Methods, apparatus and equipment for accurate modeling and approximation of feasible regions for flexible resource aggregation

Abstract

This application relates to the field of flexible resource aggregation technology, and particularly to a method, apparatus, and device for accurate modeling and approximation of feasible regions for flexible resource aggregation. The method includes: detecting the actual type of each flexible resource on the demand side of the power grid; constructing a single feasible region model for each flexible resource based on its actual type, and aggregating all single feasible region models to generate an aggregated feasible region model; selecting at least one constraint parameter from the constraint parameters of the aggregated feasible region model that satisfies a preset condition, and constructing an approximate feasible region model of a preset type based on the at least one constraint parameter; determining the actual allocation ratio of each flexible resource using the approximate feasible region model; and scheduling each flexible resource according to the actual allocation ratio. This solves the problems of related technologies having a relatively simple consideration of the aggregated feasible region, failing to ensure the smooth allocation of flexible resources, and having high computational complexity.

Description

Technical Field

[0001] This application relates to the field of flexible resource aggregation technology, and in particular to a method, apparatus and equipment for accurate modeling and approximation of feasible domains for flexible resource aggregation. Background Technology

[0002] The high penetration rate of intermittent renewable energy sources such as wind and solar power has increased the demand for grid regulation capabilities. Against this backdrop, the grid can no longer rely solely on the regulation role of traditional generating units; leveraging the regulation capabilities of demand-side flexible resources is imperative. Some typical demand-side flexible resources include distributed generation, electric vehicles, distributed energy storage, and thermal load control. The participation of demand-side flexible resources in grid regulation can ensure the safe and stable operation of the grid, promote the consumption of new energy sources, and reduce electricity costs for users.

[0003] Because considering all demand-side resource parameters during grid dispatch is overly complex and detrimental to protecting electricity user privacy, flexible demand-side resources need to be aggregated before participating in grid regulation. In this model, flexible resource aggregators submit the feasible domain of total power to the grid. After the grid operator completes clearing or dispatch calculations, the total power of each aggregator is determined, and the aggregator then allocates the power to each resource for execution. Therefore, it is necessary to accurately construct the feasible domain of aggregated resources to ensure smooth allocation without leading to excessive computational complexity.

[0004] Current ancillary service market rules tend to have simplistic considerations regarding the convergent feasible region. For example, the PJM powerline market only considers the upper and lower limits of power adjustment. The North China market considers both power and energy capacity for energy storage and V2G charging piles, while only considering power regulation capacity for controllable loads such as ordinary charging piles and electric heating. The feasible regions described by these methods generally differ significantly from the actual convergent feasible region.

[0005] However, there is a consensus in related technologies on the aggregation feasible region of computationally flexible resources, namely, it is a problem of finding the Minkowski sum of a high-dimensional polyhedron, and there is generally no efficient algorithm; at the same time, related technologies often focus on approximating this feasible region from the inside or outside, without a modeling method for accurately aggregating the feasible region. Summary of the Invention

[0006] This application provides a method, apparatus, and device for accurate modeling and approximation of the feasible region for flexible resource aggregation, in order to solve the problems of related technologies having a relatively simple consideration of the feasible region for aggregation, failing to ensure the smooth allocation of flexible resources, and having high computational complexity.

[0007] The first aspect of this application provides a method for accurate modeling and approximation of the feasible region of flexible resources aggregation, comprising the following steps: detecting the actual type of each flexible resource on the demand side of the power grid; constructing a single feasible region model for each flexible resource based on the actual type, and aggregating all single feasible region models to generate an aggregated feasible region model; screening at least one constraint parameter among the constraint parameters of the aggregated feasible region model that satisfies a preset condition, and constructing an approximate feasible region model of a preset type based on the at least one constraint parameter; determining the actual allocation ratio of each flexible resource using the approximate feasible region model; and scheduling each flexible resource according to the actual allocation ratio.

[0008] Optionally, in one embodiment of this application, the step of constructing a single feasible domain model for each flexible resource based on the actual type includes: dividing a preset time window into multiple time periods according to a preset time interval; calculating the power and energy boundary information for each time period based on the actual type of each flexible resource; and constructing the single feasible domain model based on the power and energy boundary information for each time period.

[0009] Optionally, in one embodiment of this application, the step of aggregating all individual feasible region models to obtain an aggregated feasible region model includes: aggregating the cumulative energy or power consumed by all individual feasible region models to obtain decision variables; generating a complete binary tree based on the energy consumed in each time period, and using the set of all paths of the complete binary tree as a set of constraint parameters; and constructing the aggregated feasible region model based on the decision variables and the set of constraint parameters.

[0010] Optionally, in one embodiment of this application, the preset type of approximate feasible region model includes any one of the following: k-order approximate feasible region model, long and short time period approximate feasible region model, and time period grouping approximate feasible region model.

[0011] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the k-th order approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: selecting constraint branches from the at least one constraint parameter according to the actual order of the k-th order approximate feasible region model; constructing the approximate feasible region model corresponding to the actual order based on the constraint branches, wherein the k-th order approximate model is... Where E represents the column vector of variables E(t) from T to 1. Let T represent the set of all paths in a complete binary tree of height T+1. v l Let (·) represent the vector formed by the data of all nodes on path l. T Indicates vector transpose; f l and These represent the boundary values ​​corresponding to l.

[0012] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the long and short time period approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: dividing the time period according to different time intervals, wherein the minimum time period length is equal to the preset time interval; and constructing the long and short time period approximate feasible region model according to the constraint parameters in each time period.

[0013] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the time period grouping approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: dividing a preset time window into multiple time periods according to a preset time interval; dividing the multiple time periods into multiple time period groups; and constructing the time period grouping approximate feasible region model according to the constraint parameters in each time period group.

[0014] A second aspect of this application provides a device for accurate modeling and approximation of the feasible region of flexible resources, comprising: a detection module for detecting the actual type of each flexible resource on the demand side of the power grid; a generation module for constructing a single feasible region model of each flexible resource according to the actual type, and aggregating all single feasible region models to generate an aggregated feasible region model; and a scheduling module for filtering at least one constraint parameter among the constraint parameters of the aggregated feasible region model that satisfies a preset condition, constructing an approximate feasible region model of a preset type based on the at least one constraint parameter, determining the actual allocation ratio of each flexible resource using the approximate feasible region model, and scheduling each flexible resource according to the actual allocation ratio.

[0015] Optionally, in one embodiment of this application, the generation module is further configured to divide a preset time window into multiple time periods according to a preset time interval, calculate the power and energy boundary information of each time period according to the actual type of each flexible resource, and construct the single feasible domain model according to the power and energy boundary information of each time period.

[0016] Optionally, in one embodiment of this application, the generation module is further configured to aggregate the cumulative energy or power consumed by all individual feasible domain models to obtain decision variables, generate a complete binary tree based on the energy consumed in each time period, and use the set of all paths of the complete binary tree as a set of constraint parameters, and construct the aggregated feasible domain model based on the decision variables and the set of constraint parameters.

[0017] Optionally, in one embodiment of this application, the preset type of approximate feasible region model includes any one of the following: k-order approximate feasible region model, long and short time period approximate feasible region model, and time period grouping approximate feasible region model.

[0018] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the k-th order approximate feasible region model, the scheduling module is further configured to select the constraint branch among the at least one constraint parameter according to the actual order of the k-th order approximate feasible region model, and construct the approximate feasible region model corresponding to the actual order based on the constraint branch, wherein the k-th order approximate model is Where E represents the column vector of variables E(t) from T to 1. Let T represent the set of all paths in a complete binary tree of height T+1. v l Let (·) represent the vector formed by the data of all nodes on path l. T Indicates vector transpose; f l and These represent the boundary values ​​corresponding to l.

[0019] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the long and short time period approximate feasible region model, the scheduling module is further used to divide the time period according to different time intervals, wherein the minimum time period length is equal to the preset time interval, and to construct the long and short time period approximate feasible region model according to the constraint parameters in each time period.

[0020] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is the time period grouping approximate feasible region model, the scheduling module is further configured to divide the preset time window into multiple time periods according to the preset time interval, divide the multiple time periods into multiple time period groups, and construct the time period grouping approximate feasible region model according to the constraint parameters in each time period group.

[0021] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the flexible resource aggregation feasible domain accurate modeling and approximation method as described in the above embodiments.

[0022] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the flexible resource aggregation feasible domain accurate modeling and approximation method as described in the above embodiments.

[0023] Therefore, this application has at least the following beneficial effects:

[0024] Based on the actual type of each flexible resource on the power grid demand side, a single feasible region model is constructed for each flexible resource. All single feasible region models are then aggregated to generate an aggregated feasible region model. Constraints are extracted from the aggregated feasible region model to form a series of approximate feasible region models. These approximate models are used to determine the actual allocation ratio of each flexible resource. Scheduling each flexible resource according to this actual allocation ratio can minimize the operating cost or maximize the profit of the flexible resource aggregator while ensuring the feasibility of power allocation. This solves the problems of related technologies having a relatively simple consideration of the aggregated feasible region, failing to ensure the smooth allocation of flexible resources, and having high computational complexity.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0027] Figure 1 This is a flowchart illustrating a flexible resource aggregation feasible region accurate modeling and approximation method according to an embodiment of this application;

[0028] Figure 2 The vector v corresponding to all paths l provided according to the embodiments of this application. l Schematic diagram;

[0029] Figure 3 This is a block diagram of a device for accurate modeling and approximation of a flexible resource aggregation feasible region according to an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0031] Explanation of reference numerals in the attached diagram: Detection module-100, Generation module-200, Scheduling module-300, Memory-401, Processor-402, Communication interface-403. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] The following describes, with reference to the accompanying drawings, a method, apparatus, electronic device, and storage medium for accurate modeling and approximation of the feasible region for flexible resource aggregation, according to embodiments of this application. Addressing the problems mentioned in the background art, this application provides a method for accurate modeling and approximation of the feasible region for flexible resource aggregation. In this method, the actual type of each flexible resource on the power grid demand side is detected; a single feasible region model for each flexible resource is constructed based on its actual type; all single feasible region models are aggregated to generate an aggregated feasible region model; at least one constraint parameter in the aggregated feasible region model is selected that satisfies a preset condition; an approximate feasible region model of a preset type is constructed based on the at least one constraint parameter; the actual allocation ratio of each flexible resource is determined using the approximate feasible region model; and each flexible resource is scheduled according to the actual allocation ratio. This solves the problems of related technologies having a relatively simple consideration of the aggregated feasible region, failing to ensure the smooth allocation of flexible resources, and having high computational complexity.

[0034] Specifically, Figure 1 This is a flowchart illustrating a method for accurate modeling and approximation of a feasible domain for flexible resource aggregation, as provided in an embodiment of this application.

[0035] like Figure 1 As shown, this method for accurate modeling and approximation of the feasible region of flexible resource aggregation includes the following steps:

[0036] In step S101, the actual type of each flexible resource on the demand side of the power grid is detected.

[0037] Because considering all demand-side resource parameters during grid dispatch is overly complex and detrimental to protecting electricity user privacy, flexible demand-side resources need to be aggregated before participating in grid regulation. This application, based on a known unified individual model of flexible resources, calculates an accurate aggregation feasible region model for large-scale flexible resources, and extracts some constraints from the accurate feasible region to form a series of approximate feasible region models. This ensures the feasibility of power allocation while maximizing the aggregator's expected revenue. Therefore, before constructing the individual feasible region model for each flexible resource, this application's embodiments first detect the actual type of each flexible resource on the grid demand side.

[0038] In step S102, a single feasible domain model for each flexible resource is constructed according to the actual type, and all single feasible domain models are aggregated to generate an aggregated feasible domain model.

[0039] The embodiments of this application can calculate a precise aggregate feasible region model for large-scale flexible resources based on a unified single-unit model of different types of flexible resources. The precision of the precise aggregate feasible region is that any power curve within the precise feasible region can be assigned as the power curve of each flexible resource that satisfies the single-unit power energy boundary of each flexible resource; any power curve that is not within the precise aggregate feasible region cannot be successfully assigned.

[0040] In one embodiment of this application, constructing a single feasible domain model for each flexible resource based on its actual type includes: dividing a preset time window into multiple time periods according to a preset time interval; calculating the power and energy boundary information for each time period based on the actual type of each flexible resource; and constructing a single feasible domain model based on the power and energy boundary information for each time period.

[0041] Specifically, this application embodiment constructs a monolithic feasible domain model for each flexible resource based on its actual type, including the following steps:

[0042] 1) Discretize the time by dividing the time window considered for optimization into a total of T time periods according to the time interval.

[0043] 2) Calculate the power and energy boundary information for each flexible resource. The lower power bound, upper power bound, lower energy bound, and upper energy bound for flexible resource i at time t are respectively... p i (t), e i (t), The boundary calculation method differs for different types of flexible resources, as detailed below:

[0044] For electric vehicles (EVs), input the arrival time, departure time, initial charge level, expected charge level, maximum battery capacity, and the rated charging / discharging power of the charging station. The power boundary represents the allowable charging power range for the EV at various times. Without considering vehicle discharge to the grid, if a vehicle is connected to the charging station, the power range for that time period is 0 to the rated power; if no vehicle is connected, the power is 0. Considering vehicle discharge to the grid, the lower power limit is the negative rated discharge power when the EV is connected, and 0 at other times. The energy boundary represents the possible charging plans for the EV. Its basic principle is that the charging process should reach the expected charge level before departure, but should not exceed the battery capacity.

[0045] For energy storage devices, the inputs are battery capacity, initial charge, rated charging power, and rated discharging power. The upper and lower bounds of the power are the rated charging power and rated discharging power, respectively. The upper bound corresponds to the energy trajectory of the energy storage device starting at the rated charging power at the initial moment, charging to full capacity, maintaining this power level, and then discharging back to the initial charge level at the final moment. The lower bound corresponds to the energy trajectory of the energy storage device starting at the rated discharging power at the initial moment, discharging to empty capacity, maintaining this power level, and then charging back to the initial charge level at the final moment.

[0046] For a thermally controlled load, input the rated heating or cooling power, the allowable temperature range, the ambient temperature prediction curve, and the initial internal temperature. Then the upper and lower bounds of the power are the rated heating or cooling power, respectively. In cooling mode, the upper energy bound is the energy consumption trajectory for maintaining the minimum allowable temperature, and the lower energy bound is the energy consumption trajectory for maintaining the maximum allowable temperature. The upper and lower energy bounds for the heating curve are defined in the opposite way to those in cooling mode.

[0047] The individual feasible domains of all demand-side flexible resources can be uniformly described as:

[0048]

[0049] Among them, e i (t) is the decision variable, representing the cumulative energy consumed by flexible resource i during time period t; It represents the collection of all flexible resources.

[0050] In one embodiment of this application, aggregating all individual feasible region models to obtain an aggregated feasible region model includes: aggregating the cumulative energy or power consumed by all individual feasible region models to obtain decision variables; generating a complete binary tree based on the energy consumed in each time period, and using the set of all paths of the complete binary tree as a set of constraint parameters; and constructing the aggregated feasible region model based on the decision variables and the set of constraint parameters.

[0051] Specifically, the decision variable in the single feasible region model is the total cumulative energy consumption E(t) of the flexible resource aggregate, which satisfies... E(0) = 0; or the total power P(t) of the aggregate satisfies P(t) = (E(t) - E(t-1)) / ΔT. The feasible region for precise aggregation of flexible resources can be concisely expressed as:

[0052]

[0053] Where E represents the column vector of variables E(t) from T to 1. Let T represent the set of all paths in a complete binary tree of height T+1. v l Let (·) represent the vector formed by the data of all nodes on path l.T Indicates vector transpose; f l and These represent the boundary values ​​corresponding to l one-to-one. Their definitions are explained in detail below.

[0054] The data in a complete binary tree is defined as follows:

[0055] (1) The data in the root node is 0;

[0056] (2) If the data in the right child of a node is 1 or -1, the data in the left child must be 0;

[0057] (3) When a node is the right child of its parent and all of its ancestors have data of 0, the data of the node is 1;

[0058] (4) When a node is the right child of its parent and has a non-zero ancestor, the sign of the node’s data is opposite to the sign of its nearest non-zero ancestor.

[0059] Based on the above definition, the vector v corresponding to all paths l in the embodiments of this application l like Figure 2 As shown.

[0060] This application's embodiment features a T-dimensional column vector u. l ,satisfy but f l and It can be defined by the following formula:

[0061]

[0062]

[0063] Among them, operators<x,y> [a:b] s represents the inner product of vectors x and y from the a-th component to the b-th component; l and r l These are the indices of the first and last non-zero element on l, respectively. p i and They are defined as follows:

[0064]

[0065] According to u l The definition of the above aggregate feasible region Equivalent to The expression for the feasible region can also be written as:

[0066]

[0067] ul Each component can be either 0 or 1. Therefore, the above formula for the exact aggregate feasible region means that the sum of energy consumed in each subset of the set of all time periods is bounded by upper and lower bounds, which correspond to the fastest and slowest energy consumption trajectories of that subset of time periods, respectively.

[0068] In step S103, at least one constraint parameter in the aggregated feasible region model that meets the preset conditions is selected, and an approximate feasible region model of the preset type is constructed based on the at least one constraint parameter. The actual allocation ratio of each flexible resource is determined using the approximate feasible region model, and each flexible resource is scheduled according to the actual allocation ratio.

[0069] Among them, the preset type of approximate feasible region model includes any one of the following: k-order approximate feasible region model, long and short time period approximate feasible region model, and time period grouping approximate feasible region model.

[0070] Understandably, since a binary tree has a total of 2 T There are 10 paths, except the leftmost path, which makes v... l =u l =0, which invalidates the constraint; all other paths correspond to a pair of valid upper and lower bound constraints. Therefore, the total feasible region for exact aggregation contains 2(2 T -1) Constraints. When T is large (e.g., T = 24 or 96), the number of constraints is too large and difficult to apply in scheduling. Therefore, an approximate feasible region model is needed to reduce the number of constraints, thereby reducing computational complexity. The approximate feasible region model is used to determine the actual allocation ratio of each flexible resource. Scheduling each flexible resource according to the actual allocation ratio can maximize the expected revenue of the aggregator while ensuring the feasibility of power allocation as much as possible.

[0071] It should be noted that the number of constraints in the aggregate feasible region model is related to the basic idea of ​​all approximate models, which is to extract paths that meet specific conditions from the binary tree of the exact feasible region, rather than considering all paths. Therefore, the number of constraints is reduced at the cost of accuracy.

[0072] In one embodiment of this application, when the preset type of approximate feasible region model is a k-th order approximate feasible region model, constructing the preset type of approximate feasible region model based on at least one constraint parameter includes: selecting constraint branches from at least one constraint parameter according to the actual order of the k-th order approximate feasible region model; constructing an approximate feasible region model corresponding to the actual order based on the constraint branches, wherein the k-th order approximate model is... Where E represents the column vector of variables E(t) from T to 1. Let T represent the set of all paths in a complete binary tree of height T+1. v lLet (·) represent the vector formed by the data of all nodes on path l. T φ represents the vector transpose. l and These represent the boundary values ​​corresponding to l.

[0073] It is understood that the k-th order approximation in this application selects the constraints to be considered based on the number of non-zero elements in the branches. Specifically, the branches selected by the k-th order approximation model are... Therefore, the k-th order approximation model can be written in the following form:

[0074]

[0075] When k = 1, the above constraint can be specifically written as:

[0076]

[0077] That is, the first-order approximation only includes the boundary of total energy, and this boundary is exactly equal to the sum of the energy boundaries of all flexible resources.

[0078] When k = 2, the above constraint can be specifically written as

[0079]

[0080] That is, the second-order approximation includes the boundary of the total energy change between any two time periods, and the upper and lower bounds correspond to the fastest and slowest energy change trajectories between these two time periods, respectively.

[0081] In one embodiment of this application, when the preset type of approximate feasible region model is a long and short time period approximate feasible region model, constructing the preset type of approximate feasible region model based on at least one constraint parameter includes: dividing the time period according to different time intervals, wherein the minimum time period length is equal to the preset time interval; and constructing a long and short time period approximate feasible region model according to the constraint parameters in each time period.

[0082] Specifically, the preset time interval can be ΔT. In this embodiment, time periods are divided according to different time intervals, where the shortest time period is equal to ΔT. Medium and long time periods can also be added. When the total time window length is fixed, the longer the time period, the fewer time periods are divided, thus allowing for a more accurate aggregated feasible region model to establish constraints.

[0083] For example, for a 24-hour period, 96 time intervals can be obtained by dividing the time into 15-minute intervals. The variables in these time intervals are constrained using a second-order approximate feasible region model. Similarly, 12 time intervals can be divided by dividing the time into 2-hour intervals. The variables in these time intervals are constrained using an exact feasible region model. By combining the constraints from the feasible region models used under different divisions, the constraints in the approximation of long and short time intervals are obtained, thus constructing an approximate feasible region model for long and short time intervals.

[0084] In one embodiment of this application, when the preset type of approximate feasible region model is a time period grouping approximate feasible region model, constructing the preset type of approximate feasible region model based on at least one constraint parameter includes: dividing a preset time window into multiple time periods according to a preset time interval; dividing the multiple time periods into multiple time period groups; and constructing a time period grouping approximate feasible region model based on the constraint parameters in each time period group.

[0085] Specifically, in this embodiment, the time period is divided into several groups, with each group containing a small number of time periods, allowing for the use of more precise constraints. For example, the 96 time periods of 24 hours can be divided into 12 groups: 1-8, 8-16, ..., 88-96. For each group, constraints can be established using an exact feasible region model, and the number of constraints will not be too large. The last time period of the previous group should be the same as the first time period of the next group to ensure coupling between groups. Based on this, levels can be further increased. For example, the 24 hours can be further divided into 96 time periods, divided into 6 groups (each group containing 16 time periods), with constraints established for each group using a second-order feasible region model; then, the 24 hours can be further divided into 12 time periods, forming one group, with constraints established using an exact feasible region model.

[0086] The method for accurate modeling and approximation of the feasible region for flexible resource aggregation proposed in this application constructs a single feasible region model for each flexible resource based on its actual type on the power grid demand side. All single feasible region models are then aggregated to generate an aggregated feasible region model. Constraints are extracted from the aggregated feasible region model to form a series of approximate feasible region models. These approximate models are used to determine the actual allocation ratio of each flexible resource, and each flexible resource is scheduled according to its actual allocation ratio. This approach minimizes the operating cost or maximizes the profit for the flexible resource aggregator while ensuring the feasibility of power allocation. Therefore, it solves the problems of related technologies that have a relatively simple consideration of the aggregated feasible region, cannot ensure the smooth allocation of flexible resources, and have high computational complexity.

[0087] Next, referring to the accompanying drawings, a flexible resource aggregation feasible domain accurate modeling and approximation device is proposed according to an embodiment of this application.

[0088] Figure 3 This is a block diagram of a flexible resource aggregation feasible domain accurate modeling and approximation device according to an embodiment of this application.

[0089] like Figure 3 As shown, the flexible resource aggregation feasible domain accurate modeling and approximation device 10 includes: a detection module 100, a generation module 200 and a scheduling module 300.

[0090] The system includes a detection module 100 for detecting the actual type of each flexible resource on the demand side of the power grid; a generation module 200 for constructing a single feasible region model for each flexible resource based on its actual type, and aggregating all single feasible region models to generate an aggregated feasible region model; and a scheduling module 300 for filtering at least one constraint parameter from the aggregated feasible region model that meets a preset condition, constructing an approximate feasible region model of a preset type based on at least one constraint parameter, determining the actual allocation ratio of each flexible resource using the approximate feasible region model, and scheduling each flexible resource according to the actual allocation ratio.

[0091] Optionally, in one embodiment of this application, the generation module 200 is further configured to divide a preset time window into multiple time periods according to a preset time interval, calculate the power and energy boundary information of each time period according to the actual type of each flexible resource, and construct a single feasible domain model based on the power and energy boundary information of each time period.

[0092] Optionally, in one embodiment of this application, the generation module 200 is further configured to aggregate the cumulative energy or power consumed by all individual feasible domain models to obtain decision variables, generate a complete binary tree based on the energy consumed in each time period, and use the set of all paths of the complete binary tree as a set of constraint parameters, and construct an aggregated feasible domain model based on the decision variables and the set of constraint parameters.

[0093] Optionally, in one embodiment of this application, the preset type of approximate feasible region model includes any one of the following: k-order approximate feasible region model, long and short time period approximate feasible region model, and time period grouping approximate feasible region model.

[0094] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is a k-th order approximate feasible region model, the scheduling module 300 is further configured to select at least one constraint branch from the constraint parameters according to the actual order of the k-th order approximate feasible region model, and construct an approximate feasible region model corresponding to the actual order based on the constraint branch, wherein the k-th order approximate model is... Where E represents the column vector of variables E(t) from T to 1. Let T represent the set of all paths in a complete binary tree of height T+1. v l Let (·) represent the vector formed by the data of all nodes on path l. T Indicates vector transpose; f l and These represent the boundary values ​​corresponding to l.

[0095] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is a long and short time period approximate feasible region model, the scheduling module 300 is further used to divide the time period according to different time intervals, wherein the minimum time period length is equal to the preset time interval, and to construct a long and short time period approximate feasible region model according to the constraint parameters in each time period.

[0096] Optionally, in one embodiment of this application, when the preset type of approximate feasible region model is a time period grouping approximate feasible region model, the scheduling module 300 is further used to divide the preset time window into multiple time periods according to the preset time interval, divide the multiple time periods into multiple time period groups, and construct the time period grouping approximate feasible region model according to the constraint parameters in each time period group.

[0097] It should be noted that the foregoing explanation of the embodiment of the method for accurate modeling and approximation of the feasible region of flexible resource aggregation also applies to the apparatus for accurate modeling and approximation of the feasible region of flexible resource aggregation in this embodiment, and will not be repeated here.

[0098] The flexible resource aggregation feasible region accurate modeling and approximation device proposed in this application constructs a single feasible region model for each flexible resource based on its actual type on the power grid demand side. All single feasible region models are then aggregated to generate an aggregated feasible region model. Constraints are extracted from the aggregated feasible region model to form a series of approximate feasible region models. These approximate models are used to determine the actual allocation ratio of each flexible resource, and each flexible resource is scheduled according to this ratio. This approach minimizes the operating cost or maximizes the profit for the flexible resource aggregator while ensuring the feasibility of power allocation. Therefore, it solves the problems of related technologies having a relatively simple consideration of the aggregated feasible region, failing to ensure the smooth allocation of flexible resources, and having high computational complexity.

[0099] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0100] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0101] When the processor 402 executes the program, it implements the flexible resource aggregation feasible domain accurate modeling and approximation method provided in the above embodiments.

[0102] Furthermore, electronic devices also include:

[0103] Communication interface 403 is used for communication between memory 401 and processor 402.

[0104] The memory 401 is used to store computer programs that can run on the processor 402.

[0105] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0106] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0107] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0108] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for accurate modeling and approximation of the feasible region for flexible resource aggregation.

[0110] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0113] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0114] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for accurate modeling and approximation of the feasible region of flexible resource aggregation, characterized in that, Includes the following steps: Detect the actual type of each flexible resource on the demand side of the power grid; Based on the actual type, construct a single feasible domain model for each flexible resource, and aggregate all single feasible domain models to generate an aggregated feasible domain model. Filter at least one constraint parameter from the constraint parameters of the aggregated feasible region model that meets the preset conditions, and construct an approximate feasible region model of a preset type based on the at least one constraint parameter. Use the approximate feasible region model to determine the actual allocation ratio of each flexible resource, and schedule each flexible resource according to the actual allocation ratio. The aggregated feasible region model, obtained by aggregating all individual feasible region models, includes: The cumulative energy or power consumed by all individual feasible domain models is aggregated to obtain the decision variables; A complete binary tree is generated based on the energy consumed in each time period, and the set of all paths of the complete binary tree is used as a set of constraint parameters. The aggregated feasible region model is constructed based on the decision variables and the set of constraint parameters. The sum of energy consumed in each subset of the set of all time periods is subject to upper and lower bounds, which correspond to the fastest and slowest energy consumption trajectories of that subset of time periods, respectively.

2. The method according to claim 1, characterized in that, The step of constructing a single feasible domain model for each flexible resource based on the actual type includes: Divide the preset time window into multiple time periods according to the preset time interval; Calculate the power and energy boundary information for each time period based on the actual type of each flexible resource; The single-unit feasible region model is constructed based on the power and energy boundary information for each time period.

3. The method according to claim 1, characterized in that, The preset type of approximate feasible region model includes any one of the following: k-order approximate feasible region model, long and short time period approximate feasible region model, and time period grouping approximate feasible region model.

4. The method according to claim 3, characterized in that, When the preset type of approximate feasible region model is the k-th order approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: Select the constraint branch from the at least one constraint parameter according to the actual order of the k-th order approximate feasible region model; Based on the constrained branches, an approximate feasible region model corresponding to the actual order is constructed, where the first... k The order approximation model is ;in, Indicates from T Variables up to 1 E ( t A column vector composed of ) ; Indicates height as T +1 is the set of all paths in a complete binary tree. , Representing a path l A vector composed of data from all nodes on the array. Indicates vector transpose; and Respectively represent and l One-to-one corresponding boundary values.

5. The method according to claim 3, characterized in that, When the preset type of approximate feasible region model is the long-short time period approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: The time periods are divided according to different time intervals, where the shortest time period is equal to the preset time interval. The approximate feasible region model for the long and short time periods is constructed based on the constraint parameters within each time period.

6. The method according to claim 3, characterized in that, When the preset type of approximate feasible region model is the time-grouped approximate feasible region model, the step of constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: Divide the preset time window into multiple time periods according to the preset time interval; The multiple time periods are divided into multiple time period groups, and an approximate feasible region model for each time period group is constructed based on the constraint parameters within each time period group.

7. A device for accurate modeling and approximation of the feasible region of flexible resource aggregation, characterized in that, include: The detection module is used to detect the actual type of each flexible resource on the demand side of the power grid; The generation module is used to construct a single feasible domain model for each flexible resource according to the actual type, and to aggregate all single feasible domain models to generate an aggregated feasible domain model. The scheduling module is used to filter at least one constraint parameter in the aggregated feasible region model that meets a preset condition, construct an approximate feasible region model of a preset type based on the at least one constraint parameter, determine the actual allocation ratio of each flexible resource using the approximate feasible region model, and schedule each flexible resource according to the actual allocation ratio. The aggregated feasible region model, obtained by aggregating all individual feasible region models, includes: The cumulative energy or power consumed by all individual feasible domain models is aggregated to obtain the decision variables; A complete binary tree is generated based on the energy consumed in each time period, and the set of all paths of the complete binary tree is used as a set of constraint parameters. The aggregated feasible region model is constructed based on the decision variables and the set of constraint parameters. The sum of energy consumed in each subset of the set of all time periods is subject to upper and lower bounds, which correspond to the fastest and slowest energy consumption trajectories of that subset of time periods, respectively.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the flexible resource aggregation feasible domain accurate modeling and approximation method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for accurate modeling and approximation of the feasible region of flexible resource aggregation as described in any one of claims 1-6.

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