Distributed flexible resource aggregation operation method and device based on feasible region decomposition

Abstract

The present disclosure relates to a distributed flexible resource aggregation operation method and device based on feasible region decomposition, and relates to the technical field of power systems, and solves the problem of low efficient aggregation operation of multiple heterogeneous flexible resources. The method comprises: acquiring basic operation data of each flexible resource distributed in a control area; based on the steady-state operation characteristics of the basic operation data, constructing an operating external characteristic model of each flexible resource; taking the maximum benefit or minimum cost of each flexible resource as the target, decomposing the feasible region of each flexible resource in the power constraint and energy constraint into multiple standard feasible region clusters; based on the Minkowski addition, the multiple standard feasible region clusters of each flexible resource are aggregated respectively to obtain the standard feasible region cluster combination of each flexible resource; based on the above standard feasible region cluster combination, the operating external characteristic model is run to obtain the optimal scheduling result of each flexible resource.

Description

Technical Field

[0001] This disclosure relates to the field of power system technology, and in particular to a distributed flexible resource aggregation operation method and apparatus based on feasible domain deconstruction. Background Technology

[0002] Guided by the "dual carbon" goals, my country's installed capacity of new energy sources continues to grow rapidly, and a new power system with new energy as its mainstay is being rapidly constructed. 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 uncertainty and volatility of new power loads will pose challenges to the load forecasting of the power grid, and new loads may concentrate on charging during specific time periods, leading to an increase in local peak loads. Therefore, distributed flexible resources, due to their ability to quickly respond to frequency and voltage fluctuations in the power grid, are becoming increasingly important for the stable operation of the power grid.

[0003] Current modeling of distributed flexible resources is mostly applicable to only one or two specific flexible resource technologies, lacking models applicable to different flexible resource technologies. Modeling only for a specific flexible resource technology hinders unified scheduling and control of multiple flexible resource technologies in the power system. Therefore, a unified modeling of the external characteristics of flexible resources of different technology types is needed. Furthermore, current research on distributed flexible resource aggregation equivalence requires significant time investment or sacrifices some modeling accuracy. Therefore, further research is needed on refined modeling methods for distributed flexible resources to improve the accuracy and efficiency of distributed flexible resource aggregation equivalence. Summary of the Invention

[0004] This invention provides a distributed flexible resource aggregation and operation method, apparatus, and system based on feasible domain deconstruction, to at least solve the problem of low efficiency in the efficient aggregation and operation of various heterogeneous flexible resources. The technical solution of this invention is as follows:

[0005] According to a first aspect of the present invention, a distributed flexible resource aggregation and operation method based on feasible domain decomposition is provided. The method includes: acquiring basic operational data of various flexible resources distributed within a control area; constructing an operational extrinsic characteristic model for each flexible resource based on the steady-state operational characteristics of the basic operational data; using multiple standard component models corresponding to each flexible resource, and with the objective of maximizing the benefit or minimizing the cost of each flexible resource, decomposing the feasible domain of each flexible resource under power constraints and energy constraints into multiple standard feasible domain clusters; aggregating the multiple standard feasible domain clusters of each flexible resource based on the Minkowski addition method to obtain a combination of standard feasible domain clusters for each flexible resource; and running the operational extrinsic characteristic model based on the combination of standard feasible domain clusters for each flexible resource to obtain the optimized scheduling result for each flexible resource.

[0006] In one implementation, the basic operational data includes the charging and discharging power, charging and discharging efficiency, and maximum charging and discharging efficiency of the flexible resources, the energy currently stored in the flexible resources, the energy dissipation rate, and the upper and lower boundaries of the stored energy.

[0007] In another implementation, the external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under the charging and discharging conditions of flexible resources, state transition constraints of energy stored in flexible resources, and upper and lower boundary constraints of energy stored in flexible resources.

[0008] In another implementation, the external characteristic model is specifically represented as follows:

[0009]

[0010]

[0011]

[0012] in, This represents the charging power of flexible resource i at time t. This represents the maximum charging power of flexible resource i. This represents the minimum charging power of flexible resource i. This represents the discharge power of flexible resource i at time t. This represents the maximum discharge power of flexible resource i. This represents the minimum discharge power of flexible resource i. e represents the maximum slope of energy conversion for flexible resource i. i (t) represents the stored energy of entity flexible resource i at time period t, θ i This represents the energy dissipation rate of the flexible resource i in a physical entity after considering the self-discharge physical process. and Let represent the charging efficiency and discharging efficiency of flexible resource i, respectively, and ΔT represent the time difference between time period t and time period (t-1).

[0013] In another implementation, the number of standard component models is a preset number. Before decomposing the feasible domain of each flexible resource under power and energy constraints into multiple standard feasible domain clusters with the goal of maximizing the benefit or minimizing the cost of each flexible resource using multiple standard component models corresponding to each flexible resource, the method further includes: using the K-means clustering method with the goal of minimizing the distance within the clusters, clustering the feasible power trajectory set of each flexible resource under power and energy constraints under different operating states within the control area based on a preset number of clusters, to obtain the parameters of a preset number of standard component models; constructing each standard component model of the preset number of standard component models based on the parameters of the preset number of standard component models, and determining the preset number of standard component models as multiple standard component models.

[0014] In another implementation, multiple standard component models corresponding to each flexible resource are used to decompose the feasible domain of each flexible resource under power constraints and energy constraints into multiple standard feasible domain clusters. This includes: taking the minimization of the square of the difference between the charging power of each flexible resource and the output power of each flexible resource in the response of a preset number of standard component models as the objective, using a preset number of standard component models to re-divide the feasible domain of each flexible resource under power constraints and energy constraints, thus obtaining multiple standard feasible domain clusters for each flexible resource.

[0015] In another implementation, the flexible resources include: energy storage resources, smart buildings, and time-shiftable loads.

[0016] According to a second aspect of the present invention, a distributed flexible resource aggregation and operation device based on feasible domain deconstruction is provided. The device includes: an acquisition module for acquiring basic operational data of various flexible resources distributed within a control area; a construction module for constructing an operational external characteristic model of each flexible resource based on the steady-state operational characteristics of the basic operational data; a decomposition module for decomposing the feasible domain of each flexible resource under power and energy constraints into multiple standard feasible domain clusters with the objective of maximizing the benefit or minimizing the cost of each flexible resource using multiple standard component models corresponding to each flexible resource; an aggregation module for aggregating the multiple standard feasible domain clusters of each flexible resource based on the Minkowski addition method to obtain a combination of standard feasible domain clusters of each flexible resource; and an operation module for running the operational external characteristic model based on the combination of standard feasible domain clusters of each flexible resource to obtain the optimized scheduling result of each flexible resource.

[0017] In one implementation, the basic operational data includes the charging and discharging power, charging and discharging efficiency, and maximum charging and discharging efficiency of the flexible resources, the energy currently stored in the flexible resources, the energy dissipation rate, and the upper and lower boundaries of the stored energy.

[0018] In another implementation, the external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under the charging and discharging conditions of flexible resources, state transition constraints of energy stored in flexible resources, and upper and lower boundary constraints of energy stored in flexible resources.

[0019] In another implementation, the external characteristic model is specifically represented as follows:

[0020]

[0021] in, This represents the charging power of flexible resource i at time t. This represents the maximum charging power of flexible resource i. This represents the minimum charging power of flexible resource i. This represents the discharge power of flexible resource i at time t. This represents the maximum discharge power of flexible resource i. This represents the minimum discharge power of flexible resource i. e represents the maximum slope of energy conversion for flexible resource i. i (t) represents the stored energy of entity flexible resource i at time period t, θ i This represents the energy dissipation rate of the flexible resource i in a physical entity after considering the self-discharge physical process. and Let represent the charging efficiency and discharging efficiency of flexible resource i, respectively, and ΔT represent the time difference between time period t and time period (t-1).

[0022] In another implementation, the number of standard component models is a preset number. Before decomposing the feasible domain of each flexible resource under power and energy constraints into multiple standard feasible domain clusters with the goal of maximizing the benefit or minimizing the cost of each flexible resource using multiple standard component models corresponding to each flexible resource, the device is further used to: cluster the feasible power trajectory set of each flexible resource under power and energy constraints under different operating states within the control area based on a preset number of clusters, using the K-means clustering method with the goal of minimizing the distance within the clusters, to obtain the parameters of the preset number of standard component models; and construct each standard component model of the preset number of standard component models according to the parameters of the preset number of standard component models, and determine the preset number of standard component models as multiple standard component models.

[0023] In another implementation, the decomposition module is specifically used to: with the objective of minimizing the square of the difference between the charging power of each flexible resource and the output power of each flexible resource in a preset number of standard component models, re-divide the feasible region of each flexible resource under power constraints and energy constraints using a preset number of standard component models, and obtain multiple standard feasible region clusters for each flexible resource.

[0024] In another implementation, the flexible resources include: energy storage resources, smart buildings, and time-shiftable loads.

[0025] According to a third aspect of the present invention, a power grid system is provided, which is configured to execute the distributed flexible resource aggregation operation method based on feasible domain deconstruction of the first aspect and any possible implementation thereof.

[0026] According to a fourth aspect of the present invention, a computer device is provided, comprising: a processor and a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement a distributed flexible resource aggregation operation method based on feasible domain destructuring, as described in the first aspect and any possible implementation thereof.

[0027] According to a fifth aspect of the present invention, a computer-readable storage medium is provided, on which instructions are stored, such that when the instructions in the computer-readable storage medium are executed by a processor of a computer device, an electronic device is enabled to perform the distributed flexible resource aggregation operation method based on feasible domain deconstruction as described in the first aspect.

[0028] According to a sixth aspect of the present disclosure, a computer program product is provided, the computer program product including computer instructions, which, when executed on a computer device, cause the computer device to perform the distributed flexible resource aggregation operation method based on feasible domain destructuring described in the first aspect and any possible implementation thereof.

[0029] The technical solution provided by the embodiments of the present invention brings at least the following beneficial effects: Based on the basic operational data of flexible resources within the control area, the steady-state operational characteristics of various heterogeneous flexible resources are extracted, and an external characteristic model of distributed flexible resources is constructed. Simultaneously, the diverse heterogeneous flexible resources are clustered to generate standard component clusters of the flexible resource adjustment feasible domain. Based on a profit-oriented mathematical optimization method, the adjustment feasible domain of each flexible resource is decomposed into a linear combination of standard feasible domains, and the optimal internal approximation linear combination coefficients are determined, achieving a unified representation of the adjustment feasible domains of multiple types of distributed flexible resources. Through the above method, based on a coordinated operation strategy of decomposition followed by aggregation, the problem of aggregation of flexible resource adjustment feasible domains is transformed into an algebraic addition operation of the combination coefficients of standard feasible domain clusters, improving the solution efficiency. Compared with existing technologies, this improves the modeling accuracy of multiple types of flexible resources, reduces the computation time of distributed resource aggregation scheduling, and meets the practical need for efficient solutions to the problem of embedding massive distributed flexible resources into power system optimization planning and scheduling operation.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0032] Figure 1 The flowchart illustrates a distributed flexible resource aggregation and operation method based on feasible domain deconstruction, as shown in an embodiment of the present invention.

[0033] Figure 2 This is a block diagram illustrating a distributed flexible resource aggregation operation device based on feasible domain deconstruction, according to an exemplary embodiment. Detailed Implementation

[0034] 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.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0036] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0038] This application provides the method operation steps as described in the embodiments or flowcharts, but based on conventional or non-inventive labor, it may include more or fewer operation steps. The order of steps listed in the embodiments is merely one of many possible execution orders for a distributed flexible resource aggregation operation method based on feasible domain deconstruction, and does not represent the only execution order. This method should be implementable by software and / or hardware.

[0039] Figure 1 This is a flowchart illustrating a distributed flexible resource aggregation and operation method based on feasible domain deconstruction provided in an embodiment of this application. Figure 1 As shown, the distributed flexible resource aggregation operation method based on feasible domain deconstruction includes the following steps.

[0040] S11, acquire basic operational data of various flexible resources distributed within the control area.

[0041] In one implementation, the basic operating data includes the charging and discharging power of the flexible resource, the charging and discharging efficiency, the maximum charging and discharging efficiency, the energy currently stored in the flexible resource, the energy dissipation rate, and the upper and lower boundaries of the stored energy.

[0042] S12, based on the steady-state operating characteristics of the basic operating data, construct the external characteristic model of each flexible resource.

[0043] The power system composed of various flexible resources operates in a steady-state phase, ignoring transient processes. Therefore, similar steady-state operating characteristics can be extracted from different types of flexible resources, leading to a unified operating model that characterizes the external properties of each flexible resource. Thus, by uniformly modeling flexible resources with equivalent energy storage, the intrinsic connections between their external operating characteristics can be uncovered. This allows various heterogeneous flexible resources to participate in power system operation and electricity market transactions in the form of energy storage, enabling the reuse of existing energy storage operation control strategies and business models.

[0044] S13 adopts multiple standard component models corresponding to each flexible resource, with the goal of maximizing the benefit or minimizing the cost of each flexible resource, and decomposes the feasible domain of each flexible resource under power constraints and energy constraints into multiple standard feasible domain clusters.

[0045] Considering the power and energy constraints of each flexible resource, the feasible region for aggregation and adjustment of flexible resources is explicitly characterized.

[0046] S14, based on the Minkowski addition method, aggregate multiple standard feasible domain clusters for each flexible resource to obtain a combination of standard feasible domain clusters for each flexible resource.

[0047] In some implementations, the Minkowski sum is approximated using special polyhedra, such as symmetric models, Zonotope models, homothetic polytopes, and binary hyperplane normal vectors, to reduce the complexity of calculating the Minkowski sum. However, this leads to a decrease in computational accuracy.

[0048] S15, based on the standard feasible domain cluster combination of each flexible resource, runs the external characteristic model to obtain the optimized scheduling results of each flexible resource.

[0049] In some implementations, the process for flexible resources participating in operation scheduling is as follows: First, all flexible resources participating in operation scheduling within a certain area transmit their power constraints and energy constraints to the aggregator. Second, the aggregator directly aggregates the relevant parameters of all flexible resources within the area and transmits the aggregation result to the Independent System Operator (ISO) in the power system. Finally, the ISO performs optimization planning and formulates operation strategies based on the transmitted parameters. However, in practical applications, the operation efficiency often becomes slow when the number of flexible resources reaches a certain level. Alternatively, the aggregator can use a special polyhedron to approximate the flexible resources and obtain the Minkowski sum. The relevant parameters of the Minkowski sum are then transmitted to the ISO. The ISO performs optimization planning and formulates operation strategies based on the obtained Minkowski sum parameters. However, due to the inherent lack of accuracy in the Minkowski sum obtained based on the special polyhedron, this method prevents the ISO from fully utilizing the flexible resources within the area, reducing resource utilization and the economic benefits of flexible resource scheduling.

[0050] Through the above implementation methods, based on the operational baseline data of flexible resources within the control area, steady-state operational characteristics of various heterogeneous flexible resources are extracted, and an external characteristic model of distributed flexible resources is constructed. Simultaneously, multi-dimensional heterogeneous flexible resources are clustered to generate standard component clusters of the flexible resource adjustment feasible domains. Based on a profit-oriented mathematical optimization method, the adjustment feasible domain of each flexible resource is decomposed into a linear combination of standard feasible domains, and the optimal inner approximation linear combination coefficients are determined, achieving a unified representation of the adjustment feasible domains of multiple types of distributed flexible resources. Thus, based on a coordinated operation strategy of decomposition followed by aggregation, the problem of flexible resource adjustment feasible domain aggregation is transformed into an algebraic addition operation of the combination coefficients of standard feasible domain clusters, improving solution efficiency. Compared to existing technologies, this approach improves the modeling accuracy of multiple types of flexible resources, reduces the computation time for distributed resource aggregation scheduling, and meets the practical need for efficient solutions to power system optimization planning and scheduling operation problems after the aggregation of massive distributed flexible resources.

[0051] As one implementation method, the external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under the charging and discharging conditions of flexible resources, state transition constraints of energy stored in flexible resources, and upper and lower boundary constraints of energy stored in flexible resources.

[0052] Specifically, the operational external characteristic model is expressed as follows: formulas (1) to (5). Among them, the operational external characteristic model includes the input power constraint of the flexible resource as shown in formula (1), the output power constraint of the flexible resource as shown in formula (2), the slope constraint of energy conversion under the charging and discharging conditions of the flexible resource as shown in formula (3), the state transition constraint of the energy stored in the flexible resource as shown in formula (4), and the upper and lower boundary constraints of the energy stored in the flexible resource as shown in formula (5).

[0053]

[0054] in, This represents the charging power of flexible resource i at time t. This represents the maximum charging power of flexible resource i. This represents the minimum charging power of flexible resource i. This represents the discharge power of flexible resource i at time t. This represents the maximum discharge power of flexible resource i. This represents the minimum discharge power of flexible resource i. e represents the maximum slope of energy conversion for flexible resource i. i (t) represents the stored energy of entity flexible resource i at time period t, θ i This represents the energy dissipation rate of the flexible resource i in a physical entity after considering the self-discharge physical process. and Let represent the charging efficiency and discharging efficiency of flexible resource i, respectively, and ΔT represent the time difference between time period t and time period (t-1).

[0055] In some implementations, the number of models in the multiple standard component models is a preset number.

[0056] Based on this, before performing step S13 to decompose the feasible region, multiple standard component models are first constructed. The specific construction steps are as follows.

[0057] First, with the goal of minimizing the distance within clusters, the K-means clustering method is adopted. Based on a preset number of clusters, the feasible power trajectory set of each flexible resource under power and energy constraints in different operating states within the control area is clustered to obtain the parameters of a preset number of standard component models.

[0058] Secondly, based on the parameters of a preset number of standard component models, each standard component model is constructed, and the preset number of standard component models are determined as multiple standard component models.

[0059] Based on the implementation principle of the K-means clustering method, each flexible resource is assigned to the cluster with the shortest distance to it. At the same time, the distance between the flexible resources contained in each cluster and the center point of the cluster is minimized. Thus, it can be seen that the standard component model obtained by this method extracts the operational characteristics of flexible resources within the aggregator's jurisdiction to the greatest extent.

[0060] Through the identification and modeling of the aforementioned standard feasible domain clusters, and based on the K-means clustering method, the aggregator can divide the flexible resources within its jurisdiction into several clusters. The operating parameters of the center points of these clusters can be extracted as parameters for several standard feasible domains. The standard feasible domains obtained through this method extract the operating characteristics of the flexible resources within the aggregator's jurisdiction to the greatest extent, laying the foundation for the next step of decomposing the flexible resources based on the standard feasible domain clusters.

[0061] As one implementation method, the specific implementation steps of step S13 to decompose the feasible region are as follows: with the goal of minimizing the square of the difference between the charging power of each flexible resource and the output power of each flexible resource in the response of a preset number of standard component models, the feasible region of each flexible resource under power constraints and energy constraints is re-divided using a preset number of standard component models to obtain multiple standard feasible region clusters for each flexible resource.

[0062] In another implementation, the flexible resources include: energy storage resources, smart buildings, and time-shiftable loads.

[0063] Specifically, flexible resources are based on the decomposition of standard feasible domain clusters. According to the economic dispatch theory of power systems, when a series of incentives with varying electricity prices over time are given, a series of power responses for flexible resources and standard feasible domain clusters can be obtained by taking the maximization of revenue for flexible resources and standard feasible domain clusters as the objective function.

[0064]

[0065] Formula (6) represents the objective function of minimizing the squared difference between the output power responses of the flexible resources and the K weighted feasible regions, and derives the sequence of fitting coefficients required to fit the flexible resources using the cluster of feasible regions.

[0066] As a decomposition method, it is based on the aggregate equivalence approach to aggregate and equivalence flexible resources within the control area. Specifically, each flexible resource is based on the standard feasible region B. k The sequence (k = 1, 2, ..., K) is decomposed, and the standard feasible region B corresponding to each fitted sequence is calculated. k The sequence of fitting coefficients {β} for (k = 1, 2, ..., K) k}, so that each flexible resource can be represented by fitting a standard feasible region as

[0067] To achieve the above functions, the distributed flexible resource aggregation operation device based on feasible domain deconstruction includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art will readily recognize that, based on the algorithmic steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] This disclosure also provides an embodiment such as Figure 2 The device shown is a distributed flexible resource aggregation and operation device based on feasible domain deconstruction. The device includes: an acquisition module 201, a construction module 202, a decomposition module 203, an aggregation module 204, and an operation module 205.

[0069] The acquisition module 201 is used to acquire basic operational data of various flexible resources distributed within the control area.

[0070] Module 202 is used to construct the external characteristics model of each flexible resource based on the steady-state operating characteristics of the basic operating data.

[0071] The decomposition module 203 is used to decompose the feasible domain of each flexible resource under power and energy constraints into multiple standard feasible domain clusters by adopting multiple standard component models corresponding to each flexible resource, with the goal of maximizing the benefit or minimizing the cost of each flexible resource.

[0072] The aggregation module 204 is used to aggregate multiple standard feasible domain clusters of each flexible resource based on Minkowski addition, to obtain a combination of standard feasible domain clusters of each flexible resource.

[0073] The running module 205 is used to run the external characteristic model based on the standard feasible domain cluster combination of each flexible resource to obtain the optimized scheduling results of each flexible resource.

[0074] In one implementation, the basic operational data includes the charging and discharging power, charging and discharging efficiency, and maximum charging and discharging efficiency of the flexible resources, the energy currently stored in the flexible resources, the energy dissipation rate, and the upper and lower boundaries of the stored energy.

[0075] In another implementation, the external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under the charging and discharging conditions of flexible resources, state transition constraints of energy stored in flexible resources, and upper and lower boundary constraints of energy stored in flexible resources.

[0076] In another implementation, the external characteristic model is specifically represented as follows:

[0077]

[0078]

[0079]

[0080] in, This represents the charging power of flexible resource i at time t. This represents the maximum charging power of flexible resource i. This represents the minimum charging power of flexible resource i. This represents the discharge power of flexible resource i at time t. This represents the maximum discharge power of flexible resource i. This represents the minimum discharge power of flexible resource i. e represents the maximum slope of energy conversion for flexible resource i. i (t) represents the stored energy of entity flexible resource i at time period t, θ i This represents the energy dissipation rate of the flexible resource i in a physical entity after considering the self-discharge physical process. and Let represent the charging efficiency and discharging efficiency of flexible resource i, respectively, and ΔT represent the time difference between time period t and time period (t-1).

[0081] In another implementation, the number of standard component models is a preset number. Before decomposing the feasible domain of each flexible resource under power and energy constraints into multiple standard feasible domain clusters with the goal of maximizing the benefit or minimizing the cost of each flexible resource using multiple standard component models corresponding to each flexible resource, the device is further used to: cluster the feasible power trajectory set of each flexible resource under power and energy constraints under different operating states within the control area based on a preset number of clusters, using the K-means clustering method with the goal of minimizing the distance within the clusters, to obtain the parameters of the preset number of standard component models; and construct each standard component model of the preset number of standard component models according to the parameters of the preset number of standard component models, and determine the preset number of standard component models as multiple standard component models.

[0082] In another implementation, the decomposition module 203 is specifically used to: with the objective of minimizing the square of the difference between the charging power of each flexible resource and the output power of each flexible resource in the response of a preset number of standard component models, re-divide the feasible domain of each flexible resource under power constraints and energy constraints using a preset number of standard component models, and obtain multiple standard feasible domain clusters for each flexible resource.

[0083] In another implementation, the flexible resources include: energy storage resources, smart buildings, and time-shiftable loads.

[0084] Regarding the apparatus in the above embodiments, the specific manner in which each unit module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0085] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0086] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A distributed flexible resource aggregation operation method based on feasible region decomposition, characterized in that, The method comprises: acquiring basic operation data of each flexible resource distributed in a control area; constructing an operation external characteristic model of each flexible resource based on a steady-state operation characteristic of the basic operation data; adopting a plurality of standard component models corresponding to each flexible resource, and decomposing a feasible region of each flexible resource under power constraint and energy constraint into a plurality of standard feasible region clusters with a maximum benefit or a minimum cost of each flexible resource as a target; respectively aggregating the plurality of standard feasible region clusters of each flexible resource based on Minkowski addition to obtain a standard feasible region cluster combination of each flexible resource; running the operation external characteristic model based on the standard feasible region cluster combination of each flexible resource to obtain an optimized scheduling result of each flexible resource; wherein a model number of the plurality of standard component models is a preset number; before the adopting a plurality of standard component models corresponding to each flexible resource, and decomposing a feasible region of each flexible resource under power constraint and energy constraint into a plurality of standard feasible region clusters with a maximum benefit or a minimum cost of each flexible resource as a target, the method further comprises: adopting a K-means clustering method to cluster a set of feasible power trajectories of each flexible resource under power constraint and energy constraint in different operation states in the control area based on the preset number of clustering clusters with minimization of intra-cluster distance as a target to obtain parameters of the preset number of standard component models; respectively constructing each standard component model of the preset number of standard component models according to the parameters of the preset number of standard component models, and determining the preset number of standard component models as the plurality of standard component models; the adopting a plurality of standard component models corresponding to each flexible resource, and decomposing a feasible region of each flexible resource under power constraint and energy constraint into a plurality of standard feasible region clusters with a maximum benefit or a minimum cost of each flexible resource as a target, comprises: adopting the preset number of standard component models to redivide the feasible region of each flexible resource under power constraint and energy constraint with minimization of square of difference between charging power of each flexible resource and output power of each flexible resource responding to the preset number of standard component models as a target to obtain the plurality of standard feasible region clusters of each flexible resource.

2. The method of claim 1, wherein, The basic operation data comprises charging power and discharging power, charging efficiency and discharging efficiency, maximum charging and discharging efficiency, current stored energy, energy dissipation rate, and upper and lower boundaries of stored energy of the flexible resource.

3. The method of claim 2, wherein, The operation external characteristic model comprises input power constraint of the flexible resource, output power constraint of the flexible resource, slope constraint of energy conversion under charging and discharging working condition of the flexible resource, state transition constraint of stored energy of the flexible resource, and upper and lower boundary constraint of stored energy of the flexible resource.

4. The method of claim 3, wherein, The operation external characteristic model is specifically represented as: wherein, represents the charging power of the flexible resource at time period t, represents the maximum charging power of the flexible resource , represents the minimum charging power of the flexible resource , represents the discharging power of the flexible resource at time period t, represents the maximum discharging power of the flexible resource , represents the minimum discharging power of the flexible resource , represents the maximum slope of energy conversion of the flexible resource , represents the stored energy of the physical flexible resource at time period t, represents the energy dissipation rate of the physical flexible resource considering the self-discharge physical process, and respectively represent the charging efficiency and discharging efficiency of the flexible resource , represents the time difference between time period t and time period (t-1).

5. The method of claim 1 to 4, wherein, The each flexible resource comprises energy storage resource, intelligent building, and time-shiftable load.

6. A device for distributed flexible resource aggregation operation based on feasible region disintegration, characterized in that, The device comprises: an acquisition module configured to acquire basic operation data of each flexible resource distributed in a control area; a construction module configured to construct an operation off-characteristic model of the each flexible resource based on a steady-state operation characteristic of the basic operation data; a decomposition module configured to decompose a feasible region of each flexible resource under power constraint and energy constraint into a plurality of standard feasible region clusters by using a plurality of standard component models corresponding to the each flexible resource, and taking maximum benefit or minimum cost of the each flexible resource as a target; an aggregation module configured to aggregate the plurality of standard feasible region clusters of the each flexible resource based on Minkowski addition, to obtain a standard feasible region cluster combination of the each flexible resource; an operation module configured to operate the operation off-characteristic model based on the standard feasible region cluster combination of the each flexible resource, to obtain an optimal scheduling result of the each flexible resource; wherein a model number of the plurality of standard component models is a preset number; the decomposition module is specifically configured to, taking minimization of intra-cluster distance as a target, use a K-means clustering method to cluster a set of feasible power trajectories of the each flexible resource under power constraint and energy constraint in different operation states in the control area based on the preset number of clustering clusters, to obtain parameters of the preset number of standard component models; each standard component model of the preset number of standard component models is constructed according to the parameters of the preset number of standard component models, and the preset number of standard component models is determined as the plurality of standard component models; taking minimization of square of difference between charging power of the each flexible resource and output power of the each flexible resource responding to the preset number of standard component models as a target, the preset number of standard component models is used to redivide the feasible region of the each flexible resource under power constraint and energy constraint, to obtain the plurality of standard feasible region clusters of the each flexible resource.

7. The apparatus for distributed flexible resource aggregation operation based on feasible region disintegration according to claim 6, wherein, The basic operation data comprises charging power and discharging power, charging efficiency and discharging efficiency, maximum charging and discharging efficiency of the flexible resource, current stored energy of the flexible resource, energy dissipation rate, and upper and lower boundaries of stored energy.

8. The apparatus for distributed flexible resource aggregation operation based on feasible region disintegration according to claim 7, wherein, The operation off-characteristic model comprises input power constraint of the flexible resource, output power constraint of the flexible resource, slope constraint of energy conversion under charging and discharging working condition of the flexible resource, state transition constraint of stored energy of the flexible resource, and upper and lower boundary constraint of stored energy of the flexible resource.

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