Distributed demand-side resource management method, device and computer equipment

Abstract

The present application provides a distributed demand-side resource management method, device and computer equipment, the method comprising: obtaining the willingness value, power generation prediction value and communication neighbor set of each flexible load resource on the demand side, to initialize the power consumption and auxiliary variables of each flexible load resource, to exchange information between each flexible load resource and each flexible load resource in its corresponding communication neighbor set, to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and to determine whether this iterative update meets the convergence judgment condition. If not, the willingness value of each flexible load resource on the demand side is updated, and the next iteration is entered. If it is satisfied, the latest power consumption of each flexible load resource is used as the optimal solution for demand-side resource management. In the case where flexible load resources can join or exit the solution process at any time, the renewable energy power station can maintain a high power consumption efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of power system regulation and management, and in particular to a distributed demand-side resource management method, device and computer equipment. Background Art

[0002] With the orderly advancement of the "dual carbon" work, distributed renewable energy power generation has been widely connected to the distribution network. While providing a green and clean source of electricity for the power system, it has also brought new challenges to the operation of the power system. At present, most power systems use distributed demand-side management methods. Compared with centralized demand-side management methods, distributed demand-side management can effectively protect flexible resources on the demand side.

[0003] However, in practical applications, existing methods are difficult to meet the needs of flexible resources to join or exit at any time, ignoring the willingness of flexible resources to participate in the demand-side management process, thereby reducing the power consumption efficiency of renewable energy power stations. Summary of the invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the prior art ignores the willingness of flexible resources to participate in the demand-side management process, thereby reducing the power consumption efficiency of renewable energy power plants.

[0005] In a first aspect, the present application provides a distributed demand-side resource management method, the method comprising:

[0006] Obtain the willingness value, power generation forecast value, and communication neighbor set of each flexible load resource on the demand side;

[0007] Initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource;

[0008] Initialize the auxiliary variables corresponding to each flexible load resource according to the predicted values ​​of power consumption and power generation corresponding to each flexible load resource;

[0009] Information is exchanged between each flexible load resource and each flexible load resource in its corresponding communication neighbor set respectively, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment conditions;

[0010] If the convergence judgment condition is not met, the willingness value of each flexible load resource on the demand side is updated and the next iteration is entered;

[0011] If the convergence judgment condition is met, the latest power consumption of each flexible load resource is used as the optimal solution for demand-side resource management.

[0012] In one of the embodiments, the expression for initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource is:

[0013]

[0014] Where P i (0) is the power consumption corresponding to the flexible load resource i, s i (0) is the unupdated willingness value corresponding to the flexible load resource i, s i (0) When it is 1, it means that the flexible load resource i is willing to participate in demand-side resource management, s i (0) When it is 0, it means that the flexible load resource i is unwilling to participate in demand-side resource management. is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

[0015] In one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the auxiliary variables corresponding to each flexible load resource are initialized according to the predicted values ​​of the electricity power and the generated power corresponding to each flexible load resource, including:

[0016] Initialize the auxiliary variables corresponding to each flexible load resource according to the following expression:

[0017] λ i (0) = U i ′ (P i (0))

[0018]

[0019] In the formula, λ i (0) is the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to the flexible load resource i, P i (0) is the power consumption corresponding to the flexible load resource i, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, ρ i (0) is the net power consumption corresponding to the flexible load resource i, is the predicted value of renewable energy power generation controlled by flexible load resource i, that is, the predicted value of power generation.

[0020] In one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by unit electricity consumption and the net electricity power; the iterative updating of the auxiliary variables and the electricity power corresponding to each flexible load resource includes:

[0021] Obtain preset step size, decision variables and electricity benefit function;

[0022] Update the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource according to the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the iterative update, the net power corresponding to each flexible load resource before the iterative update and the preset step size;

[0023] Update the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption corresponding to each flexible load resource after this iterative update, the willingness value corresponding to each flexible load resource before this iterative update, the decision variables and the power consumption benefit function;

[0024] According to the power consumption corresponding to each flexible load resource before and after this iterative update and the net power consumption corresponding to each flexible load resource before this iterative update, the net power consumption corresponding to each flexible load resource is updated.

[0025] In one embodiment, the method updates the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource according to the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource before the iterative update, the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the iterative update, the net power corresponding to each flexible load resource before the iterative update and the preset step size, including:

[0026] Update the electricity benefit generated by the unit electricity consumption of each flexible load resource according to the following expression:

[0027]

[0028] In the formula, k represents the round of this iterative update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i before this iterative update, λ j (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource j before this iterative update, ρi (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, η is the preset step size, a ii is the weight corresponding to the flexible load resource i in this iteration update, a ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where a ii and a ij The formula for determining is:

[0029]

[0030] In the formula, x is the left subscript of weight a, and y is the right subscript of weight a. is the communication neighbor set corresponding to the flexible load resource x.

[0031] In one embodiment, the updating of the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption corresponding to each flexible load resource after the iterative update, the willingness value corresponding to each flexible load resource before the iterative update, the decision variable and the power consumption benefit function includes:

[0032] Update the power consumption of each flexible load resource according to the following expression:

[0033]

[0034]

[0035] In the formula, k represents the round of this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, P i is the decision variable, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, s i (k) is the willingness value of flexible load resource i before this iterative update, is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

[0036] In one embodiment, updating the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after the current iterative update and the net power consumption corresponding to each flexible load resource before the current iterative update includes:

[0037] Update the net power consumption of each flexible load resource according to the following expression:

[0038]

[0039] In the formula, k represents the round of this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, ρ j (k) is the net power consumption of flexible load resource j before this iteration update, P i (k) is the power consumption corresponding to the flexible load resource i before this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, b ii is the weight corresponding to the flexible load resource i in this iteration update, b ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where b ii and b ij The formula for determining is:

[0040]

[0041] In the formula, x is the left subscript of the weight b, and y is the right subscript of the weight b. is the communication neighbor set corresponding to the flexible load resource y.

[0042] In one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the convergence judgment condition is:

[0043]

[0044] In the formula, k represents the round of this iterative update, ∈ λ is the first preset threshold, ∈ ρ is the second preset threshold, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i (k) is the estimated value of the electricity efficiency generated by the unit power consumption of flexible load resource i before this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iterative update.

[0045] In a second aspect, the present application provides a distributed demand-side resource management device, the device comprising:

[0046] The data acquisition module is used to obtain the willingness value, power generation forecast value, and communication neighbor set of each flexible load resource on the demand side;

[0047] A first initialization module, used to initialize the power consumption of each flexible load resource according to the willingness value of each flexible load resource;

[0048] The second initialization module is used to initialize the auxiliary variables corresponding to each flexible load resource according to the power consumption and power generation prediction values ​​corresponding to each flexible load resource;

[0049] An iterative update module is used to exchange information between each flexible load resource and each flexible load resource in its corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment conditions;

[0050] A first judgment module, configured to update the willingness value of each flexible load resource on the demand side and enter the next iteration if the convergence judgment condition is not met;

[0051] The second judgment module is used to use the latest power consumption of each flexible load resource as the optimal solution for demand-side resource management if the convergence judgment condition is met.

[0052] In a third aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the distributed demand-side resource management method as described in any of the above embodiments.

[0053] In a fourth aspect, the present application provides a computer device, comprising: one or more processors, and a memory;

[0054] The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, the steps of the distributed demand-side resource management method as described in any one of the above embodiments are performed.

[0055] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0056] The present application provides a distributed demand-side resource management method, device and computer equipment, the method comprising: obtaining the willingness value, power generation prediction value and communication neighbor set of each flexible load resource on the demand side; initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource; initializing the auxiliary variables corresponding to each flexible load resource according to the power consumption and power generation prediction values ​​corresponding to each flexible load resource, respectively exchanging information between each flexible load resource and each flexible load resource in its corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment condition; if the convergence judgment condition is not met, then updating the willingness value of each flexible load resource on the demand side and entering the next iteration; if the convergence judgment condition is met, then taking the latest power consumption of each flexible load resource as the optimal solution for demand-side resource management. In the process of solving the resource management problem on the demand side of the power grid, the willingness value of each flexible load resource is introduced to ensure that when the flexible load resources can join or exit the solution process at any time, the renewable energy power station can maintain a high power consumption efficiency and improve the utilization rate of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 A flow chart of a distributed demand-side resource management method provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a process for iteratively updating auxiliary variables and power consumption corresponding to each flexible load resource provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of the structure of a distributed demand-side resource management device provided in an embodiment of the present application;

[0061] Figure 4 An internal structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] In one embodiment, the present application provides a distributed demand-side resource management method, and the following embodiment is described by applying the method to a server. It can be understood that the distributed demand-side resource management method can be executed by a single server or a server cluster composed of multiple servers, and the present application does not make specific restrictions on this.

[0064] like Figure 1 As shown, the present application provides a distributed demand-side resource management method, the method comprising:

[0065] Step S101: Obtain the willingness value, power generation forecast value, and communication neighbor set of each flexible load resource on the demand side.

[0066] Among them, flexible load resources refer to electrical equipment with adjustable capabilities, the willingness value is used to indicate whether the corresponding flexible load resources participate in demand-side resource management, the power generation forecast value is the predicted value of renewable energy power generation controlled by the corresponding flexible load resources, and the communication neighbor set refers to the set of other flexible load resources with which the corresponding flexible load resources can exchange information.

[0067] In this step, when the on-site consumption efficiency of renewable energy is low, the resources on the demand side can be managed to improve energy utilization efficiency. At this time, the client can send instructions to the server so that the server can respond to the instruction and execute the distributed demand-side resource management method. When the server receives the instruction, it will obtain the willingness value of each flexible load resource on the demand side, the predicted power generation value of each flexible load resource, and the communication neighbor set of each flexible load resource, and perform subsequent iterative updates based on the obtained data.

[0068] Step S102: Initialize the power consumption of each flexible load resource according to the willingness value of each flexible load resource.

[0069] It can be understood that according to the preset initialization expression, the power consumption of each flexible load resource can be initialized according to its willingness value. At this time, initializing the power consumption of each flexible load resource can be understood as setting the power consumption of each flexible load resource in the zeroth iteration as the data basis for the first iteration.

[0070] Step S103: Initialize the auxiliary variables corresponding to each flexible load resource according to the predicted values ​​of power consumption and power generation corresponding to each flexible load resource.

[0071] The electric power corresponding to each flexible load resource is the electric power corresponding to each flexible load resource obtained after initialization in step S102, and the auxiliary variables include the estimated value of the electricity benefit generated by the unit electricity consumption and the net electric power.

[0072] In this step, the auxiliary variables of each flexible load resource are initialized according to the power consumption corresponding to each flexible load resource and the predicted value of the generated power corresponding to each flexible load resource, so as to provide a data basis for the first iteration.

[0073] Step S104: information is exchanged between each flexible load resource and each flexible load resource in its corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource.

[0074] To update the auxiliary variables and power consumption corresponding to each flexible load resource, it is necessary to obtain the auxiliary variables corresponding to each flexible load resource in the communication neighbor set of the flexible load resource. Therefore, before each iterative update, each flexible load resource needs to exchange information with each flexible load resource in its corresponding communication neighbor set to realize the update of the auxiliary variables and power consumption corresponding to each flexible load resource.

[0075] It can be understood that after completing the initialization of the power consumption and auxiliary variables corresponding to each flexible load resource, the iterative update can be started. The power consumption and auxiliary variables of each flexible load resource are updated according to some data before this iterative update of each flexible load resource and the data obtained by information exchange.

[0076] Step S105: determining whether the auxiliary variables corresponding to each flexible load resource before and after the current iterative update meet the preset convergence determination conditions.

[0077] Whether to terminate the iterative update is determined by judging whether the auxiliary variables corresponding to each flexible load resource before and after each iterative update meet the preset convergence judgment conditions.

[0078] It is understandable that the convergence judgment conditions can be set by power grid R&D personnel based on factors such as experience values, or a deep learning model can be used to learn the convergence judgment conditions set during demand-side resource management to determine the convergence judgment conditions. This application does not impose specific restrictions on this.

[0079] Step S106: If the convergence judgment condition is not met, the willingness value of each flexible load resource on the demand side is updated and the next iteration is entered.

[0080] When the auxiliary variables corresponding to each flexible load resource before and after this iterative update do not meet the convergence judgment conditions, the willingness values ​​of each flexible load resource on the demand side will be re-acquired, and the willingness values ​​of each flexible load resource re-acquired will be used to update the willingness values ​​of each flexible load resource used in this iteration, that is, the willingness values ​​of each flexible load resource re-acquired will be used as the willingness values ​​of each flexible load resource used in the next iterative update.

[0081] Step S107: If the convergence judgment condition is met, the latest power consumption of each flexible load resource is used as the optimal solution for demand-side resource management.

[0082] When the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the convergence judgment conditions, at this time, the latest power consumption of each flexible load resource is the optimal solution for demand-side resource management. Adjusting the power consumption of each flexible load resource with this optimal solution can improve the utilization rate of renewable energy.

[0083] In the above embodiment, it includes: obtaining the willingness value, power generation prediction value, and communication neighbor set of each flexible load resource on the demand side; initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource; initializing the auxiliary variables corresponding to each flexible load resource according to the power consumption and power generation prediction values ​​corresponding to each flexible load resource, and exchanging information between each flexible load resource and each flexible load resource in the corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and judge whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment condition. If the convergence judgment condition is not met, the willingness value of each flexible load resource on the demand side is updated, and the next iteration is entered. If the convergence judgment condition is met, the latest power consumption of each flexible load resource is used as the optimal solution for demand side resource management. In the process of solving the resource management problem on the demand side of the power grid, the willingness value of each flexible load resource is introduced to achieve that when the flexible load resource can join or exit the solution process at any time, the renewable energy power station can maintain a high power consumption efficiency and improve the utilization rate of renewable energy.

[0084] In one embodiment, according to the willingness value of each flexible load resource, the expression for initializing the power consumption of each flexible load resource is:

[0085]

[0086] Where P i (0) is the power consumption corresponding to the flexible load resource i, s i (0) is the unupdated willingness value corresponding to the flexible load resource i, s i (0) When it is 1, it means that the flexible load resource i is willing to participate in demand-side resource management, s i (0) When it is 0, it means that the flexible load resource i is unwilling to participate in demand-side resource management. is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

[0087] It is understandable that when flexible load resource i is willing to participate in demand-side resource management, that is, s i (0)=1, at this time, you can and A value is randomly selected from the constituted range as the power consumption of the flexible load resource i, or a certain strategy may be pre-set to determine the value of the selected power consumption.

[0088] In one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; and the auxiliary variables corresponding to each flexible load resource are initialized according to the predicted values ​​of the electricity power and the generated power corresponding to each flexible load resource, including:

[0089] Initialize the auxiliary variables corresponding to each flexible load resource according to the following expression:

[0090] λ i (0) = U i ′ (P i (0))

[0091]

[0092] In the formula, λ i (0) is the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to the flexible load resource i, P i (0) is the power consumption corresponding to the flexible load resource i, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, ρ i (0) is the net power consumption corresponding to the flexible load resource i, is the predicted value of renewable energy power generation controlled by flexible load resource i, that is, the predicted value of power generation.

[0093] Understandably, U i ′(P i (0)) indicates that the independent variable is P i (0) i The first derivative of .

[0094] like Figure 2 As shown, in one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the auxiliary variables and the electricity power corresponding to each flexible load resource are iteratively updated, including:

[0095] Step S201: Obtaining a preset step size, decision variables and electricity benefit function.

[0096] Among them, the preset step size refers to the distance to move forward in a certain direction, and the decision variable refers to a variable used for optimization and decision problems. The decision variable represents a controllable input, and under given constraints, different values ​​of the decision variable can lead to different results. In the demand-side resource management of this solution, the goal of the decision variable is to maximize the utilization rate of renewable energy. The Y-axis of the electricity benefit function represents the electricity benefit generated by the user's electricity consumption, in yuan (￥), and the X-axis represents the electricity power, in watts (W).

[0097] Step S202: Update the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource based on the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource before this iterative update, the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before this iterative update, the net power corresponding to each flexible load resource before this iterative update and the preset step size,

[0098] Among them, the electricity efficiency generated by unit electricity consumption can be understood as the electricity charge generated by unit electricity consumption, and the net electricity power is the difference between the total electricity power consumption and the total power generation power.

[0099] Step S203: Update the power consumption of each flexible load resource based on the estimated value of the power consumption benefit generated by the unit power consumption of each flexible load resource after this iterative update, the willingness value, decision variables and power consumption benefit function corresponding to each flexible load resource before this iterative update.

[0100] Step S204: Update the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after this iterative update and the net power consumption corresponding to each flexible load resource before this iterative update.

[0101] It can be understood that when updating the electricity consumption, the variable of the willingness value of each flexible load resource is added, allowing flexible load resources to join or exit the solution process of the grid demand side management problem at any time, and ensuring the optimality of the solution to the demand side management problem, further improving the utilization rate of renewable energy.

[0102] In one embodiment, according to the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource before the iterative update, the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the iterative update, the net power consumption corresponding to each flexible load resource before the iterative update and the preset step size, the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource is updated, including:

[0103] Update the electricity benefit generated by the unit electricity consumption of each flexible load resource according to the following expression:

[0104]

[0105] In the formula, k represents the round of this iterative update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i before this iterative update, λ j (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource j before this iterative update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, η is the preset step size, a ii is the weight corresponding to the flexible load resource i in this iteration update, a ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where a ii and a ij The formula for determining is:

[0106]

[0107] In the formula, x is the left subscript of weight a, and y is the right subscript of weight a. is the communication neighbor set corresponding to the flexible load resource x, is the number of flexible load resources in the communication neighbor set corresponding to the flexible load resource x.

[0108] Furthermore, i(k) indicates the optimization direction of the parameter of the estimated value of the electricity benefit generated per unit of electricity consumption, and η can be understood as the distance advanced in this optimization direction.

[0109] For example, a ij Substituting into the above formula, we can see that: x = i, y = j, then a ij It can be expressed as:

[0110]

[0111] In one embodiment, according to the estimated value of the power benefit generated by the unit power consumption of each flexible load resource after the current iterative update, the willingness value, decision variables and power benefit function corresponding to each flexible load resource before the current iterative update, the power consumption of each flexible load resource is updated, including:

[0112] Update the power consumption of each flexible load resource according to the following expression:

[0113]

[0114]

[0115] In the formula, k represents the round of this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, P i is the decision variable, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, s i (k) is the willingness value of flexible load resource i before this iterative update, is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

[0116] It is understandable that the general usage of the argmin function is to represent the variable value when the objective function takes the minimum value, for example: argmin x (f(x)) represents the value of x when the objective function f(x) reaches its minimum value.

[0117] In one embodiment, updating the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after the current iterative update and the net power consumption corresponding to each flexible load resource before the current iterative update includes:

[0118] Update the net power consumption of each flexible load resource according to the following expression:

[0119]

[0120] In the formula, k represents the round of this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, ρ j (k) is the net power consumption of flexible load resource j before this iteration update, P i (k) is the power consumption corresponding to the flexible load resource i before this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, b ii is the weight corresponding to the flexible load resource i in this iteration update, b ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where b ii and b ij The formula for determining is:

[0121]

[0122] In the formula, x is the left subscript of the weight b, and y is the right subscript of the weight b. is the communication neighbor set corresponding to the flexible load resource y, is the number of flexible load resources in the communication neighbor set corresponding to the flexible load resource y.

[0123] For example, b ij Substituting into the above formula, we can see that: x = i, y = j, then b ij It can be expressed as:

[0124]

[0125] In one embodiment, the auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the convergence judgment condition is:

[0126]

[0127] In the formula, k represents the round of this iterative update, ∈ λ is the first preset threshold, ∈ ρ is the second preset threshold, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i(k) is the estimated value of the electricity efficiency generated by the unit power consumption of flexible load resource i before this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iterative update.

[0128] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0129] The distributed demand-side resource management device provided in an embodiment of the present application is described below. The distributed demand-side resource management device described below and the distributed demand-side resource management method described above can be referenced to each other.

[0130] like Figure 3 As shown, the present application provides a distributed demand-side resource management device 300, the device comprising:

[0131] The data acquisition module 301 is used to obtain the willingness value, power generation prediction value, and communication neighbor set of each flexible load resource on the demand side;

[0132] A first initialization module 302 is used to initialize the power consumption of each flexible load resource according to the willingness value of each flexible load resource;

[0133] The second initialization module 303 is used to initialize the auxiliary variables corresponding to each flexible load resource according to the power consumption and power generation prediction values ​​corresponding to each flexible load resource;

[0134] The iterative update module 304 is used to exchange information between each flexible load resource and each flexible load resource in the corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after the iterative update meet the preset convergence judgment conditions;

[0135] The first judgment module 305 is used to update the willingness value of each flexible load resource on the demand side and enter the next iteration if the convergence judgment condition is not met;

[0136] The second judgment module 306 is used to use the latest power consumption of each flexible load resource as the optimal solution for demand-side resource management if the convergence judgment condition is met.

[0137] In one embodiment, the iterative update module includes:

[0138] The data acquisition submodule is used to obtain the preset step size, decision variables and electricity benefit function;

[0139] The power efficiency update submodule is used to update the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource according to the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource before the current iterative update, the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the current iterative update, the net power consumption corresponding to each flexible load resource before the current iterative update and the preset step size;

[0140] The power consumption update submodule is used to update the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption of each flexible load resource after the current iterative update, the willingness value, decision variables and power consumption benefit function corresponding to each flexible load resource before the current iterative update;

[0141] The net power consumption update submodule is used to update the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after this iterative update and the net power consumption corresponding to each flexible load resource before this iterative update.

[0142] The division of each module in the above-mentioned distributed demand-side resource management device is only for illustration. In other embodiments, the distributed demand-side resource management device can be divided into different modules as needed to complete all or part of the functions of the above-mentioned distributed demand-side resource management device. Each module in the above-mentioned distributed demand-side resource management device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0143] In one embodiment, the present application also provides a storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the distributed demand-side resource management method as described in any of the above embodiments.

[0144] In one embodiment, the present application also provides a computer device, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the distributed demand-side resource management method as described in any of the above embodiments.

[0145] Indicatively, if Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 400 may be provided as a server. Figure 4 The computer device 400 includes a processing component 402, which further includes one or more processors, and a memory resource represented by a memory 401, for storing instructions executable by the processing component 402, such as an application. The application stored in the memory 401 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 402 is configured to execute instructions to perform the distributed demand-side resource management method of any of the above embodiments.

[0146] The computer device 400 may further include a power supply component 403 configured to perform power management of the computer device 400, a wired or wireless network interface 404 configured to connect the computer device 400 to a network, and an input / output (I / O) interface 405. The computer device 400 may operate based on an operating system stored in the memory 401, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0147] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0148] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish an entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only include those elements, but also include other elements that are not clearly listed, or also include elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. Herein, the singular "one", "one" and "described / the" may also include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "include / comprise" or "have" etc. specify the existence of stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the existence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0149] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0150] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distributed demand-side resource management method, characterized in that: The method comprises: Obtain the willingness value, power generation forecast value, and communication neighbor set of each flexible load resource on the demand side; Initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource; Initialize the auxiliary variables corresponding to each flexible load resource according to the predicted values ​​of power consumption and power generation corresponding to each flexible load resource; Information is exchanged between each flexible load resource and each flexible load resource in its corresponding communication neighbor set respectively, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment conditions; If the convergence judgment condition is not met, the willingness value of each flexible load resource on the demand side is updated and the next iteration is entered; If the convergence judgment condition is met, the latest power consumption of each flexible load resource is used as the optimal solution for demand-side resource management; The auxiliary variables include an estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the auxiliary variables corresponding to each flexible load resource are initialized according to the predicted values ​​of the electricity power and the generated power corresponding to each flexible load resource, including: Initialize the auxiliary variables corresponding to each flexible load resource according to the following expression: l i (0)=U i ′ (P i (0)) In the formula, λ i (0) is the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to the flexible load resource i, P i (0) is the power consumption corresponding to the flexible load resource i, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, ρ i (0) is the net power consumption corresponding to the flexible load resource i, is the predicted value of renewable energy power generation controlled by flexible load resource i, that is, the predicted value of power generation; The iterative updating of the auxiliary variables and the power consumption corresponding to each flexible load resource includes: Obtain preset step size, decision variables and electricity benefit function; Update the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource according to the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the iterative update, the net power corresponding to each flexible load resource before the iterative update and the preset step size; Update the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption corresponding to each flexible load resource after this iterative update, the willingness value corresponding to each flexible load resource before this iterative update, the decision variables and the power consumption benefit function; According to the power consumption corresponding to each flexible load resource before and after this iterative update and the net power consumption corresponding to each flexible load resource before this iterative update, the net power consumption corresponding to each flexible load resource is updated.

2. The distributed demand-side resource management method according to claim 1, characterized in that: The expression for initializing the power consumption of each flexible load resource according to the willingness value of each flexible load resource is: Where P i (0) is the power consumption corresponding to the flexible load resource i, s i (0) is the unupdated willingness value corresponding to the flexible load resource i, s i (0) When it is 1, it means that the flexible load resource i is willing to participate in demand-side resource management, s i (0) When it is 0, it means that the flexible load resource i is unwilling to participate in demand-side resource management. is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

3. The distributed demand-side resource management method according to claim 1, characterized in that: The method of updating the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource before the iterative update, the estimated value of the power benefit generated by the unit power consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the iterative update, the net power consumption corresponding to each flexible load resource before the iterative update and the preset step size, includes: Update the electricity benefit generated by the unit electricity consumption of each flexible load resource according to the following expression: In the formula, k represents the round of this iterative update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i before this iterative update, λ j (k) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource j before this iterative update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, η is the preset step size, a ii is the weight corresponding to the flexible load resource i in this iteration update, a ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where a ii and a ij The formula for determining is: In the formula, x is the left subscript of weight a, and y is the right subscript of weight a. is the communication neighbor set corresponding to the flexible load resource x.

4. The distributed demand-side resource management method according to claim 1, characterized in that: The updating of the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption corresponding to each flexible load resource after the iterative update, the willingness value corresponding to each flexible load resource before the iterative update, the decision variables and the power consumption benefit function includes: Update the power consumption of each flexible load resource according to the following expression: In the formula, k represents the round of this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, P i is the decision variable, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, s i (k) is the willingness value of flexible load resource i before this iterative update, is the lower limit of power consumption, is the upper limit of power consumption, and These are preset values.

5. The distributed demand-side resource management method according to claim 1, characterized in that: The updating of the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after the current iterative update and the net power consumption corresponding to each flexible load resource before the current iterative update includes: Update the net power consumption of each flexible load resource according to the following expression: In the formula, k represents the round of this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iteration update, ρ j (k) is the net power consumption of flexible load resource j before this iteration update, P i (k) is the power consumption corresponding to the flexible load resource i before this iteration update, P i (k+1) is the power consumption corresponding to the flexible load resource i after this iteration update, b ii is the weight corresponding to the flexible load resource i in this iteration update, b ij is the weight of flexible load resource j in the communication neighbor set corresponding to flexible load resource i in this iterative update, where b ii and b ij The formula for determining is: In the formula, x is the left subscript of the weight b, y is the right subscript of the weight b, N y is the communication neighbor set corresponding to the flexible load resource y.

6. The distributed demand-side resource management method according to any one of claims 1 to 5, characterized in that: The auxiliary variables include the estimated value of the electricity benefit generated by the unit electricity consumption and the net electricity power; the convergence judgment condition is: In the formula, k represents the round of this iterative update, ∈ λ is the first preset threshold, ∈ ρ is the second preset threshold, λ i (k+1) is the estimated value of the electricity benefit generated by the unit power consumption of flexible load resource i after this iterative update, λ i (k) is the estimated value of the electricity efficiency generated by the unit power consumption of flexible load resource i before this iterative update, ρ i (k+1) is the net power consumption corresponding to the flexible load resource i after this iteration update, ρ i (k) is the net power consumption corresponding to the flexible load resource i before this iterative update.

7. A distributed demand-side resource management device, characterized in that: The device comprises: The data acquisition module is used to obtain the willingness value, power generation forecast value, and communication neighbor set of each flexible load resource on the demand side; A first initialization module, used to initialize the power consumption of each flexible load resource according to the willingness value of each flexible load resource; The second initialization module is used to initialize the auxiliary variables corresponding to each flexible load resource according to the power consumption and power generation prediction values ​​corresponding to each flexible load resource; An iterative update module is used to exchange information between each flexible load resource and each flexible load resource in its corresponding communication neighbor set, so as to iteratively update the auxiliary variables and power consumption corresponding to each flexible load resource, and determine whether the auxiliary variables corresponding to each flexible load resource before and after this iterative update meet the preset convergence judgment conditions; A first judgment module, configured to update the willingness value of each flexible load resource on the demand side and enter the next iteration if the convergence judgment condition is not met; The second judgment module is used to use the latest power consumption of each flexible load resource as the optimal solution for demand-side resource management if the convergence judgment condition is met; the auxiliary variables include an estimated value of the power consumption benefit generated by the unit power consumption and the net power consumption; the second initialization module includes: Initialize the auxiliary variables corresponding to each flexible load resource according to the following expression: l i (0)=U i ′ (P i (0)) In the formula, λ i (0) is the estimated value of the electricity benefit generated by the unit electricity consumption corresponding to the flexible load resource i, P i (0) is the power consumption corresponding to the flexible load resource i, U i is the preset electricity benefit function, whose independent variable and dependent variable are electricity power and electricity benefit respectively, ρ i (0) is the net power consumption corresponding to the flexible load resource i, is the predicted value of renewable energy power generation controlled by flexible load resource i, that is, the predicted value of power generation; The iterative update module comprises: The data acquisition submodule is used to obtain the preset step size, decision variables and electricity benefit function; The power efficiency update submodule is used to update the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource according to the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource before the current iterative update, the estimated value of the power efficiency generated by the unit power consumption corresponding to each flexible load resource in the communication neighbor set of each flexible load resource before the current iterative update, the net power corresponding to each flexible load resource before the current iterative update and the preset step size; The power consumption updating submodule is used to update the power consumption of each flexible load resource according to the estimated value of the power consumption benefit generated by the unit power consumption corresponding to each flexible load resource after the iterative update, the willingness value corresponding to each flexible load resource before the iterative update, the decision variables and the power consumption benefit function; The net power consumption update submodule is used to update the net power consumption corresponding to each flexible load resource according to the power consumption corresponding to each flexible load resource before and after this iterative update and the net power consumption corresponding to each flexible load resource before this iterative update.

8. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the distributed demand-side resource management method according to any one of claims 1 to 6 are performed.

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