CN114818257A - Intelligent micro-grid distributed dynamic tracking method with privacy protection effect - Google Patents

Intelligent micro-grid distributed dynamic tracking method with privacy protection effect Download PDF

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CN114818257A
CN114818257A CN202210236124.4A CN202210236124A CN114818257A CN 114818257 A CN114818257 A CN 114818257A CN 202210236124 A CN202210236124 A CN 202210236124A CN 114818257 A CN114818257 A CN 114818257A
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random number
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郭雷
高澜
余翔
徐科栋
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Hangzhou Innovation Research Institute of Beihang University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks

Abstract

The invention relates to the technical field of intelligent microgrid energy scheduling, in particular to an intelligent microgrid distributed dynamic tracking method with a privacy protection function, which comprises the following steps: s1, determining that the total quantity of the entity nodes of the intelligent microgrid is N and the real-time power consumption data of the ith node is phi i (t) combining the weight coefficients w of the nodes i Obtaining a solving objective function model of power consumption; s2, designing a dynamic tracking algorithm based on a random number perturbation mechanism; s3, specifically setting forth the execution process of the algorithm in S2; s4, determining and verifying algorithm parameters; by adopting the distributed dynamic tracking algorithm based on the random number disturbance mechanism, the microgrid can be ensuredWhile sensitive information of the middle nodes is not leaked, each node can accurately track the weighted average value of time-varying power consumption data of all nodes in the whole network, so that total power consumption data are obtained, and decision reference is provided for supply and demand load balance control.

Description

Intelligent micro-grid distributed dynamic tracking method with privacy protection effect
Technical Field
The invention relates to the technical field of intelligent micro-grid energy scheduling, in particular to an intelligent micro-grid distributed dynamic tracking method with a privacy protection function.
Background
With the development of renewable energy power generation technologies such as photovoltaic and wind power, distributed power generation gradually becomes an effective way for meeting load increase requirements, improving energy comprehensive utilization efficiency and improving power supply reliability, and is widely applied to power distribution networks. In order to integrate the advantages of distributed power generation, researchers provide the concept of an intelligent microgrid, which is defined as a system unit consisting of a group of micro power supplies, loads, an energy storage system and a control device, is an autonomous system capable of realizing self control, protection and management, and can be operated in a grid-connected mode with an external power grid or operated in an isolated mode;
the intelligent microgrid is a novel network consisting of a plurality of distributed power supplies and related components thereof according to a certain topological structure, has robustness and high cost benefit compared with a traditional centralized power grid, but needs to pay close attention to effective management of the microgrid power supply and demand load; because the intelligent microgrid is a distributed novel network structure, the traditional centralized control method is no longer applicable, and therefore, a technology called dynamic average tracking is introduced into the research field of the intelligent microgrid. The dynamic average tracking technology aims to design a distributed cooperative algorithm, so that each node in a network can track the average value of all node time-varying reference signals only by exchanging information with the neighbor nodes. In conventional dynamic tracking coordination algorithms, each node must faithfully communicate with its neighboring nodes to reach consensus on a variable of interest. However, in some practical applications, such direct information exchange may result in the leakage of sensitive information; so that a malicious or curious attacker can easily obtain such data by listening to the channel and can use the accessed power consumption data to deduce the detailed family or business activity of the target node.
Disclosure of Invention
The invention aims to develop a distributed dynamic tracking method with a privacy protection function based on a random number disturbance mechanism aiming at the problem of supply and demand load balance in an intelligent microgrid, so as to reasonably plan links of power production, storage and scheduling.
An intelligent microgrid distributed dynamic tracking method with a privacy protection effect comprises the following steps:
s1, determining that the total quantity of the entity nodes of the intelligent microgrid is N and the real-time power consumption data of the ith sum node is phi i (t) combining the weight coefficients w of the nodes i Obtaining a solution objective function model of power consumption
Figure BDA0003542267830000021
S2, designing a dynamic tracking algorithm based on a random number perturbation mechanism according to the target function model in S1 as follows
Figure BDA0003542267830000022
Figure BDA0003542267830000023
Figure BDA0003542267830000024
Figure BDA0003542267830000025
Using virtual states perturbed by random numbers
Figure BDA0003542267830000031
Mutual information exchange is carried out, so that the leakage of the real information of the nodes is avoided;
s3, specifically setting forth the execution process of the algorithm in S2;
s4, determining and verifying algorithm parameters;
further, z in S2 i (t)，x i (t) and u i (t) representing node internal states, estimated states and control inputs, respectively; parameter 0 < omega i ＜1，
Figure BDA0003542267830000032
Is the regularization weight of agent i, α is the control gain, γ > 0 is the design parameter;
Figure BDA0003542267830000033
representing a random number generated by node i and communicated to neighbor node j,
Figure BDA0003542267830000034
representing the random number received by node i from neighbor node j,
Figure BDA0003542267830000035
can be regarded as estimating the state x i (t) a dummy state scrambled by the random number;
furthermore, each node i generates a group of random numbers according to the number of the neighbor nodes j of the node i
Figure BDA0003542267830000036
The number of the generated random numbers is equal to the number of the neighbor nodes j, and then the random numbers are respectively sent to the neighbor nodes; each node i receives the random number sent by the neighbor node j
Figure BDA0003542267830000037
Then, estimating x according to the state of itself i (t) self-generated set of random numbers
Figure BDA0003542267830000038
And the received random number set
Figure BDA0003542267830000039
Computing virtual states for information transfer
Figure BDA00035422678300000310
5. Further, S3 illustrates a specific algorithm flow as follows:
step 1: initializing an algorithm, specifically comprising:
(1-1) generating N per node i i A random number
Figure BDA00035422678300000311
Wherein N is i Is its neighbor node number;
(1-2) node i transfers random numbers to its neighboring nodes (e.g., respectively
Figure BDA00035422678300000312
Neighbor node i passed to i 1 ) And receives the random number generated and transmitted by the adjacent node j
Figure BDA00035422678300000313
And 2, step: initializing an algorithm iteration time t as 0, and repeating the following steps;
and step 3: node i calculates state based on self-generated random number set
Figure BDA0003542267830000041
And 4, step 4: the node i calculates the virtual state based on the random number set transmitted by the neighbor node
Figure BDA0003542267830000042
And 5: the information exchange is carried out between the adjacent nodes by utilizing respective virtual states, and each node i carries out control input updating as follows:
Figure BDA0003542267830000043
step 6: node i updates internal state z i (t +1) and estimator state x i (t +1) is asThe following:
z i (t+1)＝z i (t)+h(-γz i (t)+u i (t)),
Figure BDA0003542267830000044
where h is the step size used by the algorithm for iteration.
And 7: updating the algorithm iteration time, and judging whether the iteration is finished, specifically comprising the following steps:
(7-1) updating an iteration time t ═ t + 1;
(7-2) judging whether iteration is finished, calculating the error of the state estimation values of the last two times, namely, | | epsilon (t) | ═ | x (t) -x (t-1) |, and finishing the algorithm if the error is smaller than a given threshold value, namely, | | epsilon (t) | < delta; otherwise, continuing to execute the step 3 to the step 7 until the algorithm is ended;
the beneficial effects of the invention are:
by adopting the invention, the proposed algorithm ensures that the information exchange between the nodes does not depend on the real state value x any more i (t) instead, a dummy state scrambled by a random number is used
Figure BDA0003542267830000045
Mutual information exchange is carried out, so that the leakage of the real information of the node is avoided, and the privacy protection function of the sensitive information of the node is achieved; meanwhile, a random number disturbance mechanism used by the invention is well designed, so that accurate dynamic target tracking can be realized finally even if information exchange between nodes is disturbed by the random number; the distributed dynamic tracking algorithm based on the random number disturbance mechanism can ensure that sensitive information of nodes in the microgrid is not leaked, and meanwhile, each node can accurately track the weighted average value of time-varying power consumption data of all nodes in the whole network, so that total power consumption data are obtained, and decision reference is provided for supply and demand load balance control.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
fig. 2 is a specific execution step of the algorithm in S3 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-2, an intelligent microgrid distributed dynamic tracking method with privacy protection function includes the following steps:
s1, the intelligent microgrid is composed of N entity nodes, and the real-time power consumption data of the ith node is phi i (t), the problem of dynamic average tracking is solved by how to make each node track all the power consumption data phi i (t) the average value, i.e., the tracking target, can be expressed as
Figure BDA0003542267830000051
The above solution problem is also referred to as the absolute average tracking problem because the tracking targets are all phi i Absolute average of (t); however, in an actual microgrid scenario, since the importance of each entity node is different, the target value to be tracked is not necessarily an absolute average; at this time, in order to reflect the difference of importance of each node, a weight coefficient w is introduced to each node i i Then the tracking target becomes all phi i (t) weighted average sum, i.e.
Figure BDA0003542267830000061
It should be noted that if all the weight coefficients are set to w i If the ratio is 1/N, the problem of dynamic weighted average tracking is solved into the problem of absolute average tracking, which means that the absolute average tracking is only a special case of weighted average tracking, and also means that the dynamic weighted average tracking has better universality;
s2, in order to solve the absolute average tracking problem, the following dynamic tracking algorithm has been proposed:
Figure BDA0003542267830000062
Figure BDA0003542267830000063
x i (t)＝z i (t)+φ i (t)
wherein z is i (t)，x i (t) and u i (t) represents the node internal state, estimated state and control input, respectively. Although the algorithm has better performance in terms of convergence speed and convergence accuracy, the final tracked target is all time-varying reference signals phi i Absolute average of (t). On the other hand, the algorithm requires all nodes in the network to directly communicate with their own neighbor nodes, that is, the true state of each node is directly transmitted through the channel, which brings a great risk to the leakage of node sensitive information.
In order to overcome the defects of the algorithm, the invention provides a dynamic weighted average tracking algorithm with privacy protection function based on a random number disturbance mechanism:
Figure BDA0003542267830000071
Figure BDA0003542267830000072
Figure BDA0003542267830000073
Figure BDA0003542267830000074
wherein z is i (t)，x i (t) and u i (t) represents the node internal state, estimated state and control input, respectively. Parameter 0 < omega i ＜1，
Figure BDA0003542267830000075
Is the regularization weight for agent i, α is the control gain, and γ > 0 is the design parameter.
Figure BDA0003542267830000076
Representing a random number generated by node i and communicated to neighbor node j,
Figure BDA0003542267830000077
representing the random number received by node i from neighbor node j,
Figure BDA0003542267830000078
can be regarded as estimating the state x i (t) a dummy state scrambled by the random number.
Specifically, each node i first generates a set of random numbers according to its own neighbor number
Figure BDA0003542267830000079
The number of the generated random numbers is equal to the number of the adjacent nodes, and then the random numbers are respectively sent to the adjacent nodes. Each node i receives the random number sent by the neighbor node
Figure BDA00035422678300000710
Then, estimating x according to the state of itself i (t) self-generated set of random numbers
Figure BDA00035422678300000711
And the received random number set
Figure BDA00035422678300000712
Computing virtual states for information transfer
Figure BDA00035422678300000713
Once all nodes complete the computation of their respective virtual states, the virtual states are exchanged between adjacent nodes in the network.
From the perspective of a communication network, the combined use of random numbers generated by nodes themselves and random numbers generated by their corresponding neighbor nodes plays a key role in ensuring the realization of accurate target tracking, because they just can achieve the effect of mutual cancellation when calculating respective virtual states and performing state fusion; it is worth emphasizing that the introduced random number perturbation mechanism is very small in terms of the increased communication load on the algorithm execution, since the generation and exchange of random numbers only takes place during the initialization phase of the algorithm execution and is a one-time operation. However, it should be noted that once the communication network topology changes, the perturbation mechanism including random number generation and random number exchange must be re-executed and initialized;
s3, step 1: initializing an algorithm, specifically comprising:
(1-1) generating N per node i i A random number
Figure BDA0003542267830000081
Wherein N is i Is its neighbor node number;
(1-2) node i transfers random numbers to its neighboring nodes (e.g., respectively
Figure BDA0003542267830000082
Neighbor i passed to i 1 ) And receives the random number generated and transmitted by the adjacent node j
Figure BDA0003542267830000083
Step 2: initializing an algorithm iteration time t as 0, and repeating the following steps;
and step 3: node i calculates state based on self-generated random number set
Figure BDA0003542267830000084
And 4, step 4: the node i calculates the virtual state based on the random number set transmitted by the neighbor node
Figure BDA0003542267830000085
And 5: the information exchange is carried out between the adjacent nodes by utilizing respective virtual states, and each node i carries out control input updating as follows:
Figure BDA0003542267830000086
step 6: node i updates internal state z i (t +1) and estimator state x i (t +1) is as follows:
z i (t+1)＝z i (t)+h(-γz i (t)+u i (t)),
Figure BDA0003542267830000087
where h is the step size the algorithm uses for the iteration.
And 7: updating the algorithm iteration time, and judging whether the iteration is finished, specifically comprising the following steps:
(7-1) updating an iteration time t ═ t + 1;
(7-2) judging whether iteration is finished, calculating the error of the state estimation values of the last two times, namely, | | epsilon (t) | ═ | x (t) -x (t-1) |, and finishing the algorithm if the error is smaller than a given threshold value, namely, | | epsilon (t) | < delta; otherwise, continuing to execute the step 3 to the step 7 until the algorithm is finished;
s4, determining and verifying algorithm parameters;
assumption 1 (connectivity): the network topology composed of N nodes is assumed to be bi-directional and connected.
Assume 2 (bounded): for any node i ∈ { 1.,. N } in the network topology, the time-varying reference signal φ i (t) and derivatives thereof
Figure BDA0003542267830000091
Are bounded, i.e. there are normal numbers
Figure BDA0003542267830000092
And σ is such that:
Figure BDA0003542267830000093
Figure BDA0003542267830000094
theorem 1: assuming that 1 and 2 are established, if the control gain α satisfies:
Figure BDA0003542267830000095
wherein
Figure BDA0003542267830000096
λ 2 Is the second small eigenvalue (i.e. non-zero minimum eigenvalue) of the Lapacian matrix corresponding to the communication topological graph, the algorithm proposed by the present invention can ensure the virtual state x after the random number disturbance + (t) carrying out a weighted average of the signals over a finite time
Figure BDA0003542267830000097
The accurate tracking of (2), namely:
Figure BDA0003542267830000098
its corresponding lower time bound t * Comprises the following steps:
Figure BDA0003542267830000099
wherein e (t) 0 ) Is the initial steady state error.
Theorem 1 above shows that the convergence time of the algorithm is bounded and exponential, corresponding to a lower time bound t * Mainly composed of a design parameter gamma, a weight matrix W and an initial steady-state error e (t) 0 ) And (6) determining. Furthermore, the lower bound of the control gain α depends on the reference signal φ (t) and its time derivative
Figure BDA00035422678300000910
The global information of (c). In practical applications, the global information of the reference signal and the upper bound of its time derivative can be obtained by running the maximum consistency algorithm during the initialization phase of the algorithm.
Theorem 2: according to the algorithm provided by the invention, the node i can protect the estimator state x of the node i i (t), a time-varying reference signal phi i The privacy of (t) is not revealed unless all neighbors of node i cooperate with each other to infer relevant state information of node i.
If all the neighbor nodes of the target node are willing to cooperate with each other to attack the node, the algorithm provided by the invention can not protect the sensitive information of the target node any more, and on the contrary, the algorithm provided by the invention can ensure the privacy of the sensitive state of the target node as long as at least one neighbor node has a non-cooperative attitude; in addition, if the attacker does not have network topology information of the system, the attacker cannot effectively attack the target, because the attacker cannot determine whether a certain node belongs to the neighbor nodes of the attack target, and cannot collect random numbers generated by all the neighbor nodes of the target and further calculate and infer real and sensitive information of the attack target;
in the specific implementation process of the invention, (1) a weight coefficient is introduced into each node in the system, and the proposed algorithm enables each node to accurately track the weighted average value of the time-varying reference signal instead of a simple absolute average value; (2) a set of privacy protection scheme is elaborately designed for a dynamic tracking algorithm based on a random number perturbation mechanism, so that the algorithm can accurately track a target point and simultaneously avoid leakage of sensitive information of a participating node; the distributed dynamic tracking algorithm based on the random number disturbance mechanism can ensure that node sensitive information in the microgrid is not leaked, and meanwhile, each node can accurately track the weighted average value of time-varying power consumption data of all nodes in the whole network, so that total power consumption data are obtained, and decision reference is provided for supply and demand load balance control.
The use of the specific embodiments of the invention described herein is intended to be illustrative only of the spirit of the invention; various modifications, additions and substitutions for the described embodiments may be made by those skilled in the art without departing from the scope and spirit of the invention as defined by the accompanying claims.

Claims (4)

1. An intelligent microgrid distributed dynamic tracking method with a privacy protection effect is characterized by comprising the following steps:
s1, determining that the total quantity of the entity nodes of the intelligent microgrid is N, and the real-time power consumption data of the ith node and the ith node is phi i (t) combining the weight coefficients w of the nodes i Obtaining a solution objective function model of power consumption
Figure FDA0003542267820000011
S2, according to the objective function model in S1, designing a distributed dynamic tracking algorithm based on a random number perturbation mechanism as follows:
Figure FDA0003542267820000012
Figure FDA0003542267820000013
Figure FDA0003542267820000014
x i (t)＝ω i -1 z i (t)+φ i (t)；
using virtual states perturbed by random numbers
Figure FDA0003542267820000015
Mutual information exchange is carried out, so that the leakage of the real information of the nodes is avoided;
s3, specifically setting forth the execution process of the algorithm in S2;
and S4, determining and verifying algorithm parameters.
2. The method of claim 1, wherein z in S2 is z i (t)，x i (t) and u i (t) representing node internal states, estimated states and control inputs, respectively; parameter 0 < omega i ＜1，
Figure FDA0003542267820000016
Is the regularization weight of agent i, α is the control gain, γ > 0 is the design parameter;
Figure FDA0003542267820000017
representing a random number generated by node i and communicated to neighbor node j,
Figure FDA0003542267820000018
representing the random number received by node i from neighbor node j,
Figure FDA0003542267820000019
can be regarded as estimating the state x i (t) a dummy state scrambled by the random number.
3. The intelligent microgrid distributed dynamic tracking method with privacy protection function as claimed in claim 2, characterized in that each node i generates a set of random numbers according to the number of its own neighbor nodes j
Figure FDA0003542267820000021
The number of the generated random numbers is equal to the number of the neighbor nodes j, and then the random numbers are respectively sent to the neighbor nodes; each node i receives the random number sent by the neighbor node j
Figure FDA0003542267820000022
Then, estimating x according to the state of itself i (t) self-generated set of random numbers
Figure FDA0003542267820000023
And the received random number set
Figure FDA0003542267820000024
Computing virtual states for information transfer
Figure FDA0003542267820000025
4. The intelligent microgrid distributed dynamic tracking method with privacy protection function as claimed in claim 3, wherein S3 describes a specific algorithm flow as follows:
step 1: initializing an algorithm, specifically comprising:
(1-1) generating N per node i i A random number
Figure FDA0003542267820000026
Wherein N is i Is its neighbor node number;
(1-2) node i transfers random numbers to its neighboring nodes (e.g., respectively
Figure FDA00035422678200000210
Neighbor node i passed to i 1 ) And receives the random number generated and transmitted by the adjacent node j
Figure FDA0003542267820000027
Step 2: initializing an algorithm iteration time t as 0, and repeating the following steps;
and 3, step 3: node i calculates state based on self-generated random number set
Figure FDA0003542267820000028
And 4, step 4: the node i calculates the virtual state based on the random number set transmitted by the neighbor node
Figure FDA0003542267820000029
And 5: the information exchange is carried out between the adjacent nodes by utilizing respective virtual states, and each node i carries out control input updating as follows:
Figure FDA0003542267820000031
step 6: node i updates internal state z i (t +1) and estimator state x i (t +1) is as follows:
z i (t+1)＝z i (t)+h(-γz i (t)+u i (t)),
Figure FDA0003542267820000032
where h is the step size used by the algorithm for iteration.
And 7: updating the algorithm iteration time, and judging whether the iteration is finished, specifically comprising the following steps:
(7-1) updating an iteration time t ═ t + 1;
(7-2) judging whether iteration is finished, calculating the error of the state estimation values of the last two times, namely, | | epsilon (t) | ═ | x (t) -x (t-1) |, and finishing the algorithm if the error is smaller than a given threshold value, namely, | | epsilon (t) | < delta; otherwise, continuing to execute the steps 3-7 until the algorithm is finished.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115378813A (en) * 2022-08-12 2022-11-22 大连海事大学 Distributed online optimization method based on differential privacy mechanism
CN115862310A (en) * 2022-11-30 2023-03-28 东南大学 Internet automatic motorcade stability analysis method under environment with uncertain traffic information

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115378813A (en) * 2022-08-12 2022-11-22 大连海事大学 Distributed online optimization method based on differential privacy mechanism
CN115378813B (en) * 2022-08-12 2023-08-15 大连海事大学 Distributed online optimization method based on differential privacy mechanism
CN115862310A (en) * 2022-11-30 2023-03-28 东南大学 Internet automatic motorcade stability analysis method under environment with uncertain traffic information
CN115862310B (en) * 2022-11-30 2023-10-20 东南大学 Network-linked automatic fleet stability analysis method under uncertain traffic information environment

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