Distribution network equipment modeling interaction method and system based on digital twin application architecture

Abstract

The application discloses a kind of based on digital twinborn application architecture's distribution network equipment modeling interaction method and system, constructs and includes the interactive communication layer, model building layer and simulation analysis layer distribution network digital twinborn space description application architecture;With distribution network digital twinborn space description application architecture, the basic element of distribution network equipment is differentially modeled;With the distribution network digital twinborn space description application architecture constructed, modeling is carried out to core distribution network equipment facility;With the interactive communication layer of distribution network digital twinborn space description application architecture constructed, the information interaction of basic element model and core distribution network equipment facility model obtained is carried out.The application not only improves the precision of simulation, but also greatly improves the calculation efficiency;At the same time, the dynamic call of control function ensures the accurate control of system and the real-time update of electrical quantity, which provides strong technical support for the control strategy verification and stability analysis of power system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system information technology, and particularly relates to a distribution network equipment modeling interaction method and system based on a digital twin application architecture. BACKGROUND

[0002] With the intensification of global atmospheric pollution and climate warming, reducing carbon emissions has become the consensus of the international community, and new energy vehicles have therefore been vigorously supported and developed. According to the new energy vehicle industry development plan (2021-2035), the number of electric vehicles is entering a stage of rapid growth, and it is predicted that by 2025, the sales volume of new energy vehicles will account for 20% of the total vehicle sales; by 2035, pure electric vehicles will become the main sales. As a new form of load, the load charging of electric vehicles has great randomness, and the changing electric vehicle load will change the power supply demand in a certain period, bringing certain negative effects to the regional distribution network.

[0003] The light storage and charging integrated power station integrates photovoltaic, energy storage and electric vehicle charging facilities together, and has the advantage of efficient utilization of local clean energy. It contains photovoltaic, energy storage, electric vehicles and other resources, and is an important means of efficient utilization of local clean energy, and is also a new object to meet the development needs of future low-carbon distribution network. In addition, the light storage and charging integration can overcome the shortcomings of single resources such as photovoltaic, energy storage and electric vehicles, not only can smooth the uncertainty of photovoltaic output through energy storage and electric vehicles, but also can jointly improve the power supply capacity of the system with electric vehicles and energy storage, and has greater fault recovery potential. Therefore, from the aspects of source, load and storage, the light storage and charging integrated power station is the best solution to this problem.

[0004] However, due to the access of renewable energy and high proportion of power electronic equipment, the composition of distribution network equipment is increasingly complex. The nonlinear characteristics of electrical equipment and the dynamic characteristics of different time scales are intertwined, which increases the difficulty of analysis, and at the same time puts forward higher requirements for the modeling and real-time simulation of the transient process of distribution network. The traditional simulation method only models the dynamic process of a certain time and space scale under local power grid, cannot describe multiple power grid forms in the system at the same time, and has the problems of large calculation amount and insufficient simulation efficiency, which is difficult to meet the needs of accurate simulation of new power system.

[0005] Digital twin (DT) is a modeling and simulation technology integrating multi-scale, multi-physical quantity and multi-probability based on technologies such as Internet of Things, data center, cloud computing, digital thread, big data analysis and artificial intelligence. The power grid industry has the characteristics of high value and non-complex technical requirements, and is very suitable as a pilot scenario for digital twin application. Although some power companies have introduced digital twin technology, it is mainly applied to the digital twinning of power plants and distribution plants, which is simply visualized. The application of other scenarios has various problems and has not formed a global and full-life-cycle application. Therefore, the digital twin technology of the light storage charging station has obvious development potential and application value. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a distribution network equipment modeling interaction method and system based on a digital twin application architecture to solve the technical problems of large calculation amount, low efficiency, difficulty in completely reflecting the dynamic process of power electronic equipment of traditional simulation methods, complex topology of light storage charging stations, various control methods, time-consuming solver model construction and real-time difficulty, and has better calculation efficiency and better real-time performance.

[0007] The present application adopts the following technical solutions:

[0008] A distribution network equipment modeling interaction method based on a digital twin application architecture, comprising the following steps:

[0009] S1, a distribution network digital twin space description application architecture including an interactive communication layer, a model building layer and a simulation analysis layer is constructed;

[0010] S2, the basic elements of the distribution network equipment are differentially modeled using the distribution network digital twin space description application architecture constructed in step S1;

[0011] S3, the core distribution network equipment facilities are modeled using the distribution network digital twin space description application architecture constructed in step S1;

[0012] S4, the basic element model obtained in step S2 and the core distribution network equipment facility model obtained in step S3 are interacted using the interactive communication layer of the distribution network digital twin space description application architecture constructed in step S1.

[0013] Preferably, the interactive communication layer is used to establish a data interaction mechanism between the physical entity and the model building layer, and to input the topology information, equipment parameters, monitoring quantities and real-time operation data of the distribution network equipment facilities, so as to realize the storage and preprocessing of data;

[0014] The model building layer takes primary equipment, secondary equipment and auxiliary sensing equipment as the basis of the digital twin model;

[0015] The simulation analysis layer uses an object-oriented modeling method to read and monitor environmental quantities, electrical quantities, physical quantities and behavior quantities of distribution network equipment and facilities based on an electromagnetic transient simulation method, collects system key node voltage, state of charge of energy storage and charging equipment and active power data.

[0016] Preferably, the differential modeling of the basic elements of the distribution network equipment is specifically:

[0017] The circuit network is numbered, and the node information and parameters of each device in the circuit topology are written into a reading record file to complete the pre-storage of data; based on the pre-stored data, a backward Euler method is selected to differentially model the basic elements.

[0018] Preferably, in the basic elements, the differential model of the inductor i L (t) is:

[0019] i L (t) = Y L u L (t) + I h_L (t-Δt)

[0020] Wherein, Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is a historical current source, u L (t) is the voltage at the ends of the inductor, and i L (t) is the current of the inductor at the current time.

[0021] Preferably, in the core distribution network equipment and facilities, the light storage and charging station equipment includes a grid-connected converter, a photovoltaic cell, an energy storage system and a charging load;

[0022] The grid-connected converter adopts a feedforward decoupling method to realize independent control of dq components.

[0023] The photovoltaic cell is a distributed power supply and is equivalent to a constant current source model.

[0024] The energy storage system includes an energy storage battery, and the energy storage battery is equivalent to a controlled voltage source model with internal resistance.

[0025] The charging load is an electric vehicle battery, and the electric vehicle battery is equivalent to a controlled voltage source model with internal resistance.

[0026] Preferably, the mathematical model of the grid-connected converter is:

[0027]

[0028] Wherein, K ip is the proportional coefficient of the PI regulator, K il is the integral coefficient of the PI regulator, and s is a complex variable.q is the q-axis component of the ac-side current, is i q is the current reference value, L is the ac-side inductance value, R is the ac-side line impedance, i d is the d-axis component of the ac-side current, is i d is the current reference value.

[0029] Preferably, the three-phase voltage source grid-connected converter is updated using the average value modeling method, and the ac-side node voltage equation and the dc-side node voltage equation are as follows:

[0030] I a = -Y on u a + D a Y on u i (1-D a )Y on u j

[0031] I b = -Y on u b + D b Y on u i (1-D b )Y on u j

[0032] I c = -Y on u c + D c Y on u i (1-D c )Y on u j

[0033]

[0034] I j 2(1-D a )Y on u a -D a (1-D a )Y on u i (1-D a ) 2 Y on u j

[0035] (1-D b )Y on ub -D b (1-D b )Y on u i -(1-D b ) 2 Y on u j

[0036] +(1-D c )Y on u c -D c (1-D c )Y on u i -(1-D c ) 2 Y on u j

[0037] where Ia, Ib, Ic are the currents flowing into the AC side nodes a, b, c, ua, ub, uc are the AC side a, b, c three-phase voltages, ui, uj are the DC side i, j voltages, D is the duty ratio, Y is the conductance, Ii, Ij are the currents flowing into the DC side nodes i, j. a b c a b c i j on i j

[0038] Preferably, the output voltage U of the energy storage system containing the controlled voltage source model of internal resistance is:

[0039]

[0040] where V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is a characteristic constant of the battery, I is the discharge current of the energy storage battery, and r represents the internal resistance of the battery.

[0041] Preferably, in the cyclic calculation of the core network equipment and facilities, a control function is first called to calculate the duty ratio of the current simulation step according to the control quantity of the last time, and the admittance matrix is updated based on the average value model;

[0042] In each cyclic calculation process, the historical current of the current simulation step is calculated from the branch voltage and the branch current of the last time;

[0043] The historical current of the current simulation step and the equivalent current of the independent voltage source of the current simulation step are used to calculate the node injection current vector In; ​​​​​​​​​​​

[0044] Solving the node voltage vector according to the network equation, and calculating the branch voltage vector and the branch current vector according to the node voltage;

[0045] Cyclically reciprocating and recording the simulation results of each simulation step until the simulation process is terminated, the simulation results including node voltage, charge and discharge power and battery SOC.

[0046] In a second aspect, the embodiment of the present application provides a distribution network equipment modeling interaction system based on a digital twin application architecture, comprising:

[0047] An architecture module constructs a distribution network digital twin space description application architecture comprising an interactive communication layer, a model building layer and a simulation analysis layer;

[0048] A basic module differentiates and models basic elements of distribution network equipment by using the constructed distribution network digital twin space description application architecture;

[0049] A core module models core distribution network equipment and facilities by using the constructed distribution network digital twin space description application architecture;

[0050] An interaction module performs information interaction on the established basic element model and core distribution network equipment and facility model by using the constructed distribution network digital twin space description application architecture.

[0051] Preferably, in the architecture module, the interactive communication layer is used to establish a data interaction mechanism between the physical entity and the model building layer, and to input topological information, equipment parameters, monitoring quantities and real-time operation data of the distribution network equipment and facilities, so as to realize storage and preprocessing of the data;

[0052] The model building layer takes primary equipment, secondary equipment and auxiliary sensing equipment as the basis of the digital twin model;

[0053] The simulation analysis layer uses an object-oriented modeling method to read and monitor environmental quantities, electrical quantities, physical quantities and behavior quantities of the distribution network equipment and facilities based on an electromagnetic transient simulation method, and collects system key node voltage, state of charge of energy storage and charging equipment and active power data.

[0054] Preferably, in the basic module, the differentiation modeling of the basic elements of the distribution network equipment is specifically:

[0055] The circuit network is numbered, and the node information and parameters of each equipment in the circuit topology are written into a reading record file to complete the pre-storage of data; based on the pre-stored data, a backward Euler method is selected to differentiate and model the basic elements, in which the differential model of the inductor is L (t) is:

[0056] i L (t) = YL u L (t)+I h_L (t-Δt)

[0057] where Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the history current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current time.

[0058] Preferably, in the core grid-connected device facility, the optical storage charging station device comprises a grid-connected converter, a photovoltaic cell, an energy storage system and a charging load;

[0059] The grid-connected converter realizes independent control of dq components in a feedforward decoupling manner;

[0060] The photovoltaic cell is a distributed power supply, which is equivalent to a constant current source model;

[0061] The energy storage system comprises an energy storage battery, which is equivalent to a controlled voltage source model with internal resistance;

[0062] The charging load is an electric vehicle battery, which is equivalent to a controlled voltage source model with internal resistance.

[0063] Preferably, the mathematical model of the grid-connected converter is:

[0064]

[0065] where K ip is the proportional coefficient of the PI regulator, K il is the integral coefficient of the PI regulator, s is a complex variable, i q is the q-axis component of the alternating current side current, is the i q current reference value, L is the alternating current side inductance value, R is the alternating current side line impedance, i d is the d-axis component of the alternating current side current, is the i d current reference value;

[0066] The three-phase voltage source grid-connected converter is updated by using the average value modeling method, and the alternating current side node voltage equation and the direct current side node voltage equation are as follows:

[0067] I a =-Y on u a +D a Y on u i +(1-D a )Y on uj

[0068] I b = -Y on u b + D b Y on u i + (1 - D b ) Y on u j

[0069] I c = -Y on u c + D c Y on u i + (1 - D c ) Y on u j

[0070]

[0071] I j = (1 - D a ) Y on u a - D a (1 - D a ) Y on u i - (1 - D a ) 2 Y on u j

[0072] + (1 - D b ) Y on u b - D b (1 - D b ) Y on u i - (1 - D b ) 2 Y on u j

[0073] + (1 - D c ) Y on u c - D c (1 - D c ) Y on u i - (1 - D c ) 2 Y on u j

[0074] where Ia , I b , I c is the current flowing into the AC side nodes a, b, c, u a , u b , u c is the three-phase voltage of the AC side a, b, c, u i , u j is the DC side i, j voltage, D is the duty ratio, Y on is the conductance, I i , I j is the current flowing into the DC side nodes i, j.

[0075] Preferably, the output voltage U of the energy storage system containing the controlled voltage source model of the internal resistance is:

[0076]

[0077] Wherein, V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is a characteristic constant of the battery, I is the discharge current of the energy storage battery, and r represents the internal resistance of the battery.

[0078] Preferably, the core network equipment facility calls the control function first in the cycle calculation, calculates the duty ratio of the current simulation step according to the control quantity of the last moment, and updates the admittance matrix based on the average value model;

[0079] In each cycle calculation process, the historical current of the current simulation step is calculated from the branch voltage and branch current of the last moment;

[0080] The historical current of the current simulation step and the equivalent current of the independent voltage source of the current simulation step are used to calculate the node injection current vector In;

[0081] The node voltage vector is solved according to the network equation, and the branch voltage vector and the branch current vector are calculated according to the node voltage;

[0082] The simulation results of each simulation step are recorded by repeating the cycle, and the simulation process is terminated until the simulation results including the node voltage, the charging and discharging power and the battery SOC.

[0083] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned power distribution equipment modeling interaction method based on the digital twin application architecture.

[0084] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-mentioned power distribution equipment modeling interaction method based on the digital twin application architecture.

[0085] Compared with the prior art, the present application has at least the following beneficial effects:

[0086] The power distribution equipment modeling interaction method based on the digital twin application architecture uses an object-oriented method, models the light storage and charging station power distribution equipment based on an electrical topology, combines the twin with a control strategy, and gives an equivalent model of a basic unit of the power distribution equipment and a control model of a power conversion unit; a layered architecture is used to optimize the modeling and simulation interaction of the power distribution equipment, the system is divided into an interactive communication layer, a model building layer and a simulation analysis layer, the functions of each layer are clear, and the accuracy and real-time interaction of different equipment models are ensured. The layered architecture not only facilitates efficient construction and flexible expansion of the model, but also improves the accuracy, real-time performance and scalability of the simulation system; by differentiating modeling and data entry of basic elements and core equipment facilities of the power distribution equipment, and with the help of the interactive communication layer, information interaction between models is realized, which effectively promotes simulation analysis and data processing of the power distribution equipment, and further improves the model accuracy and information interaction efficiency of the power distribution equipment.

[0087] Further, the interactive communication layer opens up the data interaction channel between the physical entity and the model building layer, realizes the automatic input and processing of topology information, device parameters and real-time operation data, and ensures the efficiency and consistency of data transmission; the model building layer uses an object-oriented modeling method to ensure the modularity and expandability of the device model, and ensures the standardized processing of primary equipment, secondary equipment and auxiliary equipment models; the simulation analysis layer is based on electromagnetic transient simulation, and real-time reading and monitoring are performed on electrical quantities, physical quantities and the like of the power distribution equipment, so that the simulation system has the ability of real-time analysis to support operation monitoring and decision-making in complex scenarios.

[0088] Further, the basic elements of the power distribution equipment are differentiated and modeled, and the electrical quantity changes of inductors, capacitors and the like are accurately simulated through differentiation processing, thereby improving the accuracy of simulation calculation. The backward Euler method is used for differentiation modeling, which helps to dynamically adjust the electrical quantity of the element through the change in a small time step, and ensures the accuracy and stability of the device model in the simulation process. This method can accurately capture the change of electrical parameters in simulation, improve the accuracy and reliability of simulation, and is particularly suitable for high dynamic electromagnetic transient simulation.

[0089] Further, the core distribution network equipment is modeled, including photovoltaic cells, energy storage systems and grid-connected converters and other key facilities, to simulate the electrical characteristics and control strategies in the grid-connected process of new energy equipment. The equivalent model is used to simplify the complex photovoltaic cells and energy storage systems, and the key control strategies in the grid-connected process, such as independent control of dq-axis components, are accurately calculated and adjusted through the mathematical model of the converter. The built model enhances the adaptability of the simulation system to the grid-connected process of new energy equipment, ensuring that the simulation results have practical guiding significance.

[0090] Further, the average value modeling method is used to improve the simulation efficiency of the converter and simplify the calculation complexity. Through this method, the complex electrical behavior of the three-phase voltage source grid-connected converter is simplified into a mathematical model that is easy to describe, thereby facilitating fast calculation and solution. The transient change process of the switching tube is described using the average value model, reducing the calculation demand for high-frequency details in electromagnetic transient simulation, and significantly improving the simulation efficiency. This method reduces the burden of simulation calculation, especially in large-scale power system simulation, speeds up the solution process, while ensuring sufficient calculation accuracy.

[0091] Further, the control function is called in each simulation step, the current duty cycle is calculated from the historical state quantity and the node admittance matrix is updated, and the device operating state is dynamically adjusted to ensure the real-time and accuracy of the simulation. Through the control function, the system can update the duty cycle in real time, and realize real-time update of electrical quantities combined with the average value model, to ensure accurate control in the simulation process. This method effectively ensures real-time adjustment of control signals during system operation, improves control accuracy and dynamic response capability of the system.

[0092] It can be understood that the beneficial effects of the above-mentioned second to fourth aspects can be referred to the related description in the first aspect, which will not be repeated here.

[0093] In summary, the application realizes efficient and accurate processing of electromagnetic transient simulation through the distribution network equipment modeling and interaction method based on the digital twin application architecture; the hierarchical design ensures the efficiency of data interaction, the flexibility of model building and the real-time of simulation analysis; the differential modeling and average value modeling method not only improves the simulation accuracy, but also greatly improves the calculation efficiency; at the same time, the dynamic calling of the control function ensures the accurate control of the system and the real-time update of the electrical quantities, providing strong technical support for the control strategy verification and stability analysis of the power system.

[0094] The technical solutions of the application will be further described in detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0095] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings used in the relative embodiment description are briefly introduced as follows. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0096] Figure 1 The application architecture diagram is provided for the network distribution digital twin space description of the present application.

[0097] Figure 2 The topology structure diagram is provided for the storage and charging station.

[0098] Figure 3 The overall structure diagram is provided for the simulation analysis layer.

[0099] Figure 4 The overall flow of the electromagnetic transient simulation algorithm is provided.

[0100] Figure 5 The equivalent circuit diagram of the storage battery is provided.

[0101] Fig. 6 is a storage battery charge and discharge control block diagram, wherein (a) is a constant voltage discharge mode, and (b) is a constant power charge mode.

[0102] Figure 7 The voltage and current double-loop control block diagram of the grid-connected converter is provided.

[0103] Figure 8 The topology diagram of the grid-connected converter is provided.

[0104] Figure 9 The member structure diagram of the "Battery" class is provided.

[0105] Figure 10 The simulation data result diagram is provided.

[0106] Fig. 11 is a comparison diagram of the simulator and the PLECS simulation waveform, wherein (a) is a DC bus voltage waveform, (b) is a storage battery charge power waveform, (c) is a charging pile charge power waveform, and (d) is a storage battery state of charge waveform.

[0107] Figure 12 The schematic diagram of a computer device provided by an embodiment of the present application is provided.

[0108] Figure 13 The block diagram of an electronic device provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0109] Clearly, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort are within the protection scope of the present application.

[0110] In the description of the present application, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0111] It should also be understood that the terms used in the present application specification are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0112] It should be further understood that the term "and / or" used in the present application specification is intended to mean one or more of any combination of the associated listed items and all possible combinations thereof, and includes these combinations, for example, A and / or B can mean the existence of A alone, the existence of B alone, or the existence of both A and B. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0113] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the present application to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range without departing from the scope of the embodiments of the present application.

[0114] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted to mean "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)", depending on the context.

[0115] Various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity and others omitted. The shapes and relative sizes of the various regions, layers, and their relative positions illustrated in the drawings are merely exemplary and may deviate in actuality due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed according to actual needs by those skilled in the art.

[0116] The application provides a distribution network equipment modeling interaction method based on a digital twin application architecture, designs a distribution network digital twin space description system application architecture, quickly constructs a digital twin of the distribution network equipment of the light storage and charging station based on an electrical topology, completes simulation running simulation of the light storage and charging distribution network, and has better real-time performance compared with traditional offline simulation software; the distribution network digital twin space description system application architecture based on the electrical topology can quickly construct the digital twin of the distribution network equipment of the light storage and charging station and complete simulation running simulation of the light storage and charging distribution network; this will help to improve the operation efficiency and stability of the light storage and charging distribution network and provide better decision support for users, and with the continuous development of new energy technology and digital twin technology, it is expected to be applied in a wider field.

[0117] The application provides a distribution network equipment modeling interaction method based on a digital twin application architecture, including the following steps:

[0118] S1, a distribution network digital twin space description application architecture including an interactive communication layer, a model building layer and a simulation analysis layer is constructed;

[0119] Please refer to Figure 1 In order to realize real-time updating of data and ensure real-time evolution of the model, the distribution network digital twin space description application architecture is divided into an interactive communication layer, a model building layer and a simulation analysis layer.

[0120] The interactive communication layer establishes a data interaction mechanism between the physical entity and the model building layer, inputs topological information, equipment parameters, monitoring quantities, real-time running data and the like of the distribution network equipment facilities, and realizes storage and preprocessing of data.

[0121] The model building layer takes three categories of primary equipment, secondary equipment and auxiliary sensing equipment as the basis of the digital twin model, and takes the distribution network equipment of the light storage and charging station as an example, mainly including alternating current network, distributed power supply, energy storage and charging load and the like. The alternating current network is connected to the direct current bus using a three-phase voltage source converter (VSC), and the energy storage and charging equipment is connected to the system using different power electronic equipment according to the charge and discharge characteristics thereof; the overall structure of the model of the distribution network equipment of the light storage and charging station is as shown in Figure 2 .

[0122] The simulation analysis layer uses an object-oriented modeling method to read and monitor the environmental quantity, electrical quantity, physical quantity and behavior quantity of the distribution network equipment and facilities based on an electromagnetic transient simulation algorithm, and collects data such as the voltage of key nodes of the system, the state of charge of energy storage and charging equipment, and active power.

[0123] On the one hand, the accuracy of complex physical mechanism modeling is improved; on the other hand, a data basis is provided for system state evaluation analysis and decision deduction; a schematic diagram of the simulation analysis layer is shown in Figure 3

[0124] The object-oriented modeling method constructs the entire system with objects as the basic unit. An object is defined as a combination of data and operations. An object can be understood as a container that encapsulates attributes and methods. Attributes are used to describe the state and characteristics of the object, and methods are used to describe the behavior and operations of the object. Some objects are abstracted according to commonalities and aggregated into classes. A class represents the abstraction of a class of objects, and each object represents an instance of the class. The object-oriented structure has three main features of encapsulation, inheritance and polymorphism. These features effectively protect the security of internal data and improve the reusability and scalability of the code. For the distribution network system, a device-centric approach can be adopted, that is, a specific device is taken as an object. Then the objects are abstracted into commonalities to form corresponding device classes, and the related attribute parameters and general operation methods are encapsulated into an indivisible unit, hiding the details of information that do not need to be known by the outside world.

[0125] In order to ensure generality, a single type of device needs to be selected as the minimum unit at a lower level, for example, it is better to create a "BuckConverter" and a "Battery" class respectively, rather than only creating a "charging station" class. On this basis, different classes can be linked together, and the charging station is composed of a buck converter and a charging battery, and the energy storage device is composed of a half-bridge converter and a charging battery, wherein the "Battery" class is reused, and only the parameters of the charging battery part of the two types of devices need to be modified, facilitating the management and extension of the code. In addition, in order to enhance the reusability and structural integrity of the code, an abstract class should be created at a higher level to store the information common to all derived classes. The abstract class Sim_CATL is at the topmost layer in the entire model structure, defines the outermost electromagnetic transient calculation function, instances of each distribution device class, and key electrical quantity attributes such as voltage and current of the complete electrical topology, and part of the function body further calls the function members of the instantiated instances of each distribution device subclass. The final execution of the simulation program is realized by the three functions of simInit, simLoop and simPlot, which correspond to the initialization, loop body and plotting link of the end of simulation of the program respectively, wherein the electromagnetic transient calculation functions defined in the Sim_CATL class are included.

[0126] Please refer to​Figure 4 The object-oriented system modeling and simulation running simulation of the optical storage charging station equipment based on the electromagnetic transient method of electrical topology is performed. First, the object-oriented system modeling and simulation running simulation of the optical storage charging station equipment is performed. The initialization part first completes the pre-storage of data, basic element modeling, and initialization of the electrical quantity of the device based on the modeling of the core distribution network equipment and facilities. The loop part first calls the control function to update the node admittance matrix, calculates the historical current, injected current, and solves the node voltage equation. After that, the node voltage and branch current are updated, and the next cycle is entered after the timer is triggered. The simulation data results are saved at the end of the loop, including the following steps:

[0127] S2, using the distribution network digital twin space description application architecture constructed in step S1, differentiating the modeling of the basic elements based on the electrical topology;

[0128] S201, based on the interactive communication layer, numbering the circuit network, and writing the node information and parameters of each device in the circuit topology into the reading record file, completing the pre-storage of data; the pre-stored data is used as the modeling parameters of the basic elements and core equipment and facilities.

[0129] S202, based on the model building layer, differentiating the modeling of the basic elements. After the element differentiation modeling, the initial node admittance matrix of the element is written by the equivalent admittance column of the element, and then the loop body is entered to solve the node voltage equation. For basic elements composed of resistance, inductance, and capacitance, the admittance matrix does not change with time in the loop process, so it does not need to be reconstructed.

[0130] The differentiated modeling includes the capacitance, inductance, and resistance on the DC side and their series and parallel combinations, the series and parallel combinations of three-phase capacitance, three-phase inductance, and three-phase resistance in the AC circuit, and AC and DC voltage sources. After differentiation, the backward Euler method is selected to ensure the numerical stability of the system.

[0131] Taking the DC inductance as an example, the differentiation process is briefly described as follows:

[0132] Let the nodes i and j be connected by an inductance L, and its characteristic equation is represented as:

[0133]

[0134] The backward Euler method is used for differentiation:

[0135]

[0136] After sorting:

[0137] i L (t)=Y L u L (t)+Ih_L (t-Δt)

[0138]

[0139] I h_L (t-Δt)=i L (t-Δt)

[0140] where Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the history current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current time, i L (t-Δt) is the inductor current at the last time, and L is the inductor value.

[0141] S3, utilizing the digital twin space of the distribution network constructed in step S1 to describe an application architecture, and modeling core distribution network equipment and facilities;

[0142] S301, establishing a mathematical model of core distribution network equipment and facilities and control modes:

[0143] Taking a light storage charging station equipment as an example, a grid-connected converter, a photovoltaic cell, an energy storage system and a charging station equipment are modeled, including a model part and a control part.

[0144] The model part is modeled by a unit component and an interface converter respectively, the interface converter model calls an algorithm of the control part, and the model has improved scalability and universality.

[0145] The photovoltaic cell is a distributed power supply, and transmits power to a microgrid through a direct current bus. Generally, the photovoltaic cell works in a maximum power tracking mode, and since the direct current bus voltage is approximately constant, the photovoltaic cell can be equivalent to a constant current source model.

[0146] The energy storage system includes an energy storage battery and necessary power conversion units.

[0147] The energy storage battery unit is generally a storage battery. In order to reflect the changes of the state of charge of the battery and the battery terminal voltage, the storage battery needs to be modeled in detail, and can be equivalent to a controlled voltage source with internal resistance, and an equivalent circuit model is as shown in Figure 5 .

[0148] The output voltage of the general equivalent circuit model of the storage battery is:

[0149] U=V-Ir(4)

[0150] where U represents the output voltage, V represents the output voltage of the controlled voltage source, r represents the battery internal resistance, and I is the discharge current of the energy storage battery.

[0151] The output voltage V of the controlled voltage source is obtained according to the discharge characteristic curve of the battery:

[0152]

[0153] wherein V0 represents the internal electromotive force; SOC represents the state of charge of the battery; and β is a characteristic constant of the battery, which can be calculated from the charge amount AH1 of the battery when not fully charged and the corresponding terminal voltage V1.

[0154] The state of charge SOC of the battery is determined by the initial SOC0, the capacity AH of the battery and the discharge current I:

[0155]

[0156] The state of charge SOC is obtained from the ratio of the charge amount AH1 of the battery to the capacity AH of the battery:

[0157]

[0158] The charge amount AH1 and the terminal voltage V1 of the battery at a certain state are known, the corresponding state of charge SOC is calculated, and then the characteristic constant β of the battery is calculated according to formula (2).

[0159] The power conversion unit of the energy storage system adopts a bidirectional DC / DC converter, and the bidirectional flow of electric energy is realized through the bidirectional DC / DC converter. The bidirectional DC / DC converter can work in boost mode and buck mode. When the electric energy generated by the distributed power source is excessive, the battery stores electric energy, and the bidirectional buck / boost converter works in buck mode, at this time the power flows from the grid side to the battery side; when the electric energy generated by the distributed power source is insufficient to meet all the loads, the battery releases electric energy, and the bidirectional buck / boost converter works in boost mode, at this time the power flows from the battery side to the grid side.

[0160] In order to achieve the above purpose, the bidirectional DC / DC converter has two working modes of constant voltage discharge and constant power charging.

[0161] When the constant voltage discharge is used, the target of the battery is to adjust the DC bus voltage, so that the DC bus voltage returns to the rated value, and the power balance in the entire microgrid is maintained. The constant voltage discharge control structure of the battery is shown in Fig. 6(a), wherein I b , I dis-ref are the actual values of the battery charging and discharging current and the reference value of the inner loop battery discharging current obtained by the voltage outer loop, respectively. The constant voltage discharge is realized by using voltage-current double-loop control, and the outer loop is the voltage loop and the inner loop is the current loop. In the voltage outer loop, the actual DC bus voltage V dc is collected and compared with the reference voltage V dc-refThe difference is sent to the PI regulator and is used as the inner loop current reference value I after passing through a current limiting link. dis-ref Enters the current loop and compares it with the actual current I b , comparison, the difference obtained passes through the PI regulator and enters the PWM modulation link to form a control signal.

[0162] During constant power charging, the battery acts as a power terminal to store the electric energy generated by the distributed power source and the power grid. The constant power charging control structure of the battery is shown in Figure 6(b). Constant power charging can be achieved with only one control loop. In the control loop, the battery terminal voltage V and the discharge current I are collected. b , calculate the instantaneous charging power P of the battery b , and compare it with the battery charging reference power P b-ref After comparison, the difference is sent to the PI regulator and then enters the PWM modulation link to form a control signal.

[0163] The energy storage power converter uses a switching control strategy to switch between constant voltage discharge and constant power charging control modes. The control switching signal can be given externally or based on the battery's state of charge.

[0164] When the energy storage battery is in the low limit area, the energy storage should be avoided from continuing to discharge, and charging should be switched to restore the energy storage SOC when conditions permit; when the energy storage battery is in the high limit area, its charging power should be controlled to avoid overcharging of the energy storage; only when the energy storage battery SOC is within the normal range can it be charged and discharged normally.

[0165] The charging load is an electric vehicle battery, connected to the DC bus via a buck converter for unidirectional charging. The charging load battery cells are similar to energy storage battery cells and can therefore be modeled as equivalent to energy storage batteries. The charging load's power conversion unit is a buck converter. Because the battery terminal voltage is lower than the DC bus voltage, a step-down buck converter is used for charging. This also prevents reverse current input, preventing reverse charging of the vehicle battery.

[0166] As the only interface between the DC microgrid and the distribution network, the grid-connected converter has the following characteristics:

[0167] Effectively isolate AC grid disturbances; maintain DC bus voltage stability; realize bidirectional energy flow. When the output of distributed power sources is greater than the power consumed by the charging load, energy flows to the grid side through the grid-connected converter, and the excess power is consumed by the distribution network side; when the output of distributed power sources is less than the power consumed by the charging load, energy flows to the DC bus through the grid-connected converter, thereby supporting the DC bus voltage and transmitting power to the charging load.

[0168] The grid-connected converter uses a voltage and current dual closed-loop control method, such asFigure 7 The outer voltage loop is used to stabilize the DC bus voltage, modulate the system's working state according to the voltage, and output the command current signal to the inner current loop. The inner current loop is used to make the system's input current follow the command current signal, so as to realize the bidirectional conversion of electric energy under the unit power factor.

[0169] In the three-phase stationary coordinate system, it is difficult to design the control system and to achieve zero static error control, so it is necessary to transform it to the two-phase synchronous rotating coordinate system. Meanwhile, due to the existence of the system AC side inductance, the dq components of the system are mutually coupled and influenced, so the feedforward decoupling method is used to realize the independent control of the dq components, so as to improve the dynamic response performance of the system. The mathematical model of the LC filter grid-connected interface converter in the dq coordinate system is:

[0170]

[0171] Taking Laplace transform on the above equation, we get:

[0172]

[0173] It is found through analysis that in the two-phase synchronous rotating coordinate system, the dq components of the system's mathematical model are coupled, so the decoupling control of the system's mathematical model is needed. Since the feedforward decoupling has simple control and does not affect the stability of the system, the feedforward decoupling control is introduced into the closed-loop system. The feedforward quantity ωLi d and -ωLi q are added to the output voltage, so as to offset the coupling terms -ωLi d and ωLi q in the mathematical model of the circuit topology, so as to achieve the goal of decoupling.

[0174] After decoupling, the dq components of the system are converted into two independent parts. At this time, the AC control of the system becomes DC control. In the above rotating coordinate transformation, the d-axis represents the active component and the q-axis represents the reactive component, and it is specified that the q-axis lags the d-axis by 90°. In this system, the q-axis is given as 0, so that the reactive power of the system is zero, thereby realizing the unit power factor rectification of the system.

[0175] Under the condition of balanced three-phase grid, the voltage command formula in the three-phase synchronous rotating coordinate system is:

[0176]

[0177] Combining the above two equations, we get:

[0178]

[0179] Thus, the coupling component in the two-phase synchronous rotating coordinate system is completely decomposed, and by reasonably designing the proportional coefficient K ip and the integral coefficient K il of the PI regulator, the voltage and current double-loop control can be realized.

[0180] S302, after determining the mathematical model of the core distribution network equipment and the control mode in step S301, an electromagnetic transient simulation model is established;

[0181] In the entire simulation method, for the simulation of the power electronic system level, large step simulation is adopted to reduce the number of calculations per unit time, thereby reducing the calculation amount and facilitating real-time. Therefore, the traditional binary resistance model and constant admittance model are no longer applicable, and average value model, dynamic phasor model, equivalent circuit model, etc. are often used to improve the simulation efficiency. Among them, the average value model is based on the switching function model, uses the average value of the variable in the switching period to replace the actual value, obtains the functional relationship between the duty cycle of the control signal and the port voltage and current vector, and then updates the node admittance matrix and the node voltage equation to complete the electromagnetic transient calculation. The average value modeling method only needs to describe the power frequency characteristics of the input / output port of the device, ignores the high-order harmonics generated by pulse width modulation (PWM), and thus allows a larger simulation step size, which greatly improves the simulation efficiency at the expense of a certain accuracy.

[0182] Next, taking a three-phase voltage source grid-connected converter as an example, average value modeling is performed thereon:

[0183] As shown in Figure 8 , let the DC port nodes of the converter be i and j, the AC port nodes be a, b and c, the on-resistance of the bridge arm be Ron, the conductance be Yon, the off-resistance of the bridge arm be infinite, and the duty cycle of the on-time of the k-phase upper bridge arm in one switching period be D k , k = a, b and c.

[0184] According to the KVL law, the average of the AC port voltage is:

[0185]

[0186] Among them, u a , u b , u c are the three-phase voltages a, b and c on the AC side, u i , u j are the voltages i and j on the DC side, and I a , I b , I c are the currents flowing into the nodes a, b and c on the AC side.

[0187] According to KCL, the average of the DC port current is:

[0188]

[0189] where I i , I j is the current flowing into the DC side node i, j.

[0190] The AC side node voltage equation of the grid-connected converter is obtained as:

[0191]

[0192] The DC side node voltage equation of the grid-connected converter is obtained as:

[0193]

[0194] After the node voltage equation is formed, the node admittance matrix of the grid-connected converter is reconstructed according to the duty cycle generated by the control code, and the electromagnetic transient modeling is completed.

[0195] According to this method, other power electronic devices such as buck converters and bidirectional DC / DC converters can be modeled by average values according to the relationship between port voltage and current, which will not be described here.

[0196] S303, the simulation of the operation of the light storage and charging station is simulated by using the distribution network digital twin space constructed in step S1 to describe the simulation analysis layer of the application architecture.

[0197] After the element differential modeling and the electromagnetic transient model of the core equipment and facilities are established, the initial node admittance matrix of the element can be written, and then the loop body is entered to prepare for solving the node voltage equation. Since the node admittance matrix of some elements is time-varying, such as interface converters, the admittance matrix needs to be rebuilt. For basic elements composed of resistors, inductors, capacitors, etc., the admittance matrix has been determined before the simulation starts, so it does not need to be modified, thereby saving computing resources and improving simulation efficiency.

[0198] At the beginning of the EMTP program calculation, the history current term is set according to different initialization methods. Usually, the initial value of the history current term is 0.

[0199] The loop part is realized by a timing function, and the timing period is set to 1ms, which is equal to the actual simulation step. When the time used in each calculation step does not exceed the timing period, real-time simulation of the system can be realized.

[0200] Unlike the calculation process of basic elements, the core distribution network equipment and facilities need to call the control function first in the loop calculation, calculate the duty cycle of the current simulation step according to the control quantity of the last time, and update the admittance matrix based on the average value model.

[0201] In each cycle calculation process, the historical current of the current simulation step is calculated from the branch voltage and branch current of the previous time.

[0202] The node injection current vector In is calculated from the historical current of the current simulation step and the equivalent current of the independent voltage source of the current simulation step.

[0203] The node voltage vector is solved according to the network equation, and the branch voltage vector and the branch current vector are calculated according to the node voltage.

[0204] This cycle is repeated and the simulation results of each simulation step are recorded until the simulation process is terminated. After the simulation process is terminated, the simulation results can be written into the record file, including node voltage, charge and discharge power, battery SOC, etc.

[0205] S4, based on the basic element model obtained in step S2 and the core distribution network equipment and facility model obtained in step S3, the device and element information is interacted.

[0206] The device type, node number, basic parameter and other information are entered into the electromagnetic transient model through the construction of the interactive communication layer of the application architecture of the distribution network digital twin space.

[0207] For the basic element model, the main parameters include voltage, current, inductance, capacitance, resistance and other electrical characteristics. These parameters are initialized before the simulation starts to ensure that the model has the correct initial state during the simulation process and is updated dynamically in real time during the simulation process.

[0208] For the core distribution network equipment and facility model, in addition to the basic electrical parameters, the control parameters of the device (such as switch state, control algorithm, etc.) are also introduced. In each simulation step, based on the current electrical parameters and control parameters, iterative calculation is performed to generate control variables such as duty cycle, and then update the node admittance matrix related thereto. Through the update of the node admittance matrix, the effect of the control strategy is reflected in real time during the simulation process, and finally the accurate electrical quantity update is realized.

[0209] During the simulation process, due to the electromagnetic transient decoupling characteristics between the basic element model and the core distribution network equipment and facility model, the two can run independently, so parallel processing can be used to improve the simulation efficiency. In the parameter input stage and the initialization process of the admittance matrix, node voltage and current vector, the two models perform related calculations independently and do not affect each other. In the simulation execution stage, the core distribution network equipment and facility model also needs to complete the update of the node admittance matrix in each simulation step to ensure the real-time adjustment of the control strategy to the electrical quantity.

[0210] When the update of the node admittance matrix is completed, the basic element model and the core distribution network equipment facility model are changed from parallel operation to serial execution. At this time, the system starts to perform subsequent steps such as historical current calculation and node admittance matrix solution, and finally realizes data information interaction and feedback processing between the basic element and the core distribution network equipment. This process ensures the accuracy and real-time performance of the simulation system, and provides technical support for electromagnetic transient simulation.

[0211] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method or a program product. Therefore, various aspects of the present application can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" here.

[0212] In another embodiment of the present application, a distribution network equipment modeling interaction system based on a digital twin application architecture is provided, which can be used to implement the above-mentioned distribution network equipment modeling interaction method based on the digital twin application architecture. Specifically, the distribution network equipment modeling interaction system based on the digital twin application architecture includes an architecture module, a basic module, a core module and an interaction module.

[0213] The architecture module constructs a distribution network digital twin space description application architecture including an interactive communication layer, a model building layer and a simulation analysis layer.

[0214] The basic module uses the constructed distribution network digital twin space description application architecture to differentially model the basic elements of the distribution network equipment.

[0215] The core module uses the constructed distribution network digital twin space description application architecture to model the core distribution network equipment facilities.

[0216] The interaction module uses the constructed distribution network digital twin space description application architecture to perform information interaction between the established basic element model and the core distribution network equipment facility model.

[0217] In still another embodiment of the present application, a terminal device is provided, which includes a processor and a memory, the memory being configured to store a computer program including program instructions, and the processor being configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the power distribution equipment modeling interaction method based on the digital twin application architecture, including:

[0218] constructing a power distribution digital twin space description application architecture including an interactive communication layer, a model building layer and a simulation analysis layer; differentiating modeling the basic elements of the power distribution equipment by using the power distribution digital twin space description application architecture; modeling the core power distribution equipment facilities by using the constructed power distribution digital twin space description application architecture; and performing information interaction on the obtained basic element model and the core power distribution equipment facility model by using the interactive communication layer of the constructed power distribution digital twin space description application architecture.

[0219] In still another embodiment of the present application, a computer readable storage medium (Memory) is also provided, which is a memory device in the terminal equipment, used for storing programs and data. It can be understood that the computer readable storage medium here can include the built-in storage medium in the terminal equipment, and of course can also include the expansion storage medium supported by the terminal equipment, and can be any tangible medium containing or storing programs, which can be used by or in combination with the instruction execution system, device or apparatus. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, which can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0220] The computer readable storage medium also includes a data signal carried in baseband or propagated as a carrier wave in a propagated data signal, in which the readable program code is carried. Such a propagated data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The readable storage medium can also be any readable medium that can be used to carry, propagate, or transmit the program for use by or in connection with the instruction execution system, device or apparatus. The program code contained in the readable storage medium can be transmitted in any suitable medium, including but not limited to wireless, wired, optical, RF, etc., or any suitable combination thereof.

[0221] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0222] The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for modeling and interacting with power distribution equipment based on a digital twin application architecture in the above embodiments; the one or more instructions stored in the computer-readable storage medium are loaded and executed by the processor to implement the following steps:

[0223] An application architecture of a power distribution digital twin space description is constructed, which includes an interactive communication layer, a model building layer and a simulation analysis layer; the basic elements of the power distribution equipment are differentially modeled by using the application architecture of the power distribution digital twin space description; the core power distribution equipment facilities are modeled by using the constructed application architecture of the power distribution digital twin space description; the basic element models and the core power distribution equipment facility models are interacted by using the interactive communication layer of the constructed application architecture of the power distribution digital twin space description.

[0224] Please refer to Figure 12 The terminal device is a computer device, and the computer device 60 of the embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. The computer program 63, when executed by the processor 61, implements the method for modeling and interacting with power distribution equipment based on a digital twin application architecture in the embodiment. To avoid repetition, details are not repeated here. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of each model / unit in the system for modeling and interacting with power distribution equipment based on a digital twin application architecture in the embodiment. To avoid repetition, details are not repeated here.

[0225] The computer device 60 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 12 The computer device 60 is only an example and does not constitute a limitation on the computer device 60, and can include more or fewer components than those shown, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0226] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, central processing units, graphics processing units, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, quantum computing-based data processing logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0227] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 60.

[0228] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0229] Any reference to memory, database or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0230] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0231] See also Figure 13 The terminal device 600 is an electronic device that is implemented as a general-purpose computing device. The components of the electronic device may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0232] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0233] The storage unit 620 can include a readable medium in the form of volatile storage such as random access memory (RAM) 6201 and / or cache memory 6202, and also can include a non-volatile storage such as read only memory (ROM) 6203.

[0234] The storage unit 620 also can include a program / utility 6204 having a set of programs / modules 6205, including an operating system, one or more application programs, other program modules, and program data, each of which can implement aspects of a network environment, for example, as each or some combination of these examples.

[0235] The bus 630 can represent one or more of several types of bus structures, including a storage bus or bus for storage controller, peripheral bus, graphics bus, processor or local bus using any of a variety of bus architectures.

[0236] The electronic device 600 also can communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 600; and / or any devices (e.g., a router, a modem, a printer, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via a network adapter 660. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via the bus 630. It should be appreciated that the electronic device 600 can be a part of one or more networks, such as virtual networks, which further can include more than one network.

[0237] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application but not all embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0238] To verify the solving accuracy and efficiency improvement effect of the method of the application, a photovoltaic and energy storage microgrid power generation system control model is built on the PLECS platform, and the result accuracy of the solving of the method of the application and the traditional node analysis method is compared. The object-oriented average value modeling electromagnetic transient algorithm has basically consistent response curves with the node analysis method in both steady state and transient state, verifying the correctness of the method used as an electromagnetic transient simulation algorithm.

[0239] For ease of understanding, the application will be described below taking a photovoltaic and energy storage charging station system as an example, but the protection scope of the application should not be limited thereby.

[0240] The object-oriented distribution network equipment model is the core of the digital twinning of the distribution network and is the basis for realizing simulation calculation and data analysis. The object-oriented modeling method constructs the entire system with objects as the basic unit. An object is defined as a combination of data and operations. An object can be understood as a container that encapsulates attributes and methods. Attributes are used to describe the state and characteristics of an object, and methods are used to describe the behavior and operations of an object. Some objects are abstracted according to commonality and aggregated into classes. A class represents the abstraction of the above-mentioned objects, and each object represents an instance of the class. The object-oriented structure has three main features of encapsulation, inheritance and polymorphism. These features effectively protect the security of internal data and improve the reusability and scalability of the code. For the distribution network system, a device-centered approach can be adopted, i.e., a specific device is taken as an object. Then the objects are abstracted into commonalities to form corresponding device classes, and the related attribute parameters and general operation methods (functions) are encapsulated into an indivisible unit, hiding the information details that do not need to be known by the outside world.

[0241] The definition of a class is essentially the definition of class members. The attributes of a class correspond to member variables, and the behaviors of a class correspond to member functions. Members are classified according to their allowed access levels and can be public, protected or private. Public members are not subject to access restrictions and can be accessed within and outside the class. Protected members can be accessed by the class and its derived classes. Private members can only be accessed by the class itself. These access level restrictions allow different levels of protection and concealment of class members, thereby reducing the relevance between programs and preventing the "butterfly effect". As shown in FIG. 1, the "Battery" class includes two types of public members and protected members, wherein the public members are functions included in the electromagnetic transient simulation algorithm process, and each device class needs to call these functions, and the protected members are attributes specific to the charging battery, such as the positive and negative nodes of the battery, the capacity, the internal resistance, the initial state of charge, etc. Figure 9

[0242] ​To ensure universality, a single device type should be selected as the minimum unit at a lower level. For example, rather than creating a single "Charging Station" class, it's best to create separate "BuckConverter" and "Battery" classes. This allows for interoperability between different classes. The charging station consists of a buck converter and a rechargeable battery, while the energy storage device consists of a half-bridge converter and a rechargeable battery. The "Battery" class is reused, and only the parameters of the rechargeable battery portion need to be modified for both types of devices, facilitating code management and expansion. Furthermore, to enhance code reusability and structural integrity, abstract classes should be created at a higher level to store information shared by all derived classes. The abstract class Sim_CATL, at the top level of the model, defines the outermost electromagnetic transient calculation functions, instances of each distribution device class, and key electrical properties such as voltage and current for the complete electrical topology. Some of these functions then call function members instantiated from each distribution device subclass. The final execution of the simulation program is implemented by three functions: simInit, simLoop, and simPlot, which correspond to the program initialization, loop body, and drawing link at the end of simulation, respectively, which include various electromagnetic transient calculation functions defined in the Sim_CATL class.

[0243] The electromagnetic transient simulation program was designed based on a client-server architecture. The front-end was developed using Vue, with code editing performed in VS Code. The back-end utilized the Spring Cloud microservices architecture and was developed in Java. The core simulation algorithm was written in C++, compiled into a dynamic library, and then executed through program calls. The program runs in a Docker containerized environment and managed by Kubernetes. Tomcat and Nginx were used to deploy the web container and proxy, and Nacos was used to manage the microservice registration and configuration center. The program also integrated OpenLayer as a 2D geographic information engine and implemented a client-server-based 3D engine using Cesium and WebGL. Neo4j was used for storage and management of the graph database, while MySQL was used as the relational database to store simulation data, inspection data, and spatial data. This simulation program achieves accurate simulation and data management of electromagnetic transients.

[0244] Set the simulation step size, simulation time and total number of system nodes:

[0245] SimulationTime(s),TimeStep(uS),NodeNumber

[0246] 10,1000,26

[0247] Import parameter information of each node and branch of the system:

[0248] AC line impedance:

[0249] n1, n2, R, L

[0250] 7, 8, 0.01, 0.0003

[0251] 7, 11, 0.01, 0.001

[0252] 7, 14, 0.01, 0.001

[0253] Three-phase voltage source:

[0254] n1, V

[0255] 7, 311.08

[0256] Battery:

[0257] n1 (positive), n2 (negative), Vo, R, k, S0C0, AH (A·h)

[0258] 22, 2, 600, 0.01, 0.888888889, 0.7, 0.74

[0259] 24, 2, 600, 0.01, 0.888888889, 0.7, 0.74

[0260] 26, 2, 600, 0.01, 0.888888889, 0.7, 0.74

[0261] 18, 13, 600, 0.01, 0.888888889, 0.7, 0.74

[0262] 20, 16, 600, 0.01, 0.888888889, 0.7, 0.74

[0263] 6, 2, 600, 0.01, 0.888888889, 0.7, 0.74 Buck converter:

[0264] n1 (Uin+), n2 (Uin-), n3 (Uo+)

[0265] 1, 2, 21

[0266] 1, 2, 23

[0267] 1, 2, 25

[0268] 12, 13, 17

[0269] 15, 16, 19

[0270] DC line impedance:

[0271] nl, n2, R, L, C

[0272] 1, 3, 0.0224, 0, 0

[0273] 1, 9, 0.0224, 0, 0

[0274] 2, 4, 0.0224, 0, 0

[0275] 2, 10, 0.0224, 0, 0

[0276] 17, 18, 0, 0.001, 0

[0277] 19, 20, 0, 0.001, 0

[0278] 21, 22, 0, 0.001, 0

[0279] 23, 24, 0, 0.001, 0

[0280] 25, 26, 0, 0.001, 0

[0281] 1, 2, 1e5, 0, 0.001

[0282] 5, 6, 0, 0.001, 0 DC load:

[0283] nl, R (no 1e5), C

[0284] 9, 1.00E+05, 2.00E-02

[0285] 10, 1.00E+05, 2.00E-02

[0286] 12, 1.00E+05, 2.00E-03

[0287] 13, 1.00E+05, 2.00E-03

[0288] 15, 1.00E+05, 2.00E-03

[0289] 16, 1.00E+05, 2.00E-03 bidirectional DC / DC converter:

[0290] nl (Uin+), n2 (Uin-), n3 (Uo+), numSOC

[0291] 1, 2, 5, 6

[0292] photovoltaic module:

[0293] nl (inflow), n22 (outflow), I

[0294] 3, 4, 40

[0295] Three-phase voltage source grid-connected converter:

[0296] n1(DC+),n2(DC-),n3(AC),n4(L node)

[0297] 9,10,8,7

[0298] 12,13,11,7

[0299] 15,16,14,7

[0300] After the model parameters are initialized, the initial node admittance matrix of the system is constructed;

[0301] Enter the loop body, call the control function to output the duty ratio, update the node admittance matrix based on the average value model, calculate the historical current, injected current, solve the node voltage equation, update the node voltage and branch current, update the node voltage and branch current, wait for the timer to trigger the next loop. If the simulation time reaches the set time, stop the loop, output and save the node voltage, DC bus voltage drop, energy storage battery power, charging battery power, battery state of charge, etc. to the simlog.txt file. Part of the file data is as shown in Figure 10 .

[0302] After the simulation is completed, the drawing function is called to draw the DC bus voltage curve, energy storage charging and discharging power curve, AC charging pile charging power curve, and energy storage battery state of charge curve.

[0303] The timing performance of the simulation is shown in the following figure. The total number of loops is 4000, and the single loop time is higher than 1.1ms for no more than 500 times, and lower than 0.9ms for no more than 600 times, which shows that the application has good timing performance.

[0304]

[0305] To verify the solving accuracy and efficiency improvement effect of the simulator, a light storage microgrid power generation system control model is built on the PLECS platform. The simulation parameters are the same as the electromagnetic transient simulator:

[0306] In the light storage microgrid, the DC bus rated voltage is 750V, the AC side grid line voltage is 380V, the energy storage battery rated charging power is 250kW, and the charging pile rated charging power is 120kW.

[0307] The simulation parameters of the photovoltaic micro power source are: using a 40A constant current source.

[0308] The simulation parameters of the energy storage micro power source are: the inductance L of the bidirectional DC-DC circuit is 0.001H, the battery internal resistance is 0.01Ω, the full SOC voltage V is 600V, and the initial SOC is 0.7.

[0309] AC power simulation parameters: phase voltage effective value: 220V, frequency: 50Hz, grid-connected inverter AC side inductance: 0.3mH, AC side resistance: 0.01Ω, DC side filter capacitance: 0.01F.

[0310] Charging pile simulation parameters: buck circuit inductance L = 0.001H, battery internal resistance 0.01Ω, full SOC voltage V = 600V, initial SOC = 0.7.

[0311] The simulation operating conditions are as follows:

[0312] 0-1s, the DC bus voltage is maintained at 750V by the grid-connected inverter, the energy storage is charged at a power of 250kW, and the charging pile is charged at a power of 120kW.

[0313] 1-4s, the energy storage is changed to constant voltage control, and the DC bus voltage is controlled at 750V by the energy storage and grid-connected inverter. The charging pile is charged at a power of 120kW.

[0314] Compared with the photovoltaic energy storage charging station model built in PLECS, the comparison results of the DC bus voltage curve, the energy storage charge and discharge power curve, the AC charging pile charging power curve and the energy storage battery state of charge curve of the two platforms are shown in FIG. 11, which is used to verify the accuracy requirement of the present application.

[0315] FIG. 11 is a comparison of the accuracy of the results solved by the method of the present application and the traditional node analysis method, it can be seen that the object-oriented average value modeling method has basically consistent response curves with the node analysis method in both steady state and transient state, verifying the correctness of the method used as an electromagnetic transient simulation algorithm. The performance comparison and efficiency verification results of the simulator are shown in Table 1.

[0316] Table 1 is a comparison of the simulation efficiency of the simulator and PLECS

[0317]

[0318] To verify the solving accuracy and efficiency improvement effect of the simulator, a photovoltaic energy storage microgrid power generation system control model is built on the PLECS platform, and the result accuracy and simulation efficiency of the method of the present application and the traditional node analysis method are compared. Compared with the traditional offline simulation software, the present application has better calculation efficiency, and after being compiled into a dynamic library, it can be called and run in other development environments, and has better real-time performance.

[0319] In summary, the power distribution equipment modeling interaction method and system based on the digital twin application architecture differentiates the basic elements for modeling, performs average value modeling on the power electronic converter, and develops an electromagnetic transient method for the system on the simulator, fully utilizes the hardware advantages of the simulator, and realizes real-time simulation of the photovoltaic energy storage charging station power system in millisecond steps.

[0320] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit or module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit or module are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0321] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0322] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the disclosed embodiments of the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0323] In the embodiments provided by the present application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0324] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0325] In addition, each functional unit in various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0326] The integrated module / unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the contents included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0327] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the flowcharts and / or block diagrams. Figure 1 Each flow or multiple flows and / or blocks Figure 1means for performing the function specified in the block or blocks.

[0328] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 flow or flows and / or blocks Figure 1 means for performing the function specified in the block or blocks.

[0329] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 flow or flows and / or blocks Figure 1 steps of means for performing the function specified in the block or blocks.

[0330] The above merely illustrates the technical idea of the present application, and cannot be used to limit the protection scope of the present application. Any modification made according to the technical idea of the present application, on the basis of the technical scheme, falls within the protection scope of the claims of the present application.

Claims

1. A distribution network equipment modeling interaction method based on digital twin application architecture, characterized in that: The following steps are involved: S1. Build a distribution network digital twin spatial description application architecture that includes an interactive communication layer, a model building layer, and a simulation analysis layer; S2. Using the distribution network digital twin space description application architecture constructed in step S1, perform differential modeling on the basic components of the distribution network equipment; S3. Using the distribution network digital twin spatial description application architecture constructed in step S1, model the core distribution network equipment and facilities. Among the core distribution network equipment and facilities, the photovoltaic storage charging station equipment includes a grid-connected converter, photovoltaic cells, energy storage system, and charging load; The grid-connected converter uses feedforward decoupling to achieve independent control of dq components; Photovoltaic cells are distributed power sources and are equivalent to a constant current source model; The energy storage system includes an energy storage battery, which is equivalent to a controlled voltage source model with internal resistance; The charging load is the electric vehicle battery, which is equivalent to a controlled voltage source model with internal resistance. The average value modeling method is used to update the three-phase voltage source grid-connected converter, and the AC side node voltage equation and the DC side node voltage equation are obtained as follows: in, I a , I b , I c Flow into the AC side node a , b , c The current, u a , u b , u c For AC side a , b , c Three-phase voltage, u i , u j For DC side i , j Voltage, D is the duty cycle, is the conductivity, I i , , I j Flowing into the DC side node i , j Current; S4. Using the interactive communication layer of the distribution network digital twin space description application architecture constructed in step S1, information is exchanged between the basic component model obtained in step S2 and the core distribution network equipment and facility model obtained in step S3. During the loop calculation, the core distribution network equipment and facility first calls the control function, calculates the duty cycle of the current simulation step based on the control quantity at the previous moment, and updates the admittance matrix based on the average value model. In each cycle calculation process, the historical current of the current simulation step is first calculated from the branch voltage and branch current at the previous moment; The historical current of the current simulation step is combined with the equivalent current of the independent voltage source of the current simulation step to calculate the node injection current vector In; Solve the node voltage vector according to the network equation, and calculate the branch voltage vector and branch current vector based on the node voltage; The simulation results of each simulation step are recorded repeatedly until the simulation process is terminated. The simulation results include node voltage, charge and discharge power, and battery SOC.

2. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 1 is characterized in that: The interactive communication layer is used to establish a data interaction mechanism between the physical entity and model building layers, input the topological information, equipment parameters, monitoring quantities, and real-time operation data of the distribution network equipment and facilities, and realize the storage and preprocessing of data; The model building layer uses primary equipment, secondary equipment, and auxiliary sensing equipment as the basis of the digital twin model; The simulation analysis layer uses object-oriented modeling methods and electromagnetic transient simulation methods to read and monitor the environmental, electrical, physical and behavioral quantities of distribution network equipment and facilities, and collects voltage data at key system nodes, charge status of energy storage and charging equipment, and active power data.

3. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 1 is characterized in that: The differential modeling of the basic components of the distribution network equipment is as follows: The circuit network is numbered, and the node information and parameters of each device in the circuit topology are written into the read record file to complete the pre-storage of data; based on the pre-stored data, the backward Euler method is selected to perform differential modeling of basic components.

4. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 3 is characterized in that: Among basic components, the differential model of inductor for: in, Y L is the equivalent admittance of the inductor, is the historical current source, is the inductor terminal voltage, is the inductor current at the current moment.

5. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 1 is characterized in that: The mathematical model of the grid-connected converter is: in, is the proportional coefficient of the PI regulator, is the integral coefficient of the PI regulator, is a complex variable, is the AC side current q Axis component, for Current reference value, is the AC side inductance value, is the AC side line impedance, is the AC side current d Axis component, for Current reference value.

6. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 1 is characterized in that: Output voltage of the controlled voltage source model with internal resistance of the energy storage system U for: in, represents the internal electromotive force, SOC Indicates the battery's state of charge. is the characteristic constant of the battery, I is the discharge current of the energy storage battery, r Indicates the internal resistance of the battery.

7. A distribution network equipment modeling and interaction system based on digital twin application architecture, characterized in that: include: The architecture module builds a digital twin spatial description application architecture for the distribution network, which includes an interactive communication layer, a model building layer, and a simulation analysis layer. The basic module uses the constructed distribution network digital twin space to describe the application architecture and perform differential modeling of the basic components of the distribution network equipment; The core module uses the constructed distribution network digital twin space description application architecture to model the core distribution network equipment and facilities. Among the core distribution network equipment and facilities, the photovoltaic storage charging station equipment includes grid-connected converters, photovoltaic cells, energy storage systems, and charging loads. The grid-connected converter uses feedforward decoupling to achieve independent control of dq components; Photovoltaic cells are distributed power sources and are equivalent to a constant current source model; The energy storage system includes an energy storage battery, which is equivalent to a controlled voltage source model with internal resistance; The charging load is the electric vehicle battery, which is equivalent to a controlled voltage source model with internal resistance. The average value modeling method is used to update the three-phase voltage source grid-connected converter, and the AC side node voltage equation and the DC side node voltage equation are obtained as follows: in, I a , I b , I c Flow into the AC side node a , b , c The current, u a , u b , u c For AC side a , b , c Three-phase voltage, u i , u j For DC side i , j Voltage, D is the duty cycle, is the conductivity, I i , , I j Flowing into the DC side node i , j Current; The interaction module uses the constructed distribution network digital twin space description application architecture to exchange information between the established basic component models and the core distribution network equipment and facility models. During the cyclic calculation, the core distribution network equipment and facilities first call the control function, calculate the duty cycle of the current simulation step based on the control quantity at the previous moment, and update the admittance matrix based on the average value model; In each cycle calculation process, the historical current of the current simulation step is first calculated from the branch voltage and branch current at the previous moment; The historical current of the current simulation step is combined with the equivalent current of the independent voltage source of the current simulation step to calculate the node injection current vector In; Solve the node voltage vector according to the network equation, and calculate the branch voltage vector and branch current vector based on the node voltage; The simulation results of each simulation step are recorded repeatedly until the simulation process is terminated. The simulation results include node voltage, charge and discharge power, and battery SOC.

8. The distribution network equipment modeling and interactive system based on the digital twin application architecture according to claim 7 is characterized in that: In the architecture module, the interactive communication layer is used to establish a data interaction mechanism between the physical entity and model building layers, input the topology information, equipment parameters, monitoring quantities, and real-time operation data of the distribution network equipment and facilities, and realize data storage and preprocessing; The model building layer uses primary equipment, secondary equipment, and auxiliary sensing equipment as the basis of the digital twin model; The simulation analysis layer uses object-oriented modeling methods and electromagnetic transient simulation methods to read and monitor the environmental, electrical, physical and behavioral quantities of distribution network equipment and facilities, and collects voltage data at key system nodes, charge status of energy storage and charging equipment, and active power data.

9. The distribution network equipment modeling and interactive system based on the digital twin application architecture according to claim 7 is characterized in that: In the basic module, the basic components of the distribution network equipment are differentiated and modeled as follows: Number the circuit network and write the node information and parameters of each device in the circuit topology into the read record file to complete the data pre-storage; Based on the pre-stored data, the backward Euler method is selected to perform differential modeling on the basic components. Among the basic components, the differential model of the inductor for: in, Y L is the equivalent admittance of the inductor, is the historical current source, is the inductor terminal voltage, is the inductor current at the current moment.

10. The distribution network equipment modeling and interaction system based on the digital twin application architecture according to claim 9 is characterized in that: The mathematical model of the grid-connected converter is: in, is the proportional coefficient of the PI regulator, is the integral coefficient of the PI regulator, is a complex variable, is the AC side current q Axis component, for Current reference value, is the AC side inductance value, is the AC side line impedance, is the AC side current d Axis component, for Current reference value.

11. The distribution network equipment modeling and interaction system based on the digital twin application architecture according to claim 9 is characterized in that: Output voltage of the controlled voltage source model with internal resistance of the energy storage system U for: in, represents the internal electromotive force, SOC Indicates the battery's state of charge. is the characteristic constant of the battery, I is the discharge current of the energy storage battery, r Indicates the internal resistance of the battery.

12. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 6.

13. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method according to any one of claims 1 to 6.

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