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

Abstract

The present invention provides a method, system, storage medium and electronic device for protecting the privacy of electricity load data, which are applied to the field of electrical data processing technology. The present invention collects the user's actual electricity load data and the noise data of the energy storage device in real time, controls the charging and discharging behavior of the energy storage device through electricity price linkage, and superimposes optimized noise on the actual electricity load data to generate target electricity load data that meets the transmission restrictions of the power grid. The method uses the energy storage charging and discharging noise to mask the user's actual electricity consumption pattern, and controls the energy storage charging and discharging behavior through electricity price linkage to ensure economic benefits, thereby achieving a dynamic balance between privacy strength and energy storage economy.

Description

Technical Field

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

[0002] In the process of digital transformation of the power industry, the massive electricity load data generated by measuring equipment such as smart meters contains sensitive user behavior information.

[0003] However, traditional privacy protection methods face the following challenges: 1. Anonymization and encryption technologies struggle to balance data availability and privacy, and virtual noise injection can easily cause grid instability. 2. Existing differential privacy mechanisms use algorithmically generated digital noise, which leads to random charging and discharging behavior of energy storage devices that conflicts with economic benefits. 3. There is a lack of quantitative control methods to address the individual differences between user privacy needs and economic demands. In particular, in high-precision load data analysis scenarios, attackers can identify appliance usage characteristics through non-invasive load decomposition techniques. Traditional data aggregation methods, however, suffer from high communication overhead and high terminal computing load, making them less applicable.

[0004] Therefore, how to balance the privacy protection strength of electricity load data and the economic efficiency of energy storage has become a technical problem that technical personnel in this field urgently need to solve. 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 power load data that overcomes or at least partially solves the above problems. The technical solution is as follows:

[0006] A method for protecting privacy of electricity load data, comprising:

[0007] Obtain the actual power load data of the user's electrical appliances at the current moment;

[0008] Obtaining 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 real-time electricity price data at the current moment;

[0009] The actual power load data and the noise data are superimposed to generate target power load data, wherein the target power load data satisfies the power transmission constraint without considering the power storage device feeding back power to the power grid, and the target power load data is displayed on the smart meter.

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

[0011]

[0012] in, For the moment The current stored energy in the energy storage device described below; For the moment 's index; is the initial stored energy in the energy storage device at time 0; For the The noise data generated by the energy storage device at the time;

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

[0014]

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

[0016] And / or, the charge and discharge power constraints are:

[0017]

[0018] in, is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; For the The noise data generated by the energy storage device at the time;

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

[0020]

[0021] in, For the The target power load data at the time.

[0022] Optionally, obtaining noise data generated by the charging and discharging behavior of the energy storage device at the current moment includes:

[0023] Obtaining a differentially private bounded noise probability density function of the energy storage device at the current moment, wherein the differentially private 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 charging and discharging behavior according to the differential privacy bounded noise probability density function at the current moment;

[0025] Noise data generated by the energy storage device performing charging and discharging behavior according to the differentially private bounded noise probability density function is obtained.

[0026] Optionally, the method further includes:

[0027] Based on the differentially private bounded noise probability density function, a sand cat swarm optimization algorithm is used to perform multi-objective collaborative optimization on the energy storage device, and an optimal energy storage parameter group is output, wherein the multi-objective collaborative optimization includes simultaneously collaboratively optimizing an objective function of maximizing the privacy protection effect and an objective function of minimizing the energy storage configuration cost, and the optimal energy storage parameter group includes an optimal function location parameter and an optimal function scale parameter;

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

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

[0030]

[0031] in, is the differential privacy bounded noise probability density function; represents the base of natural logarithms; For function position parameters; is the function scale parameter; Control parameters for the user's privacy requirements; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; is the function normalization factor, expressed as:

[0032] .

[0033] Optionally, the method further includes:

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

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

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

[0037] Optionally, dynamically adjusting the function position parameter of the differentially private bounded noise probability density function by using the moving average adjustment factor includes:

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

[0039]

[0040] Obtain the function position parameter of the dynamically adjusted differentially private bounded noise probability density function, where: is the position parameter of the function after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value.

[0041] A power load data privacy protection system includes: user appliances, energy storage devices, smart meters and energy management systems. The user appliances and the energy storage devices are both powered by a power grid. The smart meters are used to receive power usage information output by the user appliances and the energy storage devices, and to exchange information with the energy management system. The energy management system is used to execute the power load data privacy protection method.

[0042] A computer-readable storage medium stores a program, which implements the power load data privacy protection method when executed by a processor.

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

[0044] By means of the above technical solution, the power load data privacy protection method, system, storage medium and electronic device provided by the present invention obtain the actual power load data output by the user's electrical appliance 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 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 real-time electricity price data at the current moment; superimpose the actual power load data and the noise data to generate target power load data, wherein the target power load data satisfies the power transmission constraint without considering the power storage device feeding back to the grid, and the target power load data is displayed on the smart meter. The present invention superimposes the optimized noise data that meets the economic constraints of the energy storage device on the actual power load data of the user in real time. Under the premise of meeting the power transmission constraints of the grid, it not only uses noise to mask the actual power consumption pattern to achieve privacy protection, but also controls the energy storage charging and discharging behavior through electricity price linkage to ensure economic benefits, thereby achieving a 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 more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0047] Figure 1 A schematic diagram showing a flow chart of an implementation method of a power load data privacy protection method provided by an embodiment of the present invention;

[0048] Figure 2 A flow chart showing the principle of a sand cat colony optimization algorithm according to an embodiment of the present invention is shown;

[0049] Figure 3 A schematic diagram illustrating the principle of incorporating a bounded noise probability density function into a moving average drift mechanism provided by an embodiment of the present invention is shown;

[0050] Figure 4 A schematic diagram of the architecture of a power load data privacy protection system provided by an embodiment of the present invention is shown;

[0051] Figure 5A schematic diagram of the structure of a power load data privacy protection device provided by an embodiment of the present invention is shown;

[0052] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying 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. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0054] With the large-scale deployment of advanced measurement equipment such as smart meters and smart energy management systems, the new power system has the ability to collect massive amounts of power operation data and user electricity consumption data, which has brought unprecedented opportunities and challenges to the innovative development of the power industry.

[0055] Large-scale, high-precision electricity load data creates multi-dimensional value for all stakeholders in the power system, helping users tap into energy-saving potential, implement refined energy management, and thus reduce electricity costs. However, this data also carries with it sensitive user information. Through in-depth analysis of this data, criminals can identify the types of appliances used by users, their usage times, and even infer their daily routines and behavioral patterns, seriously infringing on their privacy.

[0056] In the context of digital transformation, the public's awareness of privacy protection has significantly increased, and users' attention to the security of electricity load data continues to increase. Therefore, it is necessary to build a security barrier to protect sensitive information such as user electricity usage behavior.

[0057] However, traditional privacy protection methods face numerous challenges in practical application. For example, anonymization methods struggle to provide adequate protection for electricity load data, while methods such as information encryption and data aggregation place high demands on terminal device performance and incur significant communication overhead. Furthermore, directly adding virtual noise to electricity load data through algorithms can severely impact the stable operation of power systems. These shortcomings significantly limit the practical value of traditional privacy protection methods.

[0058] Energy storage systems, as an important technological tool, can, to a certain extent, resolve the conflict between privacy protection and the economic benefits of electricity use. The random charging and discharging behavior caused by traditional differential privacy noise mechanisms can significantly reduce the economic benefits of energy storage devices as user-side demand response devices, making it difficult to effectively balance user privacy and economic efficiency.

[0059] In the era of big data in the power market, designing a quantifiable privacy protection method for electricity load data requires solving the following key issues: 1. How to inject noise into load data without affecting the stable operation of the power grid; 2. How to design a privacy protection method based on the differentiated privacy needs of users to achieve personalized privacy protection of electricity load data; 3. How to design an energy storage optimization solution that takes into account differentiated privacy needs and economic efficiency to ensure that the privacy of user electricity load data is fully protected without compromising the economic benefits of energy storage.

[0060] Based on this, an embodiment of the present invention provides a method for protecting the privacy of electricity load data. The method can collect the user's actual electricity load data and the noise data of the energy storage device in real time, control the charging and discharging behavior of the energy storage device through electricity price linkage, and superimpose optimized noise on the actual electricity load data to generate target electricity load data that meets the transmission restrictions of the power grid. While ensuring the economy of energy storage, electricity privacy protection is achieved, thereby balancing the dynamic relationship between privacy strength and energy storage operation efficiency. Under the premise of ensuring that no electricity is fed back to the power grid, noise is used to mask the actual electricity consumption pattern and optimize energy storage scheduling through electricity price strategy.

[0061] like Figure 1 As shown, a flowchart of an implementation of a method for protecting privacy of power load data provided by an embodiment of the present invention is provided. The method may include:

[0062] S100: Obtain actual power load data of the user's electrical appliances at the current moment.

[0063] Consumer appliances refer to various electrical devices used by households or businesses, including lighting equipment, air conditioners, refrigerators, washing machines, and televisions. Consumer appliances obtain electricity from the power grid to meet users' daily or work needs.

[0064] The actual power load data refers to the power consumed by a user's electrical appliances during a specific period of time. The actual power load data reflects the user's power demand and usage pattern at that moment.

[0065] S110. Obtain noise data generated by the current charging and discharging behavior of the energy storage device, wherein the current stored energy in the energy storage device satisfies the current energy cumulative usage constraint and the maximum capacity constraint, 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 current energy storage parameter group and real-time electricity price data.

[0066] Energy storage devices are devices that store electrical energy and release it when needed. They can act as power sources, providing the required energy for other electrical devices, or they can act as specific electrical devices, consuming electrical energy.

[0067] Noise data refers to the algebraic value of the charge and discharge power generated by the operation of the energy storage device during its charge and discharge process. When superimposed with actual power load data, noise data can be used to protect privacy, masking the user's true power usage patterns while satisfying the power constraints of the energy storage device. During the charge and discharge process of the energy storage device, its operation can be regarded as an injection of real physical noise into the power load data.

[0068] In this embodiment of the present invention, the public grid simultaneously supplies power to both the user's appliances and the energy storage device. The energy storage device can flexibly switch between supplying and consuming power based on the user's actual needs. Smart meters can simultaneously receive power usage information from both the user's appliances and the energy storage device, enabling comprehensive power usage monitoring and management.

[0069] Among them, the cumulative energy usage constraint refers to the limit on the energy usage of the energy storage device within a specific time, ensuring that the discharge or charging behavior of the energy storage device does not exceed its historical cumulative power usage, thereby helping to reasonably manage 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] in, For the moment the current stored energy in the lower energy storage device; For the moment 's index; is the initial stored energy in the energy storage device at time 0; For the Noise data generated by the energy storage device at this moment.

[0073] Among them, the maximum capacity constraint refers to the maximum storage capacity limit of the energy storage device, ensuring that the design capacity of the energy storage device is not exceeded during the charging process, thereby 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 this embodiment of the present invention is:

[0075]

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

[0077] The charge and discharge power constraint refers to the power range allowed during the energy storage device's charging and discharging process. This constraint ensures that the energy storage device does not exceed its technical specifications during charging and discharging, maintaining the device's safety and performance.

[0078] Optionally, the charge and discharge power constraints provided by the embodiment of the present invention are:

[0079]

[0080] in, is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; For the Noise data generated by the energy storage device at the moment;

[0081] The energy storage parameter group refers to a set of parameters used to describe and control the performance of the energy storage device, including function location parameters and function scale parameters. The energy storage parameter group is used to optimize the charging and discharging strategy of the energy storage device and its interaction with the power grid.

[0082] Real-time electricity price data refers to electricity price information provided by the power market or power grid at a specific time. This data typically changes over time, influencing the charging and discharging decisions of energy storage devices. This allows for optimal charging when prices are low and discharging when prices are high, thereby maximizing economic benefits.

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

[0084] Target load data refers to data generated by superimposing actual load data with noise data. This data represents the user's energy demand and satisfies specific power transmission constraints, without considering the power returned to the grid by the energy storage device. The generation of target load data aims to introduce real physical noise through the charging and discharging behavior of the energy storage device. This ensures the accuracy of electricity bill settlement and stable grid operation while protecting user privacy, thereby providing more accurate and privacy-protected electricity usage information, which is displayed on smart meters for users and managers to monitor and analyze electricity usage.

[0085] Smart meters are electricity metering devices that monitor a user's electricity usage in real time and enable two-way communication. Compared to traditional meters, smart meters offer greater accuracy and real-time data transmission capabilities, providing timely feedback to power companies and users.

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

[0087]

[0088] in, For the Target electricity load data at the moment; For the Actual power load data at the moment; For the Noise data at the moment;

[0089] Optionally, the power transmission constraints provided in the embodiment of the present invention are:

[0090]

[0091] in, For the Target power load data at the moment.

[0092] The power load data privacy protection method provided by the present invention obtains the actual power load data output by the user's electrical appliance at the current moment; obtains 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 real-time electricity price data at the current moment; superimposes the actual power load data and the noise data to generate target power load data, wherein the target power load data satisfies the power transmission constraint without considering the power storage device feeding back to the power grid, and the target power load data is displayed on the smart meter. The present invention superimposes the optimized noise data that meets the economic constraints of the energy storage device on the actual power load data of the user in real time. Under the premise of meeting the power transmission constraints of the power grid, it not only uses noise to mask the actual power consumption pattern to achieve privacy protection, but also controls the energy storage charging and discharging behavior through electricity price linkage to ensure economic benefits, thereby achieving a dynamic balance between privacy strength and energy storage economy.

[0093] Optional, in the above Figure 1 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, obtaining noise data generated by the charging and discharging behavior of the energy storage device at the current moment may specifically include:

[0094] Obtain a differentially private bounded noise probability density function for the energy storage device at the current moment, where the differentially private bounded noise probability density function satisfies the actual energy storage physical constraints of the energy storage device. Input the user's privacy requirement control parameter into the differentially private bounded noise probability density function so that the energy storage device performs charging and discharging behavior according to the differentially private bounded noise probability density function at the current moment. Obtain noise data generated by the energy storage device performing charging and discharging behavior according to the differentially private bounded noise probability density function.

[0095] The differentially private bounded noise probability density function (DPN) describes the distribution of noise generated by energy storage devices while maintaining differential privacy. This DPN ensures that sensitive user information is not leaked during the publication or processing of power load data while ensuring sufficient data accuracy. To achieve differential privacy, the design of the DPN must meet certain mathematical conditions, making it difficult for external observers to infer information about specific individuals, even when querying or analyzing the data.

[0096] Among them, actual energy storage physical constraints refer to the limitations on the technical and physical characteristics of energy storage devices. Actual energy storage physical constraints can include the maximum charging power and maximum discharge power of the energy storage device, energy storage capacity, charge and discharge efficiency, and the safe operating range of the device. Actual energy storage physical constraints ensure that the energy storage system remains safe and reliable during operation and prevent overcharging, overdischarging, 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 operational 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] in, is the differential privacy bounded noise probability density function; represents the base of natural logarithms; For function position parameters; is the function scale parameter; Control parameters for user privacy needs; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; is the function normalization factor, expressed as:

[0100] .

[0101] The user privacy demand control parameter is a dimensionless adjustment factor that quantifies the degree of privacy protection required by the user. The user privacy demand control parameter is determined by the user and aims to influence the shape and characteristics of the differentially private bounded noise probability density function, thereby adjusting the strength of data privacy protection.

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

[0103] The proof process for the differentially private bounded noise probability density function provided by the embodiments of the present invention is as follows: Consider a pair of adjacent data sets (denoted as D and D'), where the difference between the two data sets lies only in the data record of a single electrical device. In this case, a potential attacker could query the user's total electricity load during a specific time period. To ensure that the probability density function has consistent mathematical properties across different time periods, the dataset needs to be divided into several non-overlapping subsets based on the time dimension. Due to the parallel composition property of differential privacy, simply proving that the noise generated by the probability density function satisfies the differential privacy requirements at any single moment in time can deduce that the mechanism effectively provides differential privacy protection throughout the entire time period.

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

[0105]

[0106] in, is the reading of the smart meter at time t, representing the total power consumed by all the electrical devices of the user; It is a query function that queries the total power consumed by other electrical devices except the energy storage device at a certain moment; for The charging and discharging power of the energy storage device at this moment. The expression, parameter The function expression is as follows:

[0107]

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

[0109]

[0110] For general user-side energy storage devices, the maximum charge and discharge power are equal, so Rdiscmax = -Rcharmax. The constructor is as follows:

[0111]

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

[0113]

[0114] because ≥0, so we continue to derive the second half to get the formula:

[0115]

[0116] Therefore, if and only if x=0, the original function The minimum value of the function is obtained at both ends of the bounded domain, and because It is a symmetric function, so it satisfies the formula:

[0117]

[0118] So the above derivation can be further deduced to obtain the following formula:

[0119]

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

[0121]

[0122] The definition of differential privacy is satisfied and the proof is complete.

[0123] The embodiments of the present invention introduce real physical noise through the charging and discharging behavior of traditional energy storage systems, which can protect user privacy while ensuring 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 quantifiable. 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 while meeting the physical constraints of traditional energy storage systems, thereby flexibly selecting the intensity of privacy protection.

[0124] Optional, in the above Figure 1On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, the method may further include:

[0125] Based on the differential privacy bounded noise probability density function, the sand cat swarm optimization algorithm is used to perform multi-objective collaborative optimization of the energy storage device and output the optimal energy storage parameter group. The multi-objective collaborative optimization includes the simultaneous collaborative optimization of 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 location parameter and the optimal function scale parameter. Based on the optimal function location parameter and the optimal function scale parameter, the differential privacy bounded noise probability density function is updated so that the energy storage device performs charging and discharging behavior according to the updated differential privacy bounded noise probability density function.

[0126] Among them, the Sand Cat Swarm Optimization (SCSO) algorithm 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 follows: Figure 2 As shown in Figure 1, in the sand cat swarm optimization algorithm, each cat represents a candidate solution in the solution space. Guided by both individual and group experience, the cats continuously update their positions to find the optimal solution. The final output is a set of non-dominated solutions, known as the Pareto optimal solution set, which is applicable to multi-objective optimization problems.

[0127] Multi-objective collaborative optimization is a method that simultaneously considers multiple objective functions for optimization. During this optimization process, conflicts may arise between objectives, and the optimization algorithm must balance these objectives to find the optimal solution. In this embodiment of the present invention, multi-objective collaborative optimization includes the two goals of maximizing privacy protection and minimizing energy storage configuration costs.

[0128] The objective function for maximizing privacy protection aims to improve the level of protection for user privacy data, ensuring that sensitive information is effectively protected during data processing and sharing. This objective can be quantified using various metrics, such as mutual information, Fisher information, and load change rate, to assess the strength of the protection effect.

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

[0130]

[0131] in, Indicates that at a given energy storage capacity The cost function under Allocate fixed costs for energy storage; is the variable cost coefficient of energy storage; To control the diminishing marginal returns brought about by economies of scale.

[0132] By applying a differentially private bounded noise probability density function and combining it with the Sandcat swarm optimization algorithm for multi-objective collaborative optimization, this embodiment of the present invention can optimize the performance of energy storage devices while protecting user privacy, thereby simultaneously maximizing privacy protection and minimizing energy storage configuration costs. The optimal energy storage parameter set is then output, thereby not only improving data privacy security but also effectively reducing energy storage configuration costs for users. Furthermore, through the updated differentially private bounded noise probability density function, the energy storage device can achieve more intelligent and efficient charging and discharging behavior, resulting in a more balanced dynamic relationship between privacy strength and energy storage operational benefits.

[0133] Optional, in the above Figure 1 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, the method may further include:

[0134] Obtain the real-time electricity price data at the current moment; combine the price difference and future opportunity cost corresponding to the real-time electricity price data 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 charges and discharges according to the differential privacy bounded noise probability density function after adjusting the function position parameter.

[0135] The price difference is an indicator used to evaluate the premium level of the current electricity price relative to the lowest electricity price of the day. The price difference is calculated as the difference between the current electricity price and the lowest electricity price of the day, that is:

[0136]

[0137] in, for Price difference at different times; for Time-of-day electricity prices; This is the lowest electricity price of the day.

[0138] After normalization, price variability reflects the potential for charging and discharging revenue during the current period. The level of price variability directly influences the charging and discharging decisions of energy storage devices, helping to determine the economic viability of charging or discharging at a specific moment.

[0139] The future opportunity cost refers to the maximum price difference benefit that can be obtained within a preset time window (such as w time units in the future), that is:

[0140]

[0141] in, for The future opportunity cost of time, The preset time window size.

[0142] Future opportunity costs calculate the expected impact of current electricity price trends on future prices, aiming to assess the potential benefits of charging and discharging in future periods. After normalization, these future opportunity costs can provide a basis for dynamically adjusting energy storage strategies 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 future opportunity costs.

[0144] Figure 3 This figure shows the principle behind incorporating a bounded noise probability density function into a moving average drift mechanism, as provided by an embodiment of the present invention. The moving average adjustment factor plays a key role in dynamically adjusting the function location parameter (also known as the mean parameter μ) of the differentially private bounded noise probability density function. The moving average adjustment factor reflects the current market environment and future expectations, helping energy storage devices flexibly adjust their charging and discharging behavior, enabling them to more effectively respond to changes in electricity prices and maximize revenue.

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

[0146]

[0147] in, is the moving average adjustment factor.

[0148] Optionally, in this embodiment of the present invention, the moving average adjustment factor may be input into the formula:

[0149]

[0150] Obtain the function location parameters of the dynamically adjusted differential privacy bounded noise probability density function, 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 a negative value; It is the maximum charging power of the energy storage device, which is a positive value.

[0151] The embodiments of the present invention achieve electricity cost savings for users through a moving average drift mechanism, ensuring that the energy storage device can be discharged with a higher probability during peak electricity price periods and charged more frequently during low electricity price periods, thereby effectively helping users reduce electricity costs and maximize economic benefits. At the same time, it takes into account the user's privacy protection needs, making the dynamic relationship between privacy strength and energy storage operation efficiency more balanced.

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

[0153] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders 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 respect.

[0154] The embodiment of the present invention provides a power load data privacy protection system, the architecture of which is as follows: Figure 4 As shown, the system may include: user appliances, energy storage devices, smart meters and energy management systems. The user appliances and energy storage devices are both powered by the power grid. The smart meters are used to receive electricity usage information output by the user appliances and energy storage devices, and to interact with the energy management system. The energy management system is used to execute the above-mentioned electricity load data privacy protection method.

[0155] The electricity load data privacy protection system provided by the embodiment of the present invention uses the public power grid to provide electricity to the user's electrical equipment and energy storage device. According to the actual needs of the user, the energy storage device can be used as a power source to power other electrical appliances during charging, or it can act as a device that consumes electricity during discharge. The smart meter is responsible for simultaneously receiving electricity usage information from the user's electrical appliances and energy storage devices, and exchanging information 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. By introducing real noise, the electricity load curve is adjusted, thereby enhancing the privacy protection of the electricity load data.

[0156] Corresponding to the above method embodiment, the embodiment of the present invention also provides a power load data privacy protection device, the structure of which is as follows: Figure 5 As shown, the system may include: an actual power load data obtaining unit 10 , a noise data obtaining unit 20 and a target power load data generating unit 30 .

[0157] The actual power load data obtaining unit 10 is used to obtain the actual power load data output by the user's electrical appliances at the current moment.

[0158] The noise data acquisition unit 20 is configured to obtain noise data generated by the current charging and discharging behavior of the energy storage device, wherein the current stored energy in the energy storage device satisfies the current energy cumulative usage constraint and the maximum capacity constraint, 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 current energy storage parameter group and real-time electricity price data.

[0159] The target power load data generating unit 30 is used to superimpose the actual power load data and the noise data to generate target power load data, wherein the target power load data satisfies the power transmission constraints without considering the power backflow of the energy storage device to the power grid. The target power load data is displayed on the smart meter.

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

[0161]

[0162] in, For the moment the current stored energy in the lower energy storage device; For the moment 's index; is the initial stored energy in the energy storage device at time 0; For the Noise data generated by the energy storage device at the moment;

[0163] Optionally, the maximum capacity constraint is:

[0164]

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

[0166] Optionally, the charge and discharge power constraints are:

[0167]

[0168] in, is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; For the Noise data generated by the energy storage device at the moment;

[0169] Optionally, the power transfer constraint is:

[0170]

[0171] in, For the Target power load data at the moment.

[0172] Optionally, the noise data acquisition unit 20 can be specifically used to obtain the differential privacy bounded noise probability density function of the energy storage device at the current moment, wherein 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 charging and discharging behavior according to the differential privacy bounded noise probability density function at the current moment; and obtain the noise data generated by the energy storage device performing charging and discharging behavior according to the differential privacy bounded noise probability density function.

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

[0174] The energy storage parameter optimization configuration unit is used to perform multi-objective collaborative optimization of the energy storage device based on the differential privacy bounded noise probability density function using the sand cat swarm optimization algorithm, and output the optimal energy storage parameter group, wherein the multi-objective collaborative optimization includes the simultaneous collaborative optimization of 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 location parameter and the optimal function scale parameter; based on the optimal function location parameter and the optimal function scale parameter, the differential privacy bounded noise probability density function is updated so that the energy storage device performs charging and discharging behavior according to the updated differential privacy bounded noise probability density function.

[0175] Optionally, the differentially private bounded noise probability density function is:

[0176]

[0177] in, is the differential privacy bounded noise probability density function; represents the base of natural logarithms; For function position parameters; is the function scale parameter; Control parameters for user privacy needs; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; is the function normalization factor, expressed as:

[0178] .

[0179] Optionally, the electricity load data privacy protection device may further include: an electricity cost saving unit.

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

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

[0182]

[0183] Obtain the function location parameters of the dynamically adjusted differential privacy bounded noise probability density function, 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 a negative value; It is the maximum charging power of the energy storage device, which is a positive value.

[0184] The power load data privacy protection device provided by the present invention obtains the actual power load data output by the user's electrical appliances at the current moment; obtains 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 real-time electricity price data at the current moment; superimposes the actual power load data and the noise data to generate target power load data, wherein the target power load data satisfies the power transmission constraint without considering the power storage device feeding back to the power grid, and the target power load data is displayed on the smart meter. The present invention superimposes the optimized noise data that meets the economic constraints of the energy storage device on the actual power load data of the user in real time. Under the premise of meeting the power transmission constraints of the power grid, it not only uses noise to mask the actual power consumption pattern to achieve privacy protection, but also controls the energy storage charging and discharging behavior through electricity price linkage to ensure economic benefits, thereby achieving a dynamic balance between privacy strength and energy storage economy.

[0185] Regarding the power load data privacy protection device in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method and will not be elaborated here.

[0186] The power load data privacy protection device includes a processor and a memory. The above-mentioned actual power load data acquisition unit 10, noise data acquisition unit 20 and target power load data generation unit 30 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0187] The processor includes a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be set. By adjusting kernel parameters, the kernel collects real-time user power load data and noise data from the energy storage device. The charging and discharging behavior of the energy storage device is controlled through electricity price linkage. Optimized noise is superimposed on the actual power load data to generate target power load data that meets the grid's transmission constraints. This ensures the economic efficiency of energy storage while also maintaining power privacy. This balances the dynamic relationship between privacy strength and energy storage operational efficiency, ensuring that no power is fed back to the grid while using noise to mask actual power usage patterns and optimizing energy storage scheduling through electricity price strategies.

[0188] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, which implements the power load data privacy protection method when executed by a processor.

[0189] An embodiment of the present invention provides a processor, which is used to run a program, wherein the power load data privacy protection method is executed when the program is running.

[0190] like Figure 6 As 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. The processor 1001 and the memory 1002 communicate with each other via the bus 1003. The processor 1001 is configured to call program instructions in the memory 1002 to execute the above-described method for protecting the privacy of power load data. The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.

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

[0192] The present invention is described with reference to 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to produce a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or 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, and the like.

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

[0195] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed 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 used 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 must 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 various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0198] In the description of the present invention, it should be understood that if the terms "up", "down", "front", "back", "left" and "right" are used to indicate directions or positional relationships, they are based on the directions or positional relationships shown in the accompanying drawings. They are only used to facilitate the description of the present invention and simplify the description, and do not indicate or imply that the positions or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations of the present invention.

[0199] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity, or device comprising the element.

[0200] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0201] The above are merely embodiments of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be within the scope of the present invention.

Claims

1. A method for protecting the privacy of power load data, characterized in that: include: Obtain the actual power load data of the user's electrical appliances at the current moment; Obtaining 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 real-time electricity price data at the current moment; superimposing the actual power load data and the noise data to generate target power load data, wherein the target power load data satisfies the power transmission constraint without considering the power backflow of the energy storage device to the power grid, and the target power load data is displayed on the smart meter; Wherein, obtaining the noise data generated by the charging and discharging behavior of the energy storage device at the current moment includes: obtaining a differentially private bounded noise probability density function of the energy storage device at the current moment, wherein the differentially private bounded noise probability density function satisfies the actual energy storage physical constraints of the energy storage device; inputting a user privacy requirement control parameter into the differentially private bounded noise probability density function so that the energy storage device performs charging and discharging behavior according to the differentially private bounded noise probability density function at the current moment; obtaining the noise data generated by the charging and discharging behavior of the energy storage device according to the differentially private bounded noise probability density function; Based on the differentially private bounded noise probability density function, the energy storage device is subjected to multi-objective collaborative optimization using a sand cat swarm optimization algorithm to output an optimal energy storage parameter group, wherein the multi-objective collaborative optimization includes simultaneously collaboratively optimizing an objective function of maximizing the privacy protection effect and an objective function of minimizing the energy storage configuration cost, and the optimal energy storage parameter group includes an optimal function location parameter and an optimal function scale parameter, wherein 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; Based on the optimal function position parameter and the optimal function scale parameter, the differentially private bounded noise probability density function is updated so that the energy storage device performs charging and discharging behavior according to the updated differentially private bounded noise probability density function.

2. The method according to claim 1, characterized in that The energy cumulative usage constraint is: ， in, For the moment The current stored energy in the energy storage device described below; For the moment 's index; is the initial stored energy in the energy storage device at time 0; For the The noise data generated by the energy storage device at the time; And / or, the maximum capacity constraint is: ， in, is the maximum storage capacity of the energy storage device; And / or, the charge and discharge power constraints are: ， in, is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; For the The noise data generated by the energy storage device at the time; And / or, the power transmission constraint is: ， in, For the The target power load data at the time.

3. The method according to claim 1, characterized in that The differential privacy bounded noise probability density function is: ， in, is the differential privacy bounded noise probability density function; represents the base of natural logarithms; For function position parameters; is the function scale parameter; Control parameters for the user's privacy requirements; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value; is the function normalization factor, expressed as: 。 4. The method according to claim 1, wherein Also includes: Obtaining the real-time electricity price data at the current moment; Combining the price difference and future opportunity cost corresponding to the real-time electricity price data, obtaining the moving average adjustment factor at the current moment; The moving average adjustment factor is used to dynamically adjust the function position parameter of the differentially private bounded noise probability density function, so that the energy storage device performs charging and discharging behavior according to the differentially private bounded noise probability density function after adjusting the function position parameter.

5. The method according to claim 4, characterized in that The dynamically adjusting the function position parameter of the differentially private bounded noise probability density function by using the moving average adjustment factor includes: Enter the moving average adjustment factor into the formula: ， Obtain the function position parameter of the dynamically adjusted differentially private bounded noise probability density function, where: is the position parameter of the function after dynamic adjustment; is the moving average adjustment factor; is the maximum discharge power of the energy storage device, which is a negative value; is the maximum charging power of the energy storage device, which is a positive value.

6. A power load data privacy protection system, characterized in that: include: User appliances, energy storage devices, smart meters and energy management systems, wherein the user appliances and the energy storage devices are both powered by a power grid, the smart meter is used to receive electricity usage information output by the user appliances and the energy storage device, and to exchange information with the energy management system, and the energy management system is used to execute the electricity load data privacy protection method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the power load data privacy protection method according to any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: The electronic device includes at least one processor, and at least one memory and bus connected to the processor; 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 load data privacy protection method as described in any one of claims 1 to 5.

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