Distributed energy storage device and energy storage system

Abstract

The invention provides a distributed energy storage device and an energy storage system. The distributed energy storage device and the distributed energy storage system comprise an energy storage unit used for storing energy; the power conversion unit is used for converting the direct current of the energy storage unit into alternating current; the controller is used for controlling the charging and discharging process of the energy storage unit; and the communication module is used for communicating with an energy storage system. The distributed energy storage device and the distributed energy storage system provided by the invention have the advantages that the use is convenient, and the artificial intelligence of the energy management system is realized through the steps of data preprocessing, feature engineering, model selection, model training and optimization, model verification and testing, model deployment and updating and the like.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and in particular relates to a distributed energy storage device and an energy storage system. Background Art

[0002] With the transformation of the global energy structure and the need for environmental protection, the role of distributed energy storage devices and energy storage systems in power systems is becoming increasingly important. Distributed energy storage devices, such as batteries, supercapacitors or flywheel batteries, can provide energy buffering at different levels such as the power generation end, transmission and distribution end and user end, thereby improving the stability and reliability of the power system. The energy storage system is a complex system composed of multiple distributed energy storage devices, power conversion units, controllers, communication modules, etc., which can optimize the energy distribution of energy storage devices and improve the overall performance of the energy storage system.

[0003] However, the optimal operation of energy storage systems involves multiple factors, such as the status of energy storage devices, the operating status of the power grid, meteorological data, etc. Traditional energy storage system optimization methods mainly rely on manual experience and rules, which are difficult to cope with complex and changing operating environments.

[0004] Therefore, it is necessary to provide a new distributed energy storage device and energy storage system to solve the above technical problems. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a distributed energy storage device and energy storage system that is easy to use and realizes the artificial intelligence of the energy management system through steps such as data preprocessing, feature engineering, model selection, model training and optimization, model verification and testing, model deployment and updating.

[0006] In order to solve the above technical problems, the distributed energy storage device and energy storage system provided by the present invention include: an energy storage unit, used for storing energy;

[0007] A power conversion unit, used to convert the direct current of the energy storage unit into alternating current;

[0008] A controller, used to control the charging and discharging process of the energy storage unit;

[0009] A communication module, used for communicating with the energy storage system;

[0010] The specific steps are as follows:

[0011] S1 Design and manufacture of distributed energy storage devices: Select appropriate energy storage units, such as batteries, supercapacitors or flywheel batteries, and integrate them into the device;

[0012] S2 Design and manufacture of power conversion unit: Select appropriate power converters, such as DC-AC inverters, and integrate them into the device to convert DC power from the energy storage unit into AC power;

[0013] Design and programming of S3 controllers: Develop controller software that can control the charging and discharging process of the energy storage unit according to preset strategies or received signals;

[0014] Integration of S4 communication module: Integrate the communication module into the distributed energy storage device to enable wireless or wired communication with the energy storage system;

[0015] S5 Deployment of distributed energy storage devices: Deploy the manufactured devices to target application scenarios, such as solar power stations, wind farms, or grid nodes;

[0016] S6 Energy Storage System Design and Implementation: Build the energy storage system architecture, including the energy storage device manager, energy management system, and communication network;

[0017] Development and deployment of S7 Energy Storage Device Manager: Deploy the Energy Storage Device Manager into the energy storage system to monitor and manage multiple distributed energy storage devices;

[0018] S8 Energy Management System Development and Deployment: Deploy energy management systems into energy storage systems to optimize energy distribution and improve performance of energy storage systems;

[0019] S9 Communication and coordination between distributed energy storage devices and energy storage systems: Through the communication module, distributed energy storage devices communicate with the energy storage system in real time to ensure the optimized operation of the energy storage system;

[0020] Debugging and optimization of S10 energy storage system: Debug and optimize the energy storage system in actual operation to ensure that it can operate efficiently and stably in various application scenarios.

[0021] As a further solution of the present invention, it also includes: an environmental sensor for monitoring environmental parameters around the device;

[0022] Safety protection circuit, used to ensure the safe operation of the device.

[0023] As a further solution of the present invention, it also includes: an energy storage device manager, which is used to manage multiple distributed energy storage devices;

[0024] Energy management systems to optimize energy distribution across energy storage systems;

[0025] A communication network is used to connect the energy storage device manager and the energy management system.

[0026] As a further solution of the present invention, it also includes: a meteorological data server for providing meteorological data;

[0027] The power grid data server is used to provide power grid data.

[0028] As a further solution of the present invention, the energy storage unit includes a battery, a supercapacitor or a flywheel battery.

[0029] As a further solution of the present invention, the communication module adopts wireless communication.

[0030] As a further solution of the present invention, the energy storage device manager adopts cloud computing technology.

[0031] As a further aspect of the present invention, the energy management system uses an artificial intelligence algorithm;

[0032] The artificial intelligence algorithm is as follows:

[0033] Data preprocessing: preprocess the historical data, meteorological data, and power grid data generated by the energy storage system, including data cleaning, data normalization, and feature extraction;

[0034] Feature engineering: Perform feature engineering on the preprocessed data, including feature selection, feature extraction, and feature construction, to select features that have a significant impact on the operation of the energy storage system;

[0035] Model selection: Select appropriate AI algorithms, such as deep learning, reinforcement learning, and machine learning, based on the needs of the energy storage system;

[0036] Model training and optimization: Use preprocessed and feature-engineered data to train the selected algorithm model; during the training process, use cross-validation and grid search methods to optimize model hyperparameters to improve model performance;

[0037] Model verification and testing: Apply the trained model to the actual operating data of the energy storage system to verify and test the model to ensure its effectiveness and accuracy in actual applications;

[0038] Model deployment and update: Deploy the verified model to the energy management system of the energy storage system to achieve the optimized operation of the energy storage system; at the same time, regularly update and optimize the model to improve the performance and stability of the energy storage system;

[0039] Through the above refined algorithm content, the artificial intelligence of the energy management system is realized, thereby improving the operating efficiency and stability of distributed energy storage devices and energy storage systems.

[0040] Compared with the related art, the distributed energy storage device and energy storage system provided by the present invention have the following beneficial effects:

[0041] The present invention provides a distributed energy storage device and energy storage system: by optimizing the operation of the energy storage system through artificial intelligence algorithms, energy loss can be reduced and the overall efficiency of the energy storage system can be improved. For example, by analyzing the historical data of the energy storage system through deep learning algorithms, the future demand of the energy storage system can be predicted, thereby optimizing the charging and discharging process of the energy storage device and reducing energy waste.

[0042] The present invention provides a distributed energy storage device and energy storage system: the refined algorithm can monitor the operating status of the energy storage system in real time, detect and handle abnormal situations in time, and thus enhance the stability of the energy storage system. For example, by using a reinforcement learning algorithm to analyze the operating data of the energy storage system in real time, the charging and discharging strategy of the energy storage device can be adjusted in time to avoid system instability caused by energy storage device failure or power grid fluctuations.

[0043] The present invention provides a distributed energy storage device and energy storage system: the refined algorithm can optimize the energy distribution of the energy storage system and reduce the operating cost. For example, by analyzing the historical data of the energy storage system through a machine learning algorithm, the charging and discharging strategy of the energy storage device can be optimized and the electricity expenditure of the energy storage system can be reduced. In addition, the refined algorithm can also optimize the maintenance strategy of the energy storage system and reduce the maintenance cost.

[0044] The present invention provides a distributed energy storage device and energy storage system: the refined algorithm can support the scalability of the energy storage system, so that it can adapt to different application scenarios and requirements. For example, by analyzing the historical data of the energy storage system through feature engineering and model selection algorithms, an energy storage system model suitable for different application scenarios can be constructed to improve the adaptability of the energy storage system. In addition, the refined algorithm can also support the online upgrade and optimization of the energy storage system, so that it can adapt to the evolving energy storage technology. DETAILED DESCRIPTION

[0045] The distributed energy storage device and energy storage system include: an energy storage unit for storing energy;

[0046] A power conversion unit, used to convert the direct current of the energy storage unit into alternating current;

[0047] A controller, used to control the charging and discharging process of the energy storage unit;

[0048] A communication module, used for communicating with the energy storage system;

[0049] The specific steps are as follows:

[0050] S1 Design and manufacture of distributed energy storage devices: Select appropriate energy storage units, such as batteries, supercapacitors or flywheel batteries, and integrate them into the device;

[0051] S2 Design and manufacture of power conversion unit: Select appropriate power converters, such as DC-AC inverters, and integrate them into the device to convert DC power from the energy storage unit into AC power;

[0052] Design and programming of S3 controllers: Develop controller software that can control the charging and discharging process of the energy storage unit according to preset strategies or received signals;

[0053] Integration of S4 communication module: Integrate the communication module into the distributed energy storage device to enable wireless or wired communication with the energy storage system;

[0054] S5 Deployment of distributed energy storage devices: Deploy the manufactured devices to target application scenarios, such as solar power stations, wind farms, or grid nodes;

[0055] S6 Energy Storage System Design and Implementation: Build the energy storage system architecture, including the energy storage device manager, energy management system, and communication network;

[0056] Development and deployment of S7 Energy Storage Device Manager: Deploy the Energy Storage Device Manager into the energy storage system to monitor and manage multiple distributed energy storage devices;

[0057] S8 Energy Management System Development and Deployment: Deploy energy management systems into energy storage systems to optimize energy distribution and improve performance of energy storage systems;

[0058] S9 Communication and coordination between distributed energy storage devices and energy storage systems: Through the communication module, distributed energy storage devices communicate with the energy storage system in real time to ensure the optimized operation of the energy storage system;

[0059] Debugging and optimization of S10 energy storage system: Debug and optimize the energy storage system in actual operation to ensure that it can operate efficiently and stably in various application scenarios.

[0060] Also included: an environmental sensor for monitoring environmental parameters around the device;

[0061] Safety protection circuit, used to ensure the safe operation of the device.

[0062] Also included: an energy storage device manager for managing a plurality of distributed energy storage devices;

[0063] Energy management systems to optimize energy distribution across energy storage systems;

[0064] A communication network is used to connect the energy storage device manager and the energy management system.

[0065] The invention also includes: a meteorological data server for providing meteorological data;

[0066] The power grid data server is used to provide power grid data.

[0067] The energy storage unit includes a battery, a supercapacitor or a flywheel battery.

[0068] The communication module adopts wireless communication mode.

[0069] The energy storage device manager adopts cloud computing technology.

[0070] The energy management system adopts artificial intelligence algorithm;

[0071] The artificial intelligence algorithm is as follows:

[0072] Data preprocessing: preprocess the historical data, meteorological data, and power grid data generated by the energy storage system, including data cleaning, data normalization, and feature extraction;

[0073] Feature engineering: Perform feature engineering on the preprocessed data, including feature selection, feature extraction, and feature construction, to select features that have a significant impact on the operation of the energy storage system;

[0074] Model selection: Select appropriate AI algorithms, such as deep learning, reinforcement learning, and machine learning, based on the needs of the energy storage system;

[0075] Model training and optimization: Use preprocessed and feature-engineered data to train the selected algorithm model; during the training process, use cross-validation and grid search methods to optimize model hyperparameters to improve model performance;

[0076] Model verification and testing: Apply the trained model to the actual operating data of the energy storage system to verify and test the model to ensure its effectiveness and accuracy in actual applications;

[0077] Model deployment and update: Deploy the verified model to the energy management system of the energy storage system to achieve the optimized operation of the energy storage system; at the same time, regularly update and optimize the model to improve the performance and stability of the energy storage system;

[0078] Through the above refined algorithm content, the artificial intelligence of the energy management system is realized, thereby improving the operating efficiency and stability of distributed energy storage devices and energy storage systems. Specific embodiments

[0080] Select energy storage unit: Choose lithium-ion battery as the energy storage unit, which has the characteristics of high energy density, long life and fast charging and discharging.

[0081] Design of power conversion unit: IGBT-based DC-AC inverter is selected as the power conversion unit, which has high efficiency and stable output performance.

[0082] Develop controller: Develop microcontroller-based controller software to achieve precise control of the charging and discharging process of the energy storage unit.

[0083] Integrated communication module: Select a Wi-Fi-based wireless communication module to achieve real-time communication between distributed energy storage devices and the energy storage system.

[0084] Deploy distributed energy storage devices: Deploy the manufactured devices to solar power stations to provide a stable power supply to the power grid.

[0085] Build energy storage system architecture: Build energy storage system architecture including energy storage device manager, energy management system and communication network.

[0086] Develop an energy storage device manager: Deploy an energy storage device manager based on cloud computing technology to achieve real-time monitoring and management of multiple distributed energy storage devices.

[0087] Develop energy management system: Deploy energy management system based on artificial intelligence algorithm to achieve optimized energy distribution of energy storage system.

[0088] Realize communication and coordination: Through the wireless communication module, the distributed energy storage device communicates with the energy storage system in real time to ensure the optimized operation of the energy storage system.

[0089] Debug and optimize energy storage systems: Debug and optimize energy storage systems in actual operation to ensure that they can operate efficiently and stably in various application scenarios.

[0090] In addition, it also includes environmental sensors and safety protection circuits to monitor environmental parameters around the device and ensure the safe operation of the device. At the same time, the energy storage device manager uses cloud computing technology and the energy management system uses artificial intelligence algorithms to improve the operating efficiency and stability of the energy storage system.

[0091] Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments or applied directly or indirectly without departing from the principles and spirit of the present invention. In other related technical fields, the scope of the present invention is defined by the appended claims and their equivalents, which are equally included in the scope of patent protection of the present invention.

Claims

1. A distributed energy storage device and energy storage system, characterized in that: include: An energy storage unit for storing energy; A power conversion unit, used to convert the direct current of the energy storage unit into alternating current; A controller, used to control the charging and discharging process of the energy storage unit; A communication module, used for communicating with the energy storage system; The specific steps are as follows: Design and manufacture of S1 distributed energy storage devices: Selecting appropriate energy storage units, such as batteries, supercapacitors or flywheel batteries, and integrating them into the device; Design and manufacture of S2 power conversion units: Selecting a suitable power converter, such as a DC-AC inverter, and integrating it into the device to convert the DC power from the energy storage unit into AC power; Design and programming of S3 controller: Develop controller software that can control the charging and discharging process of the energy storage unit according to preset strategies or received signals; Integration of S4 communication module: Integrate communication modules into distributed energy storage devices to enable wireless or wired communication with the energy storage system; Deployment of S5 distributed energy storage device: Deploy the manufactured devices to target application scenarios, such as solar power plants, wind farms, or grid nodes; Design and implementation of S6 energy storage system: Build the energy storage system architecture, including the energy storage device manager, energy management system, and communication network; Development and deployment of S7 Energy Storage Device Manager: Deploy an energy storage device manager into the energy storage system to monitor and manage multiple distributed energy storage devices; Development and deployment of S8 energy management system: Deploy energy management systems into energy storage systems to optimize energy distribution and improve performance; Communication and coordination between S9 distributed energy storage devices and energy storage systems: Through the communication module, the distributed energy storage device communicates with the energy storage system in real time to ensure the optimized operation of the energy storage system; Debugging and optimization of S10 energy storage system: Debug and optimize the energy storage system in actual operation to ensure its efficient and stable operation in various application scenarios.

2. The distributed energy storage device and energy storage system according to claim 1, characterized in that: Also includes: Environmental sensors, used to monitor environmental parameters around the device; Safety protection circuit, used to ensure the safe operation of the device.

3. The distributed energy storage device and energy storage system according to claim 1, characterized in that: Also includes: An energy storage device manager, used to manage multiple distributed energy storage devices; Energy management systems to optimize energy distribution across energy storage systems; A communication network is used to connect the energy storage device manager and the energy management system.

4. The distributed energy storage device and energy storage system according to claim 1, characterized in that: Also includes: Meteorological data server, used to provide meteorological data; The power grid data server is used to provide power grid data.

5. The distributed energy storage device and energy storage system according to claim 1, characterized in that: The energy storage unit includes a battery, a supercapacitor or a flywheel battery.

6. The distributed energy storage device and energy storage system according to claim 1, characterized in that: The communication module adopts wireless communication mode.

7. The distributed energy storage device and energy storage system according to claim 1, characterized in that: The energy storage device manager adopts cloud computing technology.

8. The distributed energy storage device and energy storage system according to claim 1, characterized in that: The energy management system adopts artificial intelligence algorithm; The artificial intelligence algorithm is as follows: Data preprocessing: preprocess the historical data, meteorological data, and power grid data generated by the energy storage system, including data cleaning, data normalization, and feature extraction; Feature engineering: Perform feature engineering on preprocessed data. Including feature selection, feature extraction, and feature construction to select features that have a significant impact on the operation of the energy storage system; Model selection: Select appropriate AI algorithms, such as deep learning, reinforcement learning, and machine learning, based on the needs of the energy storage system; Model training and optimization: Use preprocessed and feature-engineered data to train the selected algorithm model; during the training process, use cross-validation and grid search methods to optimize model hyperparameters to improve model performance; Model verification and testing: Apply the trained model to the actual operating data of the energy storage system to verify and test the model to ensure its effectiveness and accuracy in actual applications; Model deployment and update: Deploy the verified model to the energy management system of the energy storage system to achieve the optimized operation of the energy storage system; at the same time, regularly update and optimize the model to improve the performance and stability of the energy storage system; Through the above refined algorithm content, the artificial intelligence of the energy management system is realized, thereby improving the operating efficiency and stability of distributed energy storage devices and energy storage systems.