Distributed flexible resource aggregation operation method and device based on feasible domain deconstruction

Abstract

The invention relates to a distributed flexible resource aggregation operation method and device based on feasible domain deconstruction, relates to the technical field of power systems, and solves the problem of low efficient aggregation operation efficiency of various heterogeneous flexible resources. The method comprises the following steps: 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 operation external characteristic model of each flexible resource; decomposing a feasible region of each flexible resource in each flexible resource into a plurality of standard feasible region clusters under power constraint and energy constraint by taking the maximum income or minimum cost of each flexible resource as a target; on the basis of the Minkowski addition, the multiple standard feasible domain clusters of each flexible resource are aggregated, and a standard feasible domain cluster combination of each flexible resource is obtained; and operating the operation external characteristic model based on the standard feasible domain cluster combination to obtain an optimal scheduling result of each flexible resource.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power systems, and in particular to a distributed flexible resource aggregation operation method and device based on feasible domain deconstruction. Background Art

[0002] Guided by the "dual carbon" goal, my country's new energy installed capacity continues to grow rapidly, and a new power system with new energy as the main body is being built at an accelerated pace. On the power generation side, the penetration rate of intermittent and unstable renewable energy such as wind power and photovoltaics in the power grid system continues to increase, and its intermittency and instability pose challenges to the stable operation of the power system. On the electricity consumption side, the uncertainty and volatility of new power loads will bring challenges to the load forecasting of the power grid. At the same time, new loads may be concentrated in specific time periods for charging, resulting in an increase in local peak loads. Therefore, distributed flexible resources are becoming increasingly important in the stable operation of the power grid due to their ability to quickly respond to frequency and voltage fluctuations in the power grid.

[0003] At present, most of the modeling of distributed flexible resources is only applicable to a single or two specific flexible resource technology types. There is no model applicable to different flexible resource technology types. Modeling only for a specific flexible resource technology is not conducive to the unified dispatch and control of multiple flexible resource technologies by the power system. Therefore, it is necessary to unify the external characteristic models of flexible resources of different technical types. At the same time, the current research on the aggregation equivalence of distributed flexible resources requires a high time cost or needs to sacrifice a certain modeling accuracy. Therefore, it is necessary to further study the refined modeling method of distributed flexible resources to improve the accuracy and efficiency of the aggregation equivalence of distributed flexible resources. Summary of the invention

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

[0005] According to a first aspect of an embodiment of the present invention, a distributed flexible resource aggregation operation method based on feasible domain deconstruction is provided, the method comprising: 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 the steady-state operation characteristics of the basic operation data; using multiple standard component models corresponding to each flexible resource, with the goal 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; based on Minkowski addition, aggregating the multiple standard feasible domain clusters of each flexible resource respectively to obtain a standard feasible domain cluster combination of each flexible resource; based on the standard feasible domain cluster combination of each flexible resource, running the operation external characteristic model to obtain an optimized scheduling result for each flexible resource.

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

[0007] In another implementation, the operating external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under 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 running external characteristic model is specifically expressed as:

[0009]

[0010]

[0011]

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

[0013] In another implementation, the number of models of the multiple standard component models is a preset number; before using the multiple standard component models corresponding to each flexible resource, with the goal of maximizing the benefit or minimizing the cost of each flexible resource, decomposing the feasible domain of each flexible resource in each flexible resource under power constraints and energy constraints into multiple standard feasible domain clusters, the method also includes: with the goal of minimizing the distance within the clustering cluster, using the K-means clustering method, based on a preset number of clustering clusters, clustering the feasible power trajectory sets of each flexible resource under power constraints and energy constraints in different operating states in the control area, to obtain parameters of a preset number of standard component models; 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 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, including: 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 response to a preset number of standard component models as the goal, and using a preset number of standard component models to re-divide the feasible domain of each flexible resource under power constraints and energy constraints to obtain 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 an embodiment of the present invention, a distributed flexible resource aggregation operation device based on feasible domain deconstruction is provided, and the device includes: an acquisition module, which is used to acquire basic operation data of each flexible resource distributed in a control area; a construction module, which is used to construct an operation external characteristic model of each flexible resource based on the steady-state operation characteristics of the basic operation data; a decomposition module, which is used to adopt multiple standard component models corresponding to each flexible resource, and decompose the feasible domain of each flexible resource under power constraints and energy constraints into multiple standard feasible domain clusters with the goal of maximizing the benefit or minimizing the cost of each flexible resource; an aggregation module, which is used to aggregate multiple standard feasible domain clusters of each flexible resource based on Minkowski addition to obtain a standard feasible domain cluster combination of each flexible resource; an operation module, which is used to run the operation external characteristic model based on the standard feasible domain cluster combination of each flexible resource to obtain an optimized scheduling result for each flexible resource.

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

[0018] In another implementation, the operating external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under 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 running external characteristic model is specifically expressed as:

[0020]

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

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

[0023] In another implementation, the decomposition module is specifically used to: take minimizing the square of the difference between the charging power of each flexible resource and the output power of each flexible resource in response to a preset number of standard component models as the goal, and use a preset number of standard component models to re-divide the feasible domain of each flexible resource under power constraints and energy constraints to obtain multiple standard feasible domain 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 an embodiment of the present invention, there is provided a power grid system, which is configured to execute the distributed flexible resource aggregation operation method based on feasible domain deconstruction of the above-mentioned first aspect and any possible implementation thereof.

[0026] According to a fourth aspect of an embodiment 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 executable instructions to implement a distributed flexible resource aggregation operation method based on feasible domain deconstruction as in the first aspect and any possible implementation thereof.

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

[0028] According to a sixth aspect of an embodiment of the present disclosure, a computer program product is provided, the computer program product comprising computer instructions, which, when executed on a computer device, enables the computer device to execute the distributed flexible resource aggregation operation method based on feasible domain deconstruction of the above-mentioned first aspect and any possible implementation manner thereof.

[0029] The technical solution provided by the embodiment of the present invention brings at least the following beneficial effects: based on the basic operation data of flexible resources in the control area, the steady-state operation characteristics of various types of heterogeneous flexible resources are extracted, and the external characteristic model of distributed flexible resources is constructed. At the same time, the multivariate heterogeneous flexible resources are clustered to generate the standard component cluster of the flexible resource regulation feasible domain; based on the benefit-oriented mathematical optimization method, the regulation feasible domain of each flexible resource is decomposed into a linear combination of the standard feasible domain, and the linear combination coefficient of the optimal internal approximation is determined to achieve a unified representation of the regulation feasible domain of multiple types of distributed flexible resources. Through the above-mentioned method, based on the coordinated operation strategy of decomposition first and aggregation later, the aggregation problem of the flexible resource regulation feasible domain is converted into the algebraic addition operation of the standard feasible domain cluster combination coefficient, thereby improving the solution efficiency. Compared with the prior art, the modeling accuracy of multiple types of flexible resources can be improved, the calculation time of distributed resource aggregation scheduling can be reduced, and the actual demand for efficient solution of massive distributed flexible resources after aggregation and embedding into the optimization planning and scheduling operation problems of the power system.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0032] Figure 1 A flowchart of a distributed flexible resource aggregation operation method based on feasible domain deconstruction is shown in an embodiment of the present invention;

[0033] Figure 2 It is a block diagram of a distributed flexible resource aggregation operation device based on feasible domain deconstruction according to an exemplary embodiment. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0036] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

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

[0038] Furthermore, the present application provides method operation steps as described in the embodiments or flow charts, but more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only a distributed flexible resource aggregation operation method based on feasible domain deconstruction among many step execution orders, and does not represent the only execution order. The method should be implemented by software and / or hardware.

[0039] Figure 1 Schematic diagram of a distributed flexible resource aggregation operation method based on feasible domain deconstruction provided in an embodiment of the present application. Figure 1 As shown, the distributed flexible resource aggregation operation method based on feasible domain deconstruction includes the following steps.

[0040] S11, obtaining basic operation data of each flexible resource distributed in the control area.

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

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

[0043] The power system composed of various flexible resources is in the steady-state operation stage and its transient process is ignored. Therefore, different types of flexible resources can extract similar steady-state operation characteristics, and thus obtain a unified operation model that characterizes the external operation characteristics of each flexible resource. In this way, by uniformly modeling the flexible resources with equivalent electric energy storage, the internal connection of their external operation characteristics is explored, so that various heterogeneous flexible resources can participate in the operation of the power system and the power market transactions in the form of energy storage, and the existing energy storage operation control strategy and business model can be reused.

[0044] S13, using multiple standard component models corresponding to each flexible resource, with the goal 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.

[0045] Considering the power and energy constraints of each flexible resource, the feasible domain of aggregate regulation of flexible resources is explicitly characterized.

[0046] S14, based on Minkowski addition, multiple standard feasible domain clusters of each flexible resource are aggregated respectively to obtain a standard feasible domain cluster combination of each flexible resource.

[0047] In some embodiments, the Minkowski sum calculation is generally approximated by special polyhedrons, such as symmetric models, Zono polyhedron models (Zonotope), homothetic polytope, two-dimensional hyperplane normal vectors model (Binary hyperplane normal vectors), etc., so as to reduce the complexity of calculating the Minkowski sum, but this will result in a decrease in calculation accuracy.

[0048] S15, based on the standard feasible domain cluster combination of each flexible resource, the external characteristic model is run to obtain the optimal scheduling result of each flexible resource.

[0049] In some embodiments, the process for flexible resources to participate in operation scheduling is as follows. First, all flexible resources participating in operation scheduling in a certain area transmit all their power constraints and energy constraints to the aggregator. Secondly, the aggregator directly aggregates the relevant parameters of all flexible resources contained in the area, and transmits the aggregation results to the independent dispatching agency (Independent System Operator) in the power system. Finally, the independent dispatching agency optimizes planning and formulates operation strategies based on the relevant parameters obtained by transmission, but in practical applications, slower operation efficiency is often obtained because the number of flexible resources reaches a certain level. In addition, the aggregator can also use special polyhedrons to approximate modeling of flexible resources and obtain the Minkowski sum, and transmit the relevant parameters of the Minkowski sum to the independent dispatching agency. The independent dispatching agency optimizes planning and formulates operation strategies based on the relevant parameters of the obtained Minkowski sum, but due to the lack of accuracy of the Minkowski sum obtained based on the special polyhedron, this method will make the independent dispatching agency unable to fully utilize the flexible resources contained in the area, reduce the utilization rate of resources and the economic benefits of flexible resource scheduling and operation.

[0050] Through the above implementation method, based on the basic operation data of flexible resources in the control area, the steady-state operation characteristics of various heterogeneous flexible resources are extracted, and the external characteristic model of distributed flexible resources is constructed. At the same time, the multivariate heterogeneous flexible resources are clustered to generate the standard component cluster of the flexible resource regulation feasible domain; based on the benefit-oriented mathematical optimization method, the regulation feasible domain of each flexible resource is decomposed into a linear combination of the standard feasible domain, and the linear combination coefficient of the optimal internal approximation is determined to achieve a unified representation of the regulation feasible domain of multiple types of distributed flexible resources. In this way, based on the coordinated operation strategy of decomposition first and aggregation later, the aggregation problem of the flexible resource regulation feasible domain is converted into the algebraic addition operation of the standard feasible domain cluster combination coefficient, which improves the solution efficiency. Compared with the existing technology, it can improve the modeling accuracy of multiple types of flexible resources, reduce the calculation time of distributed resource aggregation scheduling, and meet the actual needs of embedding massive distributed flexible resources into the optimization planning and scheduling operation problems of the power system for efficient solution.

[0051] As an implementation method, the operating external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under 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 operation external characteristic model is specifically expressed as the following formula (1) to formula (5). Among them, the operation external characteristic model includes the input power constraint of the flexible resource as expressed in formula (1), the output power constraint of the flexible resource as expressed in formula (2), the slope constraint of energy conversion under the charging and discharging conditions of the flexible resource as expressed in formula (3), the state transition constraint of the flexible resource storage energy as expressed in formula (4), and the upper and lower boundary constraints of the flexible resource storage energy as expressed in formula (5).

[0053]

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

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

[0056] Based on this, before executing step S13 to decompose the feasible domain, a plurality of standard component models are first constructed, and the specific construction steps are as follows.

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

[0058] Secondly, according to the parameters of the preset number of standard component models, each standard component model of the preset number of standard component models is constructed, and the preset number of standard component models are determined as a plurality of 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. It can be seen that the standard component model obtained by this method extracts the operating characteristics of the flexible resources within the jurisdiction of the aggregator to the greatest extent.

[0060] Through the identification and modeling of the above-mentioned standard feasible domain clusters, based on the K-means clustering method, the aggregator can divide the flexible resources within the jurisdiction into several clusters. The operating parameters of the center points of several clusters can be extracted as the parameters of several standard feasible domains. The standard feasible domain obtained by this method extracts the operating characteristics of the flexible resources within the jurisdiction of the aggregator 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 an implementation mode, the specific implementation steps of the above-mentioned step S13 for decomposing the feasible domain are as follows: 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 response to a preset number of standard component models as the goal, a preset number of standard component models are used to re-divide the feasible domain of each flexible resource under power constraints and energy constraints, and obtain multiple standard feasible domain 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 theory of economic dispatch of power systems, when a series of incentives with electricity prices changing over time are given, the revenue maximization of flexible resources and standard feasible domain clusters is taken as the objective function, and a series of power responses of flexible resources and standard feasible domain clusters can be obtained.

[0064]

[0065] Formula (6) indicates that the objective function is to minimize the square of the difference between the output power response of the flexible resource and the weighted superposition of K standard feasible domains, and to obtain the fitting coefficient sequence required for fitting the flexible resource using the standard feasible domain cluster.

[0066] As a decomposition method, the aggregation equalization method is used to aggregate the flexible resources in the control area. Specifically, each flexible resource is based on the standard feasible domain B k ((k=1,2,…,K) is decomposed and the standard feasible domain B corresponding to each fitting sequence is calculated k The fitting coefficient sequence {β k}, so that each flexible resource can be expressed by fitting the standard feasible domain as

[0067] In order to realize the above functions, the distributed flexible resource aggregation operation device based on the feasible domain deconstruction includes the hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0068] The present disclosure also provides a Figure 2 A distributed flexible resource aggregation and operation device based on feasible domain deconstruction is shown, and 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 operation data of each flexible resource distributed in the control area.

[0070] The construction module 202 is used to construct an operation external characteristic model of each flexible resource based on the steady-state operation characteristics of the basic operation data.

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

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

[0073] The operation module 205 is used to operate the external characteristic model based on the standard feasible domain cluster combination of each flexible resource to obtain the optimization scheduling result of each flexible resource.

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

[0075] In another implementation, the operating external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under 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 running external characteristic model is specifically expressed as:

[0077]

[0078]

[0079]

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

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

[0082] In another implementation, the decomposition module 203 is specifically used to: take 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 response to a preset number of standard component models as the goal, and use a preset number of standard component models to re-divide the feasible domain of each flexible resource under power constraints and energy constraints to 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 device in the above embodiment, the specific manner in which each unit module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0085] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0086] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A distributed flexible resource aggregation operation method based on feasible domain deconstruction, characterized in that: The method comprises: Obtain basic operating data of each flexible resource distributed within the control area; Based on the steady-state operation characteristics of the basic operation data, constructing an operation external characteristic model of each flexible resource; Adopting a plurality of standard component models corresponding to the respective flexible resources, with the goal of maximizing the benefit or minimizing the cost of the respective flexible resources, decomposing the feasible domain of each of the respective flexible resources under power constraints and energy constraints into a plurality of standard feasible domain clusters; Based on Minkowski addition, the plurality of standard feasible domain clusters of each flexible resource are aggregated respectively to obtain a standard feasible domain cluster combination of each flexible resource; Based on the standard feasible domain cluster combination of each flexible resource, the operation external characteristic model is run to obtain an optimized scheduling result for each flexible resource.

2. The flexible resource aggregation operation method based on feasible domain deconstruction according to claim 1 is characterized in that: The basic operation data includes the charging power and discharging power, charging efficiency and discharging efficiency and maximum charging and discharging efficiency of the flexible resource, the energy currently stored by the flexible resource, the energy dissipation rate, and the upper and lower limits of the stored energy.

3. The flexible resource aggregation operation method based on feasible domain deconstruction according to claim 2 is characterized in that: The operation external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under flexible resource charging and discharging conditions, state transition constraints of flexible resource stored energy, and upper and lower boundary constraints of flexible resource stored energy.

4. The flexible resource aggregation operation method based on feasible domain deconstruction according to claim 3 is characterized in that: The operation external characteristic model is specifically expressed as: in, represents the charging power of flexible resource i at time period t, represents the maximum charging power of flexible resource i, represents the minimum charging power of flexible resource i, represents the discharge power of flexible resource i at time period t, represents the maximum discharge power of flexible resource i, represents the minimum discharge power of flexible resource i, represents the maximum slope of energy conversion of flexible resource i, e i (t) represents the stored energy of entity flexible resource i at time period t, θ i represents the energy dissipation rate of entity flexible resource i after considering the self-discharge physical process, and They represent the charging efficiency and discharging efficiency of flexible resource i respectively, and ΔT represents the time difference between time period t and time period (t-1).

5. The flexible resource aggregation operation method based on feasible domain deconstruction according to any one of claims 1 to 4, characterized in that: The number of the multiple standard component models is a preset number; before adopting the multiple standard component models corresponding to the respective flexible resources, with the goal of maximizing the benefit or minimizing the cost of the respective flexible resources, and decomposing the feasible domain of each of the respective flexible resources under the power constraint and the energy constraint into a plurality of standard feasible domain clusters, the method further includes: Taking minimization of the distance within the cluster as the goal, a K-means clustering method is used to cluster the feasible power trajectory sets of the flexible resources under power constraints and energy constraints in different operating states in the control area based on the preset number of clusters, so as to obtain parameters of the preset number of standard component models; According to the parameters of the preset number of standard component models, each standard component model of the preset number of standard component models is constructed, and the preset number of standard component models are determined as the plurality of standard component models.

6. The flexible resource aggregation operation method based on feasible domain deconstruction according to claim 5 is characterized in that: The method adopts a plurality of standard component models corresponding to the flexible resources, takes the maximum benefit or the minimum cost of the flexible resources as the goal, and decomposes the feasible domain of each flexible resource under power constraints and energy constraints into a plurality of standard feasible domain clusters, including: 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 response to the preset number of standard component models, the preset number of standard component models are used to re-divide the feasible domain of each flexible resource under power constraints and energy constraints to obtain the multiple standard feasible domain clusters of each flexible resource.

7. The flexible resource aggregation operation method based on feasible domain deconstruction according to any one of claims 1 to 4, characterized in that: The various flexible resources include: energy storage resources, smart buildings and time-shiftable loads.

8. A distributed flexible resource aggregation operation device based on feasible domain deconstruction, characterized in that: The device comprises: An acquisition module is used to acquire basic operation data of various flexible resources distributed in the control area; A construction module, configured to construct an operation external characteristic model of each flexible resource based on the steady-state operation characteristics of the basic operation data; a decomposition module, configured to adopt a plurality of standard component models corresponding to the respective flexible resources, and decompose the feasible domain of each flexible resource under power constraints and energy constraints into a plurality of standard feasible domain clusters with the goal of maximizing the benefit or minimizing the cost of the respective flexible resources; An aggregation module, configured to aggregate the plurality of standard feasible domain clusters of each flexible resource based on Minkowski addition to obtain a standard feasible domain cluster combination of each flexible resource; An operation module is used to operate the operation external characteristic model based on the standard feasible domain cluster combination of each flexible resource to obtain an optimized scheduling result for each flexible resource.

9. The flexible resource aggregation operation device based on feasible domain deconstruction according to claim 8 is characterized in that: The basic operation data includes the charging power and discharging power, charging efficiency and discharging efficiency and maximum charging and discharging efficiency of the flexible resource, the energy currently stored by the flexible resource, the energy dissipation rate, and the upper and lower limits of the stored energy.

10. The flexible resource aggregation operation device based on feasible domain deconstruction according to claim 9 is characterized in that: The operation external characteristic model includes input power constraints of flexible resources, output power constraints of flexible resources, slope constraints of energy conversion under flexible resource charging and discharging conditions, state transition constraints of flexible resource stored energy, and upper and lower boundary constraints of flexible resource stored energy.

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