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

Abstract

The invention relates to an active power distribution network distributed operation optimization method and device based on privacy protection, equipment, a medium and a product, and relates to the technical field of power grids. The method comprises the following steps: in response to an operation optimization instruction of a target active power distribution network, obtaining an interaction variable between a first power grid cluster and a second power grid cluster which have an adjacent relationship; performing operation optimization analysis based on the interaction variables and basic operation data of the target active power distribution network to obtain variable values of the interaction variables and operation optimization data; homomorphic encryption is carried out on the variable value through the first power grid cluster, and an obtained encryption result is sent to a second power grid cluster; based on a decryption result obtained by performing homomorphic decryption on the encryption result by the second power grid cluster, performing error analysis on the interaction variable to obtain a variable error; and operating the target active power distribution network according to the operation optimization data under the condition that the variable error is smaller than the error threshold value. By adopting the method, the data security of the active power distribution network can be ensured.

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 network refers to a distribution network that has a large number of distributed power sources and bidirectional power flow. With the rapid development of power grid technology, how to optimize the operation of active distribution network is an important issue that needs to be solved urgently.

[0003] Traditional distribution network operation optimization usually adopts a centralized collection method, that is, all data of the active distribution network is centrally integrated 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 distributed operation optimization method, device, computer equipment, computer-readable storage medium and computer program product for an active distribution network based on privacy protection, which can ensure the data security of the active distribution network in response to the above-mentioned 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 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; based on the interaction variables and basic operation data of the target active distribution network, performing 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 encrypting the variable values ​​through the first power grid cluster to obtain an encryption result of the variable values, and sending the encryption result to the second power grid cluster; homomorphically decrypting the encryption result based on the second power grid cluster, obtaining the decrypted result, 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 of the embodiments, 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 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 of the embodiments, 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 of the embodiments, the method also includes: obtaining 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; 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 the 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 the operation optimization result of the target active distribution network.

[0009] In one of the embodiments, a variable value is homomorphically encrypted to obtain an encrypted result of the variable value, including: performing a 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 of the embodiments, the encryption result is homomorphically decrypted based on the second power grid cluster, and the decrypted result obtained is used to perform error analysis on the interactive variable to obtain the variable error of the interactive variable, including: performing vector analysis on the decrypted result obtained by homomorphically decrypting the encryption result by the second power grid cluster to obtain a target vector of the decrypted result; and taking the product of the target vector and the variable position matrix as the variable error of the interactive 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 interactive 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 interactive 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 value of the interactive variable 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 value through the first power grid cluster, obtain an encrypted result of the variable value, and send the encrypted result to the second power grid cluster; an error analysis module, which is used to perform homomorphic decryption on the encrypted result based on the second power grid cluster, obtain the decrypted result, perform error analysis on the interactive variable, and obtain the variable error of the interactive variable; 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, the memory storing a computer program, and the processor implementing 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 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 encryption result of the variable values, and sending the encryption result to the second power grid cluster; performing homomorphic decryption on the encryption result based on the second power grid cluster, obtaining a decryption result, 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 the computer program is executed by a processor: in response 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 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, perform 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 operation optimization data of the target active distribution network; homomorphically encrypt the variable values ​​through the first power grid cluster to obtain an encryption result of the variable values, and send the encryption result to the second power grid cluster; based on the second power grid cluster, perform homomorphic decryption on the encryption result, obtain the decryption result, perform error analysis on the interaction variables, and obtain the variable error of the interaction variables; when the variable error is less than the error threshold, operate 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, including a computer program, which implements the following steps when executed by a processor: in response 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 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, perform 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 encrypt the variable values ​​through the first power grid cluster to obtain an encryption result of the variable values, and send the encryption result to the second power grid cluster; based on the second power grid cluster, perform homomorphic decryption on the encryption result, obtain the decryption result, perform error analysis on the interaction variables, and obtain the variable error of the interaction variables; when the variable error is less than the error threshold, operate the target active distribution network according to the operation optimization data.

[0015] The above-mentioned distributed operation optimization method, device, computer equipment, computer-readable storage medium and computer program product of the active distribution network based on privacy protection, when receiving the operation optimization instruction for the target active distribution network, first determine the first power grid cluster in the target active distribution network, and the second power grid cluster that is adjacent to the first power grid cluster. It should be noted that the active distribution network often contains multiple power grid clusters, and data will be interacted between adjacent power grid clusters. This part of the 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 interactive variables between the first power grid cluster and the second power grid cluster, so as to perform 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, and obtain the 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. Since the interactive variable is a variable that interacts between the first power grid cluster and the second power grid cluster, in order to ensure the security of the data, the present application introduces homomorphic encryption technology, that is, the variable value is homomorphically encrypted through the first power grid cluster to obtain the encrypted result of the variable value, and the encrypted result is sent to the second power grid cluster. The second power grid cluster decrypts it and obtains the corresponding decryption result. Based on the decryption result, the interaction variable is analyzed for error to obtain the variable error of the interaction variable. When the variable error is less than the error threshold, the target active distribution network is operated according to the operation optimization data. The distributed operation optimization process of the entire active distribution network is thus completed. In this way, the present application uses homomorphic encryption technology to ensure the security of the data interacting between the power grid clusters, thereby ensuring the data security of the distributed operation optimization process of the entire 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 drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. 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 creative work.

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

[0018] FIG2 is a schematic diagram of a process flow of a distributed operation optimization method for an active power distribution network based on privacy protection in one embodiment;

[0019] FIG3 is a schematic diagram of a construction process of a variable position matrix and a constraint matrix in one embodiment;

[0020] FIG4 is a schematic diagram of a process for constructing an objective function in one embodiment;

[0021] FIG5 is a schematic diagram of a homomorphic encryption process in one embodiment;

[0022] FIG6 is a schematic diagram of an error analysis process of an interactive variable in one embodiment;

[0023] FIG7 is a schematic diagram of the topological structure and partition structure of an active power distribution network in a specific embodiment;

[0024] FIG8 is a schematic flow chart of a method for optimizing distributed operation of an active power distribution network based on privacy protection in a specific embodiment;

[0025] FIG9 is a curve diagram of fluctuation coefficient variation at various moments in a specific embodiment;

[0026] FIG10 is a graph showing a network loss change rate in a specific embodiment;

[0027] FIG11 is a graph showing convergence error of interaction variables in a specific embodiment;

[0028] FIG12 is a graph showing convergence error of an equality constraint in a specific embodiment;

[0029] FIG13 is a graph showing a convergence error curve of an interaction variable of Scheme 4 in a specific embodiment;

[0030] FIG14 is a graph showing a convergence error curve of an equality constraint of solution 4 in a specific embodiment;

[0031] FIG15 is a structural block diagram of an active power distribution network distributed operation optimization device based on privacy protection in one embodiment;

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

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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 in the application environment shown in FIG1. ​​The terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. In response to the operation optimization instruction for the target active distribution network initiated by the terminal 102, the server 104 first determines the first power grid cluster in the target active distribution network and the second power grid cluster that is adjacent to the first power grid cluster, and obtains the interaction variables between the first power grid cluster and the second power grid cluster; based on the interaction variables and the basic operation data of the target active distribution network, the target active distribution network is subjected to operation optimization analysis to obtain the operation optimization result of the target active distribution network; the operation optimization result includes the variable value of the interaction variable and the operation optimization data of the target active distribution network; the variable value is homomorphically encrypted through the first power grid cluster to obtain the encryption result of the variable value, and the encryption result is sent to the second power grid cluster; based on the encryption result of the second power grid cluster, the encryption result is homomorphically decrypted, and the interaction variable is analyzed for the decrypted result to obtain the variable error of the interaction variable; when the variable error is less than the error threshold, the target active distribution network is operated according to the operation optimization data. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and Internet of Things devices. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0035] In an exemplary embodiment, as shown in FIG2 , a method for optimizing distributed operation of an active power distribution network based on privacy protection is provided, which is described by taking the method applied to the server 104 in FIG1 as an example, and includes the following steps:

[0036] Step S202, 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 adjacent to the first power grid cluster, and acquiring interaction variables between the first power grid cluster and the second power grid cluster.

[0037] Among them, the active distribution network refers to a distribution network that is connected to a large number of distributed power sources and has bidirectional power flow, also known as an active distribution network. The main difference between the active distribution network and the ordinary distribution network lies in the access of distributed power sources and the direction of power flow. The active distribution network is connected to a large number of distributed power sources to achieve bidirectional power flow, while the ordinary distribution network mainly relies on the power supply of the upper power grid and the power flows unidirectionally. The target active distribution network may refer to an active distribution network that needs to be optimized for operation, and the operation optimization instruction may refer to an instruction to optimize the operation of the target active distribution network. It is understandable that in order to solve the problem of data attribution, clustering has gradually become an emerging distribution network operation control structure, which has strengthened the data barriers between distribution network clusters. Therefore, there will be multiple power grid clusters in the target active distribution network. The first power grid cluster can be any power grid cluster in the target active distribution network, and of course it can also refer to multiple power grid clusters. The second power grid cluster is a cluster that is adjacent to the first power grid cluster. The adjacent relationship represents that the first power grid cluster and the second power grid cluster are adjacent. In one example, the second power grid cluster can include the upstream cluster and the downstream cluster of the first power grid cluster. The upstream cluster can be understood as a cluster that sends active power to the first power grid cluster, and the downstream cluster can be understood as a cluster that absorbs active power from the first power grid cluster. Data interaction occurs between adjacent power grid clusters, so interactive variables are generated. Interactive variables can refer to variables that interact between the first power grid cluster and the second power grid cluster, including but not limited to the voltage between the first power grid cluster and the downstream cluster, the active power of the line connecting the first power grid cluster and the upstream cluster, and the reactive power of the line connecting the first power grid cluster and the upstream cluster. Active power refers to the AC energy actually generated or consumed per unit time, which is the average power in a cycle. Reactive power refers to the situation in which the electric field or magnetic field in an AC circuit with reactance absorbs energy from the power supply during a part of a cycle and releases energy during another part of the cycle. The average power is zero in the entire cycle, but energy is constantly exchanged between the power supply and the reactance element (capacitor, inductor). The maximum value of this exchange rate is the reactive power.

[0038] Exemplarily, when the server receives the operation optimization instruction for the target active distribution network initiated by the terminal, it can first select any one or more clusters in the target active distribution network as the first power grid cluster, so as to determine the second power grid cluster that is adjacent to the first power grid cluster, including the upstream cluster and the downstream cluster of the first power grid cluster. Then, the variables that interact between the first power grid cluster and the upstream cluster and the downstream cluster are obtained, that is, the voltage between the first power grid cluster and the downstream cluster, the active power of the line connecting the first power grid cluster and the upstream cluster, and the reactive power of the line connecting the first power grid cluster and the upstream cluster.

[0039] Step S204, 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.

[0040] Among them, the 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] Exemplarily, after obtaining the interaction variables between the first power grid cluster and the second power grid cluster, the server can simultaneously obtain the topological structure of the target active distribution network, the cluster partitioning method, and the basic data such as the access location, type, and capacity of the distributed power source. In order to construct an operation optimization analysis model for the target active distribution network based on the interaction variables and these basic data, and then solve the operation optimization analysis model to obtain the variable values ​​of the interaction variables, as well as the distributed power source output data, node voltage data, and network loss data of the target active distribution network.

[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 the interaction variables and basic operation data, the server can also combine the fluctuation coefficients of the distributed power source and the load output to build an operation optimization analysis model. The load output can refer to the sum of the power consumed by all electrical devices in the target active distribution network. The fluctuation coefficient can be used to characterize the change of the power of the distributed power source or the load output over time. In addition, the server can also assign an initialization value to the interaction variable in advance to solve the model.

[0044] In one example, an optimization solver may be used to solve and run an optimization analysis model. An optimization solver is a software tool or library for solving mathematical optimization problems. The core task of an optimization solver is to find the optimal solution based on a mathematical model provided by a user, 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 for interaction 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 interactive variable, the first power grid cluster can synchronously obtain the variable value, and the first power grid cluster will homomorphically encrypt 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 interactive variables based on the decrypted result obtained, and obtaining the variable error of the interactive variables.

[0049] Among them, 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 encryption result to plain text, that is, the variable value, after the encrypted data is homomorphically encrypted. The decryption result may refer to the result obtained after homomorphic decryption of the encryption result, and the decryption result may be the variable value. In order to ensure the accuracy of the operation optimization, the interactive variables also need to be analyzed for errors in this embodiment, and the variable error can be understood as the iteration error of the interactive variables.

[0050] Exemplarily, after receiving the encryption result for the interactive variable sent by the first power grid cluster, the second power grid cluster can use the characteristic decryption method to homomorphically decrypt the encryption result, thereby obtaining the decrypted result, that is, the variable value. The server can synchronously obtain the variable value and calculate the iteration error of the interactive variable based on the variable value.

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

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

[0053] Exemplarily, after calculating the variable error of the interactive variable, the server may compare the variable error with a preset error threshold. If the variable error is less than the error threshold, it means that the interactive variable is in a convergence state at this time, and the operation optimization data can be directly output, and based on the operation optimization data, a corresponding operation optimization strategy is formulated to control the operation of the target active distribution network. If the variable error is not less than the error threshold, it means that the interactive variable is not in a convergence state at this time, and the initialization value of the interactive variable can be updated to the variable value, and step S204 is executed.

[0054] In this embodiment, when the server receives the operation optimization instruction for the target active distribution network, it first determines the first power grid cluster in the target active distribution network and the second power grid cluster that is adjacent to the first power grid cluster. It should be noted that the active distribution network often contains multiple power grid clusters, and data will be exchanged between adjacent power grid clusters. This part of the interactive data is one of the important data for optimizing the distributed operation of the active distribution network. Therefore, this embodiment needs to obtain the interactive variables between the first power grid cluster and the second power grid cluster, so as to perform 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, and obtain the 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. Since the interactive variable is a variable that interacts between the first power grid cluster and the second power grid cluster, in order to ensure the security of the data, the present application introduces homomorphic encryption technology, that is, the variable value is homomorphically encrypted by the first power grid cluster to obtain the encrypted result of the variable value, and the encrypted result is sent to the second power grid cluster. It is decrypted by the second power grid cluster to obtain the corresponding decryption result. Based on the decryption results, the interactive variables are analyzed for errors to obtain the variable errors of the interactive variables. When 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 uses homomorphic encryption technology to ensure the security of the data interacting between the power grid clusters, thereby ensuring the data security of the distributed operation optimization process of the entire active distribution network.

[0055] In an exemplary embodiment, as shown in FIG3 , 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, 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 the 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 ​​mainly constructed based on the number of interaction variables. The variable position matrix may be a matrix for describing 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 the Yth row and Zth column 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 the Yth row and Zth column is an internal variable, the value in the matrix is ​​-1; when the variable in the Yth row and Zth column is an interaction variable, the value in the matrix is ​​1; when the variable in the Yth row and Zth column is neither an internal variable nor an interaction variable, the value in the matrix is ​​0.

[0061] It should be noted that the internal variable refers to a variable that the first power grid cluster does not interact with the second power grid cluster, and can also be understood as a variable of the first power grid cluster itself. It may include but is not limited to the active power, reactive power and current of all lines within the first power grid cluster, the active injection power, reactive injection power and node voltage of all nodes within the first power grid cluster. Therefore, in one embodiment, the step further includes: obtaining the internal variable of the first power grid cluster, and the internal variable refers to a variable 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, and the variable position matrix can be used to subsequently construct the objective function.

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

[0065] Among them, the constraint matrix can include an equality constraint matrix and an inequality constraint matrix. An equality constraint matrix refers to a matrix used to represent equality constraints in an optimization problem. An equality constraint matrix is ​​usually used to restrict the solution of an optimization problem to a set that satisfies specific equality conditions. An inequality constraint matrix refers to a matrix used to represent inequality constraints in a matrix optimization problem. An inequality constraint matrix is ​​usually used to restrict the solution of an optimization problem to a set that satisfies specific inequality conditions.

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

[0067]

[0068] Wherein, 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 may 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] Exemplarily, the server can formulate corresponding inequality constraints and equality constraints for the first power grid cluster according to the basic operation data of the target active distribution network, thereby establishing corresponding inequality constraint matrices and equality constraint matrices according to the inequality constraints and equality constraints. It should be noted that specific inequality constraints and equality constraints can be flexibly formulated according to actual conditions, such as power balance constraints, voltage and current constraints, load constraints, etc., and the specific constraints are not limited in this embodiment.

[0073] Step S306: performing 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, thereby generating an operation optimization analysis model based on the objective function and the inequality constraint matrix, and then perform an operation optimization analysis on the target active distribution network to obtain an operation optimization result of the target active distribution network.

[0075] In one example, the expression for running the optimization analysis model may 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 power grid cluster n. B represents the inequality constraint matrix of the active distribution network, b represents the constant vector of the 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 operation optimization of the active distribution network.

[0080] In an exemplary embodiment, as shown in FIG4 , 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, 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 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] Among them, the Lagrangian function is a function that describes the dynamic state of the entire physical system under the action of only conservative forces in the mechanical system. In this embodiment, by introducing the Lagrangian function, the optimization problem with constraints is transformed into an unconstrained problem, thereby simplifying the solution process. This improves the efficiency of model solution. The objective function is a function that aims to minimize the variable error.

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

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] In the formula, 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 represents the Lagrange multiplier and Lagrange dual multiplier of the equality constraint matrix of the first grid cluster n at the kth iteration, and represents the Lagrange multiplier and Lagrange dual multiplier of the variable position matrix of the first grid cluster n at the kth iteration, Represents the first power grid cluster The network loss function is represents the equality constraint matrix of the first power grid cluster n, represents the column corresponding to the interaction variable of the first power 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 length, and represents the over-relaxation coefficient.

[0092] Exemplarily, the server constructs an objective function with the goal of minimizing variable errors based on the equality constraint matrix, the variable position matrix and the Lagrangian function. Thus, according to the objective function and the inequality constraint matrix, the operation optimization analysis of the target active distribution network is performed to obtain the operation optimization result of 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 solving and further improves the efficiency of active distribution network operation optimization.

[0094] In an exemplary embodiment, as shown in FIG5 , homomorphic encryption is performed on the variable value to obtain an encryption 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 values, the first power grid cluster may first perform data format transformation on the variable values. In this embodiment, it is set to perform non-negative integer transformation on the variable values ​​to obtain corresponding transformed values.

[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 power 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 conversion 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 of homomorphic encryption can be:

[0104]

[0105] in, represents the encryption result, E() represents the encryption function, g and r represent two random numbers, It represents the product of p and q, where p and q are two pre-selected large prime numbers and p and q need to satisfy the following condition: the greatest common factor of p*q, p-1 and q-1 is 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 a decryption function, R() represents an intermediate function, and the intermediate function is used to execute the decryption function. In one example, the intermediate function may be an Evaluate algorithm. x represents a 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, as shown in FIG6 , the encryption result is homomorphically decrypted based on the second power grid cluster, and the decrypted result is obtained, and 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 of the second power grid cluster to obtain a target vector of the decryption result.

[0116] Step S604: taking the product of the target vector and the variable position matrix as the variable error of the interaction variable.

[0117] The 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 power grid cluster n in the variable position matrix, x represents the target vector, Represents the error threshold.

[0121] Exemplarily, 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 interactive variable. First, the vector of the encryption result, i.e., the target vector, can be calculated, and then the target vector is multiplied by the variable position matrix to obtain the variable error of the interactive variable. The variable error is compared with the error threshold to determine whether the interactive variable converges 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, the product between the target vector and the variable position matrix is ​​used as the variable error, which improves the accuracy of the error analysis of the interactive variables, thereby improving the accuracy of the operation optimization analysis of the active distribution network.

[0123] In a specific embodiment, taking an active distribution network with 80 nodes as an example, its topology and partition structure are shown in Figure 7, which is divided into 4 clusters, and the black circles represent the nodes in the clusters. The active distribution network is connected to 16 groups of photovoltaic power sources. The specific access locations and capacity information are shown in Table 1, where the active capacity refers to the electric power required for the normal operation of the photovoltaic power source, the reactive capacity involves the exchange and maintenance of energy, but does not involve the loss or increase of energy, and the 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] FIG8 shows specific implementation steps, including:

[0127] S1: Obtain basic operation data, interactive variables and internal variables of the active distribution network. Specifically, the server responds to the operation optimization instruction for the above-mentioned active distribution network, and selects the first power grid cluster and the second power grid cluster that is adjacent to the first power grid cluster in each cluster, including the upstream cluster and the downstream cluster, so as to obtain the interactive variables between the first power grid cluster and the second power grid cluster, including the voltage connected between the first power grid cluster and the downstream cluster, the active power of the line connected to the first power grid cluster and the upstream cluster, and the reactive power of the line connected to the first power grid cluster and the upstream cluster. At the same time, the internal variables of the first power grid cluster, that is, the variables that the first power grid cluster does not interact with the second power grid cluster, are obtained, including the active power, reactive power and current of all lines in the first power grid cluster, the active injection power, reactive injection power and node voltage of all nodes in the first power grid cluster. And obtain the basic operation data of the active distribution network, including the topological structure of the active distribution network, the cluster partitioning method and the access location, type and capacity of the distributed power source. In addition, the server can also assign an initialization value to the interactive variable.

[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, a variable position matrix is ​​constructed based on the number of variables of the interaction variables and the internal variables. 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 both the distributed power source and the load output into the operation optimization analysis model, as shown in Figure 9, which shows the changes in the fluctuation coefficients of different types of distributed power sources at various times.

[0129] S3: Solve the operation optimization model to obtain the operation optimization result of the active distribution network. Specifically, use the optimization solver 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, through the first power grid cluster, perform a non-negative integer transformation on the variable value to obtain a corresponding transformation value. The transformation value is homomorphically encrypted based on the encryption function to obtain an encryption result of the variable value, and the encryption result is sent to the second power grid cluster;

[0131] S5: Perform homomorphic decryption on the encrypted result. Perform homomorphic decryption on the encrypted result through the second power grid cluster to obtain a decrypted result. Perform vector calculation on the decrypted result to obtain a 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: When the variable error is less than the error threshold, output the running optimization data. When 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, centrally integrating all data of the active distribution network 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 network loss value of centralized optimization in time period h, Represents the optimized network loss value in time period h.

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

[0139] Table 2 Operation optimization results of each scheme

[0140]

[0141] Figure 10 shows the network loss change rate curve of this embodiment. Figure 11 shows the convergence error curve of the interactive variables of the scheme of this embodiment. Figure 12 shows the convergence error curve of the equality constraint of the scheme of this embodiment. Figure 13 shows the convergence error curve of the interactive variables of scheme 4. Figure 14 shows the convergence error curve of the equality constraint of scheme 4.

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

[0143] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0144] Based on the same inventive concept, the embodiment of the present application also provides a privacy-protected active distribution network distributed operation optimization device for implementing the privacy-protected active distribution network distributed operation optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more privacy-protected active distribution network distributed operation optimization device embodiments provided below can refer to the limitations of the privacy-protected active distribution network distributed operation optimization method above, and will not be repeated here.

[0145] In an exemplary embodiment, as shown in FIG15 , a distributed operation optimization device for an active distribution network based on privacy protection is provided, including: a variable acquisition module 1502, 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 interactive variables between the first power grid cluster and the second power grid cluster; an optimization analysis module 1504, which is used to perform 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, and obtain the operation 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; the homomorphic encryption module 1506 is used to perform homomorphic encryption on the variable value through the first power grid cluster to obtain the encryption result of the variable value, and send the encryption result to the second power grid cluster; the error analysis module 1508 is used to perform homomorphic decryption on the encryption result based on the second power grid cluster, obtain the decrypted result, perform error analysis on the interactive variable, and obtain the variable error of the interactive variable; the operation module 1510 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.

[0146] In one of the embodiments, the optimization analysis module 1504 is also 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; perform 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.

[0147] In one of the embodiments, the constraint matrix includes an equality constraint matrix and an inequality constraint matrix; the optimization analysis module 1504 is also 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 according to the objective function and the inequality constraint matrix to obtain an operation optimization result of the target active distribution network.

[0148] In one of the embodiments, the device is also 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 also 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 the operation optimization results of the target active distribution network.

[0149] In one of the embodiments, the homomorphic encryption module 1506 is also used to: perform data format transformation on the variable value to obtain the transformed value of the variable value; 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 the 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 above-mentioned privacy-protection-based distributed operation optimization device for active distribution networks can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0152] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG16. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the 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 the 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 the structure shown in FIG. 16 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme 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 arrangement of components.

[0154] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, 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 an interaction variable 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 encryption result of the variable values, and sending the encryption result to the second power grid cluster; homomorphically decrypting the encryption result based on the second power grid cluster, obtaining a decryption result, performing an error analysis on the interaction variables to obtain a variable error of the interaction variables; and operating the target active distribution network according to the operation optimization data when the variable error is less than an error threshold.

[0155] In one embodiment, when the processor executes the computer program, the following steps are also implemented: constructing a variable position matrix based on the attribute data of the interactive variables; constructing a constraint matrix based on the basic operation data of the target active distribution network; performing 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.

[0156] In one embodiment, when the processor executes the computer program, the following steps are also 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.

[0157] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining 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; based on the interactive variables and the basic operating data of the target active distribution network, the target active distribution network is subjected to operation optimization analysis to obtain the 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, the target active distribution network is subjected to operation optimization analysis to obtain the operation optimization result of the target active distribution network.

[0158] In one embodiment, when the processor executes the computer program, the following steps are also implemented: performing data format transformation on the variable value to obtain a transformed value of the variable value; and 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 the processor executes the computer program, the following steps are also implemented: performing vector analysis on the 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.

[0160] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: in response to an operation optimization instruction for a target active distribution network, 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 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 encryption result of the variable values, and the encryption result is sent to the second power grid cluster; based on the second power grid cluster, the encryption result is homomorphically decrypted to obtain the decrypted result, and an error analysis is performed on the interaction variables to obtain a variable error of the interaction variables; when the variable error is less than an error threshold, the target active distribution network is operated according to the operation optimization data.

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

[0162] In one embodiment, when the computer program is executed by the processor, the following steps are also 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 also 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 transformation 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 also implemented: performing vector analysis on the 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, 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; homomorphically encrypting the variable values ​​through the first power grid cluster to obtain an encryption result of the variable values, and sending the encryption result to the second power grid cluster; homomorphically decrypting the encryption result based on the second power grid cluster, obtaining the decryption result, performing error analysis on the interaction variables to obtain a variable error of the interaction variables; 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 also implemented: constructing a variable position matrix based on the attribute data of the interactive variables; constructing a constraint matrix based on the basic operation data of the target active distribution network; performing 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.

[0168] In one embodiment, when the computer program is executed by the processor, the following steps are also 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 also 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 transformation 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 also implemented: performing vector analysis on the 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 can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0174] The technical features of the above embodiments may be combined arbitrarily. 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 only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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 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 acquiring 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; By means of the first power grid cluster, the variable value is homomorphically encrypted to obtain an encryption result of the variable value, and the encryption result is sent to the second power grid cluster; Performing homomorphic decryption on the encrypted result based on the second power grid cluster, and obtaining the decrypted result, performing error analysis on the interactive variable to obtain the variable error of the interactive variable; When the variable error is less than an error threshold, the target active power distribution network is operated according to the operation optimization data.

2. The method according to claim 1, characterized in that The step of performing 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 operation data of the target active distribution network; According to 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.

3. The method according to claim 2, characterized in that The constraint matrix includes an equality constraint matrix and an inequality constraint matrix; the operation optimization analysis of the target active distribution network is performed according to the variable position matrix and the constraint matrix to obtain the 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; According to 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.

4. The method according to claim 1, characterized in that The method further comprises: 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 step of performing 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 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.

5. The method according to claim 1, characterized in that The performing homomorphic encryption on the variable value to obtain an encryption result of the variable value includes: Performing data format conversion on the variable value to obtain a converted value of the variable value; The transformed value is homomorphically encrypted based on an encryption function to obtain an encrypted result of the variable value.

6. The method according to claim 2, characterized in that The step of performing homomorphic decryption on the encryption result based on the second power grid cluster to obtain the decrypted result, performing error analysis on the interactive variable to obtain the variable error of the interactive 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 of the target vector and the variable position matrix is ​​taken as the variable error of the interaction variable.

7. An active distribution network distributed operation optimization device 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 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 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 interactive 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 value of the interactive variable and the operation optimization data of the target active distribution network; a homomorphic encryption module, configured to 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; an error analysis module, configured to perform homomorphic decryption on the encryption result based on the second power grid cluster, obtain the decrypted result, perform error analysis on the interactive variable, and obtain the variable error of the interactive variable; An operation module is used to operate the target active distribution network according to the operation optimization data when the variable error is less than an error threshold.

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 6 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 6 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 6 are implemented.

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