Load power consumption management control method and system based on time-of-use electricity price

Abstract

The invention relates to the field of power management, in particular to a load power consumption management control method and system based on time-of-use electricity price, and the method comprises the steps: obtaining the historical power consumption data of each user, and constructing a fitness function of a load scheduling scheme; setting an initial population; calculating a variable coefficient of each user in each time period; for each individual, weighting a preset initial variation rate according to the variation coefficient of each user in each time period, and obtaining a weighted variation rate of each user in each time period; and iteratively updating the initial population by using the weighted variation rate, outputting the individual with the highest fitness in the current population as the optimal load scheduling scheme, and performing optimal scheduling of the user load according to the optimal load scheduling scheme. According to the method, the variation rate is dynamically adjusted, and excellent individuals can be quickly identified in the iteration process, so that redundancy calculation is reduced, the efficiency of the algorithm is improved, and the algorithm can give an optimal scheduling scheme in time.

Description

Technical Field

[0001] The present invention relates to the field of power management, and more specifically, to a load power management and control method and system based on time-of-use electricity prices. Background Art

[0002] Time-of-use electricity pricing is a pricing mechanism that divides 24 hours a day into multiple time periods according to the load changes of the power system, and sets different electricity price levels for each time period. Through price signals, users are guided to adjust their electricity consumption behavior, encouraging them to use more electricity during periods with lower loads in the power system and less electricity during peak hours, thereby achieving the effect of shaving peaks and filling valleys, ensuring the safe and stable operation of the power system and improving the overall utilization efficiency of the system.

[0003] Prior art, such as a patent application document with publication number CN116012078A, discloses an electricity price optimization method based on a genetic algorithm, the electricity price optimization method comprising: obtaining M original populations, each of the original populations corresponding to a preset time period the day before, the original population comprising a plurality of individuals, the plurality of individuals being initial electricity prices at different times within the preset time period the day before; for each of the original populations, performing iterative update processing on the original population using a genetic algorithm, and obtaining a target population when an iteration termination condition is met, the target population comprising a plurality of target sub-individuals, the target sub-individuals being updated electricity prices after updating the original electricity prices corresponding to some individuals in the original population; and determining the optimized electricity price for each preset time period of the day based on each of the target sub-individuals in each of the target populations.

[0004] However, the genetic algorithm involved in the above electricity price optimization method has a mutation rate that is too large or too small, resulting in redundant calculations or low convergence efficiency in the optimization process of user load scheduling plans for different controllable loads. Summary of the invention

[0005] In order to solve the technical problem that the algorithm cannot provide the optimal scheduling solution in time due to redundant calculation or slow convergence caused by improper mutation rate setting, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a load power consumption management and control method based on time-of-use electricity price includes: Obtain the historical electricity consumption data of each user and construct the fitness function of the load scheduling plan; Optimizing the fitness function to obtain an optimal load scheduling solution; Optimize user load scheduling according to the optimal load scheduling plan; The optimization process includes: Setting an initial population, wherein the initial population consists of a plurality of individuals, each of which represents a load scheduling scheme; Calculate the coefficient of variation of each user in each time period; for each individual, weight the preset initial variation rate by the coefficient of variation of each user in each time period to obtain the weighted variation rate of each user in each time period; the coefficient of variation is positively correlated with the controllability of the user over electricity consumption and negatively correlated with the stability of electricity consumption; The weighted mutation rate is applied to iteratively update the initial population, and the individual with the highest fitness in the current population is output as the optimal load scheduling solution.

[0007] The present invention firstly obtains the historical electricity consumption data of each user, so as to more accurately understand the electricity consumption needs and habits of the users, and provide a solid data basis for constructing the fitness function of the load scheduling scheme. Further, by optimizing the load scheduling scheme, the supply and demand relationship of the power system can be better balanced, and the overload or overload of the power system can be avoided, thereby enhancing the stability of the power system. In the optimization process, the controllability of the user's electricity consumption and the stability of electricity consumption are taken into account, and a weighted mutation rate is calculated. The weighted mutation rate allows the algorithm to dynamically adjust its parameters according to the actual needs and behaviors of the users. In the iterative process, excellent individuals can be identified more quickly and individuals with poor performance can be eliminated, thereby reducing redundant calculations and improving the efficiency of the algorithm, so that the algorithm can give the optimal scheduling plan in time.

[0008] Preferably, the coefficient of variation satisfies the relationship: ; In the formula, For users In the The coefficient of variation for each period, For users In the The controllability of electricity consumption in each period, For users In the The stability of electricity consumption during each period.

[0009] By introducing the controllability and stability of electricity consumption, the calculation formula of the coefficient of variation can comprehensively consider the adjustability and stability of users' electricity consumption behavior, thereby more comprehensively evaluating the characteristics of users' electricity consumption behavior in specific periods of time, and providing important reference for the optimization, management and decision-making of the power system.

[0010] Preferably, the controllability satisfies the relationship: ; In the formula, For users In the The controllability of electricity consumption in each period, For users In the The controllable factors of electricity consumption in each period, , These are all pre-set parameters. It is an exponential function with the natural constant e as its base.

[0011] The formula nonlinearly maps the controllable factors and controllability of electricity consumption in the form of an exponential function, which can more flexibly reflect changes in users' electricity consumption behavior; by calculating the controllability of different users in different time periods, the power system can more accurately identify which users are more likely to respond to regulation needs in which time periods.

[0012] Preferably, the stability satisfies the relationship: ; In the formula, For users In the The stability of electricity consumption in each period, For the history Day users In the The maximum power consumption in a period of time, For the history Day users In the The minimum power consumption in each period, Indicates normalization processing.

[0013] By calculating the difference between the maximum and minimum power consumption in the same period of time in the historical data and normalizing it, a specific value can be obtained to represent the user's power consumption stability in that period. The smaller this value is, the more stable the user's power consumption in that period is.

[0014] Preferably, the iterative updating includes: According to the value of the fitness function, some individuals are selected from the current population to enter the next generation; Perform crossover operations on the selected individuals to simulate the hybridization process in the biological world and produce new offspring individuals; Performing mutation operations on offspring individuals according to the weighted mutation rate to generate new offspring individuals; The selection, crossover and mutation operations are repeated to form a new generation of population; when the preset maximum number of iterations is reached, the iteration is terminated.

[0015] Preferably, the process of obtaining the controllable power consumption factor includes: Calculate the deviation between the power consumption of any user in each period and the historical average power consumption; Calculate the deviation between the electricity price of any user in each period and the historical average electricity price; The normalized value of the deviation between the user's electricity consumption in each time period and the historical average electricity consumption and the deviation between the electricity price in each time period and the historical average electricity price are multiplied, and then divided by the total number of days of historical data to obtain the controllable factor of the user's electricity consumption in the corresponding time period.

[0016] By multiplying the normalized values ​​of electricity consumption deviation and electricity price deviation and dividing them by the total number of days of historical data, we can get an indicator that comprehensively considers changes in electricity consumption and electricity prices. This indicator reflects the user's sensitivity to electricity price changes during a specific period of time and their ability to adjust their electricity consumption behavior.

[0017] Preferably, the process of acquiring the stability includes: Calculate the maximum and minimum power consumption of the user in the current period on any day in history, and normalize the product of the difference between the maximum and minimum power consumption and the absolute value of the autocorrelation coefficient of the user's power consumption in the current period as the stability of the user's power consumption in the current period.

[0018] In a second aspect, a load power consumption management and control system based on time-of-use electricity prices comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the load power consumption management and control methods based on time-of-use electricity prices is implemented.

[0019] The present invention has the following effects: The present invention realizes efficient management of load scheduling through intelligent optimization algorithm combined with users' historical electricity consumption data and time-of-use electricity price information. It not only improves convergence efficiency and reduces redundant calculations, but also provides users with practical electricity consumption suggestions, which helps to build a greener, more efficient and reliable power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1 to S3 in a load power consumption management and control method based on time-of-use electricity prices in an embodiment of the present invention.

[0021] Figure 2 It is a schematic diagram of steps S20 to S22 in a load power consumption management and control method based on time-of-use electricity prices in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The application scenario of the present invention is: using an improved genetic algorithm to find an optimal load scheduling solution for users under a time-of-use electricity price.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 A load power management and control method based on time-of-use electricity price includes steps S1 to S3, which are as follows: S1: Obtain the historical electricity consumption data of each user and construct the fitness function of the load scheduling plan.

[0026] Through the user management system, the historical electricity consumption data of each user is collected, including daily, monthly or annual electricity consumption, peak hours, off-peak hours, etc. In an embodiment of the present invention, the historical electricity consumption data of each user is obtained from the user management system, and the data includes the electricity consumption in each period in the past 30 days.

[0027] Clean and organize historical electricity consumption data, remove outliers, fill in missing values ​​and perform normalization.

[0028] In addition, a day is divided into 24 time periods, and the average electricity price in each time period is obtained as the electricity price for that time period.

[0029] Since electricity prices vary in different time periods, some periods may have higher prices and others may have lower prices. In order to minimize the user's expenses, users should be arranged to use electricity during periods with lower electricity prices, and reduce electricity use during periods with higher electricity prices. At the same time, the load balance of the power system must be ensured. Load balance means that the power consumption of each user during different periods should be kept balanced as much as possible to avoid excessive power consumption in some periods and too little power consumption in other periods. This can ensure the stable operation of the power system, avoid insufficient or excessive power supply in certain periods, and improve the utilization efficiency of power resources.

[0030] In summary, when formulating a load dispatching plan, it is necessary to consider both reducing the user's electricity bill and ensuring the load balance of the power system. These two goals may sometimes conflict with each other, so it is necessary to use an intelligent optimization algorithm to find an optimal dispatching plan to achieve the best balance between the two goals.

[0031] Furthermore, in order to find the optimal load scheduling solution, it is necessary to construct a fitness function that comprehensively considers the total electricity bill of the user and the degree of load balancing.

[0032] Specifically, the total number of users is obtained from the user management system, the total electricity charges of each user in all time periods are calculated, and the variance of the electricity consumption of all users in each time period, that is, the degree of load balancing, is calculated.

[0033] The fitness function of the load scheduling scheme is constructed based on the above total electricity charges and load balancing degree, that is, the relationship is satisfied:

[0034] In the formula, is the fitness of the load dispatching scheme, is the total number of users, is the total number of divided time periods (24 in the embodiment of the present invention), For the The electricity price for each period, For users In the The electricity consumption in each period, represents the normalization function, Represents an exponential function.

[0035] in, Indicates user The total electricity cost for all periods of time, It represents the variance of electricity consumption of all users in each time period.

[0036] S2: Optimize the fitness function to obtain the optimal load scheduling solution.

[0037] As an optimization algorithm that simulates the natural evolution process, genetic algorithm has significant advantages, such as strong global search capability, strong adaptability, parallel processing capability, high robustness, etc. It can handle various complex, nonlinear, and multidimensional optimization problems.

[0038] In genetic algorithms, mutation rate is an important parameter for controlling population diversity and search capabilities. For users with flexible electricity consumption time, they have more controllable loads, which means they have more freedom to optimize in the solution space. However, the electricity consumption habits of these users may make electricity consumption more stable in certain periods and difficult to adjust. In these periods, the freedom to optimize is low. In order to avoid redundant calculations for users with less controllable loads when the mutation rate is too large (because their loads are difficult to change no matter how they are adjusted), and slow optimization for users with more controllable loads when the mutation rate is too small (because more time is needed to explore a larger solution space), we need to obtain the controllability of each user's electricity consumption and the stability of electricity consumption in different time periods. By identifying the freedom to optimize for each user in each time period in the solution space, we can dynamically adjust the mutation rate: when a user has a high degree of freedom to optimize in a certain period, a higher mutation rate is given to speed up the search; otherwise, the mutation rate is reduced to avoid invalid search.

[0039] Reference Figure 2 The process of optimizing the fitness function includes steps S20 to S22, which are as follows: S20: Setting an initial population, wherein the initial population consists of a plurality of individuals, each of which represents a load scheduling scheme.

[0040] In genetic algorithms, an initial population needs to be generated first. This population consists of multiple individuals, each of which represents a possible solution.

[0041] Specifically, the gene range is first determined. In the embodiment of the present invention, the minimum value is set to 0, the maximum value is set to twice the maximum power consumption in the historical power consumption data of all users, and the minimum unit of the solution space is two decimal places.

[0042] Then, within the above range, a population of 500 individuals is randomly generated, each of which represents a load scheduling scheme, including the power consumption of each user in each time period.

[0043] S21: Calculate the coefficient of variation of each user in each time period; for each individual, weight the preset initial variation rate with the coefficient of variation of each user in each time period to obtain the weighted variation rate of each user in each time period; the coefficient of variation is positively correlated with the controllability of the user over electricity consumption and negatively correlated with the stability of electricity consumption.

[0044] By analyzing the user's electricity consumption behavior and habits, we can determine the controllability and stability of the user's electricity consumption in different time periods, and further use this information to adjust the mutation rate of each user in order to more effectively find the optimal electricity allocation solution in the solution space. This can improve search efficiency, avoid unnecessary calculations, and ultimately achieve more efficient power management.

[0045] In an embodiment of the present invention, first, for each user in each time period, the deviation of his / her electricity consumption from the historical average electricity consumption (i.e., the variation of electricity consumption) and the deviation of the electricity price corresponding to each time period from the historical average electricity price (i.e., the variation of electricity price) are calculated, and then the variation of electricity consumption is multiplied by the normalized value of the variation of electricity price, and all historical electricity consumption data are summed up, and finally divided by the total number of days of historical data to obtain the controllable electricity consumption factor of the user in the corresponding time period. The larger the controllable electricity consumption factor is, the more sensitive the user is to the change in electricity price during the time period.

[0046] Then the above controllable factors of electricity consumption satisfy the relationship:

[0047] In the formula, For users In the The controllable factors of electricity consumption in each period, For the history Day users In the The electricity consumption in each period, is the total number of days of historical collection, For the history Tiandi The electricity price for each period, Indicates normalization processing.

[0048] When historical electricity consumption data shows In the When the increase of electricity consumption in a period is higher than the historical average of electricity consumption in the same period, and the decrease of electricity price is higher than the historical electricity price in the same period, the user In the For example, if the power consumption is large and the electricity price is low, or if the power consumption is small and the electricity price is high, this factor will be close to 1 or 0.

[0049] The above power consumption controllable factors are further used to quantify the controllability of the user's power consumption in a specific period of time, that is, the relationship is satisfied:

[0050] In the formula, For users In the The controllability of electricity consumption in each period, For users In the The controllable factors of electricity consumption in each period, , These are all pre-set parameters. is an exponential function with the natural constant e as the base. Among them, The empirical value is set to 30 to adjust the shape of the formula to ensure that when the power controllability factor approaches 0 or 1, the power controllability gradually approaches 1; It is used to control that when the power consumption controllability factor is at an intermediate value (such as 0.5), the power consumption controllability reaches the minimum value of 0. This reflects that when the power consumption behavior is close to the average behavior, the user's power consumption controllability is the lowest.

[0051] Among them, when near hour, The value of is small, and the result of the exponential function is close to 1, so Close to 0, it means that the controllability of electricity consumption is low; on the contrary, when keep away (that is, close to 0 or 1), As the value of is large, the result of the exponential function decreases, so A value close to 1 indicates that electricity consumption is highly controllable.

[0052] It should be noted that for users with a small fluctuation range of electricity consumption, since their electricity consumption is relatively stable, it is more difficult to adjust their electricity consumption (for example, to save electricity bills or respond to grid demand). This is because these users have formed relatively fixed electricity consumption habits, which are relatively difficult to change.

[0053] Therefore, the stability of the user's electricity consumption during a specific period of time is evaluated by calculating the difference between the user's maximum electricity consumption and the minimum electricity consumption during the specific period of time and performing linear normalization on it.

[0054] Then the above stability satisfies the relationship:

[0055] In the formula, For users In the The stability of electricity consumption in each period, For the history Day users In the The maximum power consumption in a period of time, For the history Day users In the The minimum power consumption in each period, Indicates normalization processing.

[0056] By calculating the stability of a user's electricity consumption during a specific period of time, we can understand the fluctuation of the user's electricity consumption behavior. The more stable the user, the smaller the fluctuation of his electricity consumption, and the more difficult it is to adjust the electricity consumption.

[0057] Then, according to the above and Calculate the user In the The coefficient of variation of each period satisfies the relationship:

[0058] In the formula, For users In the The coefficient of variation for each period, For users In the The controllability of electricity consumption in each period, For users In the The stability of electricity consumption during each period.

[0059] According to the above user In the The coefficient of variation for each period is calculated in the same way as the user The coefficient of variation in other time periods and the coefficient of variation of other users in each time period.

[0060] Finally, the initial mutation rate in the genetic algorithm is preset to 0.3 according to the empirical value, and the preset initial mutation rate is weighted by the coefficient of variation of each user in each time period to obtain the weighted mutation rate of each user in each time period.

[0061] For example, the user For example, the user In the The weighted mutation rate of each period satisfies the following relationship:

[0062] In the formula, For users In the The weighted mutation rate of each period, For users In the The coefficient of variation for each period, is the initial mutation rate, is the total number of time periods, is the total number of users.

[0063] According to user In the The weighted mutation rate of each time period can be calculated by the same method to get the weighted mutation rate of all users in all time periods.

[0064] S22: Apply the weighted mutation rate to iteratively update the initial population, and output the individual with the highest fitness in the current population as the optimal load scheduling solution.

[0065] Specifically, in the embodiment of the present invention, the weighted mutation rate calculated in step S21 is introduced into the genetic algorithm, and then the general steps of searching for the optimal load scheduling solution according to the genetic algorithm are as follows: According to the value of the fitness function, some individuals are selected from the current population to enter the next generation; common selection methods include roulette selection, tournament selection, etc. Perform crossover operations on the selected individuals to simulate the hybridization process in the biological world and produce new offspring individuals; Performing mutation operations on offspring individuals according to the weighted mutation rate to generate new offspring individuals; Repeat the selection, crossover and mutation operations to form a new generation of population; when the preset maximum number of iterations is reached (such as setting the iteration to 100 times), the iteration is terminated; In the final population after the iteration stops, find the individual with the highest fitness, which is the optimal load scheduling plan. The plan contains the optimal power consumption arrangement for each user in each time period, as well as the corresponding recommended power consumption and other information.

[0066] S3: Optimize user load scheduling according to the optimal load scheduling plan.

[0067] According to the optimal load dispatching plan, the recommended power consumption information is directly delivered to users by sending text messages.

[0068] In another embodiment, with the user In the The calculation formula of the coefficient of variation of each period is different. Considering the contribution of the controllability of electricity consumption and the stability of electricity consumption, the weight coefficient is introduced, then the user In the The coefficient of variation of each period satisfies the relationship:

[0069] In the formula, For users In the The coefficient of variation for each period, For users In the The controllability of electricity consumption in each period, For users In the The stability of electricity consumption during each period.

[0070] , are weight coefficients, which can be determined based on experience, experimental data or expert opinions. For example, if we believe that the controllability of electricity consumption has a greater impact on the coefficient of variation, we can set a larger value; and vice versa.

[0071] In another embodiment, with the user In the The calculation methods of the stability of electricity consumption in different time periods are different:

[0072] In the formula, For users In the The stability of electricity consumption in each period, For the history Day users In the The maximum power consumption in a period of time, For the history Day users In the The minimum power consumption in each period, represents normalization processing, Indicates user In the The absolute value of the autocorrelation coefficient of electricity consumption in each period.

[0073] The system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a load power consumption management and control method based on time-of-use electricity price according to the first aspect of the present invention is implemented.

[0074] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0075] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0076] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0077] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A load power management and control method based on time-of-use electricity price, characterized in that: include: Obtain the historical electricity consumption data of each user and construct the fitness function of the load scheduling plan; Optimizing the fitness function to obtain an optimal load scheduling solution; Optimize user load scheduling according to the optimal load scheduling plan; The optimization process includes: Setting an initial population, wherein the initial population consists of a plurality of individuals, each of which represents a load scheduling scheme; Calculate the coefficient of variation of each user in each time period; for each individual, weight the preset initial variation rate by the coefficient of variation of each user in each time period to obtain the weighted variation rate of each user in each time period; the coefficient of variation is positively correlated with the controllability of the user over electricity consumption and negatively correlated with the stability of electricity consumption; The weighted mutation rate is applied to iteratively update the initial population, and the individual with the highest fitness in the current population is output as the optimal load scheduling solution.

2. A load power management and control method based on time-of-use electricity price according to claim 1, characterized in that: The coefficient of variation satisfies the relationship: ; In the formula, For users In the The coefficient of variation for each period, For users In the The controllability of electricity consumption in each period, For users In the The stability of electricity consumption during each period.

3. A load power management and control method based on time-of-use electricity price according to claim 2, characterized in that: The controllability satisfies the relationship: ; In the formula, For users In the The controllability of electricity consumption in each period, For users In the The controllable factors of electricity consumption in each period, , These are all pre-set parameters. It is an exponential function with the natural constant e as its base.

4. A load power management and control method based on time-of-use electricity price according to claim 3, characterized in that: The stability satisfies the relationship: ; In the formula, For users In the The stability of electricity consumption in each period, For the history Day users In the The maximum power consumption in a period of time, For the history Day users In the The minimum power consumption in each period, Indicates normalization processing.

5. A load power management and control method based on time-of-use electricity price according to claim 4, characterized in that: The iterative update includes: According to the value of the fitness function, some individuals are selected from the current population to enter the next generation; Perform crossover operations on the selected individuals to simulate the hybridization process in the biological world and produce new offspring individuals; Performing mutation operations on offspring individuals according to the weighted mutation rate to generate new offspring individuals; The selection, crossover and mutation operations are repeated to form a new generation of population; when the preset maximum number of iterations is reached, the iteration is terminated.

6. A load power management and control method based on time-of-use electricity price according to claim 5, characterized in that: The process of obtaining the controllable factor of electricity consumption includes: Calculate the deviation between the power consumption of any user in each period and the historical average power consumption; Calculate the deviation between the electricity price of any user in each period and the historical average electricity price; The normalized value of the deviation between the user's electricity consumption in each time period and the historical average electricity consumption and the deviation between the electricity price in each time period and the historical average electricity price are multiplied, and then divided by the total number of days of historical data to obtain the controllable factor of the user's electricity consumption in the corresponding time period.

7. A load power management and control method based on time-of-use electricity price according to claim 1, characterized in that: The process of obtaining the stability includes: Calculate the maximum and minimum power consumption of the user in the current period on any day in history, and normalize the product of the difference between the maximum and minimum power consumption and the absolute value of the autocorrelation coefficient of the user's power consumption in the current period as the stability of the user's power consumption in the current period.

8. A load power management and control system based on time-of-use electricity prices, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a load power consumption management and control method based on time-of-use electricity price according to any one of claims 1 to 7 is implemented.

Images