Virtual power plant load cluster regulation and control method, device and equipment

Abstract

The invention provides a virtual power plant load cluster regulation and control method, device and equipment, and relates to the technical field of virtual power plant operation. According to the method, the operation parameters of the virtual power plant load cluster are obtained in real time, the task completion rate and the user will violation rate are calculated, and the regulation and control requirements are accurately judged; according to dual-objective optimization of minimum accumulated electric quantity and maximum user power utilization comfort level, generating an adjustable load preliminary plan of each family; according to the method, the to-be-regulated total quantity of the cluster is distributed according to the proportion of the household adjustable load, a final plan containing reduction information and transfer information is formed, accurate decomposition of a cluster level optimization target to a household level task is realized, the matching of a regulation task and household load characteristics is ensured, and finally, efficient virtual power plant load cluster regulation meeting user requirements is achieved. When the power grid is adjusted, the power utilization experience of the user is considered at the same time.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant operation technology, and in particular to a method, apparatus and equipment for load cluster control of a virtual power plant. Background Technology

[0002] Data released by the National Energy Administration shows that in 2024, the electricity consumption of urban and rural residents reached 1.4942 trillion kilowatt-hours, an increase of 10.6% year-on-year. With the growth of household electricity demand and the improvement of residents' living standards, peak electricity consumption occurs frequently, power shortages are emerging one after another, and the pressure on the power grid to regulate peak loads is increasing year by year.

[0003] Against this backdrop, demand-side response (DSR) strategies have emerged. DSR strategies refer to power companies using economic incentives to guide users to reduce their load, thus assisting the power grid in peak shaving. Demand response can be divided into two categories: price-based and incentive-based. In price-based DSR, electricity prices rise during peak hours to guide users to reduce electricity consumption, achieving peak shaving. In incentive-based DSR, power companies gain load control over users by offering rewards, directly controlling user electricity consumption behavior during peak hours to achieve load reduction. Residential load, as a crucial component of the overall social electricity load, has peak load periods that highly overlap with the power grid's peak periods, indicating significant potential for adjustment.

[0004] In existing technologies, the comfort of residents' electricity use is often overlooked when regulating the power grid, resulting in a failure to simultaneously balance power grid regulation and users' electricity experience. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for virtual power plant load cluster regulation, in order to solve the problem that power grid regulation and user electricity experience cannot be simultaneously considered.

[0006] In a first aspect, embodiments of the present invention provide a method for regulating a load cluster in a virtual power plant, comprising: acquiring operating parameters of a load cluster within a virtual power plant; calculating the task completion rate and user intention violation rate of the virtual power plant based on the operating parameters; performing target optimization based on the task completion rate, user intention violation rate, and operating parameters, with the goal of minimizing cumulative electricity consumption and maximizing user electricity comfort, to determine the optimal regulation solution, wherein the optimal regulation solution includes a preliminary regulation plan for the controllable load of each household in the load cluster; performing a summary calculation based on the optimal regulation solution to determine the total load to be regulated in the load cluster; and performing a secondary load allocation based on the total load to be regulated in the load cluster and the ratio between the controllable load of each household and the sum of the controllable loads of all households, to determine the final regulation plan for each household, wherein the final regulation plan includes reduction information for loads that can be reduced and transfer information for loads that can be transferred.

[0007] Secondly, embodiments of the present invention provide a virtual power plant load cluster control device, comprising: The communication module is used to acquire the operating parameters of the load cluster within the virtual power plant; the processing module is used to calculate the task completion rate and user intention violation rate of the virtual power plant based on the operating parameters; based on the task completion rate, user intention violation rate, and operating parameters, target optimization is performed with the goal of minimizing cumulative electricity consumption and maximizing user electricity comfort to determine the optimal control solution, which includes the preliminary control plan for the controllable load of each household in the load cluster; based on the optimal control solution, a summary calculation is performed to determine the total load to be controlled in the load cluster; based on the total load to be controlled in the load cluster and the ratio between the controllable load of each household and the sum of the controllable loads of all households, a secondary load allocation is performed to determine the final control plan for each household, which includes information on load reduction that can be reduced and information on load transfer that can be transferred.

[0008] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0009] In this embodiment of the invention, by acquiring the operating parameters of the virtual power plant load cluster in real time, the task completion rate and user intention violation rate are calculated to accurately determine the control needs and avoid blindly executing control measures. Based on the above indicators and operating parameters, optimization is performed with the dual objectives of minimizing cumulative power consumption and maximizing user power comfort, generating a preliminary control plan for the controllable load of each household in the load cluster. This achieves a balance between grid economy and user experience while providing a precise optimization direction for subsequent allocation. After determining the total load to be controlled in the load cluster based on this preliminary control plan, a secondary allocation is performed according to the ratio of each household's controllable load to the total controllable load of all households, forming a final household control plan that includes information on load reduction and transfer. This achieves precise decomposition of the cluster-level optimization objectives into household-level tasks, ensuring the matching of control tasks with household load characteristics, and ultimately achieving efficient and user-friendly virtual power plant load cluster control. This ensures that grid regulation and user power experience are considered simultaneously. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the implementation of the virtual power plant load cluster control method provided in this embodiment of the invention. Figure 2 This is a logical schematic diagram of the virtual power plant load cluster control method provided in an embodiment of the present invention; Figure 3 This is a logical schematic diagram of the intelligent optimization algorithm provided in an embodiment of the present invention; Figure 4This is a diagram showing the power consumption of each load under the condition that the household does not participate in the regulation, as provided in an embodiment of the present invention; Figure 5 This is a diagram of the power of each load when implementing strategy 1 according to the embodiments of the present invention; Figure 6 This is a diagram of the power of each load when implementing strategy 2 according to the embodiment of the present invention; Figure 7 This is a diagram of the power of each load when implementing strategy 3 according to the embodiments of the present invention; Figure 8 This is a graph showing the total power reduction when implementing strategy 1 according to the embodiments of the present invention; Figure 9 This is a graph showing the total power reduction when implementing strategy 2 according to the embodiments of the present invention; Figure 10 This is a graph showing the total power reduction when implementing strategy 3 according to the embodiments of the present invention; Figure 11 This is a schematic diagram of the structure of the virtual power plant load cluster control device provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0012] See Figure 1 The document illustrates a flowchart of the implementation of the virtual power plant load cluster control method provided in this embodiment of the invention, which is described in detail below: Step 101: Obtain the operating parameters of the load cluster within the virtual power plant.

[0013] In some embodiments, operating parameters are the core data inputs supporting the entire control method. These operating parameters include parameters related to load attributes, parameters related to control tasks, parameters related to constraints, and parameters related to user experience. Parameters related to load attributes include rigid load parameters, load reduction parameters, and load transfer parameters. Parameters related to control tasks include the required capacity for the control task, the power during the task period, and the start and end times of loads participating in the control task. Parameters related to constraints include the maximum load capacity of the load line, the load correlation coefficient, and the earliest / latest start time of one of two correlated loads. Parameters related to user experience include the power and operating status before load optimization at a preset time, the power and operating status after load optimization at a preset time, the completion rate threshold, and the violation rate threshold.

[0014] For example, the rigid load parameter is the total power of the rigid load within a preset time period, such as the power of unadjustable loads like refrigerators and lighting.

[0015] For example, load reduction parameters include the power and quantity of load that can be reduced for each household, the earliest / latest time that users are willing to turn on the load, the shortest acceptable continuous usage time for users, the actual regulation capacity, and the violation rate impact coefficient (determined based on load energy consumption and regulation priority).

[0016] For example, the transferable load parameters include the power and quantity of each household's transferable load, the on / off state function (describing the operating state at different times), the earliest / latest time when the user intends to turn on the load, the maximum / minimum load power threshold, the shortest continuous operating time of the load, and the violation rate impact coefficient (determined based on load energy consumption and adjustment priority).

[0017] As one possible implementation, the operating parameters of this invention are a comprehensive dataset covering load attributes, control tasks, constraints, and user experience, providing a data input basis for the entire virtual power plant load cluster control method, ensuring that subsequent calculation, optimization, and allocation processes are based on evidence, feasible, and accurate.

[0018] Step 102: Based on the operating parameters, calculate the task completion rate and user intention violation rate of the virtual power plant.

[0019] In some embodiments, the task completion rate is a quantitative indicator that measures the degree to which the actual performance of the virtual power plant load cluster in the power grid regulation task matches the target requirements, and it primarily reflects the degree to which the regulation task is achieved.

[0020] For example, the formula for calculating the completion rate of virtual power plant cluster regulation tasks is as follows:

[0021] In the formula, Adjust the task completion rate for the cluster; This represents the load capacity of all communities within the virtual power plant during a specific task period. This indicates the capacity required for the virtual power plant's regulation tasks. and Indicates the start and end times of the load's participation in the regulation task; Indicates the power during the task period; Indicates adjusting the capacity required for the task. The corresponding load power.

[0022] In some embodiments, the user intention violation rate is a quantitative indicator that measures the degree to which regulatory behavior interferes with users' electricity usage habits and comfort, and it primarily reflects the degree of conflict between regulation and user intention.

[0023] For example, the formula for calculating the user's willingness violation rate is:

[0024] In the formula, Indicates the amount of load that can be reduced; Indicates the number of transferable loads; This represents the impact coefficient of the violation rate on load reduction. The deviation rate impact coefficient represents the impact of transferable loads. This indicates the actual regulating capacity that can reduce the load; This indicates the electrical power consumption that can be reduced to reduce the load. The switch state function represents the load transferability; 1 indicates operation and 0 indicates shutdown.

[0025] For example, the formulas for calculating the violation rate impact coefficient of load that can be reduced and the violation rate impact coefficient of load that can be transferred are as follows:

[0026]

[0027]

[0028] In the formula, This represents the impact coefficient of the violation rate on load reduction. The deviation rate impact coefficient represents the impact of transferable loads. Indicates the basic coefficient for user type; Indicates the priority of load regulation; , This represents the power threshold for classifying energy-consuming loads into high, medium, and low categories. This represents the total energy consumption of a certain load within one month; Indicates low energy consumption load; Indicates medium energy consumption load; This indicates a high energy-consuming load.

[0029] In this embodiment, the task completion rate is a quantitative indicator of the degree to which the power grid control task is achieved, and the user intention violation rate is a quantitative indicator of the degree of interference of the control on users. The original operating parameters are transformed into decision indicators to provide a basis for the start of control, connect data collection and target optimization, and ensure that subsequent control is both necessary and accurate, ultimately achieving a balance between power grid demand and user experience.

[0030] Step 103: Based on the task completion rate, user intention violation rate, and operating parameters, with the goal of minimizing cumulative electricity consumption and maximizing user electricity comfort, perform target optimization to determine the optimal control solution. The optimal control solution includes the preliminary control plan for the controllable load of each household in the load cluster.

[0031] In some embodiments, the cumulative power consumption is the total power consumption of the virtual power plant load cluster during a preset control period, which is a core indicator for measuring the economic efficiency of power grid operation (the smaller the cumulative power consumption, the lower the power grid energy consumption and the better the economic efficiency).

[0032] In some embodiments, user power comfort is a quantitative indicator that measures the degree of interference of regulation behavior on user power habits and experience (the higher the value, the better the user experience and the less interference the regulation has on the user).

[0033] In some embodiments, objective optimization is the process of finding a balanced solution that simultaneously minimizes cumulative electricity consumption and maximizes user electricity comfort by solving algorithms, under the premise of satisfying load regulation constraints.

[0034] In some embodiments, the optimal control solution is the optimal balancing scheme obtained after the objective optimization process converges, and it forms the basis for subsequent load allocation. The optimal control solution includes preliminary plans for load reduction and load transfer for each household.

[0035] In some embodiments, the preliminary control plan refers to an initial control scheme for the controllable load of each household in the load cluster, generated through target optimization. It is a household-level control arrangement formed after optimization based on the dual objectives of minimizing cumulative electricity consumption and maximizing user electricity comfort, combined with operating parameters and constraints (such as line capacity, user willingness, etc.), and serves as the basis for subsequent calculation of the total cluster load and formulation of the final plan.

[0036] As one possible implementation, in this embodiment of the invention, the cumulative power consumption and user power comfort are the dual objectives of regulation (corresponding to the power grid economy and user experience, respectively). Target optimization is the process of balancing the dual objectives (combining constraints through algorithm iteration). The optimal regulation solution is the preliminary regulation plan obtained through optimization. The core function of this step is to transform regulation requirements into specific plans, balance the interests of the power grid and users, and provide a basis for subsequent allocation. It is the core link to achieve efficient regulation that meets user needs.

[0037] As one possible implementation, step 103 can be specifically implemented as steps 1031-1032.

[0038] Step 1031: Make a conditional judgment based on the task completion rate and the user intention violation rate to obtain the judgment result.

[0039] As one possible implementation, step 1031 can be specifically implemented as steps A101-A102.

[0040] Step A101: If the task completion rate is higher than the completion rate threshold and the user intention violation rate is higher than the violation rate threshold, then the judgment result is in compliance with the control conditions.

[0041] Step A102: If the task completion rate is lower than or equal to the completion rate threshold, or the user intention violation rate is lower than or equal to the violation rate threshold, then the judgment result is that the control conditions are not met.

[0042] In some embodiments, the completion rate threshold is a critical value for determining whether the task completion rate meets the standard, and is one of the prerequisite standards for initiating regulation and optimization.

[0043] In some embodiments, the violation rate threshold is a critical value for judging whether the user's willingness violation rate is too high, and it is another prerequisite standard for initiating regulation and optimization.

[0044] For example, the completion rate threshold ranges from 0.8 to 0.85, and the violation rate threshold ranges from 0.8 to 0.9.

[0045] As one possible implementation, embodiments of the present invention can define quantitative standards for meeting / not meeting control conditions by defining completion rate thresholds, violation rate thresholds, and judgment logic. This transforms condition judgment from abstract logic into executable rules, ensuring that control optimization is only initiated in necessary scenarios where the task is achieved and the user experience needs improvement. This provides a basis for balancing grid demand and user experience, while also improving the operability of the technical solution.

[0046] Step 1032: When the judgment result meets the control conditions, perform target optimization.

[0047] In some embodiments, the task completion rate is a quantitative indicator that measures the degree to which the actual performance of the virtual power plant load cluster in the power grid regulation task matches the target requirements, and it primarily reflects the degree to which the regulation task is achieved.

[0048] In some embodiments, the user intention violation rate is a quantitative indicator that measures the degree to which load regulation behavior interferes with users' electricity usage habits and comfort. It primarily reflects the degree of conflict between regulation and user intention (the higher the value, the stronger the user's dissatisfaction with regulation).

[0049] In some embodiments, the condition judgment is based on two quantitative indicators, task completion rate and user intention violation rate, which are compared with preset threshold standards to determine whether the target optimization logic process needs to be initiated.

[0050] In some embodiments, target optimization is a process of solving the optimal control scheme by combining operating parameters and constraints (such as line capacity, load correlation, etc.) with the dual objectives of minimizing cumulative power consumption (power grid economic objective) and maximizing user power comfort (user experience objective) under the premise of meeting control conditions.

[0051] As one possible implementation, embodiments of the present invention can establish a condition judgment mechanism to accurately screen out the scenarios that need optimization, connect the technical process, and ensure the necessity and pertinence of the optimization. This is a key transition link from decision-making to execution in the entire control method.

[0052] As one possible implementation, step 1032 can be specifically implemented as steps B101-B107.

[0053] Step B101: Using cumulative electricity consumption and user electricity comfort as objective functions, and with load line carrying capacity constraints, load association constraints, load reduction constraints, and load transfer constraints as constraints, construct a multi-objective optimization function.

[0054] As one possible implementation, step B101 can be specifically implemented as steps C101-C107.

[0055] Step C101: Sum the total rigid load power, the total load power that can be reduced, and the total load power that can be transferred for the preset time period in the operating parameters to obtain the summation result.

[0056] Step C102: Calculate the difference between the maximum load capacity of the load line and the load margin to obtain the difference result.

[0057] Step C103: When the summation result is less than or equal to the difference result, it is determined that the load line carrying capacity constraint is satisfied.

[0058] Step C104: Determine the range of start-up times based on the correlation coefficient between two different loads in the operating parameters, and the earliest and latest start-up times of one of the two loads.

[0059] Step C105: If the start time of another load is within the start time range, then the load association constraint is satisfied.

[0060] Step C106: If the earliest start time of the user is between the earliest start time and the latest start time of the user, and the continuous running time after a single start is greater than or equal to the shortest load continuous usage time acceptable to the user, then it is determined that the load reduction constraint is satisfied.

[0061] Step C107: If the power of the transferable load is between the maximum load power threshold and the minimum load power threshold, and the start time of the transferable load is within the range of the earliest start time to the latest start time of the user, and the duration of a single operation is greater than or equal to the shortest continuous operation time of the load, then the state constraints of the transferable load are determined to be satisfied.

[0062] In some embodiments, load line carrying constraints are rigid limitations set to prevent power grid lines from being overloaded due to excessive load, ensuring the safe operation of the lines during regulation.

[0063] In some embodiments, load association constraints are constraints set for loads that are associated in the virtual power plant (such as washing machines and dryers, air conditioners and electric auxiliary heating) to ensure that the operation sequence of associated loads conforms to user habits or equipment logic.

[0064] In some embodiments, the load reduction constraint is a constraint set for loads that can be reduced (such as non-essential lighting or backup equipment) to ensure that the reduction does not affect the user's basic electricity needs.

[0065] In some embodiments, the state constraints of the transferable load are constraints set for the transferable load (such as a charging pile or dishwasher) to ensure that the operating state (power, timing) of the transferred load meets the equipment performance and user requirements.

[0066] For example, the formula for load line carrying capacity constraint is:

[0067] In the formula, This indicates the maximum power capacity that the load line can carry; Indicates load margin; P B ( t ) for in t Total power of rigid load during the period P I ( t ) for in t The total load power can be reduced during certain periods. P T ( t ) for in t The total power of the load that can be transferred during the time period; in order to avoid line overload, a Puncon load margin is set, which is generally taken as 0.1~0.2Pmax.

[0068] For example, the formula for load-related constraints is:

[0069] In the formula, r ij Indicates load i With load j The correlation coefficient between them. Indicates the time when the load is started; Indicates user load j The earliest time to initiate the intention; Indicates user load jThe latest time to open the intention.

[0070]

[0071] Representing the load respectively Start and stop times; Representing the load respectively The start and stop times.

[0072] For example, load reduction constraints include:

[0073]

[0074]

[0075] In the formula, express Load can be reduced at any time The start / stop status corresponds to a value of 1 or 0; This indicates the time period during which users wish to reduce their load. Indicates users whose load can be reduced. The earliest time to initiate the intention; Indicates users whose load can be reduced. The latest time to open the intention; This indicates the minimum acceptable duration of continuous use under heavy load; Indicates load exist The moment is now on. express Load can be reduced at any time The start / stop status.

[0076] For example, transferable load constraints include:

[0077]

[0078]

[0079]

[0080] In the formula, for t The first moment i The power of a transferable load; These represent the maximum and minimum load power thresholds, respectively. Indicates user load The earliest time to initiate the intention; Indicates user load The latest time to open the intention; This indicates the shortest continuous operating time of the load. express Rigid load at all times The start / stop status.

[0081] Step B102: Based on the operating parameters and the preset multi-objective optimization algorithm, calculate the feasible solutions for load reduction and load transfer for each household.

[0082] Step B103: In each iteration, calculate the cumulative electricity consumption based on the feasible solutions for load reduction and load transfer for each household.

[0083] As one possible implementation, step B103 can be specifically implemented as follows: calculate the cumulative power of the total load of the virtual power plant within a preset time period based on the total power of the rigid load, the total power of all loads that can be reduced within the preset time period, and the total power of all loads that can be transferred within the preset time period in the operating parameters.

[0084] In some embodiments, the preset time period is a control time cycle pre-set before the virtual power plant performs load control, and is a time reference for calculating cumulative electricity consumption and comfort level.

[0085] In some embodiments, rigid loads are loads within a virtual power plant whose operating state cannot be changed through regulation. Their power and operating time are determined by the user's basic electricity demand and are not affected by regulation strategies.

[0086] In some embodiments, load reduction is a load within a virtual power plant that can be reduced by decreasing operating power or shortening operating time, without affecting the user's core needs during regulation (only reducing non-essential energy consumption).

[0087] In some embodiments, transferable loads are loads within a virtual power plant that cannot be reduced but whose operating periods can be adjusted. Their total electricity consumption remains essentially unchanged, but they can be transferred from peak electricity consumption periods to off-peak periods to achieve peak shifting.

[0088] In some embodiments, the preset time is a key time point pre-selected when calculating the user's power comfort, which is usually the time when the user is most sensitive to the load operating status.

[0089] In some embodiments, the operating state is the working mode of the load at a certain moment. The core is whether it runs according to the user's wishes, which is a key dimension for judging comfort.

[0090] Step B104: Calculate user electricity comfort based on the feasible solutions for load reduction and load transfer for each household.

[0091] As one possible implementation, step B104 can be specifically implemented as follows: calculate the user's power comfort level based on the power before load optimization, the power after optimization, the operating state before load optimization at the preset time, and the operating state after load optimization at the preset time in the operating parameters.

[0092] In some embodiments, user power comfort is a quantitative indicator that measures the degree to which users' power experience is satisfied after load regulation. The core is to determine whether the regulation deviates from the user's power consumption intention (such as operating time and power demand).

[0093] As one possible implementation, embodiments of the present invention can transform abstract objectives into concrete and operable calculation steps, clarifying the calculation methods for cumulative amounts and user electricity comfort.

[0094] Step B105: Calculate the objective function value based on the cumulative electricity consumption and user electricity comfort level in the current iteration process.

[0095] Step B106: Based on the objective function value of the current iteration and the objective function value of the previous iteration, determine whether the stopping iteration condition has been met.

[0096] Step B107: If the stopping iteration condition is met or the preset maximum number of iterations is reached, then stop the iteration and take the current feasible solution as the optimal solution for regulation; otherwise, adjust the current feasible solutions for load reduction and load transfer of each household and continue iterating.

[0097] In some embodiments, the objective function is a quantitative indicator that needs to be optimized (minimized or maximized) during the optimization process, and it is the core standard for measuring the quality of the solution. The objective function includes cumulative electricity consumption and user electricity comfort.

[0098] In some embodiments, constraints are boundary conditions that limit the optimization process, ensuring that the solved feasible solution is executable in a real-world scenario (without causing security risks or severe degradation of user experience). Constraints include load line carrying capacity constraints, load association constraints, load reduction constraints, and load transfer constraints.

[0099] In some embodiments, a feasible solution is a potential control scheme (but not necessarily the optimal one) that satisfies all constraints during the optimization process.

[0100] In some embodiments, the objective function value is a specific value of the cumulative electricity consumption and user electricity comfort calculated based on the current feasible solution, used to quantitatively evaluate the merits of the feasible solution.

[0101] In some embodiments, the optimal control solution is the best feasible solution obtained after the optimization process is completed, which is the optimal balance scheme that takes into account both the minimum cumulative power consumption and the maximum user power comfort.

[0102] For example, the formula for calculating the cumulative electricity consumption of the total load of a virtual power plant is as follows:

[0103] In the formula, A function representing the cumulative electricity consumption of the total load of the virtual power plant; For virtual power plants t Total power of rigid loads during the time period For the time of operation under rigid load, For virtual power plants t Total power of all loads that can be reduced during the time period To reduce the uptime of the load, For virtual power plants t Total power of all transferable loads during the time period This refers to the runtime of the transferable load.

[0104] For example, the formula for calculating user electricity comfort is:

[0105] In the formula, A function representing user electricity comfort; They represent t Time load i Power before and after optimization; These represent the load before and after optimization, respectively. i exist t The operating status at any given time, specifically whether it is on or off; m In order to reduce the total load, n This represents the total number of transferable loads.

[0106] For example, the optimization process includes initialization, iteration, and termination of iteration. In the initialization phase, the population size is set. In the virtual power plant, the particle dimension corresponds to the number of loads, and individual particles correspond to equipment start-up and shutdown plans. The particle swarm's position and velocity are initialized, the archive is initialized, and the initial Pareto optimal value is added to the archive. During the iteration phase, particle velocity and position are updated according to the formula, and dynamic inertia weights are set. The archive and particle swarm's... Find the Pareto optimal solution among the new particles and add it to an external archive; check if the archive capacity exceeds the limit. If it does, update the archive and the particle swarm in descending order of density distance. and In the termination phase, when the number of iterations reaches its maximum or the Pareto front converges, the optimal solution set in the archive is output, and the optimal start-up and shutdown times for each appliance are extracted. The specific steps of the algorithm are as follows: (1) Initialization. Input the usage time and power initialization parameters for each load. The particle dimension corresponds to the number of loads, and the population size is... Each particle has a corresponding load scheduling plan, and the particle is guaranteed to continuously seek the optimal solution throughout the day; (2) Objective function determination. Individual particles are input into the model as variables, and the external documentation is initialized. , and put them into the initial values ​​of the Pareto optimal solution; (3) Determine the velocity and position of the particle according to the velocity and position update formula, and set the dynamic inertia weight; (4) Update external documents and particle swarms ,for The update follows these rules: First, determine the particles. initial position , if the particle exist The position of the next iteration is Dominate Then the optimal value for the updated individual is... Otherwise, select the individual with the largest number of dominant particles as the optimal value. (5) Update the Pareto optimal solution and put it back into the external document. In the middle, it is determined whether the number of solutions exceeds the document capacity. If it does, the solution is updated and pruned based on the density distance, and then found... ; (6) If the mutation requirement is met at this time, a particle is randomly selected and allowed to undergo random mutation to perform the mutation operation; (7) Number of iterations Return to step 3 and continue running until... ; (8) Terminate the iteration to obtain the optimal position of each particle, that is, the optimal start time of each household appliance. The algorithm flow for solving the model is as follows: Figure 3 As shown.

[0107] As one possible implementation, embodiments of the present invention can find an optimal balance between two objectives, such as: minimizing cumulative power consumption while ensuring user comfort does not fall below a threshold; or maximizing user comfort while meeting grid requirements for cumulative power consumption. This balance avoids extreme adjustments based solely on economics or user experience.

[0108] Step 104: Based on the optimal control solution, perform summary calculations to determine the total load to be controlled in the load cluster.

[0109] In some embodiments, the total load to be regulated by the load cluster is the total load scale that the virtual power plant load cluster needs to regulate through two methods, namely reduction and transfer, within a preset regulation period. It serves as the total benchmark for subsequent secondary allocation.

[0110] In some embodiments, the initial control plan for the controllable load of each household in the load cluster includes the start-up and shutdown status of the load that can be reduced and transferred for each household.

[0111] As one possible implementation, step 104 can be specifically implemented as steps D101-D102.

[0112] Step D101: Calculate the total power of the load that can be reduced based on the power of the load that can be reduced and the start / stop status of the load that can be reduced in the optimal control solution.

[0113] Step D101: Calculate the total power of the transferable load based on the power of the transferable load and the start / stop status of the transferable load in the optimal control solution.

[0114] For example, the formula for calculating the total power that can be reduced is as follows:

[0115] In the formula, For the first i A load that can be reduced during its working hours. The power is ; express Load can be reduced at any time The power; express Load can be reduced at any time The start / stop status corresponds to a value of 1 or 0; This indicates the amount of load that can be reduced.

[0116] For example, the formula for calculating the total power of transferable load is as follows:

[0117] In the formula, For the first i One transferable load power; during the user's desired time period The internal power is ; express Load can be transferred at any time The power; express Load can be transferred at any time Usage status; Indicates the number of portable devices.

[0118] For example, the formula for calculating the total power of a rigid load is:

[0119] In the formula, express Rigid load at all times The power; express Rigid load at all times The start / stop status corresponds to a value of 1 or 0; k The number of rigid loads.

[0120] As one possible implementation, embodiments of the present invention can transform decentralized household-level schemes into a cluster-level total allocation, which clarifies the overall scale of regulation, verifies the feasibility of the optimal solution, and provides a fair and reasonable benchmark for subsequent secondary allocation. Step 105: Based on the total load to be controlled in the load cluster, and the ratio between the controllable load of each household and the sum of the controllable loads of all households, perform secondary load allocation to determine the final control plan for each household. The final control plan includes information on load reduction that can be reduced and information on load transfer that can be transferred.

[0121] In some embodiments, the total load to be regulated in a load cluster includes the total power of loads that can be reduced and the total power of loads that can be transferred.

[0122] As one possible implementation, step 105 can be specifically implemented as steps 1051-1053.

[0123] Step 1051: Based on the total power of load that can be reduced, the total power of load that can be transferred, and the ratio between the controllable load of each community and the sum of the controllable loads of all communities, calculate the load reduction command value and the load transfer command value for each community.

[0124] Step 1052: Based on the load reduction command value and load transfer command value of each community, and the ratio between the controllable load of each household and the sum of the controllable loads of all households in the community, determine the load reduction command value and load transfer command value of each household.

[0125] Step 1053: Determine the reduction information based on the load reduction instruction value for each household, and determine the transfer information based on the load transfer instruction value.

[0126] For example, the formula for calculating the load reduction instruction value issued by the power grid to the community is as follows:

[0127] In the formula, for The virtual power plant grid side allocates power to the first The load reduction command value for each community; Indicates according to the first The first community, the One user, in the past The electricity consumption habits predicted by the day The load power that can be reduced at any time; The number of users in the community; For all community users The total power that can be reduced at any time. ; This represents the total number of communities. P I ( t ) for in t The total load power can be reduced during certain periods.

[0128] For example, the formula for calculating the transferable load command value issued by the power grid to the community is as follows:

[0129] In the formula, for The virtual power plant grid side allocates power to the first The transferable load command value for each community; Indicates according to the first The first community, the One user, in the past The electricity consumption habits predicted by the day The transferable load power at any given time; For all community users Total transferable load power at any given time. . P T ( t ) for in t Total transferable load power during the time period.

[0130] For example, the formula for calculating the load reduction directive value allocated to each household is as follows:

[0131] In the formula, for Time of the first The community is the first The load power that can be reduced for each user.

[0132] For example, the formula for calculating the transferable load command value allocated to each household is as follows:

[0133] In the formula, for Time of the first The community is the first The transferable load power allocated to each user.

[0134] As one possible implementation, embodiments of the present invention can decompose the total workload into personalized tasks for each household through a proportional allocation rule, thereby shifting the control target from the cluster level to the household level. This decomposition ensures that each household's task matches its actual control capacity (those with stronger capacity undertake more tasks, and those with weaker capacity undertake less), avoiding a disconnect between task allocation and actual capacity, such as allocating a control workload far exceeding the capacity of households with low workloads. Figure 2 This is a logical schematic diagram of the virtual power plant load cluster control method provided in the embodiments of the present invention.

[0135] In this embodiment of the invention, by acquiring the operating parameters of the virtual power plant load cluster in real time, the task completion rate and user intention violation rate are calculated to accurately determine the control needs and avoid blindly executing control measures. Based on the above indicators and operating parameters, optimization is performed with the dual objectives of minimizing cumulative power consumption and maximizing user power comfort, generating a preliminary control plan for the controllable load of each household in the load cluster. This achieves a balance between grid economy and user experience while providing a precise optimization direction for subsequent allocation. After determining the total load to be controlled in the load cluster based on this preliminary control plan, a secondary allocation is performed according to the ratio of each household's controllable load to the total controllable load of all households, forming a final household control plan that includes information on load reduction and transfer. This achieves precise decomposition of cluster-level optimization objectives into household-level tasks, ensuring the matching of control tasks with household load characteristics, and ultimately achieving efficient virtual power plant load cluster control that meets user needs.

[0136] Optionally, after step 105, the economic benefits for each household may be calculated.

[0137] For example, the formula for calculating economic benefits is as follows:

[0138] In the formula, The economic benefits obtained by users; for Time-of-use electricity pricing at any given moment; Off-peak electricity pricing; Electricity price when supplying power to energy storage; Reduce capacity to meet the load requirements of the task; Reduce capacity to meet actual load; This represents the actual load transfer capacity. To replace electricity with energy storage facilities; The reward coefficient is related to... The value is related; and This represents the boundary value used to adjust the completion status of the task.

[0139] As one possible implementation, embodiments of the present invention can solve the problem of unclear economic returns for users participating in load regulation by clearly quantifying benefits and stratifying incentive tasks, while guiding user behavior to align with the regulation objectives of the virtual power plant, thus ensuring the sustainability of load cluster regulation.

[0140] This invention, while ensuring the economic efficiency and fairness of the distribution network system operation and scheduling, provides more flexible and precise control over the distribution of revenue from shared energy storage resources and the cost coordination within the microgrid alliance than traditional optimization scheduling methods, thereby improving the efficiency and system stability of the coordinated operation of microgrids and shared energy storage.

[0141] This invention provides a virtual power plant load cluster control method, which establishes a multi-objective optimization model of household electricity consumption behavior that considers the correlation of home appliances, provides users with optimized electricity consumption plans, guides residents' electricity consumption, and makes a comparative analysis of the adjustable potential of households under different optimization objectives. The proposed method can improve the adjustable potential of residents.

[0142] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0143] The above embodiments are in Figure 1 Based on the method shown, each step will be discussed in detail. To facilitate understanding of the complete execution process, the overall method flow will be discussed below with reference to an embodiment.

[0144] In this invention, the virtual power plant contains several communities, and each community contains several users. The electricity consumption data in the embodiment is based on a survey questionnaire. The 1,000 households involved in the questionnaire are analyzed as a community. The ratio of high, medium and low energy-consuming households is 1:2:1. The power of each household appliance is shown in Table 1. The calculation of theoretical peak power only includes forward-loading electrical equipment and does not consider the discharge power of electric vehicles.

[0145] Considering that the adjustable capacity of a typical household is 20% to 40% of the total power, a task of 5000kW is set for a community with a theoretical peak power of approximately 14,000 to 20,000 kW. The adjustment period is from 1 PM to 4 PM. To minimize inconvenience for users, each household is required to purchase 2 kWh of energy storage in the community, with a maximum output power of 800W. The electricity price for the energy storage facility is based on a photovoltaic price of 0.5 yuan / kWh, as energy storage is considered for peak electricity prices. The economic compensation for completing the adjustment task varies depending on the completion rate: 2 yuan / kWh for a completion rate of 0.75% to 1%, 0.8 yuan / kWh for a completion rate of 0.25% to 0.75%, and no compensation for a completion rate of 0% to 0.25%.

[0146] Table 1

[0147] This embodiment sets up three adjustment strategies: Strategy 1 adjusts based on the baseline load of residents; Strategy 2 introduces an adjustable potential function; and Strategy 3 involves energy storage participating in the adjustment as proposed in this paper. Figure 4 This indicates the power status of each load when the user does not participate in the adjustment; Figures 5-7 These represent the adjustment situations under virtual power plant implementation strategies 1, 2, and 3, respectively.

[0148] In Strategy 1, the community allocated 1.8 [units of currency] to the family. kW The capacity requirement was as follows: From 1 PM to 2 PM, users improved the task completion rate by turning off the washing machine and air conditioner, but since fewer appliances were working during this period, the task could not be completed; from 2 PM to 3 PM, the air conditioner was still turned off, and in order to complete the task, users chose to turn off the water heater, exceeding the task requirement, but the user's electricity consumption behavior changed significantly during this period, and the user's willingness to disobey was high; from 3 PM to 4 PM, users turned on the air conditioner and turned off the dryer, and the task completion rate was high during this period, but the task was not completed.

[0149] Strategy 2 allocates 1.3 to this family. kW The capacity requirement is determined and an adjustable potential model is introduced. If the adjustable capacity reaches the potential limit but the task is not completed, no further adjustment is required. Figure 6 It can be seen that from 13:00 to 15:00, the air conditioner is no longer directly turned off as in Strategy 1, but the temperature is adjusted to reduce the power and also reduce the energy consumption of the water heater. Overall, the user's willingness to disobey is reduced, but the overall task completion rate is low during this period. From 15:00 to 16:00, the dryer is turned off, and the task is over-fulfilled, but the overall task completion rate is still lower than that of Strategy 1.

[0150] Strategy 3 introduces energy storage power to the home, which allows washing machines that were turned off between 1 PM and 2 PM in strategies 1 and 2 to be restarted; air conditioners do not need to be turned off between 2 PM and 3 PM, and users can use vacuum cleaners in advance as they wish, improving the task completion rate while significantly reducing the rate of user disobedience.

[0151] The adjustment capacity of a family cluster within a certain community during this period is as follows: Figures 8-10 As shown: Strategy 1 achieves a very high task completion rate by turning off most home appliances during the adjustment period, sacrificing users' willingness to use electricity. The adjustment amount during most periods is generally between 6 and 8. kW The rate of completion of tasks in Strategy 2 decreased between 2 PM and 3 PM due to the introduction of the adjustable potential function. In Strategy 3, the task completion rate and the rate of user disobedience are balanced by community energy storage power supply, and the load regulation is relatively stable. The overall advantages are better than other strategies.

[0152] Table 2 shows a comparison of the three adjustable potential evaluation indicators: Strategy 1 is characterized by high task completion rate and high user willingness violation rate; Strategy 2 takes into account the adjustable potential limit, which reduces the violation rate of low energy consumption households by about half; Strategy 3 improves both the task completion rate and user willingness violation rate compared to Strategy 2, and has better overall benefits, thus verifying the feasibility of the energy storage power supply replacement strategy.

[0153] Table 2

[0154] In Table 2, , The benefits of lower electricity costs, , The economic rewards for completing load regulation tasks. for Time-of-use electricity pricing at any given moment; Off-peak electricity pricing; Electricity price when supplying power to energy storage; As the reward coefficient, Reduce capacity to meet actual load; This represents the actual load transfer capacity. To replace electricity with energy storage facilities.

[0155] Strategy 1 resulted in excessive electricity expenditures that went against user wishes. Strategy 2 alleviated the user wish refusal rate but had the lowest task completion rate. Strategy 3 balanced task completion rate, user wish refusal rate, and overall economic benefits. Although the overall economic benefits were slightly lower than the first two strategies, it resulted in the lowest refusal rate and was therefore advantageous overall.

[0156] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0157] Figure 11 A schematic diagram of the virtual power plant load cluster control device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 11 As shown, the virtual power plant load cluster control device 11 includes: Communication module 111 is used to obtain the operating parameters of the load cluster within the virtual power plant; Processing module 112 is used to calculate the task completion rate and user intention violation rate of the virtual power plant based on operating parameters; based on the task completion rate, user intention violation rate, and operating parameters, it performs target optimization with the goal of minimizing cumulative electricity consumption and maximizing user electricity comfort, and determines the optimal control solution. The optimal control solution includes the preliminary control plan of the controllable load of each household in the load cluster; based on the optimal control solution, it determines the total load to be controlled in the load cluster; based on the total load to be controlled in the load cluster and the ratio between the controllable load of each household and the sum of the controllable loads of all households, it performs secondary load allocation and determines the final control plan for each household. The final control plan includes information on load reduction that can be reduced and information on load transfer that can be transferred.

[0158] In this embodiment of the invention, by acquiring the operating parameters of the virtual power plant load cluster in real time, the task completion rate and user intention violation rate are calculated to accurately determine the control needs and avoid blindly executing control measures. Based on the above indicators and operating parameters, optimization is performed with the dual objectives of minimizing cumulative power consumption and maximizing user power comfort, generating a preliminary control plan for the controllable load of each household in the load cluster. This achieves a balance between grid economy and user experience while providing a precise optimization direction for subsequent allocation. After determining the total load to be controlled in the load cluster based on this preliminary control plan, a secondary allocation is performed according to the ratio of each household's controllable load to the total controllable load of all households, forming a final household control plan that includes information on load reduction and transfer. This achieves precise decomposition of cluster-level optimization objectives into household-level tasks, ensuring the matching of control tasks with household load characteristics, and ultimately achieving efficient virtual power plant load cluster control that meets user needs.

[0159] Figure 12 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 12As shown, the electronic device 12 of this embodiment includes a processor 120 and a memory 121. The memory 121 stores a computer program 122. When the processor 120 executes the computer program 122, it implements the steps in the various method embodiments described above. Alternatively, when the processor 120 executes the computer program 122, it implements the functions of each module / unit in the various device embodiments described above.

[0160] For example, computer program 122 may be divided into one or more modules / units, which are stored in memory 121 and executed by processor 120 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 122 in electronic device 12.

[0161] Electronic device 12 may include, but is not limited to, processor 120 and memory 121. Those skilled in the art will understand that... Figure 12 This is merely an example of electronic device 12 and does not constitute a limitation on electronic device 12. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 12 may also include input / output devices, network access devices, buses, etc.

[0162] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0163] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0164] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A virtual power plant load cluster regulation method, characterized in that, The method comprises the following steps: obtaining operation parameters of a load cluster in a virtual power plant; based on the operation parameters, calculating a task completion rate of the virtual power plant and a user-willingness violation rate; based on the task completion rate and the user-willingness violation rate, and the operation parameters, performing target optimization to determine a control optimal solution, the control optimal solution comprising a preliminary control plan of each controllable load in the load cluster; based on the control optimal solution, performing summary calculation to determine a total amount of loads to be controlled in the load cluster; based on the total amount of loads to be controlled in the load cluster, and a proportion between each controllable load and a sum of controllable loads of each household, performing secondary load distribution to determine a final control plan of each household, the final control plan comprising reduction information of a reducible load and transfer information of a transferable load. 2.The virtual power plant load cluster regulation method of claim 1, wherein, The total amount of loads to be controlled in the load cluster comprises a total power of the reducible load and a total power of the transferable load; The secondary load distribution based on the total amount of loads to be controlled in the load cluster, and the proportion between each controllable load and a sum of controllable loads of each household, to determine a final control plan of each household, comprises: based on the total power of the reducible load, the total power of the transferable load, and a proportion between each controllable load and a sum of controllable loads of each community, calculating an instruction value of the reducible load and an instruction value of the transferable load of each community; based on the instruction value of the reducible load and the instruction value of the transferable load of each community, and a proportion between each controllable load and a sum of controllable loads of each household in the community, determining the instruction value of the reducible load and the instruction value of the transferable load of each household; based on the instruction value of the reducible load of each household, determining the reduction information, and based on the instruction value of the transferable load, determining the transfer information. 3.The virtual power plant load cluster regulation method of claim 1, wherein, The target optimization based on the task completion rate and the user-willingness violation rate, and the operation parameters, to determine the control optimal solution, comprises: performing conditional judgment based on the task completion rate and the user-willingness violation rate to obtain a judgment result; when the judgment result is in line with a control condition, performing the target optimization.

4. The virtual power plant load cluster regulation method of claim 3, wherein, The conditional judgment based on the task completion rate and the user-willingness violation rate to obtain a judgment result, comprises: if the task completion rate is higher than a completion rate threshold value, and the user-willingness violation rate is higher than a violation rate threshold value, the judgment result is in line with the control condition; if the task completion rate is lower than or equal to the completion rate threshold value, or the user-willingness violation rate is lower than or equal to the violation rate threshold value, the judgment result is not in line with the control condition.

5. The virtual power plant load cluster regulation method of claim 3, wherein, The target optimization when the judgment result is in line with the control condition, comprises: constructing a multi-objective optimization function with a cumulative electricity quantity and a user electricity consumption comfort degree as objective functions, and with a load line bearing constraint, a load correlation constraint, a reducible load constraint, and a state constraint of a transferable load as constraint conditions; based on the operation parameters and a preset multi-objective optimization algorithm, calculating a feasible solution of each household reducible load and transferable load; In each round of iteration, based on the feasible solution of the current household curable load and transferable load, the cumulative power is calculated; Based on the feasible solution of the current household curable load and transferable load, the user power consumption comfort is calculated; Based on the cumulative power and the user power consumption comfort of the current iteration process, the objective function value is calculated; Based on the objective function value of the current iteration process and the objective function value of the previous round of iteration, it is judged whether the stop iteration condition is reached; If the stop iteration condition is reached or the preset maximum iteration number is reached, the iteration is stopped, and the current feasible solution is taken as the optimal solution of regulation and control; otherwise, the feasible solution of the current household curable load and transferable load is adjusted to continue iteration.

6. The virtual power plant load cluster regulation method of claim 5, wherein, The cumulative power is calculated based on the feasible solution of the current household curable load and transferable load, comprising: According to the total power of the rigid load in the virtual power plant in the running parameter in the preset time period, the total power of all curable loads in the preset time period and the total power of all transferable loads in the preset time period, the cumulative power of the virtual power plant total load is calculated; The user power consumption comfort is calculated based on the feasible solution of the current household curable load and transferable load, comprising: According to the power before load optimization, the power after optimization, the running state at the preset time before load optimization and the running state at the preset time after load optimization in the running parameter, the user power consumption comfort is calculated.

7. The virtual power plant load cluster regulation method of claim 5, wherein, The multi-objective optimization function is constructed with the cumulative power and the user power consumption comfort as the objective function, and the load line carrying constraint, the load correlation constraint, the curable load constraint and the state constraint of the transferable load as the constraint condition, comprising: The sum of the total power of the rigid load, the total power of the curable load and the total power of the transferable load in the preset period in the running parameter is summed to obtain the sum result; The difference between the maximum load line carrying power and the load margin is obtained to obtain the difference result; When the sum result is less than or equal to the difference result, it is determined that the load line carrying constraint is satisfied; The opening time range is determined according to the correlation coefficient between the two different loads in the running parameter, the earliest opening willingness time and the latest opening willingness time of one of the two loads; If the opening time of the other load is located in the opening time range, it is determined that the load correlation constraint is satisfied; If the user's earliest opening time is located between the user's earliest opening willingness time and the latest opening willingness time, and the continuous running time after single opening is greater than or equal to the user's acceptable minimum load continuous use time, it is determined that the curable load constraint is satisfied; If the power of the transferable load is between the maximum load power threshold and the minimum load power threshold, the opening time of the transferable load is within the range of the user's earliest opening willingness time to the latest opening willingness time, and the continuous running time of single operation is greater than or equal to the minimum load continuous running time, it is determined that the state constraint of the transferable load is satisfied. 8.The virtual power plant load cluster regulation method of claim 1, The preliminary regulation and control plan of the household controllable load in the load cluster includes the start-stop state of the household curable load and transferable load; Based on the optimal solution of regulation and control, the total amount of the load to be regulated in the load cluster is determined by summary calculation, comprising: According to the power of the cuttable load and the start-stop state of the cuttable load in the regulation optimal solution, the total power of the cuttable load is calculated; According to the power of the transferable load and the start-stop state of the transferable load in the regulation optimal solution, the total power of the transferable load is calculated.

9. A virtual power plant load cluster regulation device, characterized in that, The method comprises the following steps: The communication module is configured to obtain operation parameters of the load cluster in the virtual power plant; The processing module is configured to calculate a task completion rate and a user willingness violation rate of the virtual power plant based on the operation parameters, and perform target optimization based on the task completion rate and the user willingness violation rate and the operation parameters, so as to determine a regulation optimal solution, wherein the regulation optimal solution comprises a preliminary regulation plan of each controllable load in the load cluster; Based on the regulation optimal solution, the total amount of loads to be regulated in the load cluster is determined through summary calculation; Based on the total amount of loads to be regulated in the load cluster and the proportion between the controllable load of each household and the sum of controllable loads of all households, a secondary load distribution is performed to determine a final regulation plan of each household, wherein the final regulation plan comprises cutback information of the cuttable load and transfer information of the transferable load.

10. An electronic device, comprising: The method comprises the following steps: The method comprises the following steps:

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