A data management system for an energy storage power plant

Abstract

The application provides a data management system of an energy storage power station, which can collect data of energy storage power station equipment through an edge network device, and perform analysis, map data in a communication protocol and a data point table to a corresponding object model, and publish the data as a communication topic, and send corresponding data according to a communication topic subscribed by a cloud server; the cloud server maintains a standard object model of the energy storage power station equipment; and the energy storage power station equipment is registered, and the energy storage power station equipment is bound to the corresponding standard object model; after the energy storage power station equipment is online, the corresponding communication topic of the energy storage power station equipment is subscribed, and data and control instructions are received and sent through the communication topic; the received data is stored, and a data service access interface is provided to an upper layer application, so that standardized management of energy storage power station data and real-time response of equipment abnormalities are realized, data access complexity of the energy storage power station equipment is reduced, development efficiency of corresponding data management software is improved, and a standardized data cornerstone is provided for the upper layer application.

Description

Technical Field

[0001] This invention relates to the field of energy storage data management, and in particular to a data management system for an energy storage power station. Background Technology

[0002] To achieve safe, economical, efficient, and reliable operation of energy storage power stations, unified data management is essential. Currently, energy storage power stations consist of various devices such as battery stacks, battery clusters, power conversion systems (PCS), temperature control systems, and fire protection systems. These devices are inherently heterogeneous and use diverse communication protocols. Furthermore, the use of proprietary, non-open communication protocols and data formats by different suppliers for each type of device exacerbates the difficulty of standardizing data management in energy storage power stations.

[0003] Therefore, a technical solution is needed for standardized management and anomaly response of equipment data in energy storage power stations. Summary of the Invention

[0004] One objective of this application is to provide a data management system for energy storage power stations, which addresses the problems of difficulty in standardizing the management of data from various devices in energy storage power stations under existing technologies, and the difficulty in responding to abnormal states of energy storage power stations based on real-time data.

[0005] To achieve the above objectives, some embodiments of this application provide a data management system for an energy storage power station. This system includes an edge network device and a cloud server. The edge network device collects data from energy storage power station devices, parses data from different energy storage power station devices, maps communication protocols and data point tables to the corresponding physical models of the energy storage power station devices, publishes the physical models as communication topics, and sends corresponding data according to the communication topics subscribed to by the cloud server. The cloud server maintains standard physical models of the energy storage power station devices, registers the energy storage power station devices, binds the energy storage power station devices to their corresponding standard physical models, subscribes to the communication topics corresponding to the energy storage power station devices after they go online, receives data and sends control commands through the communication topics, stores the received data, and provides data service access interfaces to upper-layer applications.

[0006] Furthermore, edge network devices are used to collect data from energy storage power station equipment, preprocess the acquired data, and upload the processing results to the cloud server through a preset communication topic upload strategy.

[0007] Furthermore, the upload strategies for communication topics include triggered upload, fixed-period upload, and variable-frequency upload.

[0008] Furthermore, the object model includes attributes, events, and functions. Attributes are used to describe the status data of the energy storage power station equipment, events are used to describe the alarm-related data of the energy storage power station equipment, and functions are used to describe the control functions of the energy storage power station equipment called by the cloud server.

[0009] Furthermore, the communication topic includes message type and message body. Message type includes uplink and downlink. Uplink message type corresponds to the attributes and events of the object model, and downlink message type corresponds to the functions of the object model. Message body includes device type, device number, timestamp, verification status, and the attributes, events, and functions of the object model.

[0010] Furthermore, it is also used for:

[0011] Edge network devices collect alarm events from the first energy storage power station equipment, convert the alarm events into standard alarm events corresponding to the physical model of the first energy storage power station equipment, and upload the standard alarm events to the cloud server through the corresponding event communication topic according to the preset communication topic upload strategy.

[0012] After receiving a standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event.

[0013] The cloud server sends control commands to the third energy storage power station equipment through functional communication topics based on the risk level and preset security policies.

[0014] The cloud server obtains feedback attributes through the attribute communication topic corresponding to the third energy storage power station equipment and determines the response result of the standard alarm event.

[0015] Furthermore, the cloud server is used to store the attributes and alarm events of the first energy storage power station equipment, the attributes and control commands of the second energy storage power station equipment, and the feedback attributes of the third energy storage power station equipment.

[0016] Furthermore, energy storage power station equipment includes: energy storage equipment, thermal management equipment, fire protection equipment, and other equipment; energy storage equipment includes battery equipment and energy storage converter equipment, battery equipment includes battery stacks, battery clusters, and battery cells, and energy storage converter equipment includes main control modules, valve control modules, and energy storage converter modules; thermal management equipment includes air-cooled equipment and liquid-cooled equipment; fire protection equipment includes sensing devices, fire protection devices, alarm devices, and alarm button devices; other equipment includes switching devices, I / O devices, and meter devices.

[0017] Furthermore, the first energy storage power station equipment is the battery cell of the battery equipment in the energy storage equipment, the second energy storage power station equipment is the temperature sensor and gas sensor in the fire protection equipment, and the third energy storage power station equipment is the energy storage converter equipment in the energy storage equipment and the fire protection device in the fire protection equipment.

[0018] Furthermore, after receiving a standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event, which is then used for:

[0019] After receiving a cell temperature alarm event, the cloud server obtains the ambient temperature attribute of the temperature sensor and the hydrogen concentration attribute of the gas sensor. When the ambient temperature attribute exceeds a preset temperature threshold and the hydrogen concentration attribute exceeds a preset hydrogen concentration threshold, the risk level corresponding to the cell temperature alarm event is determined to be high risk.

[0020] Compared with existing technologies, the solution provided in this application can collect data from energy storage power station equipment through edge network devices, parse data from different energy storage power station equipment, map communication protocols and data point tables to corresponding object models and publish them as communication topics, and send corresponding data according to the communication topics subscribed to by the cloud server; maintain standard object models of energy storage power station equipment through the cloud server; register energy storage power station equipment and bind it to the corresponding standard object model; after the energy storage power station equipment goes online, subscribe to the communication topics corresponding to the energy storage power station equipment and receive data and send control commands through the communication topics; store the received data and provide data service access interfaces to upper-layer applications, thereby realizing standardized management of energy storage power station data, enabling real-time response to equipment anomalies, reducing the complexity of data access for energy storage power station equipment, improving the development efficiency of corresponding data management software, and providing a standardized data foundation for upper-layer applications. Attached Figure Description

[0021] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0022] Figure 1 This is a structural block diagram of a data management system for an energy storage power station, provided for some embodiments of this application.

[0023] Figure 2 This is a schematic diagram of a data management architecture for an energy storage power station, provided for some embodiments of this application. Detailed Implementation

[0024] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0025] Here, the data management system for energy storage power stations in this application embodiment is suitable for scenarios involving standardized management of equipment data in energy storage power stations and real-time response to abnormal equipment conditions.

[0026] In this scenario, the energy storage power station includes a variety of devices, each using different communication protocols and data formats. At the device data acquisition and access level, a large amount of data adaptation and data parsing work is required. This not only presents problems such as complex data access and the potential for data silos, but also leads to a reduction in the efficiency of corresponding software development.

[0027] The complexity of equipment data access stems from several factors: 1) Equipment heterogeneity: Energy storage power station equipment consists of battery systems, power storage converters (PCS), battery management systems (BMS), temperature control, fire protection, and other equipment from multiple manufacturers; 2) Inconsistent communication protocols: Equipment from different manufacturers may use one or more proprietary protocols such as Modbus, CANopen, IEC 104, OPC UA, etc.; 3) Closed or heterogeneous data point tables: Even when using the same protocol (such as Modbus), different manufacturers have completely different definitions of register addresses and data formats (such as integers, floating-point numbers, and byte order); 4) Lack of a unified data modeling standard: There is no universal language to describe the data and capabilities of energy storage power station equipment, resulting in customized access for each piece of equipment.

[0028] In addition, the updating or upgrading of energy storage power station equipment leads to a lot of repetitive work in the development or upgrading of adapting software, reducing software development efficiency. This is reflected in the following scenarios: 1) Adding an energy storage converter: The energy storage power station originally used PCS from vendor A, but now, due to capacity expansion, it needs to connect to PCS from vendor B. Vendor A uses Modbus. 1) TCP protocol: The data address table is privately defined. Manufacturer B uses the CANopen protocol or another private Modbus mapping. Software developers need to study Manufacturer B's protocol manual, write new parsing code, and modify the data acquisition program. The access logic of Manufacturer A's equipment cannot be reused. 2) Fire protection system upgrade: The original fire protection system only provides simple dry contact signals (switching signals). The upgraded intelligent fire protection system supports communication via Ethernet and provides detailed early warning information, equipment self-test data, etc. The adaptation software needs to change from collecting switching signals to parsing network packets, which means that the entire access architecture and data processing logic need to be redeveloped. 3) Iteration of equipment models from the same manufacturer: The battery management system (BMS) is upgraded from V2.0 to V3.0. Although the communication protocol (such as CAN bus) remains unchanged, the definition, address, or scaling ratio of some signals have changed. This means that the data acquisition end must develop new parsing rules for V3.0 devices, otherwise incorrect data will be collected.

[0029] Therefore, the development of existing data management systems for energy storage power stations suffers from problems such as tightly coupled development, repetitive work, and complex joint debugging and testing, resulting in low software development efficiency. The problem of tightly coupled development lies in the fact that application development (such as monitoring dashboards and data analysis algorithms) heavily relies on the readiness and stability of energy storage power station equipment. If the equipment is not connected or the protocol changes, the upper-layer application cannot be developed or requires rework. The problem of repetitive work is that for each new energy storage power station or new type of energy storage equipment connected, application developers need to redo protocol parsing, data mapping, and application adaptation, resulting in extremely low code reuse. The problem of complex joint debugging and testing is that application developers, equipment providers, and system integrators need to frequently conduct joint debugging to pinpoint whether the problem lies on the device side, the protocol parsing side, or the application side, leading to high communication costs and low debugging efficiency.

[0030] Furthermore, some energy storage power station equipment only provides cluster-level or system-level data of the energy storage device in order to save traffic costs or transmission bandwidth. This makes it difficult to capture the rapid changes in the energy storage device and to use it for high-precision analysis and high-frequency control in upper-level applications.

[0031] In addition, managing data in energy storage power stations presents several challenges: 1) Data in energy storage power stations exhibits strong time-series and state-series characteristics. Due to the strong time-series nature of the data and the need for high-precision estimation of battery states such as SOC and SOH, the requirements for data continuity and quality are extremely high. 2) The data volume is large. The large number of measurement points in energy storage stations results in a massive amount of data. For example, a supercapacitor energy storage power station with tens of MVA has more than 50,000 measurement points, each measuring point has 2 bytes (approximately 100kB), and the data acquisition interval is 1 second, with an estimated monthly flow rate of 260G. 3) High-frequency and multi-scale data coexist. It requires both second-level or even millisecond-level electrical transient data for protection and control, as well as hourly and daily-level slow-varying data for life assessment, making data management extremely complex. 4) The control closed-loop real-time requirements are high. Because data management needs to be tightly coupled with the control system, the delay requirements from data analysis to control command issuance are very strict.

[0032] The data management system for energy storage power stations provided in this application embodiment can collect data from energy storage power station devices through edge network devices, parse data from different energy storage power station devices, map communication protocols and data point tables to corresponding object models and publish them as communication topics, and send corresponding data according to the communication topics subscribed to by the cloud server; maintain standard object models of energy storage power station devices through the cloud server; register energy storage power station devices and bind them to corresponding standard object models; after the energy storage power station devices are online, subscribe to the communication topics corresponding to the energy storage power station devices and receive data and send control commands through the communication topics; store the received data and provide data service access interfaces to upper-layer applications, thereby realizing standardized management of energy storage power station data, enabling real-time response to device anomalies, reducing the complexity of data access for energy storage power station devices, improving the development efficiency of corresponding data management software, and providing a standardized data foundation for upper-layer applications.

[0033] Some embodiments of this application provide a data management system 100 for an energy storage power station, such as... Figure 1 As shown, the system includes an edge network device 101 and a cloud server 102.

[0034] Edge network device 101 is used to collect data from energy storage power station equipment, parse data from different energy storage power station equipment, map communication protocols and data in the data point table to the physical model of the corresponding energy storage power station equipment, publish the physical model as a communication topic, and send corresponding data according to the communication topics subscribed to by the cloud server.

[0035] Cloud server 102 is used to maintain the standard object model of energy storage power station equipment, register the energy storage power station equipment, bind the energy storage power station equipment to the corresponding standard object model, subscribe to the communication topic corresponding to the energy storage power station equipment after the energy storage power station equipment goes online, receive data and send control commands through the communication topic, store the received data, and provide data service access interface to upper layer applications.

[0036] Here, the energy storage power station includes a variety of energy storage power station equipment. In some embodiments of this application, the energy storage power station equipment can be divided into energy storage equipment, thermal management equipment, fire protection equipment and other equipment according to their functions and uses.

[0037] Energy storage devices can include battery devices and energy storage converter devices. Battery devices can include, but are not limited to, battery stacks, battery clusters, and battery cells. Energy storage converter devices can include, but are not limited to, main control modules, valve control modules, and energy storage converter modules.

[0038] Thermal management equipment may include, but is not limited to, air-cooled equipment and liquid-cooled equipment.

[0039] Firefighting equipment may include, but is not limited to, sensing devices, fire-fighting devices, alarm devices, and alarm button devices. Sensing devices may include, for example, temperature sensors, humidity sensors, smoke sensors, gas sensors, and composite sensors.

[0040] Other equipment may include, but is not limited to, switching devices, I / O devices, and metering devices.

[0041] In some embodiments of this application, the edge network device is used to collect data from the energy storage power station equipment, preprocess the acquired data, and upload the processing results to the cloud server through the communication topic according to a preset communication topic upload strategy.

[0042] Here, preprocessing may include, but is not limited to, protocol conversion, data normalization, data statistics, and data judgment. Edge network devices parse and map raw data from different protocols and data point tables onto a predefined standard object model, ensuring that regardless of changes in the energy storage power station equipment, the device data uploaded to the cloud server is uniform, object model-based, and in a standard format. The data format can be determined according to actual needs, such as JSON format.

[0043] Each type of energy storage power station device has a corresponding object model. The object model maps the energy storage power station device into standardized, identifiable, controllable, and interactive data. By establishing object models for the energy storage power station devices, data management of the entire energy storage power station can be achieved. In some embodiments of this application, the object model includes attributes, events, and functions. Attributes describe the status data of the energy storage power station device, events describe alarm-related data, and functions describe the control functions of the energy storage power station device invoked by the cloud server. By defining the list of attributes, functions, and events of the object model, the information contained in the energy storage power station device can be comprehensively described, and the capabilities of the energy storage power station device can be standardized, solving the problem of data fragmentation in energy storage power station devices.

[0044] Specifically, attributes and events refer to the data reported by energy storage power station devices to the cloud server, while functions refer to the control data issued by the cloud server to the energy storage power station devices. Attributes describe the inherent properties and operating status of the energy storage power station devices, and may include, but are not limited to, device type, attribute name, data type, unit, and value range. Events describe alarms and other warnings from the energy storage power station devices, and may include, but are not limited to, device type, event name, data type, and enumeration list. Functions describe the functions that the cloud server can actively invoke from the energy storage power station devices, and may include, but are not limited to, device type, function name, data type, unit, and value range. By establishing a physical model corresponding to the energy storage power station devices, it is possible to integrate the message data from different devices and establish a data format that meets the enterprise's own business requirements, thereby achieving cost reduction.

[0045] The standard object model corresponding to energy storage power station equipment includes a list of standard attributes, events, and functions. If a certain type of energy storage power station equipment has multiple sub-models, to ensure the compatibility of the object model, a standard list of attributes, events, and functions for that type of energy storage power station equipment can be constructed based on the union of the attribute, event, and function lists corresponding to each model. For newly connected energy storage power station equipment, if the standard object model cannot cover it, a corresponding object model for the newly connected energy storage power station equipment is established according to the aforementioned method.

[0046] In addition, if the standard object model can cover all energy storage power station equipment, but the attributes, events, and function lists are inconsistent, only a small amount of work such as custom fine-tuning of attributes, events, and function lists is required, which can easily make it compatible with other energy storage power station equipment. The complexity of the equipment of each energy storage power station is smoothed out, and it also supports developers to carry out application development and equipment research and development in parallel, shortening the research and development cycle, saving costs, and making it easy to complete the adaptation development work, thus improving development efficiency.

[0047] In some embodiments of this application, edge network devices publish unified communication topics. These topics include message types and message bodies. Message types include uplink and downlink. Uplink message types correspond to the attributes and events of the object model, while downlink message types correspond to the functions of the object model. The message body may include, but is not limited to, device type, device number, timestamp, verification status, and the object model's attributes, events, and functions. Here, the object model's attributes, events, and functions can be represented in key-value pair format. A communication topic can be represented, for example, as / device / {productKey} / {deviceName} / property / post. The cloud server only needs to subscribe to these standard topics and does not need to know which specific energy storage power station device the data originates from.

[0048] By using physical models as the data source to connect to the cloud server, regardless of how the underlying energy storage power station equipment is replaced, the data processing applications on the cloud server and the upper-layer applications do not need to be adjusted at any code level, providing a stable data foundation for the entire software system.

[0049] In some embodiments of this application, the upload strategy for communication topics may include, but is not limited to, triggered upload, fixed-period upload, and variable-frequency upload. Triggered upload, for example, may involve uploading immediately upon the occurrence of an alarm event; fixed-period upload may involve uploading the status data of the energy storage power station equipment every 30 seconds; variable-frequency upload may involve uploading data once per minute under normal conditions and once per second under abnormal conditions. By setting different upload strategies for communication topics, the network load can be balanced while ensuring the real-time nature of critical data.

[0050] In some embodiments of this application, edge network devices can collect data from energy storage power station devices at a high frequency, preprocess it locally, and then upload it according to the communication topic upload strategy. This allows for the use of refined data locally while avoiding network pressure caused by uploading all high-frequency data. Here, because energy storage converter devices and battery devices generate rapidly changing data at the millisecond level during grid frequency fluctuations or charging / discharging transitions, if the edge network device's acquisition frequency is set to the second or minute level, these critical transient processes cannot be recorded and cannot be used for accident inversion and advanced control. By setting the acquisition frequency to a high-frequency millisecond level, this data can be obtained, supporting the accident analysis functions of upper-layer applications.

[0051] Furthermore, in some embodiments of this application, the attributes in the physical model corresponding to the battery devices in the energy storage device are defined at the cell level. Here, due to bandwidth constraints or limitations imposed by the battery management system, many battery devices only provide aggregated data at the battery cluster or stack level (such as total voltage and average temperature). However, when an early failure occurs in a cell, its anomaly is masked by the average value and cannot be detected in time, leading to safety hazards. Moreover, high-precision state of health (SOH) analysis and equalization strategy optimization both require cell-level data. By forcing the attributes provided by the battery devices to the cell level (such as cell voltage and cell temperature) through the physical model, more refined data can be provided to upper-layer applications, enabling faster detection of early battery device failures and improving safety.

[0052] In some embodiments of this application, the physical model also defines a variety of non-electrical parameters. If the physical model only focuses on core electrical parameters such as voltage and current, while ignoring non-electrical parameters such as fire-fighting gas cylinder pressure, liquid cooling system pipeline flow rate, and hydrogen concentration inside the chamber, it will lead to a decrease in the safety early warning capability of the energy storage power station and make it difficult to achieve real-time response to faults.

[0053] Therefore, once the object model is defined, it becomes the standard between edge network devices and cloud server applications. Application developers can develop application logic and interfaces based on the standardized object model and test using simulated data. Device developers and integrators can simultaneously connect energy storage power station devices and deploy edge network devices. Both parties only need to ensure that the data conforms to the object model, thus achieving decoupling and parallel development between the front-end and back-end, significantly reducing commissioning time. In addition, the standard object model established for a certain type of energy storage power station device (such as air-cooled temperature control equipment) can be reused by all similar devices in all energy storage power stations. When a new energy storage power station is connected, only simple device identifier mapping is required, without redevelopment, realizing the reusability of the object model. Furthermore, upper-layer applications no longer program to specific energy storage power station device addresses or communication protocol commands, but rather to high-level interfaces such as object model attributes, events, and functions. When the underlying energy storage power station devices are replaced or upgraded, as long as their object model interfaces remain consistent, the application code does not need to be modified, improving software development efficiency.

[0054] The following uses a supercapacitor energy storage power station with tens of MVA as an example to present a data management architecture for such an energy storage power station. Figure 2 As shown.

[0055] The equipment list for this energy storage power station is as follows:

[0056] There are several sets of supercapacitor stacks (i.e. battery stacks). Each supercapacitor stack contains dozens of supercapacitor clusters (i.e. battery clusters), and each supercapacitor cluster contains hundreds of supercapacitor cells (i.e. cells).

[0057] There are several PCS systems (i.e., energy storage converter equipment), each PCS system contains several main control modules, several valve control modules, and dozens of PCS modules.

[0058] Several sets of liquid cooling equipment.

[0059] Several sets of air conditioners (i.e., air-cooled equipment).

[0060] Several sets of fire protection systems (i.e. fire protection equipment).

[0061] Several switching devices.

[0062] Several electricity meter devices.

[0063] All data from the above energy storage power station equipment is collected at the site's EMS (Energy Management System) terminal, and then forwarded by the EMS terminal to the data upload device (the EMS terminal and the data upload device constitute an edge network device), which then uploads the data to the cloud platform (i.e., the cloud server).

[0064] Energy storage power station equipment is classified according to its function and purpose. For multiple energy storage power station devices of the same type, only one standard object model needs to be established. Therefore, the energy storage power station equipment for which an object model needs to be established is:

[0065] Energy storage systems (i.e., energy storage devices) include supercapacitor stacks, supercapacitor clusters, supercapacitor cells, main control, valve control, and PCS modules.

[0066] A thermal management system (i.e., thermal management equipment) includes liquid cooling equipment and air conditioning.

[0067] Fire protection system.

[0068] Other equipment, including switching devices and metering devices.

[0069] There are physical models of four categories and several energy storage power station devices.

[0070] A standard object model is created for each type of energy storage power station equipment. The object model includes attributes, events, and functions. Attributes and events are reported items from the equipment, and functions are controlled items under the cloud platform.

[0071] Define the standard attributes, events, and functions of each energy storage power station device. Attributes correspond to the telemetry signals of the energy storage power station device, events correspond to the remote signaling signals of the energy storage power station device, and functions correspond to the remote adjustment and remote control signals of the energy storage power station device. Table 1 below shows an example of the attributes of an overcapacity stack.

[0072] Table 1

[0073] Define the communication topic of the object model. The communication topic includes the message type and message body. An example of an uplink topic for an overcapacity heap is as follows:

[0074] {

[0075] Message type: "up",

[0076] "Message Body": {

[0077] Device type: "stack"

[0078] "Heap number": 0,

[0079] "Timestamp": 1760685658,

[0080] Device ID: 250324200115

[0081] "Attribute reporting": {

[0082] "Identifier": value,

[0083] "Identifier": value,

[0084] "Identifier": value

[0085] },

[0086] "Incident Reporting": {

[0087] "Event identifier": {

[0088] "Event Parameters": Value,

[0089] "Event timestamp": value

[0090] }

[0091] }

[0092] }

[0093] }

[0094] The upload strategy for the above topics can be set to upload at fixed intervals, such as uploading one frame every 30 seconds.

[0095] The cloud platform (i.e., the cloud server) obtains relevant data by subscribing to topics. Subscribing to topics can be achieved, for example, through software such as Message Queuing Telemetry Transport (MQTT).

[0096] In some embodiments of this application, the cloud server performs centralized data modeling management and energy storage power station equipment lifecycle management. In data modeling management, the cloud server maintains a standard object model library. All defined object models are registered and managed in the standard object model library. The standard object model library supports version control. When the capabilities of energy storage power station equipment are upgraded, a corresponding new version of the object model can be created, and its distribution and compatibility can be managed, thereby achieving centralized and unified governance of the data schema.

[0097] In the lifecycle management of energy storage power station equipment, each energy storage power station device needs to be logically registered on the cloud server before being connected. During registration, a unique device identifier (such as ProductKey and DeviceName) is assigned to the device, and the device is bound to a standard object model. After the energy storage power station equipment is online, the cloud server can monitor the online / offline status of the device in real time through the MQTT protocol heartbeat mechanism or periodic data reporting, completing the full lifecycle management of the device from registration, authentication, status monitoring to disposal.

[0098] In some embodiments of this application, the cloud server persistently stores the received data from the energy storage power station equipment and provides data access services. Regarding data storage, the cloud server stores the attributes and events reported by the energy storage power station equipment into optimized databases. For example, it stores massive amounts of energy storage power station equipment attribute data into a time-series database to meet the needs of efficient writing and fast querying by time range, and stores event data into a database with strong retrieval capabilities to facilitate fault tracing and analysis.

[0099] In terms of data access services, the cloud server provides a unified set of data access service APIs based on the stored data. These APIs provide standard data access interfaces to upper-layer applications (such as monitoring dashboards, data analysis systems, and operation and maintenance management systems). Upper-layer applications do not need to query the database but can obtain the latest status of energy storage power station equipment, query historical data, subscribe to real-time data push, or issue control commands through the corresponding APIs.

[0100] Therefore, the data management system for the energy storage power station in this application embodiment can complete the entire process from data acquisition, standardization, storage to service provisioning of energy storage power station equipment, realizing end-to-end data management. Upper-layer applications are developed based on stable and unified data access service APIs, achieving decoupling from the underlying energy storage power station equipment and enabling the rapid construction of various data applications such as real-time monitoring, intelligent alarms, performance analysis, and predictive maintenance.

[0101] In some embodiments of this application, the data management system of the energy storage power station can automatically respond based on the abnormal state of the energy storage power station equipment, including the following steps:

[0102] 1) Edge network devices collect alarm events from the first energy storage power station equipment, convert the alarm events into standard alarm events corresponding to the physical model of the first energy storage power station equipment, and upload the standard alarm events to the cloud server through the corresponding event communication topic according to the preset communication topic upload strategy;

[0103] 2) After receiving a standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event;

[0104] 3) The cloud server sends control commands to the third energy storage power station equipment through functional communication topics based on the risk level and preset security policies;

[0105] 4) The cloud server obtains feedback attributes through the attribute communication topic corresponding to the third energy storage power station equipment and determines the response result of the standard alarm event.

[0106] In some embodiments of this application, the first energy storage power station equipment is the battery cell of the battery equipment in the energy storage equipment, the second energy storage power station equipment is the temperature sensor and gas sensor in the fire protection equipment, and the third energy storage power station equipment is the energy storage converter equipment in the energy storage equipment and the fire protection device in the fire protection equipment.

[0107] Specifically, after receiving a standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event, and can perform the following steps:

[0108] After receiving a cell temperature alarm event, the cloud server obtains the ambient temperature attribute of the temperature sensor and the hydrogen concentration attribute of the gas sensor. When the ambient temperature attribute exceeds a preset temperature threshold and the hydrogen concentration attribute exceeds a preset hydrogen concentration threshold, the risk level corresponding to the cell temperature alarm event is determined to be high risk level, and the corresponding risk response is executed according to the high risk level.

[0109] In addition, in some embodiments of this application, the cloud server stores the attributes and alarm events of the first energy storage power station device, the attributes of the second energy storage power station device, the control commands, and the feedback attributes of the third energy storage power station device. The stored data can be used for subsequent event review.

[0110] The following will illustrate this through a specific data management scenario.

[0111] In a large electrochemical energy storage power station, a cell in a battery cluster may start to heat up due to an internal short circuit, posing a risk of thermal runaway. The spread of thermal runaway may cause the entire battery compartment to catch fire and explode, resulting in huge safety and economic losses.

[0112] Management Objectives: To achieve early warning, multi-source confirmation, automatic linkage, and fault tracing. Specifically, before an accident occurs, through cross-verification of data from various energy storage power station devices, a series of safety measures are automatically and rapidly implemented, and all process data is recorded for post-accident analysis.

[0113] The energy storage power station equipment involved in the scenario is as follows. Each energy storage power station equipment interacts with the cloud server through a corresponding physical model.

[0114] 1. Battery Management System (BMS)

[0115] Material model properties: individual cell voltage, individual cell temperature, total cluster voltage, cluster current, etc.

[0116] Physical model events: internal short circuit warning, voltage abnormality alarm, temperature difference alarm, etc.

[0117] 2. Temperature sensor (deployed within the battery compartment environment)

[0118] Object model attribute: ambient temperature.

[0119] 3. Gas detector (to monitor hydrogen gas, etc., released during battery thermal runaway)

[0120] Material model attribute: hydrogen concentration.

[0121] Physical model event: Warning of excessive hydrogen concentration.

[0122] 4. Firefighting equipment

[0123] Physical model attributes: extinguishing agent pressure, system status, etc.

[0124] Physical model functions include: activating fire extinguishing devices, releasing fire extinguishing agents, and resetting system status.

[0125] 5. Energy storage converter equipment

[0126] Object model attributes: operating status, charging and discharging power, etc.

[0127] Object model functions: emergency stop, etc.

[0128] The interaction flow in the scenario is as follows:

[0129] 1) Reporting of abnormal events

[0130] The BMS monitors the cell data in real time and detects a slight drop in voltage and a rapid rise in temperature in cell number 15, triggering a thermal runaway event report. After the edge network device collects the data from the BMS, it immediately sends a high-level event to the cloud server. The event data is as follows: Event identifier: thermalRunawayRisk, Event parameters: {"cluster_id": "B01", "cell_id": 15, "current_temperature": 65.0, "temperature_rise_rate": 1.5}.

[0131] 2) Multi-source data aggregation and risk identification

[0132] The cloud server's intelligent alarm engine subscribes to event topics for all energy storage power station devices, and is immediately triggered upon receiving a thermalRunawayRisk event.

[0133] The intelligent alarm engine instantly retrieves auxiliary data from relevant areas via a unified data query API and performs joint analysis.

[0134] - Checking the ambient temperature properties of the temperature sensor inside the battery compartment revealed that the value had risen to 55°C (far higher than the normal value), confirming localized high temperature.

[0135] - Checking the hydrogen concentration properties of the combustible gas detector revealed a concentration of 200 ppm (exceeding the safety threshold), which is direct evidence of gas evolution from the battery cell.

[0136] Management Decision: Based on three independent evidence chains—BMS event, high temperature properties, and hydrogen properties—the intelligent alarm engine confirms the thermal runaway risk level as "high risk" and automatically generates a disposal work order.

[0137] 3) Intelligent linkage and active control

[0138] The intelligent alarm engine, based on predetermined security policies, sends control commands to relevant devices via function downlink topics, forming a control closed loop:

[0139] - Sending instructions to PCS: Instructions are sent through the emergency stop function Topic of the PCS object model. Upon receiving the instruction, the PCS immediately stops charging and discharging and cuts off the energy input.

[0140] - Issue instructions to fire-fighting equipment: Issue instructions through the fire-fighting equipment physical model's fire extinguishing device activation Topic, specifying the fire extinguishing area as "the compartment where the B battery cluster is located". Upon receiving the instructions, the fire-fighting equipment will immediately activate and release extinguishing agent to suppress the fire.

[0141] All function call commands, receiving time, target device and other information are recorded.

[0142] 4) Status monitoring and feedback confirmation

[0143] After the command is issued, the cloud server continuously monitors the execution results:

[0144] - By reporting the Topic through the properties of the subscribed PCS, confirm that its running status property has changed to "stopped";

[0145] - Confirm that the system status attribute of the subscribed fire equipment has changed to "spraying" by reporting the attributes of the equipment.

[0146] 5) Data archiving and panoramic traceability

[0147] All data from the entire event, including:

[0148] -BMS primitive properties and events.

[0149] - Properties of temperature sensors and gas detectors.

[0150] - The control function commands and parameters issued.

[0151] - Feedback attributes of each device.

[0152] This time-stamped data is stored completely in the time-series database and log system. Operations personnel can accurately review the entire process of an event by querying historical data afterward, which can be used for incident analysis, model optimization, and liability determination.

[0153] In summary, the solution provided in this application can collect data from energy storage power station devices through edge network devices, parse data from different energy storage power station devices, map communication protocols and data point tables to corresponding object models and publish them as communication topics, and send corresponding data according to the communication topics subscribed to by the cloud server; maintain standard object models of energy storage power station devices through the cloud server; register energy storage power station devices and bind them to corresponding standard object models; after the energy storage power station devices are online, subscribe to the corresponding communication topics of the energy storage power station devices and receive data and send control commands through the communication topics; store the received data and provide data service access interfaces to upper-layer applications, thereby realizing standardized management of energy storage power station data, enabling real-time response to device anomalies, reducing the complexity of data access for energy storage power station devices, improving the development efficiency of corresponding data management software, and providing a standardized data foundation for upper-layer applications.

[0154] It should be noted that this application can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, a magnetic or optical drive, a floppy disk, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0155] In a typical configuration of this application, both the terminal and the network device include one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0157] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0158] Furthermore, a portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. The program instructions invoking the methods of this application may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal carrying medium, and / or stored in the working memory of a computer device operating according to the program instructions. Here, one embodiment of this application includes a device comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run methods and / or technical solutions based on the foregoing embodiments of this application.

[0159] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A data management system for an energy storage power station, characterized in that, The system includes edge network devices and cloud servers. The edge network device is used to collect data from energy storage power station equipment, parse the data from different energy storage power station equipment, map the communication protocol and data in the data point table to the physical model of the corresponding energy storage power station equipment, publish the physical model as a communication topic, and send corresponding data according to the communication topic subscribed to by the cloud server. The cloud server is used to maintain the standard object model of the energy storage power station equipment, register the energy storage power station equipment, bind the energy storage power station equipment to the corresponding standard object model, subscribe to the communication topic corresponding to the energy storage power station equipment after the energy storage power station equipment goes online, receive data and send control commands through the communication topic, store the received data, and provide data service access interface to upper layer applications.

2. The system according to claim 1, characterized in that, The edge network device is used to collect data from the energy storage power station equipment, preprocess the acquired data, and upload the processing results to the cloud server through the communication topic according to a preset communication topic upload strategy.

3. The system according to claim 1, characterized in that, The upload strategies for the communication topics include triggered upload, fixed-period upload, and variable-frequency upload.

4. The system according to claim 1, characterized in that, The object model includes attributes, events, and functions. The attributes are used to describe the status data of the energy storage power station equipment, the events are used to describe the alarm-related data of the energy storage power station equipment, and the functions are used to describe the control functions of the energy storage power station equipment invoked by the cloud server.

5. The system according to claim 4, characterized in that, The communication topic includes message type and message body. The message type includes uplink and downlink. The uplink message type corresponds to the attributes and events of the object model. The downlink message type corresponds to the functions of the object model. The message body includes device type, device number, timestamp, verification status, and the attributes, events, and functions of the object model.

6. The system according to claim 4, characterized in that, Also used for: The edge network device collects alarm events from the first energy storage power station device, converts the alarm events into standard alarm events corresponding to the object model of the first energy storage power station device, and uploads the standard alarm events to the cloud server through the corresponding event communication topic according to the preset communication topic upload strategy. After receiving the standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event. The cloud server sends control commands to the third energy storage power station equipment through functional communication topics based on the risk level and the preset security policy. The cloud server obtains feedback attributes through the attribute communication topic corresponding to the third energy storage power station equipment and determines the response result of the standard alarm event.

7. The system according to claim 6, characterized in that, The cloud server is used to store the attributes and alarm events of the first energy storage power station device, the attributes of the second energy storage power station device, the control commands, and the feedback attributes of the third energy storage power station device.

8. The system according to claim 6, characterized in that, The energy storage power station equipment includes: energy storage equipment, thermal management equipment, fire-fighting equipment, and other equipment; the energy storage equipment includes battery equipment and energy storage converter equipment, the battery equipment includes battery stacks, battery clusters, and battery cells, and the energy storage converter equipment includes a main control module, a valve control module, and an energy storage converter module; the thermal management equipment includes air-cooled equipment and liquid-cooled equipment; the fire-fighting equipment includes sensing devices, fire-fighting devices, alarm devices, and alarm button devices; the other equipment includes switching devices, I / O devices, and meter devices.

9. The system according to claim 8, characterized in that, The first energy storage power station equipment is the battery cell of the battery equipment in the energy storage equipment; the second energy storage power station equipment is the temperature sensor and gas sensor in the fire protection equipment; and the third energy storage power station equipment is the energy storage converter equipment in the energy storage equipment and the fire protection device in the fire protection equipment.

10. The system according to claim 9, characterized in that, Upon receiving the standard alarm event, the cloud server obtains the attributes of the corresponding preset second energy storage power station equipment and determines the risk level corresponding to the standard alarm event, for the following purposes: After receiving a cell temperature alarm event, the cloud server obtains the ambient temperature attribute of the temperature sensor and the hydrogen concentration attribute of the gas sensor. When the ambient temperature attribute exceeds a preset temperature threshold and the hydrogen concentration attribute exceeds a preset hydrogen concentration threshold, the risk level corresponding to the cell temperature alarm event is determined to be a high-risk level.

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