid cluster to obtain a converted value of the variable value; Performing homomorphic encryption on the transformed value based on an encryption function to obtain an encryption result of the variable value, and sending the encryption result to the second power grid cluster; Performing homomorphic decryption on the encrypted result based on the second power grid cluster, performing error analysis on the interaction variable to obtain a variable error of the interaction variable; When the variable error is less than an error threshold, operating the target active power distribution network according to the operation optimization data; Acquire internal variables of the first power grid cluster; the internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; The performing of an operation optimization analysis on the target active distribution network based on the interactive variables and the basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network includes: Based on the internal variables, the interactive variables, and the basic operating data of the target active distribution network, performing an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network; The performing of an operation optimization analysis on the target active distribution network based on the interactive variables and the basic operation data of the target active distribution network to obtain an operation optimization result of the target active distribution network includes: Constructing a variable position matrix based on the attribute data of the interaction variables; Constructing a constraint matrix based on basic operating data of the target active distribution network; An operation optimization analysis is performed on the target active distribution network according to the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

2. The method according to claim 1, characterized in that The constraint matrix includes an equality constraint matrix and an inequality constraint matrix; performing an operation optimization analysis on the target active distribution network based on the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network includes: constructing an objective function based on the equality constraint matrix, the variable position matrix, and the Lagrangian function; An operation optimization analysis is performed on the target active power distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active power distribution network.

3. The method according to claim 1, characterized in that The step of performing homomorphic decryption on the encryption result based on the second power grid cluster, obtaining a decrypted result, and performing error analysis on the interaction variable to obtain a variable error of the interaction variable includes: Performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result by the second power grid cluster to obtain a target vector of the decryption result; The product between the target vector and the variable position matrix is ​​used as the variable error of the interaction variable.

4. The method according to claim 1, wherein The second power grid cluster includes an upstream cluster and a downstream cluster of the first power grid cluster.

5. A distributed operation optimization device for active distribution network based on privacy protection, characterized in that: The device comprises: a variable acquisition module, configured to, in response to an operation optimization instruction for a target active power distribution network, determine a first power grid cluster in the target active power distribution network and a second power grid cluster that is adjacent to the first power grid cluster, and acquire interaction variables between the first power grid cluster and the second power grid cluster; an optimization analysis module, configured to perform an operation optimization analysis on the target active distribution network based on the interaction variables and the basic operation data of the target active distribution network, and obtain an operation optimization result of the target active distribution network; the operation optimization result includes the variable values ​​of the interaction variables and the operation optimization data of the target active distribution network; a homomorphic encryption module, configured to perform data format conversion on the variable value through the first power grid cluster to obtain a transformed value of the variable value; perform homomorphic encryption on the transformed value based on an encryption function to obtain an encrypted result of the variable value; and send the encrypted result to the second power grid cluster; an error analysis module, configured to perform homomorphic decryption on the encryption result based on the second power grid cluster, and perform error analysis on the interaction variable using the obtained decryption result to obtain a variable error of the interaction variable; an operation module, configured to operate the target active power distribution network according to the operation optimization data when the variable error is less than an error threshold; The device is further configured to: obtain internal variables of the first power grid cluster; the internal variables refer to variables that the first power grid cluster does not interact with the second power grid cluster; The optimization analysis module is further configured to: perform an operation optimization analysis on the target active distribution network based on the internal variables, the interactive variables, and the basic operation data of the target active distribution network, to obtain an operation optimization result of the target active distribution network; The optimization analysis module is further configured to: construct a variable position matrix based on the attribute data of the interaction variables; construct a constraint matrix based on the basic operation data of the target active distribution network; and perform an operation optimization analysis on the target active distribution network according to the variable position matrix and the constraint matrix to obtain an operation optimization result of the target active distribution network.

6. The device according to claim 5, characterized in that The constraint matrix includes an equality constraint matrix and an inequality constraint matrix; the optimization analysis module is further used for: constructing an objective function based on the equality constraint matrix, the variable position matrix, and the Lagrangian function; An operation optimization analysis is performed on the target active power distribution network according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active power distribution network.

7. The device according to claim 5, characterized in that The error analysis module is also used to: Performing vector analysis on a decryption result obtained by homomorphically decrypting the encryption result by the second power grid cluster to obtain a target vector of the decryption result; The product between the target vector and the variable position matrix is ​​used as the variable error of the interaction variable.

8. 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 steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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