Source network load storage real-time monitoring cooperation system based on edge calculation

Abstract

The invention belongs to the technical field of source network load storage monitoring, and particularly provides a source network load storage real-time monitoring cooperation system based on edge calculation, and the system mainly comprises a data collection and standardization module which obtains the collected output power of a photovoltaic inverter, the frequency deviation of a power grid, the switching state of an important load, and the charging and discharging rate data of an energy storage system, performing multi-source data fusion to obtain a system monitoring data set; the dynamic characteristic evaluation module is used for carrying out multi-dimensional characteristic analysis based on the system monitoring data set and obtaining a system adjustment parameter set through dynamic characteristic evaluation; and the coordination control construction module is used for constructing control logic according to the system adjustment parameter set and outputting a coordination control task scheme through a coordination control generation process. According to the method, the multi-source data fusion precision is improved, the dynamic characteristic matching capability of the system is enhanced, the source-network load-storage collaborative response efficiency is improved, the coping capability for power mutation is enhanced, and stable operation of the power grid frequency is effectively guaranteed.

Description

Technical Field

[0001] This application belongs to the field of source-grid-load-storage monitoring technology, and specifically relates to a collaborative system for real-time monitoring of source-grid-load-storage based on edge computing. Background Technology

[0002] In distribution networks with a high proportion of renewable energy, current methods involve collecting data such as photovoltaic output, grid frequency, load switching, and energy storage charging and discharging. This data is then integrated from multiple sources to form monitoring information. Subsequently, the dynamic characteristics of the system are analyzed to derive control parameters, construct control logic, allocate regulation tasks, and finally execute the tasks and provide feedback on the operating status.

[0003] However, the volatility of distributed power output, the randomness of load changes, and the lag in energy storage response result in multiple time scale differences. These differences make it difficult to match and coordinate the dynamic characteristics of multiple links such as power source, grid, load, and storage within a second. Consequently, the system's response capability cannot meet the grid frequency stability requirements when faced with sudden power changes. Summary of the Invention

[0004] This application provides a real-time monitoring and coordination system for power generation, grid, load, and energy storage based on edge computing. This system effectively solves the problems in existing technologies where fluctuations in distributed power output, random load changes, and time-scale differences in energy storage response lead to difficulties in dynamic matching of multiple links in the power generation, grid, load, and energy storage system, insufficient response to power surges, and inability to meet the requirements for grid frequency stability. The system improves the accuracy of multi-source data fusion, enhances the system's dynamic characteristic matching capability, increases the efficiency of power generation, grid, load, and energy storage coordination response, strengthens the ability to cope with power surges, and effectively ensures the stable operation of the power grid frequency.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a source-grid-load-storage real-time monitoring and collaborative system based on edge computing, comprising: Data acquisition and standardization module: Acquires data on photovoltaic inverter output power, grid frequency deviation, key load switching status, and energy storage system charge and discharge rate, performs multi-source data fusion, and obtains system monitoring dataset.

[0006] Dynamic characteristic evaluation module: Based on the system monitoring dataset, multi-dimensional feature analysis is carried out, and the system adjustment parameter set is obtained through dynamic characteristic evaluation.

[0007] Coordination and control construction module: Constructs control logic based on the system adjustment parameter set, and outputs a coordination and control task scheme through the coordination and control generation process.

[0008] Distributed collaborative computing module: Distributes the coordination and control task scheme into a distributed task allocation and generates a coordination and control instruction set with the help of edge collaborative computing.

[0009] Optimization Decision Module: Performs multi-objective optimization analysis on the coordinated control instruction set to obtain an optimized control scheme.

[0010] Execution and Feedback Module: Executes the optimized control scheme and completes the effect evaluation, and obtains the system operation index set through the execution feedback mechanism.

[0011] Furthermore, multi-source data fusion is performed to obtain the system monitoring dataset, including: Standardized communication data is obtained by parsing communication protocols and unifying data formats from multi-source data. The standardized communication data is timestamped and synchronized, and time synchronization monitoring data is output. The time-synchronized monitoring data is subjected to quality verification and data cleaning to form the system monitoring dataset.

[0012] Furthermore, based on the system monitoring dataset, multi-dimensional feature analysis is performed, and a set of system regulation parameters is obtained through dynamic characteristic evaluation, including: Power fluctuation characteristics are extracted from the system monitoring dataset to obtain power fluctuation characteristic indicators.

[0013] The frequency response characteristics are evaluated based on the power fluctuation characteristic index to obtain the system frequency regulation capability index.

[0014] The safe operating boundary is calculated based on the system frequency regulation capability index, and the system stability boundary parameters are obtained.

[0015] The adjustment requirements are sorted according to the system stability boundary parameters to generate the system adjustment parameter set.

[0016] Furthermore, control logic is constructed based on the system adjustment parameter set, and a coordinated control task scheme is output through the coordinated control generation process, including: The control requirements in the system's adjustment parameter set are analyzed to generate coordinated control requirements.

[0017] Match the control objects corresponding to the coordination and control requirements, and determine the set of coordination object identifiers.

[0018] Add runtime constraints to the set of coordinated object identifiers to form the coordination control task scheme.

[0019] Furthermore, the coordinated control task scheme is distributed and tasks are allocated in a distributed manner. A coordinated control instruction set is generated using edge collaborative computing, including: The coordination and control task scheme is decomposed into distributed subtasks to generate distributed control subtasks.

[0020] Allocate computing resources to the distributed control subtasks and formulate node computing schemes.

[0021] The node computation scheme is executed in parallel to obtain a local control instruction set.

[0022] The timing relationships between the local control instruction sets are coordinated to generate the coordinated control instruction set.

[0023] Furthermore, a multi-objective optimization analysis is performed on the coordinated control instruction set to obtain an optimized control scheme, including: The execution effect of the aforementioned coordination control instruction set was simulated to achieve the expected results.

[0024] By comparing and analyzing the expected effects of various control schemes, an optimized control scheme is generated.

[0025] Furthermore, the optimized control scheme is executed and its effectiveness evaluated. A set of system operation indicators is obtained through a feedback mechanism, including: The optimized control scheme is converted into device-executable instructions, and execution control instructions are generated.

[0026] Verify the security of the execution control instructions and obtain security control instructions.

[0027] The security control command is sent to the terminal device to obtain the command execution status.

[0028] The control effect of the instruction execution state is analyzed to obtain the system operation index set.

[0029] Furthermore, communication protocol parsing and data format unification processing are performed on multi-source data to obtain standardized communication data, including: The communication protocol of multi-source data is analyzed to obtain data in a unified format.

[0030] Verify the integrity of the unified format data to obtain a complete data packet.

[0031] The data format of the complete data packet is standardized to ultimately generate standardized communication data.

[0032] Furthermore, power fluctuation characteristics are extracted from the system monitoring dataset to obtain power fluctuation characteristic indicators, including: The calculation system monitors the fluctuation amplitude in the dataset to obtain the power fluctuation amplitude index.

[0033] The power fluctuation frequency index is obtained by statistically analyzing the fluctuation frequency of the power fluctuation amplitude index.

[0034] The changing trend of the power fluctuation frequency index is analyzed, and the power fluctuation characteristic index is finally generated.

[0035] Further, the control objects corresponding to the coordination control requirements are matched, and a set of coordination object identifiers is determined, including: Identify the types of equipment required for coordinated control and determine the types of adjustable equipment.

[0036] Query the real-time status of the adjustable device type to obtain a list of available devices.

[0037] The list of available devices is sorted by priority to form a set of coordination object identifiers.

[0038] Secondly, this application provides a collaborative method for real-time monitoring of source-grid-load-storage systems based on edge computing, including: Data on photovoltaic inverter output power, grid frequency deviation, key load switching status, and energy storage system charge and discharge rate are acquired and fused from multiple sources to obtain a system monitoring dataset.

[0039] Based on the system monitoring dataset, multi-dimensional feature analysis is conducted, and a set of system regulation parameters is obtained through dynamic characteristic evaluation.

[0040] The control logic is constructed based on the system adjustment parameter set, and the coordinated control task scheme is output through the coordinated control generation process.

[0041] The coordination and control task scheme is distributed and task allocation is performed, and a coordination and control instruction set is generated with the help of edge collaborative computing.

[0042] A multi-objective optimization analysis is performed on the coordinated control instruction set to obtain an optimized control scheme.

[0043] The optimized control scheme is executed and its effectiveness is evaluated. A set of system operation indicators is obtained through the execution feedback mechanism.

[0044] Thirdly, this application provides a source-grid-load-storage real-time monitoring and coordination device based on edge computing, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the source-grid-load-storage real-time monitoring and coordination method based on edge computing.

[0045] Fourthly, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of a source-grid-load-storage real-time monitoring and coordination method based on edge computing.

[0046] Fifthly, this application provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of implementing a source-grid-load-storage real-time monitoring and coordination method based on edge computing are provided.

[0047] The beneficial effects of this application are: This application, by employing modules for data acquisition and standardization, and dynamic characteristic evaluation, and relying on edge computing to achieve data fusion, parameter acquisition, task allocation, and instruction generation and execution, effectively solves the problems in existing technologies where distributed power output fluctuations, random load changes, and energy storage response delays lead to difficulties in dynamic matching of multiple links in the power source, grid, load, and storage sectors, insufficient response to power surges, and inability to meet the requirements of grid frequency stability. It improves the accuracy of multi-source data fusion, enhances the system's dynamic characteristic matching capability, improves the collaborative response efficiency of power source, grid, load, and storage, strengthens the ability to cope with power surges, and effectively ensures the stable operation of the power grid frequency.

[0048] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A schematic diagram of a source-grid-load-storage real-time monitoring collaborative system based on edge computing, as described in this application, is shown. Detailed Implementation

[0051] To address the problems raised in the background technology, this application constructs modules for data acquisition and standardization, dynamic characteristic evaluation, coordinated control construction, distributed collaborative computing, global optimization decision-making, execution, and feedback. Relying on edge computing, it achieves source-grid-load-storage monitoring coordination, which includes data fusion, parameter acquisition, task allocation, instruction generation, optimization decision-making, and execution feedback. This enhances the source-grid-load-storage coordination capability and effectively ensures the stable operation of the power grid frequency.

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] In some embodiments, such as Figure 1As shown, this application provides a source-grid-load-storage real-time monitoring and collaborative system based on edge computing, including: Data acquisition and standardization module: Acquires data on photovoltaic inverter output power, grid frequency deviation, key load switching status, and energy storage system charge and discharge rate, performs multi-source data fusion, and obtains system monitoring dataset.

[0054] Dynamic characteristic evaluation module: Based on the system monitoring dataset, multi-dimensional feature analysis is carried out, and the system regulation parameter set is obtained through dynamic characteristic evaluation.

[0055] Coordination and control construction module: Constructs control logic based on the system adjustment parameter set, and outputs coordination and control task scheme through the coordination and control generation process.

[0056] Distributed collaborative computing module: Distributes the coordination and control task scheme into a distributed task allocation and generates a coordination and control instruction set with the help of edge collaborative computing.

[0057] Optimization Decision Module: Performs multi-objective optimization analysis on the coordinated control instruction set to obtain an optimized control scheme.

[0058] Execution and Feedback Module: Executes the optimized control scheme and completes the effect evaluation, and obtains the system operation index set through the execution feedback mechanism.

[0059] The output power of the photovoltaic inverter refers to the actual power value transmitted to the grid after the photovoltaic power generation system is converted into AC power by the inverter, which can be collected by the power monitoring unit built into the photovoltaic inverter.

[0060] The power grid frequency deviation represents the difference between the actual operating frequency of the power grid and the rated frequency, which can be collected by a frequency transmitter.

[0061] The critical load switching status refers to the operating / stopping status of key electrical equipment in the system, which can be collected by the status monitoring unit in the smart circuit breaker.

[0062] The charge / discharge rate of an energy storage system represents the rate at which the energy of the energy storage system changes per unit time, and can be collected by the battery management system.

[0063] In some embodiments, multi-source data fusion is performed to obtain a system monitoring dataset, including: S11. Perform communication protocol parsing and data format unification processing on multi-source data to obtain standardized communication data.

[0064] S12. Perform timestamp synchronization processing on standardized communication data, update quality flags, mark data with time deviations as invalid, and output time synchronization monitoring data.

[0065] A time synchronization network can be built using the IEEE 1588 precise time protocol to output synchronization monitoring data.

[0066] S13. Perform quality verification and data cleaning on the time synchronization monitoring data to form a system monitoring dataset.

[0067] Perform range verification, mutation verification, and continuity verification on time synchronization monitoring data.

[0068] When a single data point is marked as suspicious, it is automatically correlated with data from other devices within the same time window for collaborative verification. For example, when photovoltaic power experiences a sudden change, the charging and discharging rate of the energy storage system is simultaneously verified to ensure it remains stable within 2%, and the grid frequency deviation is less than 0.05Hz.

[0069] If the associated data is not synchronized abnormally, it is determined to be a single point acquisition failure, and the data is marked as invalid.

[0070] The final output system monitoring dataset includes numerical values, original timestamps, and synchronization timestamps. The numerical values ​​include normal photovoltaic inverter output power, grid frequency deviation, critical load switching status, and energy storage system charge / discharge rates.

[0071] In some embodiments, S11 involves parsing the multi-source data using communication protocols and unifying the data format to obtain standardized communication data, including: S111. Parse the communication protocol of multi-source data to obtain data in a unified format.

[0072] A multi-protocol parsing engine is used to parse the four types of data, remove the protocol header information and extract the valid data values, and output intermediate data in a unified format, including device identifier, raw value and collection timestamp.

[0073] S112. Verify the integrity of the uniform format data and obtain the complete data packet.

[0074] Check whether the data packet length conforms to the protocol specification, and use the CRC-16 algorithm to calculate the checksum to verify data integrity.

[0075] Data packets that fail verification are discarded, while data packets that pass verification are marked as complete data packets.

[0076] A complete data packet contains a device identifier, raw values, acquisition timestamp, and integrity flag.

[0077] S113. Standardize the data format of the complete data packet to ultimately generate standardized communication data.

[0078] Standardization includes numerical formatting, timestamp formatting, and data structure encapsulation. A standardized communication dataset contains a unique device identifier, a formatted valid numerical value, a standardized timestamp, and a data integrity flag.

[0079] In some embodiments, multi-dimensional feature analysis is performed based on the system monitoring dataset to obtain a set of system regulation parameters through dynamic characteristic evaluation, including: S21. Extract power fluctuation characteristics from the system monitoring dataset to obtain power fluctuation characteristic indicators.

[0080] S22. Evaluate the frequency response characteristics based on power fluctuation characteristic indicators to obtain the system frequency regulation capability index.

[0081] When the photovoltaic frequency trend is "increasing", increase the system sensitivity gain coefficient: When the trend is "decreasing", reduce the sensitivity gain: ;in, Represents sensitivity gain. Representing the basic gain coefficient, it can be calculated through regression analysis of power grid size, renewable energy penetration rate, and historical disturbance response data. Represents the trend strength index. This represents the gain enhancement factor, with a default value of 0.15. This represents the gain attenuation factor, with a default value of 0.1. , It can be determined based on historical disturbance response regression analysis.

[0082] When the power grid trend label is "rising", the corrected stability margin coefficient is: When the trend is "declining", the corrected stability margin coefficient is: ;in, This represents the corrected stability margin coefficient. Representing the baseline margin, a benchmark value can be determined through regression analysis of power grid size, load characteristics, and historical fault data. This represents the margin enhancement factor, and its baseline value can be determined through simulation analysis of historical large disturbance events. The default value is 0.2. This represents the margin decay coefficient, which can be determined based on the minimum stability margin constraint of the power grid, combined with regression analysis of load fluctuation characteristics. The default value is 0.15. This represents the strength index of the power grid trend.

[0083] When the energy storage trend label is "rising", the additional damping coefficient is calculated according to... Calculations are performed to determine when the trend is "declining". ;in, Represents the additional damping coefficient. This represents the damping enhancement coefficient. A baseline value can be determined based on the step response test data of the lithium battery pack, through regression analysis of the measured damping effect and frequency disturbance. The default value is 0.2. This represents the damping attenuation coefficient, which can be determined based on the system's minimum stable damping ratio constraint and the aging characteristics of the energy storage device. The default value is 0.1. Represents the frequency of energy storage fluctuations. This represents the strength of the energy storage trend.

[0084] Using formula Calculate the rate of change of frequency; where, Represents the rate of change of frequency. R represents the photovoltaic fluctuation amplitude, and R represents the governor response coefficient, which is an inherent equipment parameter of the engine set.

[0085] Using formula Calculate the settling time; where, Represents the frequency settling time. This represents the baseline stability time, set by the system scheduling agency based on historical stable operation data. Represents the total damping coefficient, characterizing the overall ability to suppress frequency oscillations. , The basic damping coefficient is a parameter that characterizes the inherent damping properties of the system and is usually obtained through system identification or typical fault simulation analysis.

[0086] S23. Calculate the safe operating boundary based on the system frequency regulation capability index to obtain the system stability boundary parameters.

[0087] By frequency change rate and stabilization time The final security status label is determined jointly by two dimensions: First, determine whether the safe state is dangerous; specifically, determine whether... or If so, the safe state is determined to be dangerous; among them, The dangerous threshold representing the rate of frequency change can be determined based on the maximum tolerable rate of frequency mutation in the system. This represents the warning threshold for the rate of change of frequency, specifically... 60%.

[0088] If the safe state is not dangerous, then determine whether the safe state is now a warning state; specifically, determine whether... or If so, the state is determined to be under alert; among which, This represents the critical threshold for stabilization time, which can be determined based on the system's minimum allowable stabilization recovery time. Represents the stable time warning threshold, which can be taken as... 67%.

[0089] If a safe state is neither dangerous nor alert, then the safe state is determined to be safe.

[0090] The output system stability boundary parameters include the frequency change rate threshold. Stable time threshold and security status label.

[0091] S24. Based on the system stability boundary parameters, sort the adjustment requirements and generate a set of system adjustment parameters.

[0092] Equipment priority is determined by safety status. If the safety status is dangerous, the priority is P1; if it is alert, the priority is P2; if it is safe, the priority is P3.

[0093] Adjustments to the calculation equipment: For photovoltaic or energy storage devices, the adjustment amount is: For load equipment: ;in, This represents the adjustment amount, i.e., the power value that needs to be adjusted. This represents the power regulation weight that photovoltaic or energy storage devices need to bear under a unit frequency change rate. The power regulation weight that the load equipment needs to coordinate with under a unit time deviation can be obtained online by solving a comprehensive objective function that minimizes the frequency deviation and regulation cost, based on the current frequency fluctuation of the system and the equipment status. , .

[0094] The final system regulation parameter set includes device identifier, priority level, and regulation amount.

[0095] In some embodiments, extracting power fluctuation characteristics from the system monitoring dataset in step S21 to obtain power fluctuation characteristic indicators includes: S211. Calculate the fluctuation amplitude in the system monitoring dataset to obtain the power fluctuation amplitude index.

[0096] The sliding window range method is used to calculate the difference between the maximum and minimum values ​​within each window as the instantaneous fluctuation amplitude. The resulting power fluctuation amplitude index includes photovoltaic fluctuation amplitude, grid frequency fluctuation amplitude, and energy storage fluctuation amplitude.

[0097] S212. Calculate the fluctuation frequency of the power fluctuation amplitude index to obtain the power fluctuation frequency index.

[0098] Fast Fourier Transform was used to analyze the amplitude variation spectrum and extract the main fluctuation frequency components.

[0099] The number of times the amplitude change exceeds a specified threshold (e.g., 5%) per unit time is counted and marked as the power fluctuation frequency index, including photovoltaic fluctuation frequency, grid frequency fluctuation frequency, and energy storage fluctuation frequency.

[0100] S213. Analyze the changing trend of power fluctuation frequency index and finally generate power fluctuation characteristic index.

[0101] By evaluating the continuous variation characteristics of photovoltaic fluctuation frequency, grid frequency fluctuation frequency, and energy storage fluctuation frequency, each trend is characterized by three state labels: "rising", "falling", or "stable".

[0102] Calculate the strength of each trend: The intensity of the photovoltaic frequency trend is: ;in, Represents the intensity of photovoltaic frequency trends. Represents the percentage of increasing points. The significantly increasing threshold can be determined based on historical cloud movement disturbance regression analysis, with a default value of 0.8.

[0103] The strength of the power grid frequency trend is: ;in, Represents the strength of the power grid frequency trend. This represents the rate of change in the proportion of high-frequency fluctuations. This represents the threshold for significant fluctuations, which can be defined according to the power system stability guidelines, and the default value is 0.15.

[0104] The strength of the energy storage trend is: ;in, Represents the strength of the energy storage trend. This represents the response frequency offset. This represents the rated response reference, determined based on the dynamic response data from equipment type testing, and is 0.2Hz by default.

[0105] The final output includes power fluctuation characteristic indicators that include photovoltaic frequency trends, grid frequency trends, energy storage frequency trends, and trend strengths.

[0106] In some embodiments, control logic is constructed based on a set of system adjustment parameters, and a coordinated control task scheme is output through a coordinated control generation process, including: S31. Analyze the control requirements in the system's adjustment parameter set and generate coordinated control requirements.

[0107] The control requirement field in the system adjustment parameter set is analyzed to extract three core data items: equipment identifier, priority level, and adjustment amount.

[0108] The priority and adjustment amount of the equipment are dynamically combined to generate equipment-level control requirements.

[0109] Specifically, if the equipment priority level is P1, the control requirement is "immediate execution"; if the priority level is P2, the control requirement is "planned execution" based on the adjustment amount in the system adjustment parameter set; and if the priority level is P3, the control requirement is "deferred execution". Simultaneously, these control requirements are integrated with the adjustment amount to generate equipment-level control requirements.

[0110] For example, the input is {Device ID: "ESS_01", Priority: "P1", Adjustment amount: -150kW}, and the output is {"ESS_01": "Execute immediately, -150kW"}.

[0111] Demand aggregation is performed based on grid topology: Photovoltaic inverters aggregate their regulation based on the booster station; for example, the regulation of three inverters in booster station A (-80kW, -100kW, -120kW) is aggregated into a total regulation of -300kW. Energy storage systems are aggregated based on battery clusters, and critical loads are aggregated based on feeder groups.

[0112] The final output is a set of coordinated control requirements, which includes equipment type classification, topology unit identifier, and execution instruction details.

[0113] For example, in photovoltaic control, booster station 1 corresponds to a planned execution of -300.5kW, and in energy storage control, battery cluster 3 corresponds to an immediate execution of 150.0kW.

[0114] S32. Match the control objects corresponding to the coordination and control requirements, and determine the set of coordination object identifiers.

[0115] S33. Add operational constraints to the set of coordinate object identifiers to form a coordination control task scheme.

[0116] Specifically, the following constraints are dynamically bound to each device: Regarding power constraints, the maximum adjustment amount for photovoltaic and energy storage equipment is the minimum of the equipment's adjustable margin and the system's safe power increment, and the maximum power reduction amount set for load equipment does not exceed the adjustable load ratio.

[0117] Regarding state constraints, the energy storage system sets charge and discharge boundaries, and the photovoltaic inverter sets its output range to 10%-90% of its rated capacity.

[0118] Regarding time constraints, a minimum operation interval is set for all devices.

[0119] Create an independent task unit for each device, which includes the device's unique identifier and the specific values ​​and units of the bound power constraints, state constraints, and time constraints.

[0120] All task units are integrated according to the order of their identifier sets to form a structured coordination and control task scheme.

[0121] In some embodiments, the control object corresponding to the matching and coordination control requirement in S32, and the determination of the coordination object identifier set, include: S321. Identify the types of equipment required for coordinated control and determine the types of adjustable equipment.

[0122] Specifically, keyword matching algorithms can be used to determine the type of adjustable device.

[0123] For example, if the "Photovoltaic Control" field exists, the output will be "Photovoltaic Inverter"; if the "Energy Storage Control" field exists, the output will be "Energy Storage System"; if the "Load Control" field exists, the output will be "Important Load".

[0124] S322. Query the real-time status of adjustable device types and obtain a list of available devices.

[0125] Specifically, the system obtains the ratio of the current output of the photovoltaic inverter to its rated power, the state of charge of the energy storage system, and the switching status of important loads.

[0126] The availability of devices is determined based on their real-time status, and a list of available devices is output.

[0127] S323. Sort the list of available devices by priority to form a set of coordination object identifiers.

[0128] Quantify the response capability of each device : The response capability of the photovoltaic inverter is: ;in, Represents the actual rate of change of power. The representative baseline rate of change can be determined based on the statistical coverage value of the equipment's physical limits.

[0129] The energy storage system's response capability is: ;in, Represents the actual switching time. The reference switching time can be determined based on the minimum time window for system transient stability.

[0130] The critical load response capability is: ;in, This represents the actual time of the action.

[0131] Computer device adjustment potential score : The adjustable margin of photovoltaics is: ;in, Represents adjustable margin, This represents the rated power.

[0132] The energy storage discharge margin is: ;in, Represents the real-time state of charge. Represents the lower limit of the state of charge. This represents the upper limit of the state of charge.

[0133] The load adjustability margin is: ;in, This represents the adjustable load ratio.

[0134] Calculate a priority index for each device. : .

[0135] All devices are sorted from largest to smallest according to their Π value, generating an ordered list of coordination object identifiers containing unique device identifiers.

[0136] In some embodiments, the coordination control task scheme is distributed and task allocation is performed, and a coordination control instruction set is generated with the help of edge collaborative computing, including: S41. Decompose the coordination and control task scheme into distributed subtasks, and generate distributed control subtasks.

[0137] Specifically, based on the physical location of the equipment, photovoltaic equipment tasks are grouped according to their respective booster stations, energy storage equipment tasks according to their respective battery clusters, and load equipment tasks according to their respective feeder groups; further, according to the task operation type, they are divided into power regulation tasks and state switching tasks. Power regulation tasks are used for power adjustment operations, while state switching tasks are used for state change operations.

[0138] This ultimately forms a set of distributed control subtasks, which includes tasks grouped by physical location and tasks grouped by operation type.

[0139] S42. Allocate computing resources for the distributed control subtasks and formulate node computing schemes.

[0140] The task is grouped by physical location and assigned to a processing unit. The photovoltaic group task is assigned to the power station local processing unit, the energy storage group task is assigned to the energy storage management unit, and the load group task is assigned to the distribution gateway unit.

[0141] Tasks are grouped by operation type and assigned to processing units: power regulation tasks are assigned to high computing power units, and state switching tasks are assigned to low latency processing units.

[0142] The final output node computation scheme contains the mapping relationship between tasks and processing units.

[0143] S43. Parallel execution of node computation scheme to obtain local control instruction set.

[0144] Each compute node executes its assigned subtask synchronously: For power regulation tasks, calculate the power adjustment of photovoltaic equipment: ;in, Represents the power adjustment amount. The regulation coefficient is determined by the ratio of the equipment's maximum power change rate to the system's maximum frequency deviation. This represents the local frequency deviation.

[0145] Calculate the charging and discharging power of energy storage devices: ;in, Represents the energy storage charging and discharging power. Represents the adjustable margin of energy storage. This represents the system's required power.

[0146] For state transition tasks, calculate the timing of load device actions: ;in, Represents the load action time. The base time is usually the current time. Represents frequency deviation. The load action threshold is determined by the rate of change of frequency corresponding to the load thermal recovery time.

[0147] The calculation results are converted into controllable commands for the device.

[0148] The output local control instruction set includes device identifiers and instructions.

[0149] S44. Coordinate the timing relationships between local control instruction sets to generate a coordinated control instruction set.

[0150] Identify the device type, operation nature, and parameter characteristics of each instruction, and establish the relationship between instructions.

[0151] Spatial conflict detection and functional conflict detection are performed. Spatial conflict detection specifically analyzes whether there is time overlap in the instructions of devices in the same area, while functional conflict detection specifically identifies the possible mutual influence between different operation types.

[0152] Determine the minimum safe time interval between various instructions, generate a globally ordered execution sequence, ensure that all safe interval requirements are met, and assign a precise execution timestamp to each instruction.

[0153] Output a standardized set of instructions with timing relationships, including the coordination scheme generation timestamp, scheme validity period identifier, device unique identifier, specific control instruction content, precise execution timestamp, minimum time interval constraint between instructions, and conflict resolution rule description.

[0154] In some embodiments, multi-objective optimization analysis is performed on the coordinated control instruction set to obtain an optimized control scheme, including: S51. Simulate the execution effect of the coordinated control instruction set to achieve the expected results.

[0155] Based on the current power grid topology, equipment parameters, and operating status, a dynamic digital simulation model is established. Control actions are executed in the model according to the command timestamp sequence to simulate the dynamic response process of the power grid. The expected results include frequency stability, economy, and equipment safety.

[0156] Among these, frequency stability can be specifically defined as the maximum frequency deviation of the system; economy can be specifically defined as the total cost of adjustment losses; and equipment safety can be specifically defined as the overload rate of key equipment.

[0157] S52. Compare and analyze the expected effects of various control schemes to generate an optimized control scheme.

[0158] Specifically, alternative schemes can be designed. Scheme A only adjusts the power command amplitude, such as within the range of ±10%–20%; Scheme B only compresses the command execution time interval, such as shortening it to 50%–80% of the original interval; Scheme C adopts a fixed combination strategy, such as combining power adjustment ±15% with time compression of 30%.

[0159] Each scheme was executed under the same simulation conditions, and the expected effects of frequency stability, economy, and equipment safety were compared.

[0160] Finally, following the principle of safety first, schemes with excessive overload rates are eliminated. Then, among the safe schemes, the one with the best frequency stability and economy is selected as the comprehensive optimal scheme, which is the optimized control scheme.

[0161] In some embodiments, an optimized control scheme is executed and its effectiveness is evaluated. A set of system operation indicators is obtained through a feedback mechanism, including: S61. Convert the optimized control scheme into executable instructions for the equipment and generate execution control instructions.

[0162] Specifically, parameters such as power adjustment ratio and time compression rate in the optimized control scheme are parsed, converted into device communication protocol compatible instructions, and encapsulated with communication fields such as device address code, function code, and check bit to obtain the execution control instructions.

[0163] S62. Verify the security of the execution control instructions and obtain the security control instructions.

[0164] Specifically, the system verifies whether the command parameters exceed the safe operating boundaries of the device, checks for logical conflicts between the command and the current state of the device, and intercepts over-limit commands based on real-time adjustable margin to obtain safety control commands.

[0165] S63. Send security control commands to the terminal device and obtain the command execution status.

[0166] Commands are sent to terminal devices through the edge gateway, the device response signals are collected in real time, and timeout retransmission or fault isolation mechanisms are triggered. The command execution status set, including response time and execution result code, is output.

[0167] S64. Analyze the control effect of instruction execution status to obtain a set of system operation indicators.

[0168] Conduct frequency stability analysis, economic analysis, and equipment safety analysis, and finally output a set of system operation indicators, including frequency stability, economic efficiency, and equipment safety.

[0169] In some embodiments, this application provides a collaborative method for real-time monitoring of source-grid-load-storage systems based on edge computing, including: Data on photovoltaic inverter output power, grid frequency deviation, key load switching status, and energy storage system charge and discharge rate are acquired and fused from multiple sources to obtain a system monitoring dataset.

[0170] Multi-dimensional feature analysis is conducted based on the system monitoring dataset, and the system regulation parameter set is obtained through dynamic characteristic evaluation.

[0171] The control logic is constructed based on the system adjustment parameter set, and the coordinated control task scheme is output through the coordinated control generation process.

[0172] The coordination and control task scheme is distributed and task allocation is carried out in a distributed manner, and the coordination and control instruction set is generated by edge collaborative computing.

[0173] Multi-objective optimization analysis is performed on the coordinated control instruction set to obtain an optimized control scheme.

[0174] Implement the optimized control scheme and complete the effect evaluation, and obtain the system operation index set through the execution feedback mechanism.

[0175] In some embodiments, this application provides a source-grid-load-storage real-time monitoring and coordination device based on edge computing, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the source-grid-load-storage real-time monitoring and coordination method based on edge computing.

[0176] In some embodiments, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of a source-grid-load-storage real-time monitoring and coordination method based on edge computing.

[0177] In some embodiments, this application provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, steps are taken to implement a source-grid-load-storage real-time monitoring and coordination method based on edge computing.

[0178] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0179] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0180] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A source-grid-load-storage real-time monitoring and collaborative system based on edge computing, characterized in that, include: Data acquisition and standardization module: Acquires data on photovoltaic inverter output power, grid frequency deviation, key load switching status, and energy storage system charge and discharge rate, performs multi-source data fusion, and obtains system monitoring dataset; Dynamic characteristic evaluation module: Based on the system monitoring dataset, conduct multi-dimensional feature analysis and obtain the system regulation parameter set through dynamic characteristic evaluation; Coordination control construction module: Constructs control logic based on the system adjustment parameter set, and outputs a coordination control task scheme through the coordination control generation process; Distributed collaborative computing module: Distributes the coordination and control task scheme into a distributed task allocation and generates a coordination and control instruction set with the help of edge collaborative computing; Optimization Decision Module: Performs multi-objective optimization analysis on the coordinated control instruction set to obtain an optimized control scheme; Execution and Feedback Module: Executes the optimized control scheme and completes the effect evaluation, and obtains the system operation index set through the execution feedback mechanism.

2. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, Multi-source data fusion is performed to obtain the system monitoring dataset, including: Standardized communication data is obtained by parsing communication protocols and unifying data formats from multi-source data. The standardized communication data is timestamped and synchronized, and time synchronization monitoring data is output. The time-synchronized monitoring data is subjected to quality verification and data cleaning to form the system monitoring dataset.

3. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, Based on the system monitoring dataset, multi-dimensional feature analysis is conducted, and a set of system regulation parameters is obtained through dynamic characteristic evaluation, including: Power fluctuation characteristics are extracted from the system monitoring dataset to obtain power fluctuation characteristic indicators; Based on the power fluctuation characteristic index, the frequency response characteristics are evaluated to obtain the system frequency regulation capability index. Calculate the safe operating boundary based on the system frequency regulation capability index to obtain the system stability boundary parameters; The adjustment requirements are sorted according to the system stability boundary parameters to generate the system adjustment parameter set.

4. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, Based on the system adjustment parameter set, control logic is constructed, and a coordinated control task scheme is output through the coordinated control generation process, including: Analyze the control requirements in the system's set of adjustment parameters to generate coordinated control requirements; Match the control objects corresponding to the coordination and control requirements, and determine the set of coordination object identifiers; Add runtime constraints to the set of coordinated object identifiers to form the coordination control task scheme.

5. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, The coordination and control task scheme is distributed and tasks are allocated in a distributed manner. A coordination and control instruction set is generated using edge collaborative computing, including: The coordination and control task scheme is decomposed into distributed subtasks to generate distributed control subtasks; Allocate computing resources to the distributed control subtasks and formulate node computing schemes; The node computation scheme is executed in parallel to obtain a local control instruction set; The timing relationships between the local control instruction sets are coordinated to generate the coordinated control instruction set.

6. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, A multi-objective optimization analysis is performed on the coordinated control instruction set to obtain an optimized control scheme, including: The execution effect of the coordinated control instruction set was simulated to obtain the expected results; By comparing and analyzing the expected effects of various control schemes, an optimized control scheme is generated.

7. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, The optimized control scheme is executed and its effectiveness is evaluated. A set of system operation indicators is obtained through the execution feedback mechanism, including: The optimized control scheme is converted into device-executable instructions, and execution control instructions are generated. Verify the security of the execution control instructions and obtain security control instructions; The security control command is sent to the terminal device to obtain the command execution status; The control effect of the instruction execution state is analyzed to obtain the system operation index set.

8. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 2, characterized in that, Standardized communication data is obtained by parsing communication protocols and unifying data formats from multi-source data, including: Analyze the communication protocols of multi-source data to obtain data in a unified format; Verify the integrity of the unified format data to obtain a complete data packet; The data format of the complete data packet is standardized to ultimately generate standardized communication data.

9. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 3, characterized in that, Power fluctuation features are extracted from the system monitoring dataset to obtain power fluctuation feature indicators, including: The calculation system monitors the fluctuation amplitude in the dataset to obtain power fluctuation amplitude indicators; The power fluctuation frequency index is obtained by statistically analyzing the fluctuation frequency of the power fluctuation amplitude index. The changing trend of the power fluctuation frequency index is analyzed, and the power fluctuation characteristic index is finally generated.

10. The source-grid-load-storage real-time monitoring and coordination system based on edge computing according to claim 1, characterized in that, Match the control objects corresponding to the coordination and control requirements, and determine the set of coordination object identifiers, including: Identify the types of equipment required for coordinated control and determine the types of adjustable equipment; Query the real-time status of the adjustable device type to obtain a list of available devices; The list of available devices is sorted by priority to form a set of coordination object identifiers.

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