CN113744078A - Smart electric meter user privacy protection method with smaller mutual information - Google Patents

Smart electric meter user privacy protection method with smaller mutual information Download PDF

Info

Publication number
CN113744078A
CN113744078A CN202110854025.8A CN202110854025A CN113744078A CN 113744078 A CN113744078 A CN 113744078A CN 202110854025 A CN202110854025 A CN 202110854025A CN 113744078 A CN113744078 A CN 113744078A
Authority
CN
China
Prior art keywords
power
storage battery
electric meter
household
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110854025.8A
Other languages
Chinese (zh)
Inventor
殷小超
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to CN202110854025.8A priority Critical patent/CN113744078A/en
Publication of CN113744078A publication Critical patent/CN113744078A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00302—Overcharge protection
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00306—Overdischarge protection
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H02J2310/14—The load or loads being home appliances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02B90/20—Smart grids as enabling technology in buildings sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/12—Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/221—General power management systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances

Abstract

The invention discloses a method for protecting user privacy of an intelligent electric meter with smaller mutual information, which comprises the steps of firstly constructing a probability space in a mode that the frequency is approximately equal to the probability, and describing a household electricity utilization mode; then, constructing an electric meter target data set by adopting integral multiples of half of the maximum charge-discharge power of the storage battery; and finally, regarding the original power consumption requirement as an information source, regarding the target reading of the electric meter as a code word, regarding the storage battery scheduling strategy as a coding mode, constructing an information source coding model based on a rate distortion theory, and solving by adopting a BA algorithm to obtain an optimal storage battery scheduling strategy.

Description

Smart electric meter user privacy protection method with smaller mutual information
Technical Field
The invention relates to an algorithm in the technical field of intelligent electric meter user privacy protection. In particular to a method for protecting the privacy of users of an intelligent electric meter with smaller mutual information.
Background
With the rapid development of the smart grid technology, a high-level measurement system with a smart meter as a core is also rapidly applied to power management and measurement. By means of communication, control, internet of things and computer technology, the intelligent electric meter can realize measurement and remote communication of fine-grained power utilization information, and great convenience is brought to energy management. Meanwhile, fine-grained data of the intelligent electric meter also brings great threat to the personal privacy of the user. In order to solve the problem that personal privacy such as user electricity utilization modes, personal habits, behavior preferences and the like is revealed due to fine-grained data recorded by the intelligent electric meter, a plurality of solutions are developed at home and abroad.
According to the adopted method and the different protection objects, the schemes for protecting the power utilization mode of the user can be roughly divided into the following four categories: 1) anonymizing non-aggregated data; 2) homomorphic encryption is used for aggregating data; 3) injecting noise into the real-time electricity utilization data; 4) the charging and discharging of the storage battery disturb the real-time electricity utilization data.
In the first type of scheme, the core idea is to store fine-grained real-time electricity utilization data in an anonymized manner, so that although an attacker can analyze the household electricity utilization mode by using algorithms such as non-intrusive load monitoring and the like, the electricity utilization mode cannot be associated with a specific user. The algorithm has the advantages that the privacy safety of users and fine-grained power consumption data required by a power grid company are guaranteed, and the independence of user data is also guaranteed. On the basis, an identity privacy protection technology based on a certificate is also provided, and a specific algorithm is to separate electricity charge metering data with coarse granularity from electricity consumption data recorded by an electricity meter with fine granularity. The electric power company can obtain only coarse-grained electricity charge metering data, fine-grained electricity consumption data are stored in an anonymous account by a trusted third party, but the scheme depends on the trusted third party, the credibility of the third party cannot be guaranteed, and therefore privacy of users can be leaked.
According to the second scheme, the intelligent electric meters with different keys belonging to the intelligent electric meters in the cluster are aggregated, and the intelligent electric meters encrypt the electricity utilization data recorded by the intelligent electric meters and upload the electricity utilization data to the electricity utilization company in a unified mode. Because homomorphic encryption is adopted, the aggregator can not influence the statistics of the electric quantity after the ciphertexts are overlapped, and the privacy protection effect is ensured. However, the algorithm needs to generate a large number of blind signatures in advance, higher requirements are put on hardware of the smart meter, and algorithm overhead and transmission overhead are large.
In the third scheme, a distributed time series data differential privacy aggregation algorithm is adopted, namely Laplace noise generated by adding Gaussian random data to real-time power utilization power is added, and privacy protection of users is further achieved. But since the injected noise cannot be strictly Laplace noise, privacy leakage will eventually also occur.
In the fourth type of scheme, the basic idea is to keep the power consumption at the current moment the same as the power consumption at the previous moment. But due to the presence of battery hardware constraints: 1) the maximum charge and discharge power of the storage battery, 2) the capacity of the storage battery, and finally the strategy can also leak the user power consumption mode. But the strategy can protect the user power utilization mode from the source, and as long as reasonable optimization is carried out, the influence of the hardware constraint of the storage battery on the strategy is weakened, and a very good privacy protection effect can be obtained.
After the four user privacy protection schemes are comprehensively considered, the method is improved and optimized on the basis of the fourth scheme, and the household power utilization mode is described in a discrete memoryless information source mode. And (3) analyzing the historical electricity utilization data, and constructing an electricity meter target indication set by adopting a K-Means algorithm. The household power consumption is regarded as an information source, the target reading of the electric meter is regarded as a code word, the scheduling strategy of the storage battery is regarded as a coding mode, a discrete memoryless information source coding model is constructed based on a rate distortion theory, and a BA algorithm is adopted for solving. The related simulation results show that: the finally obtained strategy has a good privacy protection effect, and the phenomenon of overcharge/overdischarge of the storage battery basically cannot occur.
Disclosure of Invention
The invention provides a smart meter user privacy protection method with smaller mutual information in order to solve the defects of the prior art.
The technical scheme adopted by the invention is as follows:
a user privacy protection method for an intelligent electric meter with smaller mutual information is characterized in that an intelligent electric meter, an energy management unit and a storage battery are arranged at a user side, the energy management unit is a control center for user power utilization management, a control strategy for the storage battery is preset, and the method comprises the following steps: the charging and discharging behaviors of the storage battery and the power value obtained from the power grid; the household appliance continuously generates power utilization requirements according to the behaviors of the user and sends the power utilization requirements to the energy management unit; a storage battery, which is used as a household electric appliance to consume electric energy and is also used as another electric power supply device to supply energy for the household electric appliance;
for the power consumption requirement of a user at a certain moment, the energy management unit controls the charging and discharging of the storage battery according to a preset storage battery scheduling strategy, so that the intelligent electric meter records the current power consumption requirement as other data, and the protection of the household power consumption mode of the user is realized; the method comprises the following steps:
step one, constructing a power utilization power probability space to describe a household power utilization mode;
step two, constructing an ammeter target reading set;
thirdly, constructing an evaluation model for selecting target data from the ammeter target indication set by the energy management unit at the time t;
step four, taking the original power consumption as an information source, taking the target reading of the electric meter as a code word, constructing an information source coding model based on a rate distortion theory, converting the exploration of the optimal storage battery scheduling strategy into the exploration of the optimal coding mode in the information source coding, and establishing a corresponding optimization equation;
step five, reconstructing the equation in the step four into the minimum relative entropy between the power consumption and the target indication set of the electric meter, and solving the equation based on a BA algorithm; and obtaining a transition probability matrix after solving, namely the final storage battery scheduling strategy.
In the above technical solution, for the step one, the probability space of a discrete memoryless information source is used to describe the power consumption mode of the household, and the description formula is as follows:
Figure BDA0003183423200000031
wherein the following variables are involved:
1) a: a set of historical household power usage;
2) g: the set obtained after the repeated elements in the set A are removed, because the household electricity utilization modes are very close to each other every day, a large amount of repeated data exist in the historical electricity utilization data set A, and a set for describing the household electricity utilization characteristics needs to be constructed in a mode of removing the repeated elements;
3) ω: ω represents an element in set G;
4) h (·): a function for calculating the number of times an element appears in its belonging set;
5) g (·): function, which is used to calculate the number of all elements in a certain set.
For the construction of the probability space, the following convention is used:
1) the source alphabet is the same as the set G;
2) the probability of the source symbol is equal to the probability of the element in the set G appearing in the historical power utilization set A.
In the above technical solution, for the second step, the target indication set of the electric meter needs to satisfy the following constraint conditions:
1)
Figure BDA0003183423200000032
2)
Figure BDA0003183423200000033
3) sorting elements in the set output load set y from small to large, wherein y isiAnd yi+1Is any two adjacent elements in the set, then
Figure BDA0003183423200000034
4)
Figure BDA0003183423200000035
Wherein the content of the first and second substances,
Figure BDA0003183423200000036
the maximum charge and discharge power of the storage battery is represented, and y is an ammeter target indication set;
constraint 1 and constraint 2 are used for ensuring that all elements in the constructed target number set of the electric meter can be utilized;
constraints 3 and 4 are to ensure that at least one target datum can be found in the established electricity metering number set through charging and discharging of the storage battery for any source symbol.
In the above-described aspect, in the second step, an integral multiple of half of the maximum charge/discharge power of the storage battery is selected as the electricity index.
In the above technical solution, in the third step, the established evaluation model is:
Figure BDA0003183423200000041
wherein, XtIs the power demand at time t, YtThe energy management unit selects target data used as electric meter readings from a preset electric meter target reading set.
In the above technical solution, in step four, the established optimization equation is:
R(D)＝minI(X；Y)
Figure BDA0003183423200000042
wherein the intermediate variable D of the objective function is calculated by the following formula,
Figure BDA0003183423200000043
x is a variable representing real-time power consumption, Y is a variable representing the number of readings of the smart meter, XiIs a specific value, Y, representing the real-time power usagejIs a specific numerical value representing the number of the intelligent electric meter.
The invention has the following advantages and beneficial effects:
the invention describes different household power utilization modes in a probability mode, thereby improving the application range of the strategy; secondly, data recorded by the intelligent ammeter basically come from a constructed ammeter target reading set, so that decoupling of association between ammeter readings and family activities is realized; finally, the strategy provided by the invention is considered from the overall perspective, and the storage battery basically cannot be in an overcharged/overdischarged state in the scheduling process.
Drawings
FIG. 1 is a scene model diagram of a smart meter user privacy protection strategy with smaller mutual information according to the invention;
FIG. 2 is a system block diagram abstracted from the scene model of FIG. 1;
FIG. 3 is a diagram of a mathematical model based on source coding;
FIG. 4 is a flow chart of the solution of the optimization problem constructed by the present invention;
FIG. 5 is a simulation result diagram of different strategies for simulating a household power consumption scene by using real power consumption data of a household A for one day;
fig. 6 is a simulation result diagram of different strategies in a period of time by using the real electricity consumption data of the family a to simulate the household electricity consumption scene (the electricity consumption power of the period is intercepted because the electricity consumption data is dense in one day);
FIG. 7 is a diagram of simulation results of different strategies for a period of time in a household power consumption scenario simulated using real power consumption data of family B;
fig. 8 is a histogram of mutual information between electricity meter data and original electricity consumption data and average charging and discharging power of a storage battery obtained after corresponding strategies are adopted by A, B two-family households;
fig. 9 is a comparison relationship diagram of the number of data in the non-target data set in the electricity meter data after A, B two-family households adopt corresponding strategies.
Detailed Description
The following detailed description is made with reference to the examples and the accompanying drawings, wherein the storage battery scheduling privacy protection method provided by the invention has smaller mutual information.
Referring to fig. 1 and 2, the entire privacy protecting system includes: 5 parts of a power grid company, a smart meter, an energy management unit, household appliances and energy storage equipment. The energy management unit is a control center for household electricity management, wherein a control strategy for the storage battery is preset, and the control strategy comprises the following steps: the charging and discharging behaviors of the storage battery and the power value obtained from the power grid; the household appliance continuously generates power utilization requirements according to the behaviors of the user and sends the power utilization requirements to the energy management unit; the energy storage device adopts a storage battery, and can be used as household electric equipment to consume electric energy and also can be used as another electric power supply device to supply energy for household appliances.
For the household power demand at a certain moment, the energy management unit can control the charging and discharging of the storage battery according to a preset storage battery scheduling strategy, so that the intelligent electric meter records the current power demand as other data, and the protection of the household power mode of the user is realized.
Because the storage battery is subject to hardware constraints (the maximum charge-discharge power of the storage battery and the capacity of the storage battery), a storage battery scheduling strategy which is preset in the energy management unit, can enable the original power consumption and the electric meter data to have small correlation degree, and enables the storage battery to have a small overcharge/overdischarge state needs to be explored. Specifically, the method for protecting the user privacy of the smart meter with smaller mutual information provided by the invention is mainly realized by the following steps:
step one, constructing a power utilization probability space to describe the power utilization mode of a family
Due to the difference of different household power consumption modes, the invention firstly describes the household power consumption mode which is the main body of privacy protection in a probability space mode, so that the obtained strategy has good applicability.
The power consumption at each moment is independent, so that the power consumption mode of the household can be described by using a probability space of a discrete memoryless information source, and the description formula is as follows:
Figure BDA0003183423200000051
wherein the following variables are involved:
1) a: a set of historical household power usage;
2) g: the set obtained after the repeated elements in the set A are removed, because the household electricity utilization modes are very close to each other every day, a large amount of repeated data exist in the historical electricity utilization data set A, and a set for describing the household electricity utilization characteristics needs to be constructed in a mode of removing the repeated elements;
3) ω: ω represents an element in set G;
4) h (·): a function for calculating the number of times an element appears in its belonging set;
5) g (·): function, which is used to calculate the number of all elements in a certain set.
For the construction of the probability space, the following convention is used:
1) the source alphabet is the same as the set G;
2) the probability of the source symbol is equal to the probability of the element in the set G appearing in the historical power utilization set A.
Step two, constructing a target reading set of the electric meter
In order to decouple the association between the electric meter readings and the household electricity consumption activities, an electric meter reading set is preset by the strategy provided by the invention, and the electric meter target reading set needs to meet the following constraint conditions:
1)
Figure BDA0003183423200000061
2)
Figure BDA0003183423200000062
3) sorting elements in the set output load set y from small to large, wherein y isiAnd yi+1Is any two adjacent elements in the set, then
Figure BDA0003183423200000063
4)
Figure BDA0003183423200000064
Wherein the content of the first and second substances,
Figure BDA0003183423200000065
indicating batteriesMaximum charge-discharge power, y is target reading set of electric meter
Constraints 1 and 2 are to ensure that all elements in the constructed target number set of the electric meter are utilized. Taking constraint 1 as an example: for a certain element y in the constructed electric meter target number setiIs provided with
Figure BDA0003183423200000066
Therefore, no matter how the energy management unit controls the charging and discharging of the storage battery, the data recorded by the intelligent ammeter cannot be yi。
Constraints 3 and 4 are to ensure that at least one target datum can be found in the established electricity metering number set through charging and discharging of the storage battery for any source symbol.
In order to make: 1) the final electric meter presents data in the electric meter target indication set, and the difference between the data is integral multiple of a fixed numerical value; 2) the energy management unit has a plurality of data when selecting the target indication, and the target data different from the existing algorithm is single; 3) from the point of view of single-point numerical values, the difference between the specific numerical value representing the current power and the power utilization representing the turning-on/turning-off of the useful equipment is half of the maximum charging and discharging power of the storage battery, and the privacy protection capability is strong. The integral multiple of half of the maximum charge-discharge power of the storage battery is selected as an electric indication number, namely:
Figure BDA0003183423200000067
where p represents the maximum charge-discharge power of the battery.
Step three, constructing an evaluation model for selecting target data from the electric meter target number set by the energy management unit at the time t
The factors influencing the strategy of the storage battery mainly comprise two aspects: 1) maximum charge-discharge power of the storage battery; 2) the capacity of the battery, both factors ultimately affect what data the energy management unit can select from the set of target readings for the meter to represent. Based on the idea of penalty function in optimization theory, for the readings selected by the energy management unit from the target reading set of the electric meter at the time t, the following evaluation model is established:
Figure BDA0003183423200000071
wherein, XtIs the power demand at time t, YtThe energy management unit selects target data used as electric meter readings from a preset electric meter target reading set.
And step four, constructing a strategy optimization model.
According to the analysis, the original power consumption is taken as an information source, the target reading of the electric meter is taken as a code word, an information source coding model is constructed on the basis of a rate distortion theory, the exploration of the optimal storage battery scheduling strategy is converted into the exploration of the optimal coding mode in the information source coding, and a corresponding optimization equation is established:
R(D)＝minI(X；Y)
Figure BDA0003183423200000072
wherein the intermediate variable D of the objective function is calculated by equation (5),
Figure BDA0003183423200000073
x is a variable representing real-time power consumption, Y is a variable representing the number of readings of the smart meter, XiIs a specific value, Y, representing the real-time power usagejIs a specific value representing the indication of the smart meter, the first two constraints being due to the nature of the transition probability matrix itself, the intermediate variable D not only being a given finite distortion but also being numerically equal to the desired power of the battery, the third constraint being due to: the search strategy is different depending on the error allowed by the energy management unit, that is, depending on the desired charge/discharge power of the battery.
Step five, the optimization equation in the step four is difficult to solve directly, so that the target equation is reconstructed into the minimum relative entropy between the power consumption and the target indication set of the ammeter, and the equation is solved based on a BA algorithm; and obtaining a transition probability matrix after solving, namely the final storage battery scheduling strategy. Referring to fig. 4, the specific solution is as follows:
knowledge in connection with information theory: 1) mutual information I (X; y) is defined as the joint distribution p (X; y) and product distribution p (X; y) relative entropy between; 2) let p (X) p (Y | X) be a given joint distribution, then the relative entropy is such that
Figure BDA0003183423200000081
Minimum distribution r*(Y) is the edge distribution of the joint probability distribution p (X) p (Y | X), i.e.:
r*(Y)＝∑xp(X)p(Y|X) (7)
the original equation can thus be reconstructed in the form of a double minimization:
Figure BDA0003183423200000082
and transforming the objective function which is difficult to solve the minimum mutual information into the minimum distance between two convex sets p (Y | X) and p (Y) through reconstruction.
Reconstructing the objective function as equation 8, and solving the minimum value of mutual information in the original function also translates to solving the minimum value of the distance between two probability sets p (Y) and p (Y | X). The solving steps of the objective function are as follows:
(1) fixing p (Y | X), and under the constraint of D ∑ p (X) p (Y | X) D (X, Y) and ∑ p (Y) 1, obtaining an extremum for p (Y) of r (D).
Let the auxiliary function be:
Figure BDA0003183423200000083
order to
Figure BDA0003183423200000084
Namely:
Figure BDA0003183423200000085
according to the constraint condition, eliminating lambda, and obtaining an extreme point:
p(Y)*＝∑p(X)p(Y|X) (11)
the "+" representation is the extreme point of one iteration and not the final result.
(2) Fixing p (Y), and under the same constraint condition, obtaining R (D) relative to p (Y | X).
As with (1), an assist function Q is set:
Figure BDA0003183423200000086
let μ be p (x) ln (λ), then
Figure BDA0003183423200000091
Order to
Figure BDA0003183423200000092
Namely:
Figure BDA0003183423200000093
simplifying the constraint conditions, and obtaining extreme points as follows:
Figure BDA0003183423200000094
(3) p (Y) obtained above*And p (Y | X)*Substituting the target equation, one can obtain:
Figure BDA0003183423200000095
D(S)＝∑p(X)p(Y|X)*d(X,Y) (17)
it can be found that the original equation is converted into a parametric equation, and the value of the minimum rate-distortion function under the condition of the corresponding limited distortion degree and the distortion degree can be obtained by selecting the proper value of S.
And (3) experimental verification:
the invention adopts real power utilization data to simulate a household power utilization scene to carry out simulation analysis on the strategy (BSS-CDH for short) provided by the invention, and carries out simulation verification on the strategy and the classical Best Effort (BE for short) and non-intrusive load balancing (NILL) strategies from the following aspects.
Referring to fig. 5-9, fig. 5 is a simulation result diagram of different strategies for simulating a household power consumption scene by using real power consumption data of a household a for one day; fig. 6 is a simulation result diagram of different strategies in a period of time by using the real electricity consumption data of the family a to simulate the household electricity consumption scene (the electricity consumption power of the period is intercepted because the electricity consumption data is dense in one day); FIG. 7 is a diagram of simulation results of different strategies for a period of time in a household power consumption scenario simulated using real power consumption data of family B; fig. 8 is a histogram of mutual information between electricity meter data and original electricity consumption data and average charging and discharging power of a storage battery obtained after corresponding strategies are adopted by A, B two-family households; fig. 9 is a comparison relationship diagram of the number of data in the non-target data set in the electricity meter data after A, B two-family households adopt corresponding strategies. From the accompanying fig. 5-9, the following conclusions can be drawn:
1. mutual information. The method is supported by taking an information theory as a theory, can directly calculate the size of original information contained in the electric meter data, is a measure of the correlation degree between the electricity utilization data recorded by the electric meter and the real electricity utilization data from the whole perspective, compares the final data formed by the method (BSS-CDH) with the traditional BE and NILL methods, and BSS-CDH has smaller mutual information.
2. And charging and discharging the average power of the storage battery. The charge-discharge power of the storage battery can directly influence the energy management unit to adjust the range of the electric readings, and simultaneously: 1) the smaller the charging and discharging power of the storage battery is, the longer the service life of the storage battery can be ensured, and the cost of the strategy is reduced; 2) the smaller the charge and discharge power of the storage battery is, the smaller the influence of the charge and discharge power of the storage battery on the strategy is. Compared with the traditional strategy, the BSS-CDH method has smaller average charging and discharging power of the storage battery.
3. Number of non-target set data. The evaluation index can be changed to better measure whether the strategy can run for a long time. Simulation results prove that the BSS-CDH method has smaller number of non-target set data and can operate for a long time according to a preset strategy.

Claims (6)

1. A smart electric meter user privacy protection method with smaller mutual information is characterized in that:
be provided with smart electric meter, energy management unit and battery at the user, energy management unit is the control maincenter of user's power consumption management, has wherein preset the control strategy to the battery, includes: the charging and discharging behaviors of the storage battery and the power value obtained from the power grid; the household appliance continuously generates power utilization requirements according to the behaviors of the user and sends the power utilization requirements to the energy management unit; a storage battery, which is used as a household electric appliance to consume electric energy and is also used as another electric power supply device to supply energy for the household electric appliance;
for the power consumption requirement of a user at a certain moment, the energy management unit controls the charging and discharging of the storage battery according to a preset storage battery scheduling strategy, so that the intelligent electric meter records the current power consumption requirement as other data, and the protection of the household power consumption mode of the user is realized;
the method comprises the following steps:
step one, constructing a power utilization power probability space to describe a household power utilization mode;
step two, constructing an ammeter target reading set;
thirdly, constructing an evaluation model for selecting target data from the ammeter target indication set by the energy management unit at the time t;
step four, taking the original power consumption as an information source, taking the target reading of the electric meter as a code word, constructing an information source coding model based on a rate distortion theory, converting the exploration of the optimal storage battery scheduling strategy into the exploration of the optimal coding mode in the information source coding, and establishing a corresponding optimization equation;
step five, reconstructing the equation in the step four into the minimum relative entropy between the power consumption and the target indication set of the electric meter, and solving the equation based on a BA algorithm; and obtaining a transition probability matrix after solving, namely the final storage battery scheduling strategy.
2. The smart meter user privacy protection method according to claim 1, characterized in that:
for the first step, the power utilization mode of the household is described by adopting a probability space of a discrete memoryless information source, and the description formula is as follows:
Figure FDA0003183423190000011
wherein the following variables are involved:
1) a: a set of historical household power usage;
2) g: the set obtained after the repeated elements in the set A are removed, because the household electricity utilization modes are very close to each other every day, a large amount of repeated data exist in the historical electricity utilization data set A, and a set for describing the household electricity utilization characteristics needs to be constructed in a mode of removing the repeated elements;
3) ω: ω represents an element in set G;
4) h (·): a function for calculating the number of times an element appears in its belonging set;
5) g (·): function, which is used to calculate the number of all elements in a certain set.
For the construction of the probability space, the following convention is used:
1) the source alphabet is the same as the set G;
2) the probability of the source symbol is equal to the probability of the element in the set G appearing in the historical power utilization set A.
3. The smart meter user privacy protection method according to claim 2, characterized in that:
for the second step, the target indication set of the electric meter needs to satisfy the following constraint conditions:
1)
Figure FDA0003183423190000021
2)
Figure FDA0003183423190000022
3) sorting elements in the set output load set y from small to large, wherein y isiAnd yi+1Is any two adjacent elements in the set, then
Figure FDA0003183423190000023
4)
Figure FDA0003183423190000024
Wherein the content of the first and second substances,
Figure FDA0003183423190000025
the maximum charge and discharge power of the storage battery is represented, and y is an ammeter target indication set;
constraint 1 and constraint 2 are used for ensuring that all elements in the constructed target number set of the electric meter can be utilized;
constraints 3 and 4 are to ensure that at least one target datum can be found in the established electricity metering number set through charging and discharging of the storage battery for any source symbol.
4. The smart meter user privacy protection method according to claim 1, characterized in that: in the second step, the integral multiple of half of the maximum charge-discharge power of the storage battery is selected as the electric indication number.
5. The smart meter user privacy protection method according to claim 3, wherein: in step three, the established evaluation model is as follows:
Figure FDA0003183423190000026
wherein, XtIs the power demand at time t, YtThe energy management unit selects target data used as electric meter readings from a preset electric meter target reading set.
6. The smart meter user privacy protection method according to claim 5, wherein: in step four, the established optimization equation is:
R(D)＝min I(X；Y)
Figure FDA0003183423190000027
wherein the intermediate variable D of the objective function is calculated by the following formula,
Figure FDA0003183423190000031
x is a variable representing real-time power consumption, Y is a variable representing the number of readings of the smart meter, XiIs a specific value, Y, representing the real-time power usagejIs a specific numerical value representing the number of the intelligent electric meter.
CN202110854025.8A 2021-07-28 2021-07-28 Smart electric meter user privacy protection method with smaller mutual information Pending CN113744078A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110854025.8A CN113744078A (en) 2021-07-28 2021-07-28 Smart electric meter user privacy protection method with smaller mutual information

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110854025.8A CN113744078A (en) 2021-07-28 2021-07-28 Smart electric meter user privacy protection method with smaller mutual information

Publications (1)

Publication Number Publication Date
CN113744078A true CN113744078A (en) 2021-12-03

Family

ID=78729231

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110854025.8A Pending CN113744078A (en) 2021-07-28 2021-07-28 Smart electric meter user privacy protection method with smaller mutual information

Country Status (1)

Country Link
CN (1) CN113744078A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111224393A (en) * 2018-11-27 2020-06-02 青岛海尔智能技术研发有限公司 Intelligent household electric energy scheduling optimization method and device and storage medium
CN112564151A (en) * 2020-12-07 2021-03-26 燕山大学 Multi-microgrid cloud energy storage optimization scheduling method and system considering privacy awareness

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111224393A (en) * 2018-11-27 2020-06-02 青岛海尔智能技术研发有限公司 Intelligent household electric energy scheduling optimization method and device and storage medium
CN112564151A (en) * 2020-12-07 2021-03-26 燕山大学 Multi-microgrid cloud energy storage optimization scheduling method and system considering privacy awareness

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
X. YIN AND H. AN: "A Novel Algorithm with Reduced Mutual Information for Smart Meter Privacy Protection", 2020 IEEE 5TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS (ICCCBDA), pages 265 - 269 *

Similar Documents

Publication Publication Date Title
Rajagopalan et al. Smart meter privacy: A utility-privacy framework
Giaconi et al. Privacy-aware smart metering: Progress and challenges
Tan et al. Increasing smart meter privacy through energy harvesting and storage devices
Chin et al. Privacy-protecting energy management unit through model-distribution predictive control
Giaconi et al. Smart meter privacy with renewable energy and an energy storage device
Yang et al. Cost-effective and privacy-preserving energy management for smart meters
Jawurek et al. Sok: Privacy technologies for smart grids–a survey of options
Tan et al. Privacy-cost trade-offs in demand-side management with storage
Yang et al. Optimal privacy-preserving energy management for smart meters
Egarter et al. Load hiding of household's power demand
Yao et al. The privacy analysis of battery control mechanisms in demand response: Revealing state approach and rate distortion bounds
Farokhi Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling
Sayfutdinov et al. Alternating direction method of multipliers for the optimal siting, sizing, and technology selection of Li-ion battery storage
Nafisi et al. Energy loss minimization using PHEVs as distributed active and reactive power resources: a convex quadratic local optimal solution
Zhu et al. Privacy protection using a rechargeable battery for energy consumption in smart grids
Giaconi et al. Smart meter privacy with an energy harvesting device and instantaneous power constraints
Hossain et al. Cost-friendly differential privacy of smart meters using energy storage and harvesting devices
Giaconi et al. Optimal demand-side management for joint privacy-cost optimization with energy storage
CN109587070B (en) Data aggregation method with privacy protection and load balancing functions in smart power grid
Avula et al. Design framework for privacy-aware demand-side management with realistic energy storage model
Sun et al. Combining electric vehicle and rechargeable battery for household load hiding
Adewole et al. DFTMicroagg: a dual-level anonymization algorithm for smart grid data
Giaconi et al. Smart meter data privacy
Shateri et al. Privacy-cost management in smart meters with mutual-information-based reinforcement learning
Koo et al. RL-BLH: Learning-based battery control for cost savings and privacy preservation for smart meters

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination