A secondary system adaptive construction method, device, equipment, medium and product

Abstract

The application discloses a secondary system adaptive construction method and device, equipment, medium and product, and relates to the technical field of micro-grid control. The method comprises the following steps: acquiring operation scene characteristic information and source-load boundary state of a micro-grid, constructing a scene characteristic vector, and constructing a twin state variable according to electrical operation state information and the source-load boundary state; determining a corrected twin state according to the twin state variable, the scene characteristic vector and real-time state of the micro-grid; determining a function requirement, screening a target module combination in a candidate module set according to the function requirement, matching a target communication protocol and a resource allocation result corresponding to the target module combination, and obtaining a configuration strategy; determining a target edge device, mapping the target module to the target edge device, and constructing a micro-grid secondary system according to the target edge device; determining a current corrected twin state, determining a current function requirement according to the current corrected twin state, and reconstructing and updating the micro-grid secondary system according to the current function requirement and the function requirement.

Description

Technical Field

[0001] This invention relates to the field of microgrid control technology, and in particular to a method, apparatus, equipment, medium, and product for adaptive construction of a secondary system. Background Technology

[0002] In recent years, with the large-scale integration of new energy equipment such as photovoltaics and energy storage in rural areas, rural microgrids have gradually shown characteristics such as high penetration of new energy sources, multi-source coordination, and flexible operation modes. At the same time, issues such as increased fluctuations in new energy output, frequent switching between off-grid and grid-connected operation, and dynamic changes in source-load boundaries have made the operation of rural microgrids more complex, placing higher demands on the adaptability of secondary systems.

[0003] However, most existing rural microgrid secondary systems are constructed using pre-planning and fixed configuration methods. Their protection, measurement and control, communication, and coordination control functions are typically designed and deployed during the system construction phase. In actual operation, when the output of new energy sources, load characteristics, energy storage status, or operating mode changes, existing secondary systems struggle to adjust their configurations in a timely manner according to the changing operating scenarios. This often necessitates manual analysis and reconfiguration, resulting in delayed system response and high operation and maintenance costs.

[0004] Furthermore, existing digital twin technology is mainly used for operational status monitoring, fault analysis, and simulation evaluation. Its digital twin model is typically used as an operational aid tool and has not yet been effectively integrated with the secondary system configuration generation process. Therefore, it cannot automatically identify system functional requirements and generate corresponding configuration schemes based on the digital twin's status. Consequently, existing technologies generally suffer from a disconnect between operational scenario perception and secondary system configuration, making it difficult to achieve autonomous adjustment and continuous optimization of the secondary system as operational status changes. Therefore, there is an urgent need for a scenario-adaptive secondary system construction method for rural microgrids with high renewable energy penetration. Summary of the Invention

[0005] This invention provides a method, apparatus, device, medium, and product for adaptive construction of secondary systems, in order to solve the problem that existing secondary systems are difficult to adjust their configurations in a timely manner according to changes in the operating scenario, and often require manual analysis and manual reconstruction.

[0006] According to one aspect of the present invention, a method for adaptive construction of a quadratic system is provided, wherein the method includes: Obtain the operating scenario feature information and source-load boundary state of the microgrid, construct a scenario feature vector according to the operating scenario feature information and the source-load boundary state, obtain the electrical operating state information of the microgrid nodes, and construct twin state variables according to the electrical operating state information and the source-load boundary state. The twin prediction state is determined based on the twin state variables and the scene feature vector, the real-time state of the microgrid is obtained, and the corrected twin state is determined based on the real-time state of the microgrid and the twin prediction state. Based on the corrected twin state, functional requirements are determined. Target module combinations are screened from the candidate module set according to the functional requirements. The target communication protocol and resource allocation result corresponding to the target module combination are matched. The target module combination, the target communication protocol, and the resource allocation result are used as the configuration strategy. Based on the configuration strategy and the operating scenario feature information, target edge devices are determined, target module combinations are mapped to target edge devices, and microgrid secondary systems are constructed according to the target edge devices. Determine the current calibration twin state, determine the current functional requirements based on the current calibration twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and the functional requirements.

[0007] According to another aspect of the present invention, a secondary system adaptive construction apparatus is provided, wherein the apparatus comprises: The information acquisition module is used to acquire the operating scenario feature information and source-load boundary state of the microgrid, construct a scenario feature vector according to the operating scenario feature information and the source-load boundary state, acquire the electrical operating state information of the microgrid nodes, and construct twin state variables according to the electrical operating state information and the source-load boundary state. The state correction module is used to determine the twin prediction state based on the twin state variables and the scene feature vector, obtain the real-time state of the microgrid, and determine the corrected twin state based on the real-time state of the microgrid and the twin prediction state. The strategy determination module is used to determine functional requirements based on the corrected twin state, filter target module combinations in the candidate module set according to the functional requirements, match the target communication protocol and resource allocation result corresponding to the target module combination, and use the target module combination, the target communication protocol and the resource allocation result as the configuration strategy. The system construction module is used to determine the target edge device according to the configuration strategy and the operating scenario feature information, map the target module combination to the target edge device, and construct the microgrid secondary system according to the target edge device; A system update module is used to determine the current calibration twin state, determine the current functional requirements based on the current calibration twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and the functional requirements. According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the secondary system adaptive construction method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the secondary system adaptive construction method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the secondary system adaptive construction method of any embodiment of the present invention.

[0010] The technical solution of this invention obtains the operational scenario feature information and source-load boundary states of a microgrid, constructs a scenario feature vector based on these information, obtains the electrical operational status information of microgrid nodes, constructs twin state variables based on the electrical operational status information and source-load boundary states, determines the twin predicted state based on the twin state variables and the scenario feature vector, obtains the real-time state of the microgrid, and determines the corrected twin state based on the real-time state and the twin predicted state, thereby reducing the possibility of misjudgment or missed judgment of control requirements. Furthermore, it determines functional requirements based on the corrected twin state, selects target module combinations from the candidate module set according to the functional requirements, and matches the target module combinations with the corresponding target communication protocols and resource allocation structures. The result is that the target module combination, target communication protocol and resource allocation results are used as configuration strategies. Based on the configuration strategies and the characteristics of the operating scenario, the target edge devices are determined. The target module combination is mapped to the target edge devices. The microgrid secondary system is constructed according to the target edge devices. The current correction twin state is determined. Based on the current correction twin state, the current functional requirements are determined. Based on the current functional requirements and the functional requirements, the microgrid secondary system is reconstructed and updated. This solves the problems of the existing rural distribution microgrid secondary system under the condition of high proportion of new energy access, such as the disconnect between the change of operating scenario and system configuration, the difficulty of the digital twin state to directly drive the secondary system configuration generation and the insufficient adaptive adjustment capability. It realizes the continuous optimization and adaptive evolution of the secondary system.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a secondary system adaptive construction method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a secondary system adaptive construction method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a secondary system adaptive construction device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements a secondary system adaptive construction method according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1This is a flowchart of a secondary system adaptive construction method according to Embodiment 1 of the present invention. This embodiment is applicable to the construction of a scenario-adaptive secondary system for rural power distribution networks. The method can be executed by a secondary system adaptive construction device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the operating scenario feature information and source-load boundary state of the microgrid, construct the scenario feature vector according to the operating scenario feature information and source-load boundary state, obtain the electrical operating state information of the microgrid nodes, and construct twin state variables according to the electrical operating state information and source-load boundary state.

[0017] In this context, a microgrid can be understood as a small, self-manageable local power grid. Operational scenario characteristic information can be understood as the basic collected data characterizing the microgrid's external dynamic disturbance conditions. For example, operational scenario characteristic information may include, but is not limited to, the actual output power of generation units, the real-time active power of load nodes, the reactive power of load nodes, and on-site environmental parameters (such as ambient temperature, relative humidity, and air dust concentration). Source-load boundary state refers to the topological isolation state of the microgrid's source-load partitions, mainly referring to the opening and closing states of the grid connection switches between the microgrid and the upper-level distribution network, as well as the flexible switches within the microgrid's internal partitions. Generally, the source-load boundary state can be represented using binary matrix quantization. The scenario feature vector can be understood as a time-series feature vector formed after standardizing and fusing the operational scenario characteristic information and the source-load boundary state. Electrical operating status information can be understood as the core state data of the microgrid's internal electrical operating conditions. Generally, electrical operating status information may include at least the voltage amplitude, voltage phase angle, node active power, node reactive power, and real-time state of charge of energy storage units at each node of the microgrid. Twin state variables are structured state variables that integrate the internal electrical operating state of a microgrid with the source-load boundary topology state.

[0018] In this embodiment, microgrid operation scenario characteristic information, source-load boundary states, and electrical operation status information of microgrid nodes can be collected. A scenario feature vector and twin state variables are constructed based on these information. During actual operation, real-time output power of each power generation unit, real-time active and reactive loads of each load node, and ambient temperature, relative humidity, and air dust concentration are collected. Microgrid grid-connected interconnection switches are collected, and a binary matrix is ​​constructed based on the operation status of all boundary switches to quantize the source-load boundary states. The real-time output power of the power generation units, real-time active and reactive loads of the load nodes, ambient temperature, relative humidity, air dust concentration, and source-load boundary states are integrated to construct a time-series scenario feature vector with unified dimensions as the scenario feature vector. Voltage amplitude, voltage phase angle, node active power, and node reactive power information of all microgrid nodes are collected. Simultaneously, the initial state of charge, real-time charging and discharging power, and rated energy capacity of each energy storage unit are collected, and the real-time state of charge of the energy storage is iteratively calculated. Integrate all electrical and energy storage data to form complete electrical operating status information. Combine the electrical operating status information with the source-load boundary states to construct twin state variables.

[0019] S120. Determine the twin prediction state based on the twin state variables and scenario feature vectors, obtain the real-time state of the microgrid, and determine the corrected twin state based on the real-time state of the microgrid and the twin prediction state.

[0020] The twin predicted state can be understood as the simulated operating state of the microgrid at the next moment, derived from the deduction. In actual operation, the twin predicted state can be determined based on the digital twin discrete state-space model, using twin state variables and scene feature vectors. The real-time state of the microgrid refers to the physically measured state of the microgrid collected in real time by various sensors. For example, the real-time state of the microgrid may include, but is not limited to, parameters such as voltage amplitude, voltage phase angle, active power, reactive power, topology switch status, and energy storage operating status. The corrected twin state can be understood as the corrected twin predicted state. In one embodiment, a Kalman filter algorithm can be used to correct the deviation between the twin predicted state and the real-time measured state of the microgrid to obtain the corrected twin state.

[0021] In this embodiment, the scene feature vector can be transformed into a standardized input vector adapted to the discrete state space model of the digital twin by performing a dimensionality transformation on a preset scene input mapping matrix. A preset state transition matrix is ​​retrieved, and the inherent state evolution components of the power grid are calculated in conjunction with the current twin state variables. This is then superimposed with the standardized input vector obtained from the scene feature vector mapping. Through iterative calculation in the discrete state space, the twin predicted state is obtained. Data such as node electrical quantities, energy storage status, and topology switch status are then collected as the real-time state of the microgrid. The difference between the twin predicted state and the real-time state of the microgrid is calculated as the prediction error. A Kalman filter algorithm is used to iteratively correct the predicted state according to the prediction error to obtain the corrected twin state.

[0022] S130. Determine functional requirements based on the corrected twin state, select target module combinations from the candidate module set according to the functional requirements, match the target communication protocol and resource allocation results corresponding to the target module combinations, and use the target module combinations, target communication protocols and resource allocation results as configuration strategies.

[0023] The functional requirements can be secondary system operation requirements identified based on information such as operating condition overruns and topology switching characteristics from the corrected twin state. For example, functional requirements may include voltage overrun control requirements, energy storage state of charge overrun control requirements, and source-load boundary switching stability control requirements. The candidate module set is a pre-built standardized software functional module library for the microgrid secondary system. Candidate modules may include protection modules, measurement and control modules, energy storage coordination modules, voltage control modules, fault diagnosis modules, and communication forwarding modules. Each candidate module can have its functional attributes, communication protocol adaptation type, and hardware resource consumption parameters pre-set. The target module combination is the optimal set of functional modules selected from the candidate module set. Generally, the target module combination can be selected with the goal of minimizing resource consumption. The target communication protocol is a pre-set standardized communication protocol that is compatible with the target module combination, such as Controller Area Network (CAN) and Message Queuing Telemetry Transport (MQTT) protocols. Resource allocation results refer to the resource consumption requirements of the target module combination. These results may include hardware resource quotas for edge devices, such as the Central Processing Unit (CPU), memory, and network bandwidth. Configuration strategies are secondary system configuration schemes that integrate the target module combination, target communication protocol, and hardware resource allocation results. These schemes can be directly deployed to edge devices to complete system deployment.

[0024] In this embodiment, multi-dimensional operating condition detection can be performed based on the corrected twin state. For example, it can traverse the voltage data of all network nodes to determine if there are voltage over-limit conditions and generate voltage over-limit control requirements; traverse the energy storage state of charge data to determine if there are overcharge or over-discharge over-limit conditions and generate energy storage over-limit control requirements; compare the current and historical source-load boundary states to identify operating conditions such as topology switch changes or operating mode switching, and generate source-load boundary switching stability control requirements. These voltage over-limit control requirements, energy storage over-limit control requirements, and source-load boundary switching stability control requirements are then used as functional requirements. A pre-built set of candidate modules for the secondary system is retrieved. Candidate module combinations that meet the functional requirements are selected from the candidate module set. When there are multiple candidate module combinations, the optimal candidate module combination is obtained by minimizing overall resource consumption. The target communication protocol corresponding to the target module combination is then matched, and resource consumption parameters are matched as resource allocation results. The target module combination, target communication protocol, and resource allocation results are used as a configuration strategy.

[0025] S140. Determine the target edge device based on the configuration strategy and the characteristic information of the operating scenario, map the target module combination to the target edge device, and construct the microgrid secondary system according to the target edge device.

[0026] In this context, target edge devices refer to hardware terminals adaptable to the current operating conditions and serve as the hardware deployment carrier for the secondary system. In practical applications, a candidate edge device set can be pre-built, and then selected from this set based on the configuration strategy and operational scenario characteristics to become the target edge devices. The microgrid secondary system refers to the automated system that monitors, controls, communicates with, and manages the primary equipment of the microgrid; it is crucial for the safe and stable operation of the microgrid.

[0027] In this embodiment, a preliminary selection of candidate edge devices can be made from a pre-built set of candidate edge devices based on the generated configuration strategy and the field environment parameters in the runtime scenario feature information. Then, from the preliminary selection of candidate edge devices, edge devices that simultaneously meet the requirements of hardware resource capacity matching resource allocation and the target communication protocol are selected as the final target edge devices. Generally, each target module is deployed to only one edge device to avoid resource conflicts and functional redundancy. The mapping and deployment of the target module to the target edge device is then completed. In one embodiment, after the mapping and deployment of the target module to the target edge device, resource verification, communication verification, and environment verification can be performed. This confirms that the edge device's hardware resources can fully support the module's operational requirements, that the edge device's protocol is compatible and data transmission is normal, and that the device can adapt to complex field operating environments. If any conditions are not met, the target edge device can be replaced until all verifications are satisfied, completing the construction of the microgrid secondary system.

[0028] S150. Determine the current correction twin state, determine the current functional requirements based on the current correction twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and functional requirements.

[0029] The current calibrated twin state refers to the real-time microgrid state obtained after Kalman filtering correction of the latest collected twin state variables within the dynamic monitoring cycle. The current functional requirements are the secondary system functional requirement vectors identified and updated in real-time based on the current calibrated twin state. Secondary system dynamic reconfiguration refers to the process of automatically restarting the entire modeling, configuration, and deployment process, updating the secondary system hardware and software configuration, and achieving adaptive iterative optimization of the system when the real-time functional requirements do not match the reference functional requirements corresponding to the historical configuration.

[0030] In this embodiment, after creating the microgrid secondary system, real-time operating data of the microgrid can be continuously collected and updated in real time to obtain the current corrected twin state, ensuring that the twin state is always synchronized with the physical grid operating conditions. Functional requirements are determined based on the latest current corrected twin state as the current functional requirements. The functional requirements from the previous system deployment are retrieved, and the current functional requirements are compared dimension by dimension to determine the adaptability of the current secondary system configuration. If there are differences between the two sets of functional requirements, it indicates that the existing secondary system configuration cannot adapt to the current grid operating conditions, and a dynamic reconfiguration mechanism is immediately triggered, automatically restarting the process from S110 to S140, and regenerating a completely new secondary system configuration scheme. In one embodiment, after the reconfiguration is completed, the current corrected twin state and current functional requirements can be updated to a new reference benchmark.

[0031] This invention, in its embodiments, acquires the operational scenario feature information and source-load boundary states of a microgrid, constructs a scenario feature vector based on these information, obtains the electrical operational status information of microgrid nodes, constructs twin state variables based on the electrical operational status information and source-load boundary states, determines the twin predicted state based on the twin state variables and the scenario feature vector, acquires the real-time state of the microgrid, and determines the corrected twin state based on the real-time state and the twin predicted state, reducing the possibility of misjudgment or missed judgment of control requirements. Furthermore, it determines functional requirements based on the corrected twin state, filters target module combinations from the candidate module set according to the functional requirements, matches the target module combinations with the corresponding target communication protocols and resource allocation results, and... The target module combination, target communication protocol, and resource allocation results serve as the configuration strategy. Based on the configuration strategy and operational scenario characteristics, target edge devices are determined, and the target module combination is mapped to the target edge devices. The microgrid secondary system is constructed according to the target edge devices, the current correction twin state is determined, the current functional requirements are determined based on the current correction twin state, and the microgrid secondary system is reconstructed and updated based on the current functional requirements and functional requirements. This solves the problems of existing rural distribution microgrid secondary systems under high-proportion renewable energy access conditions, such as the disconnect between operational scenario changes and system configuration, the difficulty of the digital twin state directly driving the secondary system configuration generation, and insufficient adaptive adjustment capabilities. It enables continuous optimization and adaptive evolution of the secondary system.

[0032] Example 2 Figure 2 This is a flowchart of a secondary system adaptive construction method according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments, and can be combined with various optional technical solutions in the above embodiments. Figure 2 As shown, the method includes: S201. Collect the real-time active load and real-time reactive load of the load node as the load power, and collect the actual output power of the power generation unit in the microgrid.

[0033] In a microgrid, a load node refers to the terminal node that bears the electricity load, such as residential or industrial loads, and is an energy-consuming unit. Real-time active load refers to the actual active power consumed by the load node at the current moment, while real-time reactive load refers to the actual reactive power consumed by the load node at the current moment. Active load represents the effective work done, while reactive load represents the energy exchange and loss within the grid. A generation unit refers to the basic generation unit in the microgrid, which can be a distributed generation unit that uses clean energy as its energy source and converts natural renewable energy into alternating current, such as photovoltaic (PV) and wind power generation units. Actual output power refers to the actual power generated by the generation unit within the microgrid at the current moment.

[0034] In the embodiments, the operation data of each load node can be collected in real time through devices such as a smart meter of the microgrid, the real-time active load and real-time reactive load of each load node are extracted, and the real-time active load and real-time reactive load are integrated as the load power of the microgrid. Meanwhile, the power output of the power generation unit is collected, and abnormal sudden change data is removed to obtain the actual output power of the power generation unit.

[0035] S202. Collect the source-load boundary states and construct them into a binary matrix, collect the ambient temperature, relative humidity and air dust concentration as environmental parameters, take the power output, active load, reactive load, source-load boundary states and environmental parameters as operation scenario feature information, and integrate the operation scenario feature information to construct a scenario feature vector.

[0036] In the embodiments, the on-off states of all boundary switches of the microgrid can be collected, and a standardized source-load boundary binary matrix is constructed according to the on-off states of the boundary switches. The ambient temperature, relative humidity and air dust concentration are synchronously collected by an environmental sensor as environmental parameters. The collected actual new energy output power, load active power, load reactive power, source-load boundary states and environmental parameters are uniformly integrated into the operation scenario feature information.

[0037] S203. Collect the voltage amplitude, voltage phase angle, active power and reactive power of all microgrid nodes.

[0038] In the embodiments, the operation parameters of all electrical nodes of the microgrid can be collected through synchronous traversal, including the voltage amplitude, voltage phase angle, real-time active power and real-time reactive power of each node.

[0039] S204. Determine the historical state of charge, active charging and discharging power, energy storage charging and discharging efficiency, and rated energy capacity of the energy storage unit, and determine the current state of charge of the energy storage unit as the energy storage state according to the historical state of charge, active charging and discharging power, energy storage charging and discharging efficiency, and rated energy capacity.

[0040] Herein, the energy storage unit refers to the battery energy storage device supporting the microgrid. The historical state of charge refers to the state of charge of the energy storage unit at the previous sampling moment. The active charging and discharging power refers to the power interaction value of the energy storage unit at the current moment, a positive value is the charging power, and a negative value is the discharging power. The rated energy capacity refers to the maximum energy storage capacity calibrated at the factory of the energy storage unit. The current state of charge refers to the percentage of the real-time remaining capacity of the energy storage unit at the current sampling moment occupying the rated energy capacity. The energy storage state is the real-time operation state of the energy storage determined comprehensively by combining the historical state of charge, active charging and discharging power, energy storage charging and discharging efficiency, and rated energy capacity.

[0041] In one embodiment, the historical state of charge (SOC), active power, charge / discharge efficiency, and rated energy capacity of the energy storage units stored in the energy storage management system can be read. Based on the time-series recursive formula for energy storage capacity, the current SOC of the energy storage unit is determined as the energy storage state. In one embodiment, the energy storage state can be determined as follows: ;in, It is in energy storage state (current state of charge). Historical charge state; To improve the charging and discharging efficiency of energy storage; This refers to the active power of charging and discharging. The sampling time interval; This is the rated energy capacity of the energy storage; among which, and The state of charge (SOC) is determined by the nameplate parameters of the energy storage device and the energy storage management system. In one embodiment, to ensure that the energy storage state meets the device's operational constraints, upper and lower limits are set for the SOC. ;in, and These represent the minimum and maximum states of charge (SOC) that the energy storage unit is allowed to operate, respectively, and are determined by the protection parameters of the energy storage management system or the equipment operating procedures.

[0042] S205. Use voltage amplitude, voltage phase angle, active power, and reactive power as electrical operating status information, and construct twin state variables with the source-load boundary state.

[0043] In one embodiment, the collected voltage amplitude, voltage phase angle, active power and reactive power of all network nodes can be used as electrical operating status information, and the electrical operating status information can be fused with the source-load boundary binary matrix to form twin state variables.

[0044] S206. Map the scene feature vector to the digital twin model input vector using a preset scene input mapping matrix.

[0045] The preset scene input mapping matrix refers to a pre-set matrix that maps scene feature vectors to digital twin model input vectors. The digital twin model input vector refers to the scene feature vectors after standardization by the mapping matrix, providing external perturbation input for state prediction.

[0046] In one embodiment, a preset scene input mapping matrix can be retrieved. The scene input mapping matrix has fixed dimension transformation and weight allocation functions, which maps the scene feature vectors and transforms them into digital twin model input vectors that are adapted to the calculation rules of the digital twin model.

[0047] S207. Determine the product of the preset state transition matrix and the twin state variables, and use the sum of the product and the input vector of the digital twin model as the twin prediction state.

[0048] Among them, the state transition matrix refers to the preset power grid state evolution matrix, which is used to characterize the electrical inertia and state timing evolution of the microgrid itself.

[0049] In this embodiment, a preset state transition matrix can be retrieved, and the state transition matrix can be multiplied with the current twin state variables to obtain the time-series evolution state component of the microgrid based on its own electrical inertia. This time-series evolution state component is then superimposed and summed with the preprocessed digital twin model input vector to obtain the twin prediction state of the microgrid at the next moment.

[0050] S208. Collect the real-time status of the microgrid, determine the difference between the twin prediction status and the real-time status of the microgrid as the prediction error, and perform Kalman filtering correction on the twin prediction status according to the prediction error to obtain the corrected twin status.

[0051] The prediction error refers to the difference between the twin predicted state and the real-time measured state of the microgrid, including model errors, measurement noise, and other operational disturbances. Kalman filter correction is an algorithm based on iterative correction of the simulation state based on the prediction error. It is used to filter out noise, compensate for model biases, and achieve synchronization between the virtual and real grid states.

[0052] In this embodiment, real-time data of the entire microgrid's actual operation can be collected as the real-time state of the microgrid. The difference between the twin predicted state and the real-time state of the microgrid is calculated dimension by dimension to obtain the prediction error vector. This quantifies the deviation between the model prediction and the actual operating conditions. Based on the prediction error, a Kalman filter algorithm is used for iterative correction to obtain the corrected twin state.

[0053] S209. Determine the voltage over-limit requirements and energy over-limit requirements by performing over-limit detection according to the node voltage state and energy storage state in the corrected twin state.

[0054] Among them, voltage over-limit demand refers to the voltage regulation and reactive power optimization function requirements generated by the system when the node voltage amplitude exceeds the preset safety range (upper or lower limit). Energy storage over-limit demand refers to the energy storage charging limitation, discharging limitation, and coordinated control requirements generated by the system when the energy storage state of charge exceeds the safety threshold (overcharging upper limit or over-discharging lower limit).

[0055] In this embodiment, voltage state data in the corrected twin state can be extracted and compared one by one with preset upper and lower voltage safety thresholds. If the node voltage exceeds the safe range, it is determined that there is a voltage over-limit in the corresponding area, and a voltage over-limit control requirement is generated. Energy storage state data in the corrected twin state can be extracted and compared with the energy storage overcharge and over-discharge safety thresholds. If the energy storage charge state exceeds the limit, a corresponding energy storage over-limit control requirement is generated.

[0056] S210. Extract the source-load boundary state at historical time, determine the source-load boundary switching requirements according to the source-load boundary state at historical time, and take the voltage over-limit requirements, energy storage over-limit requirements, and source-load boundary switching requirements as functional requirements.

[0057] Among them, the source-load boundary switching requirement refers to the adaptive mode requirement of power grid stability control generated when the current source-load boundary state changes from the historical boundary state, the topology switch changes position, or the operating mode is switched.

[0058] In this embodiment, historical source-load boundary states can be retrieved and compared with the current source-load boundary states for analysis. The system identifies the positional changes of grid-connected switches and internal sectional switches, as well as switching behaviors such as microgrid grid-connected / islanded operation modes, to determine and generate source-load boundary switching stability control requirements. The identified voltage over-limit requirements, energy storage over-limit requirements, and source-load boundary switching requirements are then summarized and integrated to form a set of functional requirements for the microgrid secondary system.

[0059] S211. With the goal of minimizing resource consumption, select target module combinations from the candidate module set according to functional requirements, determine the target communication protocol and resource allocation results corresponding to the target module combinations, and use the target module combinations, target communication protocols, and resource allocation results as configuration strategies.

[0060] The candidate module set refers to a pre-built standardized secondary system functional module library, which includes functional modules such as protection, measurement and control, voltage regulation and energy storage coordination. Each module has preset functional attributes, resource consumption and communication protocol parameters.

[0061] In this embodiment, the optimization objective is to minimize the overall resource consumption of edge devices (CPU, memory, network bandwidth, etc.), while ensuring complete coverage of all functional requirements. An optimal solution is found within a pre-defined set of candidate modules to select a target module combination suitable for the current operating conditions. Based on the functional attributes and interface characteristics of the target module combination, a suitable target communication protocol is matched, and hardware resources are quantified to obtain a resource allocation result. The target module combination, target communication protocol, and resource allocation result are then used as a configuration strategy.

[0062] S212. Determine the functional description of each target module in the target module combination in the configuration strategy, and filter the candidate edge device set according to the functional description and the environmental parameters in the running scenario feature information.

[0063] Among them, the candidate edge device set is a cluster of edge devices with deployment and adaptation potential that is initially screened based on the functional requirements of the target module combination and the on-site environmental parameters.

[0064] In this embodiment, the functional descriptions of each target module in the configuration strategy can be parsed, and environmental parameters such as ambient temperature, relative humidity and air dust concentration in the characteristic information of the operating scenario can be extracted. Combined with the module functional descriptions and the on-site environmental tolerance requirements, a cluster of devices that can adapt to the functions and tolerate the on-site environment can be initially screened from the preset edge device library to form a candidate edge device set.

[0065] S213. Match candidate edge devices that meet the resource allocation results and target communication protocol in the candidate edge device set as target edge devices, and deploy the target module to the target edge device.

[0066] Each target module is deployed to only one target edge device.

[0067] In this embodiment, the hardware resource capacity of each candidate edge device can be verified one by one to see if it meets the resource allocation result of the configuration strategy and whether the device communication interface matches the target communication protocol. Edge devices that meet both the resource allocation result and the target communication protocol are identified as target edge devices. A mapping relationship between target modules and edge devices is established. Each target module is deployed independently to only one target edge device to avoid functional overlap and resource contention, thus completing the hardware deployment of all target modules.

[0068] S214. Perform resource verification, communication verification, and environment verification on the target edge devices that have been deployed. After successful verification, complete the construction of the microgrid secondary system.

[0069] Among them, resource verification verifies the hardware resource carrying capacity, communication verification verifies the interoperability of communication protocols, and environment verification verifies the equipment environment adaptability.

[0070] In this embodiment, the remaining and total hardware resources of the target edge devices of the deployed modules can be verified to ensure stable operation of the modules; the device protocols can be verified to ensure normal data interaction; and the device tolerance parameters can be verified to ensure they are suitable for the field environment. After all resource verification, communication verification, and environmental verification are passed, the edge module deployment is confirmed to be effective, and the overall construction of the microgrid secondary system is completed.

[0071] S215. Determine the current calibration twin state at the current sampling time as the current calibration twin state, obtain the calibration twin state corresponding to the currently executed configuration strategy as the reference state, and determine the functional requirements corresponding to the reference state as the reference functional requirements.

[0072] In this embodiment, the current calibration twin state of the current sampling period can be determined as the current calibration twin state, the currently executing configuration strategy can be retrieved, the calibration twin state corresponding to the generation of the configuration strategy can be extracted as the reference state, and the functional requirements corresponding to the reference state can be used as the reference functional requirements.

[0073] S216. Identify the current functional requirements based on the current calibration twin state, and compare the differences between the current functional requirements and the reference functional requirements.

[0074] In the embodiment, the current functional requirements can be compared with the reference functional requirements dimension by dimension to quantify the difference between the current functional requirements and the reference functional requirements, and to determine whether the current secondary system configuration is suitable for the real-time power grid operating conditions.

[0075] S217. If the current functional requirements are inconsistent with the reference functional requirements, the dynamic reconfiguration of the microgrid secondary system will be triggered, and the configuration scheme will be regenerated and deployed.

[0076] In this embodiment, when the current functional requirements are inconsistent with the reference functional requirements, the system can re-collect operating condition information, perform modeling and simulation, filter target modules, match edge devices, and deploy a new configuration scheme to achieve secondary system adaptive updates.

[0077] S218. After the microgrid secondary system is reconfigured, update the current correction twin state to the new reference state and update the current functional requirements to the new reference functional requirements.

[0078] In this embodiment, after the secondary system dynamic reconstruction deployment is completed, the current corrected twin state obtained from this reconstruction can be updated to a brand new reference state, and the current functional requirements can be updated to brand new reference functional requirements.

[0079] In this embodiment of the invention, power output, active load, reactive load, source-load boundary state, and environmental parameters are used as operational scenario feature information. This operational scenario feature information is integrated to construct a scenario feature vector. The current state of charge of the energy storage unit is determined as the energy storage state based on the current state of charge, historical state of charge, active charging and discharging power, energy storage charging and discharging efficiency, and rated energy capacity. Voltage amplitude, voltage phase angle, active power, and reactive power are used as electrical operational state information. The electrical operational state information and source-load boundary state are constructed as twin state variables. A preset scenario input mapping matrix maps the scenario feature vector to a digital twin model input vector. The product of the preset state transition matrix and the twin state variables is determined. The product, summed with the input vector of the digital twin model, is used as the twin prediction state. Real-time microgrid status is collected, and the difference between the twin prediction state and the real-time microgrid state is determined as the prediction error. Kalman filtering is applied to the twin prediction state based on this prediction error to obtain the corrected twin state, improving its accuracy. Voltage over-limit requirements, energy storage over-limit requirements, and source-load boundary switching requirements are considered as functional requirements. With the goal of minimizing resource consumption, target module combinations are selected from the candidate module set according to these functional requirements. The target communication protocol and resource allocation results corresponding to each target module combination are determined. These target module combinations, target communication protocols, and resource allocation results are used as configuration strategies, based on functional descriptions and operating conditions. The environmental parameters in the scene feature information are used to filter the candidate edge device set. From the candidate edge device set, those that meet the resource allocation results and target communication protocol in the configuration strategy are matched as target edge devices. The target module is deployed to the target edge device. Resource verification, communication verification, and environmental verification are performed on the deployed target edge devices. After successful verification, the microgrid secondary system is constructed, realizing the automated construction of the microgrid secondary system. The current calibration twin state is determined as the current calibration twin state, and the calibration twin state corresponding to the currently executed configuration strategy is obtained as the reference state. The functional requirements corresponding to the reference state are determined as reference functional requirements. Based on the current calibration twin state... The system identifies the current functional requirements and compares them with reference functional requirements. If the current functional requirements are inconsistent with the reference functional requirements, a dynamic reconfiguration of the microgrid secondary system is triggered, a new configuration scheme is generated and deployed. After the microgrid secondary system reconfiguration is completed, the current correction twin state is updated to the new reference state, and the current functional requirements are updated to the new reference functional requirements. This realizes the transformation of rural distribution microgrid secondary systems from traditional manual planning and configuration to autonomous sensing, autonomous decision-making, and autonomous reconfiguration. It can effectively improve the adaptability, flexibility, and operational reliability of rural distribution microgrid secondary systems under conditions of high new energy penetration, and provide reliable technical support for the safe, stable, and efficient operation of rural distribution microgrids.

[0080] Example 3 In one embodiment, this embodiment is based on the above embodiment, taking a rural power distribution network as a microgrid, a new energy power generation unit as a power generation unit, and the operation scenario characteristics, electrical operation status information, and twin state variables all in matrix form, and digital twin state variables as twin state variables, to further illustrate a method for adaptive construction of a secondary system.

[0081] S1. To address the challenges of diverse operating scenarios, dynamic changes in source-load boundaries, and a lack of unified characterization in rural microgrids under high renewable energy penetration, this paper proposes a method for identifying operating scenarios and acquiring multi-dimensional parameters for rural microgrids. This method achieves accurate identification and quantitative description of the current operating scenarios of rural microgrids through the collaborative acquisition and characteristic modeling of distributed renewable energy output characteristics, source-grid-load topology, off-grid / on-grid switching status, and environmental parameters. The specific implementation steps are as follows: S11. At the sampling time, each new energy power generation unit in the rural distribution microgrid... The actual output power is collected, where the first... The actual output power of each new energy power generation unit is denoted as: The actual output power is obtained in real time by the new energy inverter, smart meter, or distribution automation terminal; at the same time, the rated power of the new energy power generation unit is also obtained. The rated power is determined based on the equipment nameplate parameters or grid connection file. The calculation is based on the actual output power and the rated power. Unitized output coefficient of each new energy power generation unit: ;in, The value range is [0,1], representing the sampling time. The ratio of the actual output of the new energy power generation unit to its rated output. Therefore, each new energy power generation unit... At sampling time The actual output power can be formed as a vector. : ;right Each new energy unit forms a new energy unit output matrix: As a new energy-side input feature for subsequent digital twin state construction.

[0082] S12. At the sampling time, the load nodes in the rural microgrid are sampled. Real-time active and reactive loads are collected. Among them, the first... The real-time active load of each load node is denoted as Real-time reactive load is recorded as Real-time active and reactive loads are obtained in real time from smart meters, distribution area data acquisition terminals, distribution automation terminals, or energy management systems. The determination of the first... Baseline active load of each load node and baseline reactive load The baseline active load and baseline reactive load are determined based on the user's applied capacity, transformer area records, or equipment capacity verification values. The calculation is based on the real-time load and the baseline load. Active load fluctuation coefficient and reactive load fluctuation coefficient of each load node: ;in, This is the active power load fluctuation coefficient; ; This represents the reactive load fluctuation coefficient. Each load node at the sampling time The active and reactive loads are expressed as follows: ;in, Active load; ,in, This is a reactive load.

[0083] Form an active load matrix for all load nodes: and reactive load matrix .in, This indicates the number of nodes. The source-load boundary switching state is represented as a binary matrix, i.e., the source-load boundary state matrix. ,in: .

[0084] S13. Collect environmental parameters: ;in, Temperature (°C) Relative humidity (%) Air dust concentration (mg / m³) 3 Standardize the parameters into unified indicators: The environmental feature vector is .

[0085] S14. Integrate new energy output, load, boundary state, and environmental parameters into a scene feature vector: .

[0086] in, This represents the output matrix of the new energy unit. Represents the active load matrix. Represents the reactive load matrix. Represents the source load boundary state matrix. This represents the environmental feature vector. Finally, a time series is formed. This is used as input for subsequent digital twin models.

[0087] S2. To address the problem that traditional secondary systems cannot reflect the source-load fluctuations and operational status changes of rural distribution microgrids in real time, a digital twin modeling and real-time mapping method for rural distribution microgrids is proposed. This method constructs a collaborative digital model of sources, grid, load, and storage that corresponds one-to-one with the physical microgrid, and maps operational scenario parameters to the digital space in real time, achieving synchronous perception of the physical microgrid's operational status and the controlled objects. The specific implementation steps are as follows: S21, For microgrid nodes The voltage and phase angle are expressed as Active power and reactive power The relationship with voltage is as follows: ; .

[0088] in, and For nodes The real and imaginary parts of the equivalent admittance matrix form the power matrix of the entire network: active power matrix. and reactive power matrix Active power matrix and reactive power matrix As the node power input for the digital twin model, it is used for subsequent node voltage state prediction, power balance constraint calculation, and secondary system configuration strategy generation.

[0089] S22, for the first Each energy storage unit has a state of charge at sampling time t, denoted as […]. Initial state of charge This information is obtained in real time from the energy storage management system or energy storage converter. A rural microgrid has One energy storage unit. Define the energy storage access mapping function: ,in ; indicates that the s-th energy storage unit is connected to the n-th microgrid node. For any node n, the set of energy storage units connected to it is represented as: ;in This indicates that node n is not connected to an energy storage unit; , indicating that node n is connected to an energy storage unit; Let represent that node n is connected to multiple energy storage units. Let the active charging and discharging power of the s-th energy storage unit at sampling time t be . ,in Indicates charging; This indicates discharge. Let the energy storage charge / discharge efficiency be... Rated energy capacity is The sampling time interval is Then the energy storage state of charge is updated as follows: ;in, and The state of charge (SOC) is determined by the nameplate parameters of the energy storage device and the energy storage management system. To ensure that the energy storage state meets the equipment's operational constraints, upper and lower limits are set for the SOC: ;in, and These represent the minimum and maximum states of charge (SOCs) that the energy storage unit is allowed to operate at, respectively, and are determined by the protection parameters of the energy storage management system or the equipment operating procedures. (For all...) Each energy storage unit forms an energy storage state vector: ;in, This represents the total number of energy storage units participating in digital twin modeling within the rural distribution microgrid. The energy storage state vector is used to describe the operating state of the energy storage side and participates in digital twin state prediction together with the power matrix in step S21.

[0090] S23. Construct digital twin state variables based on node power states (active and reactive power), node voltage states (voltage amplitude and phase angle), energy storage states, and network topology states (source-load boundary states). Define the microgrid digital twin state vector as: ;in Represents the node voltage magnitude vector. Represents the node phase angle vector. Represents the active power matrix of the nodes. Represents the nodal reactive power matrix. Represents the energy storage state vector. This represents the source-load boundary state matrix. In one embodiment, the node voltage state can be described by the dynamic relationship between the node current and the admittance matrix. For nodes... Its current is denoted as This represents the node equivalent current resulting from the combined effects of the new energy output current, energy storage charging and discharging current, and load consumption current. It is calculated from the node power and node voltage. ;in, Let be the imaginary unit in the complex field, satisfying Form a current vector for all nodes: Let the nodal admittance matrix be... Then we have: .in, Represents the conductance matrix. Represents the susceptance matrix; when the state of the boundary switch or tie switch changes, Updated synchronously based on the latest topology connections. Based on the node electrical dynamics, voltage state changes can be represented as: .

[0091] In one embodiment, the initial digital twin state The initial measurement data is determined by the physical microgrid, which includes node voltage, phase angle, node power, energy storage state of charge, and switching status. This data is acquired by the distribution automation system, energy management system, energy storage management system, and field acquisition terminals.

[0092] S24. The running scene feature vector obtained in step S1 is... Mapped to the input vector of the digital twin model Used to drive digital twin state prediction: The state evolution of the digital twin is described using a discrete state-space model. Where B represents the scene input mapping matrix, , ; For the next sampling time t+1, the i-th state component in the twin state variable X (such as the voltage of a node, the SOC of a certain energy storage, the active power of a node, etc.). For the current sampling time t, the j-th perturbation component of the scene feature vector F (such as new energy output, load power, temperature, source-load boundary state components, etc.). Represents the digital twin input mapping matrix. , ; Let be the j-th state component of the twin state vector X at time t; Indicating digital twin models in The predicted state at any given time is used to achieve discrete prediction of the state of the digital twin.

[0093] S25. Calculate the synchronization error and perform Kalman filtering correction on the digital twin prediction state. Let... Sampling time The measured physical state of the microgrid is acquired from the distribution automation system, energy management system, energy storage management system, and field acquisition terminals. This includes node voltage, node phase angle, node power, energy storage state of charge, and switching status. A digital twin prediction error vector is defined as follows: Kalman correction is performed on the digital twin state based on the prediction error to obtain the corrected digital twin state: ;in, , H represents the prediction error covariance matrix, which is obtained from the state prediction error statistics of the digital twin model; H represents the state measurement matrix, which is determined based on the state variables that can be obtained by the field measurement devices. The measurement error covariance matrix is ​​determined based on the measurement accuracy and noise statistical characteristics of smart meters, power distribution automation terminals, energy storage management systems, and environmental sensors.

[0094] S3. To address the problem that existing microgrid secondary system architectures have fixed configurations and are difficult to adapt to the diverse operational needs of rural areas, a digital twin-based adaptive configuration method for the secondary system architecture is proposed. This method involves simulation evaluation and strategy matching for different operational scenarios within a digital twin model, automatically generating secondary system hardware and software configuration schemes that meet the current operational requirements of rural microgrids, thus achieving adaptive adjustment of the system architecture as the scenario changes. The specific implementation steps are as follows: S31. Utilize the corrected digital twin state obtained in step S25 This represents the current overall operational status of the rural microgrid; based on the functional requirements of the digital twin status identification system, a functional requirement vector is constructed. Among them, when a node voltage exceeds the limit: ;in, S2 indicates that the voltage magnitude vector of the microgrid nodes is obtained. Minimum and maximum voltage values ​​at microgrid nodes; when energy storage state of charge exceeds limits: ;in, Represents the energy storage state vector. This represents the minimum value of the energy storage state vector. This represents the maximum value of the energy storage state vector; when a source-load boundary switching requirement is detected: ; This represents the source load boundary state matrix; from this, the functional requirements are formed: .

[0095] Establish a standardized candidate module set The standardized candidate module set is predefined by the rural microgrid secondary system function library, including protection modules, measurement and control modules, energy storage coordination modules, communication forwarding modules, voltage control modules, and fault diagnosis modules. Among them, the first... Each candidate module is represented as: .

[0096] in, This describes the functions of the above modules, including the protection module, measurement and control module, energy storage coordination module, fault diagnosis module, voltage control module, and communication forwarding module. This indicates the interface type of the above modules, including IEC61850, IEC104, Modbus Transmission Control Protocol (Modbus-TCP), CAN, and MQTT protocols, etc. This represents the resource consumption vector of the above modules, including CPU resource usage, memory resource usage, and network bandwidth requirements.

[0097] S32. Based on the functional requirements vector Perform combined optimization on candidate modules. Construct the system configuration matrix: ;in, Let the first... The resource consumption vector of each module is represented as follows: The total system resource consumption is then expressed as: Construct the feature coverage matrix: ;in, An optimization model is constructed with the goal of minimizing system resource consumption: The following functional coverage constraints must be met: Wherein, a functional requirement is considered satisfied when it is covered by at least one candidate module; therefore, the product of the functional coverage matrix and the system configuration matrix should not be less than the functional requirement vector. Module selection constraints: This yields a system configuration matrix that meets current operational requirements while minimizing resource consumption. .

[0098] S33. For the optimized module combination results, construct a communication compatibility matrix: ;in, Define the digital twin communication requirement vector: ;in, The final communication configuration matrix is ​​then represented as: ;in, This indicates element-wise multiplication.

[0099] S34. Generate a secondary system configuration strategy based on the system configuration matrix and the communication configuration matrix. .in, This indicates the final set of modules selected; This represents the result of resource allocation; let the first... The resource consumption vector of each candidate module is: ;in, Representation module Required CPU resources Representation module Required memory resources Representation module Required network bandwidth resources. This indicates the generation result of S33. The generated configuration strategy. The data is directly distributed to edge devices and the upper-level control system to achieve adaptive configuration of the rural microgrid secondary system architecture driven by digital twin status.

[0100] S4. To address the challenges of harsh deployment environments, diverse equipment models, and complex on-site installation and maintenance for rural microgrid edge devices, a modular assembly and deployment method for multi-protocol compatible edge devices with resilience to harsh environments is proposed. Based on the secondary system configuration strategy generated in step S3, selected functional modules are mapped to a standardized edge device component library. Combined with communication protocol matching, module self-testing, and environmental adaptability verification, rapid deployment and stable operation of edge devices in complex environments are achieved. The specific implementation steps are as follows: S41. Based on the secondary system configuration strategy generated in step S3 The process involves selecting edge devices from a standardized edge device component library that can support the selected functional modules. Specifically, step S31 involves a standardized candidate module set. Belonging to the software functional layer, it describes secondary system functional modules such as protection modules, measurement and control modules, energy storage coordination modules, communication forwarding modules, voltage control modules, and fault diagnosis modules. The standardized edge device component library in this step belongs to the hardware resource layer, describing hardware device resources such as edge computing terminals, communication gateways, protection and measurement and control terminals, and field control devices. Step S4 maps the software functional modules selected in step S3 to specific hardware devices. Let the standardized edge device component library be represented as: ;in, This indicates the total number of edge device modules in the component library; each edge device module Represented as: .

[0101] Among them, based on S31, This represents a set of module functions; This represents the set of interface types supported by the module. This represents the environmental adaptation feature vector. The environmental adaptation feature vector is defined as follows: ;in, Indicates the highest ambient temperature at which the module is allowed to operate; Indicates the maximum relative humidity that the module is allowed to operate at; This indicates the maximum dust concentration that the module is allowed to operate at. These parameters are obtained from the equipment nameplate parameters, product technical manual, or factory test report. Represents a resource capacity vector; a resource capacity vector is represented as ;in, This indicates the available CPU resource capacity. This indicates the available memory resource capacity. This indicates the available network bandwidth capacity; the on-site environment feature vector is obtained from step S13: When the edge device module When a device simultaneously meets the functional carrying capacity, environmental adaptability, and resource capacity requirements, it is determined to be a candidate edge device. , , , , .in, Indicates the selected functional module Functional description, Indicates the selected functional module The resource consumption vector forms a candidate set of edge devices. .

[0102] S42. Based on the resource allocation results in step S34 Select the set of functional modules Mapped to edge device candidate set Define the module-to-device resource mapping matrix: .in: .

[0103] To ensure that each selected module is deployed to an edge device, and to meet the requirements... ;in, Representation module Selected Representation module Not selected. To ensure that the resource capacity of edge devices is not exceeded, the following must be met: .

[0104] in, For module The resource consumption vector, For edge device modules The resource capacity vector is defined. Through the above constraints, resource allocation from the secondary system functional modules to the edge devices is completed.

[0105] S43. Based on the communication configuration matrix generated in step S33 The system performs communication protocol matching and interface adaptation on the candidate set of edge devices that have already been mapped. Let the set of communication protocols supported by the system be represented as: The communication protocols include IEC61850, Modbus-TCP, IEC104, CAN, and MQTT. A communication compatibility matrix is ​​constructed as follows: .in: Based on the module and device resource mapping matrix With communication configuration matrix The deployment communication adaptation matrix is ​​obtained as follows: .in, This indicates element-wise multiplication. If The corresponding element in the middle is This indicates that the corresponding edge device can meet the communication protocol requirements of the deployed functional modules; if the corresponding element is... This indicates a communication protocol mismatch, requiring the selection of a new edge device or adjustment of resource mapping.

[0106] S44. After completing resource mapping and communication adaptation, deploy and verify the results of the secondary system construction. Define the deployment and verification state vector: ;in: When the following conditions are met: At that time, the secondary system construction is considered complete. Among them, This represents a vector consisting entirely of 1s. This leads to the construction result of the quadratic system: in, This indicates the current secondary system construction result, used to characterize the selected functional modules, edge device resource mapping relationships, communication protocol adaptation results, and deployment verification status. This result serves as the basis for determining whether the current secondary system configuration is still applicable in step S5.

[0107] S5. To address the issue of rural microgrids experiencing fluctuations in renewable energy output, load changes, and operational mode switching, which cause the existing secondary system to gradually deviate from actual operational requirements, a dynamic optimization and reconfiguration method for the secondary system based on digital twin state feedback is proposed. By comparing the functional requirements corresponding to the current corrected digital twin state with the reference functional requirements corresponding to the secondary system configuration at the time of generation, it is determined whether the current secondary system configuration scheme is still applicable. When functional requirements change, the secondary system is automatically reconstructed, achieving continuous adaptive operation of the secondary system. The specific implementation steps are as follows: S51. Obtain the current calibration digital twin status. After completing the secondary system construction in step S4, continuously collect real-time operation data of the rural microgrid and update the digital twin model according to the method in step S2 to obtain the sampling time corresponding to the current evaluation period. Corrected digital twin status: ;in, Indicates the sampling time The overall operating status of the rural microgrid after digital twin synchronization correction; Represents the node voltage magnitude vector. Represents the node phase angle vector. Represents the active power matrix of the nodes. Represents the nodal reactive power matrix. Represents the energy storage charge state vector. This represents the source load boundary state matrix.

[0108] S52. When generating the current secondary system configuration strategy in step S3, record the corresponding corrected digital twin state as a reference state. Let the current running configuration strategy be generated at time [time missing]. The reference correction digital twin state is then represented as: ;in, This indicates the corrected digital twin state adapted when the current secondary system configuration scheme was generated. This indicates the sampling time corresponding to the generation of the configuration scheme. Simultaneously, following the functional requirement identification method in step S3, the reference functional requirement vector corresponding to the generation of the configuration strategy is recorded: ;in, This represents the functional requirement vector corresponding to the current secondary system configuration scheme generation. This indicates that step S3 occurs at the sampling time. The functional requirement vector is obtained based on the corrected digital twin state recognition.

[0109] S53. Based on the functional requirement identification method in step S3, determine the current corrected digital twin state. Generate the current functional requirement vector: ;in, This indicates the current node voltage exceeding the limit requirement. This indicates that the current energy storage state of charge exceeds the demand limit. This represents the current source load boundary switching requirement; the determination method for each requirement component is consistent with the functional requirement identification method in step S3. Calculate the difference between the current functional requirement vector and the reference functional requirement vector: Define functional requirement change indicators: ;in, Describing the norm .because and Both are binary functional requirement vectors, when When the current functional requirements are consistent with those generated during configuration, the current secondary system configuration remains applicable; when This indicates that the current functional requirements have changed, and there is a mismatch between the current secondary system configuration and the actual operational requirements.

[0110] S54. Perform secondary system dynamic reconfiguration. When the following conditions are met... When this occurs, the secondary system dynamic reconstruction mechanism is triggered. Steps S1 to S4 are re-executed to reconstruct the scene feature vector, digital twin state, functional requirements, and secondary system configuration scheme based on the current running scene data.

[0111] After the reconstruction is completed, a new secondary system construction result is generated and the current running configuration is replaced. At the same time, the corrected digital twin state corresponding to the completion of this reconstruction is updated to the new reference corrected digital twin state, and the functional requirement vector corresponding to this reconstruction is updated to the new reference functional requirement vector for configuration applicability judgment in the next evaluation cycle.

[0112] By continuously executing the above process, the secondary system of the rural microgrid can be dynamically optimized and adaptively reconfigured according to changes in new energy output, load fluctuations, source-load boundary adjustments, and operation mode switching.

[0113] This invention addresses the problems of existing rural microgrid secondary systems, such as the disconnect between operational scenario changes and system configuration, the difficulty of directly driving secondary system configuration generation using digital twin states, and insufficient system adaptability. It proposes a scenario-adaptive secondary system construction method for rural microgrids with high renewable energy penetration. Specifically, firstly, an operational scenario feature model is constructed, including renewable energy output status, load status, source-load boundary status, and environmental parameters, enabling real-time perception and unified representation of rural microgrid operational scenarios. Secondly, a digital twin-driven state mapping model is established, reflecting the collaborative operational status of the source-grid-load-storage system in real time through the digital twin state, enabling adaptive configuration and construction of functional modules such as protection, measurement and control, communication, and coordinated control. Finally, a dynamic reconfiguration mechanism based on digital twin state feedback is constructed, continuously comparing the differences between the current operational state and the reference operational state during configuration generation, enabling continuous optimization and dynamic adjustment of system configuration as operational scenarios change. By forming a closed-loop system of "operation scenario perception - digital twin mapping - functional requirement generation - configuration strategy generation - dynamic reconfiguration", this invention realizes the transformation of rural microgrid secondary systems from traditional manual planning and configuration to autonomous perception, autonomous decision-making and autonomous reconfiguration. It can effectively improve the adaptability, flexibility and operational reliability of rural microgrid secondary systems under the conditions of high penetration of new energy, and provide reliable technical support for the safe, stable and efficient operation of rural microgrids.

[0114] Example 4 Figure 3 This is a schematic diagram of a secondary system adaptive construction device provided according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an information acquisition module 31, a status correction module 32, a strategy determination module 33, a system construction module 34, and a system update module 35.

[0115] Among them, the information acquisition module 31 is used to acquire the operating scenario feature information and source-load boundary state of the microgrid, construct a scenario feature vector according to the operating scenario feature information and source-load boundary state, acquire the electrical operating status information of the microgrid nodes, and construct twin state variables according to the electrical operating status information and source-load boundary state.

[0116] The state correction module 32 is used to determine the twin predicted state based on the twin state variables and the scene feature vector, obtain the real-time state of the microgrid, and determine the corrected twin state based on the real-time state of the microgrid and the twin predicted state.

[0117] The strategy determination module 33 is used to determine functional requirements based on the corrected twin state, filter target module combinations in the candidate module set according to the functional requirements, match the target communication protocol and resource allocation results corresponding to the target module combinations, and use the target module combinations, target communication protocols and resource allocation results as configuration strategies.

[0118] System construction module 34 is used to determine the target edge device based on the configuration strategy and the characteristic information of the operating scenario, map the target module combination to the target edge device, and construct the microgrid secondary system according to the target edge device.

[0119] The system update module 35 is used to determine the current correction twin state, determine the current functional requirements based on the current correction twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and functional requirements.

[0120] The technical solution of this invention involves an information acquisition module that obtains the operational scenario feature information and source-load boundary states of a microgrid. A scenario feature vector is constructed based on these features. Electrical operational status information of microgrid nodes is then obtained. Twin state variables are constructed based on the electrical operational status information and source-load boundary states. A state correction module determines the twin predicted state based on the twin state variables and the scenario feature vector. The real-time state of the microgrid is then obtained, and a corrected twin state is determined based on the real-time state and the twin predicted state, reducing the possibility of misjudgments or missed adjustments. A strategy determination module determines functional requirements based on the corrected twin state, filters target module combinations from a candidate module set according to these requirements, and matches the target module combinations with corresponding target communication protocols and resources. The allocation results use the target module combination, target communication protocol, and resource allocation results as configuration strategies. The system construction module determines the target edge devices based on the configuration strategies and operational scenario characteristics, maps the target module combination to the target edge devices, and constructs the microgrid secondary system according to the target edge devices. The system update module determines the current correction twin state, determines the current functional requirements based on the current correction twin state, and reconstructs and updates the microgrid secondary system based on the current functional requirements and functional requirements. This solves the problems of existing rural distribution microgrid secondary systems under high-proportion renewable energy access conditions, such as the disconnect between operational scenario changes and system configuration, the difficulty of the digital twin state directly driving the secondary system configuration generation, and insufficient adaptive adjustment capabilities. It enables continuous optimization and adaptive evolution of the secondary system.

[0121] In one embodiment, the information acquisition module 31 includes: The power acquisition unit is used to acquire the real-time active load and real-time reactive load of the load node as the load power, and to acquire the actual output power of the power generation unit in the microgrid. The scene feature determination unit is used to collect source-load boundary states and construct them into a binary matrix, collect ambient temperature, relative humidity and air dust concentration as environmental parameters, and use power output, active load, reactive load, source-load boundary states and environmental parameters as operating scene feature information, and integrate the operating scene feature information to construct a scene feature vector; The data acquisition unit is used to collect the voltage amplitude, voltage phase angle, active power and reactive power of all microgrid nodes; The energy storage state determination unit is used to determine the historical state of charge, active charge and discharge power, energy storage charge and discharge efficiency, and rated energy capacity of the energy storage unit, and to determine the current state of charge of the energy storage unit as the energy storage state based on the historical state of charge, active charge and discharge power, energy storage charge and discharge efficiency, and rated energy capacity. The twin state determination unit is used to use voltage amplitude, voltage phase angle, active power, and reactive power as electrical operating state information, and to construct twin state variables from the electrical operating state information and the source-load boundary state.

[0122] In one embodiment, the state correction module 32 includes: The input vector determination unit is used to map scene feature vectors into digital twin model input vectors through a preset scene input mapping matrix; The state prediction unit is used to determine the product of the preset state transition matrix and the twin state variables, and the sum of the product and the input vector of the digital twin model is used as the twin predicted state. The state correction unit is used to collect the real-time state of the microgrid, determine the difference between the twin predicted state and the real-time state of the microgrid as the prediction error, and perform Kalman filtering correction on the twin predicted state according to the prediction error to obtain the corrected twin state.

[0123] In one embodiment, the strategy determination module 33 includes: The demand determination unit is used to determine voltage over-limit demand and energy over-limit demand by performing over-limit detection according to the node voltage state and energy storage state in the correction twin state, respectively. The functional requirement determination unit is used to extract the source-load boundary state at historical time, determine the source-load boundary switching requirements according to the source-load boundary state at historical time, and take the voltage over-limit requirements, energy storage over-limit requirements and source-load boundary switching requirements as functional requirements. The strategy determination unit is used to select target module combinations from the candidate module set according to functional requirements with the goal of minimizing resource consumption, determine the target communication protocol and resource allocation results corresponding to the target module combinations, and use the target module combinations, target communication protocols and resource allocation results as configuration strategies.

[0124] In one embodiment, the system construction module 34 includes: The set filtering unit is used to determine the functional description of each target module in the target module combination in the configuration strategy, and to filter the candidate edge device set according to the functional description and environmental parameters in the running scenario feature information; The device deployment unit is used to match candidate edge devices that meet the resource allocation results and target communication protocol in the configuration strategy from the candidate edge device set as target edge devices, and deploy the target module to the target edge device; wherein, each target module is deployed to only one target edge device; The data verification unit is used to perform resource verification, communication verification, and environmental verification on the deployed target edge devices, and completes the construction of the microgrid secondary system after successful verification.

[0125] In one embodiment, the system update module 35 includes: The state extraction unit is used to determine the current correction twin state at the current sampling time as the current correction twin state, obtain the correction twin state corresponding to the currently executed configuration strategy as the reference state, and determine the functional requirements corresponding to the reference state as the reference functional requirements. The difference determination unit is used to identify the current functional requirement based on the current calibration twin state and compare the difference between the current functional requirement and the reference functional requirement. The system reconfiguration unit is used to trigger the dynamic reconfiguration of the microgrid secondary system if the current functional requirements are inconsistent with the reference functional requirements, and to regenerate and deploy the configuration scheme. The information storage unit is used to update the current correction twin state to a new reference state and the current functional requirements to new reference functional requirements after the microgrid secondary system reconfiguration is completed.

[0126] The secondary system adaptive construction device provided in the embodiments of the present invention can execute the secondary system adaptive construction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0127] Example 5 Figure 4 This is a schematic diagram of an electronic device implementing a secondary system adaptive construction method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0128] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0129] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a secondary system adaptive construction method.

[0131] In some embodiments, a secondary system adaptive construction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the secondary system adaptive construction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a secondary system adaptive construction method by any other suitable means (e.g., by means of firmware).

[0132] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0136] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0137] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0138] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements a secondary system adaptive construction method according to any embodiment of the present invention.

[0139] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0141] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for adaptive construction of a quadratic system, characterized in that, include: Obtain the operating scenario feature information and source-load boundary state of the microgrid, construct a scenario feature vector according to the operating scenario feature information and the source-load boundary state, obtain the electrical operating state information of the microgrid nodes, and construct twin state variables according to the electrical operating state information and the source-load boundary state. The twin prediction state is determined based on the twin state variables and the scene feature vector, the real-time state of the microgrid is obtained, and the corrected twin state is determined based on the real-time state of the microgrid and the twin prediction state. Based on the corrected twin state, functional requirements are determined. Target module combinations are screened from the candidate module set according to the functional requirements. The target communication protocol and resource allocation result corresponding to the target module combination are matched. The target module combination, the target communication protocol, and the resource allocation result are used as the configuration strategy. Based on the configuration strategy and the operating scenario feature information, target edge devices are determined, target module combinations are mapped to target edge devices, and microgrid secondary systems are constructed according to the target edge devices. Determine the current calibration twin state, determine the current functional requirements based on the current calibration twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and the functional requirements.

2. The method according to claim 1, characterized in that, The process of acquiring the microgrid's operational scenario feature information and source-load boundary states, constructing a scenario feature vector based on the operational scenario feature information and the source-load boundary states, acquiring the electrical operational state information of the microgrid nodes, and constructing twin state variables based on the electrical operational state information and the source-load boundary states includes: The real-time active and reactive loads of load nodes are collected as load power, and the actual output power of the power generation units in the microgrid is also collected. The source-load boundary state is collected and constructed into a binary matrix. The ambient temperature, relative humidity and air dust concentration are collected as environmental parameters. The power output, active load, reactive load, source-load boundary state and environmental parameters are used as operating scenario feature information. The operating scenario feature information is integrated to construct a scenario feature vector. Collect the voltage amplitude, voltage phase angle, active power, and reactive power of all microgrid nodes; Determine the historical state of charge, active charge and discharge power, energy storage charge and discharge efficiency, and rated energy capacity of the energy storage unit. Determine the current state of charge of the energy storage unit as the energy storage state based on the historical state of charge, active charge and discharge power, energy storage charge and discharge efficiency, and rated energy capacity. The voltage amplitude, voltage phase angle, active power, and reactive power are used as electrical operating status information, and the electrical operating status information and the source-load boundary state are used to construct twin state variables.

3. The method according to claim 1, characterized in that, The step of determining the twin prediction state based on the twin state variables and the scene feature vector, obtaining the real-time state of the microgrid, and determining the corrected twin state based on the real-time state of the microgrid and the twin prediction state includes: The scene feature vector is mapped to the digital twin model input vector by a preset scene input mapping matrix; Determine the product of the preset state transition matrix and the twin state variables, and use the sum of the product and the input vector of the digital twin model as the twin predicted state; The real-time status of the microgrid is collected, and the difference between the twin predicted status and the real-time status of the microgrid is determined as the prediction error. The twin predicted status is then corrected by Kalman filtering according to the prediction error to obtain the corrected twin status.

4. The method according to claim 1, characterized in that, The step of determining functional requirements based on the corrected twin state, filtering target module combinations from the candidate module set according to the functional requirements, matching the target module combinations with the corresponding target communication protocols and resource allocation results, and using the target module combinations, the target communication protocols, and the resource allocation results as a configuration strategy includes: Based on the node voltage state and energy storage state in the corrected twin state, over-limit detection is performed to determine the voltage over-limit requirement and the energy over-limit requirement. Extract the source-load boundary state at historical time, determine the source-load boundary switching requirement according to the source-load boundary state at historical time, and take the voltage over-limit requirement, the energy storage over-limit requirement, and the source-load boundary switching requirement as functional requirements; With the goal of minimizing resource consumption, target module combinations are selected from the candidate module set according to the functional requirements. The target communication protocol and resource allocation results corresponding to the target module combinations are determined, and the target module combinations, the target communication protocol, and the resource allocation results are used as the configuration strategy.

5. The method according to claim 1, characterized in that, The step of determining the target edge device based on the configuration strategy and the operating scenario characteristic information, mapping the target module combination to the target edge device, and constructing the microgrid secondary system according to the target edge device includes: Determine the functional description of each target module in the target module combination in the configuration strategy, and filter the candidate edge device set according to the functional description and the environmental parameters in the running scenario feature information; From the set of candidate edge devices, select the candidate edge devices that satisfy the resource allocation results in the configuration strategy and the target communication protocol as target edge devices, and deploy the target module to the target edge devices; wherein, each target module is deployed to only one target edge device; Resource verification, communication verification, and environment verification are performed on the deployed target edge devices respectively. After successful verification, the construction of the microgrid secondary system is completed.

6. The method according to claim 1, characterized in that, The process of determining the current correction twin state, determining the current functional requirements based on the current correction twin state, and reconstructing and updating the microgrid secondary system based on the current functional requirements and the functional requirements includes: The current calibration twin state at the current sampling time is determined as the current calibration twin state, the calibration twin state corresponding to the currently executed configuration strategy is obtained as the reference state, and the functional requirements corresponding to the reference state are determined as the reference functional requirements. Identify the current functional requirement based on the current corrected twin state, and compare the difference between the current functional requirement and the reference functional requirement; If the current functional requirements are inconsistent with the reference functional requirements, the dynamic reconfiguration of the microgrid secondary system is triggered, the configuration scheme is regenerated and deployed. After the microgrid secondary system is reconfigured, the current correction twin state is updated to the new reference state, and the current functional requirements are updated to the new reference functional requirements.

7. A secondary system adaptive construction device, characterized in that, include: The information acquisition module is used to acquire the operating scenario feature information and source-load boundary state of the microgrid, construct a scenario feature vector according to the operating scenario feature information and the source-load boundary state, acquire the electrical operating state information of the microgrid nodes, and construct twin state variables according to the electrical operating state information and the source-load boundary state. The state correction module is used to determine the twin prediction state based on the twin state variables and the scene feature vector, obtain the real-time state of the microgrid, and determine the corrected twin state based on the real-time state of the microgrid and the twin prediction state. The strategy determination module is used to determine functional requirements based on the corrected twin state, filter target module combinations in the candidate module set according to the functional requirements, match the target communication protocol and resource allocation result corresponding to the target module combination, and use the target module combination, the target communication protocol and the resource allocation result as the configuration strategy. The system construction module is used to determine the target edge device according to the configuration strategy and the operating scenario feature information, map the target module combination to the target edge device, and construct the microgrid secondary system according to the target edge device; The system update module is used to determine the current calibration twin state, determine the current functional requirements based on the current calibration twin state, and reconstruct and update the microgrid secondary system based on the current functional requirements and the functional requirements.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the secondary system adaptive construction method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the secondary system adaptive construction method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the secondary system adaptive construction method according to any one of claims 1-6.

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