A statistical data modeling method and system for electrochemical energy storage based on IEC61850

Abstract

A statistical data modeling method and system for electrochemical energy storage based on IEC61850 was developed. IEC61850 common data classes with linked data functionality were designed, namely, the Related Data Status (Related DataStatus) and the Related Data Meased Value (Related Data Measured Value). The Related Data Status (RDS) contains not only the basic status value, quality, and time, but also a trigger value (triVal) and a location number (locNum) to describe the corresponding measured value and location of the change when the status value changes. The Related Data Measured Value (RDMV) contains not only the basic measurement value, quality, and time, but also a location number (locNum) to describe the location of the analog value change. Modeling statistical data using this common data class enables structured data processing throughout the entire process, significantly improving the data processing efficiency of distributed energy storage systems, enabling automatic data configuration, and supporting big data analysis through linked data.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy integrated automation systems, and in particular relates to an electrochemical energy storage statistical data modeling method and system based on IEC61850. Background Art

[0002] With the growing demand for building an economically viable, environmentally friendly, and low-carbon society, DER (Distributed Energy Resource) technologies, such as photovoltaics and wind power, have garnered widespread attention and become a research hotspot in the energy sector both domestically and internationally. DER plays a crucial role in strengthening power supply system reliability, improving power quality, and enabling large-scale access to renewable energy. Combining power generation and energy storage technologies within DERs can create a flexible, integrated energy supply system, further improving energy utilization, reducing fossil energy consumption and CO2 emissions, promoting the rapid development of low-carbon electricity and a low-carbon society, and accelerating the achievement of carbon neutrality.

[0003] To achieve stable and reliable operation of electrochemical energy storage power stations, it is necessary to implement full data collection and efficient processing of the electrochemical energy storage system. When using IEC61850 to model the voltage, current, temperature, and statistical data of the electrochemical energy storage system, the public data classes SPS (Single Point Status) and MV (Measured Value) are traditionally used to model the state and measurement quantities, respectively. This modeling approach requires independent modeling of all state and measurement quantities at a single point, splitting the associated structured data into independent points to generate data object instances.

[0004] For example, in the event of a battery cell overvoltage alarm, it is necessary to use multiple data objects to model the voltage over-limit status, voltage over-limit value, and battery cell location where the over-limit value is located. During the data model analysis and configuration process of the energy storage monitoring background, three independent data points will be generated: voltage over-limit status, voltage over-limit value, and battery cell location where the over-limit value is located.

[0005] When performing data alarms and screen displays in the energy storage monitoring background, the relevant data needs to be re-associated to meet user monitoring and big data analysis needs. For the statistics of the maximum value of the measured quantity of the battery unit, the location where the maximum value appears will also be independently modeled. Re-association analysis is required for display and data analysis in the energy storage monitoring background.

[0006] Through the above analysis, the existing modeling scheme has the following problems:

[0007] 1) This results in too many data points, which has a significant impact on system performance when accessing large amounts of data;

[0008] 2) The structured data generated by the battery management IED (Intelligent Electronic Device) is independently transmitted to the monitoring backend and needs to be manually reconfigured and associated, increasing the engineering configuration workload and the probability of errors;

[0009] 3) Since data points need to be re-associated, the data structure complexity of the energy storage monitoring background increases, thereby reducing data processing efficiency. Summary of the Invention

[0010] The purpose of the present invention is to provide an electrochemical energy storage statistical data modeling method based on IEC61850, which is used to efficiently structure model the statistical data of the electrochemical energy storage system and store the associated information of the statistical data in the model, thereby improving the efficiency of data storage and analysis.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] A first aspect of the present invention provides an electrochemical energy storage statistical data modeling method based on IEC61850, comprising the following steps:

[0013] Step 1: Construct IEC61850 common data classes with linked data functions, which are linked data state quantities and linked data measurement quantities. The linked data state quantities are represented by RDS and the linked data measurement quantities are represented by RDMV.

[0014] Step 2: Modeling the electrochemical energy storage system. The abnormal information of the battery cells is modeled using RDS, and the maximum statistical data of the battery cells in the battery cluster is modeled using RDMV.

[0015] Preferably, in step 1, RDS includes not only the basic state value, quality and time, but also a trigger value and a location number. The trigger value is represented by triVal and the location number is represented by locNum, which are used to describe the measurement value and location of the corresponding abnormal device when the state quantity changes.

[0016] Preferably, in step 1, the RDMV includes not only the basic measurement value, quality and time, but also a location number, which is represented by locNum and is used to describe the location of the unit device corresponding to the measurement quantity.

[0017] Preferably, in step 2, the abnormal information of the battery cell is modeled using RDS, including: recording the abnormal status information through the data attribute stVal in the data object, recording the measurement quantity corresponding to the battery cell that generates the abnormal information through the data attribute triVal in the data object, and recording the location number of the battery cell that generates the abnormal information through the data attribute locNum.

[0018] Preferably, in step 2, the maximum value statistics of the battery cells in the battery cluster are modeled using RDMV, including: recording the maximum value of the statistical data through the data attribute mxVal in the data object, and recording the position number of the battery cell where the maximum value is located through the data attribute locNum.

[0019] Preferably, the method further includes: step 3, when the energy storage monitoring background parses the battery management IED model for data configuration, the associated data of the RDS and RDMV data objects are imported into the energy storage monitoring background real-time database as attributes of state quantities and analog quantities.

[0020] Preferably, the method further includes: step 4, when the energy storage monitoring background is running, the statistical data objects calculated in real time by the battery management IED are uploaded through the IEC61850 reporting service, and the energy storage monitoring background performs alarms, displays and historical data storage based on the associated data.

[0021] Preferably, after the battery management IED detects abnormal information and the measurement quantity and position number of the corresponding battery unit in real time, it writes the data value into the data object instance, triggering the data object change report to be submitted; when the maximum value statistical data result changes, the maximum value and the location where the maximum value occurs are written into the data object instance at the same time, triggering the data object change report to be submitted.

[0022] Preferably, after receiving the RDS type data object, the energy storage monitoring backend parses the data structure containing the abnormal state, associated measurement values, and associated location serial numbers and sends it to the energy storage monitoring backend alarm module. The alarm module pushes abnormal state alarm information based on the structured data, and simultaneously prompts the measurement value at the time of the abnormality and the location serial number of the abnormal battery unit;

[0023] After receiving the RDMV type data object, the energy storage monitoring backend parses the data structure containing the statistical maximum value and the associated position number and sends it to the energy storage monitoring backend data processing module and displays its associated position number at the same time.

[0024] The second aspect of the present invention provides an electrochemical energy storage statistical data modeling system based on IEC61850, which runs the electrochemical energy storage statistical data modeling method based on IEC61850, including: an RDS module for modeling abnormal information of battery cells using associated data state quantities; and an RDMV module for modeling the maximum statistical data of battery cells in a battery cluster using associated data measurement quantities.

[0025] Compared with the prior art, through the above technical solution, the beneficial technical effects of the present invention include at least: 1) By modeling statistical data through the public data class, the structured processing of the entire data process can be realized, the number of data points can be reduced, and the adverse impact on system performance when large-capacity data is accessed can be avoided. 2) The data processing efficiency of the distributed energy storage system is greatly improved, automatic data configuration is realized, and data analysis is supported by associated data, which reduces the engineering configuration workload and error probability. 3) There is no need to re-establish associations between data points, which reduces the complexity of the data structure of the energy storage monitoring background. 4) The IEC61850 public data classes designed in the present invention - associated data state quantity (RDS) and associated data measurement quantity (RDMV) can also be widely used in other fields of associated data modeling, and have great use value and promotion significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of modeling abnormal information of battery cells using associated data state quantities;

[0027] Figure 2 Schematic diagram of modeling the maximum statistical data of battery cells in a battery cluster using associated data measurements;

[0028] Figure 3 is a flow chart of the electrochemical energy storage statistical data modeling method based on IEC61850 provided in Example 1 of the present invention;

[0029] Figure 4 is a flow chart of the electrochemical energy storage statistical data modeling method based on IEC61850 provided in Example 2 of the present invention; DETAILED DESCRIPTION

[0030] The technical solutions and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] The data of the energy storage battery cells and energy storage battery clusters of the energy storage monitoring system are the basis of the human-computer interaction interface and data analysis. In order to achieve efficient modeling of statistical data, embodiment 1 of the present invention provides an electrochemical energy storage statistical data modeling method based on IEC61850 for modeling electrochemical energy storage statistical data, such as Figure 3 As shown, the following steps are included:

[0032] Step 1: Construct IEC61850 common data classes with linked data functionality, including linked data status and linked data measurement. The linked data status is represented by RDS (Related Data Status) and the linked data measurement is represented by RDMV (Related Data Measured Value).

[0033] Step 2: Modeling the electrochemical energy storage system. The abnormal information of the battery cells is modeled using RDS, and the maximum statistical data of the battery cells in the battery cluster is modeled using RDMV.

[0034] It is worth noting that statistical data modeling through this public data class can be used to efficiently structure the statistical data of the electrochemical energy storage system, store the associated information of the statistical data in the model, reduce the number of data points, avoid adverse effects on system performance when large-capacity data is accessed, and improve data storage and analysis efficiency.

[0035] Furthermore, embodiment 2 of the present invention provides a method for modeling electrochemical energy storage statistical data based on IEC61850, such as Figure 4 As shown, the following steps are included:

[0036] Step 1: Construct IEC61850 common data classes with linked data functions, namely linked data status and linked data measurement. The linked data status is represented by RDS (Related Data Status) and the linked data measurement is represented by RDMV (Related Data Measured Value).

[0037] As shown in Table 1, in addition to the basic state value, quality and time, RDS also includes a trigger value and a location number. The trigger value is represented by triVal and the location number is represented by locNum, which are used to describe the measurement value and location of the corresponding abnormal device when the state quantity changes.

[0038] Table 1 Related Data Status (RDS)

[0039]

[0040] As shown in Table 2, in addition to the basic measurement value, quality and time, the RDMV also contains a location number, which is represented by locNum and is used to describe the location of the unit device corresponding to the measurement quantity.

[0041] Table 2 RelatedData MeasedValue (RDMV)

[0042]

[0043]

[0044] Step 2: When modeling the electrochemical energy storage system, the abnormal information of the battery cell is modeled using RDS, and the maximum statistical data of the battery cells in the battery cluster is modeled using RDMV.

[0045] In a preferred but non-limiting embodiment, the abnormal information of the battery cell is modeled using RDS, and the abnormal status information is recorded through the data attribute stVal in the data object, the measurement value corresponding to the battery cell generating the abnormal information is recorded through the data attribute triVal in the data object, and the location number of the battery cell generating the abnormal information is recorded through the data attribute locNum, such as Figure 1 shown.

[0046] In a preferred but non-limiting embodiment, the maximum value statistics of the battery cells in the battery cluster are modeled using RDMV, and the maximum value of the statistics is recorded through the data attribute mxVal in the data object, and the position number of the battery cell where the maximum value is located is recorded through the data attribute locNum, such as Figure 2 shown.

[0047] Step 3: When the energy storage monitoring background parses the battery management IED model for data configuration, it imports the associated data of the RDS and RDMV data objects as state quantity and analog quantity attributes into the energy storage monitoring background real-time database.

[0048] It is worth noting that the present invention greatly improves the data processing efficiency of the distributed energy storage system, realizes automatic data configuration and supports data analysis through associated data, reducing the engineering configuration workload and error probability.

[0049] In step 4, while the energy storage monitoring backend is running, the battery management IED's real-time calculations of alarms, maximum values, and other statistical data objects are uploaded via the IEC61850 reporting service. The energy storage monitoring backend then uses the associated data to generate detailed alarms, display, and store historical data. This eliminates the need to re-associate data points, reducing the complexity of the energy storage monitoring backend's data structure.

[0050] In a preferred but non-restrictive embodiment, the statistical data objects such as alarms and maximum values ​​calculated in real time by the battery management IED are uploaded through the IEC61850 reporting service. After the battery management IED detects abnormal information and the measurement quantity and position number of the corresponding battery unit in real time, the data value is written into the data object instance, triggering the data object change report to be uploaded; when the maximum value statistical data result changes, the maximum value and the location where the maximum value occurs are simultaneously written into the data object instance, triggering the data object change report to be uploaded.

[0051] In a preferred but non-limiting embodiment, the energy storage monitoring backend performs detailed alarming, display, and historical data storage based on the associated data. Upon receiving an RDS-type data object, the energy storage monitoring backend parses the data structure containing the abnormal state, associated measurement values, and associated location numbers, and sends it to the energy storage monitoring backend alarm module. The alarm module pushes abnormal state alarm information based on the structured data, and simultaneously displays the measurement value at the time of the abnormality and the location number of the abnormal battery cell. Upon receiving an RDMV-type data object, the energy storage monitoring backend parses the data structure containing the statistical maximum value and associated location number, and sends it to the energy storage monitoring backend data processing module and stores it in a real-time database. The energy storage monitoring backend screen displays the statistical maximum value and its associated location number based on the real-time database information.

[0052] In a preferred but non-limiting embodiment, the alarm module pushes abnormal status alarm information based on structured data, simultaneously indicating the measured value at the time of the abnormality and the location number of the abnormal battery cell. To implement formatted alarms, parameter symbols are used in the object attribute description during data object modeling. When the energy storage monitoring backend processes the alarm information, the parameter symbols in the alarm description are replaced with the measured value at the time of the abnormality and the location number of the abnormal battery cell, achieving automated and standardized description of alarm information with data parameters.

[0053] It is worth noting that Example 2 can not only realize the structured modeling of statistical data of the electrochemical energy storage system and store the associated information of the statistical data in the model; it can also realize the structuring of the entire process from data modeling, parsing, processing, storage, analysis, and display, and realize automatic association in the data configuration module to achieve automatic configuration and standardized display of associated data, thereby improving the efficiency of energy storage monitoring background data storage and data analysis.

[0054] Embodiment 3 of the present invention provides an electrochemical energy storage statistical data modeling system based on IEC61850, which runs an electrochemical energy storage statistical data modeling method based on IEC61850 as described above, including: an RDS module for modeling abnormal information of battery cells using associated data state quantities; and an RDMV module for modeling the maximum statistical data of battery cells in a battery cluster using associated data measurement quantities.

[0055] The above embodiments have introduced the implementation methods of the present invention. It is understandable that the IEC61850 common data classes designed in the present invention, namely, Linked Data State Quantity (RDS) and Linked Data Measurement Quantity (RDMV), can also be widely applied to other fields of Linked Data modeling, and have great practical value and promotion significance.

[0056] The above embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A statistical data modeling method for electrochemical energy storage based on IEC61850, characterized in that: The following steps are involved: Step 1: Construct IEC61850 common data classes with linked data functionality, including linked data state quantities and linked data measurement quantities. Linked data state quantities are represented by RDS. In addition to basic state values, quality, and time, RDS also contains trigger values ​​and location numbers. Trigger values ​​are represented by triVal, and location numbers are represented by locNum. They are used to describe the measurement value and location of the corresponding abnormal device when the linked data state quantity changes. Linked data measurement quantities are represented by RDMV. In addition to basic measurement values, quality, and time, RDMV also contains location numbers. Location numbers are represented by locNum. They are used to describe the location of the unit device corresponding to the linked data measurement quantity. Step 2: Modeling the electrochemical energy storage system. The abnormal information of the battery cell is modeled using RDS, including: recording the abnormal status information through the data attribute stVal in the data object, recording the measurement value corresponding to the battery cell that generates the abnormal information through the data attribute triVal in the data object, and recording the location number of the battery cell that generates the abnormal information through the data attribute locNum. The maximum value statistical data of the battery cells in the battery cluster are modeled using RDMV, including: The maximum value of the statistical data is recorded through the data attribute mxVal in the data object, and the position number of the battery cell where the maximum value is located is recorded through the data attribute locNum.

2. The electrochemical energy storage statistical data modeling method based on IEC61850 according to claim 1, characterized in that: The method further includes: step 3, when the energy storage monitoring background parses the battery management IED model for data configuration, the associated data of the RDS and RDMV data objects are imported into the energy storage monitoring background real-time database as attributes of state quantities and analog quantities.

3. The electrochemical energy storage statistical data modeling method based on IEC61850 according to claim 2, characterized in that: The method further includes: step 4, when the energy storage monitoring background is running, the statistical data objects calculated in real time by the battery management IED are sent to the energy storage monitoring background through the IEC61850 reporting service, and the energy storage monitoring background performs alarms, displays and stores historical data based on the associated data.

4. The electrochemical energy storage statistical data modeling method based on IEC61850 according to claim 3, characterized in that: After the battery management IED detects abnormal information and the measurement quantity and location number of the corresponding battery unit in real time, it writes the data value into the data object instance, triggering the data object change report to be sent; when the maximum value statistical data result changes, the maximum value and the location where the maximum value occurs are written into the data object instance at the same time, triggering the data object change report to be sent.

5. The electrochemical energy storage statistical data modeling method based on IEC61850 according to claim 3 or 4, characterized in that: After receiving the RDS data object, the energy storage monitoring backend parses the data structure containing the abnormal status, associated measurement values, and associated location numbers and sends it to the energy storage monitoring backend alarm module. The alarm module pushes abnormal status alarm information based on the structured data, and also indicates the measurement value at the time of the abnormality and the location number of the abnormal battery cell. After receiving the RDMV type data object, the energy storage monitoring backend parses the data structure containing the statistical maximum value and the associated position number and sends it to the energy storage monitoring backend data processing module and displays its associated position number at the same time.

6. An electrochemical energy storage statistical data modeling system based on IEC61850, running an electrochemical energy storage statistical data modeling method based on IEC61850 according to any one of claims 1 to 5, characterized in that: include: RDS module, used to model abnormal information of battery cells using associated data state quantities; The RDMV module is used to model the maximum value statistics of battery cells in a battery cluster using associated data measurements.

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