Power grid load storage equipment edge control method and related equipment

Abstract

The embodiment of the invention provides a power grid load storage equipment edge control method and related equipment, and belongs to the technical field of power grid control. The method is applied to an edge computing node of distributed load storage equipment, and comprises the following steps: acquiring operation parameters of the load storage equipment in an area administered by the edge computing node; determining a state evaluation set of the load storage equipment based on the operation parameters; the state evaluation set comprises at least one state evaluation index value; performing control strategy optimization on the load storage equipment according to the state evaluation set to obtain an optimal strategy; the optimal strategy represents a control instruction and an instruction communication requirement of the target control object; and issuing the control instruction to the target control object according to the instruction communication requirement through an edge communication channel based on the software defined network. According to the invention, the edge control collaboration of the power grid load storage equipment can be improved.

Description

Technical Field

[0001] This application relates to the field of power grid control technology, and in particular to an edge control method and related equipment for power grid load storage equipment. Background Technology

[0002] Grid-load-storage equipment is a key technological component in new power systems, enabling coordinated operation of the power grid, new energy sources, loads, and energy storage. Its core function is to achieve dynamic balance between power supply and demand through intelligent regulation. Currently, the power grid employs a centralized cloud-based control model to address the coordination of distributed load-storage equipment, which has fundamental flaws: First, all network data needs to be transmitted back to the central processing center, resulting in a lengthy decision-making chain and severe response delays, failing to meet millisecond-level real-time regulation requirements; second, the direct transmission of massive amounts of equipment operating parameters to the cloud causes communication channel congestion. Based on this, related technologies utilize edge nodes to coordinate and control their respective load-storage equipment, improving the sensitivity of distributed load-storage equipment control. However, even with edge nodes for coordinated control, communication between edge nodes and various energy storage or load devices remains unstable. Different commands under the same strategy experience varying delays when reaching their corresponding devices, leading to poor coordination of grid-load-storage equipment under edge nodes. Summary of the Invention

[0003] The main objective of this application is to propose an edge control method and related equipment for grid load-storage devices, aiming to improve the coordination of edge control of grid load-storage devices.

[0004] To achieve the above objectives, one aspect of this application proposes an edge control method for grid-connected storage devices, applied to the edge computing nodes of distributed storage devices. The edge control method for grid-connected storage devices includes the following steps: Collect the operating parameters of the load storage equipment within the area covered by the edge computing node; The state assessment set of the load storage equipment is determined based on the operating parameters; the state assessment set includes at least one state assessment index value. The optimal strategy is obtained by optimizing the control strategy of the load storage device based on the state assessment set; the optimal strategy represents the control commands and command communication requirements of the target control object. The control commands are sent to the target controlled object through the edge communication channel based on the software-defined network, in accordance with the command communication requirements.

[0005] In some embodiments, the operating parameters of the load-storage equipment include load unit parameters and energy storage unit parameters; determining the state assessment set of the load-storage equipment based on the operating parameters includes the following steps: The load margin calculation model is used to calculate the load margin index value based on the load unit parameters; An energy storage margin calculation model is used to calculate the energy storage margin index value based on the parameters of the energy storage unit.

[0006] In some embodiments, the step of optimizing the control strategy of the storage device based on the state assessment set to obtain the optimal strategy includes the following steps: Determine whether any state assessment index value in the state assessment set exceeds the expected range of the corresponding state assessment index; If a state evaluation index value in the state evaluation set exceeds the expected range of the corresponding state evaluation index, the control strategy of the load storage device is optimized based on the current network state and the current device state to obtain the optimal strategy.

[0007] In some embodiments, optimizing the control strategy of the load storage device based on the current network state and the current device state to obtain the optimal strategy includes the following steps: Obtain the dynamic policy matrix representing multiple control policies; Based on the current network state and the current device state, the multi-objective optimization function of the dynamic policy matrix is ​​solved to obtain the optimal policy; The optimization elements of the multi-objective optimization function include the future utility of the control strategy, the expected load impact of strategy execution on the edge communication network, and the device health profile.

[0008] In some embodiments, the instruction communication requirements include execution intensity and utility expectation window. The step of sending the control instructions to the target controlled object via an edge communication channel based on a software-defined network, according to the instruction communication requirements, includes the following steps: The downlink priority of the control command is determined based on the execution intensity and the utility expectation window; The target communication mode is determined based on the downlink priority and the current quality of each link. The control command is encapsulated according to the target communication mode, and the encapsulated command is sent to the target control object.

[0009] In some embodiments, the grid-load-storage device edge control method further includes the following steps: After issuing the control command to the target control object, monitor the response signal from the target control object; If the response signal is not received within a preset period, the optimal strategy is cached, and the control command represented by the optimal strategy is reissued according to a preset degradation mode.

[0010] In some embodiments, the grid-load-storage device edge control method further includes the following steps: The execution result of the control command and the state update data of the target controlled object are compressed to obtain the first data; The data blocks of the first data are grouped and encrypted to obtain the second data; The second data is uploaded to the cloud device.

[0011] To achieve the above objectives, another aspect of this application proposes a grid-connected energy storage device edge control system, applied in the edge computing node of a distributed energy storage device. The grid-connected energy storage device edge control system includes: The data acquisition module is used to collect the operating parameters of the load storage equipment within the area covered by the edge computing node; The status assessment module is used to determine the status assessment set of the load storage equipment based on the operating parameters; the status assessment set includes at least one status assessment index value. The instruction generation module is used to optimize the control strategy of the load storage device based on the state evaluation set to obtain the optimal strategy; the optimal strategy represents the control instructions and instruction communication requirements of the target control object; The edge execution module is used to send the control commands to the target controlled object according to the command communication requirements through the edge communication channel based on the software-defined network.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, and program product for edge control of power grid load-storage equipment. This solution collects operating parameters of the load-storage equipment within the jurisdiction of the edge computing node of the distributed load-storage equipment. Based on the operating parameters, a state evaluation set of the load-storage equipment is determined. The state evaluation set includes at least one state evaluation index value. The optimal strategy is obtained by optimizing the control strategy of the load-storage equipment according to the state evaluation set. The optimal strategy represents the control commands and command communication requirements of the target controlled object. The control commands are then sent to the target controlled object according to the command communication requirements through an edge communication channel based on a software-defined network. This solution significantly shortens the response latency by executing the load-storage equipment state evaluation and adaptive control command generation locally at the edge computing node. During the strategy command optimization process, the communication requirements of the commands are simultaneously optimized. Then, the control commands are sent to the target controlled object according to the command communication requirements through the edge communication channel of the software-defined network, enabling coordinated control of the command communication process, thereby improving the coordination of edge control of power grid load-storage equipment. Attached Figure Description

[0016] Figure 1 This is a flowchart of the edge control method for grid load storage equipment provided in the embodiments of this application; Figure 2 This is a schematic diagram of the edge control system for the power grid load storage device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0019] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0020] LZMA (Lempel-Ziv-Markov chain-Algorithm) is a high-efficiency lossless compression algorithm based on improvements to the LZ77 and Deflate algorithms. It employs adaptive dictionary compression technology, supports variable dictionaries up to 4GB, matches repeating data sequences using the improved LZ77 algorithm, and achieves bit-level probability prediction by combining range encoders.

[0021] The AES-256 (Advanced Encryption Standard-256) algorithm is a symmetric encryption algorithm that uses a 256-bit key length. It is widely recognized as one of the most secure encryption standards and is extensively used in data transmission, storage, and network security. This algorithm uses the same key for both encryption and decryption, and achieves efficient data processing through mathematical operations (such as byte substitution, row shifting, and column obfuscation).

[0022] The current power grid uses a centralized cloud-based control model to address the coordination of distributed load and storage devices, which has fundamental flaws: First, all network data needs to be transmitted back to the central processing center, resulting in a lengthy decision-making chain and severe response delays, failing to meet the millisecond-level real-time control requirements. Second, the direct transmission of massive amounts of device operating parameters to the cloud causes communication channel congestion. Third, fixed threshold control strategies are difficult to adapt to the dynamic coupling characteristics of load and storage devices, frequently leading to malfunctions. Fourth, the communication status between edge nodes and various energy storage or load devices is unstable, and communication coordination is poor when different instructions under the same strategy are issued to their corresponding devices. Fifth, plaintext transmission of control instructions carries the risk of malicious tampering. In short, current edge computing solutions only focus on monitoring individual devices and fail to resolve the systemic contradictions between real-time decision-making, communication efficiency, and strategy adaptability in load-storage coordination scenarios.

[0023] In view of this, this application provides an edge control method and related equipment for grid load-storage equipment. This scheme performs load-storage equipment status assessment and adaptive control command generation locally through edge computing nodes, which greatly shortens the response delay. During the adaptive optimization process of strategy commands, the communication requirements of the commands are optimized simultaneously. Then, through the edge communication channel of the software-defined network, the control commands are sent to the target control object according to the command communication requirements. The command communication process is coordinated and controlled, thereby improving the coordination of edge control of grid load-storage equipment.

[0024] The edge control method for power grid load-storage equipment provided in this application relates to the field of power grid control technology. This edge control method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the edge control method for power grid load-storage equipment, but is not limited to the above forms.

[0025] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] Distributed load-storage devices are the core component of distributed energy storage systems. They refer to energy storage units that are distributed and deployed at nodes of the power system (such as the user side and the distribution network side). By storing and releasing electrical energy to the load system, they achieve power supply and demand balance in the region and improve grid stability. Each region is equipped with a corresponding edge computing node to monitor and control the status of the load units and energy storage units in that region. The grid load-storage device edge control method in this embodiment can be applied to the aforementioned edge computing nodes.

[0027] Figure 1 This is an optional flowchart of the edge control method for grid load storage equipment provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0028] S101, collects the operating parameters of the load storage equipment within the area covered by the edge computing node; S102, Determine the condition assessment set of the load storage equipment based on the operating parameters; the condition assessment set includes at least one condition assessment index value. S103, Based on the state assessment set, optimize the control strategy of the load storage equipment to obtain the optimal strategy; the optimal strategy represents the control commands and command communication requirements of the target control object; S104 sends control commands to the target controlled object according to the command communication requirements through the edge communication channel based on software-defined network.

[0029] Steps S101 to S104 as shown in the embodiments of this application execute the status assessment and adaptive control command generation of the load storage equipment locally through the edge computing node, which greatly shortens the response delay. During the strategy command optimization process, the communication requirements of the command are optimized at the same time. Then, through the edge communication channel of the software-defined network, the control command is sent to the target control object according to the command communication requirements. The command communication process is coordinated and controlled, thereby improving the coordination of edge control of the grid load storage equipment.

[0030] In step S101 of some embodiments, the edge computing node collects the operating parameters of the load-storage devices within its jurisdiction. The load-storage devices may include, but are not limited to, load units and energy storage units. Load units refer to devices that transmit electrical loads to the power grid, such as photovoltaic inverters; energy storage units refer to energy storage devices in the power grid, such as lithium battery packs. The operating parameters of the load-storage devices may include photovoltaic output power, battery SOC, bus voltage, and operating temperature. Specifically, the edge computing node collects in real time the operating parameters of the load units (i.e., load unit parameters) and the operating parameters of the energy storage units (energy storage unit parameters) within its jurisdiction.

[0031] In step S102 of some embodiments, by using the constructed calculation model of the state assessment index and inputting the corresponding operating parameters into the calculation model, the corresponding state assessment index value can be obtained. Multiple different state assessment index values ​​form a state assessment set for the load-storage equipment. For example, the state assessment set may include, but is not limited to, load margin index values ​​and energy storage margin index values.

[0032] In step S103 of some embodiments, the optimal strategy is obtained by optimizing the control strategy of the load-storage equipment based on the state assessment set. Specifically, if any state assessment index value in the state assessment set exceeds the expected range of the corresponding state assessment index, the edge computing node is triggered to optimize the control strategy of the load-storage equipment to obtain the optimal strategy. Based on the optimal strategy, the load-storage equipment is then coordinated to adjust it to the desired state. In this embodiment, the control strategy optimization process not only optimizes the control commands for the controlled object but also seeks the communication requirements of the commands. Subsequently, communication resources for the commands can be reasonably allocated based on the communication requirements, and the command communication process can be coordinated to improve the coordination of the edge control of the grid load-storage equipment. In this embodiment, the command communication requirements may include, but are not limited to, encoding strategies, execution strength, and utility expectation windows. Execution strength characterizes the contribution of executing the command to adjusting the load-storage equipment to the desired state, and the utility expectation window characterizes the timeframe for the command to be sent to the target controlled object. For example, when the state evaluation index value exceeds the expected range of the state evaluation index, a dynamic policy matrix containing multiple control strategies is constructed using edge computing nodes, and the optimal strategy is selected from the dynamic policy matrix based on the real-time network status and device performance profile, thereby generating adaptive control instructions for the target control object.

[0033] In step S104 of some embodiments, the edge communication channel refers to the communication channel from the edge computing node to each controlled object (such as photovoltaic, lithium battery, etc.), and this communication channel can be wired or wireless. The edge communication channel of a software-defined network refers to the dynamic and coordinated transmission of control commands through software-defined control. For example, through the edge communication channel based on a software-defined network, control commands are sent to the target load storage device (target controlled object) according to the downlink priority indicated by the command communication requirements in the optimal strategy, for execution by the target load storage device. Based on optimizing the communication requirements of the control commands, this embodiment combines software-defined networks to efficiently map the command communication requirements to corresponding communication resources, enabling each control command to use appropriate communication resources for interaction, thereby improving the coordination of load storage device adjustment and control throughout the entire area.

[0034] According to some embodiments of this application, step S102 may include, but is not limited to, the following steps: S201 uses a load margin calculation model to calculate the load margin index value based on the load unit parameters; S202 uses an energy storage margin calculation model to calculate the energy storage margin index value based on the energy storage unit parameters.

[0035] In this embodiment, the load margin calculation model is used to calculate the load margin index between the current power output of the load unit and its rated capacity. The energy storage margin calculation model is used to calculate the energy storage margin index between the current power of the energy storage unit and its critical depth of discharge. By inputting the load unit parameters into the load margin calculation model, the load margin index value is obtained; by inputting the energy storage unit parameters into the energy storage margin calculation model, the energy storage margin index value is obtained. This embodiment, by evaluating the load margin of the load unit and the energy storage margin of the energy storage unit, adjusts the system based on the evaluated state, thereby avoiding situations where the system exceeds the energy storage and load performance, and improving system operational safety.

[0036] It is understood that the evaluation status indicators in this application embodiment may include not only load margin indicators and energy storage margin indicators, but also energy conversion efficiency, charge and discharge efficiency, etc. This application embodiment does not impose specific limitations.

[0037] According to some embodiments of this application, step S103 may include, but is not limited to, the following steps: S301, Determine whether any state assessment index value in the state assessment set exceeds the expected range of the corresponding state assessment index. S302, when there are state evaluation index values ​​in the state evaluation set that exceed the expected range of the corresponding state evaluation index, optimize the control strategy of the load storage device based on the current network state and the current device state to obtain the optimal strategy.

[0038] In this embodiment, it is determined whether the value of each state evaluation index in the state evaluation set exceeds the expected range of the corresponding state evaluation index. If there is a state evaluation index value in the state evaluation set that exceeds the expected range of the corresponding state evaluation index, for example, the load margin index value of a load unit exceeds the load margin threshold or the energy storage margin index value of an energy storage unit exceeds the energy storage margin threshold, then the control strategy of the load-storage equipment is optimized based on the current network state and the current equipment state to obtain the optimal strategy. The current network state can refer to the latency, bandwidth, transmission rate, and congestion status of each communication channel, etc., and the current equipment state can include the current state evaluation index value, operating parameters, and performance parameters of each equipment unit in the distributed load-storage equipment. The control strategy optimization can be implemented through heuristic algorithms, metaheuristic algorithms, or reinforcement learning search strategy algorithms. Heuristic algorithms can be greedy algorithms or dynamic programming algorithms, etc., and metaheuristic algorithms can be genetic algorithms, ant colony algorithms, particle swarm optimization, or simulated annealing algorithms, etc. For example, taking a reinforcement learning search strategy algorithm, the current network state and current device state can be input into a trained policy network for policy optimization, thereby outputting an optimal policy that satisfies the optimization objective. The optimization objective may include, but is not limited to, the future utility of executing the control policy, the expected load impact of policy execution on the edge communication network, and device health profiles. This embodiment determines the timing of load storage device adjustments by judging whether the state evaluation index value of the load storage device exceeds a suitable range, enabling timely adjustments to the load storage device and preventing it from remaining in an abnormal state for extended periods. When adjustment is needed, the control strategy for the load storage device is automatically optimized based on the current network state and current device state to obtain the optimal policy. Then, based on the optimal policy, the load storage device is coordinated and controlled, adaptively ensuring that the adjustment values ​​of the load storage devices in the region meet the optimization objective.

[0039] According to some embodiments of this application, step S302 may include, but is not limited to, the following steps: S401, Obtain the dynamic policy matrix representing multiple control policies; S402, Solve the multi-objective optimization function of the dynamic policy matrix based on the current network state and the current device state to obtain the optimal policy; Among them, the optimization elements of the multi-objective optimization function include the future utility of the control policy, the expected load impact of policy execution on the edge communication network, and the device health profile.

[0040] In this embodiment, a pre-trained lightweight neural network model can be fine-tuned based on historical data, enabling the model to predict the future utility of control strategies. Specifically, the pre-trained lightweight neural network model refers to a general strategy evaluation model, and the historical data refers to sample data of future utility obtained after executing a certain control strategy under the real-time state assessment index of the storage and load facility. By fine-tuning the pre-trained lightweight neural network model using historical data, the lightweight neural network model can be used to accurately predict the future utility of control strategies and improve model training efficiency.

[0041] In this embodiment, during the control strategy optimization process, a dynamic strategy matrix representing multiple control strategies is first constructed. The current state evaluation index and the control strategies in the dynamic strategy matrix are input into the trained lightweight neural network model to obtain the future utility of different control strategies. Relevant algorithm models are used to calculate the expected load impact of executing the control strategy on the edge communication network and the device health profile. Using the future utility of different control strategies, the expected load impact of strategy execution on the edge communication network, and the device health profile as decision factors, a multi-objective optimization function is constructed with the optimization objectives of improving future utility, reducing expected load impact, and improving device health profile. The multi-objective optimization function is solved to find the comprehensive optimal strategy from the dynamic strategy matrix. Based on this optimal strategy, adaptive control instructions and instruction communication requirements including strategy encoding, execution strength, and utility expectation window are generated. This embodiment improves strategy optimization efficiency by constructing a dynamic strategy matrix representing multiple control strategies and then finding the optimal control strategy from the dynamic strategy matrix based on the multi-objective optimization function.

[0042] According to some embodiments of this application, step S104 may include, but is not limited to, the following steps: S501 determines the downlink priority of control commands based on execution intensity and expected utility window; S502 determines the target communication mode based on downlink priority and the current quality of each link; S503 encapsulates the control commands according to the target communication mode and sends the encapsulated commands to the target control object.

[0043] In this embodiment, the optimal strategy characterizes how to control each controlled object in the region, i.e., the control commands for each controlled object, and the command communication requirements of the control commands. The command communication requirements include encoding strategy, execution strength, and utility expectation window. The downlink priority of the control commands is determined based on the execution strength and utility expectation window using a query method or formula mapping method. Higher execution strength results in higher downlink priority, improving the reliability of important command communication; a shorter utility expectation window results in better downlink priority, improving the communication efficiency of more urgent commands. Using a software-defined network controller, the optimal communication mode (i.e., the target communication mode) is dynamically selected and switched for the control commands based on the downlink priority and the current quality of each link. This ensures that the control commands occupy the corresponding communication resources according to the specified downlink priority during the issuance process, achieving coordinated communication among the control commands. For example, the selected communication modes may include, but are not limited to, highly reliable, low-latency TSN links and high-throughput Wi-SUN links. The control commands are encapsulated and issued according to the protocol specifications of the selected target communication mode. Furthermore, after determining the target communication mode, the control commands can be encoded according to the encoding strategy indicated in the command communication requirements. The encoded control commands are then encapsulated and sent according to the protocol specifications of the target communication mode. By using an appropriate encoding strategy to encode the control commands, the efficiency of command communication can be further improved, provided that the target controlled object can successfully receive the control commands.

[0044] According to some embodiments of this application, the edge control method for grid load-storage equipment in this application may also include, but is not limited to, the following steps: S601, after issuing control commands to the target controlled object, monitors the response signal from the target controlled object; S602, if no response signal is received within a preset period, caches the optimal strategy and reissues the control instructions represented by the optimal strategy according to the preset degradation mode.

[0045] In this embodiment, if the target payload storage device (i.e., the target controlled object) does not return an acknowledgment signal within the set timeout period, meaning the edge computing node does not detect its response signal, the edge computing node activates its local caching strategy, regenerates the downlink priority of the adaptive control command according to the optimal strategy based on a preset degradation mode, and then issues it. The preset degradation mode refers to the downward adjustment method of the downlink priority of the control command compared to the previous one. For example, if the downlink priority of the first issued control command is level one, and the target controlled object fails to issue the command in the current communication round, the downlink priority of the second issued control command can be adjusted to a lower level, such as level two or level three, to reissue the control command and avoid excessive consumption of communication resources for other normally issued control commands during reissue.

[0046] According to some embodiments of this application, the edge control method for grid load-storage equipment in this application may also include, but is not limited to, the following steps: S701, compress the execution result of the control command and the status update data of the target controlled object to obtain the first data; S702, perform block encryption on the first data to obtain the second data; S703 uploads the second data to the cloud device.

[0047] In this embodiment, edge computing nodes compress and encrypt the execution results of control commands and the device status update data of the target load storage device before uploading them to the cloud center for storage. This achieves data synchronization between the cloud and the edge, facilitating cloud-based management of distributed load storage devices. Execution results, including information such as execution failure or success, can be fed back to the edge computing nodes by the target load storage device. Specifically, the execution results and device status update data can be losslessly compressed using the LZMA algorithm, and then the compressed data blocks are encrypted using the AES-256 algorithm.

[0048] Furthermore, the cloud center performs blockchain notarization on the data uploaded by the edge computing nodes, and then writes the data hash value into the consortium blockchain node to achieve secure data storage.

[0049] The following section will provide a detailed description and explanation of the solutions in this application embodiment, using specific application examples. The specific implementation process for deploying edge computing nodes in a distributed photovoltaic (PV) grid area is as follows: S1. Data Acquisition: Edge computing nodes collect real-time operating parameters of rooftop photovoltaic inverters (load units) and secondary lithium battery packs (energy storage units) within their jurisdiction via the RS485 / MODBUS protocol, including photovoltaic output power, battery SOC, bus voltage, and ambient temperature.

[0050] S2, Status Assessment: Edge computing nodes calculate the status assessment indicators of storage and load cells based on the collected data: For photovoltaic inverters, calculate the load margin index between the current power output and the rated capacity; For lithium battery packs, calculate the energy storage margin index between the current charge level and the critical depth of discharge.

[0051] S3, Instruction Generation: When the load margin index of a photovoltaic cluster is detected to exceed the threshold, the edge computing node performs the following: Based on the real-time energy storage margin of the lithium battery pack, it determines the dispatchable energy storage support capacity, performs strategy optimization based on the energy storage support capacity and network status, and generates control instructions containing power adjustment values, duration, and instruction communication requirements to indicate execution priority.

[0052] S4, Edge Execution: The instructions are encapsulated into JSON format data packets via the CoAP protocol, encrypted with AES, and sent to the target device. After receiving the instructions, the photovoltaic inverter adjusts its output power, and the lithium battery pack performs charging / discharging operations according to the instructions.

[0053] S5, cloud synchronization: Edge computing nodes acquire command execution results and updated device status data, then compress the data volume using the LZMA algorithm, encrypt the data in blocks using AES-256, and finally upload it to the power grid cloud control platform via HTTPS protocol.

[0054] This application embodiment utilizes edge computing nodes to collect real-time operating parameters of load storage equipment within its jurisdiction; it uses edge computing nodes to calculate status evaluation indicators of the load storage equipment based on the operating parameters; when the status evaluation indicators exceed a preset threshold, it uses edge computing nodes to construct a dynamic strategy matrix containing multiple control strategies, and selects the optimal strategy from the dynamic strategy matrix based on real-time network status and equipment performance data to generate adaptive control commands for the target load storage equipment; through a multimodal edge communication channel based on a software-defined network, the adaptive control commands are sent to the target load storage equipment according to the downlink priority required by the strategy for execution by the target load storage equipment; the execution results of the adaptive control commands and the equipment status update data of the target load storage equipment are compressed and encrypted by the edge computing nodes and then uploaded to the cloud center. This application embodiment executes load storage equipment status evaluation and adaptive control command generation locally by edge computing nodes, significantly shortening response latency; the use of a lightweight adaptive edge communication protocol significantly reduces transmission load and improves communication efficiency; the command strategy that dynamically matches equipment status effectively avoids malfunctions; the command execution results are uploaded to the cloud after layered encryption and compression, simultaneously improving communication efficiency and data security, and constructing a complete edge-based control closed loop.

[0055] Please refer to Figure 2 This application also provides an edge control system for grid-connected storage equipment, applied in the edge computing nodes of distributed storage equipment. The grid-connected storage equipment edge control system includes: The data acquisition module is used to collect the operating parameters of the load storage equipment within the area covered by the edge computing node; The condition assessment module is used to determine the condition assessment set of the load storage equipment based on operating parameters; the condition assessment set includes at least one condition assessment index value. The instruction generation module is used to optimize the control strategy of the load storage equipment based on the state assessment set to obtain the optimal strategy; the optimal strategy represents the control instructions and instruction communication requirements of the target control object; The edge execution module is used to send control commands to the target controlled object according to the command communication requirements through the edge communication channel based on the software-defined network.

[0056] Furthermore, the edge control system for the power grid load storage device in this application embodiment also includes a cloud synchronization module, which is used to compress and encrypt the execution results of the adaptive control command and the device status update data of the target load storage device and upload them to the cloud center using the edge computing node.

[0057] It is understood that the methods described in the above method embodiments are applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0058] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including a tablet computer, an edge server, or similar device.

[0059] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0060] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 302 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301. Input / output interface 303 is used to implement information input and output; The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304); The processor 301, memory 302, input / output interface 303, and communication interface 304 are connected to each other within the device via bus 305.

[0061] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0062] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0063] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0064] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0065] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0066] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0067] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0068] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0070] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application 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 this application 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 non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0071] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0072] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0073] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0075] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for edge control of grid load-storage equipment, characterized in that, The edge control method for grid-connected energy storage devices, applied to edge computing nodes of distributed load-storage devices, includes the following steps: Collect the operating parameters of the load storage equipment within the area covered by the edge computing node; The state assessment set of the load storage equipment is determined based on the operating parameters; the state assessment set includes at least one state assessment index value. The optimal strategy is obtained by optimizing the control strategy of the load storage device based on the state assessment set; the optimal strategy represents the control commands and command communication requirements of the target control object. The control commands are sent to the target controlled object through the edge communication channel based on the software-defined network, in accordance with the command communication requirements.

2. The method according to claim 1, characterized in that, The operating parameters of the load-storage equipment include load unit parameters and energy storage unit parameters; determining the state assessment set of the load-storage equipment based on the operating parameters includes the following steps: The load margin calculation model is used to calculate the load margin index value based on the load unit parameters; An energy storage margin calculation model is used to calculate the energy storage margin index value based on the parameters of the energy storage unit.

3. The method according to claim 1, characterized in that, The process of optimizing the control strategy for the storage device based on the state assessment set to obtain the optimal strategy includes the following steps: Determine whether any state assessment index value in the state assessment set exceeds the expected range of the corresponding state assessment index; If a state evaluation index value in the state evaluation set exceeds the expected range of the corresponding state evaluation index, the control strategy of the load storage device is optimized based on the current network state and the current device state to obtain the optimal strategy.

4. The method according to claim 3, characterized in that, The process of optimizing the control strategy for the load storage device based on the current network status and the current device status to obtain the optimal strategy includes the following steps: Obtain the dynamic policy matrix representing multiple control policies; Based on the current network state and the current device state, the multi-objective optimization function of the dynamic policy matrix is ​​solved to obtain the optimal policy; The optimization elements of the multi-objective optimization function include the future utility of the control strategy, the expected load impact of strategy execution on the edge communication network, and the device health profile.

5. The method according to claim 1, characterized in that, The command communication requirements include execution intensity and utility expectation window. The step of sending the control commands to the target controlled object via an edge communication channel based on a software-defined network, according to the command communication requirements, includes the following steps: The downlink priority of the control command is determined based on the execution intensity and the utility expectation window; The target communication mode is determined based on the downlink priority and the current quality of each link. The control command is encapsulated according to the target communication mode, and the encapsulated command is sent to the target control object.

6. The method according to any one of claims 1 to 5, characterized in that, The edge control method for grid load storage equipment also includes the following steps: After issuing the control command to the target control object, monitor the response signal from the target control object; If the response signal is not received within a preset period, the optimal strategy is cached, and the control command represented by the optimal strategy is reissued according to a preset degradation mode.

7. The method according to claim 6, characterized in that, The edge control method for grid load storage equipment also includes the following steps: The execution result of the control command and the state update data of the target controlled object are compressed to obtain the first data; The data blocks of the first data are grouped and encrypted to obtain the second data; The second data is uploaded to the cloud device.

8. An edge control system for grid load storage equipment, characterized in that, The grid-connected energy storage device edge control system, applied in the edge computing nodes of distributed energy storage devices, includes: The data acquisition module is used to collect the operating parameters of the load storage equipment within the area covered by the edge computing node; The status assessment module is used to determine the status assessment set of the load storage equipment based on the operating parameters; the status assessment set includes at least one status assessment index value. The instruction generation module is used to optimize the control strategy of the load storage device based on the state evaluation set to obtain the optimal strategy; the optimal strategy represents the control instructions and instruction communication requirements of the target control object; The edge execution module is used to send the control commands to the target controlled object according to the command communication requirements through the edge communication channel based on the software-defined network.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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