Hierarchical full-factor simulation modeling method and system for green power guarantee of power grid

Abstract

The application provides a layered full-factor simulation modeling method and system for green power guarantee of a power grid, relates to the technical field of power grid operation, and comprises the following steps: obtaining full-factor equipment parameters and topological information; dividing the distribution network into at least two simulation levels according to the topological information, configuring different step lengths, and defining boundary nodes between the levels; converting each equipment into a unified equivalent model, grouping similar equipment into the same group based on dynamic response characteristics, and aggregating to form a reduced-order model of each level; independently advancing simulation of each level according to the respective step length, and interacting between the levels through the boundary nodes at a preset interaction time; monitoring the electrical quantity of the boundary nodes, maintaining the reduced-order model simulation when the electrical quantity is within a preset range, and switching the corresponding region to a detailed model when the electrical quantity exceeds the preset range. Through the organic combination of layered simulation and model reduction, and the self-adaptive switching of working condition perception, the application significantly reduces the calculation amount while ensuring the accuracy, and realizes the coordination and unity of simulation efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of power grid operation technology, and more specifically, to a hierarchical, full-element simulation modeling method and system for ensuring green electricity supply to the power grid. Background Technology

[0002] With the large-scale development of renewable energy and the rapid growth of advanced manufacturing industries, a large number of distributed photovoltaic systems, energy storage systems, power electronic converters, and complex industrial loads are being connected to regional distribution networks. This has led to increasingly complex equipment configurations and dynamic response characteristics exhibiting significant multi-timescale features in these networks. Electromagnetic transient simulation has become an indispensable technical tool to support the planning, design, operation analysis, and stability assessment of regional distribution networks.

[0003] However, the relevant technologies have at least one of the following problems: In the existing regional power distribution network simulation methods, due to the inherent architecture of a globally unified simulation step size and a unified model accuracy, it is impossible to perform hierarchical and partitioned processing according to the differences in the dynamic response characteristics of the equipment, nor can it adjust the model fidelity in real time according to the system operating status. This results in a contradiction between computational efficiency and simulation accuracy during the simulation process, making it difficult to achieve a balance between the two under all operating conditions. Summary of the Invention

[0004] The technical problem solved by this invention is that existing regional power distribution network simulation methods are limited by the inherent architecture of a globally unified simulation step size and a unified model accuracy. They cannot perform hierarchical and partitioned processing based on the differences in the dynamic response characteristics of equipment, nor can they adjust the model fidelity in real time according to the system operating status. This results in a structural contradiction between computational efficiency and simulation accuracy during the simulation process, making it difficult to achieve a balance between the two under all operating conditions.

[0005] To address the aforementioned issues, this invention provides a hierarchical, full-element simulation modeling method for ensuring green electricity supply in power grids. The method includes: acquiring parameter information and topology connection information of all elements in the distribution network of the target area; dividing the distribution network of the target area into at least two simulation levels based on the topology connection information, defining a simulation step size for each simulation level, and defining boundary nodes between simulation levels; converting the simulation levels into unified equivalent models based on the parameter information; grouping equivalent models within the same simulation level according to the dynamic response characteristics of each equivalent model, and grouping equivalent models whose dynamic response characteristics meet preset similarity conditions into the same group, aggregating each group into an aggregated model to form a reduced-order model for each simulation level; controlling each simulation level to independently advance the simulation according to its own simulation step size, and exchanging information between adjacent levels through boundary nodes at preset interaction times; monitoring the electrical changes at boundary nodes; maintaining the reduced-order model for simulation when the electrical changes do not exceed a preset range; and switching the aggregated model in the area affected by the electrical changes to the corresponding detailed model when the electrical changes exceed the preset range.

[0006] Compared with existing technologies, the technical effects achieved by this solution are as follows: This application solves the computational redundancy problem caused by the globally uniform step size in existing simulation methods by dividing the target distribution network into at least two simulation levels according to the topological connection relationship and configuring differentiated step sizes. This allows each level to advance independently at a time scale that matches its own dynamics, reducing the overall computational burden. On this basis, by converting each device into an equivalent model with a unified interface and grouping and aggregating them according to dynamic response characteristics, the aggregated model replaces all equivalent models within the group in the simulation, reducing the total number of system state variables and further improving simulation efficiency. During the simulation process, by setting preset interaction times and boundary node information exchange mechanisms between levels, the accuracy of the coupling relationship can be maintained while each level operates independently. At the same time, by monitoring the electrical changes of boundary nodes in real time and switching the aggregated model in the corresponding area to a detailed model when the changes exceed a preset range, the simulation speed under normal operating conditions is guaranteed, while the reproduction accuracy of key transient processes under large disturbance scenarios is also guaranteed.

[0007] In one embodiment of the present invention, the target area distribution network is divided into at least two simulation levels based on topology connection information, and a simulation step size is defined for each simulation level, as well as boundary nodes between simulation levels. This includes: acquiring dynamic response characteristic parameters and voltage level information of each device within the target area distribution network; classifying each device into different response speed levels based on the dynamic response characteristic parameters; dividing the target area distribution network into levels based on the voltage level information and response speed levels to generate a hierarchical topology structure containing at least two simulation levels; configuring a simulation step size for each simulation level that matches the corresponding dynamic response characteristic parameters; and defining the electrical connection points between each simulation level as boundary nodes, and storing the topology location information and initial electrical parameters of the boundary nodes.

[0008] Compared with existing technologies, the technical effects achieved by this solution are as follows: Based on dynamic response characteristic parameters, each device is divided into different response speed levels, and the levels are hierarchically divided by combining voltage level information and response speed level. Each level is configured with a simulation step size that matches its dynamic response characteristics. Levels with fast dynamic response speeds are configured with smaller simulation step sizes to capture high-frequency transient details, while levels with slow dynamic response speeds are configured with larger simulation step sizes to avoid redundant calculations. This eliminates the computational redundancy problem caused by using a globally uniform minimum simulation step size from the source.

[0009] In one embodiment of the present invention, the simulation hierarchy is transformed into a unified equivalent model based on parameter information, including: identifying the equipment type of each device within the same simulation hierarchy; extracting the electrical characteristic parameters of each device based on the parameter information corresponding to the equipment type; and transforming each device into an equivalent model with a unified interface form according to its respective equipment type and electrical characteristic parameters using a unified preset modeling rule.

[0010] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by identifying the equipment types of each device within the same simulation level and extracting the corresponding electrical characteristic parameters, devices with different physical characteristics are transformed into equivalent models with a unified interface form according to a unified preset modeling rule. This enables each device to have a consistent access method and interaction specification when accessing the simulation framework, eliminating the constraint of model heterogeneity between heterogeneous devices on the scalability of the simulation framework.

[0011] In one embodiment of the present invention, equivalent models within the same simulation level are grouped according to their dynamic response characteristics, and equivalent models whose dynamic response characteristics meet preset similarity conditions are grouped into the same group. This includes: extracting the dynamic response characteristics of each equivalent model; determining the similarity between equivalent models within the same simulation level based on the dynamic response characteristics, and grouping equivalent models whose similarity meets preset similarity conditions into the same group; wherein, the dynamic response characteristics include at least the response speed level and inertia support capability level of each equivalent model under preset disturbance conditions.

[0012] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: Based on the dynamic response characteristics, the similarity between equivalent models within the same simulation level is determined, and equivalent models whose similarity meets the preset conditions are grouped into the same group. This makes the grouping criteria directly correspond to the actual behavior of the equipment in the dynamic process of the system, rather than relying on the simple classification of equipment type or capacity, thereby ensuring the homogeneity of the dynamic response characteristics of each equipment within the same group.

[0013] In one embodiment of the present invention, determining the similarity between equivalent models within the same simulation level based on dynamic response characteristics, and grouping equivalent models whose similarity meets a preset similarity condition into the same group, includes: quantizing the response speed level and inertia support capability level into corresponding numerical components; combining the numerical components to form the cluster feature vector of each equivalent model; calculating the vector distance between the cluster feature vectors of each equivalent model within the same simulation level; comparing the vector distance with a preset distance threshold; when the vector distance is less than the preset distance threshold, determining that the similarity between the corresponding two equivalent models meets the preset similarity condition; and grouping the equivalent models that meet the preset similarity condition into the same group.

[0014] Compared with existing technologies, the technical effects achieved by this solution are as follows: by comparing the numerical calculation results of vector distance with the preset distance threshold to determine whether equivalent models meet the similarity conditions, the grouping criteria are freed from dependence on human experience. At the same time, the vector distance measurement method can comprehensively reflect the differences in dynamic response characteristics of multiple dimensions, avoiding misgrouping that may be caused by single-dimensional judgment.

[0015] In one embodiment of the present invention, each family is aggregated into an aggregate model to form a reduced-order model for each simulation level. This includes: obtaining the rated capacity, inertia time constant, and equivalent impedance of each equivalent model within each family; summing the rated capacities of each equivalent model within the family to obtain the aggregate rated capacity of the aggregate model; weighting the inertia time constant of each equivalent model within the family according to its rated capacity to obtain the aggregate inertia time constant of the aggregate model; performing parallel conversion on the equivalent impedance of each equivalent model within the family to obtain the aggregate equivalent impedance of the aggregate model; assigning the aggregate rated capacity, aggregate inertia time constant, and aggregate equivalent impedance to the aggregate model, and replacing all equivalent models within the family with the aggregate model to reduce the total number of state variables at each simulation level, thereby forming a reduced-order model for each simulation level.

[0016] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: the rated capacity is added together, the inertia time constant is weighted and averaged according to the capacity, and the equivalent impedance is converted in parallel and then assigned to the aggregated model. This aggregated model replaces all equivalent models in the group to participate in the simulation, so that the independent state variables originally occupied by multiple independent devices are compressed into a unified aggregated state variable, reducing the total number of state variables at each simulation level.

[0017] In one embodiment of the present invention, controlling each simulation level to independently advance the simulation according to its own simulation step size, and to conduct information exchange between adjacent levels through boundary nodes at preset interaction times, includes: between two adjacent interaction times, each simulation level independently advances the simulation calculation according to its corresponding simulation step size; when the current preset interaction time arrives, the independent advancement of each simulation level is paused; the first boundary electrical quantity information reported by adjacent levels in each simulation level is obtained through boundary nodes, and the second boundary electrical quantity information of this level is sent down to the adjacent levels; the boundary conditions of the boundary nodes of this level are updated according to the obtained first boundary electrical quantity information of the adjacent levels, and the simulation calculation of the next time period continues to advance based on the updated boundary conditions until the next preset interaction time arrives.

[0018] Compared with existing technologies, the technical effects achieved by this solution are as follows: between two adjacent interaction times, each simulation level is controlled to independently advance the simulation according to its own step size; at the interaction time, the independent advancement of each level is paused and boundary electrical quantity information is exchanged through boundary nodes; after updating the boundary conditions of the current level based on the acquired information of the adjacent level, the simulation continues to advance to the next time period until the next interaction time. This ensures that each level runs independently at its own time scale to maintain computational efficiency, and also realizes the accurate transmission of electrical coupling relationships between levels through periodic interaction.

[0019] In one embodiment of the present invention, when the electrical change exceeds a preset range, the aggregated model in the area affected by the change is switched to the corresponding detailed model, including: marking the boundary node where the electrical change exceeds the preset range as a trigger node; determining the area affected by the change based on the trigger node; locating all aggregated models to be switched in the area affected by the change, and obtaining the original equipment information of the family corresponding to each aggregated model; expanding each aggregated model into multiple corresponding original equipment models based on the original equipment information; replacing the original aggregated model in the area affected by the change with the original equipment model, while maintaining the reduced-order model in the area outside the area affected by the change.

[0020] Compared with existing technologies, the technical effects achieved by this solution are as follows: the boundary node where the electrical change exceeds the preset range is marked as the trigger node, and the area involved in the change is determined accordingly. Within this area, the aggregated model to be switched is located and the corresponding original equipment information of the group is obtained. The aggregated model is expanded into the corresponding multiple original equipment models to replace the original aggregated model. At the same time, the reduced-order model is kept unchanged in the area outside the area involved in the change, so that the adjustment range of the aggregated model is precisely limited to the local area where a large disturbance actually occurs.

[0021] In one embodiment of the present invention, after replacing the original aggregated model in the area affected by the change with the original equipment model, the method further includes: continuing to monitor the electrical changes of the boundary nodes; determining the restored aggregated area after the electrical changes recover to a preset range and continue for a preset duration; the restored aggregated area is the entire range of the area affected by the change that has been switched to the original equipment model; obtaining the electrical parameters of each original equipment model in the restored aggregated area; re-aggregating each original equipment model in the restored aggregated area into an aggregated model according to a preset equivalence rule; replacing the original original equipment models in the restored aggregated area with the re-aggregated aggregated model to restore the global reduced-order simulation.

[0022] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: after the aggregated model is expanded into the original equipment model, the electrical changes of the boundary nodes continue to be monitored. When the electrical changes recover to within the preset range and continue for a preset time, the range that has been switched to the original equipment model is determined as the recovery aggregation region. According to the preset equivalence rules, each original equipment model is re-aggregated into an aggregated model to restore the global reduced-order simulation, so that the adjustment of model fidelity has a bidirectional switching capability.

[0023] In one embodiment of the present invention, a hierarchical full-element simulation modeling system for ensuring green electricity supply to the power grid is also provided. This system can apply any of the hierarchical full-element simulation modeling methods described above. The hierarchical full-element simulation modeling system includes: a data acquisition module, used to acquire parameter information and topology connection information of all elements of the distribution network in the target area; a hierarchical configuration module, used to divide the distribution network in the target area into at least two simulation levels based on the topology connection information, and to define the simulation step size for each simulation level, as well as the boundary nodes between the simulation levels; and a model reduction module, used to transform the simulation levels into a unified equivalent model based on the parameter information, and to determine the model's parameters based on the equivalent model's parameters. The system uses dynamic response characteristics to group equivalent models within the same simulation level. Equivalent models whose dynamic response characteristics meet preset similarity conditions are grouped into the same family, and each family is aggregated into an aggregated model to form a reduced-order model for each simulation level. The collaborative simulation module controls each simulation level to independently advance the simulation according to its own simulation step size, and performs information exchange between adjacent levels through boundary nodes at preset interaction times. The switching control module monitors the electrical changes at boundary nodes. When the electrical changes do not exceed the preset range, the reduced-order model is maintained for simulation. When the electrical changes exceed the preset range, the aggregated model in the area affected by the change is switched to the corresponding original equipment model.

[0024] Compared with existing technologies, the technical effects achieved by adopting this technical solution are: it can achieve the technical effects in any of the above examples, which will not be elaborated here.

[0025] By adopting the technical solution of the present invention, the following technical effects can be achieved: (1) This application solves the computational redundancy problem caused by the global uniform step size in the existing simulation method by dividing the target distribution network into at least two simulation levels according to the topological connection relationship and configuring differentiated step sizes. This allows each level to advance independently at a time scale that matches its own dynamics, reducing the overall computational burden. On this basis, by converting each device into an equivalent model with a unified interface and grouping and aggregating it according to the dynamic response characteristics, the aggregated model replaces all equivalent models in the group to participate in the simulation, reducing the total number of system state variables and further improving the simulation efficiency. During the simulation process, by setting the preset interaction time between levels and the information exchange mechanism of boundary nodes, the accuracy of the coupling relationship can be maintained while each level operates independently. At the same time, by monitoring the electrical changes of boundary nodes in real time and switching the aggregated model in the corresponding area to a detailed model when it exceeds the preset range, the simulation speed under normal operating conditions is guaranteed, and the reproduction accuracy of key transient processes under large disturbance scenarios is also guaranteed. (2) By identifying the equipment type of each device in the same simulation level and extracting electrical characteristic parameters, devices with different physical characteristics are transformed into equivalent models with a unified interface according to a unified preset modeling rule, so that each device has a consistent access method and interaction specification when accessing the simulation framework, thus eliminating the constraint of model heterogeneity between heterogeneous devices on the scalability of the simulation framework. (3) The numerical calculation result of vector distance is compared with the preset distance threshold to determine whether the equivalent models meet the similarity conditions, so that the grouping judgment criteria are free from the dependence on human experience. At the same time, the vector distance measurement method can comprehensively reflect the differences in dynamic response characteristics of multiple dimensions, avoiding misgrouping that may be caused by single-dimensional judgment. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 The flowchart illustrates a hierarchical, full-element simulation modeling method for ensuring green electricity supply to the power grid, as provided in this embodiment of the invention. Detailed Implementation

[0027] Embodiments of the present invention will now be described in detail. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a link, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] See Figure 1 , Figure 1 A flowchart illustrating a hierarchical, full-element simulation modeling method for ensuring green electricity supply to the power grid, provided as an embodiment of the present invention; specifically, a hierarchical, full-element simulation modeling method for ensuring green electricity supply to the power grid includes: S1: Obtain parameter information and topology connection information of all elements of equipment in the target area's power distribution network; S2: Divide the target area distribution network into at least two simulation levels based on the topology connection information, and define the simulation step size for each simulation level, as well as the boundary nodes between the simulation levels. S3: Based on the parameter information, the simulation hierarchy is transformed into a unified equivalent model; S4: Based on the dynamic response characteristics of each equivalent model, the equivalent models within the same simulation level are grouped together, and the equivalent models whose dynamic response characteristics meet the preset similarity conditions are grouped into the same family. Each family is aggregated into an aggregated model to form a reduced-order model for each simulation level. S5: Control each simulation level to advance the simulation independently according to its own simulation step size, and conduct information exchange between adjacent levels through boundary nodes at preset interaction times. S6: Monitor electrical changes at boundary nodes; S61: When the electrical change does not exceed the preset range, maintain the reduced-order model for simulation; S62: When the electrical change exceeds the preset range, switch the aggregate model in the area affected by the change to the corresponding detailed model.

[0031] Among them, all-element equipment refers to all types of electrical equipment covered in the distribution network, including distributed power sources, energy storage devices, distribution lines, transformers, switching equipment, and various loads; parameter information includes at least the rated capacity, rated voltage, control mode, response time constant, inertia time constant, and equivalent impedance of each device; topology connection information includes the electrical connection relationship between each device, line impedance parameters, and network topology.

[0032] Specifically, since there are many types of equipment and different parameter formats in the regional distribution network, the parameter information and topology connection information of all equipment in the target regional distribution network are first obtained. These data are then uniformly acquired and processed under the same simulation framework to avoid inconsistent modeling due to inconsistent data dimensions.

[0033] Next, due to the differences in dynamic response times among different devices in the regional power distribution network, the large-scale power grid is decomposed into multiple spatially decoupled sub-regions, i.e., simulation levels, based on voltage levels and dynamic response characteristic parameters. Devices with similar dynamic response characteristic parameters are grouped into the same level. On this basis, a simulation step size matching its dynamic response characteristics is assigned to each simulation level. For example, levels with fast dynamic response speeds are configured with smaller simulation step sizes, while levels with slow dynamic response speeds are configured with larger simulation step sizes, allowing each level to proceed independently at a time scale matching its own dynamic response characteristics. At the same time, the electrical connection points between each simulation level are defined as boundary nodes, and their topological location information and initial electrical parameters are stored. It should be noted that the definition of boundary nodes is to define a unique channel for information exchange between adjacent levels in subsequent hierarchical independent simulations, avoiding chaotic coupling between levels.

[0034] Subsequently, since devices with different physical characteristics have completely different model structures, if the interface form is not unified before grouping and aggregation, the subsequent similarity comparison and aggregation operations cannot be implemented. Therefore, all devices are transformed into equivalent models with a unified interface form through unified preset modeling rules, so that all types of devices that originally had different model structures are presented in the same interface form.

[0035] Furthermore, only devices with similar dynamic response characteristics can have their aggregated models accurately represent the overall characteristics of the original group during the modeling process. Therefore, dynamic response characteristics are chosen as the basis for grouping, rather than device class or capacity. If devices with significantly different dynamic response characteristics are forcibly aggregated, the dynamic response of the aggregated model cannot represent any of the original devices, resulting in simulation distortion. Therefore, the purpose of grouping is to ensure that the dynamic behavior of each device within the same group has a sufficiently high homogeneity, so that the aggregated model can represent the overall dynamic characteristics of the group. Subsequently, the aggregated model replaces all equivalent models within the group, thereby forming a reduced-order model for each simulation level. This aggregation operation compresses the independent state variables originally occupied by multiple independent devices into a unified set of aggregated state variables, directly reducing the total number of state variables at each simulation level.

[0036] Then, each simulation level runs independently between two adjacent interaction times. At each interaction time, it pauses its independent progress and exchanges boundary electrical quantity information. Based on the acquired information from adjacent levels, it updates the boundary conditions of its current simulation level and continues to advance to the next time period. This cycle repeats until the simulation ends. The reason for using discrete interaction instead of continuous interaction is that if each simulation level interacts at every simulation step, the communication overhead between simulation levels will completely offset the efficiency improvement brought by layering. That is, the benefit of layered simulation comes from the independent parallel computation of each simulation level. If the interaction between simulation levels is too frequent, the time cost of the interaction itself will replace the time saving brought by computation, thus rendering layering meaningless. Therefore, by setting a reasonable interaction interval, each simulation level runs independently for most of the time to maintain efficiency, and exchanges information only when necessary to ensure coupling accuracy.

[0037] Meanwhile, the reduced-order model can maintain sufficient simulation accuracy when the system is in a steady state or weak dynamic process. However, when the system experiences large disturbances (such as short-circuit faults, large-capacity equipment disconnection, etc.), the reduced-order model cannot accurately reproduce the transient details of the fault near the fault. If a detailed model is used in all operating conditions, the computational load will be unacceptable; if a reduced-order model is used in all operating conditions, the simulation results under large disturbance scenarios will be distorted. The model fidelity is adaptively switched by monitoring electrical changes. When the electrical changes do not exceed the preset range, the reduced-order model is maintained for simulation; when the electrical changes exceed the preset range, the aggregated model in the area involved in the change is switched to the corresponding detailed model. The reduced-order model can be understood as a model after aggregating multiple devices. It has fewer state variables and is computationally fast, but its accuracy is insufficient when large disturbances occur. The detailed model, on the other hand, is a complete model of each device after the reduced-order model is expanded and restored. It retains all transient details, has high accuracy, but requires a large amount of computation.

[0038] Furthermore, based on the topology connection information, the target area distribution network is divided into at least two simulation levels, and a simulation step size is defined for each simulation level, as well as the boundary nodes between simulation levels, including: Obtain dynamic response characteristic parameters and voltage level information of each device in the target area's power distribution network; Based on dynamic response characteristic parameters, each device is divided into different response speed levels; The target area distribution network is divided into levels based on voltage level information and response speed level, generating a hierarchical topology structure containing at least two simulation levels. Configure simulation step sizes for each simulation level that match the dynamic response characteristic parameters corresponding to each level; The electrical connection points between each simulation level are defined as boundary nodes, and the topological location information and initial electrical parameters of the boundary nodes are stored.

[0039] Specifically, dynamic response characteristic parameters include the control response bandwidth or electromechanical transient time constant of each device. The control response bandwidth directly reflects the device's ability to respond to high-frequency disturbances. The voltage level determines the electrical position of the device in the power grid, and devices of different voltage levels usually assume different functional roles. Therefore, the two are combined to form the basis for the division of simulation levels.

[0040] Next, devices with control response bandwidth higher than a first preset value or electromechanical transient time constant lower than a second preset value are classified as fast response level, and devices with control response bandwidth lower than the first preset value or electromechanical transient time constant higher than the second preset value are classified as slow response level; wherein, the first preset value is 100Hz and the second preset value is 100ms.

[0041] Based on this, voltage level information and response speed level are combined to hierarchically divide the target area's distribution network, generating a hierarchical topology structure containing at least two simulation levels. For example, in an industrial park distribution network, the corresponding hierarchical structure includes a system-level main network layer, a feeder-level distribution layer, and a user-level equipment layer. The system-level main network layer corresponds to the high-voltage distribution bus and above, including main network equipment with slower dynamic response speeds; the feeder-level distribution layer corresponds to medium-voltage feeders and line segments; and the user-level equipment layer corresponds to terminal substations and various access devices, including power electronic equipment with the fastest dynamic response speeds. Therefore, voltage level and response speed level are used as dual criteria for hierarchical division because voltage level alone cannot distinguish the differences in response speed between different devices at the same voltage level, and response speed alone cannot reflect the positional relationship of devices in the topology. Only by combining the two can a hierarchical structure that conforms to electrical topology rules and can adapt to differences in dynamic characteristics be achieved.

[0042] Based on this, since the dynamic response characteristics of the equipment have been limited to a similar range through hierarchical division, a uniform and applicable simulation step size can be configured for it. At the same time, the electrical connection points between each simulation level are defined as boundary nodes. Boundary nodes are usually selected from locations such as the high / low voltage side busbars of the transformer and the beginning of the feeder. The topological location information and initial electrical parameters of the boundary nodes are stored as interfaces for information interaction between subsequent levels. This allows each level to focus only on the boundary information of its adjacent levels without having to perceive the full state of all levels, thereby maintaining the relative independence and scalability of each level.

[0043] Furthermore, based on the parameter information, the simulation hierarchy is transformed into a unified equivalent model, including: Identify the device type of each device within the same simulation level; Extract the electrical characteristic parameters of each device based on the parameter information corresponding to the device type; Each device is transformed into an equivalent model with a unified interface according to its own device type and electrical characteristic parameters using a unified preset modeling rule.

[0044] The equipment types include at least: power supply equipment, energy storage equipment, load equipment, and line equipment.

[0045] Specifically, by identifying equipment types, we can understand the control methods and characteristics of different types of equipment, and determine specific parameter mapping rules based on equipment type for subsequent modeling and transformation. For example, the equivalent model of a photovoltaic inverter should focus on reflecting its maximum power point tracking control and low voltage ride-through capability; while the equivalent model of a motor load should focus on reflecting the balance between mechanical torque and speed. These two types of equipment differ greatly in model structure and parameter dimensions, belonging to two completely different sets of things; therefore, it is necessary to adopt unified modeling rules, using the same method to abstract the characteristics of any type of equipment into a unified equivalent model.

[0046] Then, electrical characteristic parameters of each device are extracted based on the parameter information of each device. The electrical characteristic parameters include at least the rated capacity, control mode (grid-connected or grid-connected), and response time constant of each device. Among them, the electrical characteristic parameters determine the external characteristics of the model under steady-state and transient conditions. That is, the rated capacity determines the power reference of the model, the control mode determines the response mode of the model when the grid frequency / voltage changes, and the response time constant determines the speed at which the model transitions from one state to another. The above parameters together constitute a complete set describing the electrical operation of the equipment.

[0047] Finally, since devices with different physical characteristics originally have completely different mathematical models, if each participates in subsequent operations with its original model form, the device models within the same simulation level cannot be compared and processed in a unified manner. However, if the same preset modeling rules are used to abstract the device types and electrical characteristic parameters of each device into equivalent models with the same mathematical form and a unified interface, then the devices become comparable and operable, providing a basis for subsequent grouping and aggregation based on dynamic response characteristics.

[0048] Furthermore, based on the dynamic response characteristics of each equivalent model, equivalent models within the same simulation level are grouped, and equivalent models whose dynamic response characteristics meet preset similarity conditions are grouped into the same family, including: Extract the dynamic response features of each equivalent model; Based on dynamic response characteristics, the similarity between equivalent models within the same simulation level is determined, and equivalent models whose similarity meets the preset similarity conditions are grouped into the same group. Among them, the dynamic response characteristics include at least the response speed level and inertia support capability level of each equivalent model under the preset disturbance conditions.

[0049] Specifically, response speed level and inertia support capability level are selected as the core dimensions of dynamic response characteristics because both indicators reflect the key behaviors of the equipment in the dynamic process of the system: that is, response speed determines how quickly the equipment starts to act after a disturbance occurs, and inertia support capability determines how much support the equipment can provide; the two correspond to the speed and strength of the equipment's dynamic response, respectively, and can comprehensively reflect the dynamic behavior characteristics of the equipment.

[0050] Using dynamic response characteristics as the basis for similarity calculation is a prerequisite for subsequent aggregation of devices with similar dynamic behaviors. Only when devices with similar dynamic response characteristics can the aggregation model reflect the overall characteristics of the original group. If devices with significantly different response speeds or inertia support capabilities are forcibly aggregated, the dynamic response trajectory of the aggregation model will deviate from the actual behavior of all original devices, resulting in simulation distortion. Therefore, the purpose of grouping is to ensure that the dynamic behavior of devices within the same group has a sufficiently high homogeneity.

[0051] Finally, the above-mentioned grouping method makes the grouping objective and quantifiable, and can ensure the consistency and repeatability of the grouping results; at the same time, it avoids misgrouping that may be caused by a single dimension; for example, two devices with similar response speeds but huge differences in inertia support capabilities should not be classified into the same group.

[0052] Furthermore, based on dynamic response characteristics, the similarity between equivalent models within the same simulation level is determined, and equivalent models whose similarity meets preset similarity conditions are grouped into the same family, including: The response speed level and inertia support capability level are quantified into corresponding numerical components; The numerical components are combined to form the cluster feature vectors of each equivalent model; Calculate the vector distance between the cluster eigenvectors of each equivalent model within the same simulation level; Compare the vector distance with a preset distance threshold; When the vector distance is less than a preset distance threshold, the similarity between the two equivalent models is determined to meet the preset similarity condition. Equivalent models that meet the preset similarity conditions are grouped into the same family.

[0053] Specifically, since qualitative descriptions such as fast and slow cannot be directly expressed in detail, it is necessary to convert the response speed level and inertia support capability level into calculable numerical forms before vector distance calculation and comparison can be performed. The quantization rule is to assign a value to each level that can reasonably reflect the differences in its actual dynamic characteristics. For example, fast response speed is mapped to 0.9, medium response speed to 0.5, and slow response speed to 0.1, and high inertia support capability is mapped to 0.8, medium inertia support capability to 0.5, and low inertia support capability to 0.2. The differences in these values ​​directly correspond to the actual differences in the control response bandwidth and inertia time constant of the device, so that the quantized numerical components have actual physical meaning in the dynamic response characteristics.

[0054] Then, by combining multi-dimensional dynamic response features into feature vectors, each equivalent model has a unique coordinate position in the dynamic response feature space. Subsequently, since vector distance can comprehensively reflect the overall proximity between two points in a multi-dimensional space, vector distance is chosen as a measure of similarity. The smaller the vector distance, the smaller the comprehensive difference between the two equivalent models in all dynamic response feature dimensions, and the higher their dynamic behavior similarity.

[0055] Finally, the calculated vector distance is compared with a preset distance threshold. When the vector distance is less than the preset distance threshold, the similarity between the two equivalent models is determined to meet the preset similarity condition. The equivalent models that meet the condition are then classified into the same group.

[0056] Furthermore, each population is aggregated into an aggregated model to form reduced-order models for each simulation level, including: Obtain the rated capacity, inertia time constant, and equivalent impedance of each equivalent model within each group; The nominal capacity of each equivalent model within the population is summed to obtain the nominal capacity of the aggregate model. The inertia time constants of each equivalent model within the group are weighted and averaged according to their respective rated capacities to form the aggregated inertia time constant of the aggregated model. The equivalent impedances of each equivalent model within the group are converted into the aggregate equivalent impedance of the aggregate model by parallel conversion. The aggregated rated capacity, aggregated inertia time constant, and aggregated equivalent impedance are assigned to the aggregated model, and the aggregated model replaces all equivalent models in the family, so as to reduce the total number of state variables at each simulation level and form a reduced-order model at each simulation level.

[0057] Specifically, the three parameters—rated capacity, inertia time constant, and equivalent impedance—correspond to the equipment's parameters in three dimensions: scale characterization (rated capacity), inertia response characteristics (inertia time constant), and port electrical characteristics (equivalent impedance), respectively. These are the basic data for calculating the parameters of the aggregation model. Therefore, the rated capacities of each equivalent model within the cluster are added together to obtain the aggregated rated capacity of the aggregation model, reflecting that the overall power capacity provided by the cluster is equal to the sum of the power capacities of each member device. The inertia time constants of each equivalent model within the cluster are weighted and averaged according to their respective rated capacities to obtain the aggregated inertia time constant of the aggregation model, making the aggregated inertia more accurately reflect the overall inertia response characteristics of the cluster. The equivalent impedances of each equivalent model within the cluster are paralleled and converted to obtain the aggregated equivalent impedance of the aggregation model because the devices within the cluster are connected in parallel to the same bus in the topology, and the comprehensive impedance characteristics presented by their ports are consistent with the impedance relationship when multiple devices are connected in parallel.

[0058] Finally, the parameters calculated above are assigned to the aggregated model, and this aggregated model is used to replace all equivalent models in the group in subsequent simulation calculations, so that the independent state variables of multiple devices are compressed into a set of aggregated state variables.

[0059] It should be noted that the reduction ratio of the total number of state variables depends on the number of devices in the population; the more devices there are, the better the order reduction effect.

[0060] Furthermore, each simulation level is controlled to independently advance the simulation according to its own simulation step size, and information exchange between adjacent levels is carried out through boundary nodes at preset interaction times, including: Between two adjacent interaction times, each simulation level independently advances the simulation calculation according to its corresponding simulation step size; When the current preset interaction time arrives, pause the independent advancement of each simulation level; The first boundary electrical quantity information reported by the adjacent levels in each simulation level is obtained through the boundary nodes, and the second boundary electrical quantity information of this level is sent down to the adjacent levels. The boundary conditions of the current level boundary nodes are updated based on the first boundary electrical quantity information of the adjacent levels. Based on the updated boundary conditions, the simulation calculation for the next time period continues until the next preset interaction time arrives.

[0061] The preset interaction time is a pre-defined time node for information exchange between different levels, which can be set periodically.

[0062] Specifically, the purpose of solving independently within each level without interference is that each level can continue to run without waiting for other levels to complete their calculations, thus maximizing the utilization of the computing resources of each level. On this basis, if each level interacts at every simulation step, the communication overhead between levels will completely offset the efficiency gains brought by the layering. However, if there is no interaction at all, the levels will be completely decoupled, and the simulation results will lose overall consistency. Therefore, periodic interaction between simulation levels is required rather than continuous interaction.

[0063] Furthermore, the first boundary electrical quantity information refers to the electrical quantity data (such as voltage amplitude, frequency, and power) received from the adjacent layer at the boundary node, while the second boundary electrical quantity information refers to the electrical quantity data at the boundary node sent from this layer to the adjacent layer. The bidirectional exchange is used because the coupling relationship between adjacent layers is bidirectional, that is, the state of the upper network affects the operating boundary of the lower network, and the power injection of the lower network also affects the power flow distribution of the upper network. Unidirectional exchange cannot meet the accuracy requirements of bidirectional round-trip coupling. Therefore, the exchange of boundary electrical quantity information between adjacent layers is adopted.

[0064] Finally, the boundary conditions of the boundary nodes of this level are updated based on the first boundary electrical quantity information of the adjacent levels, and the simulation calculation of the next time period is continued based on the updated boundary conditions. After the boundary conditions are updated, each level can use the correct boundary values ​​as the boundary constraints for solving the differential equation system when it proceeds independently in the next time period, which ensures the electrical consistency of the layered simulation as a whole.

[0065] Furthermore, when the electrical change exceeds a preset range, the aggregated model within the affected area is switched to the corresponding detailed model, including: Mark boundary nodes where electrical changes exceed preset ranges as trigger nodes; The area affected by the change is determined based on the trigger node; Locate all aggregate models to be switched within the area affected by the change, and obtain the original device information of the family corresponding to each aggregate model; Based on the original device information, each aggregated model is expanded into a corresponding multiple original device models; The original aggregate model within the area affected by the change is replaced with the original equipment model, while the reduced-order model is maintained in the area outside the area affected by the change.

[0066] The detailed model is further concretized into the original device model, which refers to the complete detailed model parameters and topological connections of each device in the group before aggregation. This information has been stored during the initial aggregation.

[0067] Specifically, since boundary nodes are the electrical coupling channels between simulation levels, an electrical change exceeding the preset range indicates a large disturbance, and the impact of a large disturbance will inevitably be reflected in the electrical quantities of the boundary nodes. Therefore, using boundary nodes as monitoring points can cover the coupling paths between all levels with the fewest monitoring points, while also avoiding the redundancy of setting monitoring points for each internal node.

[0068] In power distribution networks, the impact of large disturbances is usually localized, with areas far from the fault point experiencing less disturbance. Therefore, boundary nodes exceeding a preset range are marked as trigger nodes. The area extending outward from the trigger node within a preset electrical distance is defined as the area affected by the change. The reduced-order model maintains sufficient simulation accuracy outside this area.

[0069] Furthermore, based on the original equipment information, each aggregated model is expanded into multiple corresponding original equipment models. During the expansion process, the initial state variables of each original equipment model are assigned the port voltage, current, and phase information output by the aggregated model before expansion, to ensure power continuity before and after the switch. Finally, the original aggregated model in the affected area is replaced by the original equipment model, while the reduced-order model is maintained in the area outside the affected area. After the switch is completed, the system enters a hybrid simulation mode with local detail and global reduced order.

[0070] Furthermore, after replacing the original aggregation model within the area affected by the change with the original equipment model, it also includes: Continue to monitor electrical changes at boundary nodes; Once the electrical change returns to the preset range and remains there for a preset duration, the recovery aggregation area is determined; the recovery aggregation area is the entire range of areas involved in the change that has been switched to the original equipment model; Obtain the electrical parameters of each original device model within the restored aggregation area; According to the preset equivalence rules, the original device models within the restored aggregation area are re-aggregated into an aggregated model; The original device models within the restored aggregation area are replaced with the re-aggregated aggregation model to restore the global reduced-order simulation.

[0071] Specifically, since a disturbance is a transient event with a time process—meaning that after a disturbance occurs, the system will undergo a transient process and tend towards a new steady state or return to the state before the fault—after replacing the original aggregated model in the area affected by the change with the original equipment model, the electrical changes at the boundary nodes continue to be monitored. The reason for continuing monitoring rather than stopping is to enable the system to perceive the time point at which the disturbance subsides.

[0072] Considering that electrical quantities may fluctuate again after returning to the normal range due to system oscillations, switching of other equipment, etc., if aggregation recovery is triggered only at the moment of recovery, it may lead to frequent back-and-forth switching between expansion and recovery, resulting in numerical oscillations. Therefore, a dual judgment condition is set. When the quantity recovers to within the preset range and continues for a preset duration, it can be determined that the system has stabilized in the working state. Therefore, the recovery aggregation area can be determined, thereby avoiding invalid switching caused by temporary fluctuations.

[0073] Subsequently, the electrical parameters of each original device model within the restored aggregation area are obtained, and the models are re-aggregated into an aggregate model according to the same preset equivalence rules as the initial aggregation. The re-aggregated aggregate model then replaces the original original device models, restoring the global reduced-order simulation.

[0074] Furthermore, this invention also provides a hierarchical full-element simulation modeling system for ensuring green electricity supply to the power grid. This system can apply any of the hierarchical full-element simulation modeling methods described above. The hierarchical full-element simulation modeling system includes: a data acquisition module, a hierarchical configuration module, a model reduction module, a collaborative simulation module, and a switching control module. The data acquisition module acquires parameter information and topology connection information of all elements of the distribution network in the target area. The hierarchical configuration module divides the distribution network in the target area into at least two simulation levels based on the topology connection information, assigns a simulation step size to each simulation level, and defines the boundary nodes between simulation levels. The model reduction module transforms the simulation levels into [the required parameters] based on the parameter information. A unified equivalent model is used to group equivalent models within the same simulation level based on their dynamic response characteristics. Equivalent models whose dynamic response characteristics meet preset similarity conditions are grouped into the same family, and each family is aggregated into an aggregated model to form a reduced-order model for each simulation level. The collaborative simulation module is used to control each simulation level to advance the simulation independently according to its own simulation step size, and to exchange information between adjacent levels through boundary nodes at preset interaction times. The switching control module is used to monitor the electrical changes at boundary nodes. When the electrical changes do not exceed the preset range, the reduced-order model is maintained for simulation. When the electrical changes exceed the preset range, the aggregated model in the area affected by the change is switched to the corresponding original equipment model.

[0075] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A hierarchical, full-element simulation modeling method for ensuring green electricity supply to the power grid, characterized in that, include: Obtain parameter information and topology connection information of all elements of equipment in the target area's power distribution network; The target area distribution network is divided into at least two simulation levels based on the topology connection information, and a simulation step size is defined for each simulation level, as well as the boundary nodes between the simulation levels are defined. Based on the parameter information, the simulation hierarchy is transformed into a unified equivalent model; Based on the dynamic response characteristics of each equivalent model, the equivalent models within the same simulation level are grouped together, and the equivalent models whose dynamic response characteristics satisfy the preset similarity conditions are grouped into the same family. Each family is then aggregated into an aggregated model to form a reduced-order model for each simulation level. Each simulation level is controlled to advance the simulation independently according to its own simulation step size, and information exchange between adjacent levels is carried out through the boundary nodes at preset interaction times. Monitor the electrical changes at the boundary nodes; When the electrical change does not exceed the preset range, the reduced-order model is maintained for simulation. When the electrical change exceeds the preset range, the aggregate model in the area affected by the change is switched to the corresponding detailed model.

2. The hierarchical full-element simulation modeling method according to claim 1, characterized in that, The step of dividing the target area distribution network into at least two simulation levels based on the topology connection information, defining a simulation step size for each simulation level, and defining boundary nodes between the simulation levels includes: Obtain the dynamic response characteristic parameters and voltage level information of each device in the target area distribution network; Based on the dynamic response characteristic parameters, each device is divided into different response speed levels; The target area distribution network is hierarchically divided according to the voltage level information and the response speed level, generating a hierarchical topology structure containing at least two simulation levels; Configure a simulation step size for each simulation level that matches the dynamic response characteristic parameters corresponding to each level; The electrical connection points between each simulation level are defined as boundary nodes, and the topological location information and initial electrical parameters of the boundary nodes are stored.

3. The hierarchical full-element simulation modeling method according to claim 1, characterized in that, The step of converting the simulation hierarchy into a unified equivalent model based on the parameter information includes: Identify the device type of each device within the same simulation level; Extract electrical characteristic parameters of each device based on the parameter information corresponding to the device type; Each device is transformed into an equivalent model with a unified interface according to its respective device type and electrical characteristic parameters using a unified preset modeling rule.

4. The hierarchical full-element simulation modeling method according to claim 1, characterized in that, The step of grouping equivalent models within the same simulation level based on their dynamic response characteristics, and classifying equivalent models whose dynamic response characteristics satisfy a preset similarity condition into the same group, includes: Extract the dynamic response features of each of the equivalent models; Based on the dynamic response characteristics, the similarity between the equivalent models within the same simulation level is determined, and the equivalent models whose similarity satisfies the preset similarity conditions are grouped into the same group. The dynamic response characteristics include at least the response speed level and inertia support capability level of each equivalent model under preset disturbance conditions.

5. The hierarchical full-element simulation modeling method according to claim 4, characterized in that, The step of determining the similarity between equivalent models within the same simulation level based on the dynamic response characteristics, and grouping equivalent models whose similarity satisfies the preset similarity conditions into the same group, includes: The response speed level and the inertia support capability level are respectively quantified into corresponding numerical components; The numerical components are combined to form the cluster feature vectors of each of the equivalent models; Calculate the vector distance between the cluster feature vectors of each equivalent model within the same simulation level; The vector distance is compared with a preset distance threshold; When the vector distance is less than the preset distance threshold, it is determined that the similarity between the two corresponding equivalent models satisfies the preset similarity condition. The equivalent models that satisfy the preset similarity conditions are grouped into the same family.

6. The hierarchical full-element simulation modeling method according to claim 5, characterized in that, The step of aggregating each of the aforementioned populations into an aggregated model to form a reduced-order model for each of the aforementioned simulation levels includes: Obtain the rated capacity, inertia time constant, and equivalent impedance of each equivalent model within each group; The rated capacity of each equivalent model within the group is added together to obtain the aggregate rated capacity of the aggregate model. The inertia time constant of each equivalent model within the group is weighted and averaged according to its rated capacity to obtain the aggregate inertia time constant of the aggregate model. The equivalent impedances of each equivalent model within the group are converted into the aggregated equivalent impedance of the aggregated model by parallel conversion. The aggregated rated capacity, the aggregated inertia time constant, and the aggregated equivalent impedance are assigned to the aggregated model, and the aggregated model replaces all the equivalent models in the family, so as to reduce the total number of state variables in each simulation level and form the reduced-order model of each simulation level.

7. The hierarchical full-element simulation modeling method according to claim 1, characterized in that, The control of each simulation level independently advances the simulation according to its respective simulation step size, and performs information exchange between adjacent levels through the boundary nodes at preset interaction times, including: Between two adjacent interaction times, each simulation level independently advances the simulation calculation according to its corresponding simulation step size; When the preset interaction time is reached, the independent advancement of each simulation level is paused; The boundary nodes are used to obtain the first boundary electrical quantity information reported by the adjacent levels in each simulation level, and the second boundary electrical quantity information of the current level is sent down to the adjacent levels. The boundary conditions of the boundary nodes at this level are updated based on the first boundary electrical quantity information of the adjacent levels. Based on the updated boundary conditions, the simulation calculation for the next time period continues until the next preset interaction time arrives.

8. The hierarchical full-element simulation modeling method according to claim 1, characterized in that, When the electrical change exceeds the preset range, the aggregated model within the affected area is switched to the corresponding detailed model, including: The boundary node whose electrical change exceeds the preset range is marked as a trigger node; The area involved in the change is determined based on the trigger node; Locate all the aggregated models to be switched within the area affected by the change, and obtain the original device information of the family corresponding to each aggregated model; Based on the original device information, each aggregated model is expanded into multiple corresponding original device models; The original aggregate model within the area affected by the change is replaced with the original device model, while the reduced-order model is maintained in the area outside the area affected by the change.

9. The hierarchical full-element simulation modeling method according to claim 8, characterized in that, After replacing the original aggregate model within the area affected by the change with the original device model, the method further includes: Continue to monitor the electrical changes at the boundary nodes; Once the electrical change returns to the preset range and remains there for a preset duration, a recovery aggregation area is determined; the recovery aggregation area is the entire range of the area involved in the change that has been switched to the original device model; Obtain the electrical parameters of each of the original device models within the restored aggregation area; According to the preset equivalence rules, the original device models within the restored aggregation area are re-aggregated into the aggregated model; The original device models within the restored aggregation region are replaced with the re-aggregated aggregation model to restore the global reduced-order simulation.

10. A hierarchical, full-element simulation modeling system for ensuring green electricity supply to the power grid, characterized in that, The hierarchical full-element simulation modeling system, capable of applying the hierarchical full-element simulation modeling method as described in any one of claims 1 to 9, comprises: The data acquisition module is used to acquire parameter information and topology connection information of all elements of the distribution network in the target area; A hierarchical configuration module is used to divide the target area distribution network into at least two simulation levels according to the topology connection information, and to divide the simulation step size for each simulation level, and to define the boundary nodes between the simulation levels. The model reduction module is used to transform the simulation level into a unified equivalent model according to the parameter information, to group the equivalent models within the same simulation level according to the dynamic response characteristics of each equivalent model, to group the equivalent models whose dynamic response characteristics meet the preset similarity conditions into the same group, and to aggregate each group into an aggregated model to form a reduced model for each simulation level. A collaborative simulation module is used to control each simulation level to independently advance the simulation according to its own simulation step size, and to conduct information interaction between adjacent levels through the boundary nodes at a preset interaction time. A switching control module is used to monitor the electrical changes of the boundary nodes. When the electrical changes do not exceed a preset range, the reduced-order model is maintained for simulation. When the electrical changes exceed the preset range, the aggregated model in the area affected by the change is switched to the corresponding original device model.

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