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

Abstract

The invention discloses a distribution network equipment modeling interaction method and system based on a digital twin application architecture. The method comprises the following steps: constructing a distribution network digital twin space description application architecture comprising an interactive communication layer, a model building layer and a simulation analysis layer; the method comprises the following steps: describing an application architecture by using a distribution network digital twin space, and carrying out differential modeling on basic elements of distribution network equipment; using the constructed distribution network digital twin space to describe an application architecture, and modeling core distribution network equipment facilities; and describing an interactive communication layer of the application architecture by using the constructed digital twin space of the distribution network, and performing information interaction on the obtained basic element model and the core distribution network equipment facility model. According to the method, the simulation precision is improved, and the calculation efficiency is greatly improved; meanwhile, the accurate control of the system and the real-time updating of the electrical quantity are ensured through the dynamic calling of the control function, and powerful technical support is provided for the control strategy verification and the stability analysis of the power system.

Description

Background Art

[0002] As global air pollution and climate warming continue to intensify, reducing carbon emissions has become a consensus of the international community, and new energy vehicles have therefore received strong support and development. According to the New Energy Vehicle Industry Development Plan (2021-2035), the number of electric vehicles is entering a stage of rapid growth. It is expected that by 2025, the sales of new energy vehicles will account for 20% of the total vehicle sales; by 2035, pure electric vehicles will become the mainstream of sales. As an emerging form of load, electric vehicles have a great deal of randomness in load charging. The changing load of electric vehicles will change the power supply demand in a certain period of time, bringing certain negative impacts to the regional distribution network.

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

[0004] However, due to the access of renewable energy and a high proportion of power electronic equipment, the composition of distribution network equipment is becoming 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 puts higher requirements on transient process modeling and real-time simulation of distribution networks. Traditional simulation methods only model the dynamic process of a local power grid at a certain time and space scale. They cannot describe multiple power grid forms in the system at the same time, and there are problems such as large computational complexity and insufficient simulation efficiency, which makes it difficult to meet the needs of accurate simulation of new power systems.

[0005] Digital twin (DT) is based on technologies such as the Internet of Things, data middle platform, cloud computing, digital thread, big data analysis, and artificial intelligence. It is an integrated multi-scale, multi-physical quantity, and multi-probability modeling and simulation technology. The power grid industry has the characteristics of high value and simple technical requirements, and is very suitable as a leading scenario for the application of digital twins. Although some power companies have introduced digital twin technology, it is mainly used in the digital twinning of power plants and distribution plants, which is simply visualization. There are various problems with the application of other scenarios, and it has not formed a global and full life cycle application. Therefore, the digital twin technology of photovoltaic storage charging stations has obvious development potential and application value. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a distribution network equipment modeling interaction method and system based on the digital twin application architecture to address the deficiencies in the above-mentioned prior art, which is used to solve the technical problems that traditional simulation methods have large amount of calculation, insufficient efficiency, and difficulty in fully reflecting the dynamic process of power electronic equipment; the topology structure of photovoltaic storage charging stations is complex and the control methods are diverse; the solver model construction is time-consuming and difficult to realize in real time, and has better computing efficiency and better real-time performance.

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

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

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

[0010] S2. Using the distribution network digital twin space description application architecture constructed in step S1, differential modeling is performed on the basic components of the distribution network equipment;

[0011] S3. Use the distribution network digital twin space description application architecture constructed in step S1 to model the core distribution network equipment and facilities;

[0012] S4. Utilize the interactive communication layer of the distribution network digital twin space description application architecture constructed in step S1 to perform information exchange between the basic component model obtained in step S2 and the core distribution network equipment and facility model obtained in step S3.

[0013] Preferably, the interactive communication layer is used to establish a data interaction mechanism in the physical entity and model building layer, 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 the data;

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

[0015] 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 system key node voltages, charge status of energy storage and charging equipment, and active power data.

[0016] Preferably, the basic components of the distribution network equipment are modeled by differentiation as follows:

[0017] 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.

[0018] Preferably, in the basic element, 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] Among them, Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the historical current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current moment.

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

[0022] The grid-connected converter uses feed-forward decoupling to achieve independent control of dq components;

[0023] Photovoltaic cells are distributed power sources, equivalent to a constant current source model;

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

[0025] The charging load is the 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] Among them, 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 AC side current, for i q Current reference value, L is the inductance value on the AC side, R is the line impedance on the AC side, i d is the d-axis component of the AC side current, for i d Current reference value.

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

[0030] I a =-Y on ua +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 u b -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-Dc ) 2 Y on u j

[0037] Among them, I a , I b , I c is the current flowing into nodes a, b, c on the AC side, u a ,u b ,u c is the three-phase voltage of a, b, c on the AC side, u i ,u j is the DC side i, j voltage, D is the duty cycle, Y on is the conductivity, I i ,,I j is the current flowing into the DC side node i, j.

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

[0039]

[0040] Among them, V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is the 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, the core distribution network equipment and facilities first call the control function during the cyclic calculation, calculate the duty cycle of the current simulation step according to the control amount at the previous moment, and update the admittance matrix based on the average value model;

[0042] 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;

[0043] 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;

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

[0045] 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.

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

[0047] The architecture module builds a distribution network digital twin space description application architecture that includes an interactive communication layer, a model building layer, and a simulation analysis layer;

[0048] The basic module uses the constructed distribution network digital twin space to describe the application architecture and perform differential modeling on the basic components of the distribution network equipment;

[0049] The core module uses the constructed distribution network digital twin space to describe the application architecture and model the core distribution network equipment and facilities;

[0050] The interactive module uses the constructed distribution network digital twin space to describe the application architecture and conduct information interaction with the established basic component models and core distribution network equipment and facility models.

[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, 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 the data;

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

[0053] 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 system key node voltages, charge status of energy storage and charging equipment, and active power data.

[0054] Preferably, in the basic module, the basic elements of the distribution network equipment are modeled by differentiation as follows:

[0055] 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 on the basic components. Among the basic components, the differential model of the inductor i L (t) is:

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

[0057] Among them, Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the historical current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current moment.

[0058] Preferably, in the core distribution network equipment facilities, the photovoltaic storage charging station equipment includes a grid-connected converter, a photovoltaic cell, an energy storage system and a charging load;

[0059] The grid-connected converter uses feed-forward decoupling to achieve independent control of dq components;

[0060] Photovoltaic cells are distributed power sources, equivalent to a constant current source model;

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

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

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

[0064]

[0065] Among them, 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 AC side current, for i q Current reference value, L is the inductance value on the AC side, R is the line impedance on the AC side, i d is the d-axis component of the AC side current, for i d Current reference value;

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

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

[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 ui +(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] Among them, I a , I b , I c is the current flowing into nodes a, b, c on the AC side, u a ,u b ,u c is the three-phase voltage of a, b, c on the AC side, u i ,u j is the DC side i, j voltage, D is the duty cycle, Y on is the conductivity, I i ,,I j is the current flowing into the DC side node i, j.

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

[0076]

[0077] Among them, V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is the 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 distribution network equipment and facilities first call the control function during the cyclic calculation, calculate the duty cycle of the current simulation step according to the control amount at the previous moment, and update the admittance matrix based on the average value model;

[0079] 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;

[0080] 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;

[0081] Solve the node voltage vector according to the network equation, and calculate the branch voltage vector and branch current vector according to the node voltage;

[0082] 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.

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

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

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

[0086] The present invention provides a distribution network equipment modeling interaction method based on digital twin application architecture. It uses an object-oriented method to model the distribution network equipment of the photovoltaic storage charging station based on the electrical topology, combines the twin with the control strategy, and provides the equivalent model of the basic unit of the distribution network equipment and the control model of the power conversion unit; it adopts a hierarchical architecture to optimize the modeling and simulation interaction of the distribution network equipment, and divides the system into an interactive communication layer, a model building layer, and a simulation analysis layer. The functions of each layer are clear, ensuring the accuracy and real-time interaction of different equipment models. The hierarchical architecture is not only conducive to the efficient construction and flexible expansion of the model, but also improves the accuracy, real-time and scalability of the simulation system; by differentially modeling and data entry of the basic components and core equipment facilities of the distribution network equipment, and realizing information interaction between models with the help of the interactive communication layer, it effectively promotes the simulation analysis and data processing of the distribution network equipment, and further improves the model accuracy and information interaction efficiency of the distribution network equipment.

[0087] Furthermore, the interactive communication layer opens up the data interaction channel between the physical entity and the model building layer, realizes the automatic entry and processing of topological information, equipment parameters and real-time operation data, and ensures the efficiency and consistency of data transmission; the model building layer ensures the modularity and extensibility of the equipment model through object-oriented modeling, 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 performs real-time reading and monitoring of electrical quantities and physical quantities of distribution network equipment, ensuring that the simulation system has the ability of real-time analysis to support operation monitoring and decision-making in complex scenarios.

[0088] Furthermore, the basic components of the distribution network equipment were differentially modeled, and the electrical quantity changes of components such as inductors and capacitors were accurately simulated through differential processing, thereby improving the accuracy of simulation calculations. Differential modeling based on the backward Euler method helps to dynamically adjust the electrical quantities of components through changes in small time steps, ensuring the accuracy and stability of the equipment model during the simulation process. This method can accurately capture the changes in electrical parameters in the simulation, improve the accuracy and reliability of the simulation, and is particularly suitable for electromagnetic transient simulations with high dynamic changes.

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

[0090] Furthermore, the average value modeling method is adopted to improve the simulation efficiency of the converter and simplify the computational 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, so as to facilitate fast calculation and solution. The transient change process of the switch tube is characterized by the average value model, which reduces the computational requirements for high-frequency details in electromagnetic transient simulation and significantly improves the simulation efficiency. This method reduces the burden of simulation calculation, especially in large-scale power system simulation, speeds up the solution process, and ensures sufficient calculation accuracy.

[0091] Furthermore, 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, the equipment operation status is dynamically adjusted, and the real-time and accuracy of the simulation are ensured. Through the control function, the system can update the duty cycle in real time, and realize the real-time update of electrical quantities in combination with the average value model to ensure accurate control during the simulation process. This method effectively ensures the real-time adjustment of the control signal when the simulation system is running, and improves the control accuracy and the dynamic response capability of the system.

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

[0093] In summary, the present invention realizes efficient and accurate processing of electromagnetic transient simulation through the distribution network equipment modeling 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 performance of simulation analysis; the use of differential modeling and average value modeling not only improves the accuracy of simulation, but also greatly improves the calculation efficiency; at the same time, the dynamic calling of the control function ensures the precise control of the system and the real-time update of the electrical quantities, which provides strong technical support for the control strategy verification and stability analysis of the power system.

[0094] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0096] Figure 1 This is a diagram of the application architecture of the digital twin space description of the distribution network of the present invention;

[0097] Figure 2 This is the topological diagram of the storage and charging station;

[0098] Figure 3 This is the overall structure diagram of the simulation analysis layer;

[0099] Figure 4 It is the overall process of electromagnetic transient simulation algorithm;

[0100] Figure 5 is the equivalent circuit diagram of the battery;

[0101] FIG6 is a block diagram of battery charge and discharge control, wherein (a) is a constant voltage discharge mode, and (b) is a constant power charge mode;

[0102] Figure 7 It is the voltage and current double closed-loop control block diagram of the grid-connected converter;

[0103] Figure 8 is the topology diagram of the grid-connected converter;

[0104] Fig. 9 This is the member structure diagram of the "Battery" class;

[0105] Fig.10 This is the simulation data result diagram;

[0106] Figure 11 is a comparison diagram of the simulator and PLECS simulation waveforms, where (a) is the DC bus voltage waveform, (b) is the energy storage battery charging power waveform, (c) is the charging pile charging power waveform, and (d) is the energy storage battery state of charge waveform;

[0107] Fig.12 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0108] Fig.13 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0109] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

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

[0112] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

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

[0114] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0115] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

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

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

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

[0119] See also Figure 1 ,In order to realize real-time update of data and ensure real-time ,evolution of the model, the spatial description application architecture of the ,distribution network digital twin is divided into interactive communication layer, ,model building layer, and simulation analysis layer.

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

[0121] The model building layer is composed of three types of equipment: primary equipment, secondary equipment, and auxiliary sensing equipment as the basis of the digital twin model. Taking the photovoltaic storage charging station distribution network equipment as an example, it mainly includes AC power grid, distributed power supply, energy storage, charging load and other equipment. The AC power grid uses a three-phase voltage source converter (VSC) to access the DC bus. The energy storage and charging equipment are connected to the system using different power electronic equipment according to their own charging and discharging characteristics; the overall structure of the photovoltaic storage charging station distribution network equipment access model is as follows Figure 2 shown.

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

[0123] On the one hand, it improves the accuracy of complex physical mechanism modeling; on the other hand, it provides a data basis for system status evaluation analysis and decision-making deduction; the schematic diagram of the simulation analysis layer is shown in Figure 3 shown.

[0124] The object-oriented modeling method uses objects as the basic unit to build the entire system. Objects are defined as a combination of data and operations. Objects can be understood as containers that encapsulate attributes and methods. Attributes are used to describe the state and characteristics of objects, and methods are used to describe the behavior and operations of objects. Some objects are abstracted based on their commonalities and aggregated into classes. A class represents the abstraction of the above-mentioned class of objects, and each object represents an instance of the class. The object-oriented structure has three main characteristics: encapsulation, inheritance, and polymorphism. These characteristics 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 such objects are abstracted into commonalities to form corresponding device classes, and their related attribute parameters and general operation methods are encapsulated into an indivisible unit to hide information details that do not need to be known by the outside world.

[0125] In order to ensure universality, it is necessary to select a single type of equipment as the minimum unit at a lower level. For example, it is better to create "BuckConverter" and "Battery" classes separately, rather than just creating a "charging station" class. On this basis, different classes can be linked. The charging station is composed of a buck converter and a rechargeable battery, while the energy storage device is composed of a half-bridge converter and a rechargeable battery. The "Battery" class is reused, and only the parameters of the rechargeable battery part of the two types of equipment need to be modified, which is convenient for code management and expansion. 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 shared by all its derived classes. The abstract class Sim_CATL is at the top level of the entire model structure, defining the outermost electromagnetic transient calculation function, instances of each distribution equipment class, and key electrical quantity attributes such as voltage and current of the complete electrical topology. Some of the function bodies then call the function members of each distribution equipment subclass instantiation. 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 phase at the end of simulation, respectively, which include various electromagnetic transient calculation functions defined in the Sim_CATL class.

[0126] See also Figure 4, an electromagnetic transient method based on electrical topology is used to perform object-oriented system modeling and simulation operation of the photovoltaic charging station equipment. First, the object-oriented system modeling and simulation operation of the photovoltaic charging station equipment are performed. The initialization part first completes the pre-storage of data and the modeling of basic components. On this basis, the core distribution network equipment facilities are modeled and the electrical quantities of the equipment are initialized; the loop part first calls the control function to update the node admittance matrix, calculates the historical current and the injected current, and after the node voltage equation is solved, the node voltage and branch current are updated, and finally the timer is triggered to enter the next loop; 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, differential modeling of basic components based on electrical topology is performed;

[0128] S201. Number the circuit network based on the interactive communication layer, and write the node information and parameters of each device in the circuit topology into the read record file to complete the pre-storage of data; the pre-stored data is used as the modeling parameters of the basic components and core equipment facilities.

[0129] S202. Based on the model building layer, the basic components are differentially modeled. After the components are differentially modeled, the initial node admittance matrix of the components is written out from the equivalent admittance columns of the components, and then the loop body is entered to prepare for the loop to solve the node voltage equation. For the basic components composed of resistors, inductors, capacitors, etc., their admittance matrix does not change with time during the loop process, so there is no need to reconstruct it.

[0130] Differential modeling such as capacitors, inductors, resistors and their series-parallel combinations on the DC side, series-parallel combinations of three-phase capacitors, three-phase inductors and three-phase resistors in AC circuits, AC and DC voltage sources, etc. The backward Euler method is selected for differential analysis to ensure the numerical stability of the system.

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

[0132] Assume that the node i and the node j are connected by an inductor L, and the characteristic equation is expressed as:

[0133]

[0134] Differencing it using the backward Euler method:

[0135]

[0136] After finishing:

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

[0138]

[0139] I h_L (t-Δt)=i L (t-Δt)

[0140] Among them, Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the historical current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current moment, i L (t-Δt) is the inductor current at the previous moment, and L is the inductance value.

[0141] S3. Use the distribution network digital twin space description application architecture constructed in step S1 to model the core distribution network equipment and facilities;

[0142] S301. Establish a mathematical model of core distribution network equipment and facilities and control methods:

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

[0144] The model part is divided into unit components and interface converters, and the interface converter model calls the algorithm of the control part to improve the scalability and versatility of the model.

[0145] Photovoltaic cells are distributed power sources that transmit power to the microgrid via the DC bus. Usually, photovoltaic cells work in maximum power point tracking mode. Since the DC bus voltage is approximately constant, photovoltaic cells can be equivalent to a constant current source model.

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

[0147] Energy storage battery units are generally batteries. In order to reflect the battery charge state and the change of battery terminal voltage, it is necessary to model the battery in detail. It can be equivalent to a controlled voltage source with internal resistance. The equivalent circuit model is as follows: Figure 5 shown.

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

[0149] U=V-Ir(4)

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

[0151] According to the discharge characteristic curve of the battery, the output voltage V of the controlled voltage source is:

[0152]

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

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

[0155]

[0156] At the same time, the state of charge SOC is obtained by the ratio of the battery's charge AH1 to its capacity AH:

[0157]

[0158] Given the charge AH1 and terminal voltage V1 of the battery in a certain state, the corresponding state of charge SOC can be calculated, and then the characteristic constant β of the battery can be calculated according to formula (2).

[0159] The power conversion unit of the energy storage system adopts a bidirectional DC / DC converter, which realizes the bidirectional flow of electric energy. The bidirectional DC / DC converter can work in boost mode or buck mode. When the electric energy generated by the distributed power source is in excess, the battery stores the electric energy, and the bidirectional buck / boost converter works in buck mode, at which time the power flows from the grid side to the battery side; when the electric energy generated by the distributed power source is not enough to meet all loads, the battery releases the electric energy, and the bidirectional buck / boost converter works in boost mode, at which time the power flows from the battery side to the grid side.

[0160] To achieve the above purpose, the bidirectional DC / DC converter has two working modes: constant voltage discharge and constant power charging.

[0161] During constant voltage discharge, the battery aims to adjust the DC bus voltage so that the DC bus voltage returns to the rated value and maintains the power balance in the entire microgrid. The control structure of the battery constant voltage discharge is shown in Figure 6(a). b ,I dis-ref They are the actual value of the battery charge and discharge current and the reference value of the inner loop battery discharge current obtained by the voltage outer loop. Constant voltage discharge is achieved by voltage and current dual loop control, with the outer loop being the voltage loop and the inner loop being the current loop. In the voltage outer loop, the actual DC bus voltage V is collected. dc Compare it with the reference voltage V dc-refThe difference is sent to the PI regulator and used as the inner loop current reference value I after a current limiting link. dis-ref Enters the current loop and compares 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 according to 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 the battery of the electric vehicle, which is connected to the DC bus through a buck converter to achieve unidirectional charging. The battery unit of the charging load is similar to the energy storage battery unit, so it can be modeled according to the equivalent model of the energy storage battery. The power conversion unit of the charging load is a buck converter. Since the battery terminal voltage is lower than the DC bus voltage, a step-down buck converter is used for charging. At the same time, it can also prevent reverse current input and avoid the problem of reverse charging of the car 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 two-way flow of energy. When the output of distributed power sources is greater than the power consumed by the charging load, the energy flows to the grid side through the grid-connected converter, and the excess electric energy is consumed by the distribution network side; when the output of distributed power sources is less than the power consumed by the charging load, the 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 as Figure 7 The voltage outer loop is used to stabilize the DC bus voltage, modulate the system's working state according to the voltage, and output a command current signal to the current inner loop. The function of the current inner loop is to enable the input current of the system to follow the command current signal, thereby realizing the bidirectional conversion of electric energy under unity power factor.

[0169] In the three-phase stationary coordinate system, since the control system is difficult to design and difficult to achieve zero-static error control, it is necessary to transform it into a two-phase synchronous rotating coordinate system. At the same time, due to the existence of the system AC side inductance, there is a phenomenon of mutual coupling and mutual influence between the dq components of the system, so the feedforward decoupling method is used to achieve independent control of the dq components to improve the dynamic response performance of the system. Based on the LC filter grid-connected interface converter topology, the mathematical model in the dq coordinate system is:

[0170]

[0171] Performing Laplace transform on it yields:

[0172]

[0173] After analysis, it is found that in the two-phase synchronous rotating coordinates, there is a coupling phenomenon between the dq components of the mathematical model of the system, so it is necessary to decouple the mathematical model of the system. Because feedforward decoupling has simple control and does not affect the stability of the system, feedforward decoupling control is introduced in the closed-loop system. Add the feedforward amount ωLi to the output voltage d and -ωLi q , so that it is coupled with the coupling term -ωLi in the circuit topology mathematical model d and ωLi q They offset each other to achieve the goal of decoupling.

[0174] After decoupling, the dq components of the system are transformed into two independent parts. At this time, the AC control of the system is changed to DC control. In the above rotating coordinate transformation, the d axis represents the active component, the q axis represents the reactive component, and the q axis is specified to lag 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 achieving unity power factor rectification of the system.

[0175] Under the condition of three-phase power grid balance, the voltage command formula under three-phase synchronous rotating coordinates is:

[0176]

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

[0178]

[0179] It can be concluded that the coupling component in the two-phase synchronous rotating coordinate system has been completely decomposed, and the proportional coefficient K of the PI regulator is reasonably designed. ip and the integral coefficient K il , the voltage and current dual closed-loop control can be achieved.

[0180] S302: After determining the mathematical model of the core distribution network equipment and facilities and the control method in step S301, a corresponding electromagnetic transient simulation model is established;

[0181] In the whole simulation method, for the simulation of power electronics system level, large step simulation is used to reduce the number of calculations per unit time, thereby reducing the amount of calculations and facilitating real-time. Therefore, the traditional binary resistance model and constant admittance model are no longer applicable, and the 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 switch function model, and the average value of the variable in the switch cycle is used to replace its actual value to obtain the functional relationship between its control signal duty cycle and the port voltage and current vector, and then update the node admittance matrix and 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, ignoring the high-order harmonics generated by pulse width modulation (PWM), thus allowing a larger simulation step size to be used, which greatly improves the simulation efficiency by sacrificing a certain degree of accuracy.

[0182] The following takes the three-phase voltage source grid-connected converter as an example to model its average value:

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

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

[0185]

[0186] Among them, u a ,u b ,u c is the three-phase voltage of a, b, c on the AC side, u i ,u j is the voltage at DC side i, j, I a , I b , I c is the current flowing into the AC side nodes a, b, c.

[0187] According to the KCL law, the DC port current is averaged:

[0188]

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

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

[0191]

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

[0193]

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

[0195] According to this method, other power electronic devices such as buck converters, bidirectional DC / DC converters, etc. can be modeled based on the port voltage-current relationship, which will not be described in detail here.

[0196] S303, using the simulation analysis layer of the distribution network digital twin space description application architecture constructed in step S1 to simulate the operation of the photovoltaic storage charging station.

[0197] After the component differential modeling and the establishment of the electromagnetic transient model of the core equipment and facilities, the initial node admittance matrix of the component can be written out, and then the loop body is entered to prepare for the cyclic solution of the node voltage equation. Since the node admittance matrix of some components is time-varying, such as the interface converter, it is necessary to rebuild the admittance matrix. For the basic components composed of resistors, inductors, capacitors, etc., their admittance matrix has been determined before the simulation begins, so no modification is required, thereby saving computing resources and improving simulation efficiency.

[0198] At the beginning of the EMTP program calculation, the historical current item is set according to different initialization methods, and the initial value of the historical current item is usually 0.

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

[0200] Different from the calculation process of basic components, the core distribution network equipment and facilities need to call the control function first during the cyclic calculation, calculate the duty cycle of the current simulation step according to the control quantity at the previous moment, and update the admittance matrix based on the average value model.

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

[0202] 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.

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

[0204] This cycle repeats and records the simulation results of each simulation step 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 component model obtained in step S2 and the core distribution network equipment facility model obtained in step S3, the equipment and component information are interacted.

[0206] By constructing the interactive communication layer of the distribution network digital twin space description application architecture, information such as equipment type, node number, and basic parameters are entered into the electromagnetic transient model.

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

[0208] For the core distribution network equipment and facility model, in addition to the basic electrical parameters, the control parameters of the equipment (such as switch status, control algorithm, etc.) are also introduced. In each simulation step, iterative calculations are performed based on the current electrical parameters and control parameters to generate control variables such as duty cycle, and then update the related node admittance matrix. By updating the node admittance matrix, it is ensured that the equipment reflects the role of the control strategy in real time during the simulation process, and ultimately achieves accurate electrical quantity updates.

[0209] During the simulation process, due to the electromagnetic transient decoupling characteristics between the basic component model and the core distribution network equipment and facility model, the two can run independently, so the simulation efficiency can be improved through parallel processing. During the parameter entry stage and the initialization of the admittance matrix, node voltage and current vector, the two models perform related calculations independently without affecting 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 node admittance matrix is ​​updated, the basic component model and the core distribution network equipment and facility model are transformed from parallel operation to serial execution. At this point, the system begins to execute subsequent steps such as historical current calculation and node admittance matrix solution, and finally realizes data information interaction and feedback processing between the basic components 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] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0212] In another embodiment of the present invention, a distribution network equipment modeling interaction system based on the digital twin application architecture is provided. The system 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] Among them, the architecture module builds 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 to describe the application architecture and perform differential modeling on the basic components of the distribution network equipment;

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

[0216] The interactive module uses the constructed distribution network digital twin space to describe the application architecture and conducts information interaction with the established basic component models and core distribution network equipment and facility models.

[0217] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or 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 devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distribution network equipment modeling interaction method based on the digital twin application architecture, including:

[0218] Construct a distribution network digital twin space description application architecture that includes an interactive communication layer, a model building layer, and a simulation analysis layer; use the distribution network digital twin space description application architecture to perform differential modeling on the basic components of the distribution network equipment; use the constructed distribution network digital twin space description application architecture to model the core distribution network equipment and facilities; use the interactive communication layer of the constructed distribution network digital twin space description application architecture to perform information exchange between the obtained basic component models and the core distribution network equipment and facility models.

[0219] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions 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] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0221] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0222] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the distribution network equipment modeling interaction method based on the digital twin application architecture in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0223] Construct a distribution network digital twin space description application architecture that includes an interactive communication layer, a model building layer, and a simulation analysis layer; use the distribution network digital twin space description application architecture to perform differential modeling on the basic components of the distribution network equipment; use the constructed distribution network digital twin space description application architecture to model the core distribution network equipment and facilities; use the interactive communication layer of the constructed distribution network digital twin space description application architecture to perform information exchange between the obtained basic component models and the core distribution network equipment and facility models.

[0224] See also Fig.12 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the distribution network device modeling interaction method based on the digital twin application architecture in the embodiment is implemented. To avoid repetition, it is not described here one by one. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the distribution network device modeling interaction system based on the digital twin application architecture in the embodiment are implemented. To avoid repetition, it is not described here one by one.

[0225] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Fig.12 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

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

[0227] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may 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] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0229] Any reference to memory, database or other media used in the embodiments provided in the present application 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0230] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0231] See also Fig.13 The terminal device 600 is an electronic device, and the electronic device is in the form of a general 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), a display unit 640, etc.

[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 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0234] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0235] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0236] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0237] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0238] In order to verify the solution accuracy and efficiency improvement effect of the method of the present invention, a control model of a photovoltaic storage microgrid power generation system was built on the PLECS platform, and the accuracy of the results obtained by the method of the present invention and the traditional node analysis method was compared. The electromagnetic transient algorithm of object-oriented average value modeling has a basically consistent response curve with the node analysis method, whether in steady state or transient conditions, which verifies the correctness of this method as an electromagnetic transient simulation algorithm.

[0239] For ease of understanding, the present invention will be described below using a photovoltaic charging station system as an example, but this should not limit the scope of protection of the present invention.

[0240] The object-oriented distribution network equipment model is the core of the distribution network digital twin and the basis for realizing simulation calculation and data analysis. The object-oriented modeling method uses objects as the basic unit to build the entire system. Objects are defined as a combination of data and operations. Objects can be understood as containers that encapsulate attributes and methods. Attributes are used to describe the state and characteristics of objects, and methods are used to describe the behavior and operation of objects. Some objects are abstracted according to their commonalities and aggregated into classes. Classes represent the abstraction of the above-mentioned class of objects, and each object represents an instance of the class. The object-oriented structure has three main characteristics: encapsulation, inheritance, and polymorphism. These characteristics 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 such objects are abstracted into commonalities to form corresponding device classes, and their related attribute parameters and general operation methods (functions) are encapsulated into an indivisible unit to hide information details that do not need to be known by the outside world.

[0241] The definition of a class is essentially the definition of its members. The attributes of a class correspond to its member variables, and the behavior of a class corresponds to its 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 both within and outside the class. Protected members can be accessed by this class and its derived classes. Private members can only be accessed by the class itself. These access level restrictions enable different degrees of protection and concealment of class members, thereby reducing the correlation between programs and preventing the "butterfly effect". For example Fig. 9 As shown in the figure, the "Battery" class includes two types: public members and protected members. Public members are functions included in the electromagnetic transient simulation algorithm process, and each device class needs to call these functions, while protected members are properties unique to rechargeable batteries, such as the positive and negative nodes of the battery, capacity, internal resistance, initial state of charge, etc.

[0242] In order to ensure universality, it is necessary to select a single type of equipment as the smallest unit at a lower level. For example, it is better to create "BuckConverter" and "Battery" classes separately, rather than just creating a "charging station" class. On this basis, different classes can be linked. The charging station is composed of a buck converter and a rechargeable battery, while the energy storage device is composed of a half-bridge converter and a rechargeable battery. The "Battery" class is reused, and only the parameters of the rechargeable battery part of the two types of equipment need to be modified, which is convenient for code management and expansion. 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 shared by all its derived classes. The abstract class Sim_CATL is at the top level of the entire model structure, defining the outermost electromagnetic transient calculation function, instances of each distribution equipment class, and key electrical quantity attributes such as voltage and current of the complete electrical topology. Some of the function bodies then call the function members of each distribution equipment subclass instantiation. 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 is designed based on the B / S architecture. The front end is developed with Vue and the code is edited in VScode. The back end uses the Spring Cloud microservice architecture and is developed in Java. The core simulation algorithm is written in C++ and compiled into a dynamic library and run by program call. The program runs in the Docker container environment and is managed by K8S. The web container and proxy are deployed using Tomcat and Nginx, and the microservice registration and configuration center is managed using Nacos. The program also integrates OpenLayer as a 2D geographic information engine, and uses Cesium and WebGL to implement a 3D engine based on the B / S architecture. Neo4j is used for storage and management of the graph database, and MySQL is used for the relational database to store simulation data, inspection data, and spatial data. The simulation program realizes accurate simulation and data management of electromagnetic transients.

[0244] Set the simulation step, 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 electrode), n2 (negative electrode), 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, 0.74Buck 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] n1, 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] n1(Uin+), n2(Uin-), n3(Uo+), numSOC

[0291] 1,2,5,6

[0292] Photovoltaic modules:

[0293] n1(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 cycle, update the node admittance matrix based on the average value model, calculate the historical current and injection current, solve the node voltage equation, update the node voltage and branch current, update the node voltage and branch current, and wait for the timer to trigger the next loop. If the simulation time reaches the set time, stop the loop, output the node voltage, DC bus voltage drop, energy storage battery power, charging battery power, battery charge state, etc. and save them in the simlog.txt file. Some file data are as follows Fig.10 shown.

[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 charge state curve.

[0303] The timing performance of the simulation is shown in the figure below. When the total number of cycles is 4000, the number of single cycles exceeding 1.1 ms does not exceed 500 times, and the number of cycles below 0.9 ms does not exceed 600 times, which indicates that the present invention has good timing performance.

[0304]

[0305] In order to verify the accuracy and efficiency improvement of the simulator, a control model of the photovoltaic energy storage microgrid power generation system was built on the PLECS platform. The simulation parameters are the same as those of the electromagnetic transient simulator:

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

[0307] Photovoltaic micro power supply simulation parameters: Use 40A constant current source.

[0308] Energy storage micro power supply simulation parameters: bidirectional DC-DC circuit inductance L = 0.001H, battery internal resistance 0.01Ω, full SOC voltage V = 600V, initial SOC = 0.7.

[0309] AC power supply simulation parameters: phase voltage effective value: 220V, frequency: 50Hz, AC side inductance of grid-connected inverter: 0.3mH, AC side resistance: 0.01Ω, DC side filter capacitor: 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 operation conditions are as follows:

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

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

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

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

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

[0317]

[0318] In order to verify the solving accuracy and efficiency improvement effect of the simulator, a control model of a photovoltaic energy storage microgrid power generation system was built on the PLECS platform. The solution accuracy and simulation efficiency of the method of the present invention were compared with those of the traditional node analysis method. Compared with traditional offline simulation software, the method has better computing efficiency. 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 present invention provides an interactive method and system for modeling distribution network equipment based on a digital twin application architecture, which performs differential modeling on basic components and average value modeling on power electronic converters. A systematic electromagnetic transient method is developed on the simulator, which fully utilizes the hardware advantages of the simulator and realizes real-time simulation of the power system of a photovoltaic charging station with a millisecond step size.

[0320] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0321] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

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

[0323] In the embodiments provided by the present invention, 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. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0324] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0325] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0326] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained 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, computer-readable media do not include electric carrier signals and telecommunication signals.

[0327] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0328] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0329] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0330] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A distribution network equipment modeling interaction method based on digital twin application architecture, characterized in that: The following steps are involved: S1. Construct a distribution network digital twin spatial description application architecture including 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, differential modeling is performed on the basic components of the distribution network equipment; S3. Use the distribution network digital twin space description application architecture constructed in step S1 to model the core distribution network equipment and facilities; S4. Utilize the interactive communication layer of the distribution network digital twin space description application architecture constructed in step S1 to perform information exchange between the basic component model obtained in step S2 and the core distribution network equipment and facility model obtained in step S3.

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 layer, input the topological information, equipment parameters, monitoring quantity, 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 system key node voltages, charge states 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 basic components of the distribution network equipment are differentiated and modeled 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 the basic components, the differential model of the inductor is L (t) is: i L (t)=Y L u L (t)+I h_L (t-Δt) Among them, YL is the equivalent admittance of the inductor, I h_L (t-Δt) is the historical current source, u L (t) is the inductor terminal voltage, i L (t) 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: 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 feed-forward decoupling to achieve independent control of dq components; Photovoltaic cells are distributed power sources, 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, and the electric vehicle battery is equivalent to a controlled voltage source model with internal resistance.

6. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 5 is characterized in that: The mathematical model of the grid-connected converter is: Among them, 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 AC side current, for i q Current reference value, L is the inductance value on the AC side, R is the line impedance on the AC side, i d is the d-axis component of the AC side current, for i d Current reference value.

7. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 6 is characterized in that: The three-phase voltage source grid-connected converter is updated by the average value modeling method, and the AC side node voltage equation and the DC side node voltage equation are obtained as follows: I a -Yonu a +D a Y on u i +(1-D a )Y on u j I b =-Y on u b +D b Y on u i +(1-D b )Y on u j I c =-Y on u c +D c Y on u i +(1-D c )Y on u j 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 +(1-D b )Y on u b -D b (1-D b )Y on u i -(1-D b ) 2 Y on u j +(1-D c )Y on u c -D c (1-D c )Y on u i -(1-D c ) 2 Y on u j Among them, I a , I b , I c is the current flowing into nodes a, b, c on the AC side, u a ,u b ,u c is the three-phase voltage of a, b, c on the AC side, u i ,u j is the DC side i, j voltage, D is the duty cycle, Y on is the conductivity, I i ,,I j is the current flowing into the DC side node i, j.

8. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 5 is characterized in that: The output voltage U of the controlled voltage source model with internal resistance of the energy storage system is: Among them, V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is the characteristic constant of the battery, I is the discharge current of the energy storage battery, and r represents the internal resistance of the battery.

9. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 1 is characterized in that: When the core distribution network equipment and facilities perform cyclic calculations, they 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 according to 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.

10. A distribution network equipment modeling interaction system based on digital twin application architecture, characterized in that: include: The architecture module builds a distribution network digital twin space description application architecture that 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 on the basic components of the distribution network equipment; The core module uses the constructed distribution network digital twin space to describe the application architecture and model the core distribution network equipment and facilities; The interactive module uses the constructed distribution network digital twin space to describe the application architecture and conduct information interaction with the established basic component models and core distribution network equipment and facility models.

11. The distribution network equipment modeling interaction system based on the digital twin application architecture according to claim 10 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 layer, input the topological information, equipment parameters, monitoring quantity, 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 system key node voltages, charge status of energy storage and charging equipment, and active power data.

12. The distribution network equipment modeling interaction system based on the digital twin application architecture according to claim 10, characterized in that: In the basic module, the basic components of the distribution network equipment are differentiated and modeled 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 on the basic components. Among the basic components, the differential model of the inductor i L (t) is: i L (t)=Y L u L (t)+I h_L (t-Δt) Among them, Y L is the equivalent admittance of the inductor, I h_L (t-Δt) is the historical current source, u L (t) is the inductor terminal voltage, i L (t) is the inductor current at the current moment.

13. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 10 is characterized in that: 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 feed-forward decoupling to achieve independent control of dq components; Photovoltaic cells are distributed power sources, 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, and the electric vehicle battery is equivalent to a controlled voltage source model with internal resistance.

14. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 13 is characterized in that: The mathematical model of the grid-connected converter is: Among them, 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 AC side current, for i q Current reference value, L is the inductance value on the AC side, R is the line impedance on the AC side, i d is the d-axis component of the AC side current, for i d Current reference value; The three-phase voltage source grid-connected converter is updated by the average value modeling method, and the AC side node voltage equation and the DC side node voltage equation are obtained as follows: I a =-Y on u a +D a Y on u i +(1-D a )Y on u j I b =-Y on u b +D b Y on u i +(1-D b )Y on u j I c =-Y on u c +D c Y on u i +(1-D c )Y on u j 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 +(1-D b )Y on u b -D b (1-D b )Y on u i -(1-D b ) 2 Y on u j +(1-D c )Y on u c -D c (1-D c )Y on u i -(1-D c ) 2 Y on u j Among them, I a , I b , I c is the current flowing into nodes a, b, c on the AC side, u a ,u b ,u c is the three-phase voltage of a, b, c on the AC side, u i ,u j is the DC side i, j voltage, D is the duty cycle, Y on is the conductivity, I i ,,I j is the current flowing into the DC side node i, j.

15. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 13 is characterized in that: The output voltage U of the controlled voltage source model with internal resistance of the energy storage system is: Among them, V0 represents the internal electromotive force, SOC represents the state of charge of the battery, β is the characteristic constant of the battery, I is the discharge current of the energy storage battery, and r represents the internal resistance of the battery.

16. The distribution network equipment modeling interaction method based on the digital twin application architecture according to claim 10 is characterized in that: When the core distribution network equipment and facilities perform cyclic calculations, they 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 according to 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.

17. 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 9.

18. 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 9.

Images