Energy storage power station control strategy updating method based on cloud edge cooperation technology

Abstract

The invention relates to an energy storage power station control strategy updating method based on a cloud-side cooperation technology, and the method comprises the steps: starting to call a data processing program when an edge calculation gateway collects data or receives alarm information or a task processing request actively transmitted by an energy storage power station; an edge computing gateway data processing program judges the type of a currently executed task, if the currently executed task is alarm information, an alarm information analysis processing program is executed, if the currently executed task is a computing task actively requested by the energy storage power station, a computing task analysis processing program is executed, and if the currently executed task is a high-complexity computing task, a cloud server is requested to assist computing; the two-way updating method of the control strategy is designed, both the edge control gateway and the cloud server can actively initiate strategy updating, and compared with an edge-side or cloud-side-based one-way strategy updating method in the prior art, the method provided by the invention has more applicability.

Description

Technical Field

[0001] The present invention belongs to the field of cloud-edge collaborative data update technology, and specifically is a method for updating a control strategy of an energy storage power station based on cloud-edge collaborative technology. Background Art

[0002] At present, new energy power generation mainly includes wind power generation, photovoltaic power generation, biomass power generation and ocean power generation. As the installed capacity expands year by year, the impact on the power grid is gradually increasing. Due to the large uncertainty of the output of new energy, the traditional distribution network supported by thermal power and hydropower has been greatly challenged. In order to improve the utilization rate of new energy and enhance the stability of the power grid, the fluctuation of new energy can be smoothed by adding energy storage systems. At the same time, the power supply problem in some areas without city power can be solved by adding new energy storage systems. Therefore, in recent years, energy storage technology has been rapidly upgraded, and the number and capacity of energy storage power stations have also increased rapidly. At present, the control system of energy storage power stations mostly adopts the form of local EMS system combined with battery BMS. BMS is generally installed inside the battery PACK and consists of multi-level acquisition and controller to realize the bottom-level protection function of the battery. The control response time is in milliseconds; local EMS generally obtains information from BMS and other system equipment through communication, and performs second-level control on the battery. The response time is generally hundreds of milliseconds to a few seconds.

[0003] The control of traditional energy storage power stations is mostly limited to the internal equipment and status of the power stations, and it is unable to obtain more external operating conditions of the power grid, which may cause the control effect to be inconsistent with the target. At the same time, it is difficult to perform more complex calculations and analysis due to the limited computing power and storage capacity of the local controller. At the same time, various newly built cloud services have obtained a large amount of edge data, and can obtain the optimized control strategy of the energy storage power station through data analysis methods such as big data and artificial intelligence, but they cannot be sent to the corresponding local controller in a timely and accurate manner to achieve the optimal control goal. Therefore, there is an urgent need for a method to realize cloud-edge collaboration of energy storage power stations, and to achieve the safe and timely issuance of control strategies through this method. This patent designs and implements a control strategy update method and corresponding systems and equipment to solve this problem.

[0004] Among the existing patents, the Chinese invention patent with application number "CN202410488909" and name "A cloud-edge collaborative data acquisition and control method for new energy power stations" proposes a cloud-edge collaborative data acquisition and control method and realizes a cloud-edge collaborative control method. However, the edge cloud on the edge side uses a lightweight server. Although the computing speed has been improved, the communication response still cannot meet the real-time requirements of the energy storage power station for the control system. The proposed method is more suitable for cloud-edge collaborative scenarios that do not have high requirements for real-time but have high requirements for computing complexity.

[0005] Among the existing patents, the Chinese invention patent with application number "CN202110851674" and name "Active distribution network cloud-edge collaborative control method, device and terminal equipment" also proposes an active distribution network cloud-edge collaborative control method, which can realize the coordinated control of cloud and edge power in distributed energy distribution networks, but it concentrates all the main computing power on the cloud. The edge controller itself has no computing power and policy update capabilities, resulting in an increase in cloud resource demand. At the same time, the update of simple strategies is not timely enough. Faced with the gradual increase in the number of accesses, it will inevitably lead to untimely updates of single site strategies.

[0006] Among the existing patents, the Chinese invention patent with application number "CN202310102664" and name "Energy management method and system based on cloud-edge collaboration" proposes a cloud-edge collaborative energy management method, which can realize targeted control management of local power grids, photovoltaics, wind power and other equipment, and also has the ability to update control strategies. However, its edge nodes must obtain prediction data from the cloud before they can perform corresponding control updates. The edge nodes cannot use their own computing resources to update control strategies. They are still not applicable in scenarios with high real-time requirements and are overly dependent on the computing power of the cloud.

[0007] Although the above-mentioned existing technologies can use cloud-edge collaboration capabilities to realize partial control strategy updates, they do not take into account that for energy storage systems, some control and protection functions have high real-time requirements. There will be some shortcomings if the control strategy updates are placed on the edge or cloud alone. A control strategy update method with full cloud-edge collaboration must be designed to solve the above shortcomings. Summary of the invention

[0008] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0009] In view of the following technical problems in the prior art: the existing edge nodes must obtain prediction data from the cloud before performing corresponding control updates.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for updating the control strategy of an energy storage power station based on cloud-edge collaborative technology, comprising:

[0011] Step 1: When the energy storage power station equipment or BMS, EMS, etc. generates an alarm or a task that requires computing and processing, it is sent to the edge computing gateway through communication, or the edge computing gateway periodically queries the energy storage power station operation data;

[0012] Step 2: When the edge computing gateway collects data or receives alarm information or task processing requests actively sent by the energy storage power station, it starts calling the data processing program;

[0013] Step 3: The edge computing gateway data processing program determines the type of task currently being executed. If it is an alarm message, it proceeds to step 4. If it is a computing task actively requested by the energy storage power station, it proceeds to step 7. If the edge gateway obtains data regularly, it proceeds to step 11.

[0014] Step 4: The edge computing gateway immediately performs alarm information analysis and processing, executes the corresponding control strategy, sends control commands to the energy storage power station, and sends the alarm information to the cloud platform.

[0015] Step 5: The cloud platform receives the alarm information, calls the corresponding processing module to perform data analysis, infers other chain problems that may occur, updates the control strategy and sends it to the edge computing gateway.

[0016] Step 6: The edge computing gateway receives the control strategy update request from the cloud platform, updates the control strategy at an appropriate time, and re-issues the control command to the energy storage power station, and then proceeds to step 12.

[0017] Step 7: The edge computing gateway immediately performs the computing task analysis and processing procedure to evaluate the complexity of the computing task. For low-complexity tasks, it calls the edge computing gateway computing power for processing and sends the results to the energy storage power station, and then proceeds to step 12.

[0018] Step 8: For highly complex tasks, request cloud computing assistance.

[0019] Step 9: After receiving the request for assisting computing, the cloud platform calls the corresponding processing module, requests the edge computing gateway to transmit the required computing data, waits for the edge computing gateway to complete the transmission, and then performs the calculation and sends the calculation results to the edge computing gateway.

[0020] Step 10: The edge computing gateway receives the calculation results sent by the cloud platform, updates its own control strategy, and sends the calculation results to the energy storage power station, and then proceeds to step 12.

[0021] Step 11: The edge computing gateway processes the acquired data according to the current control strategy, updates the control command, sends the control command to the energy storage power station, and then proceeds to step 12.

[0022] Step 12: The edge computing gateway updates the key data of the energy storage power station to its own storage, waiting for the cloud to obtain data or uploading it at a scheduled time.

[0023] As a preferred technical solution for the control strategy update method of energy storage power station based on cloud-edge collaborative technology, the cloud server assists in computing the following:

[0024] Step 71: The cloud server periodically obtains key data of the energy storage site connected to the edge computing gateway from the edge computing gateway, including remote signaling, telemetry, remote control, and remote adjustment data of each device, metering equipment data, protection device data, line load data, line voltage, current, frequency, power factor and other electrical data, power station revenue-related electricity price data, time period data, and stores them in the cloud database waiting for computing task calls;

[0025] Step 72: The cloud server executes the strategy optimization calculation task according to the set period, performs the optimization calculation of each site control strategy, and stores the optimization result in the cloud storage, waiting to be sent to the edge computing gateway;

[0026] Step 73: If a user performs a remote policy update operation on the cloud server, if the optimization result is used, the optimization policy stored in the cloud storage is called; if the control policy set by the user is used, the user's setting policy is called, and a policy update request is sent to the edge computing gateway. After the response, the corresponding policy is sent to the edge computing gateway, and then the process goes to step 71.

[0027] As a preferred technical solution for the control strategy update method of energy storage power station based on cloud-edge collaborative technology, the cloud server assists in computing the following:

[0028] Step 74: If the cloud server reaches the timing period for the scheduled remote update strategy for a specific energy storage site, first check whether the user has set the automatic remote optimization function to be prohibited. If prohibited, go to step 71. If not prohibited, call the optimization strategy stored in the cloud storage, send a strategy update request to the edge computing gateway, and after the response, send the corresponding strategy to the edge computing gateway, and then go to step 71.

[0029] Step 75: If the cloud server receives the alarm information sent by the edge computing gateway, it calls the corresponding processing module to perform data analysis, infers other chain problems that may occur, updates the control strategy and sends it to the edge computing gateway, and then proceeds to step 71;

[0030] Step 76: If the cloud server receives a request for assisting computing sent by the edge computing gateway, it requests to obtain the corresponding data and tasks of the computing task from the corresponding edge computing gateway, performs task calculations in combination with the data in the cloud database, and sends the calculation results to the edge computing gateway. At the same time, it re-performs the control strategy optimization calculation based on the task, stores the optimization results in the cloud storage, and then proceeds to step 71.

[0031] As a preferred technical solution of a method for updating a control strategy of an energy storage power station based on cloud-edge collaboration technology, the edge gateway collects data including: preprocessing the collected data, including:

[0032] Data cleaning, filtering out outliers and noise data;

[0033] Anomaly detection, using statistical methods or machine learning algorithms to detect possible failures or abnormal conditions;

[0034] Feature extraction: extract key feature parameters that affect the control strategy.

[0035] As a preferred technical solution for a method for updating a control strategy of an energy storage power station based on cloud-edge collaboration technology, the method for determining the type of task currently being executed includes:

[0036] The edge computing gateway evaluates the task request and calculates the task priority P.

[0037] The calculation formula of task priority P is:

[0038] P=α·C+β·R-γ·L-δ·U

[0039] in:

[0040] C: task complexity, R: real-time requirement, L: network delay, U: edge computing resource utilization, α, β, γ, δ: weight coefficients.

[0041] As a preferred technical solution for the control strategy update method of energy storage power station based on cloud-edge collaborative technology, tasks are assigned to the edge or cloud for collaborative processing according to the task priority P, and P0 is defined as the priority threshold;

[0042] Edge processing: For tasks with P>P0, the edge computing gateway processes locally, updates the control strategy and applies it;

[0043] Cloud collaborative processing: For tasks with P≤P0, the edge computing gateway initiates a request for assistance to the cloud. The cloud returns the result after processing, and the edge computing gateway updates the control strategy and applies it.

[0044] As a preferred technical solution for the control strategy update method of energy storage power stations based on cloud-edge collaborative technology, the edge processing flow includes:

[0045] Call local algorithm modules, such as simple threshold judgment, rule judgment, etc.;

[0046] Update control strategies, such as adjusting charging and discharging power, switching operating modes, etc.;

[0047] Send control instructions to the energy storage power station execution equipment.

[0048] As an optimal technical solution for the control strategy update method of energy storage power stations based on cloud-edge collaborative technology, the cloud-side collaborative processing process includes:

[0049] The edge computing gateway packages the task request and related data, encrypts them and sends them to the cloud server;

[0050] After receiving the request, the cloud server calls on high-performance computing resources and complex algorithms for processing;

[0051] The processing results are encrypted and returned to the edge computing gateway;

[0052] The edge computing gateway receives the results, updates the control strategy, and sends it to the energy storage power station execution device

[0053] Beneficial effects of the invention: This patent designs a two-way update method for control strategies. Both the edge control gateway and the cloud server can actively initiate policy updates. Compared with the previous one-way policy update method in which the cloud-edge collaborative strategy is mainly based on the edge side or the cloud side, the method of this patent has more applicability. It can respond better to both the high real-time requirements and the high complexity requirements on the edge side. At the same time, the policy update on the cloud side does not have to rely on the request on the edge side, and the optimization strategy can be sent to the edge side more timely. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0055] Figure 1 This is a schematic diagram of the edge computing gateway processing flow of the present invention;

[0056] Figure 2 This is a schematic diagram of the cloud server processing flow of the present invention;

[0057] Figure 3 The present invention is a schematic diagram of the implementation process of the method. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0062] Example 1

[0063] Reference Figure 1 This embodiment provides a method and system for updating the control strategy of an energy storage power station based on cloud-edge collaboration to improve the deficiencies in the existing system. The specific work is as follows:

[0064] The present invention provides a method for updating the control strategy of an energy storage power station based on cloud-edge collaboration, and the method adopts a method in which both the cloud and the edge can actively initiate a request for updating the strategy. First, for the edge, the edge computing gateway is used to collect and analyze the real-time needs of the energy storage power station, and the tasks are processed separately according to the complexity of the task and the real-time requirements. For simple tasks and tasks with high real-time requirements, the edge computing gateway completes the processing locally; for tasks with high complexity, an assist processing request is actively initiated, and cloud resources are called to jointly complete the task processing and local control strategy update. Secondly, for the cloud, you can wait for the assist processing request of the edge gateway, or you can collect the key data of the energy storage power station stored by the edge computing gateway through timed polling and intelligent reasoning algorithms. If necessary, you can remotely request the real-time data of a specific gateway, and obtain the optimization results of different edge gateway control strategies through big data analysis and artificial intelligence algorithms. When the current strategy is compared and it is found that it really needs to be updated, a control strategy update request is actively issued, and the strategy update is realized after the edge computing gateway responds.

[0065] The present invention develops an edge computing gateway with computing capabilities, control strategy execution capabilities, and data interaction capabilities with cloud servers, which can realize data collection, data storage, and data analysis capabilities. The edge computing gateway is used to collect energy storage power station data, and after analysis, the control strategy is updated and implemented to meet the real-time requirements of the system. At the same time, key data is stored waiting for cloud requests to call. If it is found that the edge computing capability is insufficient, the cloud can also be actively requested to assist in computing. The developed gateway can fully meet the real-time control requirements of the energy storage power station, save cloud computing resources and storage resources, and reduce the amount of data transmitted between the cloud and the edge. At the same time, when there are complex computing requirements, the cloud can be requested to assist in computing in a timely manner to meet the control optimization requirements.

[0066] Through the collaboration of cloud servers and edge computing gateways, the control strategy of the energy storage power station can be updated in a timely manner without affecting some control requirements with higher real-time requirements. For complex computing requirements, cloud resources can also be used to optimize the calculation. It can be applicable to the control strategy update of most energy storage sites. The cost on the edge side is on par with the existing cloud-edge collaborative system, which can reduce the configuration of cloud resources to a certain extent.

[0067] A method for updating the control strategy of an energy storage power station based on cloud-edge collaboration is adopted in which both the cloud and the edge can actively initiate a request for updating the strategy. First, for the edge, the edge computing gateway is used to collect and analyze the real-time needs of the energy storage power station, and the tasks are processed separately according to the complexity and real-time requirements of the tasks. For simple tasks and tasks with high real-time requirements, the edge computing gateway processes them locally; for tasks with high complexity, an assist processing request is actively initiated, and cloud resources are called to jointly complete the task processing and local control strategy updates.

[0068] Secondly, for the cloud, you can wait for the edge gateway to assist in processing the request, or you can collect key data of the energy storage power station stored in the edge computing gateway through timed polling and intelligent reasoning algorithms. If necessary, you can remotely request real-time data from a specific gateway, and obtain optimization results of different edge gateway control strategies through big data analysis and artificial intelligence algorithms. When the current strategy is compared and it is found that it does need to be updated, it will actively issue a control strategy update request and wait for the edge computing gateway to respond before implementing the strategy update.

[0069] In the above technical solution, further, after the edge computing gateway collects the energy storage power station data and task requests, it stores the key data in the edge computing gateway memory, and calls the edge computing gateway data processing program to perform data analysis and task processing, and immediately responds to tasks with high real-time requirements. After the data processing is completed, the task complexity is evaluated, different processing methods are selected according to the evaluation results, and the control strategy update is gradually completed.

[0070] When the energy storage power station equipment or BMS, EMS, etc. generates an alarm or a task that requires computing and processing, it is sent to the edge computing gateway through communication, or the edge computing gateway periodically queries the energy storage power station operation data;

[0071] The edge gateway collects data including: preprocessing the collected data, including:

[0072] Data cleaning, filtering out outliers and noise data;

[0073] Anomaly detection, using statistical methods or machine learning algorithms to detect possible failures or abnormal conditions;

[0074] Feature extraction: extract key feature parameters that affect the control strategy.

[0075] The determining of the type of task currently being executed includes:

[0076] The edge computing gateway evaluates the task request and calculates the task priority P.

[0077] Task sources include:

[0078] 1. Warning information: such as battery over-temperature, over-voltage, etc., which need to be handled immediately.

[0079] 2. Scheduled tasks: such as regular optimization of control strategies.

[0080] 3. Request task: computing task actively requested by the energy storage power station.

[0081] The calculation formula of task priority P is:

[0082] P=α·C+β·R-γ·L-δ·U

[0083] in:

[0084] C: task complexity, R: real-time requirement, L: network delay, U: edge computing resource utilization, α, β, γ, δ: weight coefficients.

[0085] Furthermore, C (Complexity) is the complexity of a task. Complex tasks have high C values, while simple tasks have low C values.

[0086] R (Real-time demand): According to the response time requirements of the task, the task with high real-time requirements has a high RRR value.

[0087] L (Latency): Current network latency. The higher the latency, the higher the LLL value.

[0088] U (Utilization): The utilization rate of the CPU, memory and other resources of the edge computing gateway. The higher the utilization rate, the higher the U value.

[0089] α, β, γ, δ: weight coefficients set according to actual needs.

[0090] P0: Task priority threshold, which determines the task processing position.

[0091] According to the task priority P, the task is assigned to the edge or cloud for collaborative processing.

[0092] Edge processing: For tasks with P>P0, the edge computing gateway processes locally, updates the control strategy and applies it;

[0093] Cloud collaborative processing: For tasks with P≤P0, the edge computing gateway initiates a request for assistance to the cloud. The cloud returns the result after processing, and the edge computing gateway updates the control strategy and applies it.

[0094] The edge processing flow is to call the local algorithm module, such as simple threshold judgment, rule judgment, etc.

[0095] Update control strategies, such as adjusting charging and discharging power, switching operating modes, etc.

[0096] Send control instructions to the energy storage power station execution equipment.

[0097] The cloud collaborative processing process is:

[0098] The edge computing gateway packages the task request and related data, encrypts them and sends them to the cloud server.

[0099] After receiving the request, the cloud server calls on high-performance computing resources and complex algorithms (such as big data analysis and artificial intelligence algorithms) for processing.

[0100] The processing results are encrypted and returned to the edge computing gateway.

[0101] The edge computing gateway receives the results, updates the control strategy, and sends it to the energy storage power station execution equipment.

[0102] Control policy update module for policy storage: The edge computing gateway maintains the latest control policy library.

[0103] Strategy application: Generate specific control instructions based on the updated control strategy.

[0104] The execution device includes receiving control instructions: the execution device of the energy storage power station (such as the battery management system BMS, energy management system EMS) receives instructions; executes operations, and performs charging and discharging control, mode switching and other operations according to the instructions.

[0105] The edge computing gateway can upload data and provide exception feedback. Data upload is to synchronize key operating data, control strategy updates, task processing results, etc. to the cloud server. Exception feedback can notify the cloud in time when an exception or alarm occurs.

[0106] Cloud servers can perform data storage and analysis, global optimization, encrypted communication, identity authentication, and permission control. Specifically, data storage and analysis: store and analyze the received data to form a global operating situation.

[0107] Global optimization: Optimize control strategies based on global data and send new strategies to the edge computing gateway when necessary.

[0108] Encrypted communication: Data transmission between the edge computing gateway and the cloud server uses encryption protocols such as SSL / TLS.

[0109] Identity authentication: Use digital certificates, tokens, etc. to ensure the legitimacy of the identities of both communicating parties.

[0110] Permission control: Set different operation permissions to prevent unauthorized access and operation.

[0111] The edge computing gateway includes a data acquisition module: responsible for real-time acquisition of the operating data of the energy storage power station, a data processing module: data preprocessing and task evaluation, a task allocation module: determines the task processing location based on task priority, a local algorithm module: performs local task processing, such as simple calculations and policy updates, and a communication module: securely communicates with the cloud server.

[0112] The cloud server includes: a task processing module, which receives and processes the assistance request of the edge computing gateway.

[0113] The data analysis module analyzes global data and optimizes control strategies.

[0114] The policy delivery module delivers the new control policy to the edge computing gateway.

[0115] The security module is responsible for security measures such as data encryption and identity authentication.

[0116] The energy storage power station execution equipment includes: a control interface that receives and executes control instructions issued by the edge computing gateway, and a feedback module that feeds back the execution status and operating status to the edge computing gateway.

[0117] The connection relationship between each step is:

[0118] Data collection and preprocessing are the starting point of the entire process, providing the data required for subsequent task evaluation.

[0119] Task evaluation and classification determines how the task should be handled based on the collected data.

[0120] Task allocation and processing: Based on the evaluation results, tasks are allocated to the edge or cloud for processing.

[0121] Control strategy updating and execution are the application results of task processing, which directly affect the operation of energy storage power stations.

[0122] Data synchronization and feedback ensure the consistency of information between the cloud and the edge, providing data support for global optimization.

[0123] Security measures are implemented throughout the entire process to ensure the safety of data and controls.

[0124] Furthermore, the cloud-edge collaborative system has the following key points:

[0125] a: Cloud servers and edge computing gateways have multiple communication and data interaction methods. In scenarios with low real-time requirements, data can be exchanged by message push. When data exchange is urgent or the amount of data to be transmitted is large, data exchange can be quickly carried out by establishing a reliable Ethernet connection. All information and data transmission are encrypted to prevent data leakage during the process.

[0126] b: The computing power and architecture of the edge computing gateway have a certain impact on the implementation of the control strategy update method described in this patent, but the task classification uses local edge and cloud-assisted processing, the edge side prioritizes responding to alarm information, and the edge side automatically updates the control strategy without relying on the cloud, which are all control methods that do not rely on hardware.

[0127] c: This patent does not include energy storage power stations in the method of updating cloud-edge collaborative control strategies. That is, this method has general applicability, but the energy storage power station needs to have basic communication capabilities, data upload capabilities, and the ability to receive control instructions. If not, the control strategy update method described in this patent is not applicable to the power station.

[0128] The present invention has the following advantages:

[0129] This patent designs a two-way update method for control strategies. Both the edge control gateway and the cloud server can actively initiate policy updates. Compared with the previous one-way policy update method in which the cloud-edge collaborative strategy is mainly based on the edge side or the cloud side, the method of this patent has more applicability. It can respond better to both the high real-time requirements and the high complexity requirements on the edge side. At the same time, the policy updates on the cloud side do not have to rely on the requests on the edge side, and the optimization strategies can be sent to the edge side more timely.

[0130] This patent adopts a peer-to-peer design between the cloud and the edge, and does not tilt the main computing demand to either party. The purpose of increasing computing power can be achieved by expanding the hardware resources of either party. Through this peer-to-peer design, the computing resources of either party will not be wasted. When the hardware resources of either party increase, the computing amount of both parties can be adjusted by adjusting the discrimination level of the request for assistance in computing, so as to achieve the purpose of making full use of computing resources.

[0131] This patent is different from other patents in that the alarm information generated by the energy storage power station will be processed in a targeted manner at the edge computing gateway. Not only will targeted protection strategies be issued based on the alarm information, but the information will also be uploaded to the cloud to make inferences and analysis on the greater impact that the alarm may have, and to promptly control the occurrence of other chain failures.

[0132] Application Examples

[0133] Application scenario: A 100MW wind farm in a coastal area equipped with a 50MWh energy storage system

[0134] In a coastal area, there is a wind farm with an installed capacity of 100MW. Due to the volatility and uncontrollability of wind power generation, in order to smooth the output power, improve the quality of power, and meet the requirements of grid dispatch, the wind farm is equipped with a 50MWh lithium battery energy storage system.

[0135] The energy storage system needs to dynamically adjust the charging and discharging strategy based on factors such as real-time wind power generation, grid load demand, and electricity price changes to achieve optimization of economic benefits and safe operation.

[0136] The process operation of applying this technical solution:

[0137] 1. Data collection and preprocessing

[0138] 1.1 Data Collection of Edge Computing Gateway

[0139] Wind power data:

[0140] Instantaneous power generation P wind :Real-time monitoring of wind turbine output power, ranging from 0-100MW.

[0141] Wind speed V wind : Obtained through wind speed sensor, range 0-25m / s.

[0142] Energy storage system data:

[0143] Battery voltage U battery : Total voltage range 1000-1500V.

[0144] Battery pack current I battery:Charge and discharge current range -500A to +500A (negative value is discharge, positive value is charge).

[0145] Battery pack temperature T battery : Range 15℃-45℃.

[0146] State of charge SoC: range 0%-100%.

[0147] State of Health SoH: Range 80%-100%.

[0148] Grid parameters:

[0149] Grid frequency f grid : 50Hz±0.1Hz.

[0150] Grid voltage U grid : 10kV, ±5% fluctuation is allowed.

[0151] Electricity price information Price: Real-time electricity price, in yuan / kWh, with obvious differences in peak and valley electricity prices.

[0152] Environmental data: Ambient temperature T env : Range -10℃ to 40℃.

[0153] Humidity env : Range 20% - 80%.

[0154] 1.2 Data Preprocessing

[0155] Data cleaning:

[0156] Remove outliers caused by sensor failures, such as data where voltage and current are outside the physically possible range.

[0157] Battery temperature abnormality detection: When T battery When >40℃, it is judged as too high temperature.

[0158] Feature extraction: power fluctuation rate ΔP wind :Calculate the rate of change of wind power generation per unit time, which is used to evaluate the volatility of wind power.

[0159] Available Capacity C available : Calculate the current chargeable or dischargeable amount based on SoC.

[0160] 2. Task evaluation and classification

[0161] 2.1 Task Identification

[0162] Task 1: It is detected that the wind power generation power fluctuates greatly and the energy storage system is required to smooth the power.

[0163] Task 2: Detect the battery pack temperature T battery=42℃, exceeding the warning value and requiring immediate treatment.

[0164] 2.2 Task Evaluation

[0165] For Task 1:

[0166] Task complexity C 1 :It needs to calculate the power smoothing algorithm, involving short-term power prediction, with medium complexity, setting C 1 =3.

[0167] Real-time requirements 1 :There is a certain requirement for real-time performance, but a delay of several seconds is allowed. Set R 1 =2.

[0168] Network delay L: The current network delay is 100ms, L=0.1s.

[0169] Resource utilization U: The current CPU utilization of the edge computing gateway is 40%, U = 40%.

[0170] Weight coefficient: α=0.4, β=0.3, γ=0.2, δ=0.1.

[0171] Calculate the task priority P 1 :P 1 =0.4×3+0.3×2-0.2×0.1-0.1×40=1.2+0.6-0.02-4=-2.22.

[0172] For Task 2:

[0173] Task complexity C 2 , alarm processing, simple logic, set C 2 , real-time requirements R 2 , needs to be processed immediately, high real-time performance, set R 2 =5.

[0174] Calculate the task priority P 2 :

[0175] P 2 =0.4×1+0.3×5-0.2×0.1-0.1×40=0.4+1.5-0.02-4=-2.12.

[0176] 2.3 Task Allocation

[0177] Threshold P0=0.

[0178] The priorities of Task 1 and Task 2 are both lower than P0, but since Task 2 has extremely high real-time requirements, the system sets rules for local processing of urgent tasks, so Task 2 is processed at the edge.

[0179] Task 1 is more complex and has high resource utilization of the edge computing gateway, so we choose to request cloud assistance.

[0180] 3. Task allocation and processing

[0181] 3.1 Edge processing (task 2)

[0182] Execute immediately:

[0183] Trigger cooling measures: start the battery cooling system and increase the fan speed.

[0184] Limit charge and discharge power: reduce the charge and discharge current of the battery to reduce heat.

[0185] Alarm notification: Send alarm text messages and emails to operation and maintenance personnel.

[0186] Control strategy update: adjust battery operating parameters and dynamically adjust the operating parameters of the battery pack according to temperature conditions to prevent the temperature from continuing to rise.

[0187] Data synchronization: Upload alarm information and processing measures to the cloud server in real time.

[0188] 3.2 Cloud Collaborative Processing (Task 1)

[0189] The edge computing gateway requests cloud assistance: package relevant data, including wind power curve P wind (t), available capacity C available , power grid load forecast, etc., and send them to the cloud after encryption.

[0190] Short-term wind power forecast: Use time series analysis or machine learning algorithms to predict wind power for the next 15 minutes.

[0191] Make a charge and discharge plan: battery (t)=-(P wind (t)-P target )

[0192] in:

[0193] P battery (t): The charging and discharging power of the energy storage system at time ttt (positive value means discharging, negative value means charging).

[0194] P target : Target smoothed output power.

[0195] Constraints:

[0196] Battery power limit: |P battery (t)|≤P battery，max .

[0197] Battery State of Charge Limits: SoC min ≤SoC(t)≤SoC max .

[0198] The optimization objective consists in minimizing the power fluctuation rate:

[0199]

[0200] Generate optimized control strategies.

[0201] The cloud returns the result: the calculated control strategy is encrypted and sent to the edge computing gateway.

[0202] 3.3 Edge Computing Gateway Application Cloud Strategy

[0203] Receive and verify the control policy returned by the cloud.

[0204] Update the control strategy library and save the new strategy.

[0205] Issue control instructions: Issue new charging and discharging plans to the energy management system (EMS) of the energy storage system.

[0206] Energy storage system execution: EMS receives instructions and adjusts the battery's charge and discharge power according to the plan.

[0207] 4. Control strategy update and execution

[0208] 4.1 Control strategy update of edge computing gateway

[0209] Policy storage: Save the latest control policy locally for subsequent use.

[0210] Strategy execution: Monitor the execution of the energy storage system to ensure the effective implementation of the strategy.

[0211] 4.2 Execution of energy storage system

[0212] Charge and discharge control: According to the control instructions, the charge and discharge power of the battery pack is adjusted to achieve smoothing of wind power.

[0213] Status monitoring: Real-time monitoring of the battery's operating status to ensure operation within a safe range.

[0214] 5. Data synchronization and feedback

[0215] 5.1 Data upload to edge computing gateway

[0216] Operation data: Regularly upload the operation data of the energy storage system, including battery status, execution effect, etc.

[0217] Event log: upload task processing results and logs for cloud analysis.

[0218] 5.2 Analysis and Optimization of Cloud Servers

[0219] Data analysis: Conduct big data analysis on data from multiple wind farms and energy storage systems to identify common problems.

[0220] Strategy optimization: Based on the analysis results, optimize the control algorithm to improve the overall operating efficiency.

[0221] Policy delivery: The optimized policy is delivered to the relevant edge computing gateway.

[0222] 6. Security measures

[0223] 6.1 Data Encryption

[0224] Transmission encryption: Data communication between the edge computing gateway and the cloud server is encrypted using the TLS1.3 protocol to prevent data from being stolen or tampered with.

[0225] 6.2 Identity Authentication

[0226] Two-way authentication: The edge computing gateway and cloud server use digital certificates for two-way authentication to ensure the legitimacy of both communicating parties.

[0227] 6.3 Permission Control

[0228] Access control: A role-based access control (RBAC) mechanism is used to ensure that different users and devices have different permissions.

[0229] 7. Effect verification

[0230] 7.1 Real-time response

[0231] Temperature anomaly handling: After detecting that the battery temperature is too high, the edge computing gateway immediately takes cooling and power limiting measures, so that the temperature drops to a safe range (below 40°C) within 10 minutes.

[0232] Avoid accidents: Through timely processing, thermal runaway and safety accidents caused by battery overheating can be prevented.

[0233] 7.2 Strategy Optimization

[0234] Power smoothing effect: Under the guidance of cloud optimization strategies, the energy storage system successfully smoothed the fluctuations of wind power, reducing the fluctuation rate of output power by 50%.

[0235] Grid adaptability: The smoothed power curve meets the requirements of grid dispatching and improves the power quality.

[0236] 7.3 Economic Benefits

[0237] Electricity price arbitrage: Based on real-time electricity price information, the energy storage system charges during low electricity price periods and discharges during high electricity price periods, increasing operating income.

[0238] Extended equipment life: Through reasonable charging and discharging strategies, over-charging and discharging of batteries are avoided, thus extending the battery life.

[0239] 7.4 System Stability

[0240] Data reliability: Multiple security measures are adopted to ensure the integrity and reliability of data, and there is no data loss or tampering.

[0241] Collaboration efficiency: The cloud-edge collaborative working mechanism is efficient, task allocation is reasonable, and the resource utilization of edge computing gateways and cloud servers is optimized.

[0242] Through the above practical applications, it can be seen that this technical solution has significant advantages in updating the control strategy of energy storage power stations:

[0243] Real-time guarantee: For emergency tasks, the edge computing gateway can respond quickly to ensure system security.

[0244] Resource optimization: Through task evaluation, computing tasks on the edge and cloud are reasonably allocated, improving overall resource utilization.

[0245] Strategy effectiveness: Cloud servers use powerful computing power and global data to provide high-quality control strategies.

[0246] Safe and reliable: Complete safety measures ensure the stable operation of the system.

[0247] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0248] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for updating a control strategy of an energy storage power station based on cloud-edge collaboration technology, characterized in that: include, Step 1: When the energy storage power station equipment or BMS, EMS, etc. generates an alarm or a task that requires computing and processing, it is sent to the edge computing gateway through communication, or the edge computing gateway periodically queries the energy storage power station operation data; Step 2: When the edge computing gateway collects data or receives alarm information or task processing requests actively sent by the energy storage power station, it starts calling the data processing program; Step 3: The edge computing gateway data processing program determines the type of task currently being executed. If it is an alarm message, it proceeds to step 4. If it is a computing task actively requested by the energy storage power station, it proceeds to step 7. If the edge gateway obtains data regularly, it proceeds to step 11. Step 4: The edge computing gateway immediately performs alarm information analysis and processing, executes the corresponding control strategy, sends control commands to the energy storage power station, and sends alarm information to the cloud platform; Step 5: The cloud platform receives the alarm information, calls the corresponding processing module to perform data analysis, infers other chain problems that may occur, updates the control strategy and sends it to the edge computing gateway; Step 6: The edge computing gateway receives the control strategy update request from the cloud platform, updates the control strategy at an appropriate time, and re-issues the control command to the energy storage power station, and then proceeds to step 12; Step 7: The edge computing gateway immediately performs the computing task analysis and processing procedure to evaluate the complexity of the computing task. For low-complexity tasks, the edge computing gateway computing power is called for processing, and the results are sent to the energy storage power station, and then the process goes to step 12; Step 8: For highly complex tasks, request the cloud to assist in computing; Step 9: The cloud platform receives the request for assisting computing, calls the corresponding processing module, requests the edge computing gateway to transmit the required computing data, waits for the edge computing gateway to complete the transmission, performs the calculation, and sends the calculation results to the edge computing gateway; Step 10: The edge computing gateway receives the calculation results sent by the cloud platform, updates its own control strategy, and sends the calculation results to the energy storage power station, and then proceeds to step 12; Step 11: The edge computing gateway processes the acquired data according to the current control strategy, updates the control command, sends the control command to the energy storage power station, and then proceeds to step 12; Step 12: The edge computing gateway updates the key data of the energy storage power station to its own storage, waiting for the cloud to obtain data or uploading it at a scheduled time.

2. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 1 is characterized in that: Cloud servers assist in computing including: Step 71: The cloud server periodically obtains key data of the energy storage site connected to the edge computing gateway from the edge computing gateway, including remote signaling, telemetry, remote control, and remote adjustment data of each device, metering equipment data, protection device data, line load data, line voltage, current, frequency, power factor and other electrical data, power station revenue-related electricity price data, time period data, and stores them in the cloud database waiting for computing task calls; Step 72: The cloud server executes the strategy optimization calculation task according to the set period, performs the optimization calculation of each site control strategy, and stores the optimization result in the cloud storage, waiting to be sent to the edge computing gateway; Step 73: If a user performs a remote policy update operation on the cloud server, if the optimization result is used, the optimization policy stored in the cloud storage is called; if the control policy set by the user is used, the user's setting policy is called, and a policy update request is sent to the edge computing gateway. After the response, the corresponding policy is sent to the edge computing gateway, and then the process goes to step 71.

3. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 2 is characterized in that: Cloud server-assisted computing also includes: Step 74: If the cloud server reaches the timing period for the scheduled remote update strategy for a specific energy storage site, first check whether the user has set the automatic remote optimization function to be disabled. If it is disabled, proceed to step 71. If it is not disabled, call the optimization strategy stored in the cloud storage, send a strategy update request to the edge computing gateway, send the corresponding strategy to the edge computing gateway after the response, and then proceed to step 71. Step 75: If the cloud server receives the alarm information sent by the edge computing gateway, it calls the corresponding processing module to perform data analysis, infers other chain problems that may occur, updates the control strategy and sends it to the edge computing gateway, and then proceeds to step 71; Step 76: If the cloud server receives a request for assisting computing sent by the edge computing gateway, it requests to obtain the corresponding data and tasks of the computing task from the corresponding edge computing gateway, performs task calculations in combination with the data in the cloud database, and sends the calculation results to the edge computing gateway. At the same time, it re-performs the control strategy optimization calculation based on the task, stores the optimization results in the cloud storage, and then proceeds to step 71.

4. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 3 is characterized in that: The edge gateway collects data including: preprocessing the collected data, including: Data cleaning, filtering out outliers and noise data; Anomaly detection, using statistical methods or machine learning algorithms to detect possible failures or abnormal conditions; Feature extraction: extract key feature parameters that affect the control strategy.

5. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 4 is characterized in that: The determining of the type of task currently being executed includes: The edge computing gateway evaluates the task request and calculates the task priority P. The calculation formula of task priority P is: P=α·C+β·R-γ·L-δ·U in: C: task complexity, R: real-time requirement, L: network delay, U: edge computing resource utilization, α, β, γ, δ: weight coefficients.

6. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 5 is characterized in that: According to the task priority P, the task is assigned to the edge or cloud for collaborative processing, and P0 is defined as the priority threshold; Edge processing: For tasks with P>P0, the edge computing gateway processes locally, updates the control strategy and applies it; Cloud collaborative processing: For tasks with P≤P0, the edge computing gateway initiates a request for assistance to the cloud. The cloud returns the result after processing, and the edge computing gateway updates the control strategy and applies it.

7. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 6 is characterized in that: The edge processing flow includes: Call local algorithm modules, such as simple threshold judgment, rule judgment, etc.; Update control strategies, such as adjusting charging and discharging power, switching operating modes, etc.; Send control instructions to the energy storage power station execution equipment.

8. The method for updating the control strategy of an energy storage power station based on cloud-edge collaboration technology according to claim 7 is characterized in that: The cloud collaborative processing process includes: The edge computing gateway packages the task request and related data, encrypts them and sends them to the cloud server; After receiving the request, the cloud server calls on high-performance computing resources and complex algorithms for processing; The processing results are encrypted and returned to the edge computing gateway; The edge computing gateway receives the results, updates the control strategy, and sends it to the energy storage power station execution equipment.

Images