Aggregation method and device for virtual hybrid energy storage of power distribution network, medium and product

Abstract

The embodiment of the invention provides an aggregation method and device for virtual hybrid energy storage of a power distribution network, a medium and a product. The method comprises the following steps: establishing a control model corresponding to each virtual converter in a virtual hybrid energy storage cluster, and determining a second characteristic parameter of an aggregation equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, a first characteristic parameter in the control model, a power distribution network model and a cooperative control law model, and establishing an aggregation equivalence model in combination with the control model, and then obtaining a target parameter set of the aggregation equivalence model according to the second modal set. Besides, according to the control model and the power distribution network model, a physical layer model corresponding to the virtual hybrid energy storage cluster is established, and finally, according to the aggregation equivalent model, the target parameter set, the physical layer model and the cooperative control law model, an aggregation result of the virtual hybrid energy storage cluster is obtained. According to the method, the number of state variables related to characteristic parameters in the modeling process is always maintained at a certain scale, so that the effect of reducing the complexity of the model is achieved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, device, medium and product for virtual hybrid energy storage aggregation in power distribution networks. Background Technology

[0002] Virtual energy storage technology is a new type of energy storage solution in the fields of smart grids and energy internet. As a supplement to physical energy storage, it integrates distributed flexible load resources through information technology to form a virtual system with energy storage characteristics, which can reduce the scale and cost of traditional energy storage.

[0003] In existing virtual energy storage aggregation schemes, the full-order equivalent model of the virtual energy storage system is adopted. The full-order small-signal model based on the state space form has good modeling accuracy and versatility in time-domain and frequency-domain analysis.

[0004] However, as the number of distributed resources within the virtual energy storage increases, the state space order of this model will suffer from the "curse of dimensionality," resulting in an overly complex model. Summary of the Invention

[0005] This application provides a method, device, medium, and product for aggregating virtual hybrid energy storage in a power distribution network, which aims to reduce the complexity of the model while ensuring its accuracy.

[0006] In a first aspect, embodiments of this application provide a method for aggregating virtual hybrid energy storage in a distribution network, comprising:

[0007] Establish a control model containing the first characteristic parameter corresponding to each virtual converter in the virtual hybrid energy storage cluster.

[0008] Based on the control model, the first characteristic parameter, the distribution network model, and the cooperative control law model, the second characteristic parameter of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster is determined.

[0009] Based on the control model and the second characteristic parameter, an aggregated equivalent model is established.

[0010] Based on the second feature parameter and the second mode set, the target parameter set of the aggregated equivalent model is obtained.

[0011] Based on the control model and the distribution network model, a physical layer model corresponding to the virtual hybrid energy storage cluster is established.

[0012] Based on the aggregated equivalent model, target parameter set, physical layer model, and cooperative control law model, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0013] In one possible implementation, after establishing the control model corresponding to each virtual converter in the virtual hybrid energy storage cluster in conjunction with the first aspect, the method further includes:

[0014] Determine the input and output interfaces corresponding to the control model.

[0015] Based on the unique grid connection point, and according to the input and output interfaces, the virtual hybrid energy storage cluster is divided into a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage group module.

[0016] The virtual synchronous converter module is nested within the virtual hybrid energy storage module, and the virtual hybrid energy storage module is nested within the virtual hybrid energy storage cluster module.

[0017] In one possible implementation, in conjunction with the first aspect, based on the control model, the first characteristic parameter, the distribution network model, and the cooperative control law model, the second characteristic parameter of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster is determined, including:

[0018] Based on the network admittance matrix, a distribution network model corresponding to the distribution network is established.

[0019] A cooperative control law model is established based on the communication topology and control parameter set.

[0020] The control model, distribution network model, and cooperative control law model in the virtual hybrid energy storage module are subjected to unified model fusion processing to obtain the full-time scale model corresponding to the virtual hybrid energy storage module.

[0021] The second feature parameter is determined based on the first feature parameter and the full-time-scale model.

[0022] In one possible implementation, in conjunction with the first aspect, determining the second characteristic parameter based on the first characteristic parameter and the full-time-scale model includes:

[0023] Based on the first feature parameter, the first mode set related to the first feature parameter is obtained.

[0024] Based on the first mode set, establish a feature parameter optimization model corresponding to the aggregated equivalent model.

[0025] Solve the feature parameter optimization model to obtain the second feature parameter of the aggregated equivalent model.

[0026] In one possible implementation, in conjunction with the first aspect, the target parameter set of the aggregated equivalent model is obtained based on the second feature parameters and the second modality set, including:

[0027] The modes in the second mode set are clustered according to the time scale to obtain multiple mode subsets.

[0028] A parameter optimization model is established based on multiple modal subsets and the second feature parameter.

[0029] By solving the parameter optimization model, the target parameter set of the aggregated equivalent model is obtained.

[0030] In one possible implementation, in conjunction with the first aspect, based on the physical layer model and the cooperative control law model, the aggregation result of the virtual hybrid energy storage cluster is obtained, including:

[0031] The physical layer model is sampled to obtain the first discrete model corresponding to the physical layer model.

[0032] Discretize the cooperative control law model to obtain a second discrete model corresponding to the cooperative control law model.

[0033] Based on the first discrete model and the second discrete model, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0034] In one possible implementation, in conjunction with the first aspect, the aggregation result of the virtual hybrid energy storage cluster is obtained based on the aggregated equivalent model, the target parameter set, the first discrete model, and the second discrete model, including:

[0035] Substituting the second discrete model into the first discrete model and performing a Z-transform yields the characteristic equation.

[0036] The spatiotemporal stable boundary is obtained based on the characteristic equation.

[0037] The spatiotemporal stability boundary, target parameter set, and aggregated equivalent model are determined as the aggregation result of the virtual hybrid energy storage cluster.

[0038] Secondly, embodiments of this application provide a virtual hybrid energy storage aggregation device for a power distribution network, comprising:

[0039] The first establishment module is used to establish a control model containing the first characteristic parameters corresponding to each virtual converter in the virtual hybrid energy storage cluster.

[0040] The second module is used to determine the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameters, the distribution network model, and the cooperative control law model. The aggregated equivalent model is then established based on the control model and the second characteristic parameters.

[0041] The first module is used to obtain the target parameter set of the aggregated equivalent model based on the second feature parameters and the second modality set.

[0042] The second module is used to establish the physical layer model corresponding to the virtual hybrid energy storage cluster based on the control model and the distribution network model; and to obtain the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the physical layer model and the cooperative control law model.

[0043] In one possible implementation, in conjunction with the second aspect, after the first establishing module establishes the control model corresponding to each virtual converter in the virtual hybrid energy storage cluster, it is further used for:

[0044] Determine the input and output interfaces corresponding to the control model.

[0045] Based on the unique grid connection point, and according to the input and output interfaces, the virtual hybrid energy storage cluster is divided into a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage group module.

[0046] The virtual synchronous converter module is nested within the virtual hybrid energy storage module, and the virtual hybrid energy storage module is nested within the virtual hybrid energy storage cluster module.

[0047] In one possible implementation, in conjunction with the second aspect, the second establishing module determines the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameters, the distribution network model, and the cooperative control law model, specifically for:

[0048] Based on the network admittance matrix, a distribution network model corresponding to the distribution network is established.

[0049] A cooperative control law model is established based on the communication topology and control parameter set.

[0050] The control model, distribution network model, and cooperative control law model in the virtual hybrid energy storage module are subjected to unified model fusion processing to obtain the full-time scale model corresponding to the virtual hybrid energy storage module.

[0051] The second feature parameter is determined based on the first feature parameter and the full-time-scale model.

[0052] In one possible implementation, in conjunction with the second aspect, the second establishing module determines the second feature parameters based on the first feature parameters and the full-time-scale model, specifically for:

[0053] Based on the first feature parameter, the first mode set related to the first feature parameter is obtained.

[0054] Based on the first mode set, establish a feature parameter optimization model corresponding to the aggregated equivalent model.

[0055] Solve the feature parameter optimization model to obtain the second feature parameter of the aggregated equivalent model.

[0056] In one possible implementation, in conjunction with the second aspect, the first obtaining module obtains the target parameter set of the aggregated equivalent model based on the second feature parameters and the second modality set, specifically for:

[0057] The modes in the second mode set are clustered according to the time scale to obtain multiple mode subsets.

[0058] A parameter optimization model is established based on multiple modal subsets and the second feature parameter.

[0059] By solving the parameter optimization model, the target parameter set of the aggregated equivalent model is obtained.

[0060] In one possible implementation, in conjunction with the second aspect, the second obtaining module obtains the aggregation result of the virtual hybrid energy storage cluster based on the physical layer model and the cooperative control law model, specifically for:

[0061] The physical layer model is sampled to obtain the first discrete model corresponding to the physical layer model.

[0062] Discretize the cooperative control law model to obtain a second discrete model corresponding to the cooperative control law model.

[0063] Based on the first discrete model and the second discrete model, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0064] In one possible implementation, in conjunction with the second aspect, the second obtaining module obtains the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the first discrete model, and the second discrete model, specifically for:

[0065] Substituting the second discrete model into the first discrete model and performing a Z-transform yields the characteristic equation.

[0066] The spatiotemporal stable boundary is obtained based on the characteristic equation.

[0067] The spatiotemporal stability boundary, target parameter set, and aggregated equivalent model are determined as the aggregation result of the virtual hybrid energy storage cluster.

[0068] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor.

[0069] The memory stores the instructions that the computer executes.

[0070] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0071] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0072] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0073] This application provides a method, device, medium, and product for aggregating virtual hybrid energy storage in a distribution network. It establishes a control model corresponding to each virtual converter in the virtual hybrid energy storage cluster. Based on the control model and its first characteristic parameters, the distribution network model, and the cooperative control law model, it determines the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster. The aggregated equivalent model is then established in conjunction with the control model. Finally, based on the second characteristic parameters and the second mode set, the target parameter set of the aggregated equivalent model is obtained. Furthermore, based on the control model and the distribution network model, a physical layer model corresponding to the virtual hybrid energy storage cluster is established. Finally, based on the aggregated equivalent model, the target parameter set, the physical layer model, and the cooperative control law model, the aggregation result of the virtual hybrid energy storage cluster is obtained. By determining the second characteristic parameters based on the first characteristic parameters and establishing the aggregated equivalent model based on the control model, the number of state variables related to the characteristic parameters during the modeling process is maintained at a certain scale, thus reducing the complexity of the model. Attached Figure Description

[0074] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0075] Figure 1 A schematic diagram of a scenario for a virtual hybrid energy storage aggregation method for a power distribution network provided in this application;

[0076] Figure 2 A flowchart illustrating a virtual hybrid energy storage aggregation method for a distribution network provided in this application. Figure 1 ;

[0077] Figure 3 A flowchart illustrating a virtual hybrid energy storage aggregation method for a distribution network provided in this application. Figure 2 ;

[0078] Figure 4 A flowchart illustrating a virtual hybrid energy storage aggregation method for a distribution network provided in this application. Figure 3 ;

[0079] Figure 5 A block diagram of the virtual energy storage control strategy provided in this application;

[0080] Figure 6 A block diagram illustrating the partitioning of the virtual energy storage cluster module provided in this application;

[0081] Figure 7A schematic diagram of the structure of a virtual hybrid energy storage aggregation device for a power distribution network provided in this application;

[0082] Figure 8 A schematic diagram of the structure of the electronic device provided in this application.

[0083] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0084] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0085] First, the terms used in this application will be explained:

[0086] Virtual energy storage technology generally refers to technologies that simulate energy storage functions by aggregating adjustable resources. In power systems, it refers to integrating flexible resources such as loads and distributed power sources to achieve the spatial and temporal transfer of energy to smooth out fluctuations.

[0087] Virtual converter: Generally refers to a virtual device that simulates the function of a converter through software. In power systems, it refers to simulating the power conversion and control characteristics of a converter on a digital platform for system analysis and testing.

[0088] Virtual hybrid energy storage clusters: These generally refer to clusters that virtually integrate multiple forms of energy storage. In power systems, this means forming complementary energy storage clusters by coordinating and controlling different types of virtual energy storage resources to enhance system resilience.

[0089] Characteristic parameters: In power systems, these are key parameters that characterize the performance of equipment or systems, such as the inertial time constant of a generator.

[0090] Virtual moment of inertia: In power systems, this refers to the moment of inertia of an inverter that is made similar to that of a synchronous machine through control strategies, thereby suppressing frequency fluctuations.

[0091] Damping coefficient: In power systems, it refers to the parameter characterizing the system's ability to suppress oscillations. A damping coefficient that is too low can easily lead to system instability.

[0092] Reactance coefficient: In a power system, it refers to the reactance parameter of equipment such as lines and transformers, which affects power flow distribution and voltage stability.

[0093] Active frequency droop factor: In a power system, it refers to the proportional coefficient of the inverter's active power output as the frequency deviation changes, and is used for frequency regulation.

[0094] Reactive voltage droop factor: In a power system, it refers to the proportional coefficient of the inverter's reactive output as a function of voltage deviation, and is used for voltage regulation.

[0095] Voltage droop coefficient: In a power system, it refers to the adjustment coefficient of the generator or voltage regulator output voltage as reactive current changes, which affects the stability of parallel operation.

[0096] Grid connection point: In a power system, this refers to the connection point between distributed power sources, microgrids and the main grid, which is a key node for power exchange and control.

[0097] Network admittance matrix: Generally, this refers to a matrix that describes the admittance relationships between network nodes. In power systems, it refers to a matrix that characterizes the electrical connection strength between nodes and is used for power flow calculations and stability analysis.

[0098] Mode set: In power systems, it generally refers to the set of all characteristic modes in small-signal analysis of the system, reflecting the overall characteristics of different oscillation modes of the system.

[0099] Mode: In power systems, mode generally refers to the pattern that characterizes the oscillation characteristics of the system in small-signal stability analysis, such as local mode and interval mode.

[0100] Discretization generally refers to the process of converting continuous quantities into discrete quantities. In power systems, it refers to converting continuous system models or signals into discrete forms for digital simulation and controller design.

[0101] Z-transform: Generally refers to the transformation of discrete-time signals into the complex frequency domain. In power systems, it is used for the analysis and design of discrete systems, such as the stability verification of digital controllers.

[0102] Spatiotemporal stability boundary: In power systems, this refers to the boundary conditions under which the system remains stable under changes in time and space, and is used to predict safe operation outcomes.

[0103] Full-order small-signal model: Generally refers to a small-signal model that includes all state variables of the system. In power systems, it refers to a linearized model that fully describes the dynamic characteristics of each component of the system and is used for accurate analysis of small-signal stability.

[0104] Participation factor analysis (PFA) is a method that analyzes the contribution of variables to oscillation modes. In power systems, it is used to determine the degree of influence of each state variable on the system's oscillation modes, guiding the design of stability control strategies.

[0105] Parameter space: Generally refers to a multidimensional space composed of multiple parameters. In power systems, it refers to the space consisting of all possible values ​​of key system parameters, used to analyze the impact of parameter variations on system performance.

[0106] Secondly, the application background of the embodiments of this application will be explained:

[0107] Virtual energy storage technology is a novel energy storage solution in the fields of smart grids and the energy internet. As a supplement to physical energy storage, it integrates distributed flexible load resources through information technology to form a virtual system with energy storage characteristics, reducing the scale and cost of traditional energy storage. Existing virtual energy storage aggregation schemes employ a full-order equivalent model of the virtual energy storage system. This full-order small-signal model, based on state-space representation, possesses good modeling accuracy and versatility in time and frequency domain analysis. However, as the number of distributed resources within the virtual energy storage increases, the state-space order of this model suffers from the "curse of dimensionality," resulting in an overly complex model.

[0108] To address the aforementioned issues, the inventors investigated whether model complexity could be reduced by reusing characteristic parameters. They proposed an aggregation method for virtual hybrid energy storage in distribution networks. This method establishes control models for each virtual converter based on the dynamic characteristics of various flexible resources. Based on the input and output characteristics of these control models, the virtual hybrid energy storage cluster is divided into three nested modules, and a collaborative control framework is established between these modules. From the inside out, these modules are: a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage cluster module. The control model corresponds to the virtual synchronous converter module. Then, combining the distribution network model and the collaborative control law model, an aggregated equivalent model is constructed for the virtual hybrid energy storage module. This aggregated equivalent model has the same structure as the control model, and its characteristic parameters are determined based on the characteristic parameters of the control model. The target parameter set of this aggregated equivalent model is solved, and its spatiotemporal stability boundary is determined based on the control model, the distribution network model, and the collaborative control law model. The aggregated equivalent model, the target parameter set, and the spatiotemporal stability boundary are collectively used to determine the aggregation result of the virtual hybrid energy storage cluster. By employing the above methods, the reuse of modules based on feature parameters is achieved, thereby reducing the complexity of the model.

[0109] Taking the aggregation method of virtual hybrid energy storage in a city's power distribution network as an example, combined with Figure 1 This illustrates the specific application scenario of the virtual hybrid energy storage aggregation method for distribution networks provided in this application. For example... Figure 1As shown, the specific application scenario of this application includes a power grid control system 101, a virtual hybrid energy storage cluster 102, physical energy storage devices 103, and a source-load entity 104. The physical energy storage devices 103 are connected to the edge computing nodes of the virtual hybrid energy storage cluster 102 through their respective converters. The power grid control system 101 establishes connections with the virtual hybrid energy storage cluster 102 and the source-load entity 104 through communication lines. The source-load entity 104 collects its own operating data in real time and uploads it to the power grid control system 101, while simultaneously pushing it to the virtual hybrid energy storage cluster 102. The physical energy storage devices 103 feed back their own operating data to the virtual hybrid energy storage cluster 102. The edge computing platform in the virtual hybrid energy storage cluster 102, based on the aggregated equivalent model, aggregates the acquired operating data to form an equivalent cluster operating status, which is then reported to the power grid control and management system. On the other hand, it processes the control commands sent by the power grid control system 101 and sends them to the converters of each physical energy storage device 103. The power grid control system 101, based on the supply and demand data of the source-load entity 104 and the status feedback of the virtual hybrid energy storage cluster 102, formulates a global control strategy and sends it to the virtual hybrid energy storage cluster 102. Finally, the edge computing platform in the virtual hybrid energy storage cluster 102 decomposes the data into specific execution commands based on the aggregated equivalent model, driving the physical energy storage devices 103 to perform corresponding actions.

[0110] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0111] Figure 2 A flowchart illustrating a virtual hybrid energy storage aggregation method for a distribution network provided in this application. Figure 1 ,like Figure 2 As shown, the method includes:

[0112] S201. Establish a control model containing the first characteristic parameter corresponding to each virtual converter in the virtual hybrid energy storage cluster.

[0113] In this step, the first characteristic parameters include virtual moment of inertia, active power frequency droop coefficient, reactive power voltage droop coefficient, damping coefficient, reactance coefficient, and voltage droop coefficient. After establishing the control model, the input and output interfaces corresponding to each virtual converter can be determined based on the control model. Using a unique grid connection point as the dividing criterion, the virtual hybrid energy storage cluster is divided into virtual synchronous converter modules, virtual hybrid energy storage modules, and virtual hybrid energy storage group modules based on the input and output interfaces. The virtual synchronous converter modules are nested within the virtual hybrid energy storage modules, and the virtual hybrid energy storage modules are nested within the virtual hybrid energy storage group modules.

[0114] S202. Based on the control model, the first characteristic parameter, the distribution network model, and the cooperative control law model, determine the second characteristic parameter of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster.

[0115] In this step, firstly, a distribution network model corresponding to the distribution network is established based on the network admittance matrix; then, a cooperative control law model is established based on the communication topology and control parameter set; next, the control model, distribution network model, and cooperative control law model in the virtual hybrid energy storage module are subjected to unified model fusion processing to obtain a full-time-scale model corresponding to the virtual hybrid energy storage module. Finally, the second characteristic parameters of the aggregated equivalent model are determined based on the first characteristic parameters of the control model and the full-time-scale model. Specifically, the process of determining the second characteristic parameters of the aggregated equivalent model based on the first characteristic parameters includes: obtaining a first mode set related to the first characteristic parameters; establishing a characteristic parameter optimization model corresponding to the aggregated equivalent model based on the first mode set; and solving the characteristic parameter optimization model to obtain the second characteristic parameters of the aggregated equivalent model.

[0116] S203. Based on the control model and the second characteristic parameter, establish an aggregated equivalent model.

[0117] In this step, based on the same structure as the control model, and using the second characteristic parameter as the model characteristic parameter, an aggregated equivalent model corresponding to the virtual hybrid energy storage module in the virtual hybrid energy storage cluster is established.

[0118] S204. Based on the second feature parameter and the second mode set, obtain the target parameter set of the aggregated equivalent model.

[0119] In this step, the modes in the second mode set are clustered according to time scale to obtain multiple mode subsets. Based on the multiple mode subsets and the second feature parameter, a parameter optimization model is established. By solving the parameter optimization model, the target parameter set of the aggregated equivalent model is obtained.

[0120] S205. Based on the control model and the distribution network model, establish the physical layer model corresponding to the virtual hybrid energy storage cluster.

[0121] S206. Based on the aggregated equivalent model, target parameter set, physical layer model, and cooperative control law model, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0122] In this step, the physical layer model is sampled to obtain a first discrete model corresponding to the physical layer model. The cooperative control law model is discretized to obtain a second discrete model corresponding to the cooperative control law model. Based on the first and second discrete models, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0123] Specifically, the second discrete model is substituted into the first discrete model for Z-transformation to obtain the characteristic equation. The spatiotemporal stability boundary is then obtained based on the characteristic equation. The spatiotemporal stability boundary, the target parameter set, and the aggregated equivalent model are determined as the aggregated result of the virtual hybrid energy storage cluster.

[0124] This application provides a method for aggregating virtual hybrid energy storage in a distribution network. The method establishes a control model corresponding to each virtual converter in the virtual hybrid energy storage cluster. Based on the control model and its first characteristic parameters, the distribution network model, and the cooperative control law model, the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster are determined. Based on the second characteristic parameters, an aggregated equivalent model is established with the same structure as the control model. Then, based on the second characteristic parameters and the second mode set, the target parameter set of the aggregated equivalent model is obtained. Furthermore, based on the control model and the distribution network model, a physical layer model corresponding to the virtual hybrid energy storage cluster is established. Finally, the aggregation result of the virtual hybrid energy storage cluster is obtained based on the aggregated equivalent model, the target parameter set, the physical layer model, and the cooperative control law model. By determining the second characteristic parameters based on the first characteristic parameters and establishing the aggregated equivalent model based on the control model, the number of state variables related to the characteristic parameters during the modeling process is maintained at a certain scale, thus reducing the complexity of the model.

[0125] Figure 3 A flowchart illustrating a virtual hybrid energy storage aggregation method for a distribution network provided in this application. Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the examples, a method for aggregating virtual hybrid energy storage in a distribution network is described in detail. This method includes:

[0126] S301. Establish a control model containing the first characteristic parameters corresponding to each virtual converter in the virtual hybrid energy storage cluster.

[0127] In this step, by establishing control models for different distributed resource converters and writing them in the form of full-order state equations, a virtual energy storage operation model based on virtual synchronous machine control is constructed.

[0128] Specifically, based on the virtual synchronous rotor motion equations, primary frequency regulation simulation equations, excitation regulation simulation equations, and reactive power droop control equations related to the distributed resource converter, the converter control-related variables are written as the sum of steady-state and dynamic changes. The dynamic changes are shown in the following full-order small-signal model:

[0129]

[0130] Where the subscript "0" indicates the initial value of each variable; Δ indicates the dynamic change of the corresponding variable; based on this, the meanings of each parameter are as follows: δ is the virtual rotor angle; ω is the angular frequency of the converter's virtual rotor; ω pcc ω is the grid connection point angular frequency; J is the virtual rotational inertia of the converter; P m P e These represent the virtual mechanical power and electrical power of the converter, respectively; ω n The rated angular frequency is K; D is the active-frequency equivalent damping coefficient; K P P is the active-frequency droop factor. r_G This is the initial command given to the converter's active power. When it is positive, the converter acts as a virtual synchronous generator (suitable for devices that generate active power, such as physical energy storage and fuel cells). In this case, P... r_G It is its initial active power output command. When it is negative, the converter acts as a virtual load synchronizing machine (such as electrical loads like air conditioners and electric vehicles). P r_G It is the initial active power consumption instruction; P e It is electrical power; E o ΔE and ΔE represent the no-load virtual internal potential and compensation, respectively; X is the equivalent reactance; U n Ratings related to the converter port voltage; K v Q is the reactive power-voltage droop factor. m Q is the reactive power setpoint; e This represents the actual reactive power output; K q U is the voltage droop coefficient; pcc This represents the effective value of the actual output voltage at the port.

[0131] The full-order small-signal model can be written in the following full-order state equation form:

[0132]

[0133] Wherein, ΔU pcc =[Δω pcc ,Δu pcc The model feature parameters corresponding to the above model are V = [J, D, X, K]. p K v K q ].

[0134] The full-order state equation of the above full-order small-signal model is determined as the control model, and the characteristic parameter corresponding to the control model is the first characteristic parameter.

[0135] S302. Determine the input and output interfaces corresponding to the control model, and divide the virtual hybrid energy storage cluster into modules accordingly.

[0136] In this step, the input and output interfaces corresponding to the control model are determined. Specifically, the input and output interfaces of each virtual converter are determined based on the control model. Then, using a unique grid connection point as the dividing line, the virtual hybrid energy storage cluster is divided into virtual synchronous converter modules, virtual hybrid energy storage modules, and virtual hybrid energy storage group modules based on the input and output interfaces. The virtual synchronous converter modules are nested within the virtual hybrid energy storage modules, and the virtual hybrid energy storage modules are nested within the virtual hybrid energy storage group modules. The modules ensure target consistency through command flow, transmit state changes through power flow, and achieve dynamic balance through feedback correction, ultimately achieving coordinated operation of all modules.

[0137] S303. Perform unified model fusion processing on the control model, distribution network model, and cooperative control law model to obtain the full-time-scale model corresponding to the virtual hybrid energy storage module.

[0138] In this step, a distribution network model corresponding to the distribution network is established based on the network admittance matrix, and its expression is as follows:

[0139]

[0140] Among them, A NET_Nor Determined by power grid parameters such as the network admittance matrix, it describes the inherent characteristics of changes in the internal state of the distribution network; Δx NET_Nor B represents the small-signal disturbance related to the state variables of the distribution network. NET_Nor For the distribution network input matrix (related to the grid connection point voltage), describe the grid connection point voltage disturbance ΔU. pcc The degree of influence on the rate of change of state variables of the distribution network; C NET_Nor This is the input matrix for the distribution network (related to the injected current). It reflects the injected current disturbance ΔI. IN The effect of the rate of change of state variables in the distribution network; ΔI IN This represents the small-signal disturbance of the injected current into the distribution network, and signifies the deviation of the injected current from its steady-state value.

[0141] Based on communication topology L VSG Given the control parameter set K, a cooperative control law model is established, the expression of which is as follows:

[0142]

[0143] Among them, A Pr_Nor The system matrix is ​​the cooperative control law, derived from the communication topology L. VSG The control parameter set K, etc., determines the dynamic relationship between state variables within the coordinated control loop; Δx Pr_Nor B represents the small-signal disturbance of the state variables in the collaborative control law model. Pr_Nor The input matrix of the cooperative control law describes the reference power disturbance ΔP.r_M The effect on the rate of change of the control state variable; ΔP r_M The small-signal disturbance is the reference power.

[0144] A unified model fusion process is performed on the control model, distribution network model, and cooperative control law model in the virtual hybrid energy storage module to obtain the full-time-scale model corresponding to the virtual hybrid energy storage module, whose expression is as follows:

[0145]

[0146] Where, x VES These are the state variables of the overall model of the virtual hybrid energy storage module.

[0147] S304. Based on the first characteristic parameter and the full-time-scale model, determine the second characteristic parameter of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster.

[0148] In this step, a first mode set related to the first feature parameter is obtained. Based on the first mode set, a feature parameter optimization model corresponding to the aggregated equivalence model is established. The feature parameter optimization model is solved to obtain the second feature parameters of the aggregated equivalence model.

[0149] Specifically, through participation factor analysis, modes strongly correlated with the first characteristic parameter V of the control model are obtained based on the full-time-scale model. These modes are then clustered to eliminate short-time-scale and crowded modes, thereby obtaining the target mode set σ. ob Finally, the following feature parameter optimization model is established:

[0150] min‖σ eq_VES -σ ob ||2

[0151] s.tσ eq_VES ∝V eq_VES

[0152] Where, σ eq_VES The second characteristic parameter V eq_VES The corresponding mode group, when solved using an intelligent algorithm to optimize the aforementioned feature parameters, allows the determination of the second feature parameter V. eq_VES =[J eq_VES D eq_VES X eq_VES K peq_VES ]. J eq_VES D is the equivalent moment of inertia. eq_VES Related to the damping coefficient, it is used to adjust the system's damping characteristics and suppress oscillations; X eq_VES These parameters, related to the equivalent reactance, affect the reactive power exchange and voltage support characteristics between the energy storage module and the grid, and also participate in shaping the system impedance characteristics, thus affecting the mode; Kpeq_VES The equivalent droop coefficient related to active power-frequency control is used to adjust the response characteristics of the active power output of the energy storage module to frequency deviation.

[0153] S305. Establish an aggregated equivalent model based on the control model and the second characteristic parameter.

[0154] In this step, based on the determined second feature parameter, an aggregated isometry model is established with the same structure as the control model. Its corresponding expression is as follows:

[0155]

[0156] Wherein, ΔU pcc =[Δω pcc ,Δu pcc The model feature parameters corresponding to the above model are: V eq_VES =[J eq_VES D eq_VES X eq_VES K peq_VES ].

[0157] S306. Based on the second characteristic parameter and the second mode set, establish a parameter optimization model.

[0158] In this step, the second mode set, which is strongly correlated with the second feature parameter in the aggregated equivalent model, is taken as the research object. The modes in the second mode set are clustered according to the time scale to obtain multiple mode subsets. Based on the multiple mode subsets and the second feature parameter, a parameter optimization model is established.

[0159] S307. Solve the parameter optimization model to obtain the target parameter set of the aggregated equivalent model.

[0160] In this step, the target parameter set of the aggregated equivalent model is obtained by solving the parameter optimization model.

[0161] Specifically, the stochastic gradient algorithm is used to solve the parameter optimization model. By randomly searching in the parameter space, the target parameter set of the aggregated equivalent model is finally obtained.

[0162] S308. Based on the control model and the distribution network model, establish the physical layer model corresponding to the virtual hybrid energy storage cluster.

[0163] In this step, the control model and the distribution network model are combined to establish a physical layer model of the virtual energy storage system that does not include the cooperative control law model.

[0164] S309. Based on the physical layer model and the cooperative control law model, the spatiotemporal stability boundary is obtained.

[0165] In this step, the physical layer model is sampled to obtain the first discrete model corresponding to the physical layer model. The cooperative control law model is discretized to obtain the second discrete model corresponding to the cooperative control law model. The second discrete model is substituted into the first discrete model for Z-transform to obtain the characteristic equation. The spatiotemporal stability boundary is obtained based on the characteristic equation. The expression of the characteristic equation is as follows:

[0166]

[0167] Among them, A VES_DIS This is the discrete state matrix of the aggregated equivalent model, which also includes the communication time delay τ. d Communication interval T s and communication topology L VSG Therefore, it can be analyzed by matrix A. VES_DIS The distribution of eigenvalues ​​is used to examine the impact of spatiotemporal parameters on the stability of virtual energy storage modules.

[0168] Finally, communication topology L VSG Its spectral radius ψ can be used as a metric, while maintaining the communication interval T. s Unchanged, proposed according to A VES_DIS The distribution of eigenvalues ​​on the unit circle is used to depict the spatiotemporal stability boundary (curve) of the virtual energy storage module under this communication interval. Ts (ψ,τ d This boundary describes the maximum time delay that the system can withstand under different topologies. Changing the communication interval T... s A set of stable boundaries can be obtained, and ultimately a spacetime stable boundary can be obtained.

[0169] S310. The spatiotemporal stability boundary, target parameter set, and aggregated equivalent model are determined as the aggregation result of the virtual hybrid energy storage cluster.

[0170] In this step, it can also be considered that the aggregated equivalent model, which adopts the target parameter set and the communication interval corresponding to the spatiotemporal stability boundary, is determined as the aggregation result of the virtual hybrid energy storage cluster.

[0171] This application provides a method for aggregating virtual hybrid energy storage in a distribution network. The method involves establishing a control model with first characteristic parameters for each virtual converter in the virtual hybrid energy storage cluster, determining its input and output interfaces, and then dividing the virtual hybrid energy storage cluster into modules: a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage group module. The control model, distribution network model, and cooperative control law model are then subjected to unified model fusion processing to obtain the full-time-scale model corresponding to the virtual hybrid energy storage module. Next, based on the first characteristic parameters and the full-time-scale model, the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster are determined. A parameter optimization model is then established and solved using the second mode set to obtain the target parameter set of the aggregated equivalent model. Finally, based on the control model and the distribution network model, a physical layer model corresponding to the virtual hybrid energy storage cluster is established. The spatiotemporal stability boundary is obtained by combining the cooperative control law model. Finally, the spatiotemporal stability boundary, the target parameter set, and the aggregated equivalent model are collectively determined as the aggregation result of the virtual hybrid energy storage cluster. In this embodiment, the accuracy of the model is guaranteed by using a full-time-scale model, and the complexity of the model is reduced by dividing the model into modules and reusing feature parameters in the corresponding models of each module. Thus, the model's accuracy is guaranteed while its complexity is reduced.

[0172] Based on any of the above embodiments, the following, in conjunction with Figure 4 This paper provides a detailed explanation of a virtual hybrid energy storage aggregation method for a power distribution network through specific examples.

[0173] S401. Establish control models for different distributed resource converters and write them in the form of full-order state equations.

[0174] In this step, by establishing control models for different distributed resource converters and writing them in the form of full-order state equations, a virtual energy storage operation model based on virtual synchronous machine control is constructed.

[0175] Flexible resources such as electric vehicles and variable frequency air conditioners are connected to the power grid through power electronic converters. They can flexibly change their operating mode according to the grid status. By modifying the converters of these flexible and controllable resources, the original control method of the converters can be changed to a virtual synchronous machine control strategy, thereby enabling the above-mentioned flexible and controllable resources to achieve a similar function to virtual energy storage.

[0176] Specifically, based on the synchronous generator rotor mechanical equations, the virtual synchronous rotor motion equations of the active-frequency control link of the distributed resource converter can be expressed by the following equations:

[0177]

[0178] Where J is the virtual moment of inertia of the converter, θ is the virtual rotor angle of the converter, and ω is the virtual rotor angular frequency of the converter. n Where ω is the rated angular frequency, D is the damping coefficient, and P is the rated angular frequency. m P e These represent the virtual mechanical power and electrical power of the converter, respectively. Virtual mechanical power P m It can be obtained from the following primary frequency modulation simulation equation:

[0179] P m =P r_G +K P (ω n -ω pcc )

[0180] Among them, K P ω is the active-frequency droop factor. pcc P is the angular frequency at the grid connection point. r_G The initial instruction value is set when the value P is set. r_G When the active power setpoint P is positive, the converter acts as a virtual synchronous generator (suitable for physical energy storage, fuel cells, and other power sources). r_G When the value is negative, the converter is a virtual load synchronizing machine (suitable for loads such as air conditioners and electric vehicles).

[0181] Furthermore, to simulate the excitation regulation system of a synchronous generator, the virtual electromotive force E of the converter is divided into two parts. The excitation regulation simulation equation related to the virtual electromotive force is as follows:

[0182]

[0183] Among them, Q m Q is the given value for reactive power. e K represents the actual reactive power output. q E represents the voltage droop coefficient. o ΔE and ΔE represent the no-load virtual internal potential and compensation, respectively. According to the synchronous motor excitation voltage regulation strategy, the reactive power setpoint Q... m This can be obtained from the following reactive power-voltage droop control equation:

[0184] Q m =Q set +ΔQ=Q set +K v (U ref -U pcc )

[0185] Among them, Q set U is the initial reactive power setting value. ref U is the reference RMS value of the converter port voltage. pcc K represents the actual effective value of the port output voltage. vThis is the reactive power-voltage droop coefficient.

[0186] The overall control block diagram of the virtual synchronous machine control strategy is as follows: Figure 5 As shown, Figure 5 ω in * E * These correspond to the virtual angular frequency and internal potential reference value of the converter, respectively. These are the reference values ​​for the dq axis of the converter output voltage, i. Ld_ref i Lq_ref These are the reference values ​​for the converter inductor current dq axis, and u. d u q and i Ld i Lq These are the dq-axis components corresponding to the actual values. Figure 5 The upper part corresponds to the four equations above, while the lower part is the common inner-loop control strategy for converters.

[0187] Based on the above, the control-related variables of each converter can be written as the sum of steady-state and dynamic changes, and the dynamic changes can be represented by a full-order small-signal model. Below, the subscript "0" indicates the initial value of each variable; Δ represents the dynamic change of the corresponding variable. The four equations mentioned in this step are expressed using a full-order small-signal model as follows:

[0188]

[0189] The remaining variables in the equation are the same as those described above, and will not be repeated here. The above full-order small-signal model can also be written in the following full-order state equation form:

[0190]

[0191] Wherein, ΔU pcc =[Δω pcc ,Δu pcc The model feature parameters corresponding to the above model are: V = [J, D, X, K]. p K v K q ]

[0192] It should be noted that the above full-order small-signal model and its full-order state equations This refers to the control model in the aforementioned embodiments, and its corresponding model feature parameter V is the first feature parameter in the aforementioned embodiments.

[0193] S402. Based on the different input / output interfaces and communication connections of the devices, the virtual hybrid energy storage cluster is divided into modules according to the modular concept.

[0194] In this step, following the modular approach, the virtual hybrid energy storage cluster is divided into modules based on the input / output interfaces, communication connections, and internal structures of different devices, thus constructing a nested module division method and a hierarchical collaborative framework.

[0195] Specifically, such as Figure 6 As shown, the virtual hybrid energy storage cluster is divided into three modules: a virtual synchronous converter module (VSG module), a virtual hybrid energy storage module (VES module), and a virtual hybrid energy storage cluster module (VESC module). Each module uses a unique grid connection point as the dividing interface, encapsulating the internal equipment and communication of each module. Each module only contains a power output interface (PCC point) and a command input interface (Pr point). This division method allows the modules to be nested, facilitating reuse in subsequent modeling. According to the above module division method, under this collaborative framework, the VESC module, based on the external command P... r_C Module output power P VESC The communication topology between VES modules within the module L VES (Laplace matrix) and power allocation strategy among internal VES modules λ VES Provides external instructions P for lower-level VES modules r_M The VES module provides instructions to the VSG module in the same form and method. The top-down instruction flow ultimately changes the output of the VSG module, while the bottom-up power flow simultaneously changes the state variables of other modules, thereby achieving coordination between different modules.

[0196] S403. Determine the single virtual converter model, distribution network model, and cooperative control law model.

[0197] In this step, the single virtual converter model is the control model in S401. Distribution network model Based on the network admittance matrix, its expression is:

[0198]

[0199] The cooperative control law model is based on the communication topology L VSG Constructed with the control parameter set K, its expression is as follows:

[0200]

[0201] The parameters above are the same as those in the previous embodiments, and will not be repeated here.

[0202] S404. Establish an equivalent model for the aggregation of virtual hybrid energy storage clusters.

[0203] After establishing the distribution network model and the cooperative control law model, the control model, distribution network model, and cooperative control law model within the virtual hybrid energy storage module are combined to obtain the full-time-scale model of the virtual hybrid energy storage module. Its expression is as follows:

[0204]

[0205] Then, through participation factor analysis, based on the above full-time-scale model... The modes that are strongly correlated with the first characteristic parameter V of the control model are obtained. These modes are then clustered to eliminate short-time-scale and crowded modes, thereby obtaining the target mode set σ. ob Finally, the following feature parameter optimization model is established:

[0206] min‖σ eq_VES -σ ob ||2

[0207] s.tσ eq_VES ∝V eq_VES

[0208] Where, σ eq_VES V is the second characteristic parameter corresponding to the aggregated equivalent model (the VES equivalent model corresponding to the virtual energy storage module). eq_VES The corresponding mode group is used to solve the above feature parameter optimization model using an intelligent algorithm to determine the second feature parameter V. eq_VES =[J eq_VES D eq_VES X eq_VES K peq_VES Based on the second feature parameter V eq_VES An aggregate equivalent model is established, the structure of which is consistent with the control model structure corresponding to the Virtual Synchronous Converter (VSG) module, and its expression is:

[0209]

[0210] Wherein, ΔU pcc =[Δω pcc ,Δu pcc ].

[0211] The model feature parameters corresponding to the above model are:

[0212] V eq_VES =[J eq_VES D eq_VES X eq_VES K peq_VES ]

[0213] The parameters above are the same as those in the previous embodiments, and will not be repeated here.

[0214] Correspondingly, equivalent modeling can also be performed on the virtual hybrid energy storage cluster modules. The equivalent models of the virtual hybrid energy storage modules, the distribution network model, and the cooperative control law model within the cluster are uniformly modeled according to S401 to obtain the full-time-scale model of the virtual hybrid energy storage cluster modules. Then, the equivalent model of the virtual hybrid energy storage cluster modules is obtained using the same method as establishing the equivalent model of the virtual hybrid energy storage modules. Its corresponding characteristic parameters are: V eq_VESC =[J eq_VESC D eq_VESC X eq_VESC K eq_VESC ].

[0215] The above-mentioned equivalent modeling method for virtual hybrid energy storage clusters based on the first characteristic parameter of the control model of the virtual synchronous converter module (VSG module) can be reused, thereby keeping the number of state variables in the modeling process at a certain scale, avoiding the complexity of modeling and enhancing the logic.

[0216] S405, Optimization of equivalent model parameters.

[0217] Finally, in the aggregate equivalence model, μ = [V1,...,V] is used as an example. N ,K1,...,K N Strongly correlated modes σ i (μ) is the research object (number N1). First, the modal σ is analyzed according to the time scale of the modality. i The modality set composed of (μ) is clustered into N2 subsets, and then the following optimal parameter optimization model is established:

[0218]

[0219] Where ρ1 and ρ2 are the weights for stability and consistency, respectively, and ε i The weights corresponding to different modes are set so that the mode weights are smaller when they are far from the imaginary axis and larger when they are close to the imaginary axis, so that the dominant poles are far away from the imaginary axis and the stability margin is increased. Let be the variance of all modes in the j-th subset, representing the consistency of the dynamic performance corresponding to the mode at that time scale.

[0220] Finally, the stochastic gradient algorithm is proposed to solve the above optimal parameter optimization model, and its expression is as follows:

[0221]

[0222] Where, μ k Let η be the control parameter space of the aggregated equivalent model obtained in the k-th iteration. k Let be the step size of the k-th iteration. Let μ be the partial derivative of the random mode with respect to the parameter space. Through a random search in the parameter space, the target parameter set μ of the aggregated equivalent model is finally obtained. op .

[0223] S406. Construct a spatiotemporally stable boundary.

[0224] In this step, the spatiotemporal stability boundary of the virtual hybrid energy storage cluster is constructed. In actual operation, communication between the virtual synchronous converter modules (VSG modules) within the virtual hybrid energy storage cluster is not real-time. Therefore, in addition to parameter μ in S404, communication parameters also affect the stability of the virtual energy storage. The main factors are time parameters and spatial parameters. The time parameter mainly includes the communication interval T. s and communication time delay τ d The spatial parameters are mainly the communication topology L. VSG To construct the spatiotemporal stability boundary of the virtual hybrid energy storage cluster, the relevant model needs to be discretized, as follows:

[0225] First, by combining the control model and the distribution network model, a physical layer model of the virtual energy storage system, excluding the cooperative control law model, is established. That is, the physical layer model corresponding to the virtual hybrid energy storage cluster is expressed as follows:

[0226]

[0227] According to the communication interval T s By sampling the above physical layer model, its discrete model can be obtained, and its expression is as follows:

[0228]

[0229] Among them, A VSG-NET and B VSG+NET These are the state matrix and input matrix of the physical layer continuous-time model, respectively. VSG+NET and E VSG+NET These are the state matrix and input matrix of the discrete-time model at the physical layer, respectively. For simplicity of analysis, the above equation ignores ΔU. pcc The impact.

[0230] Secondly, by discretizing the cooperative control law model, its discrete model can be obtained, as shown in the following expression:

[0231] ΔP r_G [k+1]=DGΔP r_G [k]+(IL vsG -D)GΔP r_G [k-τ d ]+k p (ΔP r_M [k]-FΔr VSG+NET [k])

[0232] Where, D = diag{IL VSG}, G represents the output power of the VSG module to the allocation strategy λ VSG The mapping is then applied to the physical layer discrete model. A Z-transform is performed, and the characteristic equation is established as follows:

[0233]

[0234] Among them, A VES_DIS This is the discrete state matrix of the VES module, which also includes the communication time delay τ. d Communication interval T s and communication topology L VSG Therefore, it can be analyzed by matrix A. VES_DIS The distribution of eigenvalues ​​is used to examine the impact of spatiotemporal parameters on the stability of virtual energy storage modules.

[0235] Finally, communication topology L VSG It can be measured by its spectral radius ψ, specifically, while maintaining the communication interval T. s Unchanged, proposed according to A VES_DIS The distribution of eigenvalues ​​on the unit circle is used to depict the spatiotemporal stability boundary (curve) of the virtual energy storage module under this communication interval. Ts (ψ,τ d This boundary describes the maximum time delay that the system can withstand under different topologies. Changing the communication interval T... s A cluster of stable boundaries can be obtained, ultimately leading to a spatiotemporally stable region, which represents the communication parameters that enable the system to operate stably. This provides a basis for selecting the performance of actual communication equipment and the networking method of the link.

[0236] S407. Obtain the aggregation result of the virtual hybrid energy storage cluster.

[0237] In this step, the target parameter set in S405 and the aggregated equivalent model with the spatiotemporal stability boundary in S406 will be used to determine the aggregation result of the virtual hybrid energy storage cluster.

[0238] It should be noted that, in Figure 4 The processing steps S401 to S407 shown in the embodiments do not constitute a specific limitation on a method for aggregating virtual hybrid energy storage in a distribution network. In other embodiments of this application, a method for aggregating virtual hybrid energy storage in a distribution network may include... Figure 4 The embodiments may include more or fewer steps; for example, a method for aggregating virtual hybrid energy storage in a distribution network may include... Figure 4 Some steps in the embodiments, or, Figure 4 Some steps in the embodiments can be replaced by steps with the same function, or, Figure 4Some steps in the embodiments can be broken down into multiple steps, etc.

[0239] Figure 7 A schematic diagram of a virtual hybrid energy storage aggregation device for a power distribution network provided in this application is shown below. Figure 7 As shown, the virtual hybrid energy storage aggregation device 70 for a power distribution network provided in this embodiment includes:

[0240] The first establishment module 701 is used to establish a control model containing first characteristic parameters corresponding to each virtual converter in the virtual hybrid energy storage cluster.

[0241] The second module 702 is used to determine the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameters, the distribution network model, and the cooperative control law model. The aggregated equivalent model is then established based on the control model and the second characteristic parameters.

[0242] The first obtaining module 703 is used to obtain the target parameter set of the aggregated equivalent model based on the second feature parameters and the second mode set.

[0243] The second module 704 is used to establish the physical layer model corresponding to the virtual hybrid energy storage cluster based on the control model and the distribution network model; and to obtain the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the physical layer model and the cooperative control law model.

[0244] In one possible implementation, after the first establishment module 701 establishes the control model corresponding to each virtual converter in the virtual hybrid energy storage cluster, it is further used for:

[0245] Determine the input and output interfaces corresponding to the control model.

[0246] Based on the unique grid connection point, and according to the input and output interfaces, the virtual hybrid energy storage cluster is divided into a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage group module.

[0247] The virtual synchronous converter module is nested within the virtual hybrid energy storage module, and the virtual hybrid energy storage module is nested within the virtual hybrid energy storage cluster module.

[0248] In one possible implementation, the second establishment module 702 determines the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameters, the distribution network model, and the cooperative control law model, specifically for:

[0249] Based on the network admittance matrix, a distribution network model corresponding to the distribution network is established.

[0250] A cooperative control law model is established based on the communication topology and control parameter set.

[0251] The control model, distribution network model, and cooperative control law model in the virtual hybrid energy storage module are subjected to unified model fusion processing to obtain the full-time scale model corresponding to the virtual hybrid energy storage module.

[0252] The second feature parameter is determined based on the first feature parameter and the full-time-scale model.

[0253] In one possible implementation, the second establishment module 702 determines the second feature parameters based on the first feature parameters and the full-time-scale model, specifically for:

[0254] Based on the first feature parameter, the first mode set related to the first feature parameter is obtained.

[0255] Based on the first mode set, establish a feature parameter optimization model corresponding to the aggregated equivalent model.

[0256] Solve the feature parameter optimization model to obtain the second feature parameter of the aggregated equivalent model.

[0257] In one possible implementation, the first obtaining module 703 obtains the target parameter set of the aggregated equivalent model based on the second feature parameters and the second modality set, specifically for:

[0258] The modes in the second mode set are clustered according to the time scale to obtain multiple mode subsets.

[0259] A parameter optimization model is established based on multiple modal subsets and the second feature parameter.

[0260] By solving the parameter optimization model, the target parameter set of the aggregated equivalent model is obtained.

[0261] In one possible implementation, the second obtaining module 704 obtains the aggregation result of the virtual hybrid energy storage cluster based on the physical layer model and the cooperative control law model, specifically for:

[0262] The physical layer model is sampled to obtain the first discrete model corresponding to the physical layer model.

[0263] Discretize the cooperative control law model to obtain a second discrete model corresponding to the cooperative control law model.

[0264] Based on the first discrete model and the second discrete model, the aggregation result of the virtual hybrid energy storage cluster is obtained.

[0265] In one possible implementation, the second obtaining module 704 obtains the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the first discrete model, and the second discrete model, specifically for:

[0266] Substituting the second discrete model into the first discrete model and performing a Z-transform yields the characteristic equation.

[0267] The spatiotemporal stable boundary is obtained based on the characteristic equation.

[0268] The spatiotemporal stability boundary, target parameter set, and aggregated equivalent model are determined as the aggregation result of the virtual hybrid energy storage cluster.

[0269] This embodiment provides a virtual hybrid energy storage aggregation device for a power distribution network, which can execute the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0270] Figure 8 A schematic diagram of the structure of the electronic device provided in this application. Figure 8 As shown, the electronic device 80 provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the device 80 further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0271] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0272] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0273] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0274] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0275] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0276] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0277] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0278] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0279] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0280] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0281] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0282] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0283] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0284] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0285] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for aggregating virtual hybrid energy storage in a power distribution network, characterized in that, include: Establish a control model containing the first characteristic parameter corresponding to each virtual converter in the virtual hybrid energy storage cluster; Based on the control model, the first characteristic parameter, the distribution network model, and the cooperative control law model, the second characteristic parameter of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster is determined. Based on the control model and the second feature parameter, the aggregated equivalent model is established; Based on the second feature parameters and the second modality set, the target parameter set of the aggregated equivalent model is obtained; Based on the control model and the power distribution network model, establish the physical layer model corresponding to the virtual hybrid energy storage cluster; The aggregation result of the virtual hybrid energy storage cluster is obtained based on the aggregated equivalent model, the target parameter set, the physical layer model, and the cooperative control law model.

2. The method according to claim 1, characterized in that, After establishing the control model corresponding to each virtual converter in the virtual hybrid energy storage cluster, the method further includes: Determine the input and output interfaces corresponding to the control model; Based on the unique grid connection point, and according to the input interface and the output interface, the virtual hybrid energy storage cluster is divided into a virtual synchronous converter module, a virtual hybrid energy storage module, and a virtual hybrid energy storage group module. The virtual synchronous converter module is nested within the virtual hybrid energy storage module, and the virtual hybrid energy storage module is nested within the virtual hybrid energy storage cluster module.

3. The method according to claim 2, characterized in that, The determination of the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameter, the distribution network model, and the cooperative control law model includes: Based on the network admittance matrix, a distribution network model corresponding to the distribution network is established; Based on the communication topology and control parameter set, the cooperative control law model is established; The control model, the distribution network model, and the cooperative control law model in the virtual hybrid energy storage module are subjected to unified model fusion processing to obtain a full-time scale model corresponding to the virtual hybrid energy storage module. The second feature parameter is determined based on the first feature parameter and the full-time-scale model.

4. The method according to claim 3, characterized in that, The step of determining the second feature parameter based on the first feature parameter and the full-time-scale model includes: Based on the first feature parameter, a first mode set related to the first feature parameter is obtained; Based on the first modality set, establish a feature parameter optimization model corresponding to the aggregated equivalence model; Solve the feature parameter optimization model to obtain the second feature parameters of the aggregated equivalence model.

5. The method according to claim 1, characterized in that, Based on the second feature parameters and the second modality set, the target parameter set of the aggregated equivalent model is obtained, including: The modes in the second mode set are clustered according to the time scale to obtain multiple mode subsets; Based on the multiple modal subsets and the second feature parameters, a parameter optimization model is established; By solving the parameter optimization model, the target parameter set of the aggregated equivalence model is obtained.

6. The method according to claim 1, characterized in that, The aggregation result of the virtual hybrid energy storage cluster, obtained based on the physical layer model and the cooperative control law model, includes: The physical layer model is sampled to obtain a first discrete model corresponding to the physical layer model; The cooperative control law model is discretized to obtain a second discrete model corresponding to the cooperative control law model. The aggregation result of the virtual hybrid energy storage cluster is obtained based on the first discrete model and the second discrete model.

7. The method according to claim 6, characterized in that, The process of obtaining the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the first discrete model, and the second discrete model includes: Substitute the second discrete model into the first discrete model and perform a Z-transform to obtain the characteristic equation; The spatiotemporal stable boundary is obtained based on the aforementioned characteristic equation; The spatiotemporal stability boundary, the target parameter set, and the aggregated equivalent model are determined as the aggregation result of the virtual hybrid energy storage cluster.

8. A virtual hybrid energy storage aggregation device for a power distribution network, characterized in that, include: The first establishment module is used to establish a control model containing first characteristic parameters corresponding to each virtual converter in the virtual hybrid energy storage cluster. The second establishment module is used to determine the second characteristic parameters of the aggregated equivalent model corresponding to the virtual hybrid energy storage cluster based on the control model, the first characteristic parameters, the distribution network model, and the cooperative control law model. Based on the control model and the second feature parameter, the aggregated equivalent model is established; The first obtaining module is used to obtain the target parameter set of the aggregated equivalent model based on the second feature parameters and the second modality set; The second obtaining module is used to establish a physical layer model corresponding to the virtual hybrid energy storage cluster based on the control model and the distribution network model; and to obtain the aggregation result of the virtual hybrid energy storage cluster based on the aggregated equivalent model, the target parameter set, the physical layer model and the cooperative control law model.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 7.

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