An artificial intelligence-based microgrid operation monitoring system

Abstract

The application discloses a kind of microgrid operation supervision systems based on artificial intelligence, it is related to artificial intelligence technical field;The microgrid structure is obtained by the present application, and the microgrid structure data network is obtained according to the microgrid structure;Through the data acquisition of microgrid structure data network by distributed power data unit, energy storage data unit and power consumption data unit, microgrid acquisition time group data network is established;Secondly, energy conversion data is collected using energy conversion unit and mapped in microgrid acquisition time group data network;Using energy processing unit, microgrid data group in microgrid acquisition time group data network is extracted and processed, first energy conversion loss rate and second energy scheduling data value are obtained, and then microgrid operation state group is obtained;Finally, energy coordination data monitoring model is established by energy coordination module and sent to monitoring module, abnormal coordination signal is sent to adjusting module and microgrid is managed;Effectively improve the stability of microgrid.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to an artificial intelligence-based microgrid operation monitoring system. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. The purpose of microgrids is to realize the flexible and efficient application of distributed power sources and solve the grid connection problem of a large number of diverse distributed power sources. The development and extension of microgrids can fully promote the large-scale access of distributed power sources and renewable energy, realize highly reliable supply of multiple energy forms to loads, and is an effective way to realize active distribution networks, enabling the transition of traditional power grids to smart grids.

[0003] The energy conversion device is mainly responsible for converting different forms of electrical energy to meet the needs of various load devices within the microgrid. In the microgrid, renewable energy output from distributed power sources such as photovoltaic power generation, wind power generation, fuel cells, and gas turbines is stored by storage devices, and then converted into AC power by energy exchange devices for use by households, businesses, and industries, acting as a bridge between distributed power sources and loads. However, when the power supply load is overloaded or the automatic adjustment is unbalanced, abnormal fluctuations occur during the conversion process, thus affecting the stability of the microgrid. Therefore, an artificial intelligence-based microgrid operation monitoring system is provided. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a microgrid operation monitoring system based on artificial intelligence;

[0005] The objective of this invention can be achieved through the following technical solution: a microgrid operation monitoring system based on artificial intelligence, the system comprising a status data acquisition module, a conversion and processing module, an energy coordination module, a monitoring module, and a regulation module;

[0006] The status data acquisition module includes a distributed power data unit, an energy storage data unit, and a power consumption data unit: used to acquire the microgrid structure, acquire the microgrid structure data network based on the microgrid structure; and to acquire data from the microgrid structure data network through the distributed power data unit, energy storage data unit, and power consumption data unit to establish a microgrid acquisition time group data network.

[0007] The conversion and processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it onto the microgrid acquisition time group data network; the energy processing unit is used to extract microgrid data groups from the microgrid acquisition time group data network and process them to obtain the first energy conversion loss rate and the second energy scheduling data value, thereby obtaining the microgrid operating status group;

[0008] The energy coordination module is used to map the microgrid operating status group to the time group node corresponding to the microgrid acquisition time group data network to establish an energy coordination data monitoring model.

[0009] The monitoring module is used to extract the microgrid operating status group corresponding to several consecutive time group nodes in the energy coordination data monitoring model, and to obtain abnormal coordination signals.

[0010] The regulation module is used to manage the microgrid based on abnormal coordination signals.

[0011] Furthermore, the process of acquiring the microgrid structure data network includes:

[0012] In the microgrid structure, distributed power sources, energy storage devices, energy conversion devices, and loads are correspondingly generated as power measurement nodes, storage device nodes, energy conversion nodes, and user nodes. Power grid transmission channels are set up to connect the power measurement nodes, storage device nodes, and user nodes sequentially through the power grid transmission channels, and then the energy conversion nodes are distributed on each power grid transmission channel to obtain the microgrid structure data network.

[0013] Furthermore, the process of establishing the microgrid acquisition time group data network includes:

[0014] The distributed power data unit, energy storage data unit, and electricity consumption data unit are respectively used to collect corresponding power generation data, energy storage data, and electricity consumption data based on the power measurement node, storage device node, and user node.

[0015] Set the acquisition time node t at the power measurement node i Where i is the data acquisition node number and takes a positive integer value, and the transmission time is set in the power grid transmission channel. Based on the transmission time, obtain the acquisition time nodes corresponding to the storage device node and the user node, denoted as the storage time node and the user time node, respectively. and And generate a data collection time group with the collection time node;

[0016] Furthermore, the distributed power data unit, energy storage data unit, and power consumption data unit collect corresponding power generation data, energy storage data, and power consumption data in real time according to the data acquisition time group, and connect them to generate a microgrid data group;

[0017] Several data acquisition time groups are generated into corresponding time group nodes, and microgrid data groups are mapped to the corresponding time group nodes. Then, the time group nodes are interconnected to establish a microgrid data acquisition time group network.

[0018] Furthermore, the process of acquiring the energy conversion data includes:

[0019] Based on the data acquisition time group, the first energy conversion acquisition time point and the second energy conversion acquisition time point corresponding to the energy conversion node are obtained and denoted as follows: and The energy conversion data corresponding to the energy conversion node is then collected and mapped onto the time group node corresponding to the microgrid data collection time group network; wherein, the energy conversion data includes converted energy data, pre-stored energy data and scheduled energy data.

[0020] Furthermore, the process of acquiring the microgrid operating status group includes:

[0021] Extract the microgrid data group and energy conversion data from the corresponding time group node of the microgrid acquisition time group data network, process them, and obtain the first energy conversion loss rate and the second energy dispatch data value;

[0022] The specific formula for obtaining the first energy conversion loss rate is:

[0023] ;

[0024] in, This is expressed as the first energy conversion loss rate; This represents the power generation data collected at the corresponding time point. This represents the storage energy data collected at the corresponding storage time node; This represents the converted energy data collected at the first energy conversion acquisition time point, and ;

[0025] like ,but Then, the energy conversion node corresponding to the first energy conversion acquisition time point is marked as a high-quality energy conversion node;

[0026] Conversely, when If the energy conversion node is abnormal, the energy conversion node corresponding to the first energy conversion acquisition time point will be marked as an abnormal energy conversion node, and the state corresponding to the energy conversion node will be generated as an energy conversion loss state; otherwise, the corresponding energy conversion node will be generated as a normal conversion node.

[0027] Furthermore, the specific formula for obtaining the second energy scheduling data value is as follows:

[0028] ;

[0029] in, This is represented as the second energy scheduling data value; This represents the scheduling energy data collected at the second energy conversion acquisition time point, and C represents pre-stored energy data; This represents the electricity consumption data collected by the user node.

[0030] like If the energy conversion node is normal, the energy conversion node corresponding to the second energy conversion acquisition time point will be marked as a normal scheduling node; otherwise, the corresponding energy conversion node will be marked as an abnormal scheduling node, and the state corresponding to the energy conversion node will be generated as an insufficient energy supply state.

[0031] The states corresponding to high-quality energy conversion nodes, normal conversion nodes, and normal scheduling nodes are generated into normal states, while energy conversion loss states and insufficient energy supply states are marked as abnormal states. The obtained states are then used to generate a microgrid operation state group.

[0032] Furthermore, the process by which the monitoring module acquires abnormal coordination signals includes:

[0033] Set a monitoring cycle group N, where N > 0. Extract the corresponding time group nodes based on the monitoring cycle group, obtain the corresponding microgrid operation status groups, and extract the status of the same energy conversion node in each microgrid operation status group. Then, obtain the proportion K of abnormal states of the energy conversion node in the monitoring cycle group, i.e., the calculation formula is: Where M is the number of abnormal states;

[0034] If K > 0.3, the corresponding energy conversion node will generate an abnormal coordination signal and send it to the regulation module; otherwise, no action will be taken.

[0035] Furthermore, the process by which the regulation module manages the microgrid includes:

[0036] The adjustment module is connected to a management terminal, which is used to receive abnormal coordination signals, obtain the energy conversion node corresponding to the abnormal coordination signals, and then obtain the corresponding abnormal state.

[0037] If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired;

[0038] If the energy supply is insufficient, it means that the power supply is insufficient and energy scheduling is required for the energy storage device corresponding to the storage device node connected to the energy conversion node.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains the microgrid structure and acquires a microgrid structure data network based on the microgrid structure; it collects data from the microgrid structure data network through distributed power data units, energy storage data units, and power consumption data units to establish a microgrid acquisition time group data network; secondly, it uses an energy conversion unit to collect energy conversion data and map it onto the microgrid acquisition time group data network; it uses an energy processing unit to extract and process the microgrid data groups in the microgrid acquisition time group data network to obtain the first energy conversion loss rate and the second energy scheduling data value, thereby obtaining the microgrid operating status group; finally, it establishes an energy coordination data monitoring model through an energy coordination module and sends it to a monitoring module, acquires abnormal coordination signals and sends them to a regulation module for microgrid management; thus effectively improving the stability of the microgrid. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0041] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0043] Example 1

[0044] like Figure 1 As shown, an artificial intelligence-based microgrid operation monitoring system includes a status data acquisition module, a conversion and processing module, an energy coordination module, a monitoring module, and a regulation module.

[0045] The status data acquisition module includes a distributed power data unit, an energy storage data unit, and a power consumption data unit: used to acquire the microgrid structure, acquire the microgrid structure data network based on the microgrid structure; and to acquire data from the microgrid structure data network through the distributed power data unit, energy storage data unit, and power consumption data unit to establish a microgrid acquisition time group data network.

[0046] The conversion and processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it onto the microgrid acquisition time group data network; the energy processing unit is used to extract microgrid data groups from the microgrid acquisition time group data network and process them to obtain the first energy conversion loss rate and the second energy scheduling data value, thereby obtaining the microgrid operating status group;

[0047] The energy coordination module is used to map the microgrid operating status group to the time group node corresponding to the microgrid acquisition time group data network to establish an energy coordination data monitoring model.

[0048] The monitoring module is used to extract the microgrid operating status group corresponding to several consecutive time group nodes in the energy coordination data monitoring model, and to obtain abnormal coordination signals.

[0049] The regulation module is used to manage the microgrid based on abnormal coordination signals.

[0050] Example 2

[0051] This embodiment further defines Embodiment 1, and the process of acquiring the microgrid structure data network includes:

[0052] In the microgrid structure, distributed power sources, energy storage devices, energy conversion devices, and loads are correspondingly generated as power measurement nodes, storage device nodes, energy conversion nodes, and user nodes; power grid transmission channels are set up, and the power measurement nodes, storage device nodes, and user nodes are connected sequentially through the power grid transmission channels, thereby distributing the energy conversion nodes on each power grid transmission channel to obtain the microgrid structure data network;

[0053] The process of establishing the microgrid acquisition time group data network includes:

[0054] The distributed power data unit, energy storage data unit, and electricity consumption data unit are respectively used to collect corresponding power generation data, energy storage data, and electricity consumption data based on the power measurement node, storage device node, and user node.

[0055] Set the acquisition time node t at the power measurement node i Where i is the data acquisition node number and takes a positive integer value, and the transmission time is set in the power grid transmission channel. Based on the transmission time, obtain the acquisition time nodes corresponding to the storage device node and the user node, denoted as the storage time node and the user time node, respectively. and And generate a data collection time group with the collection time node;

[0056] Furthermore, the distributed power data unit, energy storage data unit, and power consumption data unit collect corresponding power generation data, energy storage data, and power consumption data in real time according to the data acquisition time group, and connect them to generate a microgrid data group;

[0057] Several data acquisition time groups are generated into corresponding time group nodes, and microgrid data groups are mapped to the corresponding time group nodes. Then, the time group nodes are interconnected to establish a microgrid data acquisition time group network.

[0058] Example 3

[0059] This embodiment further defines Embodiment 1, and the process of acquiring the energy conversion data includes:

[0060] Based on the data acquisition time group, the first energy conversion acquisition time point and the second energy conversion acquisition time point corresponding to the energy conversion node are obtained and denoted as follows: and The system then collects energy conversion data corresponding to the energy conversion nodes and maps it to the time group nodes corresponding to the microgrid's time group data network; wherein, the energy conversion data includes converted energy data, pre-stored energy data, and scheduled energy data;

[0061] The process of acquiring the microgrid operating status group includes:

[0062] Extract the microgrid data group and energy conversion data from the corresponding time group node of the microgrid acquisition time group data network, process them, and obtain the first energy conversion loss rate and the second energy dispatch data value;

[0063] The specific formula for obtaining the first energy conversion loss rate is:

[0064] ;

[0065] in, This is expressed as the first energy conversion loss rate; This represents the power generation data collected at the corresponding time point. This represents the storage energy data collected at the corresponding storage time node; This represents the converted energy data collected at the first energy conversion acquisition time point, and ;

[0066] like ,but Then, the energy conversion node corresponding to the first energy conversion acquisition time point is marked as a high-quality energy conversion node;

[0067] Conversely, when If the energy conversion node is abnormal, the energy conversion node corresponding to the first energy conversion acquisition time point will be marked as an abnormal energy conversion node, and the state corresponding to the energy conversion node will be generated as an energy conversion loss state; otherwise, the corresponding energy conversion node will be generated as a normal conversion node.

[0068] It should be further explained that, in the specific embodiment, the energy loss during the conversion and transmission of power generation data through the power grid transmission channel is negligible. Therefore, by obtaining the loss data during the conversion process based on the power generation data, energy conversion data, and stored energy data, it is possible to determine whether the corresponding energy conversion device is abnormal.

[0069] The specific formula for obtaining the second energy scheduling data value is as follows:

[0070] ;

[0071] in, This is represented as the second energy scheduling data value; This represents the scheduling energy data collected at the second energy conversion acquisition time point, and C represents pre-stored energy data; This represents the electricity consumption data collected by the user node.

[0072] like If the energy conversion node is normal, the energy conversion node corresponding to the second energy conversion acquisition time point will be marked as a normal scheduling node; otherwise, the corresponding energy conversion node will be marked as an abnormal scheduling node, and the state corresponding to the energy conversion node will be generated as an insufficient energy supply state.

[0073] The states corresponding to high-quality energy conversion nodes, normal conversion nodes, and normal scheduling nodes are generated into normal states, and the states of energy conversion loss and insufficient energy supply are marked as abnormal states. Then, the obtained states are used to generate a microgrid operation state group.

[0074] In the above embodiments, it should be further explained that the energy conversion device plays a significant role in the microgrid, including energy conversion, energy storage, and energy release.

[0075] Example 4

[0076] This embodiment further defines Embodiment 1. The process by which the monitoring module acquires abnormal coordination signals includes:

[0077] Set a monitoring cycle group N, where N > 0. Extract the corresponding time group nodes based on the monitoring cycle group, obtain the corresponding microgrid operation status groups, and extract the status of the same energy conversion node in each microgrid operation status group. Then, obtain the proportion K of abnormal states of the energy conversion node in the monitoring cycle group, i.e., the calculation formula is: Where M is the number of abnormal states;

[0078] If K > 0.3, the corresponding energy conversion node will generate an abnormal coordination signal and send it to the regulation module; otherwise, no action will be taken.

[0079] In the above embodiments, it should be further explained that, based on the energy collaborative data monitoring model, the microgrid data acquisition time group data network collects data from the nodes corresponding to the microgrid structure in real time, generates data groups from the collected data, and stores and retrieves data from time group nodes, thereby improving the data transmission speed. By processing the data groups, the microgrid operating status group is obtained, and a monitoring cycle group is set to analyze the microgrid operating data group, avoiding the randomness of the status. In this way, the stability of the microgrid can be monitored in real time through the energy collaborative data monitoring model, thereby improving the balance state of the microgrid.

[0080] Example 5

[0081] This embodiment further defines Embodiment 1, and the process by which the regulation module manages the microgrid includes:

[0082] The adjustment module is connected to a management terminal, which is used to receive abnormal coordination signals, obtain the energy conversion node corresponding to the abnormal coordination signals, and then obtain the corresponding abnormal state.

[0083] If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired to ensure the stability of energy conversion;

[0084] If the energy supply is insufficient, it indicates that the power supply is insufficient. It is necessary to perform energy scheduling on the energy storage devices corresponding to the energy conversion nodes and reduce the load on the user nodes to ensure the stability of energy scheduling.

[0085] Working Principle: This invention acquires the microgrid structure and obtains a microgrid structure data network based on it. Data is collected from this data network using distributed power generation data units, energy storage data units, and electricity consumption data units to establish a microgrid data acquisition time group network. Next, energy conversion units collect energy conversion data and map it onto the microgrid data acquisition time group network. An energy processing unit extracts and processes the microgrid data groups from the data acquisition time group network to obtain the first energy conversion loss rate and the second energy scheduling data value, thereby obtaining the microgrid operating status group. Finally, an energy coordination module establishes an energy coordination data monitoring model and sends it to the monitoring module. Abnormal coordination signals are then sent to the regulation module for microgrid management. This effectively improves the stability of the microgrid.

[0086] The features and exemplary embodiments of various aspects of this application will be described in detail above. In order to make the purpose, technical solution and advantages of this application clearer, the application will be further described in detail above with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit this application. For those skilled in the art, this application can be implemented without some of the details in these specific details. The above description of the embodiments is only to provide a better understanding of this application by showing examples of this application.

[0087] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A microgrid operation monitoring system based on artificial intelligence, characterized in that, The system includes a status data acquisition module, a conversion and processing module, an energy coordination module, a monitoring module, and a regulation module; The status data acquisition module includes a distributed power data unit, an energy storage data unit, and a power consumption data unit: used to acquire the microgrid structure, acquire the microgrid structure data network based on the microgrid structure; and to acquire data from the microgrid structure data network through the distributed power data unit, energy storage data unit, and power consumption data unit to establish a microgrid acquisition time group data network. The conversion and processing module includes an energy conversion unit and an energy processing unit; the energy conversion unit is used to collect energy conversion data and map it onto the microgrid acquisition time group data network; the energy processing unit is used to extract microgrid data groups from the microgrid acquisition time group data network and process them to obtain the first energy conversion loss rate and the second energy scheduling data value, thereby obtaining the microgrid operating status group; The energy coordination module is used to map the microgrid operating status group to the time group node corresponding to the microgrid acquisition time group data network to establish an energy coordination data monitoring model. The monitoring module is used to extract the microgrid operating status group corresponding to several consecutive time group nodes in the energy coordination data monitoring model, and to obtain abnormal coordination signals. The regulation module is used to manage the microgrid based on abnormal coordination signals; The process of acquiring the microgrid structure data network includes: In the microgrid structure, distributed power sources, energy storage devices, energy conversion devices, and loads are correspondingly generated as power measurement nodes, storage device nodes, energy conversion nodes, and user nodes; power grid transmission channels are set up, and the power measurement nodes, storage device nodes, and user nodes are connected sequentially through the power grid transmission channels, thereby distributing the energy conversion nodes on each power grid transmission channel to obtain the microgrid structure data network; The process of establishing the microgrid acquisition time group data network includes: The distributed power data unit, energy storage data unit, and electricity consumption data unit are used to collect corresponding power generation data, stored energy data, and electricity consumption data according to the power measurement node, storage device node, and user node, respectively. Set the acquisition time node t at the power measurement node i Where i is the data acquisition node number and takes a positive integer value, and the transmission time is set in the power grid transmission channel. Based on the transmission time, obtain the acquisition time nodes corresponding to the storage device node and the user node, denoted as the storage time node and the user time node, respectively. and And generate a data collection time group with the collection time node; Furthermore, the distributed power data unit, energy storage data unit, and power consumption data unit collect corresponding power generation data, energy storage data, and power consumption data in real time according to the data acquisition time group, and connect them to generate a microgrid data group; Several data acquisition time groups are generated into corresponding time group nodes, and microgrid data groups are mapped to the corresponding time group nodes. Then, the time group nodes are interconnected to establish a microgrid data acquisition time group network.

2. The microgrid operation monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of acquiring the energy conversion data includes: Based on the data acquisition time group, the first energy conversion acquisition time point and the second energy conversion acquisition time point corresponding to the energy conversion node are obtained and denoted as follows: and The energy conversion data corresponding to the energy conversion node is then collected and mapped onto the time group node corresponding to the microgrid data collection time group network; wherein, the energy conversion data includes converted energy data, pre-stored energy data and scheduled energy data.

3. The microgrid operation monitoring system based on artificial intelligence according to claim 2, characterized in that, The process of acquiring the microgrid operating status group includes: Extract the microgrid data group and energy conversion data from the corresponding time group node of the microgrid acquisition time group data network, process them, and obtain the first energy conversion loss rate and the second energy dispatch data value; The specific formula for obtaining the first energy conversion loss rate is: ； in, This is expressed as the first energy conversion loss rate; This represents the power generation data collected at the corresponding time point. This represents the storage energy data collected at the corresponding storage time node; This represents the converted energy data collected at the first energy conversion acquisition time point, and ; like ,but Then, the energy conversion node corresponding to the first energy conversion acquisition time point is marked as a high-quality energy conversion node; Conversely, when If the energy conversion node is abnormal, the energy conversion node corresponding to the first energy conversion acquisition time point will be marked as an abnormal energy conversion node, and the state corresponding to the energy conversion node will be generated as an energy conversion loss state; otherwise, the corresponding energy conversion node will be generated as a normal conversion node.

4. The microgrid operation monitoring system based on artificial intelligence according to claim 3, characterized in that, The specific formula for obtaining the second energy scheduling data value is as follows: ； in, This is represented as the second energy scheduling data value; This represents the scheduling energy data collected at the second energy conversion acquisition time point, and C represents pre-stored energy data; This represents the electricity consumption data collected by the user node. like If the energy conversion node is normal, the energy conversion node corresponding to the second energy conversion acquisition time point will be marked as a normal scheduling node; otherwise, the corresponding energy conversion node will be marked as an abnormal scheduling node, and the state corresponding to the energy conversion node will be generated as an insufficient energy supply state. The states corresponding to high-quality energy conversion nodes, normal conversion nodes, and normal scheduling nodes are generated into normal states, while energy conversion loss states and insufficient energy supply states are marked as abnormal states. The obtained states are then used to generate a microgrid operation state group.

5. The microgrid operation monitoring system based on artificial intelligence according to claim 4, characterized in that, The process by which the monitoring module acquires abnormal coordination signals includes: Set a monitoring cycle group N, where N > 0. Extract the corresponding time group nodes based on the monitoring cycle group, obtain the corresponding microgrid operation status groups, and extract the status of the same energy conversion node in each microgrid operation status group. Then, obtain the proportion K of abnormal states of the energy conversion node in the monitoring cycle group, i.e., the calculation formula is: Where M is the number of abnormal states; If K > 0.3, the corresponding energy conversion node will generate an abnormal coordination signal and send it to the regulation module; otherwise, no action will be taken.

6. The microgrid operation monitoring system based on artificial intelligence according to claim 5, characterized in that, The process by which the regulation module manages the microgrid includes: The adjustment module is connected to a management terminal, which is used to receive abnormal coordination signals, obtain the energy conversion node corresponding to the abnormal coordination signals, and then obtain the corresponding abnormal state. If it is an energy conversion loss state, it means that the energy conversion device corresponding to the energy conversion node needs to be repaired; If the energy supply is insufficient, it means that the power supply is insufficient and energy scheduling is required for the energy storage device corresponding to the storage device node connected to the energy conversion node.

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