CN116418124B - Micro-grid control system and energy storage power station control system - Google Patents
Micro-grid control system and energy storage power station control system Download PDFInfo
- Publication number
- CN116418124B CN116418124B CN202310685401.4A CN202310685401A CN116418124B CN 116418124 B CN116418124 B CN 116418124B CN 202310685401 A CN202310685401 A CN 202310685401A CN 116418124 B CN116418124 B CN 116418124B
- Authority
- CN
- China
- Prior art keywords
- edge device
- energy storage
- edge
- control system
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 73
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 59
- 238000012545 processing Methods 0.000 claims abstract description 36
- 206010068065 Burning mouth syndrome Diseases 0.000 claims abstract description 16
- 230000002688 persistence Effects 0.000 claims abstract description 14
- 238000004891 communication Methods 0.000 claims abstract description 12
- 238000003062 neural network model Methods 0.000 claims abstract description 7
- 238000001514 detection method Methods 0.000 claims description 14
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 6
- 238000000034 method Methods 0.000 description 11
- 230000007246 mechanism Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J15/00—Systems for storing electric energy
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
- H02J7/0014—Circuits for equalisation of charge between batteries
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
Abstract
The application provides a micro-grid control system and an energy storage power station control system, wherein the micro-grid control system is applied to an energy storage power station comprising a plurality of energy storage containers; each energy storage container is provided with a BMS; setting a plurality of edge devices in the micro-grid control system, wherein each edge device corresponds to one BMS or a plurality of BMSs, each edge device is respectively in communication connection with an algorithm server and the EMS, each edge device receives operation data of an energy storage power station from the EMS or an associated BMS, performs characteristic engineering and data persistence processing on the data, and sends the processed data to the corresponding algorithm server; and the algorithm server predicts the data sent by the edge equipment through the neural network model. The real-time processing of the operation data of the energy storage power station can be completed through the edge equipment connected with one or more BMS and the algorithm server connected with each edge equipment, and the data processing performance of the system is improved.
Description
Technical Field
The application relates to the technical field of energy storage, in particular to a micro-grid control system and an energy storage power station control system.
Background
The existing energy storage power station generally adopts EMS (Energy Management System ) to monitor and collect the operation data of BMS (Battery Management System ), and then executes related data processing through other algorithm servers, so that the data processing amount is large, the operation is complex, and the performance requirement on hardware is high. Due to the limitation of hardware performance of an algorithm server, a large amount of data uploaded by the BMS cannot be processed in real time; if a separate edge device is configured for each BMS, which has the processing capability to perform the relevant calculations, etc., it is too costly for a large energy storage power station, and once a certain edge device fails, it may cause a power station operation accident.
Disclosure of Invention
The application aims to provide a micro-grid control system and an energy storage power station control system, which can perform real-time BMS data processing through edge equipment connected with one or more BMSs so as to reduce the data processing pressure of a subsequent algorithm server and improve the data processing performance of the system.
In a first aspect, the present application provides a microgrid control system for use in an energy storage power station comprising a plurality of energy storage containers; each energy storage container is provided with a BMS; a plurality of edge devices are arranged in the micro-grid control system, each edge device corresponds to one BMS or a plurality of BMSs, and each edge device is respectively in communication connection with the algorithm server and the EMS; each edge device is used for receiving the operation data of the energy storage power station from the EMS or the associated BMS, carrying out characteristic engineering and data persistence processing on the operation data of the energy storage power station, and sending the processed data to a corresponding algorithm server; the algorithm server calculates the micro-grid operation optimization measures based on the pre-trained neural network model and the data sent by the edge devices.
In a preferred embodiment of the present application, each of the edge devices mentioned above defaults to communicate with the EMS to obtain the operation data of the energy storage power station corresponding to the associated BMS; when the edge device cannot communicate with the EMS, the operation data of the energy storage power station of the corresponding part is automatically switched to be acquired from the associated BMS.
In a preferred embodiment of the present application, the algorithm server is further configured to detect a real-time operation state of all isomorphic edge devices, and when determining that a certain edge device fails, send a redirection instruction to a target BMS associated with the failed edge device, so that the target BMS redirects data to be sent, to which a BMS identifier is attached, to the normal running isomorphic edge device of the failed edge device for processing; wherein, isomorphic edge devices are edge devices that perform the same feature engineering and data persistence.
In a preferred embodiment of the present application, each of the edge devices is further configured to report its current running state information to a corresponding algorithm server in real time; the current operating state information includes at least one of: resource condition, network connection condition and working state; the algorithm server is also used for periodically checking the current running state information of each edge device, if the edge device is determined to have faults or insufficient resources according to the current running state information, rerouting is performed according to a preset replacement strategy, and the data task corresponding to the faulty or insufficient resources edge device is distributed to one edge device with the best computing performance in the isomorphic edge device for execution.
In a preferred embodiment of the present application, the algorithm server is further configured to determine, when rerouting, an edge device with the best current computing performance according to the corresponding current running state information of each isomorphic edge device and weights corresponding to multiple detection items of the edge device.
In a preferred embodiment of the present application, the plurality of detection items include: device performance, operating status, network quality, reliability; the algorithm server is also used for determining evaluation parameters corresponding to various detection items respectively according to the current running state information of the optional edge equipment for each optional edge equipment; according to the evaluation parameters and weights respectively corresponding to the multiple detection items, calculating the comprehensive score corresponding to the selectable edge equipment; and determining the edge equipment corresponding to the maximum value of the comprehensive score as the current best replacement object.
In a preferred embodiment of the present application, the algorithm server is further configured to select, as the optional edge device, an edge device that is not currently in a data transmission processing state based on a load balancing policy.
In a preferred embodiment of the present application, the energy storage power station operation data includes: cell temperature, power, current, voltage, SOC, and SOH in battery pack in each energy storage container.
In a second aspect, the present application also provides an energy storage power station control system, the energy storage power station control system comprising an EMS, a plurality of BMSs, and a microgrid control system according to the first aspect; the energy storage power station comprises a plurality of energy storage containers; each energy storage container is provided with a BMS; and a plurality of edge devices are arranged in the micro-grid control system, each edge device corresponds to one BMS or a plurality of BMSs, and each edge device is respectively in communication connection with the algorithm server and the EMS.
In a preferred embodiment of the present application, the BMS adopts a three-layered architecture.
In the micro-grid control system and the energy storage power station control system provided by the application, the micro-grid control system is applied to an energy storage power station comprising a plurality of energy storage containers; each energy storage container is provided with a BMS; a plurality of edge devices are arranged in the micro-grid control system, each edge device corresponds to one BMS or a plurality of BMSs, and each edge device is respectively in communication connection with the algorithm server and the EMS; each edge device is used for receiving the operation data of the energy storage power station from the EMS or the associated BMS, carrying out characteristic engineering and data persistence processing on the operation data of the energy storage power station, and sending the processed data to a corresponding algorithm server; the algorithm server calculates the micro-grid operation optimization measure based on the pre-trained neural network model and the data sent by the edge devices, and the application can perform real-time BMS data processing through the edge devices connected with one or more BMSs so as to reduce the data processing pressure of the subsequent algorithm server and improve the data processing performance of the system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an energy storage power station according to an embodiment of the present application;
fig. 2 is a schematic diagram of a micro-grid control system according to an embodiment of the present application;
fig. 3 is a schematic diagram of a three-layer architecture of a BMS according to an embodiment of the present application;
fig. 4 is a block diagram of a control system of an energy storage power station according to an embodiment of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, an energy storage power station generally adopts an EMS to collect running data of a BMS and execute related data processing through other algorithm servers, the data processing amount of the method is large, the operation is complex, the performance requirement on hardware is high, if each BMS is configured with a single edge device with processing capability of executing related calculation and the like, the cost is too high for a large energy storage power station, and once a certain edge device fails, the operation accident of the power station can be caused. Based on the above, the embodiment of the application provides a micro-grid control system and an energy storage power station control system, which can complete real-time processing of operation data of an energy storage power station through edge devices connected with one or more BMSs and an algorithm server connected with each edge device, and improve the data processing performance of the system.
For the sake of understanding the present embodiment, a detailed description will be given of a micro-grid control system disclosed in the embodiment of the present application.
The embodiment of the application provides a micro-grid control system, which is applied to an energy storage power station comprising a plurality of energy storage containers, and is shown in a figure 1; each energy storage container is provided with a BMS; referring to fig. 2, a plurality of edge devices 21 are arranged in the micro-grid control system, each edge device 21 corresponds to one BMS or a plurality of BMSs, and each edge device 21 is respectively in communication connection with an algorithm server 22 and an EMS; that is, the micro-grid control system includes an edge device 21 associated with each BMS or a plurality of BMS (i.e., one edge device associated with each energy storage container, or one edge device associated with several containers), and an algorithm server 22 corresponding to each edge device 21; here, one edge device 21 or a plurality of edge devices 21 may correspond to one algorithm server 22; typically, a plurality of edge devices 21 corresponds to one algorithm server 22; each edge device 21 is communicatively connected to the EMS.
Each edge device 21 is configured to receive energy storage power station operation data from the EMS or the associated BMS, perform feature engineering and data persistence processing on the energy storage power station operation data, and send the processed data to the corresponding algorithm server 22; the algorithm server 22 calculates the microgrid operational optimization measures based on the pre-trained neural network model and the data sent by the edge devices.
The micro-grid control system comprises a plurality of edge devices, wherein the edge devices are provided with double communication interfaces and can be used for communicating with an Energy Management System (EMS) and a Battery Management System (BMS) of an energy storage power station (as shown in fig. 3, the BMS is generally in a three-level architecture and comprises a slave control BMU, a master control BCU and a master control BAU); each energy storage power station is configured with an edge device, and the edge device is used for receiving energy storage power station operation data from an EMS/corresponding power station BMS to perform characteristic engineering and data persistence, wherein the energy storage power station operation data comprise cell temperature, electric quantity, power, current, voltage, SOC, SOH and the like in a battery pack in each energy storage container; the edge equipment processes the operation data of the energy storage power station and then sends the operation data to a corresponding algorithm server; the pre-trained neural network model running in the algorithm server predicts the microgrid running optimization measures based on the data received by the corresponding edge devices.
In the mode, the data processing is split, and the data processing is performed by utilizing a plurality of edge devices and corresponding algorithm servers, so that the data processing pressure of the algorithm servers can be greatly reduced, and the data processing performance of the whole system is improved.
In a preferred embodiment of the present application, each of the edge devices mentioned above defaults to communicate with the EMS to obtain the operation data of the energy storage power station corresponding to the associated BMS; when the specific edge equipment cannot communicate with the EMS, the operation data of the energy storage power station of the corresponding part is automatically switched to be obtained from the associated BMS, and the operation data of the energy storage power station of the corresponding part is subjected to characteristic engineering and data persistence processing and then is sent to the corresponding algorithm server.
In a preferred embodiment of the present application, the algorithm server is further configured to detect a real-time operation state of all isomorphic edge devices, and when determining that a certain edge device fails, send a redirection instruction to a target BMS associated with the failed edge device, so that the target BMS redirects data to be sent, to which a BMS identifier is attached, to the isomorphic edge device of the failed edge device that is in normal operation for processing; wherein, isomorphic edge devices are edge devices that perform the same feature engineering and data persistence.
In the specific implementation, the same algorithm server detects and caches the real-time running states of all edge devices (i.e. isomorphic edge devices) capable of executing the same characteristic engineering and data persistence; the specific detection mode can determine whether specific edge devices normally operate or not through heartbeat, and the current transmission state (such as whether transmission is performed or not, the available transmission bandwidth and the like) of each edge device; when determining that a certain edge device fails, the algorithm server sends a redirection instruction to the BMS associated with the failed edge device, so that the associated BMS redirects data to be sent to the selected alternative edge device for feature engineering and data persistence processing and then sends the data to the corresponding algorithm server.
The above process, namely the rerouting mechanism of the algorithm server, means that when a node or a link fails or is jammed in network communication, the system automatically adjusts the routing path to ensure that data can be normally transmitted. In the edge device micro-grid control system, the rerouting mechanism can realize automatic seamless replacement between the edge devices.
To achieve automatic seamless replacement, the system needs to implement a rerouting mechanism through some algorithm. Meanwhile, the system also needs to monitor the connection between the devices, timely detect the dropped devices and trigger an automatic seamless replacement mechanism so as to ensure the continuity of the micro-grid control system.
The specific measures are as follows:
(1) Each edge device is also used for reporting the current running state information of the edge device to the corresponding algorithm server in real time; the current operating state information includes at least one of: resource condition, network connection condition and working state; that is, each edge device needs to report its own information such as resource condition, network connection condition, working state, etc. to the corresponding algorithm server in real time.
(2) The algorithm server is further configured to periodically check current operation status information of each edge device, and if it is determined that an edge device has a failure or insufficient resources according to the current operation status information, reroute according to a preset replacement policy, and allocate a data task corresponding to the failed or insufficient resources edge device to one edge device with the best computing performance in the isomorphic edge device for execution, for example, an edge device with the most abundant resources and the best network connection.
(3) The algorithm server is further configured to determine, when rerouting, an edge device with the best current computing performance according to the corresponding current running state information of each isomorphic edge device and weights corresponding to multiple detection items of the edge devices.
In specific implementation, the plurality of detection items may include: a plurality of detection items of equipment performance, running state, network quality and reliability; the algorithm server is also used for determining evaluation parameters corresponding to various detection items respectively according to the current running state information of the optional edge equipment for each optional edge equipment; according to the evaluation parameters and weights respectively corresponding to the multiple detection items, calculating the comprehensive score corresponding to the selectable edge equipment; and determining the edge equipment corresponding to the maximum value of the comprehensive score as the current best replacement object. Or the algorithm server is further used for selecting the edge device which is not in the data transmission processing state currently as the optional edge device based on the load balancing strategy.
In summary, in the embodiment of the present application, in rerouting, the algorithm server needs to consider a plurality of factors, such as performance, operation status, network quality, reliability (such as considering network delay, integrity of data transmission, security, etc. to ensure continuity and stability of the micro-network control system) and so on. Corresponding weights can be set according to the factors, and finally the optimal equipment is selected as an alternative connection object. For example, based on load balancing, an edge device that is not currently in a data transmission/processing state and has the largest available transmission bandwidth is selected as a substitute edge device, and of course, further, the processing load (available memory, CPU, and other resources) of each edge device may also be considered.
After selecting the replacement edge device, i.e. the current best replacement object, the algorithm server needs to send a redirection instruction to the BMS (BAU) associated with the primary edge device, so that the associated BMS can send data to the replacement edge device; and sending instructions to the original edge equipment (which is not needed when the original edge fails) and replacing the edge equipment, and transferring and distributing the tasks to ensure that the tasks can be smoothly carried out.
The embodiment of the application provides a distributed micro-grid control system, which can ensure that the system can run seamlessly even if one device fails. In addition, as the edge equipment can perform feature engineering and data persistence, the efficiency of the micro-grid control system can be improved while the data transmission is reduced. Finally, since the system has a seamless device replacement mechanism, continuity of the system can be maintained in the event of a device failure. In the event of a device failure, the system has a seamless device replacement mechanism. If an edge device fails, other edge devices in the network may take over the service of the failed device to ensure that the system remains operational. In particular, when one edge device drops, the other edge device may take over the services of the device by rerouting to ensure that the entire system continues to operate. This process is automatic and does not require manual intervention.
Based on the above-mentioned micro-grid control system, an embodiment of the present application further provides an energy storage power station control system, as shown in fig. 4, where the energy storage power station control system includes an EMS41, a plurality of BMSs 42, and a micro-grid control system 43 according to the first aspect; the energy storage power station comprises a plurality of energy storage containers; each energy storage container is configured with a BMS42; the micro-grid control system 43 is provided with a plurality of edge devices 21, each edge device 21 corresponds to one BMS42 or a plurality of BMSs 42, and each edge device 21 is respectively in communication connection with the algorithm server 22 and the EMS 41.
Because the battery pack is large in scale, the energy storage BMS is mostly of a three-layer architecture, and a layer of master control is arranged on the slave control and the master control.
Slave control: the battery cell management unit BMU (Battery Module Unit, also called CSC/CSU and the like) is responsible for collecting information such as voltage and temperature of the single battery, calculating and analyzing the SOC and SOH of the battery, realizing active equalization of the single battery, and uploading abnormal information of the single battery to the battery cluster management unit BCU through CAN communication;
and (2) main control: the battery cluster management unit BCU (Battery Cluster Unit, also has high-voltage management units HVU, BCMU and the like) is responsible for collecting various battery information uploaded by the BMU, collecting battery pack voltage, battery pack temperature, battery pack charging and discharging current and total voltage information, detecting electric leakage and protecting power failure when the state is abnormal; calculating and analyzing the SOC and SOH of the battery pack, and uploading all information to a battery array management unit BAU through CAN communication;
and (3) general control: the battery array management unit BAU (Battery Array Unit, sometimes also referred to as BAMS, MBMS, etc.) centrally manages the batteries of the entire energy storage power station. Each battery cluster management unit is connected downwards, and various information uploaded by the battery cluster management units is collected; information interaction is carried out on the battery information and the energy management system, and battery operation parameters issued by the EMS system are received through battery information uploaded and collected by the Ethernet; and the battery state quantity and abnormal information are sent to the converter by the BMS through communication of the CAN or the RS485 and the converter PCS, and the energy storage converter PCS receives the BMS alarm information and then performs corresponding protection action.
The implementation principle and the generated technical effects of the energy storage power station control system provided by the embodiment of the application are the same as those of the embodiment of the micro-grid control system, and for the sake of brief description, reference may be made to corresponding contents in the embodiment of the micro-grid control system where the embodiment of the energy storage power station control system is not mentioned.
The embodiment of the application also provides a computer readable storage medium, which stores computer executable instructions that, when being called and executed by a processor, cause the processor to implement the method of the algorithm server as follows:
and carrying out microgrid operation optimization measure prediction on data sent by the corresponding edge equipment through a pre-trained neural network model.
Detecting real-time running states of all isomorphic edge devices, and when a fault of a target isomorphic edge device is determined, sending a redirection instruction to a target BMS (management system) associated with the target isomorphic edge device so as to redirect data to be sent of the target BMS to the selected edge device for substitution; wherein, isomorphic edge devices are edge devices that perform the same feature engineering and data persistence.
And periodically checking the current running state information of each edge device, if the target edge device is determined to have a fault or insufficient resources according to the current running state information, rerouting according to a preset replacement policy, and distributing the data task corresponding to the target edge device to the edge device which is the current best replacement object, for example, the edge device with the most sufficient resources and the best network connection.
For each selectable edge device, determining evaluation parameters corresponding to various detection items respectively according to the current running state information of the selectable edge device; according to the evaluation parameters and weights respectively corresponding to the multiple detection items, calculating the comprehensive score corresponding to the selectable edge equipment; and determining the edge equipment corresponding to the maximum value of the comprehensive score as the current best replacement object. Or the algorithm server is further used for selecting the edge device which is not in the data transmission processing state currently as the optional edge device based on the load balancing strategy.
The specific process may be referred to in the foregoing, and will not be described herein.
The method, the apparatus and the computer program product of the electronic device provided in the embodiments of the present application include a computer readable storage medium storing program codes, where the instructions included in the program codes may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment and will not be described herein.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the description of the present application, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. A microgrid control system, characterized in that the microgrid control system is applied to an energy storage power station comprising a plurality of energy storage containers; each of the energy storage containers is configured with a BMS; a plurality of edge devices are arranged in the micro-grid control system, each edge device corresponds to a plurality of BMSs, and each edge device is respectively in communication connection with an algorithm server and an EMS;
each edge device is used for receiving energy storage power station operation data from the EMS or the associated BMS, performing characteristic engineering and data persistence processing on the energy storage power station operation data, and sending the processed data to a corresponding algorithm server;
the algorithm server calculates micro-grid operation optimization measures based on a pre-trained neural network model and data sent by edge equipment;
each edge device defaults to communicate with the EMS to obtain energy storage power station operation data corresponding to the associated BMS; when the edge equipment cannot communicate with the EMS, automatically switching to acquire operation data of the energy storage power station of a corresponding part from the associated BMS;
the algorithm server is also used for detecting the real-time running states of all isomorphic edge devices, and when a certain edge device is determined to be faulty, a redirection instruction is sent to a target BMS (management system) associated with the faulty edge device, so that the target BMS redirects data to be sent with the BMS identifier to the isomorphic edge device of the faulty edge device for processing, wherein the isomorphic edge device is in normal operation; wherein the isomorphic edge device is an edge device executing the same feature engineering and data persistence;
the algorithm server is further used for determining evaluation parameters corresponding to various detection items respectively according to the current running state information of the isomorphic edge equipment for each isomorphic edge equipment when rerouting is performed, and calculating the comprehensive score of the isomorphic edge equipment according to the evaluation parameters and the weights corresponding to the various detection items respectively; taking isomorphic edge equipment with the maximum comprehensive score as a current best replacement object; the plurality of detection items includes: device performance, operating status, network quality, reliability.
2. The microgrid control system according to claim 1, wherein each of said edge devices is further configured to report its current running state information to said algorithm server in real time; the current operating state information includes at least one of: resource condition, network connection condition and working state;
the algorithm server is also used for periodically checking the current running state information of each edge device, if the edge device is determined to have faults or insufficient resources according to the current running state information, rerouting is performed according to a preset replacement strategy, and the data task corresponding to the faulty or insufficient resources edge device is distributed to one edge device with the best computing performance in the isomorphic edge device for execution.
3. The micro network control system of claim 1, wherein the algorithm server is further configured to select an edge device that is not currently in a data transmission processing state as the optional edge device based on a load balancing policy.
4. The microgrid control system according to claim 1, wherein said energy storage power plant operation data comprises: cell temperature, power, current, voltage, SOC, and SOH in battery pack in each energy storage container.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310685401.4A CN116418124B (en) | 2023-06-12 | 2023-06-12 | Micro-grid control system and energy storage power station control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310685401.4A CN116418124B (en) | 2023-06-12 | 2023-06-12 | Micro-grid control system and energy storage power station control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116418124A CN116418124A (en) | 2023-07-11 |
| CN116418124B true CN116418124B (en) | 2023-10-13 |
Family
ID=87056370
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310685401.4A Active CN116418124B (en) | 2023-06-12 | 2023-06-12 | Micro-grid control system and energy storage power station control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116418124B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112732443A (en) * | 2021-01-12 | 2021-04-30 | 徐州普罗顿氢能储能产业研究院有限公司 | Energy storage power station state evaluation and operation optimization system based on edge calculation |
| KR102324325B1 (en) * | 2021-03-17 | 2021-11-11 | 주식회사 트위니 | Energy storage system hierarchical management system |
| CN113644745A (en) * | 2021-09-16 | 2021-11-12 | 国网湖南省电力有限公司 | Energy storage power station intelligence operation and maintenance analytic system based on cloud pipe limit end |
| CN114547190A (en) * | 2022-01-21 | 2022-05-27 | 中国长江三峡集团有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN115291109A (en) * | 2022-07-22 | 2022-11-04 | 中国长江三峡集团有限公司 | Monitoring method and system of battery energy storage system, electronic equipment and storage medium |
| CN115604189A (en) * | 2022-09-30 | 2023-01-13 | 云南大学（Cn） | Data collaboration method and device for edge computing device |
-
2023
- 2023-06-12 CN CN202310685401.4A patent/CN116418124B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112732443A (en) * | 2021-01-12 | 2021-04-30 | 徐州普罗顿氢能储能产业研究院有限公司 | Energy storage power station state evaluation and operation optimization system based on edge calculation |
| KR102324325B1 (en) * | 2021-03-17 | 2021-11-11 | 주식회사 트위니 | Energy storage system hierarchical management system |
| CN113644745A (en) * | 2021-09-16 | 2021-11-12 | 国网湖南省电力有限公司 | Energy storage power station intelligence operation and maintenance analytic system based on cloud pipe limit end |
| CN114547190A (en) * | 2022-01-21 | 2022-05-27 | 中国长江三峡集团有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN115291109A (en) * | 2022-07-22 | 2022-11-04 | 中国长江三峡集团有限公司 | Monitoring method and system of battery energy storage system, electronic equipment and storage medium |
| CN115604189A (en) * | 2022-09-30 | 2023-01-13 | 云南大学（Cn） | Data collaboration method and device for edge computing device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116418124A (en) | 2023-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8836285B2 (en) | Power management system | |
| CN106340968B (en) | Integrated support system and method for power distribution network management and control | |
| KR101835575B1 (en) | Dynamically reconfigurable framework for a large-scale battery system | |
| WO2022193899A1 (en) | Balance monitoring control system and method for power battery, server, and storage medium | |
| CN208522051U (en) | Electrochemical energy storage system | |
| CN115902646B (en) | Energy storage battery fault identification method and system | |
| WO2015040725A1 (en) | Storage battery system | |
| KR20220095313A (en) | Digital Twin Based Distributed Energy Resource and Power Line Management Plan System and Method thereof | |
| CN109193045A (en) | Electrochemical energy storage system and its control method | |
| CN110535156B (en) | New forms of energy storage battery distributed control system | |
| CN111509319A (en) | PHM management system for energy storage power supply | |
| CN115800555A (en) | Energy storage system | |
| JP6288096B2 (en) | Battery system | |
| WO2015040723A1 (en) | Storage battery system | |
| CN116418124B (en) | Micro-grid control system and energy storage power station control system | |
| KR20230036776A (en) | System and method for fault diagnosis of fuel cell energy management system based on digital twin | |
| CN115483763B (en) | Lead-acid battery energy storage power station monitoring management system and method | |
| CN115664932B (en) | Energy block parallel communication method and device | |
| CN114978989B (en) | Space application load control method | |
| KR102443567B1 (en) | Energy storage management system and management method for active energy storage of renewable energy of the same | |
| JP7032608B2 (en) | Judgment of resilience in the microgrid | |
| KR101734149B1 (en) | Transmission Line Overload Management Method Using Energy Storage System | |
| CN117239937B (en) | Data interaction method and device of battery management system and storage medium | |
| KR101734148B1 (en) | Congestion Management System For Transmission Line Overload Mitigation | |
| KR101734151B1 (en) | Power System Management Method For Transmission Line Overload Mitigation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |