Power load data privacy protection method and system, storage medium and electronic equipment

Abstract

The invention provides an electrical load data privacy protection method and system, a storage medium and electronic equipment, and is applied to the technical field of electrical data processing. Actual electrical load data of a user and noise data of an energy storage device are collected in real time, and charging and discharging behaviors of the energy storage device are controlled through electricity price linkage; the optimization noise is superposed to the actual power utilization load data to generate target power utilization load data meeting the power grid transmission limitation, the energy storage charging and discharging noise is utilized to cover the real power utilization mode of the user, the energy storage charging and discharging behavior is controlled through electricity price linkage to guarantee the economic benefit, and the dynamic balance of the privacy intensity and the energy storage economical efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical data processing, and in particular, to a method, system, storage medium, and electronic device for protecting the privacy of electricity consumption load data. Background Art

[0002] In the process of power digital transformation, a large amount of electricity consumption load data generated by measurement devices such as smart meters contains sensitive user behavior information.

[0003] However, traditional privacy protection methods face the following problems: 1. Anonymization and encryption technologies are difficult to balance data availability and privacy, and virtual noise injection is likely to cause instability in power grid operation; 2. The existing differential privacy mechanism uses digital noise generated by algorithms, resulting in a conflict between the random charge and discharge behavior of energy storage devices and the economic benefit target; 3. There is a lack of quantitative control means for the personalized differences between user privacy needs and economic demands. Especially in the scenario of high-precision load data analysis, attackers can identify electrical appliance usage characteristics through non-intrusive load decomposition technology, and traditional data aggregation methods have defects such as large communication overhead and high terminal computing load, and poor applicability.

[0004] Therefore, how to balance the privacy protection intensity of electricity consumption load data and energy storage economy has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, system, storage medium, and electronic device for protecting the privacy of electricity consumption load data that overcomes or at least partially solves the above problems. The technical solutions are as follows:

[0006] A method for protecting the privacy of electricity consumption load data includes:

[0007] Obtaining the actual electricity consumption load data output by a user's electrical appliance at the current moment;

[0008] Obtaining noise data generated by the charge and discharge behavior of an energy storage device at the current moment, where the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charge and discharge power constraint of the energy storage device, and the charge and discharge behavior of the energy storage device is controlled by the energy storage parameter group and real-time electricity price data at the current moment;

[0009] Superimposing the actual electricity consumption load data and the noise data to generate target electricity consumption load data, where the target electricity consumption load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target electricity consumption load data is displayed on a smart meter.

[0010] Optionally, the energy cumulative usage constraint is:

[0011]

[0012] wherein, is the current stored energy in the energy storage device at time ; is the index of time ; is the initial stored energy in the energy storage device at the 0th time; is at the time, the noise data generated by the energy storage device;

[0013] and / or, the maximum capacity constraint is:

[0014]

[0015] wherein, is the maximum storage capacity of the energy storage device;

[0016] and / or, the charge-discharge power constraint is:

[0017]

[0018] wherein, is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive; is at the time, the noise data generated by the energy storage device;

[0019] and / or, the power transmission constraint is:

[0020]

[0021] wherein, is the time, the target power consumption load data.

[0022] Optionally, obtaining the noise data generated by the charge-discharge behavior of the energy storage device at the current time includes:

[0023] Obtaining the differential privacy bounded noise probability density function of the energy storage device at the current time, wherein the differential privacy bounded noise probability density function satisfies the actual energy storage physical constraints of the energy storage device;

[0024] Inputting the user privacy requirement control parameter into the differential privacy bounded noise probability density function, so that the energy storage device performs charge-discharge behavior according to the differential privacy bounded noise probability density function at the current time;

[0025] Obtain the noise data generated by the charging and discharging behavior of the energy storage device according to the differential privacy bounded noise probability density function.

[0026] Optionally, the method further includes:

[0027] Based on the differential privacy bounded noise probability density function, use the sand cat swarm optimization algorithm to perform multi-objective collaborative optimization on the energy storage device, and output an optimal energy storage parameter set, where the multi-objective collaborative optimization includes simultaneously performing collaborative optimization on the objective function of maximizing the privacy protection effect and the objective function of minimizing the energy storage configuration cost, and the optimal energy storage parameter set includes an optimal function position parameter and an optimal function scale parameter;

[0028] Based on the optimal function position parameter and the optimal function scale parameter, update the differential privacy bounded noise probability density function, so that the energy storage device performs charging and discharging behavior according to the updated differential privacy bounded noise probability density function.

[0029] Optionally, the differential privacy bounded noise probability density function is:

[0030]

[0031] Wherein, is the differential privacy bounded noise probability density function; represents the base of the natural logarithm; is the function position parameter; is the function scale parameter; is the user privacy requirement control parameter; is the maximum discharge power of the energy storage device, which is negative; is the maximum charging power of the energy storage device, which is positive; is the function normalization factor, expressed as:

[0032] .

[0033] Optionally, the method further includes:

[0034] Obtain the real-time electricity price data at the current moment;

[0035] Combine the price difference degree corresponding to the real-time electricity price data and the future opportunity cost to obtain the moving average adjustment factor at the current moment;

[0036] Use the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function, so that the energy storage device performs charging and discharging behavior according to the differential privacy bounded noise probability density function after adjusting the function position parameter.

[0037] Optionally, using the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function includes:

[0038] Input the moving average adjustment factor into the formula:

[0039]

[0040] Obtain the function position parameter of the differential privacy bounded noise probability density function after dynamic adjustment, where is the function position parameter after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive.

[0041] A power consumption load data privacy protection system includes: user appliances, an energy storage device, a smart meter, and an energy management system. Both the user appliances and the energy storage device are powered by the power grid. The smart meter is used to receive the power consumption information output by the user appliances and the energy storage device, and perform information interaction with the energy management system. The energy management system is used to execute the power consumption load data privacy protection method described above.

[0042] A computer-readable storage medium stores a program thereon, and when the program is executed by a processor, it implements the power consumption load data privacy protection method described above.

[0043] An electronic device includes at least one processor, at least one memory connected to the processor, and a bus; wherein, the processor and the memory communicate with each other through the bus; the processor is used to call the program instructions in the memory to execute the power consumption load data privacy protection method described above.

[0044] With the above technical solutions, the method, system, storage medium and electronic device for protecting the privacy of electricity load data provided by the present invention obtain the actual electricity load data output by the user's electrical appliances at the current moment; obtain the noise data generated by the charging and discharging behavior of the energy storage device at the current moment, wherein the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charging and discharging power constraint of the energy storage device, and the charging and discharging behavior of the energy storage device is controlled by the energy storage parameter group and the real-time electricity price data at the current moment; superimpose the actual electricity load data and the noise data to generate the target electricity load data, wherein the target electricity load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target electricity load data is displayed on the smart meter. By superimposing the optimized noise data that meets the economic constraints of the energy storage device on the actual electricity load data of the user in real time, the present invention not only uses noise to cover the real electricity consumption pattern to achieve privacy protection under the premise of meeting the power transmission limit of the power grid, but also guarantees the economic benefits by controlling the charging and discharging behavior of the energy storage device through electricity price linkage, achieving the dynamic balance between privacy strength and energy storage economy.

[0045] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are given below. Brief Description of the Drawings

[0046] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0047] Figure 1 It shows a schematic flow chart of an implementation manner of the method for protecting the privacy of electricity load data provided by the embodiment of the present invention;

[0048] Figure 2 It shows a schematic principle flow chart of the sand cat swarm optimization algorithm provided by the embodiment of the present invention;

[0049] Figure 3 It shows a schematic principle diagram of putting the bounded noise probability density function into the moving average drift mechanism provided by the embodiment of the present invention;

[0050] Figure 4 It shows a schematic architecture diagram of the system for protecting the privacy of electricity load data provided by the embodiment of the present invention;

[0051] Figure 5The structural schematic diagram of the power consumption load data privacy protection device provided by the embodiment of the present invention is shown;

[0052] Figure 6 The structural schematic diagram of the electronic device provided by the embodiment of the present invention is shown. Detailed implementation manners

[0053] Hereinafter, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0054] With the large-scale deployment of advanced metering devices such as smart meters and smart energy management systems, the new power system has the ability to collect a large amount of power operation data and user power consumption data, which brings unprecedented opportunities and challenges to the innovative development of the power industry.

[0055] Large-scale and high-precision power consumption load data creates multi-dimensional value for the various participants in the power system, which can help users tap the energy-saving potential, achieve refined energy management, and thus reduce the power consumption cost. However, along with it, there is sensitive user information contained in the power consumption load data. By deeply analyzing these data, lawbreakers can identify the types of electrical appliances used by users and their usage time, and even infer the users' daily work and rest patterns and behavior patterns, seriously infringing on users' privacy.

[0056] In the context of digital transformation, the public's privacy protection awareness has been significantly enhanced, and users' attention to the security of power consumption load data has continued to increase. Therefore, it is necessary to build a security protection barrier for sensitive information such as users' power consumption behaviors.

[0057] However, traditional privacy protection methods face many challenges in practical applications. For example, the anonymization method is difficult to provide sufficient protection for power consumption load data, while methods such as information encryption and data aggregation have relatively high requirements for the performance of terminal devices and will generate significant communication overheads. In addition, the method of directly adding virtual noise to power consumption load data by algorithms will seriously affect the stable operation of the power system. These defects have greatly limited the application value of traditional privacy protection methods.

[0058] As an important technical means, the energy storage system can, to a certain extent, solve the contradiction between privacy protection and power consumption economic benefits. The random charge and discharge behaviors caused by the traditional differential privacy noise mechanism may significantly reduce the economic benefits of the energy storage device as a user-side demand response device, making it difficult to effectively balance between user privacy and economy.

[0059] In the era of big data in the electricity marketization, when designing a quantifiable method for protecting the privacy of electricity consumption load data, the following key issues must be addressed: 1. How to inject noise into the load data without affecting the stable operation of the power grid; 2. How to design a privacy protection method in combination with the differentiated privacy needs of users to achieve personalized privacy protection of electricity consumption load data; 3. How to design an energy storage optimization scheme that takes into account differentiated privacy needs and economy to ensure that the privacy of users' electricity consumption load data is fully protected without compromising the economic benefits of energy storage.

[0060] Based on this, in the embodiments of the present invention, a method for protecting the privacy of electricity consumption load data is provided, which can collect the actual electricity consumption load data of users and the noise data of energy storage devices in real time, control the charging and discharging behavior of the energy storage device through electricity price linkage, and superimpose the optimized noise on the actual electricity consumption load data to generate target electricity consumption load data that meets the power grid transmission limit, realizing electricity privacy protection while ensuring the economy of energy storage, thereby balancing the dynamic relationship between privacy strength and energy storage operation benefits, and ensuring that under the premise of not feeding electricity back to the power grid, using noise to cover the real electricity consumption pattern and optimizing the energy storage scheduling through electricity price strategies.

[0061] As Figure 1 shown, a schematic flowchart of an implementation manner of the method for protecting the privacy of electricity consumption load data provided by the embodiments of the present invention is shown, and the method may include:

[0062] S100. Obtain the actual electricity consumption load data output by the user's electrical appliances at the current moment.

[0063] Among them, the user's electrical appliances refer to various power equipment used by household users or commercial users, which may include lighting equipment, air conditioners, refrigerators, washing machines, and televisions, etc. The user's electrical appliances obtain electrical energy through the power grid to meet the daily life or work needs of users.

[0064] Among them, the actual electricity consumption load data refers to the electric power consumed by the user's electrical appliances within a specific time. The actual electricity consumption load data reflects the electricity consumption demand and electricity consumption pattern of the user at this moment.

[0065] S110. Obtain the noise data generated by the charging and discharging behavior of the energy storage device at the current moment, where the current stored energy in the energy storage device meets the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data meets the charging and discharging power constraint of the energy storage device, and the charging and discharging behavior of the energy storage device is controlled by the energy storage parameter group and the real-time electricity price data at the current moment.

[0066] Among them, the energy storage device refers to a device that can store electrical energy and release it when needed. The energy storage device can act as a power source to provide the required electrical energy for other electrical equipment, or can also act as a specific electrical appliance to consume electrical energy.

[0067] Among them, the noise data refers to the algebraic value of the charge-discharge power generated during the charge-discharge process of the energy storage device due to its operation. When the noise data is superimposed on the actual power consumption load data, it can be used for privacy protection to mask the user's true power consumption pattern while meeting the power constraints of the energy storage device. During the charge-discharge behavior of the energy storage device, the operation of the energy storage device can be regarded as the injection of real physical noise into the power consumption load data.

[0068] In the embodiment of the present invention, the public power grid provides electrical energy for both user appliances and the energy storage device. According to the actual needs of the user, the energy storage device can flexibly switch between providing electrical energy and consuming electrical energy. The smart meter can simultaneously receive the power consumption information from the user appliances and the energy storage device for comprehensive power consumption monitoring and management.

[0069] Among them, the energy cumulative usage constraint refers to the limitation of the energy usage of the energy storage device within a specific time, ensuring that the discharge or charge behavior of the energy storage device does not exceed the historical cumulative used electricity, thus contributing to the reasonable management of the energy efficiency and service life of the energy storage device.

[0070] Optionally, the energy cumulative usage constraint provided by the embodiment of the present invention is:

[0071]

[0072] Among them, is the current stored energy in the energy storage device at time ; is the index of time ; is the initial stored energy in the energy storage device at the 0th time; is at the time, the noise data generated by the energy storage device.

[0073] Among them, the maximum capacity constraint refers to the maximum storage capacity limit of the energy storage device, ensuring that it does not exceed the designed capacity during the charging process, thus helping to prevent overcharging or over-discharging and ensuring the safety and reliability of the energy storage system.

[0074] Optionally, the maximum capacity constraint provided by the embodiment of the present invention is:

[0075]

[0076] Among them, is the maximum storage capacity of the energy storage device.

[0077] Among them, the charge-discharge power constraint refers to the allowable power range of the energy storage device during the charging and discharging processes. The charge-discharge power constraint is used to ensure that the energy storage device does not exceed its technical specifications during charging and discharging, maintaining the safety and performance of the device.

[0078] Optionally, the charge-discharge power constraint provided by the embodiments of the present invention is:

[0079]

[0080] Among them, is the maximum discharge power of the energy storage device, which is negative; is the maximum charging power of the energy storage device, which is positive; is the noise data generated by the energy storage device at the moment;

[0081] Among them, the energy storage parameter group refers to a series of parameters used to describe and control the performance of the energy storage device, including function position parameters and function scale parameters. The energy storage parameter group is used to optimize the charge-discharge strategy of the energy storage device and its interaction with the power grid.

[0082] Among them, the real-time electricity price data refers to the electricity price information provided by the power market or the power grid within a specific time. The real-time electricity price data usually changes with time, affecting the charge-discharge decisions of the energy storage device, so as to charge when the electricity price is low and discharge when the electricity price is high, thereby maximizing economic benefits.

[0083] S120. Superimpose the actual electricity load data and the noise data to generate target electricity load data, where the target electricity load data satisfies the power transmission constraint without considering the reverse power transmission of the energy storage device to the power grid, and the target electricity load data is displayed on the smart meter.

[0084] Among them, the target electricity load data refers to the data generated after superimposing and processing the actual electricity load data and the noise data. The target electricity load data represents the electricity demand of the user, and without considering the reverse power transmission of the energy storage device to the power grid, it satisfies specific power transmission constraints. The generation of the target electricity load data aims to introduce real physical noise through the charge-discharge behavior of the energy storage device, ensuring the accuracy of electricity bill settlement and the stable operation of the power grid while protecting user privacy, thereby providing more accurate and privacy-protected electricity usage information and displaying it on the smart meter for users and managers to monitor and analyze electricity usage.

[0085] Among them, the smart meter is an electricity metering device that can monitor the electricity usage data of users in real time and conduct two-way communication. Compared with traditional meters, the smart meter has higher accuracy and real-time data transmission capabilities, and can feedback electricity usage information to the power company and users in a timely manner.

[0086] The reading of the target electricity load data displayed on the smart meter provided by the embodiment of the present invention is the algebraic sum of the charge-discharge power of the energy storage device and the electric power consumed by the user's electrical appliances, that is:

[0087]

[0088] wherein, is the target electricity load data at the th moment; is the actual electricity load data at the th moment; is the noise data at the th moment;

[0089] Optionally, the power transmission constraint provided by the embodiment of the present invention is:

[0090]

[0091] wherein, is the target electricity load data at the th moment.

[0092] The method for protecting the privacy of electricity load data provided by the present invention obtains the actual electricity load data output by the user's electrical appliances at the current moment; obtains the noise data generated by the charge-discharge behavior of the energy storage device at the current moment, wherein the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charge-discharge power constraint of the energy storage device, and the charge-discharge behavior of the energy storage device is controlled by the energy storage parameter group and the real-time electricity price data at the current moment; superimposes the actual electricity load data and the noise data to generate the target electricity load data, wherein the target electricity load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target electricity load data is displayed on the smart meter. The present invention dynamically superimposes the optimized noise data that meets the economic constraints of the energy storage device to the actual electricity load data of the user in real time. On the premise of meeting the power transmission limit of the power grid, it not only uses noise to cover the real electricity consumption pattern to achieve privacy protection, but also ensures economic benefits by controlling the charge-discharge behavior of the energy storage device through electricity price linkage, achieving a dynamic balance between privacy strength and energy storage economy.

[0093] Optionally, based on one or more of the above Figure 1 corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, obtaining the noise data generated by the charge-discharge behavior of the energy storage device at the current moment may specifically include:

[0094] Obtain the differential privacy bounded noise probability density function of the energy storage device at the current moment, where the differential privacy bounded noise probability density function satisfies the actual energy storage physical constraints of the energy storage device. Input the user privacy requirement control parameter into the differential privacy bounded noise probability density function so that the energy storage device performs charge and discharge behaviors according to the differential privacy bounded noise probability density function at the current moment. Obtain the noise data generated by the energy storage device performing charge and discharge behaviors according to the differential privacy bounded noise probability density function.

[0095] Among them, the differential privacy bounded noise probability density function is used to describe the distribution characteristics of the noise generated by the energy storage device under the condition of satisfying the differential privacy protection mechanism. The differential privacy bounded noise probability density function ensures that the sensitive information of users will not be leaked during the publication or processing of electricity load data, and at the same time can ensure that the data is accurate enough. To achieve differential privacy, the design of the probability density function must meet certain mathematical conditions, so that even when querying or analyzing the data, it is difficult for external observers to infer the information of specific individuals.

[0096] Among them, the actual energy storage physical constraints refer to the limiting conditions of the energy storage device in terms of technical and physical characteristics. The actual energy storage physical constraints can include the maximum charging power and maximum discharging power of the energy storage device, the energy storage capacity, the charge and discharge efficiency, and the safe operation range of the device, etc. The actual energy storage physical constraints ensure that the energy storage system remains safe and reliable during operation and prevent overcharging, over-discharging or other situations that may cause equipment damage. When generating noise and its probability density function, these physical constraints must be fully considered to ensure that the generated noise is within the operable range of the energy storage device.

[0097] Optionally, the differential privacy bounded noise probability density function provided by the embodiment of the present invention is:

[0098]

[0099] Among them, is the differential privacy bounded noise probability density function; represents the base of the natural logarithm; is the function position parameter; is the function scale parameter; is the user privacy requirement control parameter; is the maximum discharging power of the energy storage device, which is negative; is the maximum charging power of the energy storage device, which is positive; is the function normalization factor, expressed as:

[0100] .

[0101] Among them, the user privacy requirement control parameter is a dimensionless adjustment factor used to quantify the degree of the user's demand for privacy protection. The user privacy requirement control parameter is determined independently by the user's personalization, aiming to affect the form and characteristics of the probability density function of the differential privacy bounded noise, so as to adjust the intensity of data privacy protection.

[0102] It can be understood that the user privacy requirement control parameter not only affects the operation behavior of the energy storage device, but also is directly related to the degree of user privacy protection. When the user's privacy requirement is low, a relatively small privacy requirement control parameter can be set to reduce the standard deviation of the probability density function, so that the generated noise is more concentrated, and the noise generated by the energy storage device during the charging and discharging process is relatively small, and the intensity of privacy protection is also relatively low. On the contrary, when the user's privacy requirement is high, the user can choose to set a relatively large privacy requirement control parameter to increase the standard deviation of the probability density function, so that the noise distribution is more dispersed, and the noise generated by the energy storage device during the charging and discharging process is relatively large, thereby enhancing the intensity of privacy protection.

[0103] The proof process that the probability density function of the differential privacy bounded noise provided by the embodiment of the present invention satisfies differential privacy is as follows: Consider a pair of adjacent data sets (denoted as D and D'), and the difference between these two data sets lies only in the data record of a single electrical device. In this case, a potential attacker may obtain the total power consumption of the user within a specific time period through queries. To ensure that the probability density function has consistent mathematical properties at different time periods, we need to divide the data set into several non-overlapping subsets according to the time dimension. According to the parallel composition property of differential privacy, it can be deduced that the mechanism can effectively provide differential privacy protection throughout the time period as long as it is proved that the noise generated by the probability density function satisfies the requirements of differential privacy protection at any single moment.

[0104] According to the differential privacy definition formula and the traditional energy storage system structure formula, the following formula can be obtained:

[0105]

[0106] Among them, is the reading of the smart meter at time t, representing the total power consumed by all electrical devices of the user; is the query function, querying the total power consumed by other electrical devices except the energy storage device at a certain moment; is the charging and discharging power of the energy storage device at time. According to the expression of parameter , the function expression of parameter is shown in the following formula:

[0107]

[0108] For the same user, the intrinsic privacy of the electricity load data benchmark is the same as the target intrinsic privacy. Therefore, the privacy requirement parameter p = p'. According to the differential privacy global sensitivity calculation formula, the above derivation formula can be transformed into the following formula:

[0109]

[0110] For a general user-side energy storage device, the maximum charge and discharge power are equal. Therefore, Rdiscmax = -Rcharmax, and the constructed function is as follows:

[0111]

[0112] Calculate the first derivative of this function as follows:

[0113]

[0114] Since ≥0, the latter part is further differentiated to obtain the formula:

[0115]

[0116] Therefore, when and only when x = 0, the original function achieves the maximum value. The minimum value of the function is obtained at both ends of the bounded domain, and since is a symmetric function, the following formula is satisfied:

[0117]

[0118] Thus, the above derivation formula can be further derived to obtain the following formula:

[0119]

[0120] where the calculation formula of ε' is as follows:

[0121]

[0122] Satisfies the differential privacy definition formula, and the proof is completed.

[0123] In the embodiment of the present invention, real physical noise is introduced through the charge and discharge behavior of the traditional energy storage system, which can not only protect the privacy of users, but also ensure the accuracy of electricity bill settlement and the stable operation of the power grid. In addition, the introduction of differential privacy makes the privacy protection process have quantifiable characteristics. At the same time, the personalized bounded noise probability density function containing privacy requirement parameters can allow users to independently adjust the morphological characteristics of the noise curve on the basis of meeting the physical constraints of the traditional energy storage system, so as to flexibly select the intensity of privacy protection.

[0124] Optionally, in the above Figure 1Based on one or more corresponding embodiments, in another alternative embodiment provided by the embodiments of the present invention, the method may further include:

[0125] Based on the differential privacy bounded noise probability density function, use the sand cat swarm optimization algorithm to perform multi-objective collaborative optimization on the energy storage device, and output the optimal energy storage parameter group. Among them, the multi-objective collaborative optimization includes simultaneously performing collaborative optimization on the objective function of maximizing the privacy protection effect and the objective function of minimizing the energy storage configuration cost. The optimal energy storage parameter group includes the optimal function position parameter and the optimal function scale parameter; based on the optimal function position parameter and the optimal function scale parameter, update the differential privacy bounded noise probability density function so that the energy storage device performs charge and discharge behaviors according to the updated differential privacy bounded noise probability density function.

[0126] Among them, the sand cat swarm optimization algorithm (Sand Cat Swarm Optimization, SCSO) is a swarm intelligence optimization algorithm based on simulating the foraging behavior of sand cats in the desert. The principle process of the sand cat swarm optimization algorithm is as Figure 2 shown. In the sand cat swarm optimization algorithm, each sand cat represents a candidate solution in the solution space. The sand cat continuously updates its own position under the joint guidance of individual experience and group experience to find the optimal solution. The final output is a set of non-dominated solutions, that is, the Pareto optimal solution set, which is suitable for multi-objective optimization problems.

[0127] Among them, multi-objective collaborative optimization is a method of optimizing while considering multiple objective functions. In this optimization process, there may be conflicts between the objectives, and the optimization algorithm needs to balance these objectives to find the optimal solution. In the embodiments of the present invention, the multi-objective collaborative optimization includes two objectives: maximizing the privacy protection effect and minimizing the energy storage configuration cost.

[0128] Among them, the objective function of maximizing the privacy protection effect aims to improve the protection level of users' privacy data and ensure that users' sensitive information is effectively protected during the data processing and sharing process. This objective can be quantified by various indicators, such as mutual information, Fisher information, load change rate and other indicators to evaluate the intensity of the protection effect.

[0129] Among them, the purpose of the objective function of minimizing the energy storage configuration cost is to reduce the overall configuration cost of the energy storage system. Minimizing the energy storage configuration cost takes into account the fixed cost and variable cost of the energy storage device and introduces the scale effect. The expression of the objective function of minimizing the energy storage configuration cost can be:

[0130]

[0131] Among them, represents the cost function at a given energy storage capacity , Fixed cost for energy storage configuration; Variable cost coefficient for energy storage; To control the marginal decreasing characteristic brought by scale effect.

[0132] In the embodiment of the present invention, by applying the differential privacy bounded noise probability density function and combining with the sand cat swarm optimization algorithm for multi-objective collaborative optimization, it is possible to optimize the performance of the energy storage device while protecting user privacy, so as to maximize the privacy protection effect and minimize the energy storage configuration cost at the same time, and output the optimal energy storage parameter group. Thus, not only the data privacy security is improved, but also the energy storage configuration cost of users is effectively reduced. In addition, through the updated differential privacy bounded noise probability density function, the energy storage device can achieve more intelligent and efficient charging and discharging behaviors, making the dynamic relationship between the privacy strength and the energy storage operation benefit more balanced.

[0133] Optionally, based on one or more corresponding embodiments above, in another optional embodiment provided by the embodiment of the present invention, the method may further include: Figure 1 Obtain the real-time electricity price data at the current moment; combine the price difference degree corresponding to the real-time electricity price data and the future opportunity cost to obtain the moving average adjustment factor at the current moment; use the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function, so that the energy storage device performs charging and discharging behaviors according to the differential privacy bounded noise probability density function after adjusting the function position parameter.

[0134] Among them, the price difference degree is an index used to evaluate the premium level of the current period electricity price relative to the lowest electricity price of the whole day. The specific calculation of the price difference degree is the difference between the electricity price at the current moment and the lowest electricity price of the whole day, that is:

[0135]

[0136]

[0137] Among them, is The price difference degree at time is The electricity price at time is the lowest electricity price of the whole day.

[0138] After the price difference degree is normalized, it can reflect the charging and discharging income potential of the current period. The level of the price difference degree directly affects the charging and discharging decision of the energy storage device, and helps to judge the economy of charging or discharging at a specific moment.

[0139] Among them, the future opportunity cost refers to the maximum price difference income that may be obtained within a preset time window (such as the next w time units), that is:

[0140] ​

[0141] Among them, is the future opportunity cost at a certain moment, and is the preset time window size.

[0142] The future opportunity cost calculates the expected impact of the current electricity price trend on future electricity prices, aiming to evaluate the potential benefits of charging and discharging in the future period. After normalization, the future opportunity cost can provide a basis for dynamically adjusting the energy storage strategy to optimize economic benefits.

[0143] Among them, the moving average adjustment factor is an adjustment parameter calculated by comprehensively considering the current electricity price difference and the future opportunity cost.

[0144] Figure 3 The following figure shows the schematic diagram of the moving average drift mechanism for the bounded noise probability density function provided by the embodiment of the present invention. The moving average adjustment factor plays a key role when dynamically adjusting the function position parameter (also called the mean parameter μ) of the differential privacy bounded noise probability density function. The moving average adjustment factor reflects the existing market environment and future expectations, helping the energy storage device flexibly adjust its charging and discharging behavior, making it more effectively respond to electricity price changes and achieve maximum benefits.

[0145] The calculation formula of the moving average adjustment factor provided by the embodiment of the present invention can be:

[0146]

[0147] Among them, is the moving average adjustment factor.

[0148] Optionally, the embodiment of the present invention can input the moving average adjustment factor into the formula:

[0149]

[0150] to obtain the function position parameter of the differentially private bounded noise probability density function after dynamic adjustment, where is the function position parameter after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive.

[0151] Embodiments of the present invention achieve cost savings for users' electricity consumption based on the moving average drift mechanism, ensuring that the energy storage device can discharge with a higher probability during peak electricity price periods and charge more during off-peak electricity price periods, thereby effectively helping users reduce electricity costs and maximize economic benefits. At the same time, the privacy protection needs of users are taken into account, making the dynamic relationship between privacy strength and energy storage operation benefits more balanced.

[0152] Although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous.

[0153] It should be understood that the various steps recited in the method embodiments of the present invention may be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0154] Embodiments of the present invention provide a system for protecting the privacy of electricity load data, and its architecture is as Figure 4 shown. The system may include: user appliances, an energy storage device, a smart meter, and an energy management system. Both the user appliances and the energy storage device are powered by the power grid. The smart meter is used to receive the electricity consumption information output by the user appliances and the energy storage device, and perform information interaction with the energy management system. The energy management system is used to execute the above-mentioned method for protecting the privacy of electricity load data.

[0155] In the system for protecting the privacy of electricity load data provided by the embodiments of the present invention, the public power grid provides electrical energy for the user's electrical equipment and energy storage device. According to the actual needs of the user, the energy storage device can act as a power source to supply power to other appliances during charging, or act as a device that consumes electrical energy during discharging. The smart meter is responsible for receiving the electricity consumption information of the user appliances and the energy storage device simultaneously, and performing information interaction with the energy management system. The energy management system generates corresponding control instructions based on the preset energy storage charging and discharging strategy and environmental information to adjust the charging and discharging behavior of the energy storage device, and adjusts the electricity load curve by introducing real noise, thereby enhancing the privacy protection of the electricity load data.

[0156] Corresponding to the above method embodiments, embodiments of the present invention also provide a device for protecting the privacy of electricity load data, and its structure is as Figure 5 shown. It may include: an actual electricity load data acquisition unit 10, a noise data acquisition unit 20, and a target electricity load data generation unit 30.

[0157] The actual electricity load data acquisition unit 10 is used to acquire the actual electricity load data output by the user appliances at the current moment.

[0158] A noise data acquisition unit 20 is configured to acquire noise data generated by the charge and discharge behavior of the energy storage device at the current moment, where the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charge and discharge power constraint of the energy storage device, and the charge and discharge behavior of the energy storage device is controlled by the energy storage parameter group and the real-time electricity price data at the current moment.

[0159] A target power consumption load data generation unit 30 is configured to superimpose the actual power consumption load data and the noise data to generate target power consumption load data, where the target power consumption load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target power consumption load data is displayed on the smart meter.

[0160] Optionally, the energy cumulative usage constraint is:

[0161]

[0162] Wherein, is the current stored energy in the energy storage device at time ; is the index of time ; is the initial stored energy in the energy storage device at the 0th moment; At the th moment, the noise data generated by the energy storage device;

[0163] Optionally, the maximum capacity constraint is:

[0164]

[0165] Wherein, is the maximum storage capacity of the energy storage device;

[0166] Optionally, the charge and discharge power constraint is:

[0167]

[0168] Wherein, is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive; At the th moment, the noise data generated by the energy storage device;

[0169] Optionally, the power transmission constraint is:

[0170]

[0171] Wherein, is the Target power consumption load data at a moment.

[0172] Optionally, the noise data acquisition unit 20 can be specifically configured to acquire the differential privacy bounded noise probability density function of the energy storage device at the current moment, where the differential privacy bounded noise probability density function satisfies the actual energy storage physical constraints of the energy storage device; input the user privacy requirement control parameter into the differential privacy bounded noise probability density function, so that the energy storage device performs charge and discharge behaviors according to the differential privacy bounded noise probability density function at the current moment; acquire the noise data generated by the energy storage device performing charge and discharge behaviors according to the differential privacy bounded noise probability density function.

[0173] Optionally, the power consumption load data privacy protection device may further include: an energy storage parameter optimization and configuration unit.

[0174] The energy storage parameter optimization and configuration unit is configured to perform multi-objective collaborative optimization on the energy storage device using the sand cat swarm optimization algorithm based on the differential privacy bounded noise probability density function, and output an optimal energy storage parameter group, where the multi-objective collaborative optimization includes simultaneously performing collaborative optimization on the objective function of maximizing the privacy protection effect and the objective function of minimizing the energy storage configuration cost, and the optimal energy storage parameter group includes an optimal function position parameter and an optimal function scale parameter; update the differential privacy bounded noise probability density function based on the optimal function position parameter and the optimal function scale parameter, so that the energy storage device performs charge and discharge behaviors according to the updated differential privacy bounded noise probability density function.

[0175] Optionally, the differential privacy bounded noise probability density function is:

[0176]

[0177] Where is the differential privacy bounded noise probability density function; represents the base of the natural logarithm; is the function position parameter; is the function scale parameter; is the user privacy requirement control parameter; is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive; is the function normalization factor, expressed as:

[0178] .

[0179] Optionally, the power consumption load data privacy protection device may further include: a power consumption cost saving unit.

[0180] The electricity cost saving unit is used to obtain real-time electricity price data at the current moment; combine the price difference degree corresponding to the real-time electricity price data and the future opportunity cost to obtain the moving average adjustment factor at the current moment; use the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function, so that the energy storage device performs charge and discharge behaviors according to the differential privacy bounded noise probability density function after adjusting the function position parameter.

[0181] Optionally, the electricity cost saving unit can be specifically used to input the moving average adjustment factor into the formula:

[0182]

[0183] Obtain the function position parameter of the differentially private bounded noise probability density function after dynamic adjustment, where is the function position parameter after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive.

[0184] The electricity load data privacy protection device provided by the present invention obtains the actual electricity load data output by the user's electrical appliances at the current moment; obtains the noise data generated by the charge and discharge behaviors of the energy storage device at the current moment, where the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charge and discharge power constraint of the energy storage device, and the charge and discharge behaviors of the energy storage device are controlled by the energy storage parameter group and the real-time electricity price data at the current moment; superimpose the actual electricity load data and the noise data to generate target electricity load data, where the target electricity load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target electricity load data is displayed on the smart meter. The present invention dynamically superimposes optimized noise data that conforms to the economic constraints of the energy storage device to the actual electricity load data of the user. On the premise of meeting the power transmission limit of the power grid, it not only uses noise to cover the real electricity consumption pattern to achieve privacy protection, but also guarantees economic benefits by controlling the charge and discharge behaviors of the energy storage device through electricity price linkage, achieving a dynamic balance between privacy strength and energy storage economy.

[0185] Regarding the electricity load data privacy protection device in the above embodiments, the specific manners in which each unit performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0186] The above-mentioned device for protecting the privacy of electricity consumption load data includes a processor and a memory. The actual electricity consumption load data acquisition unit 10, the noise data acquisition unit 20, the target electricity consumption load data generation unit 30, etc. are all stored in the memory as program units, and the processor executes the above program units stored in the memory to implement corresponding functions.

[0187] The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set. By adjusting the kernel parameters, the actual electricity consumption load data of the user and the noise data of the energy storage device are collected in real time. The charging and discharging behavior of the energy storage device is controlled through electricity price linkage. The optimized noise is superimposed on the actual electricity consumption load data to generate target electricity consumption load data that meets the grid transmission limit, realizing electricity consumption privacy protection while ensuring the economy of the energy storage, thereby balancing the dynamic relationship between privacy intensity and energy storage operation benefits. On the premise of ensuring that no electricity is fed back to the grid, the true electricity consumption pattern is masked by noise and the energy storage scheduling is optimized through electricity price strategies.

[0188] An embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the above-mentioned method for protecting the privacy of electricity consumption load data is implemented.

[0189] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein when the program runs, the above-mentioned method for protecting the privacy of electricity consumption load data is executed.

[0190] As Figure 6 shown, an embodiment of the present invention provides an electronic device 1000, which includes at least one processor 1001, at least one memory 1002 connected to the processor 1001, and a bus 1003; wherein, the processor 1001 and the memory 1002 complete communication with each other through the bus 1003; the processor 1001 is used to call program instructions in the memory 1002 to execute the above-mentioned method for protecting the privacy of electricity consumption load data. The electronic device herein can be a server, a PC, a PAD, a mobile phone, etc.

[0191] The present invention also provides a computer program product, which is suitable for executing a program for initializing the steps of the method for protecting the privacy of electricity consumption load data when executed on an electronic device.

[0192] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, electronic devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable devices produce means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0193] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, etc.

[0194] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory includes at least one storage chip. The memory is an example of computer-readable media.

[0195] Computer-readable media includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, magnetic disk storage, or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0196] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0197] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to users and the authorization of users should be obtained through appropriate means in accordance with relevant laws and regulations.

[0198] In the description of the present invention, it should be understood that if terms such as "upper", "lower", "front", "rear", "left" and "right" are used to indicate the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated position or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention.

[0199] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the element.

[0200] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] The above are only the embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included within the scope of the present invention.

Claims

1. A method for protecting the privacy of electrical load data, characterized in that, Including: Obtaining the actual power consumption load data output by the user's electrical appliance at the current moment; Obtaining the noise data generated by the charge and discharge behavior of the energy storage device at the current moment, where the current stored energy in the energy storage device satisfies the energy cumulative usage constraint and the maximum capacity constraint at the current moment, the noise data satisfies the charge and discharge power constraint of the energy storage device, and the charge and discharge behavior of the energy storage device is controlled by the energy storage parameter group and the real-time electricity price data at the current moment; Superimposing the actual power consumption load data and the noise data to generate target power consumption load data, where the target power consumption load data satisfies the power transmission constraint without considering the power reverse transmission from the energy storage device to the power grid, and the target power consumption load data is displayed on the smart meter.

2. The method according to claim 1, wherein The energy cumulative usage constraint is: Wherein, is the current stored energy in the energy storage device at time ; is the index of time ; is the initial stored energy in the energy storage device at the 0th time; is the noise data generated by the energy storage device at the th time; And / or, the maximum capacity constraint is: Among them, is the maximum storage capacity of the energy storage device; And / or, the charge and discharge power constraint is: Among them, is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive; is at the noise data generated by the energy storage device at the moment; And / or, the power transmission constraint is: Among them, is the target power consumption load data at the moment.

3. The method according to claim 1, characterized in that, The obtaining the noise data generated by the charge and discharge behavior of the energy storage device at the current moment includes: Obtaining the differential privacy bounded noise probability density function of the energy storage device at the current moment, where the differential privacy bounded noise probability density function satisfies the actual energy storage physical constraint of the energy storage device; Inputting the user privacy requirement control parameter into the differential privacy bounded noise probability density function so that the energy storage device performs charge and discharge behavior according to the differential privacy bounded noise probability density function at the current moment; Obtaining the noise data generated by the energy storage device performing charge and discharge behavior according to the differential privacy bounded noise probability density function.

4. The method according to claim 3, characterized in that, Also including: Based on the differential privacy bounded noise probability density function, using the sand cat swarm optimization algorithm to perform multi-objective collaborative optimization on the energy storage device, and outputting the optimal energy storage parameter group, where the multi-objective collaborative optimization includes simultaneously performing collaborative optimization on the objective function of maximizing the privacy protection effect and the objective function of minimizing the energy storage configuration cost, and the optimal energy storage parameter group includes the optimal function position parameter and the optimal function scale parameter; Based on the optimal function position parameter and the optimal function scale parameter, updating the differential privacy bounded noise probability density function so that the energy storage device performs charge and discharge behavior according to the updated differential privacy bounded noise probability density function.

5. The method according to claim 3, characterized in that The differential privacy bounded noise probability density function is: Among them, is the differential privacy bounded noise probability density function; represents the base of the natural logarithm; is the function position parameter; is the function scale parameter; is the user privacy requirement control parameter; is the maximum discharge power of the energy storage device, which is negative; is the maximum charge power of the energy storage device, which is positive; is the function normalization factor, expressed as: 。 6. The method according to claim 3, wherein Also including: Obtaining the real-time electricity price data at the current moment; Combining the price difference degree corresponding to the real-time electricity price data and the future opportunity cost to obtain the moving average adjustment factor at the current moment; Using the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function so that the energy storage device performs charge and discharge behavior according to the differential privacy bounded noise probability density function after adjusting the function position parameter.

7. The method according to claim 6, characterized in that The using the moving average adjustment factor to dynamically adjust the function position parameter of the differential privacy bounded noise probability density function includes: Inputting the moving average adjustment factor into the formula: Obtain the function position parameter of the differentially private bounded noise probability density function after dynamic adjustment, where, is the function position parameter after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is negative; is the maximum charging power of the energy storage device, which is positive.

8. A power consumption load data privacy protection system, characterized in that Including: A user appliance, an energy storage device, a smart meter, and an energy management system, wherein both the user appliance and the energy storage device are powered by the power grid, the smart meter is configured to receive power consumption information output by the user appliance and the energy storage device, and perform information interaction with the energy management system, and the energy management system is configured to execute the power consumption load data privacy protection method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by a processor, it implements the power consumption load data privacy protection method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes at least one processor, at least one memory connected to the processor, and a bus; wherein, the processor and the memory communicate with each other through the bus; the processor is configured to call program instructions in the memory to execute the power consumption load data privacy protection method according to any one of claims 1 to 7.

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