Distributed flexible resource aggregation scheduling method, device and medium
By constructing a robust operational external characteristic model and a standard robust component model, the feasible domain of flexible resources is decomposed into multiple standard feasible domain clusters, solving the unified modeling problem of scheduling multiple types of resources, realizing efficient and robust response and improving resource utilization efficiency, and ensuring the stability and economy of the power system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
- Filing Date
- 2026-01-16
- Publication Date
- 2026-05-29
AI Technical Summary
Existing distributed flexible resource aggregation and scheduling methods cannot uniformly model multiple types of heterogeneous flexible resources, making it difficult to efficiently handle the aggregation and collaborative optimization scheduling of the adjustment capabilities of massive flexible resources. They are also difficult to adapt to power systems with a high proportion of distributed power sources, and traditional centralized scheduling methods are difficult to cope with random disturbances and have low robustness.
By constructing a robust external characteristic model for flexible resources, the feasible domain of each flexible resource is decomposed into multiple standard robust feasible domain clusters using multiple standard robust component models. Scheduling instructions are generated in the cloud and aggregated using standard robust component models to improve computational efficiency and robustness.
It enables efficient and robust response of various heterogeneous resources in power system dispatch, improves the overall utilization efficiency of distributed generation resources, and ensures the stable and economical operation of the power system.
Smart Images
Figure CN122118765A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system dispatching and operation, and in particular to a distributed flexible resource aggregation dispatching method, equipment, and medium. Background Technology
[0002] With the continuous expansion of new energy installed capacity, a new type of power system characterized by a high proportion of renewable energy is rapidly evolving. On the generation side, the penetration rate of intermittent and unstable renewable energy sources such as wind power and photovoltaics in the power grid system is constantly increasing, and their intermittency and instability pose challenges to the stable operation of the power system. On the consumption side, the spatiotemporal aggregation characteristics of new flexible loads such as large-scale electric vehicles and intelligent temperature-controlled loads have significantly increased the peak-valley load difference rate compared to the traditional model, significantly exacerbating the pressure on power grid supply and demand balance. Distributed flexible resources have the ability to quickly respond to fluctuations in power load and renewable energy power, and their importance in the safe and economical operation of the power grid is growing.
[0003] Currently, the participation of distributed flexible resources in power system dispatch faces multiple technical bottlenecks: 1) Most studies are limited to the separate modeling of a single flexible resource type, lacking a unified representation method for the external characteristics of multiple technology types, leading to technical barriers such as incompatible model interfaces and heterogeneous control commands when coordinating the dispatch of multiple types of flexible resources. 2) Traditional centralized dispatching methods are difficult to efficiently handle the coordinated dispatching of massive flexible resources, while offline dispatching methods based on typical scenarios have high errors when dealing with random disturbances on both the source and load sides. 3) The separate modeling of flexible resource types does not consider the distribution of uncertain parameters, resulting in low robustness of the generated dispatching commands.
[0004] Existing methods for distributed flexible resource aggregation and scheduling cannot uniformly model multiple types of heterogeneous flexible resources, cannot efficiently handle the aggregation and collaborative optimization scheduling of the adjustment capabilities of massive flexible resources, and are difficult to adapt to power systems with a high proportion of distributed power sources to fully explore the scheduling capabilities of distributed flexible resources and meet the requirements for scheduling robustness. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art by providing a distributed flexible resource aggregation and scheduling method, device and medium, which realizes efficient and robust response of various heterogeneous resources in power system scheduling and improves the overall utilization efficiency of distributed generation resources.
[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a distributed flexible resource aggregation and scheduling method is provided, comprising: Based on the steady-state operating characteristics of the basic operating data of each flexible resource and the probability distribution of the uncertainty parameters, a robust operating external characteristic model of each flexible resource is constructed. We adopt multiple standard robust component models corresponding to each flexible resource to decompose the robust feasible region of each flexible resource under the influence of power constraints, energy constraints and uncertainties into multiple standard robust feasible region clusters. After aggregating multiple standard robust feasible domain clusters for each flexible resource, they are uploaded to the cloud; The cloud generates scheduling instructions based on the aggregation results and sends them to the aggregator. The aggregator then allocates scheduling instructions according to the feasible domain of each flexible resource under power and energy constraints.
[0007] Preferably, the steady-state operating characteristics of the basic operating data of the flexible resource include the charging and discharging power, charging and discharging efficiency, maximum charging and discharging efficiency, currently stored energy, energy dissipation rate, and upper and lower boundaries of stored energy. The uncertainty parameters include at least one of the following: the forecast error of renewable energy output, the fluctuation range of load demand, and the probability distribution of flexible resource availability.
[0008] Preferably, the robust operational external characteristic model of each flexible resource is specifically expressed as follows: , , , , , In the formula: This indicates the flexible resources at time period t. The charging power, Representing flexible resources Maximum charging power, Representing flexible resources Minimum charging power; This indicates the flexible resources at time period t. The discharge power, Representing flexible resources Maximum discharge power, Representing flexible resources The minimum discharge power; Representing flexible resources The maximum slope of energy conversion; This represents the entity's flexible resources at time period t. Stored energy Representing entity flexible resources The minimum and maximum stored energy; This indicates flexible resources after considering the physical process of self-discharge. Energy dissipation rate; and Representing flexible resources The charging efficiency and discharging efficiency; Indicates a time interval; , and The uncertainty buffer is determined based on historical error data or a preset confidence level.
[0009] Preferably, the method of using multiple standard robust component models corresponding to each flexible resource to decompose the robust feasible region of each flexible resource under the influence of power constraints, energy constraints, and uncertainties into multiple standard robust feasible region clusters specifically includes: S301, Regarding the first A flexible resource with power and energy constraints. The feasible domain power trajectory set is obtained through a convex polyhedron. Represented as: , In the formula: To control the amount of flexible resources currently existing within the area; and For the trajectory set parameters corresponding to the m-th flexible resource; For control variables; S302, Define the control area A flexible resource exists A standard robust component model is constructed using a standard component, and its isomorphic polyhedral properties are used to approximate the convex polyhedron of flexible resources. Flexible resources can be decomposed as follows: , In the formula: For the first A standard robust component model; For the first A standard robust component model The corresponding sequence of fitting coefficients; where the feasible region power trajectory set The corresponding number A standard robust component model , is represented as: , and For the first Trajectory set parameters corresponding to each standard component; S303. Using the standard robust component model, an approximate internal feasible region is recalculated for each flexible resource, and the feasible region of each flexible resource under power and energy constraints is decomposed into multiple standard feasible region clusters.
[0010] Preferably, the standard robust component model The calculation and solution process specifically includes: Based on the structural characteristics of isomorphic polyhedra, a standard robust component model is adopted. Express a set of power constraints and energy constraints : , , Energy confinement Through power constraint Represented as: , make , For dimensional information, for vectors Since the power constraint and energy constraint are linear, a system of linear inequalities is used. Characterizing power constraints and energy constraints ,in ; Obtain the feasible region power trajectory set convex polyhedron : , In the formula, and For the first The trajectory set parameters corresponding to each standard component.
[0011] Preferably, the step of recalculating an approximate internal feasible region for each flexible resource using a standard robust component model, and decomposing the feasible region of each flexible resource under power and energy constraints into multiple standard feasible region clusters, specifically includes: , In the formula: Indicates the first A flexible resource through based on The approximate feasible region obtained by decomposing a standard robust component model. Indicates the first The first flexible resource regarding the The scaling factor of a standard robust component model Indicates the first The first flexible resource regarding the The transformation factor of a standard robust component model.
[0012] Preferably, based on the power system economic dispatch model, for flexible resources within the control area, the capacity, maximum charging and discharging power, and charging and discharging power for each time period, as well as the power used by the flexible resource power station for charging and discharging during each time period, are determined with the objective function of optimizing the typical daily operating cost. The objective function expression is as follows: , , In the formula: and These represent time intervals. Internal flexible resources The charging power and discharging power, Representing flexible resources exist Power response within a time step This indicates that the area is in Electricity prices fluctuate over time within a given time step.
[0013] Preferably, the aggregator allocates scheduling instructions based on scheduling instructions according to the feasible region of each flexible resource under power and energy constraints, specifically including: , , , In the formula: Representing flexible resources The scheduling instructions to be executed; This indicates the scheduling instructions issued by the scheduling agency based on the feasible domain of flexible resource aggregation; For flexible resources The Middle A standard robust component model The corresponding sequence of fitting coefficients.
[0014] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.
[0015] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.
[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention transforms the aggregation problem of flexible resources into an aggregation problem of standard robust component models by first decomposing and then aggregating flexible resources. In other words, it transforms the complex geometric aggregation problem into an efficient algebraic operation problem, which greatly improves computational efficiency. At the same time, the probability distribution of uncertain parameters is introduced in the process of constructing the robust operation external characteristic model, so that the generated scheduling plan can resist a certain degree of uncertainty interference, reduce the number of plan failures and rescheduling, and ensure the efficient and robust response of various heterogeneous resources in power system scheduling. This is conducive to improving the overall utilization efficiency of distributed generation resources and ensuring the stable and economical operation of power systems with a high proportion of distributed power sources. Attached Figure Description
[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0019] Example 1 like Figure 1 As shown in the figure, this embodiment provides a distributed flexible resource aggregation and scheduling method, which includes: S1. Based on the steady-state operating characteristics of the basic operating data of each flexible resource and the probability distribution of the uncertainty parameters, construct a robust operating external characteristic model for each flexible resource.
[0020] In this embodiment, the steady-state operating characteristics of the basic operating data of each distributed flexible resource within the control area include the charging and discharging power, charging and discharging efficiency, maximum charging and discharging efficiency, currently stored energy, energy dissipation rate, and upper and lower boundaries of stored energy of the flexible resource.
[0021] Uncertainty parameters include at least one of the following: forecasting error of renewable energy output, fluctuation range of load demand, and probability distribution of flexible resource availability.
[0022] The robust external characteristic model for each flexible resource is specifically expressed as follows: (1) (2) (3) (4) (5) In the formula: This indicates the flexible resources at time period t. The charging power, Representing flexible resources Maximum charging power, Representing flexible resources Minimum charging power; This indicates the flexible resources at time period t. The discharge power, Representing flexible resources Maximum discharge power, Representing flexible resources The minimum discharge power; Representing flexible resources The maximum slope of energy conversion; This represents the entity's flexible resources at time period t. Stored energy Representing entity flexible resources The minimum and maximum stored energy; This indicates flexible resources after considering the physical process of self-discharge. Energy dissipation rate; and Representing flexible resources The charging efficiency and discharging efficiency; Indicates a time interval; , and The uncertainty buffer is determined based on historical error data or a preset confidence level.
[0023] S2. Using multiple standard robust component models corresponding to each flexible resource, the robust feasible region of each flexible resource under the influence of power constraints, energy constraints and uncertainties is decomposed into multiple standard robust feasible region clusters.
[0024] 1) For the first A flexible resource with power and energy constraints. The feasible domain power trajectory set is obtained through a convex polyhedron. Represented as: (6) In the formula: To control the amount of flexible resources currently existing within the area; and For the trajectory set parameters corresponding to the m-th flexible resource; For control variables.
[0025] 2) Define the control area A flexible resource exists A standard robust component model is constructed using standard components. This model is a set of isomorphic polyhedra used to approximate the convex polyhedron of flexible resources, leveraging its isomorphic polyhedron properties to approximate the convex polyhedron of flexible resources. Flexible resources can be decomposed as follows: , (7) In the formula: For the first A standard robust component model; For the first A standard robust component model The corresponding sequence of fitting coefficients; where the feasible region power trajectory set The corresponding number A standard robust component model , is represented as: , and For the first Trajectory set parameters corresponding to each standard component; Specifically, based on the structural characteristics of isomorphic polyhedra, a standard robust component model is adopted. Express a set of power constraints and energy constraints : (8) , (9) Energy confinement The corresponding equation (9) is obtained through power constraints. Represented as: , (10) make , For dimensional information, for vectors Since the power constraint and energy constraint are linear, a system of linear inequalities is used. Characterizing power constraints and energy constraints ,in , ; The system of linear inequalities defines the set of power trajectories in the feasible region with respect to equations (8) and (9). convex polyhedron , is represented as: (11) In the formula: and For the first The trajectory set parameters corresponding to each standard component.
[0026] 3) By using the standard robust component model, an approximate internal feasible region is recalculated for each flexible resource, and the feasible region of each flexible resource under power and energy constraints is decomposed into multiple standard feasible region clusters.
[0027] Specifically, the result obtained through equation (11) A standard robust component model is used to decompose the region. The essence of decomposing a flexible resource is to recalculate an approximate internal feasible region for each flexible resource. The decomposition process can be represented as follows: (12) Equation (12) represents the first A flexible resource through based on The approximate feasible region obtained by decomposing a standard robust component model, wherein Indicates the first The first flexible resource regarding the The scaling factor of a standard robust component model Indicates the first The first flexible resource regarding the The transformation factor of a standard robust component model.
[0028] This embodiment utilizes the basic principles of economic dispatch in power systems to manage a certain area. A flexible resource implementation based on Decomposition of standard robust components. Based on the power system economic dispatch model, for flexible resources in this region, with the objective function of optimizing typical daily operating costs, the capacity, maximum charging and discharging power, and charging and discharging power of flexible resources in each time period, as well as the power used by the flexible resource power station for charging and discharging in each time period, are determined. (13) (14) In the formula: and These represent time intervals. Internal flexible resources The charging power and discharging power, Representing flexible resources exist Power response within a time step This indicates that the area is in The electricity price fluctuates with time within a time step. Equation (13) represents the electricity price that varies with time when a series of time-varying electricity price incentives are given. At this point, maximizing the revenue of flexible resources can be equivalently expressed as minimizing the cost of obtaining electricity from flexible resources. Equation (14) represents the effect of electricity price incentives on flexible resources. Power response when maximizing returns.
[0029] S3. After aggregating multiple standard robust feasible domain clusters for each flexible resource, upload them to the cloud.
[0030] In this embodiment, based on Minkowski summaries, multiple standard robust feasible domain clusters of each flexible resource are aggregated to obtain a combination of standard robust feasible domain clusters of each flexible resource, and the aggregation result is uploaded to the cloud.
[0031] Power and energy constraints of flexible resources The feasible domain power trajectory set can be obtained through a convex polyhedron. Represented by a standard robust component model derived from flexible resource identification and modeling. With flexible resources convex polyhedron They possess the geometric characteristics of isomorphic polyhedra, and both have similar structural features.
[0032] (15) Equation (15) represents flexible resources The decomposition results are based on the standard robust component model. Each flexible resource is based on the standard robust component model. The model is decomposed, and the standard robust component model corresponding to each fitted sequence is calculated. Fitting coefficient sequence .
[0033] Based on the fundamental idea of decomposing and then aggregating flexible resources, the aggregation problem of flexible resources by the aggregator is transformed into the aggregation problem of standard robust component models. For a single standard robust component model, the physical process of aggregation can be expressed mathematically by finding the Minkowski sum. Furthermore, since a single standard robust component model has a completely consistent polyhedral shape, finding its Minkowski sum is transformed into a simple and easy-to-operate algebraic addition problem.
[0034] S4. The cloud generates scheduling instructions based on the aggregation results and sends them to the aggregator. The aggregator allocates scheduling instructions according to the feasible domain of each flexible resource under power and energy constraints.
[0035] The cloud-based dispatching agency, based on the aggregation results of various flexible resources provided by the aggregator, issues corresponding dispatching instructions according to changes in power system supply and demand balance and regulation requirements, and transmits these instructions to the aggregator. The aggregator, based on the dispatching instructions issued by the dispatching agency, allocates dispatching instructions according to the feasible region of each flexible resource under power and energy constraints.
[0036] The feasible domain allocation scheduling instructions for each flexible resource under power and energy constraints are shown in equations (16) to (18): (16) (17) (18) In the formula: Representing flexible resources The scheduling instructions to be executed; This indicates the scheduling instructions issued by the scheduling agency based on the feasible domain of flexible resource aggregation; For flexible resources The Middle A standard robust component model The corresponding sequence of fitting coefficients.
[0037] This invention innovatively proposes a distributed flexible resource aggregation and scheduling method. It achieves a unified mathematical representation of the external characteristics of different types of flexible resources at the edge through unified modeling, constructing an interface model that allows the power grid dispatching agency to allocate scheduling instructions. The cloud-based dispatching agency, based on the aggregation results of various flexible resources provided by the aggregator, issues corresponding scheduling instructions according to changes in power system supply-demand balance and regulation requirements. The aggregator, based on the scheduling instructions issued by the dispatching agency, allocates scheduling instructions according to the feasible domain of each flexible resource under power and energy constraints, effectively improving the efficiency and accuracy of distributed flexible resources participating in power system dispatching.
[0038] Example 2 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.
[0039] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0040] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).
[0041] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.
[0042] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.
[0043] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0044] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A distributed flexible resource aggregation and scheduling method, characterized in that, include: Based on the steady-state operating characteristics of the basic operating data of each flexible resource and the probability distribution of the uncertainty parameters, a robust operating external characteristic model of each flexible resource is constructed. We adopt multiple standard robust component models corresponding to each flexible resource to decompose the robust feasible region of each flexible resource under the influence of power constraints, energy constraints and uncertainties into multiple standard robust feasible region clusters. After aggregating multiple standard robust feasible domain clusters for each flexible resource, they are uploaded to the cloud; The cloud generates scheduling instructions based on the aggregation results and sends them to the aggregator. The aggregator then allocates scheduling instructions according to the feasible domain of each flexible resource under power and energy constraints.
2. The distributed flexible resource aggregation and scheduling method according to claim 1, characterized in that, The steady-state operating characteristics of the basic operating data of the flexible resources include the charging and discharging power, charging and discharging efficiency, maximum charging and discharging efficiency, currently stored energy, energy dissipation rate, and upper and lower boundaries of stored energy. The uncertainty parameters include at least one of the following: the forecast error of renewable energy output, the fluctuation range of load demand, and the probability distribution of flexible resource availability.
3. The distributed flexible resource aggregation and scheduling method according to claim 1, characterized in that, The robust operational external characteristic model of each flexible resource is specifically expressed as follows: ， ， ， ， ， In the formula: This indicates the flexible resources at time period t. The charging power, Representing flexible resources Maximum charging power, Representing flexible resources Minimum charging power; This indicates the flexible resources at time period t. The discharge power, Representing flexible resources Maximum discharge power, Representing flexible resources The minimum discharge power; Representing flexible resources The maximum slope of energy conversion; This represents the entity's flexible resources at time period t. Stored energy Representing entity flexible resources The minimum and maximum stored energy; This indicates flexible resources after considering the physical process of self-discharge. Energy dissipation rate; and Representing flexible resources The charging efficiency and discharging efficiency; Indicates a time interval; , and The uncertainty buffer is determined based on historical error data or a preset confidence level.
4. The distributed flexible resource aggregation and scheduling method according to claim 1, characterized in that, The aforementioned multiple standard robust component models corresponding to each flexible resource decompose the robust feasible region of each flexible resource under the influence of power constraints, energy constraints, and uncertainties into multiple standard robust feasible region clusters, specifically including: S301, Regarding the first A flexible resource with power and energy constraints. The feasible domain power trajectory set is obtained through a convex polyhedron. Represented as: ， In the formula: To control the amount of flexible resources currently existing within the area; and For the trajectory set parameters corresponding to the m-th flexible resource; For control variables; S302, Define the control area A flexible resource exists A standard robust component model is constructed using a standard component, and its isomorphic polyhedral properties are used to approximate the convex polyhedron of flexible resources. Flexible resources can be decomposed as follows: ， In the formula: For the first A standard robust component model; For the first A standard robust component model The corresponding sequence of fitting coefficients; where the feasible region power trajectory set The corresponding number A standard robust component model , is represented as: , and For the first Trajectory set parameters corresponding to each standard component; S303. Using the standard robust component model, an approximate internal feasible region is recalculated for each flexible resource, and the feasible region of each flexible resource under power and energy constraints is decomposed into multiple standard feasible region clusters.
5. The distributed flexible resource aggregation and scheduling method according to claim 4, characterized in that, The standard robust component model The calculation and solution process specifically includes: Based on the structural characteristics of isomorphic polyhedra, a standard robust component model is adopted. Express a set of power constraints and energy constraints : ， ， Energy confinement Through power constraint Represented as: ， make , For dimensional information, for vectors Since the power constraint and energy constraint are linear, a system of linear inequalities is used. Characterizing power constraints and energy constraints ,in ; Obtain the feasible region power trajectory set convex polyhedron : ， In the formula, and For the first The trajectory set parameters corresponding to each standard component.
6. The distributed flexible resource aggregation and scheduling method according to claim 5, characterized in that, The method involves recalculating an approximate internal feasible region for each flexible resource using a standard robust component model. This decomposes the feasible region of each flexible resource under power and energy constraints into multiple standard feasible region clusters, specifically including: ， In the formula: Indicates the first A flexible resource through based on The approximate feasible region obtained by decomposing a standard robust component model. Indicates the first The first flexible resource regarding the The scaling factor of a standard robust component model Indicates the first The first flexible resource regarding the The transformation factor of a standard robust component model.
7. The distributed flexible resource aggregation and scheduling method according to claim 4, characterized in that, Based on the power system economic dispatch model, for flexible resources within the control area, the objective function is to optimize the typical daily operating cost. This determines the capacity, maximum charging / discharging power, charging / discharging power for each time period, and the power used for charging and discharging at the flexible resource power station during each time period. The objective function expression is as follows: ， ， In the formula: and These represent time intervals. Internal flexible resources The charging power and discharging power, Representing flexible resources exist Power response within a time step This indicates that the area is in Electricity prices fluctuate over time within a given time step.
8. A distributed flexible resource aggregation and scheduling method according to claim 4, characterized in that, The aggregator allocates scheduling instructions based on scheduling instructions, according to the feasible region of each flexible resource under power and energy constraints, specifically including: ， ， ， In the formula: Representing flexible resources The scheduling instructions to be executed; This indicates the scheduling instructions issued by the scheduling agency based on the feasible domain of flexible resource aggregation; For flexible resources The Middle A standard robust component model The corresponding sequence of fitting coefficients.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.