Digital twin operation simulation system and method based on power distribution network modular development

Abstract

The invention discloses a digital twin operation simulation system and method based on modular development of a power distribution network. The system and method can realize efficient management and optimization of the power distribution network. Through the modular design, the system can flexibly meet the specific requirements of different partitions, and the reliability and stability of the power distribution network are improved. Meanwhile, through the digital twinning technology, the actual operation condition of the power distribution network can be simulated in the virtual environment, and potential problems can be found in advance and optimized, so that the risk and cost in actual operation are reduced. In addition, the system has good expansibility, function expansion and scene simulation can be carried out according to actual requirements, and support is provided for long-term development of the power distribution network. The visualization and user interface module enables a user to intuitively understand the operation state of the power distribution network, thereby facilitating decision making and operation.

Description

Technical Field

[0001] The present invention relates to the technical field of modular digital twin operation simulation of distribution networks, and in particular to a digital twin operation simulation system and method based on modular development of distribution networks. Background Art

[0002] In today's power system, the management and maintenance of distribution networks face unprecedented challenges. With the continuous expansion of the scale of distribution networks and their increasing complexity, traditional monitoring and control methods can no longer meet the needs of modern power systems. These traditional methods have many deficiencies in real-time, flexibility and accuracy, resulting in low efficiency in the management and maintenance of distribution networks and difficulty in dealing with various emergencies.

[0003] In order to meet these challenges, it is particularly important to develop a digital twin operation simulation system based on modular development. This system can achieve real-time monitoring and precise control of the distribution network through modular design, and improve the management efficiency and reliability of the distribution network. The advantage of modular development lies in its flexibility and scalability. It can be customized and expanded according to the actual needs of the distribution network, so as to better adapt to the complexity and changes of the distribution network.

[0004] The application of digital twin technology enables the operation simulation system to simulate the operation status of the distribution network more realistically and provide more accurate data support. Through digital twin technology, various simulation operations can be performed on the distribution network in a virtual environment, potential problems can be discovered in advance and optimized and adjusted, thereby improving the operation efficiency and safety of the distribution network. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a digital twin operation simulation system and method based on modular development of distribution network, which can solve the problems mentioned in the background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a digital twin operation simulation system based on modular development of a distribution network, comprising:

[0010] Power grid modular design and data acquisition module, data processing module, model building and simulation module, integration and expansion module, visualization and user interface module and cloud server;

[0011] The power grid modular design and data acquisition module is used to divide the target distribution network into several partitions, and obtain the real-time data of sensors configured in different partitions of the target distribution network, and transmit the obtained real-time data partitions to the data processing module;

[0012] The data processing module acquires the real-time data transmitted from the power grid modular design and data acquisition module, and processes the real-time data. Different partitions use different preset data processing methods, and different preset data processing methods at least include filtering, scaling and outlier processing operations, and the real-time data after data processing is sent to the model construction and simulation module.

[0013] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, it also includes:

[0014] The model construction and simulation module establishes digital twin models of distribution network modules in different partitions according to the real-time data after partitioned data processing transmitted by the data processing module, performs simulation verification on different models, and transmits the digital twin models of distribution network modules in different partitions that meet the simulation verification conditions to the integration and expansion module;

[0015] The integration and expansion module is used to integrate the digital twin models of distribution network modules in different partitions transmitted to the model construction and simulation module, and compare the integrated model with the actual scenario of the target distribution network, supplement the missing scenario model of the target distribution network through extended functions, and transmit the integrated and expanded model to the cloud server.

[0016] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, it also includes:

[0017] The visualization and user interface module is used for users to retrieve the integrated and extended model from the cloud server and display and control it through the visualization unit. Users can also export the integrated and extended model in the cloud server through the visualization and user interface module;

[0018] The cloud server is used to provide data storage and cloud computing functions for the power grid modular design and data acquisition module, data processing module, model construction and simulation module, integration and expansion module, and visualization and user interface module.

[0019] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, wherein: the modular design and data acquisition module of the power grid includes: a partition design unit, a sensor deployment unit and a data collection and transmission unit;

[0020] The partition design unit is used to formulate a first partition standard based on the physical characteristics, load type, and importance of the power grid, and to partition the entire target distribution network into a plurality of partitions according to the first partition standard, and transmit the partition result to the sensor deployment unit;

[0021] The sensor deployment unit is used to deploy sensors at the target nodes of each partition, and the types of the deployed sensors include at least voltage sensors and current sensors;

[0022] The data collection and transmission unit is used to collect data from various sensors in different partitions, and send the data to the data processing module in a predetermined partition format.

[0023] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, wherein: the model construction and simulation module includes: a model construction unit, a simulation execution unit and a verification and evaluation unit;

[0024] The model building unit receives the processed data transmitted from the power grid modular design and data acquisition module, selects target power grid performance characteristic variables, and establishes a digital twin model of the distribution network module based on different partitions based on the characteristic variables;

[0025] The simulation execution unit is used to perform simulation tests on the constructed digital twin model of the distribution network module, simulate different operating states or fault conditions, and transmit the simulation results to the verification and evaluation unit;

[0026] The verification and evaluation unit receives the evaluation simulation result transmitted from the simulation execution unit, and determines whether the simulation results of the digital twin model of different distribution network modules meet the first evaluation rule in combination with the preset first evaluation rule;

[0027] If the first evaluation rule is met, the digital twin model of the distribution network module determined by the evaluation is transmitted to the integration and expansion module;

[0028] If the first evaluation rule is not met, the model training is performed again until all models meet the first evaluation rule.

[0029] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, wherein: the integration and expansion module includes: a model integration unit, a scene matching unit and an expansion function development unit;

[0030] The model integration unit receives the digital twin models of distribution network modules in different partitions that meet the first evaluation rule transmitted from the verification and evaluation unit, and imports the digital twin models of distribution network modules in each partition into a unified framework, and defines an interface for interaction between models, so that each model communicates with each other and exchanges data, and the parameters of all partition models remain consistent globally;

[0031] The scenario matching unit collects the operation data of the real target distribution network in different time periods, and uses the model integrated by the model integration unit to simulate the operation of the distribution network in the same time period, compares the simulation result of the model with the actual operation data, calculates the deviation between the model prediction value and the actual value, and determines whether the deviation value meets the first deviation threshold;

[0032] If the first deviation threshold is not met, the deviation between the model prediction value and the actual value is output to the extended function development unit, and the extended function development unit re-establishes a new model according to the deviation.

[0033] As a preferred solution of the digital twin operation simulation system based on modular development of distribution network described in the present invention, the visualization and user interface module includes: a graphical user interface design unit, an interactive control unit and a data export and sharing unit.

[0034] In a second aspect, the present invention provides a digital twin operation simulation method based on modular development of a distribution network, comprising:

[0035] Divide the target distribution network into zones, obtain real-time data of preset sensors in different zones, and pre-process the real-time data;

[0036] According to the preprocessing results and in combination with the partitioning results, a digital twin model of the distribution network module for different partitions is established;

[0037] Integrate the digital twin models of distribution network modules in different partitions, compare the integrated distribution network model with the actual scenario of the target distribution network, and expand the missing scenario model of the target distribution network;

[0038] Based on the integrated and expanded digital twin model of the distribution network module, the digital twin operation simulation of the distribution network is completed.

[0039] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a digital twin operation simulation system and method based on the modular development of the distribution network, which can realize the efficient management and optimization of the distribution network. Through modular design, the system can flexibly respond to the specific needs of different partitions and improve the reliability and stability of the distribution network. At the same time, through digital twin technology, the actual operation of the distribution network can be simulated in a virtual environment, potential problems can be discovered in advance and optimized, thereby reducing the risks and costs in actual operation. In addition, the system of the present invention also has good scalability, and can expand functions and simulate scenarios according to actual needs, providing support for the long-term development of the distribution network. The visualization and user interface modules enable users to intuitively understand the operating status of the distribution network, which is convenient for decision-making and operation.

[0042] In practical applications, the system of the present invention can be seamlessly integrated with the existing distribution network management system to achieve data sharing and functional complementarity, further improving the intelligence level of the distribution network. Through the computing and storage capabilities of the cloud server, the system can process large-scale data and improve the efficiency of simulation and analysis.

[0043] To sum up, the digital twin operation simulation system and method based on modular development of distribution network proposed in the present invention can not only improve the operating efficiency and reliability of the distribution network, but also provide strong support for the long-term planning and optimization of the distribution network, and has significant technical advantages and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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 labor. Among them:

[0045] Figure 1 A schematic diagram of the system structure of a digital twin operation simulation system and method based on modular development of a distribution network provided by one embodiment of the present invention;

[0046] Figure 2 An internal structural diagram of a computer device for a digital twin operation simulation system and method based on modular development of a distribution network provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] Example 1

[0049] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a digital twin operation simulation system and method based on modular development of distribution network, including:

[0050] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0051] Digital Twin Model: A digital twin model is a virtual model that accurately maps and simulates the behavior and state of a physical entity (such as a device, system, or process) in the real world. Digital twins combine physical models, sensor data, historical data, and other data sources to create a dynamic, real-time digital version through advanced analytics, machine learning, and software algorithms, thereby achieving a deep understanding and optimization of physical entities.

[0052] The main features of the digital twin model are:

[0053] Real-time: The digital twin model can reflect the status of the physical entity in real time, which means that any changes in the physical entity will be immediately reflected in the digital twin.

[0054] Two-way interaction: Not only can data be collected from physical entities to update digital twins, but physical entities can also be controlled or adjusted through digital twins.

[0055] Integration: Digital twins typically integrate multiple data sources, including sensor data, historical operational data, environmental data, etc., to provide a comprehensive perspective.

[0056] Predictive: Through machine learning and other analytical tools, digital twins can help predict future behavior and potential problems so that action can be taken in advance.

[0057] Optimizability: Digital twins can be used to test different what-if scenarios, helping decision makers make more informed choices and optimize performance and efficiency.

[0058] In related technologies, there are some challenges, such as how to efficiently manage and optimize the distribution network, and how to achieve better performance in large-scale data processing and real-time monitoring.

[0059] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to establish a digital twin operation simulation system based on modular development of distribution network;

[0060] Figure 1 A system structure diagram of a digital twin operation simulation system and method based on modular development of a distribution network is shown, including:

[0061] Power grid modular design and data acquisition module 100, data processing module 200, model building and simulation module 300, integration and expansion module 400, visualization and user interface module 500 and cloud server 600;

[0062] The power grid modular design and data acquisition module 100 is used to divide the target distribution network into several partitions, and obtain the real-time data of sensors configured in different partitions of the target distribution network, and transmit the obtained real-time data partitions to the data processing module 200;

[0063] The data processing module 200 acquires the real-time data transmitted from the power grid modular design and data acquisition module 100, and processes the real-time data. Different partitions use different preset data processing methods, and the different preset data processing methods at least include filtering, scaling and outlier processing operations, and the real-time data after data processing is sent to the model construction and simulation module 300.

[0064] The model construction and simulation module 300 establishes digital twin models of distribution network modules for different partitions according to the real-time data after partitioned data processing transmitted by the data processing module 200, performs simulation verification on different models, and transmits the digital twin models of distribution network modules for different partitions that meet the simulation verification conditions to the integration and expansion module 400;

[0065] The integration and expansion module 400 is used to integrate the digital twin models of the distribution network modules in different partitions transmitted by the model construction and simulation module 300, and compare the integrated model with the actual scenario of the target distribution network, supplement the missing scenario model of the target distribution network through the extended function, and transmit the integrated and expanded model to the cloud server 600.

[0066] The visualization and user interface module 500 is used for users to retrieve the integrated and extended models from the cloud server 600 and display and control them through the visualization unit. Users can also export the integrated and extended models in the cloud server 600 through the visualization and user interface module 500;

[0067] The cloud server 600 is used to provide data storage and cloud computing functions for the power grid modular design and data acquisition module 100 , the data processing module 200 , the model construction and simulation module 300 , the integration and expansion module 400 , and the visualization and user interface module 500 .

[0068] In the embodiment of the present application, the power grid modular design and data acquisition module 100 includes: a partition design unit, a sensor deployment unit, and a data collection and transmission unit;

[0069] The partition design unit is used to formulate a first partition standard based on the physical characteristics, load type, and importance of the power grid, and to partition the entire target distribution network into a number of partitions according to the first partition standard, and transmit the partition results to the sensor deployment unit;

[0070] The sensor deployment unit is used to deploy sensors at target nodes in each partition, and the types of sensor deployment include at least voltage sensors and current sensors;

[0071] The data collection and transmission unit is used to collect data from various sensors in different partitions, and send the data to the data processing module 200 according to a predetermined partition format.

[0072] It should be noted that the physical characteristics of the power grid include consideration of factors such as the geographical distribution of the power grid, line layout, and equipment capacity. For example, the partition boundary can be determined based on the location of the substation and the power supply radius. Load types can be divided according to the nature of electricity consumption in different areas, such as industrial areas, commercial areas, residential areas, etc. Different types of loads may require different management strategies and service quality assurance. Importance can assess the criticality or sensitivity of various parts of the power grid. For example, areas where key facilities such as hospitals are located may be considered higher priority service areas.

[0073] For example, suppose the central area of ​​a city contains commercial centers and medical facilities. This part can be designated as a high-priority zone because it has not only commercial loads but also critical medical service needs; the north of the city is an industrial area, which can form another zone because of its intensive load and continuous work; the south is mainly residential, which can be further subdivided according to the size of the residential area; there is a large park in the east of the city with relatively less load, which can be merged into one zone with other lighter loads.

[0074] It should be noted that the benefit of the first zoning standard is that it can flexibly adapt to the specific needs and conditions of different power grids, ensuring that the digital twin model can more accurately reflect the operating status and characteristics of the actual distribution network. Through this modular design, refined management of the distribution network can be achieved, and the reliability and efficiency of the power grid can be improved. After determining the partition boundaries, the partition design unit will generate a detailed partition map, which will guide the sensor deployment unit to install appropriate sensors at each target node. The sensor deployment unit will select appropriate voltage sensors and current sensors based on the information provided by the partition design unit to ensure that the electrical parameters of each partition can be accurately monitored.

[0075] It should be noted that the sensors deployed at the target nodes can also include environmental monitoring devices such as temperature sensors and humidity sensors to provide a more comprehensive understanding of the operating environment and conditions of the distribution network. For example, in industrial areas, temperature sensors can monitor the heat generated by equipment during operation to ensure that the equipment operates within a safe temperature range; while in residential areas, humidity sensors can help detect and prevent electrical equipment failures caused by moisture.

[0076] In the embodiment of the present application, the model construction and simulation module 300 includes: a model construction unit, a simulation execution unit, and a verification and evaluation unit;

[0077] The model building unit receives the processed data transmitted from the power grid modular design and data acquisition module 100, selects the target power grid performance characteristic variables, and establishes the digital twin model of the distribution network module based on different partitions based on the characteristic variables;

[0078] It should be noted that key characteristic variables that affect the performance of the power grid are selected, such as voltage level, current intensity, power loss, etc. These characteristic variables are used to build a digital twin model of the distribution network module based on different partitions. The specific mathematical modeling process can use statistical methods, physics formulas or machine learning algorithms to fit the data.

[0079] The simulation execution unit is used to perform simulation tests on the constructed digital twin model of the distribution network module, simulate different operating states or fault conditions, and transmit the simulation results to the verification and evaluation unit;

[0080] It should be noted that the constructed digital twin model of the distribution network module is simulated and tested, which includes simulating the performance under normal operating conditions and the response under fault conditions. During the simulation process, input parameters such as load demand and weather conditions can be changed to observe the response of the model. The simulation results are output to the verification and evaluation unit for further processing.

[0081] The simulation test steps of this application can be as follows:

[0082] In the model building unit, a digital twin model is established based on the partition data obtained from the power grid modular design and data acquisition module 100. The model reflects the real state of the power grid and includes key parameters of the power grid, such as voltage, current, power loss, etc.

[0083] Choose a set of initial conditions, such as a specific time of day (such as during the morning rush hour), and normal weather conditions.

[0084] The input parameters of the model are set, including the load demand of the power grid (for example, the power consumption of industrial areas, commercial areas, and residential areas), weather conditions (temperature, humidity, wind speed, etc.), and other factors that may affect the operation of the power grid.

[0085] The model is started in the simulation execution unit to simulate the operation of the distribution network under set conditions.

[0086] Record the output of the model, including grid performance indicators such as voltage level, current intensity, and power factor.

[0087] Different fault conditions, such as circuit breaker failure, line short circuit, etc., are injected into the model to observe the response of the power grid under different fault conditions.

[0088] These fault conditions can be simulated by modifying certain parameters in the model, such as setting the impedance of a line to zero to simulate a short circuit.

[0089] Change the simulation conditions, such as increasing the load demand, changing the weather conditions (such as introducing a thunderstorm), and then run the simulation again.

[0090] Observe and record the impact of these changes on grid performance.

[0091] The simulation results are output to the verification and evaluation unit in preparation for the next step of evaluation.

[0092] For example, assume a scenario where a city's distribution network is divided into three main areas: industrial area, commercial area and residential area. Under normal circumstances, the load demand of each area is P industrial ,P commercial , and P residential The initial conditions are: time: 8:00 a.m. Weather: sunny Load demand: 500kW in industrial area, 300kW in commercial area, 200kW in residential area.

[0093] In this example, we go one step further and run the model to record the voltage level of each partition;

[0094] In this example, we take it a step further and simulate a short circuit fault on a line in the industrial area to see how the grid responds. We increase the load demand in the commercial area to 400kW and rerun the simulation;

[0095] In this example, we go a step further and simulate a thunderstorm to see how the power grid performs under adverse weather conditions;

[0096] In this example, further, the above simulation results are output to the verification and evaluation unit for further evaluation.

[0097] The verification and evaluation unit receives the evaluation simulation result transmitted from the simulation execution unit, and determines whether the simulation results of the digital twin model of different distribution network modules meet the first evaluation rule in combination with the preset first evaluation rule;

[0098] If the first evaluation rule is met, the distribution network module digital twin model that passes the evaluation is transmitted to the integration and expansion module 400;

[0099] If the first evaluation rule is not met, the model training is performed again until all models meet the first evaluation rule.

[0100] It should be noted that the first evaluation rule may include that the deviation between the performance indicators predicted by the model (such as voltage, current, power loss, etc.) and the actual measured values ​​should be within the allowable range. This is usually manifested as statistics such as mean square error (MSE) and mean absolute error (MAE) not exceeding a certain threshold. For example, it can be set to a mean square error of no more than 5%, or a mean absolute error of no more than 1%. If the simulation results of the model do not meet these evaluation rules, then it is necessary to readjust the model parameters or improve the model building method until the evaluation rules are met. The model should exhibit stable performance under different operating conditions, that is, there will be no drastic fluctuations or instability. For rapidly changing events (such as sudden increases in load or fault conditions), the model should be able to respond quickly and correctly. The model should have strong adaptability to various different input parameters (such as load demand, weather conditions, etc.) and be able to operate stably under different conditions. The model should be consistent between different partitions, that is, the parameters of different partition models should be consistent globally to ensure the coordinated operation of the entire system.

[0101] Exemplarily, the first evaluation rule may be specifically expressed as follows:

[0102] The voltage predicted by the model must not deviate by more than ±5%.

[0103] Under simulated fault conditions, the response time of the model should not exceed 1 second.

[0104] For different weather conditions, the difference between the load demand predicted by the model and the actual demand should be within ±10%.

[0105] It should be noted that the benefit of the first evaluation rule is that it can ensure the accuracy and reliability of the digital twin model of the distribution network module under various operating conditions. By setting strict evaluation criteria, models with excellent performance can be screened out, thus providing strong support for the optimization and maintenance of the power grid. In addition, the first evaluation rule can also help identify potential problems in the model and provide direction for subsequent model improvements.

[0106] In the embodiment of the present application, the integration and expansion module 400 includes: a model integration unit, a scene matching unit, and an expansion function development unit;

[0107] The model integration unit receives the digital twin models of distribution network modules in different partitions that meet the first evaluation rule transmitted from the verification and evaluation unit, and imports the digital twin models of distribution network modules in each partition into a unified framework, and defines the interface for interaction between models, so that each model communicates and exchanges data with each other, and the parameters of all partition models are kept consistent globally;

[0108] The scenario matching unit collects the operation data of the real target distribution network in different time periods, and uses the model integrated by the model integration unit to simulate the operation of the distribution network in the same time period, compares the simulation result of the model with the actual operation data, calculates the deviation between the model prediction value and the actual value, and determines whether the deviation value meets the first deviation threshold;

[0109] It should be noted that the first deviation threshold in the present application is similar to the first evaluation rule and is also a standard for measuring the prediction accuracy of the model, but it is usually more specific and detailed. For example, the first deviation threshold can be set as the maximum allowable deviation percentage between the model predicted voltage value and the actual voltage value, or the average absolute error between the model predicted load demand and the actual load demand within a specific time period does not exceed a fixed value. Such a setting helps to more accurately evaluate the performance of the model in different time periods and ensure that the model can maintain a high prediction accuracy in different time periods.

[0110] For example, suppose that a digital twin system for the distribution network of a city is being developed. The city is divided into three main areas: industrial area, commercial area and residential area. Each area has its own sensor network to monitor power load, voltage, current and other information in real time. The task of the system is to build a digital twin model that can simulate the operation of the distribution network in these three areas and ensure that the model is as consistent as possible with the actual situation. At this time, the first deviation threshold can be set to the maximum allowable deviation between the voltage value predicted by the model and the actual voltage value (the maximum allowable deviation is ±3%), and the average absolute error between the model predicted load demand and the actual load demand does not exceed 50kW in any continuous 24 hours. Such a setting can ensure that the model can provide prediction results that are very close to the actual operation in most time periods, providing a reliable reference for the optimization and maintenance of the power grid.

[0111] If the first deviation threshold is not met, the deviation between the model prediction value and the actual value is output to the extended function development unit, and the extended function development unit re-establishes a new model according to the deviation.

[0112] It should be noted that a digital twin system of a city's distribution network is being developed. The city is divided into three main areas: industrial, commercial, and residential. Each area has its own sensor network to monitor power load, voltage, current, and other information in real time. The task is to build a digital twin model that can simulate the operation of the distribution network in these three areas and ensure that the model is as consistent as possible with the actual situation.

[0113] Exemplarily, first, the model integration unit receives the digital twin model of the distribution network module of three partitions (industrial area, commercial area, and residential area) that have been confirmed by the verification and evaluation unit and meet the first evaluation rules.

[0114] Next, the model integration unit imports these models into a unified framework. This means that the interaction interfaces between these models need to be defined so that the various models can communicate and exchange data with each other. For example, if the power demand in an industrial area suddenly increases, this information needs to be able to be transmitted to the models in commercial and residential areas in a timely manner in order to simulate the response of the entire power grid.

[0115] During the integration process, it is necessary to ensure that the parameters of all zoning models remain consistent across the system. For example, if the voltage in the industrial area changes, this change also needs to be reflected in the models of the commercial and residential areas to maintain global consistency.

[0116] Next, the scene matching unit starts to collect the operation data of the real target distribution network in different time periods. For example, it can obtain data such as power load, voltage and current from 8 am to 8 pm every day in the past month.

[0117] Next, the model integrated by the model integration unit is used to simulate the operation of the distribution network in the same time period, for example, to simulate the changes in power load, voltage and current from 8 am to 8 pm.

[0118] Next, the simulation results of the model are compared with the actual operating data to calculate the deviation between the model prediction value and the actual value. For example, the difference between the voltage value predicted by the model at 8 o'clock in the morning and the actual voltage value is compared.

[0119] Next, it is determined whether the deviation value satisfies the first deviation threshold. Assume that the first deviation threshold is that the voltage deviation does not exceed ±5%. If it exceeds this range, it is considered that the prediction of the model is not accurate enough.

[0120] Next, if the deviation between the model prediction value and the actual value does not satisfy the first deviation threshold, the scene matching unit outputs the deviation information to the extended function development unit.

[0121] Next, the extended function development unit rebuilds a new model based on this deviation information. For example, if it is found that the model has large prediction deviations in certain time periods, the development unit may introduce new algorithms or adjust model parameters to better capture the characteristics of these time periods.

[0122] Finally, the new model will be sent back to the model integration unit for a new round of integration, matching and verification until all models meet the first deviation threshold.

[0123] In the embodiment of the present application, the visualization and user interface module 500 includes: a graphical user interface design unit, an interactive control unit, and a data export and sharing unit. The graphical user interface design unit is responsible for designing a friendly graphical user interface (GUI) so that the user can clearly see the real-time status and simulation results of the distribution network. The interface design should not only be beautiful, but more importantly, it should be intuitive and easy to understand, so that the user can quickly understand the operation of the current system.

[0124] It should be noted that data can be displayed through a variety of visual elements such as charts, dashboards, maps, etc., such as the changing trends of key parameters such as voltage, current, load, and the status of different partitions. In addition, it may also include alarm and notification systems to remind users in time when the system detects an abnormality.

[0125] The interactive control unit provides a means for users to interact with the system, allowing users to perform operations through the interface, such as adjusting simulation parameters, viewing data in a specific time period, setting alarm thresholds, etc.

[0126] It should be noted that controls such as buttons, sliders, drop-down menus, etc. allow users to easily interact with the system. For example, users can choose to view detailed information about a specific partition, or change simulation conditions to observe the performance of the grid under different scenarios.

[0127] The data export and sharing unit allows users to export integrated and extended model data to local or other systems for further analysis or sharing with others.

[0128] It should be noted that this unit can support data export in multiple formats, such as CSV, Excel, PDF, etc., so that users can choose the appropriate format according to their needs. In addition, online sharing functions may be provided, and users can directly share reports or data sets with colleagues or partners through links or emails.

[0129] For example, the graphical user interface design unit displays the grid status of different partitions through color-coded maps, such as green for normal operation, yellow for warning status, and red for faults. A dashboard is provided to display key parameters such as real-time voltage, current, and power, and a curve chart is used to display the historical trend of these parameters. When a grid anomaly is detected, the user is informed through pop-up windows, sound prompts, etc., and the abnormal area is highlighted on the interface.

[0130] Interactive control unit allows users to adjust parameters in the simulation, such as simulating load demand in different time periods, or changing weather conditions to observe the grid response. Provides search function, users can retrieve historical data by date and time range to review the past grid status. Users can set different fault scenarios, simulate the grid performance when a fault occurs, and view the system recovery.

[0131] Users can export the data in the current view to a common document format for subsequent offline analysis. Automatically or manually generate reports containing key indicators and charts to facilitate users to report work or conduct project reviews. Provide a one-click sharing function, users can send the current view or report to others via email or generate a sharing link.

[0132] In the embodiment of the present application, the cloud server 600 includes a data storage unit, a cloud computing unit, and a data access and sharing unit. The cloud server 600 provides a centralized data storage solution, namely, a data storage unit, for storing raw data, processed data, model-built data, simulation results, and finally integrated and expanded model data from the power grid modular design and data acquisition module 100. The data storage function ensures the security and reliability of all data, and also facilitates data backup and recovery.

[0133] The cloud server 600 provides powerful computing resources to support complex computing tasks in the model construction and simulation module 300, such as model training, simulation testing, and model verification based on big data. Since the digital twin operation simulation system for modular development of the distribution network needs to process a large amount of data and complex calculations, the use of cloud computing capabilities can significantly improve the processing efficiency and response speed of the system.

[0134] As a central hub, the cloud server 600 not only stores data, but also supports data exchange between different modules, such as transmitting processed data from the data processing module 200 to the model building and simulation module 300, or transmitting the integrated and extended model to the visualization and user interface module 500. Users can access the data and models in the cloud server 600 through the visualization and user interface module 500 to view, analyze and export them.

[0135] In summary, the present invention proposes a digital twin operation simulation system based on modular development of distribution network, which can realize efficient management and optimization of distribution network. Through modular design, the system can flexibly respond to the specific needs of different partitions and improve the reliability and stability of the distribution network. At the same time, through digital twin technology, the actual operation of the distribution network can be simulated in a virtual environment, potential problems can be discovered in advance and optimized, thereby reducing the risks and costs in actual operation. In addition, the system of the present invention also has good scalability, and can perform functional expansion and scenario simulation according to actual needs, providing support for the long-term development of the distribution network. The visualization and user interface module 500 enables users to intuitively understand the operating status of the distribution network, which is convenient for decision-making and operation.

[0136] This embodiment also provides a digital twin operation simulation method based on modular development of a distribution network, including:

[0137] Divide the target distribution network into zones, obtain the real-time data of preset sensors in different zones, and pre-process the real-time data;

[0138] According to the preprocessing results and combined with the partitioning results, a digital twin model of the distribution network module for different partitions is established;

[0139] Integrate the digital twin models of distribution network modules in different partitions, compare the integrated distribution network model with the actual scenario of the target distribution network, and expand the missing scenario model of the target distribution network;

[0140] Based on the integrated and expanded digital twin model of the distribution network module, the digital twin operation simulation of the distribution network is completed.

[0141] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0142] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a digital twin operation simulation method based on modular development of a distribution network is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0143] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0144] Divide the target distribution network into zones, obtain the real-time data of preset sensors in different zones, and pre-process the real-time data;

[0145] According to the preprocessing results and combined with the partitioning results, a digital twin model of the distribution network module for different partitions is established;

[0146] Integrate the digital twin models of distribution network modules in different partitions, compare the integrated distribution network model with the actual scenario of the target distribution network, and expand the missing scenario model of the target distribution network;

[0147] Based on the integrated and expanded digital twin model of the distribution network module, the digital twin operation simulation of the distribution network is completed.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0149] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0150] 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 1 A device that provides the functions specified in a block or multiple blocks.

[0151] 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.

[0152] 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 1The steps for the functions specified in one or more boxes.

[0153] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A digital twin operation simulation system based on modular development of distribution network, characterized in that: include: A power grid modular design and data acquisition module (100), a data processing module (200), a model building and simulation module (300), an integration and expansion module (400), a visualization and user interface module (500), and a cloud server (600); The power grid modular design and data acquisition module (100) is used to divide the target distribution network into a plurality of partitions, obtain real-time data of sensors configured in different partitions of the target distribution network, and transmit the obtained real-time data partitions to the data processing module (200); The data processing module (200) acquires the real-time data transmitted from the power grid modular design and data acquisition module (100), and processes the real-time data, with different partitions using different preset data processing methods, each of which at least includes filtering, scaling and outlier processing operations, and sends the processed real-time data to the model construction and simulation module (300).

2. The digital twin operation simulation system based on modular development of distribution network according to claim 1, characterized in that: Also includes: The model building and simulation module (300) establishes digital twin models of distribution network modules for different partitions according to the real-time data after the partitioned data processing transmitted by the data processing module (200), performs simulation verification on different models, and transmits the digital twin models of distribution network modules for different partitions that meet the simulation verification conditions to the integration and expansion module (400); The integration and expansion module (400) is used to integrate the digital twin models of the distribution network modules of different partitions transmitted to the model construction and simulation module (300), compare the integrated model with the actual scene of the target distribution network, supplement the missing scene model of the target distribution network through the extension function, and transmit the integrated and expanded model to the cloud server (600).

3. The digital twin operation simulation system based on modular development of distribution network according to claim 2, characterized in that: Also includes: The visualization and user interface module (500) is used for a user to retrieve the integrated and extended model from the cloud server (600), and to display and control it through a visualization unit. The user can also export the integrated and extended model in the cloud server (600) through the visualization and user interface module (500); The cloud server (600) is used to provide data storage and cloud computing functions for the power grid modular design and data acquisition module (100), the data processing module (200), the model construction and simulation module (300), the integration and expansion module (400), and the visualization and user interface module (500).

4. The digital twin operation simulation system based on modular development of distribution network according to claim 3, characterized in that: The power grid modular design and data acquisition module (100) comprises: a partition design unit, a sensor deployment unit and a data collection and transmission unit; The partition design unit is used to formulate a first partition standard based on the physical characteristics, load type, and importance of the power grid, and to partition the entire target distribution network into a plurality of partitions according to the first partition standard, and transmit the partition result to the sensor deployment unit; The sensor deployment unit is used to deploy sensors at the target nodes of each partition, and the types of the deployed sensors include at least voltage sensors and current sensors; The data collection and transmission unit is used to collect data from various sensors in different partitions, and send the data to the data processing module (200) in a predetermined partition format.

5. The digital twin operation simulation system based on modular development of distribution network according to claim 4, characterized in that: The model construction and simulation module (300) comprises: a model construction unit, a simulation execution unit and a verification and evaluation unit; The model building unit receives processed data transmitted from the power grid modular design and data acquisition module (100), selects target power grid performance characteristic variables, and establishes digital twin models of distribution network modules based on different partitions based on the characteristic variables; The simulation execution unit is used to perform simulation tests on the constructed digital twin model of the distribution network module, simulate different operating states or fault conditions, and transmit the simulation results to the verification and evaluation unit; The verification and evaluation unit receives the evaluation simulation result transmitted from the simulation execution unit, and determines whether the simulation results of the digital twin model of different distribution network modules meet the first evaluation rule in combination with the preset first evaluation rule; If the first evaluation rule is met, the distribution network module digital twin model determined by the evaluation is transmitted to the integration and expansion module (400); If the first evaluation rule is not met, the model training is performed again until all models meet the first evaluation rule.

6. The digital twin operation simulation system based on modular development of distribution network according to claim 5, characterized in that: The integration and expansion module (400) comprises: a model integration unit, a scene matching unit and an expansion function development unit; The model integration unit receives the digital twin models of distribution network modules in different partitions that meet the first evaluation rule transmitted from the verification and evaluation unit, and imports the digital twin models of distribution network modules in each partition into a unified framework, and defines an interface for interaction between models, so that each model communicates with each other and exchanges data, and the parameters of all partition models remain consistent globally; The scenario matching unit collects the operation data of the real target distribution network in different time periods, and uses the model integrated by the model integration unit to simulate the operation of the distribution network in the same time period, compares the simulation result of the model with the actual operation data, calculates the deviation between the model prediction value and the actual value, and determines whether the deviation value meets the first deviation threshold; If the first deviation threshold is not met, the deviation between the model prediction value and the actual value is output to the extended function development unit, and the extended function development unit re-establishes a new model according to the deviation.

7. The digital twin operation simulation system based on modular development of distribution network according to claim 6, characterized in that: The visualization and user interface module (500) comprises: a graphical user interface design unit, an interaction control unit and a data export and sharing unit.

8. A digital twin operation simulation method based on modular development of distribution network, characterized in that: include: Divide the target distribution network into zones, obtain real-time data of preset sensors in different zones, and pre-process the real-time data; According to the preprocessing results and in combination with the partitioning results, a digital twin model of the distribution network module for different partitions is established; Integrate the digital twin models of distribution network modules in different partitions, compare the integrated distribution network model with the actual scenario of the target distribution network, and expand the missing scenario model of the target distribution network; Based on the integrated and expanded digital twin model of the distribution network module, the digital twin operation simulation of the distribution network is completed.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to claim 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

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