EMS management system applied to energy storage power station
By designing a multi-module EMS management system in the energy storage power station and using a variety of sensors and big data analysis technologies, the problems of incomplete data acquisition and low analysis accuracy in traditional management systems are solved, and the efficient operation of the energy storage power station and the improvement of the power grid regulation capabilities are achieved.
Patent Information
- Application Number
- CN202411344218.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2025-05-23
AI Technical Summary
Traditional energy storage power station management systems have problems such as incomplete data acquisition, low analysis accuracy, single control strategy and poor communication reliability, resulting in low operating efficiency and inability to fully play the role of power grid regulation.
Design an EMS management system including data acquisition module, data analysis module, control strategy module, communication module and human-computer interaction module. It uses a variety of sensors to comprehensively collect data, uses big data analysis and machine learning algorithms for in-depth analysis, generates optimized control strategies, and ensures the reliability of data transmission through high-speed communication interfaces.
It improves the operating efficiency and stability of energy storage power plants, achieves a timely response to grid demand and changes, reduces energy waste and equipment losses, and enhances user management and monitoring capabilities, and improves the safety and reliability of the power plants.
Smart Images
Figure CN120029104A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of EMS management systems for energy storage power stations, and more specifically, to an EMS management system applied to energy storage power stations. Background Art
[0002] In energy storage power stations, an efficient energy management system (EMS) is crucial. Traditional energy storage power station management methods often have problems such as incomplete data collection, low analysis accuracy, single control strategy, and poor communication reliability; this leads to low operating efficiency of energy storage power stations and their inability to fully play their regulatory role in the power grid.
[0003] For example, early data acquisition systems may only be able to collect a limited number of key parameters, such as voltage and current, while ignoring important parameters such as temperature and power, making the assessment of the power plant's operating status inaccurate; in terms of data analysis, simple statistical methods cannot dig out the deep patterns and potential trends in the data, making it difficult to accurately predict the future operating status of the power plant.
[0004] The imperfect control strategy makes the energy storage power station respond untimely or not optimally when dealing with grid changes, resulting in energy waste and reduced grid stability. In addition, the inconsistency of communication protocols and unstable communication quality lead to frequent interruptions or errors in data transmission between the system and external devices, affecting the coordinated operation of the entire power station.
[0005] To this end, the present application proposes an EMS management system applied to an energy storage power station to solve the above-mentioned problems.
[0006] Application Contents
[0007] In order to solve the above problems, the present application provides an EMS management system applied to an energy storage power station.
[0008] The EMS management system provided in this application for energy storage power station adopts the following technical solution:
[0009] An EMS management system applied to an energy storage power station, comprising a cabinet,
[0010] The cabinet is provided with a data acquisition module, a data analysis module, a control strategy module, a communication module and a human-computer interaction module, and each module is electrically connected;
[0011] The data acquisition module collects the operating data of various equipment in the energy storage power station and transmits the collected data to the data analysis module;
[0012] The data analysis module uses big data analysis technology and machine learning algorithms to process and analyze data, and the control strategy module generates control instructions based on the analysis results;
[0013] The communication module realizes the communication between the system and the external device through the communication protocol;
[0014] The human-computer interaction module provides a user operation interface and displays the system operation status.
[0015] Furthermore, the data acquisition module includes a sensor unit and a data preprocessing unit, the sensor unit is composed of a voltage sensor, a current sensor, a temperature sensor and a power sensor and collects relevant parameters, and the data preprocessing unit filters, amplifies and performs analog-to-digital conversion on the collected data.
[0016] Through the above technical solution, the various sensors of the sensor unit can comprehensively collect key parameters, and the data preprocessing unit performs effective preliminary processing on the collected data, thereby improving the quality and availability of the data and providing a reliable basis for subsequent analysis and decision-making.
[0017] Furthermore, the big data analysis technology of the data analysis module adopts a neural network algorithm based on the Hadoop architecture machine learning algorithm to mine and analyze data to evaluate the operating status and performance of the energy storage power station.
[0018] Through the above technical solutions, data analysis based on Hadoop architecture and neural network algorithms can deeply explore the potential patterns and trends in the data, more accurately evaluate the operating status and performance of energy storage power stations, and help discover problems in advance and optimize operating strategies.
[0019] Furthermore, the control strategy module formulates an optimized charging and discharging strategy through a preset charging and discharging strategy algorithm according to the real-time operation status of the energy storage power station and user needs, generates a power allocation strategy according to the power allocation rules, and formulates an energy management strategy according to the energy management model.
[0020] Through the above technical solutions, the charging and discharging, power distribution and energy management strategies formulated according to real-time conditions and user needs can achieve efficient operation of energy storage power stations, meet the needs of different scenarios, and improve energy utilization efficiency and economic benefits.
[0021] Furthermore, the Ethernet interface of the communication module adopts a rate of 1000Mbps, the CAN bus complies with the CAN2.0B standard, and the RS485 interface has a photoelectric isolation function to ensure reliable communication with various devices in the energy storage power station and the upper monitoring system.
[0022] Through the above technical solution, a variety of high-speed and standard-compliant communication interfaces are adopted, and photoelectric isolation function is provided to ensure fast, stable and reliable data transmission, avoid interference and data loss, and ensure good communication between the system and internal and external devices.
[0023] Furthermore, the content displayed in real time by the human-computer interaction module includes various operating parameters, alarm information and historical data of the energy storage power station.
[0024] Through the above technical solution, rich content is displayed in real time, allowing users to fully and timely understand the operation of the energy storage power station so as to make decisions quickly and take corresponding measures.
[0025] Furthermore, the data analysis module and the control strategy module analyze historical load data, weather data and holiday factors, and use a combination of time series prediction algorithm and machine learning algorithm to accurately predict the load in the future, providing a basis for the operation strategy of the energy storage power station.
[0026] Through the above technical solution, load forecasting is carried out by combining multiple factors, which provides a forward-looking basis for the operation strategy of the energy storage power station, helps to optimize resource allocation and improve the adaptability of the power station.
[0027] Furthermore, the time series prediction algorithm in the load forecasting function adopts the ARIMA model, and the machine learning algorithm adopts the support vector machine algorithm. The accuracy of load forecasting is improved by weighted fusion of the results of the two algorithms.
[0028] Through the above technical solution, a specific time series prediction algorithm and machine learning algorithm are adopted and weighted fusion is performed, which significantly improves the accuracy of load forecasting and makes the operation strategy more scientific and reasonable.
[0029] In summary, the present application includes at least one of the following beneficial technical effects:
[0030] (1) This application improves the operating efficiency and stability of energy storage power stations. Through precise data collection, analysis and optimized control strategies, power stations can better adapt to grid demand and changes, and reduce energy waste and equipment loss.
[0031] (2) It enhances the user's ability to manage and monitor energy storage power stations. The rich human-computer interaction content and convenient operation interface enable users to grasp the power station operation status in a timely manner and make accurate decisions, thereby improving the safety and reliability of the power station. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 This is a schematic diagram of the structure of this application.
[0033] Explanation of the numbers in the figure: 1. Cabinet; 2. Data acquisition module; 2A. Sensor unit; 2B. Data preprocessing unit; 3. Data analysis module; 4. Control strategy module; 5. Communication module; 6. Human-computer interaction module. DETAILED DESCRIPTION
[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application; it is obvious that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application without making creative work are within the scope of protection of the present application.
[0035] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present application. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.
[0036] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "set / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.
[0037] Example:
[0038] The following is combined with Figure 1 - Provide further details on this application.
[0039] The embodiment of the present application discloses an EMS management system applied to an energy storage power station, comprising a cabinet 1,
[0040] The cabinet 1 is provided with a data acquisition module 2, a data analysis module 3, a control strategy module 4, a communication module 5 and a human-computer interaction module 6, and each module is electrically connected;
[0041] The data acquisition module 2 collects the operating data of various equipment in the energy storage power station and transmits the collected data to the data analysis module 3;
[0042] The data analysis module 3 uses big data analysis technology and machine learning algorithms to process and analyze data, and the control strategy module 4 generates control instructions based on the analysis results;
[0043] The communication module 5 realizes the communication between the system and external devices through the communication protocol;
[0044] The human-computer interaction module 6 provides a user operation interface and displays the system operation status.
[0045] See also Figure 1 The data acquisition module 2 includes a sensor unit 2A and a data preprocessing unit 2B. The sensor unit 2A is composed of a voltage sensor, a current sensor, a temperature sensor and a power sensor and collects relevant parameters. The data preprocessing unit 2B filters, amplifies and performs analog-to-digital conversion on the collected data. The various sensors of the sensor unit 2A can comprehensively collect key parameters. The data preprocessing unit 2B performs effective pre-processing on the collected data, improves the quality and availability of the data, and provides a reliable basis for subsequent analysis and decision-making.
[0046] See also Figure 1 The big data analysis technology of data analysis module 3 is based on the Hadoop architecture machine learning algorithm and adopts neural network algorithm to mine data and evaluate the operating status and performance of energy storage power stations; data analysis based on Hadoop architecture and neural network algorithm can deeply mine the potential rules and trends in the data, and more accurately evaluate the operating status and performance of energy storage power stations, which helps to discover problems in advance and optimize operation strategies.
[0047] See also Figure 1 The control strategy module 4 formulates an optimized charging and discharging strategy through a preset charging and discharging strategy algorithm according to the real-time operation status of the energy storage power station and user needs, generates a power allocation strategy according to the power allocation rules, and formulates an energy management strategy according to the energy management model; the charging and discharging, power allocation and energy management strategies formulated according to the real-time situation and user needs can realize the efficient operation of the energy storage power station, meet the needs in different scenarios, and improve energy utilization efficiency and economic benefits.
[0048] See also Figure 1 The Ethernet interface of communication module 5 adopts a rate of 1000Mbps, the CAN bus complies with the CAN2.0B standard, and the RS485 interface has a photoelectric isolation function to ensure reliable communication with various devices in the energy storage power station and the superior monitoring system; it adopts a variety of high-speed and standard-compliant communication interfaces and has a photoelectric isolation function to ensure fast, stable and reliable data transmission, avoid interference and data loss, and ensure good communication between the system and internal and external devices.
[0049] See also Figure 1 The real-time display content of the human-computer interaction module 6 includes various operating parameters, alarm information and historical data of the energy storage power station; the real-time display of rich content allows users to fully and timely understand the operation status of the energy storage power station, so as to make decisions quickly and take corresponding measures.
[0050] See also Figure 1The data analysis module 3 and the control strategy module 4 analyze historical load data, weather data and holiday factors, and use a combination of time series prediction algorithm and machine learning algorithm to accurately predict the load in the future, providing a basis for the operation strategy of the energy storage power station; combining multiple factors to predict the load provides a forward-looking basis for the operation strategy of the energy storage power station, which helps to optimize resource allocation and improve the adaptability of the power station.
[0051] See also Figure 1 The time series prediction algorithm in the load forecasting function adopts the ARIMA model, and the machine learning algorithm adopts the support vector machine algorithm. The accuracy of load forecasting is improved by weighted fusion of the results of the two algorithms; the specific time series prediction algorithm and machine learning algorithm are adopted and weighted fusion is performed, which significantly improves the accuracy of load forecasting and makes the operation strategy more scientific and reasonable.
[0052] The implementation principle of an EMS management system applied to an energy storage power station in an embodiment of the present application is as follows:
[0053] First, the sensor unit 2A in the data acquisition module 2 collects the operating data of the voltage, current, temperature, power and other equipment of the energy storage power station, and the data preprocessing unit 2B performs filtering, amplification and analog-to-digital conversion on these data. The processed data is transmitted to the data analysis module 3, which performs in-depth analysis based on technologies such as Hadoop architecture and neural network algorithms to evaluate the operating status and performance of the power station.
[0054] Next, the control strategy module 4 formulates strategies such as charging and discharging, power allocation and energy management through preset algorithms and models based on data analysis results, real-time operating conditions and user needs, and generates corresponding control instructions.
[0055] The communication module 5 uses interfaces and protocols such as Ethernet, CAN bus, RS485, etc. to transmit control instructions to related equipment and realize reliable communication with external equipment and superior monitoring system.
[0056] At the same time, the human-computer interaction module 6 displays various operating parameters, alarm information and historical data of the energy storage power station in real time, and provides an operation interface for users, and users can manage and make decisions based on this information.
[0057] In addition, the data analysis module 3 will combine historical load data, weather data, holidays and other factors, and use a combination of time series prediction algorithms and machine learning algorithms to accurately predict future loads, providing a basis for the formulation of operation strategies.
[0058] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.
Claims
1. An EMS management system applied to an energy storage power station, comprising a cabinet (1), characterized in that: The cabinet (1) is provided with a data acquisition module (2), a data analysis module (3), a control strategy module (4), a communication module (5) and a human-computer interaction module (6), and the modules are electrically connected; The data acquisition module (2) collects the operating data of various types of equipment in the energy storage power station, and transmits the collected data to the data analysis module (3); The data analysis module (3) uses big data analysis technology and machine learning algorithms to process and analyze data, and the control strategy module (4) generates control instructions based on the analysis results; The communication module (5) realizes communication between the system and external devices through a communication protocol; The human-computer interaction module (6) provides a user operation interface and displays the system operation status.
2. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The data acquisition module (2) comprises a sensor unit (2A) and a data preprocessing unit (2B); the sensor unit (2A) is composed of a voltage sensor, a current sensor, a temperature sensor and a power sensor and acquires relevant parameters; and the data preprocessing unit (2B) performs filtering, amplification and analog-to-digital conversion on the acquired data.
3. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The big data analysis technology of the data analysis module (3) is based on the Hadoop architecture machine learning algorithm and adopts a neural network algorithm to mine and analyze data to evaluate the operating status and performance of the energy storage power station.
4. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The control strategy module (4) formulates an optimized charging and discharging strategy based on the real-time operation status of the energy storage power station and user needs through a preset charging and discharging strategy algorithm, generates a power allocation strategy according to a power allocation rule, and formulates an energy management strategy based on an energy management model.
5. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The Ethernet interface of the communication module (5) adopts a rate of 1000 Mbps, the CAN bus complies with the CAN2.0B standard, and the RS485 interface has a photoelectric isolation function, thereby ensuring reliable communication with various devices in the energy storage power station and the upper-level monitoring system.
6. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The content displayed in real time by the human-computer interaction module (6) includes various operating parameters, alarm information and historical data of the energy storage power station.
7. The EMS management system applied to an energy storage power station according to claim 1, characterized in that: The data analysis module (3) and the control strategy module (4) analyze historical load data, weather data and holiday factors, and use a combination of a time series prediction algorithm and a machine learning algorithm to accurately predict the load in the future, thereby providing a basis for the operation strategy of the energy storage power station.
8. The EMS management system applied to an energy storage power station according to claim 7, characterized in that: The time series prediction algorithm in the load forecasting function adopts the ARIMA model, and the machine learning algorithm adopts the support vector machine algorithm. The accuracy of load forecasting is improved by weighted fusion of the results of the two algorithms.