Digital twinborn monitoring system and method based on intelligent micro-grid

Abstract

The invention provides a digital twinborn monitoring system and method based on an intelligent micro-grid, and relates to the technical field of intelligent micro-grids, and the monitoring system comprises a first obtaining unit which is used for obtaining power consumption data; the simulation response unit is used for performing power consumption simulation through the constructed park equipment power supply network simulation model to obtain simulation response output; the second acquisition unit is used for acquiring actual response output; the comparative analysis unit is used for carrying out comparative analysis on the actual response output and the simulation response output based on the operation load energy consumption corresponding to the power consumption data to obtain an extra energy consumption abnormal value; the anomaly analysis unit is used for performing power supply configuration rationality analysis to obtain power supply anomaly configuration; and the configuration optimization unit is used for optimizing the park equipment power supply network based on the abnormal power supply configuration. According to the system and the method provided by the invention, the power supply optimization of the park equipment power supply network can be quickly completed, and the use safety of each piece of electric equipment and power supply configuration devices is ensured.

Description

Technical Field

[0001] This invention relates to the field of smart microgrid technology, and in particular to a digital twin monitoring system and method based on smart microgrids. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. Microgrids can operate in parallel with the grid or independently off-grid, and are a key technology for achieving efficient utilization of renewable energy and improving power supply reliability and flexibility.

[0003] In the existing process of using park microgrids to power various electrical equipment in the park, the increased power consumption makes it difficult to assess the operational reliability of electrical equipment and the power supply configuration devices (such as cables, switch controllers, etc.) under high loads. Moreover, this operational reliability can lead to inaccurate assessment of abnormal equipment conditions, which in turn makes it impossible to quickly locate the cause of faults in park electrical equipment, thus prolonging the maintenance process and bringing certain adverse effects to the construction of park microgrids. Summary of the Invention

[0004] This invention provides a digital twin monitoring system and method based on a smart microgrid to solve the technical problem that in the prior art, it is difficult to assess the operational reliability of electrical equipment and the power supply configuration devices used to connect the electrical equipment under high loads. Moreover, such operational reliability can lead to inaccurate assessment of abnormal equipment status, which in turn makes it impossible to quickly locate the cause of electrical equipment failure in the park, thus prolonging the maintenance process.

[0005] To achieve the above and other related objectives, this invention provides a digital twin monitoring system based on a smart microgrid, comprising: a first acquisition unit for acquiring power consumption data of electrical equipment groups in each zone of the park; a simulation response unit for simulating power consumption based on the power consumption data using a constructed simulation model of the park's equipment power supply network to obtain a simulated response output, wherein the simulation model includes power consumption simulation models corresponding to multiple zones of electrical equipment groups, and a power supply configuration model corresponding to power supply configuration devices for power transmission between electrical equipment groups and electrical equipment within each group; a second acquisition unit for acquiring the actual response output of each electrical equipment group based on the power consumption data; a comparison and analysis unit for comparing and analyzing the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data to obtain an anomaly value for additional energy consumption; an anomaly analysis unit for performing a power supply configuration rationality analysis based on the anomaly value for additional energy consumption to obtain an anomaly power supply configuration; and a configuration optimization unit for optimizing the park's equipment power supply network based on the anomaly power supply configuration.

[0006] In one embodiment of the present invention, the simulation response unit includes a model building subunit, which includes: a data receiving module for receiving replacement data of a group of electrical equipment, the replacement data of the group of electrical equipment including first replacement data corresponding to the electrical equipment and second replacement data corresponding to the power supply configuration device; a model adjustment module for adjusting the corresponding power consumption simulation model and power supply configuration model according to the first replacement data and the second replacement data; and a model updating module for dynamically updating the park equipment power supply network simulation model with the adjusted power consumption simulation model and power supply configuration model to construct the park equipment power supply network simulation model.

[0007] In one embodiment of the present invention, it further includes: a difference calculation unit, used to calculate the difference between the power consumption data and the historical power consumption data to obtain the power consumption fluctuation data corresponding to each power supply device, wherein the calculation formula for the power consumption fluctuation data is: , This indicates power consumption data. The system displays historical power consumption data, including power consumption fluctuation data such as power consumption increase data and power consumption decrease data; the operation detection unit is used to perform operation detection on electrical equipment and power supply configuration devices based on the power consumption fluctuation data to obtain the operating load energy consumption.

[0008] In one embodiment of the present invention, the operation detection unit includes: a numerical detection subunit for detecting power consumption fluctuation data; and a model processing subunit for, when the power consumption fluctuation data is greater than a fluctuation threshold, outputting power consumption allocation data corresponding to the power consumption simulation model and the power supply configuration model through the park equipment power supply network simulation model, based on the power consumption equipment and power supply configuration devices corresponding to the power consumption fluctuation data. ,in, This represents the first power consumption allocation data corresponding to the power consumption simulation model. This section describes a system that includes: a second power consumption allocation data corresponding to the power supply configuration model; a judgment subunit, used to determine whether the current configuration specifications of the electrical equipment and power supply configuration devices meet the corresponding load requirements based on the power consumption allocation data; a first output subunit, used to directly output the simulated response to the comparison and analysis unit when the current configuration specifications of the electrical equipment and power supply configuration devices meet the corresponding load requirements, so as to compare and analyze the actual response output with the simulated response output and obtain the additional energy consumption anomaly value; and a second output subunit, used to generate the required configuration specifications corresponding to the current power consumption allocation data and send them to the equipment maintenance terminal for early warning and replacement when the current configuration specifications of the electrical equipment and power supply configuration devices do not meet the corresponding load requirements. It also generates the operating load energy consumption based on the degree of difference between the current configuration specifications and the required configuration specifications, the energy consumption conversion coefficient corresponding to the degree of difference, and the running time corresponding to the generated power consumption data. The calculation formula for the operating load energy consumption is as follows: , This indicates the degree of difference between the current configuration specification and the required configuration specification. The energy conversion coefficient represents the degree of difference. This indicates the running time corresponding to the power consumption data.

[0009] In one embodiment of the present invention, the judgment subunit includes: a lookup module, used to find the rated power consumption data according to the current configuration specifications of the electrical equipment and power supply configuration devices; and a data comparison module, used to compare the power consumption allocation data with the rated power consumption data; if the power consumption allocation data is less than the rated power consumption data, it indicates that the current configuration specifications meet the corresponding load requirements; if the power consumption allocation data is greater than the rated power consumption data, it indicates that the current configuration specifications do not meet the corresponding load requirements.

[0010] In one embodiment of the present invention, the comparison and analysis unit includes: a threshold lookup subunit, used to find the corresponding response output difference threshold based on the actual response output; a calculation subunit, used to calculate the response output influence value based on the operating load energy consumption and the response output influence coefficient; calculate the conservative response output based on the response output influence value and the simulated response output; calculate the difference between the conservative response output and the actual response output to obtain the response output difference; and an output detection subunit, used to perform threshold detection on the response output difference; when the response output difference is greater than the response output difference threshold, calculate the additional energy consumption anomaly value based on the response output difference and the response output influence coefficient, wherein the calculation formula for the additional energy consumption anomaly value is: , This represents the simulated response output. Indicates energy consumption under operating load. This represents the response output influence coefficient. This indicates the actual response output.

[0011] In one embodiment of the present invention, the anomaly analysis unit includes: a model query subunit, used to search for a power supply anomaly model based on the extra energy consumption anomaly value and the power consumption allocation data corresponding to the extra energy consumption anomaly value; a simulation operation subunit, used to add the power supply anomaly model to the power supply network simulation model of the park equipment, and perform simulation operation through the corresponding power consumption data to obtain an anomaly response output; an anomaly comparison subunit, used to compare and analyze the actual response output and the anomaly response output based on the operating load energy consumption corresponding to the power consumption data to obtain an energy consumption anomaly update value; and a threshold comparison subunit, used to perform a threshold comparison on the anomaly difference between the energy consumption anomaly update value and the extra energy consumption anomaly value, and when the anomaly difference is less than the corresponding anomaly threshold, the power supply anomaly model corresponding to the energy consumption anomaly update value is used as the power supply anomaly configuration, wherein the anomaly threshold corresponds to each power supply anomaly model.

[0012] In one embodiment of the present invention, when there are multiple power supply anomaly models corresponding to an anomaly value less than the anomaly threshold, the power supply anomaly model corresponding to the smallest anomaly value is selected as the power supply anomaly configuration.

[0013] In one embodiment of the present invention, the configuration optimization unit includes: a solution query subunit, used to find a solution corresponding to the power supply anomaly model based on the power supply anomaly configuration; a sending subunit, used to send the solution to the human end; and a configuration processing subunit, used to receive the completion signal of the solution from the human end, and add the power supply anomaly model and the repair model corresponding to the solution to the simulation model of the park equipment power supply network to optimize the park equipment power supply network. The park equipment power supply network includes power consumption equipment groups and power supply configuration devices for power transmission between power consumption equipment groups and between power consumption equipment within the power consumption equipment groups.

[0014] To achieve the above and other related objectives, the present invention also provides a digital twin monitoring method based on a smart microgrid, comprising: acquiring power consumption data of electrical equipment groups in each zone of the park through a first acquisition unit; simulating power consumption based on the power consumption data using a constructed park equipment power supply network simulation model through a simulation response unit to obtain a simulated response output, wherein the park equipment power supply network simulation model includes power consumption simulation models corresponding to multiple zones of electrical equipment groups, and power supply configuration models corresponding to power supply configuration devices used for power transmission between electrical equipment groups and between electrical equipment within electrical equipment groups; acquiring the actual response output of each electrical equipment group based on the power consumption data through a second acquisition unit; comparing and analyzing the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data through a comparison and analysis unit to obtain an abnormal value of additional energy consumption; performing a power supply configuration rationality analysis based on the abnormal value of additional energy consumption through an anomaly analysis unit to obtain an abnormal power supply configuration; and optimizing the park equipment power supply network based on the abnormal power supply configuration through a configuration optimization unit.

[0015] The beneficial effects of this invention are as follows: This invention proposes a digital twin monitoring system and method based on a smart microgrid. By acquiring power consumption data of electrical equipment groups in various zones of the park and inputting it into the park's equipment power supply network simulation model, the simulation model can generate a simulated response output based on the power consumption data. This enables the simulation of the response output of the power consumption data and the monitoring of anomalies in the response output through digital twin monitoring technology. Specifically, by acquiring the actual response output and determining the operating load energy consumption corresponding to the current power consumption data, the actual response output is compared and analyzed with the simulated response output. This allows for the identification of anomalies in additional energy consumption when the difference between the actual and simulated response outputs is significant. This method effectively ensures the accuracy of the calculation of anomalies in additional energy consumption. Furthermore, based on these anomalies, the power supply configuration can be analyzed for rationality using the park's equipment power supply network simulation model to identify and determine the abnormal power supply configuration corresponding to the occurrence of the anomaly. Then, based on the abnormal power supply configuration, a solution is generated to repair the abnormal power supply configuration and replace the electrical equipment and its spare parts, thereby quickly optimizing the power supply network of the park equipment and ensuring the safe use of each electrical device and power supply configuration component. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 A structural block diagram of a digital twin monitoring system based on a smart microgrid provided in an embodiment of the present invention; Figure 2 The diagram shown is a flowchart illustrating a digital twin monitoring method based on a smart microgrid, provided in an embodiment of the present invention.

[0018] The attached figures are labeled as follows: First acquisition unit 111; simulation response unit 112; second acquisition unit 113; comparison and analysis unit 114; anomaly analysis unit 115; configuration optimization unit 116. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Please see Figure 1 This invention provides a digital twin monitoring system based on a smart microgrid, comprising: a first acquisition unit 111 for acquiring power consumption data of electrical equipment groups in each zone of the park; a simulation response unit 112 for simulating power consumption based on the power consumption data using a constructed simulation model of the park's equipment power supply network to obtain a simulated response output, wherein the simulation model of the park's equipment power supply network includes power consumption simulation models corresponding to multiple zones of electrical equipment groups, and a power supply configuration model corresponding to power supply configuration devices for power transmission between electrical equipment groups and electrical equipment within each electrical equipment group; a second acquisition unit 113 for acquiring the actual response output of each electrical equipment group based on the power consumption data; a comparison and analysis unit 114 for comparing and analyzing the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data to obtain an anomaly value for additional energy consumption; an anomaly analysis unit 115 for performing a power supply configuration rationality analysis based on the anomaly value for additional energy consumption to obtain an anomaly power supply configuration; and a configuration optimization unit 116 for optimizing the park's equipment power supply network based on the anomaly power supply configuration.

[0023] As can be seen from the above, in the process of constructing a microgrid in the park, in order to better monitor the various electrical devices in the park, the electrical devices can be grouped into equipment groups according to the partitions. Each equipment group can generate a corresponding response output, such as a light energy response output corresponding to the conversion of electrical energy into light energy, a heat energy response output corresponding to the conversion of electrical energy into heat energy (such as air conditioning), and a production output response output corresponding to the production of a certain product. Each equipment group includes multiple electrical devices. Before monitoring the power consumption status of each shared equipment group, a corresponding power consumption simulation model can be established for each equipment group. This power consumption simulation model includes a device simulation model corresponding to each electrical device. Then, a power transmission connection is established between each electrical device through power supply configuration devices, or a power transmission connection is established between the electrical device and the power supply network through power supply configuration devices. During the power consumption status monitoring of each shared equipment group, the power consumption data of each partitioned equipment group in the park can be obtained through the first acquisition unit 111. The generated power consumption data is then input into the park equipment power supply network simulation model via the simulation response unit 112. Based on this power consumption data, the simulation model generates a simulated response output that corresponds to the power consumption data. Next, the second acquisition unit 113 acquires the actual response output uploaded by the automation monitoring system or users, such as the actual brightness of lights or the indoor temperature in the current mode. Based on the acquired actual response output, the comparison and analysis unit 114 compares and analyzes the actual response output with the simulated response output, assuming the operating load energy consumption corresponding to the current power consumption data is determined. This allows for the identification of anomaly values ​​in the power consumption when the actual and simulated response outputs differ significantly. Furthermore, the anomaly analysis unit 115 performs a rationality analysis of the power supply configuration based on these anomaly values ​​in the park equipment power supply network simulation model, identifying the abnormal power supply configuration corresponding to these anomalies. Then, the configuration optimization unit 116 generates a solution based on the abnormal power supply configuration to repair the abnormal power supply configuration and replace the electrical equipment and its spare parts, thereby quickly optimizing the power supply network of the park equipment and ensuring the safe use of each electrical equipment and power supply configuration device.

[0024] In the digital twin monitoring system based on a smart microgrid of the present invention, the simulation response unit 112 includes a model building subunit, which includes: a data receiving module for receiving replacement data of a group of electrical equipment, the replacement data of the group of electrical equipment including first replacement data corresponding to the electrical equipment and second replacement data corresponding to the power supply configuration device; a model adjustment module for adjusting the corresponding power consumption simulation model and power supply configuration model according to the first replacement data and the second replacement data; and a model updating module for dynamically updating the park equipment power supply network simulation model with the adjusted power consumption simulation model and power supply configuration model to construct the park equipment power supply network simulation model.

[0025] When simulating power consumption using the constructed park equipment power supply network simulation model through the simulation response unit 112, the simulation model can be constructed and updated through the model construction subunit. Specifically, after each maintenance personnel completes the inspection and replacement of electrical equipment, the corresponding maintenance data can be uploaded to the digital twin monitoring system of this invention. This maintenance data can include the replacement and adjustment of electrical equipment and / or power supply configuration devices. The data receiving module is used to identify the replacement data of the electrical equipment group. Then, based on the replacement data, the power consumption simulation model and the power supply configuration model are adjusted separately through the model adjustment module. After the adjustment, the park equipment power supply network simulation model is dynamically updated through the model update module, thereby obtaining a continuously updated park equipment power supply network simulation model. In addition, in the park equipment power supply network simulation model, both the power consumption simulation model and the power supply configuration model can simulate the energy consumption distribution corresponding to the power consumption data, and simulate the response output of the electrical equipment group based on each power consumption simulation model and power supply configuration model. For example, by using cables and controllers as power supply configuration devices, a power supply configuration model can be used for simulation. Corresponding light groups can be used as power consumption equipment groups and simulated through a power consumption simulation model. By using power consumption simulation models and power supply configuration models corresponding to multiple power consumption equipment groups, a simulation model of the entire park's equipment power supply network can be constructed. This allows for the simulation of the response output of each power supply equipment group in the entire park. Based on the simulated response output, anomalies in the actual response output can be monitored through digital twin monitoring technology.

[0026] The digital twin monitoring system based on a smart microgrid of the present invention may further include: a difference calculation unit, used to calculate the difference between power consumption data and historical power consumption data to obtain power consumption fluctuation data corresponding to each power supply device, wherein the calculation formula for the power consumption fluctuation data is: , This indicates power consumption data. The system displays historical power consumption data, including power consumption fluctuation data such as power consumption increase data and power consumption decrease data; the operation detection unit is used to perform operation detection on electrical equipment and power supply configuration devices based on the power consumption fluctuation data to obtain the operating load energy consumption.

[0027] To ensure the accuracy of the analysis of outlier values ​​in additional energy consumption, before comparing and analyzing the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data, a difference calculation unit can be used to calculate the difference between the power consumption data and the historical power consumption data. This difference can then be used as the power consumption fluctuation data for each power supply device, expressed by the following formula: Then, the operation detection unit uses this power consumption fluctuation data to perform operation monitoring on the electrical equipment and power supply configuration devices. This allows them to determine the changes in operating load caused by increased or decreased power consumption, and thus calculate the corresponding operating load energy consumption. For example, when the current power consumption data is higher than the historical power consumption data, it indicates that the current power consumption has increased, and the operating load of the power supply equipment group has increased, thus forming a corresponding power consumption increase data. Conversely, when the current power consumption data is lower than the historical power consumption data, it indicates that the current power consumption has decreased, and the operating load of the power supply equipment group has decreased, thus forming a corresponding power consumption decrease data.

[0028] In the digital twin monitoring system based on a smart microgrid of the present invention, the operation detection unit may further include: a numerical detection subunit for detecting power consumption fluctuation data; and a model processing subunit for, when the power consumption fluctuation data is greater than a fluctuation threshold, outputting power consumption allocation data corresponding to the power consumption simulation model and the power supply configuration model through the park equipment power grid simulation model based on the power consumption equipment and power supply configuration devices corresponding to the power consumption fluctuation data. ,in, This represents the first power consumption allocation data corresponding to the power consumption simulation model. This section describes several sub-units for different power consumption configuration models. One sub-unit, called the "Judgment Sub-unit," determines whether the current configuration specifications of the electrical equipment and power supply components meet the corresponding load requirements based on the power consumption allocation data. Another sub-unit, called the "First Output Sub-unit," outputs the simulated response directly to the comparison and analysis unit when the current configuration specifications meet the load requirements. This allows for comparison and analysis between the actual response output and the simulated response output to obtain anomalies in additional energy consumption. A third sub-unit, called the "Second Output Sub-unit," generates a required configuration specification corresponding to the current power consumption allocation data and sends it to the equipment maintenance terminal for early warning and replacement when the current configuration specifications do not meet the load requirements. It also generates the operating load energy consumption based on the degree of difference between the current and required configuration specifications, the energy conversion coefficient corresponding to the degree of difference, and the running time corresponding to the generated power consumption data. The formula for calculating the operating load energy consumption is as follows: , This indicates the degree of difference between the current configuration specification and the required configuration specification. The energy conversion coefficient represents the degree of difference. This indicates the running time corresponding to the power consumption data.

[0029] When calculating the energy consumption of operating load through the operation detection unit, the numerical detection subunit can first detect the power consumption fluctuation data calculated by the difference calculation unit. If the detected power consumption fluctuation data is less than the fluctuation threshold, no processing is performed. However, if the detected power consumption fluctuation data is greater than the fluctuation threshold, the model processing subunit uses the power supply network simulation model of the park equipment to output the power consumption allocation data corresponding to the power consumption simulation model and the power supply configuration model that show the power consumption fluctuation data. Thus achieving This represents the first power consumption allocation data corresponding to the power consumption simulation model. This represents the second power consumption allocation data corresponding to the power supply configuration model, enabling rapid analysis of the power consumption allocation of the entire park's equipment power supply network using a simulation model. After obtaining the power consumption allocation data, the judgment sub-unit can further determine whether the current configuration specifications of the equipment and power supply configuration devices meet the corresponding load requirements. When the current configuration specifications meet the load requirements, the first output sub-module controls the analog response output to be directly output to the comparison and analysis unit. The comparison and analysis unit then directly compares the actual response output with the analog response output to calculate the additional energy consumption anomaly value. Conversely, when the current configuration specifications do not meet the load requirements, the second output sub-unit, based on the current configuration specifications and the current power consumption allocation data, obtains the required configuration specifications corresponding to the current power consumption allocation data and remotely sends these specifications to the corresponding equipment maintenance terminal for configuration specification update management. Because the current configuration specification is used before the required configuration specification is changed, there will be a generation gap (specification difference) between the current configuration specification and the required configuration specification. Due to this generation gap, there will be a certain generation gap loss because the corresponding required configuration specification is not changed in time. For example, if the current configuration specification of a certain electrical device is medium power consumption, while its required configuration specification is medium to high power consumption, there will be a certain degree of difference between the two. This degree of difference can be obtained by looking up a table. For example, the generation gap loss between different configuration specifications can be determined by repeatedly testing the electrical device and the power supply configuration device with different configuration specifications. In other words, the generation gap loss can be used to represent the degree of difference between different configuration specifications.

[0030] Therefore, while the second output subunit sends the required configuration specifications to the equipment maintenance terminal for early warning and replacement, it also generates the corresponding operating load energy consumption by looking up the degree of difference between the current configuration specifications and the required configuration specifications, the energy consumption conversion coefficient corresponding to the degree of difference, and the running time corresponding to the generated power consumption data. The formula can be expressed as follows: The energy consumption conversion factor is obtained by calibrating each electrical device and power supply configuration component; it represents the conversion factor that transforms the degree of difference into energy consumption per unit time. After calculating the operating load energy consumption, a conservative approach can be taken to the simulated response output to ensure the accuracy when comparing the simulated response output with the actual response output.

[0031] The judgment subunit includes: a lookup module, used to find the rated power consumption data based on the current configuration specifications of the electrical equipment and power supply configuration devices; and a data comparison module, used to compare the power consumption allocation data with the rated power consumption data; if the power consumption allocation data is less than the rated power consumption data, it means that the current configuration specifications meet the corresponding load requirements; if the power consumption allocation data is greater than the rated power consumption data, it means that the current configuration specifications do not meet the corresponding load requirements.

[0032] In the process of determining whether the current configuration specifications of electrical equipment and power supply components meet the corresponding load requirements through the judgment subunit, the corresponding rated power consumption data can be obtained by the lookup module based on the current configuration specifications of the electrical equipment and power supply components. Specifically, in the digital twin monitoring system of this invention, the rated power consumption data corresponding to the configuration specifications of different electrical equipment and different power supply components can be pre-calibrated and stored to form a database. During the lookup, the lookup module can directly query the corresponding database based on the current configuration specifications of the electrical equipment and power supply components to find the matching rated power consumption data. Then, the power consumption allocation data is compared with the rated power consumption data by the data comparison module. If the power consumption allocation data is less than the rated power consumption data, it indicates that the current configuration specifications meet the corresponding load requirements; if the power consumption allocation data is greater than the rated power consumption data, it indicates that the current configuration specifications do not meet the corresponding load requirements. This allows for accurate classification of whether each power consumption allocation data indicates an overload situation.

[0033] In the digital twin monitoring system based on a smart microgrid of the present invention, the comparison and analysis unit 114 includes: a threshold lookup subunit, used to find the corresponding response output difference threshold based on the actual response output; a calculation subunit, used to calculate the response output influence value based on the operating load energy consumption and the response output influence coefficient; calculate the conservative response output based on the response output influence value and the simulated response output; calculate the difference between the conservative response output and the actual response output to obtain the response output difference; and an output detection subunit, used to perform threshold detection on the response output difference; when the response output difference is greater than the response output difference threshold, an additional energy consumption anomaly value is calculated based on the response output difference and the response output influence coefficient, and the calculation formula for the additional energy consumption anomaly value is: , This represents the simulated response output. Indicates energy consumption under operating load. This represents the response output influence coefficient. This indicates the actual response output.

[0034] When calculating abnormal values ​​of additional energy consumption, the comparative analysis unit 114 can use a threshold lookup subunit to obtain the corresponding monitoring threshold, i.e., the response output difference threshold, based on the actual response output obtained. Then, the calculation subunit first utilizes the energy consumption of the operating load. and response output influence coefficient Calculate the impact value of the response output. Then, based on the impact value of the response output. and simulated response output The difference is calculated to obtain the conservative response output. Then output a conservative response. and actual response output The difference between the response and output is calculated. Finally, the response output difference is threshold-checked by the output detection subunit. If the response output difference is less than the threshold, it indicates that the current response output difference is normal and no further processing is needed. However, if the response output difference is greater than the threshold, then processing is performed based on the response output difference... and response output influence coefficient The calculated anomaly value of additional energy consumption can be expressed by the formula as follows: The calculated anomaly in additional energy consumption allows for a rational analysis of abnormal power supply configurations, enabling the rapid identification and timely repair of the corresponding abnormal power supply configurations.

[0035] It is worth noting that when generating the simulated response output, the additional losses caused by power supply configuration devices such as lines, connecting devices, and switching control devices during power transmission have been optimized in advance through the simulation model of the power supply network of the park equipment. Furthermore, the energy consumption of the operating load caused by the increase or decrease in power consumption has also been calculated. Therefore, when comparing the simulated response output with the actual response output, the additional energy consumption anomaly value is accurately calculated by the comparison analysis unit 114.

[0036] In the digital twin monitoring system based on a smart microgrid of the present invention, the anomaly analysis unit 115 includes: a model query subunit, used to search for a power supply anomaly model based on the extra energy consumption anomaly value and the power consumption allocation data corresponding to the extra energy consumption anomaly value; a simulation operation subunit, used to add the power supply anomaly model to the power supply network simulation model of the park equipment, and perform simulation operation through the corresponding power consumption data to obtain anomaly response output; an anomaly comparison subunit, used to compare and analyze the actual response output and the anomaly response output based on the operating load energy consumption corresponding to the power consumption data to obtain the energy consumption anomaly update value; and a threshold comparison subunit, used to perform threshold comparison on the anomaly difference between the energy consumption anomaly update value and the extra energy consumption anomaly value. When the anomaly difference is less than the corresponding anomaly threshold, the power supply anomaly model corresponding to the energy consumption anomaly update value is used as the power supply anomaly configuration, wherein the anomaly threshold corresponds to each power supply anomaly model.

[0037] When performing power supply anomaly configuration analysis, the anomaly analysis unit 115 can use the calculated extra energy consumption anomaly value and the corresponding power consumption allocation data to find the corresponding power supply anomaly model using the model query subunit. The digital twin monitoring system of this invention also stores multiple power supply anomaly models corresponding to different extra energy consumption anomaly value ranges and different power consumption allocation data ranges, which can be configured by staff based on the power supply anomaly situation. That is, the storage module of the model query subunit stores multiple power supply anomaly models corresponding to different extra energy consumption anomaly value ranges and different power consumption allocation data ranges in advance. By using the extra energy consumption anomaly value and the corresponding power consumption allocation data to look up the table, the corresponding extra energy consumption anomaly value range and power consumption allocation data range can be determined, and the set of power supply anomaly models corresponding to the extra energy consumption anomaly value range and power consumption allocation data range can be found. Then, each power supply anomaly model in the power supply anomaly model set is sequentially added to the park equipment power supply network simulation model through the simulation running subunit, and the simulation is performed using the power consumption data corresponding to the generated extra energy consumption anomaly value, thereby obtaining the corresponding anomaly response output. Then, the anomaly comparison subunit continues to compare and analyze the actual response output with the anomaly response output based on the calculated power consumption data corresponding to the operating load energy consumption, thereby deriving the updated energy consumption anomaly value. The steps for comparing and analyzing the actual response output with the anomaly response output to derive the updated energy consumption anomaly value can be found in the formula. The calculations are as follows, and will not be elaborated here. After obtaining the updated energy consumption anomaly value, the threshold comparison subunit calculates the difference between the updated energy consumption anomaly value and the additional energy consumption anomaly value to obtain the anomaly difference value. Then, the anomaly difference value is compared with its corresponding anomaly threshold. When the anomaly difference value is less than the corresponding anomaly threshold, the power supply anomaly model corresponding to the updated energy consumption anomaly value can be used as the power supply anomaly configuration. This allows for a faster identification of solutions when additional energy consumption anomalies occur, reducing maintenance costs for staff and improving the speed and accuracy of assessments.

[0038] In addition, since there may be multiple power supply anomaly models corresponding to abnormal differences less than the abnormal threshold, when selecting a power supply anomaly configuration, the power supply anomaly model with the smallest abnormal difference can be selected as the power supply anomaly configuration when there are multiple power supply anomaly models corresponding to abnormal differences less than the abnormal threshold.

[0039] In the digital twin monitoring system based on a smart microgrid of the present invention, the configuration optimization unit 116 includes: a solution query subunit, used to find a solution corresponding to the power supply anomaly model based on the power supply anomaly configuration; a sending subunit, used to send the solution to the human end; and a configuration processing subunit, used to receive the completion signal of the solution from the human end, and add the power supply anomaly model and the repair model corresponding to the solution to the simulation model of the power supply network of the park equipment, so as to optimize the power supply network of the park equipment. The power supply network of the park equipment includes power consumption equipment groups and power supply configuration devices for power transmission between power consumption equipment groups and between power consumption equipment within the power consumption equipment groups.

[0040] After obtaining the power supply anomaly configuration, the configuration optimization unit 116 can quickly find a solution corresponding to the power supply anomaly model based on the configuration through the solution query subunit. Then, the solution is sent to the human terminal via the sending subunit to remind staff to quickly complete the equipment replacement or repair of the park's power supply network according to the provided solution. After the staff completes the solution, a completion signal is sent to the digital twin monitoring system of this invention. Correspondingly, the configuration processing subunit receives the completion signal from the human terminal and, upon receiving the signal, continues to add the power supply anomaly model and the corresponding repair model to the park's power supply network simulation model to continuously optimize the simulation model, ensuring consistency between the park's power supply network and the simulation model, and improving the accuracy and flexibility of subsequent monitoring.

[0041] Please see Figure 2 The present invention also provides a digital twin monitoring method based on a smart microgrid, comprising: Step S10: Obtain power consumption data of electrical equipment groups in each zone of the park through the first acquisition unit 111; Step S20: Based on the power consumption data, the simulation response unit 112 performs power consumption simulation through the constructed campus equipment power supply network simulation model to obtain the simulation response output. The campus equipment power supply network simulation model includes power consumption simulation models corresponding to multiple partitioned power equipment groups, as well as power supply configuration models corresponding to power supply configuration devices used for power transmission between power equipment groups and between power equipment within power equipment groups. Step S30: Obtain the actual response output of each power-consuming equipment group based on the power consumption data through the second acquisition unit 113; Step S40: By comparing and analyzing the actual response output with the simulated response output based on the power consumption data of the operating load through the comparison and analysis unit 114, the abnormal value of additional energy consumption is obtained. Step S50: The anomaly analysis unit 115 performs a power supply configuration rationality analysis based on the abnormal value of additional energy consumption to obtain the abnormal power supply configuration; Step S60: Optimize the power supply network of the park equipment based on the power supply anomaly configuration by configuring the optimization unit 116.

[0042] In summary, the digital twin monitoring system and method based on a smart microgrid disclosed in this invention acquires power consumption data of electrical equipment groups in various zones of the industrial park and inputs it into the park's power supply network simulation model. This allows the simulation model to generate a simulated response output based on the power consumption data, thus simulating the response output and monitoring anomalies in the response output through digital twin monitoring technology. Specifically, by acquiring the actual response output and determining the operating load energy consumption corresponding to the current power consumption data, a comparison and analysis of the actual and simulated response outputs can be performed. This allows for the identification of abnormal additional energy consumption values ​​when the actual and simulated response outputs differ significantly. This method effectively ensures the accuracy of calculating abnormal additional energy consumption. Furthermore, based on these abnormal additional energy consumption values, the power supply configuration can be analyzed using the park's power supply network simulation model to identify and determine the abnormal power supply configuration corresponding to the occurrence of these abnormal values. Then, based on this abnormal power supply configuration, a solution is generated to repair the abnormal power supply configuration and replace the electrical equipment and its spare parts, thereby quickly optimizing the power supply network of the park's equipment and ensuring the safe use of all electrical equipment and power supply configuration components. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0043] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A digital twin monitoring system based on a smart microgrid, characterized in that, include: The first acquisition unit is used to acquire power consumption data of electrical equipment groups in each zone of the park. The simulation response unit is used to simulate power consumption based on the power consumption data and through the constructed simulation model of the power supply network of the park equipment to obtain a simulation response output. The simulation model of the power supply network of the park equipment includes power consumption simulation models corresponding to multiple partition power consumption equipment groups, and power supply configuration models corresponding to power supply configuration devices used for power transmission between the power consumption equipment groups and the power consumption equipment within the power consumption equipment groups. The second acquisition unit is used to acquire the actual response output of each of the power-consuming equipment groups based on the power consumption data; The comparative analysis unit is used to compare and analyze the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data, and to obtain the additional energy consumption anomaly value. Anomaly analysis unit is used to perform a power supply configuration rationality analysis based on the aforementioned abnormal additional energy consumption value, and to determine the abnormal power supply configuration; and The configuration optimization unit is used to optimize the power supply network of the park equipment based on the power supply anomaly configuration.

2. The digital twin monitoring system based on a smart microgrid according to claim 1, characterized in that, The simulation response unit includes a model building subunit, which includes: The data receiving module is used to receive the replacement data of the electrical equipment group, wherein the replacement data of the electrical equipment group includes first replacement data corresponding to the electrical equipment and second replacement data corresponding to the power supply configuration device; The model adjustment module is used to adjust the corresponding power consumption simulation model and power supply configuration model according to the first replacement data and the second replacement data, respectively; and The model update module is used to dynamically update the power supply network simulation model of the park equipment by adjusting the power consumption simulation model and the power supply configuration model, so as to construct the power supply network simulation model of the park equipment.

3. The digital twin monitoring system based on a smart microgrid according to claim 1, characterized in that, Also includes: The difference calculation unit is used to calculate the difference between the power consumption data and the historical power consumption data to obtain the power consumption fluctuation data corresponding to each power supply device. The calculation formula for the power consumption fluctuation data is as follows: , This indicates power consumption data. This represents historical power consumption data, including power consumption increase data and power consumption decrease data. The operation detection unit is used to perform operation detection on the electrical equipment and the power supply configuration device based on the power consumption fluctuation data, and obtain the operating load energy consumption.

4. The digital twin monitoring system based on a smart microgrid according to claim 3, characterized in that, The operation detection unit includes: A numerical detection subunit is used to detect the power consumption fluctuation data; The model processing subunit is used to, when the power consumption fluctuation data exceeds a fluctuation threshold, output power consumption allocation data corresponding to the power-consuming equipment and the power supply configuration device corresponding to the power consumption fluctuation data through the park equipment power supply network simulation model. ,in, This represents the first power consumption allocation data corresponding to the power consumption simulation model. This represents the second power consumption allocation data corresponding to the power supply configuration model; The judgment subunit is used to determine, based on the power consumption allocation data, whether the current configuration specifications of the electrical equipment and the power supply configuration device meet the corresponding load requirements; The first output subunit is configured to directly output the simulated response output to the comparison and analysis unit when the current configuration specifications of the electrical equipment and the power supply configuration device meet the corresponding load requirements, so as to compare and analyze the actual response output with the simulated response output to obtain the additional energy consumption anomaly value; and The second output subunit is used to generate a required configuration specification corresponding to the current power consumption allocation data and send it to the equipment maintenance terminal for early warning and replacement when the current configuration specification of the electrical equipment and the power supply configuration device does not meet the corresponding load requirements. Based on the degree of difference between the current configuration specification and the required configuration specification, the energy consumption conversion coefficient corresponding to the degree of difference, and the running time corresponding to the generation of the power consumption data, the operating load energy consumption is generated. The calculation formula for the operating load energy consumption is: , This indicates the degree of difference between the current configuration specification and the required configuration specification. The energy conversion coefficient represents the degree of difference. This indicates the running time corresponding to the power consumption data.

5. The digital twin monitoring system based on a smart microgrid according to claim 4, characterized in that, The judgment subunit includes: The lookup module is used to find the rated power consumption data based on the current configuration specifications of the electrical equipment and the power supply configuration device; and The data comparison module is used to compare the power consumption allocation data with the rated power consumption data; when the power consumption allocation data is less than the rated power consumption data, it indicates that the current configuration meets the corresponding load requirements; when the power consumption allocation data is greater than the rated power consumption data, it indicates that the current configuration does not meet the corresponding load requirements.

6. The digital twin monitoring system based on a smart microgrid according to claim 1, characterized in that, The comparison analysis unit includes: The threshold lookup subunit is used to find the corresponding response output difference threshold based on the actual response output. A calculation subunit is used to calculate the response output impact value based on the operating load energy consumption and the response output impact coefficient; calculate a conservative response output based on the response output impact value and the simulated response output; and calculate the difference between the conservative response output and the actual response output to obtain the response output difference; and The detection output subunit is used to perform threshold detection on the response output difference; when the response output difference is greater than the response output difference threshold, the additional energy consumption anomaly value is calculated based on the response output difference and the response output influence coefficient, and the calculation formula for the additional energy consumption anomaly value is: , This represents the simulated response output. Indicates energy consumption under operating load. This represents the response output influence coefficient. This indicates the actual response output.

7. The digital twin monitoring system based on a smart microgrid according to claim 1, characterized in that, The anomaly analysis unit includes: The model query subunit is used to find the power supply anomaly model based on the extra energy consumption anomaly value and the power consumption allocation data corresponding to the extra energy consumption anomaly value; The simulation subunit is used to add the power supply anomaly model to the power supply network simulation model of the park equipment, and to perform simulation by using the corresponding power consumption data to obtain the anomaly response output. An anomaly comparison subunit is used to compare and analyze the actual response output with the anomaly response output based on the operating load energy consumption corresponding to the power consumption data, and to obtain an energy consumption anomaly update value; and The threshold comparison subunit is used to perform a threshold comparison on the abnormal difference between the energy consumption abnormal update value and the additional energy consumption abnormal value. When the abnormal difference is less than the corresponding abnormal threshold, the power supply abnormal model corresponding to the energy consumption abnormal update value is used as the power supply abnormal configuration. The abnormal threshold corresponds to each power supply abnormal model.

8. The digital twin monitoring system based on a smart microgrid according to claim 7, characterized in that, When there are multiple power supply anomaly models corresponding to the anomaly difference value being less than the anomaly threshold, the power supply anomaly model corresponding to the one with the smallest anomaly difference value is selected as the power supply anomaly configuration.

9. The digital twin monitoring system based on a smart microgrid according to claim 1, characterized in that, The configuration optimization unit includes: The solution query subunit is used to find a solution corresponding to the power supply anomaly model based on the power supply anomaly configuration; A sending subunit is used to send the solution to a human operator; and A configuration processing subunit is configured to receive the completion signal of the solution from the human terminal, and add the power supply anomaly model and the repair model corresponding to the solution to the simulation model of the power supply network of the park equipment to optimize the power supply network of the park equipment. The power supply network of the park equipment includes a group of electrical equipment and a power supply configuration device for power transmission between the group of electrical equipment and the electrical equipment within the group.

10. A digital twin monitoring method based on a smart microgrid, characterized in that, include: The first acquisition unit acquires power consumption data of electrical equipment groups in each zone of the park. Based on the power consumption data, the simulation response unit performs power consumption simulation through the constructed campus equipment power supply network simulation model to obtain the simulation response output. The campus equipment power supply network simulation model includes power consumption simulation models corresponding to multiple partitioned power consumption equipment groups, as well as power supply configuration models corresponding to power supply configuration devices used for power transmission between the power consumption equipment groups and the power consumption equipment within the power consumption equipment groups. The second acquisition unit acquires the actual response output of each of the electrical equipment groups based on the power consumption data. The comparative analysis unit compares and analyzes the actual response output with the simulated response output based on the operating load energy consumption corresponding to the power consumption data to obtain the additional energy consumption anomaly value. The anomaly analysis unit performs a power supply configuration rationality analysis based on the abnormal value of the additional energy consumption, and then obtains the abnormal power supply configuration. The configuration optimization unit optimizes the power supply network of the park equipment based on the power supply anomaly configuration.

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