V2G power data aggregation method and system based on local differential privacy

Abstract

The invention discloses a V2G electric power data aggregation method and system based on local differential privacy. The method comprises the following steps: acquiring a signal of charging electric quantity of an electric vehicle and generating a charging data report; based on the privacy data in the collected data report, the charging pile adopts a stepped random response mechanism to disturb the charging starting time, and adopts Laplace noise to disturb the charging duration and the charging amount to obtain disturbed power utilization data; and aggregating the disturbed power consumption data to obtain a power consumption condition curve, and adjusting a power supply strategy according to the power consumption condition. According to the invention, sensitive information of a user is protected while power data analysis is carried out, and the security of real data is ensured.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a V2G power data aggregation method and system based on local differential privacy. Background Art

[0002] Smart grid is a modern power system that realizes intelligent management of power grid by integrating advanced information technology, sensor technology, automatic control technology, etc. Vehicle-to-grid (V2G) is an important subsystem of smart grid, which brings many advantages to smart grid, including renewable energy generation, solving power loss and providing fast power supply. By taking advantage of the characteristics of electric vehicle charging and discharging, the vehicle-to-grid network can help the power grid to achieve "peak shaving and valley filling", improve the stability of the power grid, reduce energy consumption and reduce the cost of power generation. Vehicle-to-grid is also suitable for some small-scale energy management systems. Obviously, the coverage of the charging pile location will affect the degree of participation. In the case of high participation, the total cost of the energy system will decrease and the electricity price will also decrease.

[0003] At the same time, V2G networks also introduce new privacy issues. Since the existing on-board battery capacity is insufficient to support long-term and long-distance operation, electric vehicles need to be charged frequently. During the charging process, sensitive information such as remaining power, geographic location, and charging time will be uploaded to the data center and provided to third-party scientific research institutions or enterprises for analysis. Although this analysis helps to better serve electric vehicles, data security is difficult to guarantee when untrusted third-party institutions are used. Therefore, the privacy, integrity, and confidentiality of V2G must be guaranteed to provide safe and efficient services.

[0004] In order to protect the security of user electricity consumption data, researchers have proposed different solutions, which are mainly divided into two types: one is to use anonymity and pseudonym technology to hide the user's real ID so as not to disclose the user's personal information; the other is to use differential privacy (DP) to add noise to the user data to protect the user's sensitive data. However, previous studies have shown that when the attacker has some auxiliary information, the anonymized data can be easily de-anonymized. In order to overcome the shortcomings of anonymization technology, differential privacy has become an important method to protect user sensitive data, requiring that the data query results before and after the disturbance are roughly the same, so that the attacker cannot determine whether a single tuple in the database has been modified. Differential privacy can not only provide strong privacy guarantees, but also is not affected by the attacker's background knowledge, and has flexible configuration and low resource overhead.

[0005] Differential privacy is mainly divided into centralized differential privacy (CDP) and local differential privacy (LDP). Central differential privacy assumes that a trusted data collector will not steal or leak the private information of the data owner. However, this assumption does not hold true in many real-world applications, especially since data is now considered the core asset of the owner. To this end, local differential privacy has been proposed, which allows each data owner to locally perturb their data using a random mechanism and then send the perturbed data to an untrusted data collector.

[0006] Differential privacy requires that the output of data query results is roughly the same so that the adversary cannot determine whether a single tuple in the database has been modified. It has been widely used to protect user privacy in data stream publishing. Specifically, these studies can be further divided into two paradigms: event-level privacy and user-level privacy. The former protects any single event occurring at a specific timestamp, while the latter protects all events of any individual within the entire timestamp. Kellaris et al. combined event-level differential privacy and user-level differential privacy and proposed a new concept of w-event privacy on infinite streams. w-event privacy protects any sequence of events occurring within any w timestamp window. For w = 1, w-event privacy is equivalent to event-level privacy, and when w is infinite, it is equivalent to user-level privacy. Both of the above schemes assume that there is a trusted data center to collect the user's original data. After collecting all the user's data, the data center calculates and publishes data that satisfies w-event differential privacy according to established rules. However, trusted data centers have the problem of expensive infrastructure deployment and also bring additional security risks. Summary of the invention

[0007] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] In view of the above existing problems, the present invention is proposed.

[0009] Therefore, the present invention provides a V2G power data aggregation method and system based on local differential privacy to solve the problem of privacy leakage caused by multiple consumption of privacy budget.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0011] In a first aspect, the present invention provides a V2G power data aggregation method based on local differential privacy, comprising:

[0012] Collect the electric vehicle charging power signal and generate charging data reports;

[0013] Based on the privacy data in the collected data reports, the charging pile uses a step random response mechanism to perturb the charging start time, and uses Laplace noise to perturb the charging time and charging amount to obtain the perturbed power consumption data;

[0014] The disturbed power consumption data is aggregated to obtain the power consumption curve, and the power supply strategy is adjusted according to the power consumption.

[0015] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0016] The method of collecting the electric vehicle charging power signal and generating a charging data report comprises the following steps:

[0017] Collect and translate the electric vehicle charging power signal to obtain the physical signal of the electric vehicle charging;

[0018] The script cleans and calculates the data and generates a charging data report when the electric vehicle is fully charged, including the charging time and charging amount;

[0019] When an electric vehicle starts charging again, data collection for the next data report begins.

[0020] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0021] The step random response mechanism is used to disturb the charging start time, comprising the following steps:

[0022] Given the distance d between the input time s and the potential output time y, all possible output times y Divide into m groups;

[0023] According to the selected parameter c, calculate the probability distribution q(y|s) from the original data s to the perturbed data y;

[0024] According to the calculated disturbance probability distribution, the final output time y is generated by randomizing the original charging start time s.

[0025] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0026] The probability distribution q(y|s) of the calculation from the original data s to the perturbed data y is expressed as:

[0027]

[0028] Among them, α1(s),...,α m (s) is the perturbation probability based on distance.

[0029] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0030] The method of using Laplace noise to disturb the charging time and the charging amount includes the following steps:

[0031] Determine a raw data sequence consisting of the user's charging amount 1 = (l1, l2, ..., l n );

[0032] Choose a privacy budget ε0;

[0033] For each original charge l i Add Laplace noise Laplace (1 / ε0) to generate the disturbed charge

[0034] Assume that the global sensitivity of the query function is 1;

[0035] Each user's charging amount after disturbance Send to the data aggregation end;

[0036] The data aggregation end receives the perturbed data from all users and performs aggregation operations based on the data.

[0037] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0038] Aggregating the disturbed power consumption data to obtain a power consumption curve includes the following steps:

[0039] The charging start time y after the collection unit receives the disturbance from the i-th charging pile;

[0040] According to the step perturbation mechanism, using the perturbation probability distribution Estimate the original charging start time corresponding to the disturbance data y

[0041] The data aggregation end divides the time range into multiple intervals T = (T1, T2, ..., Tm) according to the analysis requirements, and counts the number of charging users and charging amounts in different time periods;

[0042] Based on the estimated original charging time and charging amount, the total charging amount and the number of charging users in different time periods are counted to form a corresponding histogram.

[0043] As a preferred solution of the V2G power data aggregation method based on local differential privacy described in the present invention, wherein:

[0044] The perturbation probability distribution It is expressed as:

[0045]

[0046] In a second aspect, the present invention provides a V2G power data aggregation system based on local differential privacy, comprising:

[0047] A data acquisition module, used to collect the electric vehicle charging power signal and generate a charging data report;

[0048] The privacy protection processing module is used to use a step random response mechanism to disturb the charging start time based on the privacy data in the collected data report, and to use Laplace noise to disturb the charging time and charging amount to obtain disturbed power consumption data;

[0049] The power supply strategy adjustment module is used to aggregate the disturbed power consumption data to obtain a power consumption curve and adjust the power supply strategy according to the power consumption.

[0050] In a third aspect, the present invention provides a computing device, comprising:

[0051] Memory, used to store programs;

[0052] A processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the V2G power data aggregation method based on local differential privacy.

[0053] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the V2G power data aggregation method based on local differential privacy are implemented.

[0054] Beneficial effects of the present invention: The present invention proposes to submit relevant charging information when each user finishes charging, and the user end adds noise based on the collected power consumption data to obtain disturbed power consumption data and sends it; a step random response mechanism is used for the time disturbance, so that it will disturb near the real data with a greater probability and disturb in an interval far away from the real data with a smaller probability; the parameters of the step random response mechanism all give the optimal selection situation, which can achieve good privacy protection while realizing data aggregation analysis; Laplace noise is added to sensitive data such as charging time and total charging amount to protect privacy, prevent attackers from background knowledge attacks and reduce computing overhead; the data aggregation end aggregates the received disturbed power consumption data to obtain a power consumption curve, and adjusts the power supply strategy according to the power consumption; the user end does not need to frequently send disturbance data to the data aggregation end, avoids privacy leakage caused by multiple consumption of privacy budget, and ensures data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0056] Figure 1 A basic flow chart of a V2G power data aggregation method based on local differential privacy provided by an embodiment of the present invention;

[0057] Figure 2 A diagram of an electric vehicle charging and discharging network architecture for a V2G power data aggregation method based on local differential privacy provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0062] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0063] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] Example 1

[0065] Reference Figure 1 , is an embodiment of the present invention, which provides a V2G power data aggregation method based on local differential privacy. The method is applied to the user end and the data aggregation end in V2G. The user end is the sender of the data, such as Figure 1 As shown, the following steps are included:

[0066] S1: Collect the electric vehicle charging power signal and generate a charging data report;

[0067] In the embodiment of the present application, the signal of the electric vehicle charging power is collected and translated to obtain the physical signal of the electric vehicle charging, and the data is cleaned and calculated through the script, and a charging data report is generated when the electric vehicle charging is completed, which at least includes information such as charging time and charging amount. When the electric vehicle starts charging again, the data collection of the next data report begins.

[0068] In the embodiment of the present application, the charging pile determines the original power consumption data (charging start time hour:min) to be formatted from the original power consumption data generated by the electric vehicle, and formats the original power consumption data to be formatted:

[0069] s=hour×60+min

[0070] Where, s represents the minute of the charging start time, hour and min represent the hours and minutes of the charging start time in 24-hour format respectively.

[0071] In the embodiment of the present application, the formatted power consumption data is obtained, and a data report including the charging start time s∈[0,1439] and the charging amount sequence l is generated.

[0072] S2: Based on the privacy data in the collected data reports, the charging pile uses a step random response mechanism to perturb the charging start time, and uses Laplace noise to perturb the charging time and charging amount to obtain the perturbed power consumption data;

[0073] In the embodiment of the present application, the charging pile collects the privacy data in the data report, and according to the preset privacy budget ε, the charging pile uses a step random response to disturb the charging start time, and uses Laplace noise to disturb the charging time and charging amount, to obtain the disturbed power consumption data. Since the charging start time and charging amount are independent of each other, there is no privacy budget segmentation, so the privacy budget taken when disturbing them is the preset budget ε, realizing ε-LDP. After the disturbance is completed, the disturbed data is sent to the corresponding convergence unit.

[0074] It should be noted that the present invention does not limit the “preset privacy budget is ε”, and it can be set by those skilled in the art according to engineering needs or experimental data.

[0075] In the embodiment of the present application, step S2, for any charging pile, is as follows:

[0076] S21: The method of using step random response to disturb the charging start time s is as follows:

[0077] Given a domain D = [0,1439], for any s∈D, all possible output times can be divided into m groups G1(s),...,G according to their distance to s. m (s). Note that this division G j (s) depends on the input time s. For each input time s, all m time periods and the perturbation probability (the probability of perturbing s to any output time y) will be calculated as:

[0078]

[0079] Among them, α1(s),...,α m (s) are the distance-based perturbation probabilities, which are used to describe the probability of perturbing from s∈D to positions in m different groups, and the perturbation probability gap between each adjacent group is the same in α1(s),...,α m (s).

[0080] In addition, for each input time s, the sum of all perturbation probabilities should satisfy: ∑ j∈[1,m] ∑ y∈Gj(s) q(y|s)=1.

[0081] S22: Use Laplace noise to perturb the charge sequence l = (l1,…,l n ) for each element l i The calculation formula is as follows:

[0082]

[0083] Among them, l i is the element of the perturbation charge sequence, ε0 is the privacy budget, and Laplace(·) represents the Laplace mechanism.

[0084] Laplace mechanism: Given an arbitrary query function Q with global sensitivity Δ(Q): D→R d , the random algorithm M(D)=Q(D)+Y satisfies ε-differential privacy if Y is randomly sampled from the Laplace distribution Lap(x|b), b=Δ(Q) / ε. The probability density function of the Laplace distribution is expressed as:

[0085]

[0086] Let Δ denote the sensitivity of l and t, and under local differential privacy, Δ=1.

[0087] In the embodiment of the present application, the user terminal sends the disturbed data After being sent to the data aggregation end, the data aggregation end performs aggregation operations based on the received data to realize the query of power consumption and number of users in different time periods.

[0088] Further, step S21 includes:

[0089] S211: Determine the grouping of output time y:

[0090] d=|ys|

[0091] Where d is the distance between the output time y and the input time s;

[0092] In the embodiment of the present application, a length vector {β1(s),…,β m (s)}, all in group G j The distance d between the data in (s) (1≤j≤m) and s does not exceed β j (s). Since G1(s) is the group closest to the input position s, β1(x)>β2(x)>…>β m (x). You can define a suitable length vector according to actual needs to find the optimal result.

[0093] S212: Determine the disturbance probability:

[0094] The output times can be divided into m groups according to their distances to the input times. Given any two output times y and y′ that are located in two adjacent groups, y∈G j (s) and y′∈G j+1 (s), we have probability q(y|s)=q(y′|s)+Δ(s), where the step size Δ(s)∈[0,1) is the constant probability difference for any two adjacent groups of input s;

[0095] Use α max (s) and α min (s) represent α1(s),...,α m The maximum and minimum probability in (s), we have α max (s) = α1(s) and α min (s) = α m (s). For all input times s∈D, we specify a constant c ≥ 1 as α max (s) and α min Therefore, for a given input time s, the constant probability difference between adjacent groups in the perturbation probability distribution is expressed as:

[0096]

[0097] For each s∈D, the sum of the perturbation probabilities of all output times is 1. Based on the difference in perturbation probabilities of output times in different groups and the number of output times in each group, all perturbation probabilities, including α max (s) and α min (s):

[0098]

[0099] α max (s) = α min (s)·c

[0100] Where d is the size of the time domain, |G j (s)|is group Gj (s). It should be noted that for different input time s, different α1(s),...,α m (s), because for different s, the size of each group |G j (s)| may be different. Therefore, the privacy upper bound ∈ can be calculated (applicable to any two input times s, s′∈D).

[0101] S213: Select parameter c:

[0102] Since ∈ is positively correlated with c, for any desired ∈-LDP, the desired c can be uniquely computed using ∈, D, and m.

[0103] S3: Aggregate the disturbed power consumption data to obtain a power consumption curve, and adjust the power supply strategy according to the power consumption.

[0104] In the embodiment of the present application, step S3 includes:

[0105] S31, the collection unit processes the disturbed charging start time, and receives the disturbance data y∈G from the i-th charging pile j (s) and the step perturbation mechanism can be obtained:

[0106]

[0107] By Estimate the original data corresponding to the perturbed data y

[0108] S32. Calculate the user's electricity consumption:

[0109] The data aggregation end can divide the time range into multiple intervals T = (T1,…,T m ), which is convenient for counting the number of users and charging amount in different time periods. Based on the estimated original starting charging interval and charging amount sequence, we can get the histogram of its charging amount changing with time. Let L i Indicates that the interval user is in T i The total charging capacity of the interval, if a user has Then L i+j = l i , all collected user data can be accumulated to get the aggregation result SL i , we can get the histogram of the charge capacity changing with time. According to the same method, let c i represents the number of users charging in the i-th time interval. If there is a Then let {c j ,…,c j+|l|} is increased by one, and a histogram of the number of users at different charging times can be drawn.

[0110] In an embodiment of the present application, the power grid control center can adjust the electricity prices in different time periods based on the curve of electricity consumption changing over time generated by the aggregation unit, and can analyze the electricity consumption in different areas according to the areas under the jurisdiction of the aggregation unit, thereby recommending that electric car owners can go to designated areas for power supply during appropriate time periods to alleviate the power supply pressure during peak hours.

[0111] This embodiment also provides a V2G power data aggregation system based on local differential privacy, including:

[0112] A data acquisition module, used to collect the electric vehicle charging power signal and generate a charging data report;

[0113] The privacy protection processing module is used to use a step random response mechanism to disturb the charging start time based on the privacy data in the collected data report, and to use Laplace noise to disturb the charging time and charging amount to obtain disturbed power consumption data;

[0114] The power supply strategy adjustment module is used to aggregate the disturbed power consumption data to obtain a power consumption curve and adjust the power supply strategy according to the power consumption.

[0115] Furthermore, it also includes:

[0116] Memory, used to store programs;

[0117] A processor is used to load the program to execute the V2G power data aggregation method based on local differential privacy.

[0118] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the V2G power data aggregation method based on local differential privacy.

[0119] The storage medium proposed in this embodiment and the V2G power data aggregation method based on local differential privacy proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0120] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0121] Example 2

[0122] This is an embodiment of the present invention, which provides a V2G power data aggregation system based on local differential privacy, including a data acquisition module, a privacy protection processing module and a power supply strategy adjustment module;

[0123] In the embodiment of the present application, the data acquisition module includes a signal for collecting the charging power of the electric vehicle and generating a charging data report;

[0124] In the embodiment of the present application, the data acquisition module is responsible for collecting raw power consumption data from the user end, and may include preliminary data processing or formatting work;

[0125] In the embodiments of the present application, the user end refers to the entity that directly generates electricity consumption data and participates in the privacy protection process. In the V2G scenario, the user end includes electric vehicles and charging piles. Electric vehicles have two-way energy trading capabilities, which help maintain the stability of the power grid. They can act as energy producers and charge batteries during peak hours. They can also charge during off-peak hours and act as energy consumers. The charging pile is responsible for data exchange between the electric vehicle and the aggregation unit. The real-time state of charging and discharging of the electric vehicle is monitored, and the relevant information is uploaded to the aggregation unit after disturbance.

[0126] In an embodiment of the present application, the user end includes adding noise to the generated power consumption data, such as charging amount, charging time and other information to obtain disturbance data, and sending the disturbance data to the data aggregation end.

[0127] In an embodiment of the present application, the user end adds noise to the power consumption data to obtain disturbance data, including the charging pile determining and processing the original power consumption data to be formatted from the original power consumption data generated by the electric vehicle, obtaining the formatted power consumption data, and generating a one-dimensional vector including the charging start time and the charging amount sequence at the specified sampling time; the charging pile uses a step random response to disturb the charging start time, and uses Laplace noise to disturb the charging amount to obtain the disturbed power consumption data.

[0128] In an embodiment of the present application, the method of adding noise includes adding noise to the charging time by using a step random response mechanism, and adding noise to the charging amount by using a Laplace mechanism.

[0129] In an embodiment of the present application, the step random response mechanism includes grouping the disturbance results according to a predetermined step structure based on the distance from the original data, and defining the optimal disturbance probability of each group. The disturbance probability difference between each adjacent group is the same and decreases with the distance, ensuring that the adjacent data interval is disturbed with a greater probability. These probabilities are used to describe the probability of the data being disturbed from the input time to the position in each group, and finally the disturbance is performed according to the determined disturbance probability.

[0130] In the embodiment of the present application, the privacy protection processing module includes implementing local differential privacy technology on the user side, and disturbing sensitive data (such as charging start time, charging duration and charging amount) by adding noise to protect user privacy. This step is also completed on the user side.

[0131] In the embodiment of the present application, the privacy protection processing module includes: based on the privacy data in the collected data report, the charging pile uses a step random response mechanism to perturb the charging start time, and uses Laplace noise to perturb the charging time and charging amount to obtain the perturbed power consumption data;

[0132] In an embodiment of the present application, the power supply strategy adjustment module includes aggregating the disturbed power consumption data to obtain a power consumption curve, and adjusting the power supply strategy according to the power consumption.

[0133] In the embodiment of the present application, the data aggregation end includes a convergence unit and a power grid control center; the convergence unit serves as a communication medium between the charging pile and the upper power grid control center, and is responsible for collecting the power data of the charging piles within its jurisdiction, and uploading it to the power grid control center, conveying the dispatching orders of the power grid control center, and completing the orderly charging and discharging operations of electric vehicles. The power grid control center is responsible for publishing the real-time power demand and electricity price of the entire power grid and other related information, while monitoring the power consumption and location information of electric vehicles, and formulating the optimal control strategy based on the monitored data and the power grid's own related needs.

[0134] In an embodiment of the present application, the data aggregation end includes determining the electricity usage of users within the jurisdiction of each aggregation unit in different time periods based on disturbance data, aggregating data, and using the electricity usage of users within the jurisdiction of each aggregation unit in different time periods to adjust electricity prices and make power supply recommendations to users, thereby improving the regulation capacity of the power system.

[0135] In an embodiment of the present application, the data aggregation method includes estimating the disturbed time data according to the characteristics of the step disturbance mechanism, and superimposing the user's electricity consumption data in the corresponding time period, thereby reducing the impact of noise and obtaining more accurate results while ensuring user privacy.

[0136] In the embodiment of the present application, the power supply strategy adjustment module includes receiving the disturbed power consumption data from the user end through the data aggregation end, summarizing and analyzing it, forming an overall power consumption curve, and adjusting the power supply strategy based on this information, such as adjusting the electricity price or power supply recommendation, etc. This part of the work is usually completed by the power grid control center or a similar data processing center.

[0137] In an embodiment of the present application, the data aggregation terminal performs statistical analysis based on the disturbance data sent by the user, estimates the changes in the electricity consumption data of all users in each area over time, and can adjust the electricity price and power supply based on the analysis results.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A V2G power data aggregation method based on local differential privacy, characterized in that: include: Collect the electric vehicle charging power signal and generate charging data reports; Based on the privacy data in the collected data reports, the charging pile uses a step random response mechanism to perturb the charging start time, and uses Laplace noise to perturb the charging time and charging amount to obtain the perturbed power consumption data; The disturbed power consumption data is aggregated to obtain the power consumption curve, and the power supply strategy is adjusted according to the power consumption.

2. The V2G power data aggregation method based on local differential privacy as claimed in claim 1, characterized in that: The method of collecting the electric vehicle charging power signal and generating a charging data report comprises the following steps: Collect and translate the electric vehicle charging power signal to obtain the physical signal of the electric vehicle charging; The script cleans and calculates the data and generates a charging data report when the electric vehicle is fully charged, including the charging time and charging amount; When an electric vehicle starts to charge again, data collection for the next data report begins.

3. The V2G power data aggregation method based on local differential privacy according to claim 1 or 2, characterized in that: The step random response mechanism is used to disturb the charging start time, comprising the following steps: Divide all possible output times y into m groups according to the distance d between the input time s and the potential output time y; According to the selected parameter c, calculate the probability distribution q(y|s) from the original data s to the perturbed data y; According to the calculated disturbance probability distribution, the final output time y is generated by randomizing the original charging start time s.

4. The V2G power data aggregation method based on local differential privacy as claimed in claim 3, characterized in that: The probability distribution q(y|s) of the calculation from the original data s to the perturbed data y is expressed as: Among them, α1(s),...,α m (s) is the perturbation probability based on distance.

5. The V2G power data aggregation method based on local differential privacy as claimed in claim 4, characterized in that: The method of using Laplace noise to disturb the charging time and the charging amount includes the following steps: Determine a raw data sequence l = (l1, l2, ..., l n ); Choose a privacy budget ε0; For each original charge l i Add Laplace noise Laplace (1 / ε0) to generate the disturbed charge Assume that the global sensitivity of the query function is 1; Each user's charging amount after disturbance Send to the data aggregation end; The data aggregation end receives the perturbed data from all users and performs aggregation operations based on the data.

6. The V2G power data aggregation method based on local differential privacy as claimed in claim 5, characterized in that: Aggregating the disturbed power consumption data to obtain a power consumption curve includes the following steps: The charging start time y after the collection unit receives the disturbance from the i-th charging pile; According to the step perturbation mechanism, using the perturbation probability distribution Estimate the original charging start time corresponding to the disturbance data y The data aggregation end divides the time range into multiple intervals T = (T1, T2, ..., Tm) according to the analysis requirements, and counts the number of charging users and charging amounts in different time periods; Based on the estimated original charging time and charging amount, the total charging amount and the number of charging users in different time periods are counted to form a corresponding histogram.

7. The V2G power data aggregation method based on local differential privacy as claimed in claim 6, characterized in that: The perturbation probability distribution It is expressed as:

8. A system based on the V2G power data aggregation method based on local differential privacy according to claim 1, characterized in that: A data acquisition module, used to collect the electric vehicle charging power signal and generate a charging data report; The privacy protection processing module is used to use a step random response mechanism to disturb the charging start time based on the privacy data in the collected data report, and to use Laplace noise to disturb the charging time and charging amount to obtain disturbed power consumption data; The power supply strategy adjustment module is used to aggregate the disturbed power consumption data to obtain a power consumption curve and adjust the power supply strategy according to the power consumption.

9. A computing device, characterized in that include: Memory, used to store programs; A processor, configured to load the program to execute the steps of the V2G power data aggregation method based on local differential privacy as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the V2G power data aggregation method based on local differential privacy as described in any one of claims 1 to 7 are implemented.

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