CN114386769B - Power output determining method and device based on privacy protection in smart grid - Google Patents
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Abstract
The invention discloses a method and equipment for determining electric energy output based on privacy protection in a smart grid. The method comprises the following steps: constructing a directed graph network according to the distribution situation of each party participating in electric energy output; establishing a decision information model of each node according to the directed graph network structure; adding independent random noise which is subjected to Laplace distribution into the energy output quantity provided by each node pair, and establishing an observation sequence p as information of communication exchange in a network; establishing a random weight adjacency matrix W according to a network structure, and determining a left eigenvector q corresponding to an eigenvalue 1 of the matrix; each node estimates the energy output quantity of the neighbor node according to the weight matrix W and the observation sequence p, and calculates a corresponding cost function; and updating and iterating the energy output quantity of each node by taking the designated step length and the left eigenvector q as parameters. The invention well realizes the electric energy output decision based on the limited information in the intelligent power grid and can well protect the privacy and safety of users.
Description
Technical Field
The invention belongs to the technical field of smart grids, and particularly relates to a method and equipment for determining electric energy output based on privacy protection in a smart grid.
Background
The intelligent power grid is a new generation power system formed by integrating new energy, new materials, new equipment, advanced sensing technology, information technology, control technology, energy storage technology and other new technologies on the basis of the traditional power system, has the characteristics of high informatization, automation, interactivity and the like, and can better realize safe, reliable, economical and efficient operation of the power grid. The intelligent power grid is developed, so that the power grid acceptance and the capability of optimizing and configuring various energy sources are further improved, and comprehensive allocation of energy source production and consumption is realized; the method is favorable for promoting the scientific utilization of clean energy and distributed energy, thereby comprehensively constructing a safe, efficient and clean modern energy guarantee system. An important link in the smart grid is electric energy transaction, and with the development of the smart grid and the opening of an electric energy transaction market, the electric energy transaction based on renewable energy sources has a wide development prospect.
From the development angle of the intelligent power grid, the participants participating in energy supply are taken as users, and the user side can communicate with other users through a network structure to adjust self decision in the process of realizing electric energy output, so that the aim of minimizing own cost function is fulfilled. However, users often cannot obtain the true value of the cost function due to their limited access to neighbor decisions; meanwhile, the limitation of the directed network also brings challenges for minimizing the cost function; in addition to this, distributed operation in large-scale network grids, users may be extremely vulnerable to various network attacks, which have become one of the most challenging problems encountered in the grid. How to cope with these challenges, it is a challenge to implement a power output decision based on limited information in a smart grid and to well protect user privacy and security.
Disclosure of Invention
The invention aims to: the invention aims to provide a method and equipment for determining electric energy output based on privacy protection in a smart grid, and solves the problems in the background technology.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a method for determining electric energy output based on privacy protection in a smart grid, comprising the following steps:
(1) Constructing a directed graph network according to the distribution condition of each party participating in electric energy output, wherein electric energy output parties are taken as nodes, and communication connection existing among the parties is taken as edges;
(2) According to the directed graph network structure, a decision information model of the node is built, and decision information x i of the ith node comprises energy output x i,i of the node and energy output x i,-i of other nodes recorded by the node;
(3) The node i adds random noise v i into the energy output quantity x i given on the basis of the decision information model, and establishes an observation sequence p i＝xi+vi as the information of communication exchange in the network;
(4) Establishing a random weight adjacency matrix W according to a directed graph network structure, and determining a left eigenvector q corresponding to an eigenvalue 1 of the matrix;
(5) The node i estimates the energy output quantity of the neighbor node according to the weight adjacent matrix W and the observation sequence p i Combining the energy output quantity x i,i of the self as estimated decision information/>And based on/>Calculate the corresponding objective function/>
(6) And updating and iterating the energy output quantity of each node by taking the designated step length and the left eigenvector q as parameters based on the objective function, and determining the final energy output quantity of each node when the iteration stop condition is met.
Further, the random noise v i in the step (3) is an independent random noise variable compliant with the Laplace distribution, and the density function isWherein b=dp k, d >0,0< p <1, k is a positive integer.
Further, the step (4) includes:
(4-1) establishing a weight adjacency matrix W taking omega ij as an ith row and jth column element, if a node j can send information to the node i, omega ij >0, and recording that the set of the node j is N i and represents an incoming neighbor set of the node i; whereas ω ij =0, while ensuring that W satisfies the row random property;
(4-2) according to Perron-Frobenius theorem, obtaining a left eigenvector q corresponding to the eigenvalue of the weight adjacency matrix W being 1, wherein q= [ q 1,q2,...,qi,...,qN]T, N is the total number of nodes.
Further, in the step (5), the node i estimates the energy output of the neighboring node according to the following formula: called the information of the incoming neighbors collected by node i.
Further, in the step (6), the node i updates its own information according to the following formula:
where proj is the projection operator, is the extended pseudo-gradient map Give weight/>Alpha is a fixed step length;
For the energy output x i,-i of other nodes recorded by the node i, the information of the incoming neighbor collected by the node i is used As an update of node information.
The invention also provides a device for determining the electric energy output based on privacy protection in the smart grid, which comprises the following steps:
The directed graph construction module is used for constructing a directed graph network according to the distribution condition of each party participating in electric energy output, taking electric energy output parties as nodes and taking communication connection existing among the parties as edges;
The decision information model building module is used for building a decision information model of a node according to the directed graph network structure, and decision information x i of an ith node comprises energy output x i,i of the node and energy output x i,-i of other nodes recorded by the node;
The observation sequence construction module is used for indicating the node i to add random noise v i to the energy output x i given based on the decision information model, and establishing an observation sequence p i＝xi+vi as the information of communication exchange in the network;
the adjacency weight matrix construction module is used for establishing a random row weight adjacency matrix W according to the directed graph network structure and determining a left eigenvector q corresponding to an eigenvalue 1 of the random row weight adjacency matrix W;
The neighbor information estimation module is used for indicating the node i to estimate the energy output quantity of the neighbor node according to the weight adjacency matrix W and the observation sequence p i Combining the energy output quantity x i,i of the self as estimated decision information/>And based on/>Calculate the corresponding objective function/>
And the information updating module is used for updating and iterating the energy output quantity of each node by taking the designated step length and the left characteristic vector q as parameters based on the objective function, and determining the final energy output quantity of each node when the iteration stopping condition is met.
Further, the adjacency weight matrix construction module includes:
A matrix establishing unit, configured to establish a weight adjacency matrix W with ω ij as an ith row and jth column element, if a node j can send information to a node i, ω ij >0, and record that a set of such node j is N i, and represent an ingress neighbor set of the node i; whereas ω ij =0, while ensuring that W satisfies the row random property;
And the solving unit is used for solving a left eigenvector q corresponding to the eigenvalue of the weight adjacent matrix W of 1 according to the Perron-Frobenius theorem, wherein q= [ q 1,q2,...,qi,...,qN]T ] and N is the total number of nodes.
Further, the neighbor information estimation module instructs the node i to estimate the energy output of the neighbor node according to the following formula: called the information of the incoming neighbors collected by node i.
The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of the privacy-preserving-based power output determining method in a smart grid as described above of the present invention.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and when the processor executes the computer program, the steps of the electric energy output determining method based on privacy protection in the intelligent power grid can be realized.
The beneficial effects are that: the invention considers the privacy protection distributed Nash equilibrium searching method for the directed network in the intelligent power grid, and ensures the information privacy of each node by adding independent random noise on each node information and then sending the independent random noise to the neighbor. At the same time, this approach is differential privacy-satisfying and the privacy level can be computationally determined. By establishing a row random weight matrix under the setting of partial decision information, introducing a left eigenvector corresponding to an eigenvalue 1 of the weight matrix according to the Perron-Frobenius theorem, and establishing a fully distributed algorithm on the directed graph, the gradual convergence of the algorithm to Nash equilibrium points is ensured. The invention realizes the electric energy output decision based on the limited information in the intelligent power grid and can well protect the privacy and safety of users.
Drawings
Fig. 1 is a flowchart of a method for determining power output based on privacy protection in a smart grid according to the present invention;
FIG. 2 is a diagram of a simulation of a renewable energy source electrical energy output participant in accordance with an embodiment of the invention;
fig. 3 is a directed network diagram constructed in accordance with the network architecture of fig. 2.
Detailed Description
For a better understanding of the features and advantages of the present invention, reference should be made to the following description of its specific embodiments and to the accompanying drawings.
Referring to fig. 1, a method for determining power output based on privacy protection in a smart grid disclosed in an embodiment of the present invention includes the following steps:
(1) Constructing a directed graph network according to the distribution condition of each party participating in electric energy output, wherein electric energy output parties are taken as nodes, and communication connection existing among the parties is taken as edges;
In this embodiment, a group of 5 energy providers is used as a user end node in the smart grid, and a renewable energy source electric energy output model in the smart grid is expressed as: the set of 5 energy providers is involved in the production of a renewable energy source that competes for 2 markets: m 1、M2, as shown in FIG. 2.
In the description herein, user end nodes, user nodes, energy providers, energy output means the same meaning and may be used interchangeably.
The decision information x i of node i is represented as: the energy provider i decides to produce and carry x i energy to the n i markets to which it is connected, thereby participating in the energy supply of these n i markets. In this process, each energy provider i wishes to minimize its own objective function J i(xi,x-i, where x -i represents the amount of energy that is produced and carried by the four other energy providers other than i. Because of privacy protection requirements, each energy provider does not want other providers to obtain their own energy output decisions. In market competition, there is no central node in two-way communication with all providers.
Because of such partial decision-making arrangements, the energy providers cannot directly observe the energy output decisions of all other energy providers, so they do local information exchange to reduce their lack of global information. The process of local information exchange can be simulated as a directed network graph, if the energy provider i can receive the decision information transmitted by the energy provider j, a directed edge (as shown in fig. 3) taking the node i as a starting point and taking the node j as an ending point exists between the nodes i and j, and the energy provider j which transmits the decision information is called an ingress neighbor of the energy provider i. Because of the setting of the partial decision messages, each energy provider i cannot obtain the true value x -i of the other energy provider decision information and thus the true value J i(xi,x-i of its own cost function. It should be understood that the directed graph structure shown in fig. 3 is only for illustrating the function of the inventive arrangements and is not limiting of the invention.
(2) Establishing a decision information model of each node according to the directed graph network structure, wherein the decision information model comprises the own energy output quantity of the node and the estimated quantity of the energy output of other nodes;
in the invention, each node is assumed to estimate the decisions of other nodes, so that the self decisions are adjusted to minimize the objective function.
Consider the set i= {1,2,..2, 5}, for an energy provider, node I, which produces and carries energyWherein Ω i is a set of production and carrying energy recorded by energy provider i, its elements are n i -dimensional real vectors, R represents a real set;
the energy quantity produced and carried by all nodes recorded in each node i is recorded as x i,xi and is an n-dimensional real vector, wherein n=n 1+n2+...+n5;xi comprises two parts, and one part is decision information of the node i, namely the energy supply quantity of the node i Another part is the estimation of other nodes/>I.e., x i＝[xi,1,xi,2,...,xi,5]T; for example, for node 1, it records decision information x 1＝[x1,1,x1,2,...,x1,5]T, where x 1,1 is the energy supply of node 1 itself, and x 1,2,x1,3,x1,4,x1,5 is an estimate of the energy supply of nodes 2-5, respectively, recorded by node 1; for example, for node 2, it records decision information x 2＝[x2,1,x2,2,...,x2,5]T, where x 2,2 is the energy supply of node 2 itself, and x 2,1,x2,3,x2,4,x2,5 is the estimate of the energy supply of node 2 to nodes 1 and 3-5, respectively. The energy produced and carried by the nodes is also referred to hereinafter as the output energy or the supplied energy.
According to the network structure of fig. 2 and 3, each energy provider is set to determine the energy quantity x i produced and carried as a random number between (5, 10), and then the product quantity of other providers is estimated through each provider, so that the value x i can be obtained.
(3) Adding independent random noise which is subjected to Laplace distribution into the energy output quantity provided by each node pair, and establishing an observation sequence p as information of communication exchange in a network;
In order to protect decision information of each user node from various network attacks, independent random noise which obeys Laplacian distribution is added to protect privacy of each user node, so that a decision information observation sequence with noise interference is generated. Specifically, consider an independent random noise variable v i that obeys the Laplace distribution, whose density function is Where b=dp k, d is a real number greater than 0, p is a real number between (0, 1), and k is a positive integer, which ensures that the variance of this noise variable decays to zero when k goes to infinity, thereby reducing interference with decision information exchange.
The observation sequence p i＝xi+vi is defined to indicate that each node adds noise as interference when propagating on the directed graph, so that the own information privacy is hoped to be ensured, and the observation sequence is used as information communicated and exchanged in the network structure. In the process of decision information exchange, the node i and the entering neighbor j are constantly in information exchange, the j and the entering neighbor j are also constantly in information exchange, and finally the node i can obtain decision information of all nodes in the directed network while protecting information privacy through sending or receiving the observation sequence p i each time.
(4) Establishing a random weight adjacency matrix W according to a network structure, and determining a left eigenvector q corresponding to an eigenvalue 1 of the matrix;
the construction method of the weight adjacency matrix is as follows: for the directed network of fig. 2, a weight adjacency matrix W with omega ij as an ith row and jth column element is established, and a node j can send information to a node i, namely, if the network has an edge pointing to the node j from the node i, omega ij >0 is recorded, and the set of the node j is N i, which represents an ingress neighbor set of the node i; whereas ω ij =0 while ensuring that W satisfies the row random property, i.e. that the sum of the elements of each row of the W matrix is 0.
According to the Perron-Frobenius theorem, a left eigenvector q corresponding to the eigenvalue of the weight adjacency matrix W being 1 is obtained, wherein q= [ q 1,q2,...,qi,...,qN]T.
(5) Each node estimates the energy output quantity of the neighbor node according to the weight matrix W and the observation sequence p, and calculates a corresponding cost function;
Consider the cost function J i(xi,x-i),x-i of node i as a decision information variable for other nodes than node i. Since node i cannot access the decisions x -i of other nodes, they cannot know the actual value of the cost function J i(xi,x-i). However, by communicating with its neighbors, node i can estimate the energy output of other user nodes P j is the observation sequence of node j, where node j is the ingress node neighbor of node i. Combining the energy output x i,i of node i itself, from x i,i and estimated/>Estimated decision information/>, constituting node iCorresponding to the decision information of the node in step (2)/>Can also be understood as being defined by/>Two-part composition, i.e./>
The objective function may be calculated based on the estimated decision information. In the embodiment of the invention, aiming at the competitive market network, a ij is established as a matrix A of the j-th column element of the i-th row, A= [ A 1,....,AN]∈R2×n, whereinIt determines which market the energy provider i participates in, defined specifically as follows: if energy provider i produces and carries [ x i]j energy to market M k, k=1 or 2, then the kth row, column element of a i is 1, otherwise 0. Where [ x i]j ] represents the j-th element of vector x i;
Setting an objective function J i(xi,x-i)＝ci(xi)-PT(Ax)Aixi of each node i, wherein Representing the total amount of energy supplied by all nodes; p: r m→Rm represents a price vector function mapping the total supply of each market to the associated market price, considering that this price function is linear, i.e. p=p '-HAx, where P' is a second order real vector with [10,20] as the element and H is a 2x 2 order real matrix with [1,3] as the element;
the local production cost per energy provider is The setting Q i is to obtain a diagonal matrix by taking the random number in [14,16] as a diagonal block, and the s i element is [1,2]. In the embodiment of the invention, under the condition of taking the values, the output value when the objective function is minimum can be obtained. It should be understood that the calculation of the objective function and the value of the cost-related variable are merely for the purpose of illustrating one implementation of the method, and are not meant to limit the method thereto.
According toWith the definition of the objective function J i(xi,x-i), the corresponding function value/>, is calculated
(6) And updating and iterating the energy output quantity of each node by taking the designated step length and the left eigenvector q as parameters.
Updating the information x i of the node i, wherein the setting formula is shown as formula (I):
in the formula (I), k=0, 1,2, … … is iteration number, proj is projection operator, and extended pseudo-gradient mapping is adopted Give weight/>Wherein alpha is a fixed step size;
For node i to estimate information for other nodes, i.e., x i,-i, information defining its collected ingress neighbors For updating node information, i.e./>Where k=0, 1,2, … … is the number of iterations.
In this embodiment, α=3×10 -5 is taken, and the energy x i produced and carried by each node i is updated.
Further, according to the definition of differential privacy, two sets of functions are consideredJ i (1)≠Ji (2) when i=i 0; when i+.i 0, J i (1)＝Ji (2); the privacy level may be calculated according to the following formula:
in formula (II), C represents the upper bound of the J i gradient.
Let the nodes 2,3,4 relate to sensitive information in the energy output process, then take i 0 = {2,3,4}, according to formula (ii), the privacy level can be calculated.
The embodiment of the invention considers the privacy protection distributed Nash equilibrium searching method for the directed network in the intelligent power grid, and the theory proves that the method meets the differential privacy by adding independent random noise on each node information and then sending the independent random noise to the neighbors, thereby ensuring the information privacy of each user node and calculating and determining the privacy level. In addition, the invention establishes a row random weight matrix under the setting of partial decision information, introduces a left feature vector corresponding to the feature value 1 of the weight matrix according to the Perron-Frobenius theorem, establishes a completely distributed algorithm on the directed graph, and ensures that the algorithm gradually converges to Nash equilibrium points. The method is suitable for being applied to the intelligent power grid to optimize the objective function of the user by taking the directed network as the communication structure, and has privacy protection, universality and flexibility.
According to the same technical concept as the method embodiment, in another embodiment, there is provided a privacy protection-based power output determining apparatus in a smart grid, including:
The directed graph construction module is used for constructing a directed graph network according to the distribution condition of each party participating in electric energy output, taking electric energy output parties as nodes and taking communication connection existing among the parties as edges;
The decision information model building module is used for building a decision information model of a node according to the directed graph network structure, and decision information x i of an ith node comprises energy output x i,i of the node and energy output x i,-i of other nodes recorded by the node;
The observation sequence construction module is used for indicating the node i to add random noise v i to the energy output x i given based on the decision information model, and establishing an observation sequence p i＝xi+vi as the information of communication exchange in the network;
the adjacency weight matrix construction module is used for establishing a random row weight adjacency matrix W according to the directed graph network structure and determining a left eigenvector q corresponding to an eigenvalue 1 of the random row weight adjacency matrix W;
The neighbor information estimation module is used for indicating the node i to estimate the energy output quantity of the neighbor node according to the weight adjacency matrix W and the observation sequence p i Combining the energy output quantity x i,i of the self as estimated decision information/>And based on/>Calculate the corresponding objective function/>
And the information updating module is used for updating and iterating the energy output quantity of each node by taking the designated step length and the left characteristic vector q as parameters based on the objective function, and determining the final energy output quantity of each node when the iteration stopping condition is met.
Wherein, the adjacency weight matrix construction module includes:
A matrix establishing unit, configured to establish a weight adjacency matrix W with ω ij as an ith row and jth column element, if a node j can send information to a node i, ω ij >0, and record that a set of such node j is N i, and represent an ingress neighbor set of the node i; whereas ω ij =0, while ensuring that W satisfies the row random property;
And the solving unit is used for solving a left eigenvector q corresponding to the eigenvalue of the weight adjacent matrix W of 1 according to the Perron-Frobenius theorem, wherein q= [ q 1,q2,...,qi,...,qN]T ] and N is the total number of nodes.
The neighbor information estimation module indicates the node i to estimate the energy output of the neighbor node according to the following formula: called the information of the incoming neighbors collected by node i.
It should be understood that the privacy protection-based power output determining device in the smart grid provided in this embodiment may implement all the technical solutions in the foregoing method embodiments, and the functions of each functional module may be specifically implemented according to the methods in the foregoing method embodiments, and the specific implementation process may refer to the relevant descriptions in the foregoing embodiments, which are not repeated herein.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The method for determining the electric energy output based on privacy protection in the smart grid is characterized by comprising the following steps of:
(1) Constructing a directed graph network according to the distribution condition of each party participating in electric energy output, wherein electric energy output parties are taken as nodes, and communication connection existing among the parties is taken as edges;
(2) According to the directed graph network structure, a decision information model of the node is built, and decision information x i of the ith node comprises energy output x i,i of the node and energy output x i,-i of other nodes recorded by the node;
(3) The node i adds random noise v i into the energy output quantity x i given on the basis of the decision information model, and establishes an observation sequence p i＝xi+vi as the information of communication exchange in the network;
(4) Establishing a random weight adjacency matrix W according to a directed graph network structure, and determining a left eigenvector q corresponding to an eigenvalue 1 of the matrix;
(5) The node i estimates the energy output quantity of the neighbor node according to the weight adjacent matrix W and the observation sequence p i Combining the energy output quantity x i,i of the self as estimated decision information/>And based on/>Calculate the corresponding objective function/>
(6) And updating and iterating the energy output quantity of each node by taking the designated step length and the left eigenvector q as parameters based on the objective function, and determining the final energy output quantity of each node when the iteration stop condition is met.
2. The method for determining power output based on privacy protection in smart grid according to claim 1, wherein the random noise v i in the step (3) is an independent random noise variable compliant with laplace distribution, and the density function isWherein b=dp k, d >0,0< p <1, k is a positive integer.
3. The method for determining power output based on privacy protection in smart grid according to claim 1, wherein the step (4) comprises:
(4-1) establishing a weight adjacency matrix W taking omega ij as an ith row and jth column element, if a node j can send information to the node i, omega ij >0, and recording that the set of the node j is N i and represents an incoming neighbor set of the node i; whereas ω ij =0, while ensuring that W satisfies the row random property;
(4-2) according to Perron-Frobenius theorem, obtaining a left eigenvector q corresponding to the eigenvalue of the weight adjacency matrix W being 1, wherein q= [ q 1,q2,...,qi,...,qN]T, N is the total number of nodes.
4. The method for determining power output based on privacy protection in smart grid according to claim 3, wherein the node i in the step (5) estimates the energy output of the neighboring node according to the following formula: called the information of the incoming neighbors collected by node i.
5. The method for determining the power output based on privacy protection in the smart grid according to claim 4, wherein the node i in the step (6) updates its own information according to the following formula:
where proj is the projection operator, is the extended pseudo-gradient map Give weight/>Alpha is a fixed step length;
For the energy output x i,-i of other nodes recorded by the node i, the information of the incoming neighbor collected by the node i is used As an update of node information.
6. An electric energy output determining device based on privacy protection in a smart grid, comprising:
The directed graph construction module is used for constructing a directed graph network according to the distribution condition of each party participating in electric energy output, taking electric energy output parties as nodes and taking communication connection existing among the parties as edges;
The decision information model building module is used for building a decision information model of a node according to the directed graph network structure, and decision information x i of an ith node comprises energy output x i,i of the node and energy output x i,-i of other nodes recorded by the node;
The observation sequence construction module is used for indicating the node i to add random noise v i to the energy output x i given based on the decision information model, and establishing an observation sequence p i＝xi+vi as the information of communication exchange in the network;
the adjacency weight matrix construction module is used for establishing a random row weight adjacency matrix W according to the directed graph network structure and determining a left eigenvector q corresponding to an eigenvalue 1 of the random row weight adjacency matrix W;
The neighbor information estimation module is used for indicating the node i to estimate the energy output quantity of the neighbor node according to the weight adjacency matrix W and the observation sequence p i Combining the energy output quantity x i,i of the self as estimated decision information/>And based on/>Calculate the corresponding objective function/>
And the information updating module is used for updating and iterating the energy output quantity of each node by taking the designated step length and the left characteristic vector q as parameters based on the objective function, and determining the final energy output quantity of each node when the iteration stopping condition is met.
7. The privacy protection based power output determining apparatus of claim 6, wherein the adjacency weight matrix construction module comprises:
A matrix establishing unit, configured to establish a weight adjacency matrix W with ω ij as an ith row and jth column element, if a node j can send information to a node i, ω ij >0, and record that a set of such node j is N i, and represent an ingress neighbor set of the node i; whereas ω ij =0, while ensuring that W satisfies the row random property;
And the solving unit is used for solving a left eigenvector q corresponding to the eigenvalue of the weight adjacent matrix W of 1 according to the Perron-Frobenius theorem, wherein q= [ q 1,q2,...,qi,...,qN]T ] and N is the total number of nodes.
8. The privacy protection-based power output determining apparatus in a smart grid according to claim 6, wherein the neighbor information estimation module instructs the node i to estimate the energy output of the neighbor node according to: called the information of the incoming neighbors collected by node i.
9. A computer device, the device comprising:
one or more processors;
a memory; and
One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processor implement the steps of the privacy-preserving-based power output determination method in a smart grid as claimed in any one of claims 1 to 5.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the steps of the privacy protection based power output determining method in a smart grid according to any of claims 1 to 5 are implemented when the computer program is executed by a processor.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108966172A (en) * | 2018-08-17 | 2018-12-07 | 苏州科技大学 | Wireless sensor and actor network second order data method for secret protection |
| CA3080373A1 (en) * | 2019-05-10 | 2020-11-10 | Royal Bank Of Canada | System and method for machine learning architecture with privacy-preserving node embeddings |
| CN111988185A (en) * | 2020-08-31 | 2020-11-24 | 重庆邮电大学 | Multi-step communication distributed optimization method based on Barzilai-Borwein step length |
| CN113688424A (en) * | 2021-08-31 | 2021-11-23 | 福建师范大学 | Personalized differential privacy protection method based on weight social network |
-
2021
- 2021-12-14 CN CN202111524525.1A patent/CN114386769B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108966172A (en) * | 2018-08-17 | 2018-12-07 | 苏州科技大学 | Wireless sensor and actor network second order data method for secret protection |
| CA3080373A1 (en) * | 2019-05-10 | 2020-11-10 | Royal Bank Of Canada | System and method for machine learning architecture with privacy-preserving node embeddings |
| CN111988185A (en) * | 2020-08-31 | 2020-11-24 | 重庆邮电大学 | Multi-step communication distributed optimization method based on Barzilai-Borwein step length |
| CN113688424A (en) * | 2021-08-31 | 2021-11-23 | 福建师范大学 | Personalized differential privacy protection method based on weight social network |
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