Energy storage monitoring method and system based on artificial intelligence

Abstract

The invention is suitable for the technical field of energy storage monitoring, and particularly relates to an energy storage monitoring method and system based on artificial intelligence, and the method comprises the steps: S100: building an energy contract template, embedding the use authority of energy-saving equipment, determining an energy consumption unit in a region, generating an energy management protocol, and issuing the use authority of the energy-saving equipment to the energy consumption unit, a revenue sharing mechanism is established; and S200, transmitting redundant electric quantity circulating in the energy-saving equipment to energy storage equipment, configuring a one-to-one correspondence relationship between the energy storage equipment and an energy consumption unit, defining the energy storage equipment as nodes, extracting attributes of the nodes, and determining a coverage range of each node. By constructing the micro-grid, peak electricity price can be avoided, the energy independence of an energy consumption unit can be improved, the energy storage equipment can be connected into the power system through an open interface, continuous power supply guarantee is provided for the energy consumption unit, peak clipping and valley leveling can be performed on the power system, and the stability of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage monitoring, and in particular to an energy storage monitoring method and system based on artificial intelligence. Background Art

[0002] Energy storage is the storage of electrical energy and its release when needed. Its main purpose is to balance the supply and demand of electricity, and to improve the stability and efficiency of the power system by storing excess electrical energy and providing electricity when demand peaks. Energy storage methods include at least: battery storage, flywheel, compressed air and pumped storage.

[0003] Energy storage monitoring refers to the process of real-time monitoring and management of energy storage systems. AI-based energy storage monitoring uses AI algorithms to process and analyze energy storage systems in real time to ensure the normal operation of the system. This technology can not only predict power demand and optimize the charging and discharging strategies of energy storage systems, but also smooth out the peaks and fill the valleys of power loads.

[0004] However, there are relatively few participants in energy storage at present, and the balancing ability of the power system is relatively limited; therefore, "how to give full play to the subjective initiative of enterprises and encourage them to store energy" is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The purpose of the present invention is to provide an energy storage monitoring method and system based on artificial intelligence to solve the problem of "how to give full play to the subjective initiative of enterprises and encourage enterprises to store energy" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: An energy storage monitoring method based on artificial intelligence, the method comprising: S100: Establish an energy contract template, embed the use rights of energy-saving equipment, determine the energy-consuming units in the area, generate an energy management agreement, issue the use rights of energy-saving equipment to the energy-consuming units, and establish a profit sharing mechanism; S200: The redundant power flowing in the energy-saving device is transmitted to the energy storage device, a one-to-one correspondence between the energy storage device and the energy-consuming unit is configured, and the energy storage device is defined as a node, the attributes of the node are extracted, the coverage of each node is determined, all nodes are aggregated, a microgrid is constructed, and the interface of the microgrid is marked; S300: traverse the loads in the node, where the loads are used to characterize the devices that are using electricity, use a machine learning algorithm to predict the load changes of the node, compare the changing loads with the redundant power, and determine whether there is a power gap in the microgrid. If so, open the interface to the power system.

[0007] Furthermore, the S100 includes: inserting a tag generated by an energy management protocol into the node; Based on the electricity consumption of the energy-consuming unit, a reward coefficient is determined and the profit sharing mechanism is modified.

[0008] Furthermore, the S100 further includes: Calculate the current electricity consumption of the energy-consuming units, conduct energy consumption evaluation, and obtain the electricity consumption of the loads; According to the evaluation results, improvement points are found, and the energy-saving equipment is deployed to determine the redundant power.

[0009] Furthermore, the S100 further includes: Integrate the redundant electricity and the preset exchange mechanism, calculate the income of the energy-consuming unit, and distribute the income using the income sharing mechanism; The revenue is refreshed according to the change of the electricity price in the power system.

[0010] Further, the S200 includes: Embed a coordination strategy into the node, generate a scheduling framework, and transfer the node into the scheduling framework to build a microgrid; An interface in the microgrid is determined, wherein the interface includes an incoming line and an outgoing line, wherein the incoming line points to the power system, and the outgoing line points to the node.

[0011] Further, the S300 includes: The microgrid is divided into two parts, an off-grid encirclement circle is constructed, and a feedback amount of electricity is determined, wherein there is no electricity consumption gap at the nodes in the off-grid encirclement circle; Based on the off-grid encirclement circle, a transmission link for feedback electricity is established.

[0012] Furthermore, the method further comprises: Integrate the nodes and loads into a virtual power plant, and based on the change in electricity price, use the virtual power plant to smooth the peak and valley of the power system; The nodes are expanded, an energy sharing platform is established, and a pre-built redundant electricity trading mechanism is integrated.

[0013] Furthermore, the system comprises: Establish a module for establishing energy contract templates, embedding the use rights of energy-saving equipment, determining energy-consuming units in the region, generating energy management agreements, issuing the use rights of energy-saving equipment to the energy-consuming units, and establishing a revenue sharing mechanism; A marking module is used to transmit the redundant power flowing in the energy-saving device to the energy storage device, configure a one-to-one correspondence between the energy storage device and the energy-consuming unit, define the energy storage device as a node, extract the attributes of the node, determine the coverage of each node, aggregate all nodes, construct a microgrid, and mark the interface of the microgrid; The judgment module is used to traverse the load in the node, wherein the load is used to characterize the equipment that is using electricity, use a machine learning algorithm to predict the load change of the node, compare the changing load and redundant power, and judge whether there is a power gap in the microgrid. If so, open the interface to the power system.

[0014] Furthermore, the establishment module includes: a generating unit, configured to insert a label generated by an energy management protocol into the node; A correction unit, used to determine a reward coefficient according to the power consumption of the energy-consuming unit and to correct the revenue sharing mechanism; An acquisition unit is used to calculate the current electricity consumption of the energy-consuming unit, conduct energy consumption evaluation, and obtain the electricity consumption of the load; A determination unit, configured to find improvement points according to the evaluation results, deploy the energy-saving equipment, and determine redundant power; A sending unit, used for integrating the redundant power and the preset exchange mechanism, calculating the income of the energy-consuming unit, and sending the income by using the income sharing mechanism; A refreshing unit is used to refresh the revenue according to the change of electricity price in the power system.

[0015] Furthermore, the marking module includes: A construction unit, used to generate a scheduling framework according to the power coordination strategy, and transfer the nodes into the scheduling framework to construct a microgrid; The pointing unit is used to determine an interface in the microgrid, wherein the interface includes an incoming line and an outgoing line, wherein the incoming line points to the power system, and the outgoing line points to the node.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. By generating energy management protocols, it is possible to reduce the power consumption of energy-consuming units, reduce electricity bills, and help energy-consuming units achieve both economic and environmental benefits. By inputting redundant power into energy storage devices, it is possible to optimize the use of energy storage and improve energy utilization. By building microgrids, it is possible to reduce the peak electricity prices of energy-consuming units and improve their energy independence. Through open interfaces, energy storage devices can be connected to the power system, which can provide continuous power supply guarantees for energy-consuming units while also shaving peaks and valleys for the power system, thereby improving the stability of the power system.

[0017] 2. By building a virtual power plant, the redundant electricity of energy-consuming units can be sold, which not only reduces the electricity costs of energy-consuming units, but also further improves the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of an energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention; Figure 2 A first sub-flow chart of the energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention; Figure 3 A second sub-flow chart of the energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention; Figure 4 A third sub-flow chart of the energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention; Figure 5 A block diagram of the composition of an energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention; Figure 6 A block diagram of the components of the establishment module in the energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of a marking module in an energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of the judgment module in the artificial intelligence-based energy storage monitoring system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] In Example 1, Figure 1 The implementation process of the energy storage monitoring method based on artificial intelligence provided by the embodiment of the present invention is shown and described in detail below: S100: Establish an energy contract template, embed the use rights of energy-saving equipment, determine the energy-consuming units in the area, generate an energy management agreement, issue the use rights of energy-saving equipment to the energy-consuming units, and establish a profit sharing mechanism.

[0021] An energy contract template is established, which is signed by the energy-consuming unit and the energy-saving equipment supplier. After the signature is completed, the energy-consuming unit is given access to the energy-saving equipment, an energy management agreement is generated, and a profit-sharing mechanism is established.

[0022] In the embodiment, the energy-consuming unit signs an energy management agreement with the energy-saving equipment supplier, and the energy-saving equipment supplier provides energy-saving equipment. The resulting revenue (i.e., the saved electricity bill) is shared by the energy-consuming unit and the energy-saving equipment supplier, and the specific sharing ratio and details are the revenue sharing mechanism. The advantage of this is that it can save the use of electric energy and improve the stability of the load of the energy-consuming unit and the power system.

[0023] S200: The redundant electricity flowing in the energy-saving device is transmitted to the energy storage device, a one-to-one correspondence between the energy storage device and the energy-consuming unit is configured, and the energy storage device is defined as a node, the attributes of the node are extracted, the coverage of each node is determined, all nodes are aggregated, a microgrid is constructed, and the interface of the microgrid is marked.

[0024] The redundant power circulating in the energy-saving device is transmitted to the energy storage device, where the redundant power is the power saved by the energy-saving device. This part of the power can be stored by the lithium battery, mechanical energy storage module, etc. in the energy-saving device, or it can be directly transmitted to the energy storage device, where the energy storage device can be a battery energy storage system, a mechanical energy storage system, or a hybrid energy storage system; one or several energy-consuming units correspond to one energy storage device, and the energy storage device is defined as a node, and the properties of the node are determined, where the properties are the capacity, number of cycles, and energy density of the energy storage device; since the capacity of the energy storage device is limited, it can only store and coordinate the redundant power of a limited number of energy-consuming units, so it is necessary to determine the coverage of the node according to the properties of the node, integrate all the nodes in the area, build a microgrid, and open the interface of the microgrid to the power system in the area, and use the microgrid to balance the load of the power system.

[0025] S300: traverse the loads in the node, where the loads are used to characterize the devices that are using electricity, use a machine learning algorithm to predict the load changes of the node, compare the changing loads with the redundant power, and determine whether there is a power gap in the microgrid. If so, open the interface to the power system.

[0026] Traverse all the loads in the node, use machine learning algorithms to predict load changes, and compare the load with redundant power. The load is the power required by the energy unit, and the redundant power is the power stored in the energy storage unit. Compare the load and redundant power. If the redundant power is less than the load, it means that there is a power gap. In this case, it is necessary to open the interface and use the power system to fill the power gap.

[0027] In Example 2, Figure 2 The implementation process of the energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention is shown, and S100 is described in detail below, as follows: S101: inserting a label generated by an energy management protocol into the node.

[0028] Insert a label into the node, where the label is generated by the energy management protocol and contains specific details of the energy management protocol.

[0029] S102: Determine a reward coefficient based on the power consumption of the energy-consuming unit and modify the profit sharing mechanism.

[0030] The reward coefficient is determined based on the electricity consumption of the energy-consuming unit and the electricity consumption after using energy-saving equipment. For example, if an energy-consuming unit saves 30% of electricity after using energy-saving equipment, different reward coefficients can be given according to the specific proportions. Rewards are given by utilizing a profit-sharing mechanism, thereby increasing the enthusiasm of energy-consuming units to use energy-saving equipment.

[0031] In Example 3, Figure 2 The implementation process of the energy storage monitoring method based on artificial intelligence provided by the embodiment of the present invention is shown, and S100 is further described in detail below, as follows: S103: Count the current electricity consumption of the energy-consuming unit, and perform energy consumption evaluation to obtain the power consumption of the load.

[0032] Statistics are compiled on the electricity consumption status of energy-consuming units, including: electricity consumption data of each production equipment in different time periods, electricity consumption of production equipment in each department, etc. Energy consumption of each production equipment is evaluated to determine the amount of electricity required for a certain periodic production.

[0033] S104: Find out improvement points according to the evaluation results, deploy the energy-saving equipment, and determine redundant power.

[0034] Based on the evaluation results of each production equipment, find out the improvement points, and deploy energy-saving equipment on the production equipment within the energy-consuming unit, and use the electricity saved by the energy-saving equipment, which is the redundant electricity.

[0035] For example, the evaluation result of a certain production equipment is that its electricity efficiency is low and it requires an additional 5% of electricity; the improvement point can be to start it during the low-power consumption period and replace the equipment with new ones.

[0036] In Example 4, Figure 2 The implementation process of the energy storage monitoring method based on artificial intelligence provided by the embodiment of the present invention is shown, and S100 is further described in detail below, as follows: S105: Integrate the redundant electricity and the preset exchange mechanism, calculate the income of the energy-consuming unit, and distribute the income using the income sharing mechanism.

[0037] Create a redemption mechanism for redundant power and revenue, where the redemption mechanism is: redeem redundant power to the power system at original price or at a discount, or reduce the electricity bills of energy-consuming units through redundant power. Calculate the revenue of energy-consuming units, determine the profit sharing between energy-consuming units and energy-saving equipment suppliers based on the revenue sharing mechanism, and issue it.

[0038] S106: Refresh the revenue according to the change of electricity price in the power system.

[0039] According to the real-time electricity price in the power system, the total electricity price of the redundant electricity is calculated, and the profits of energy-consuming units and energy-saving equipment suppliers are refreshed according to this total electricity price.

[0040] In Example 5, Figure 3 The implementation process of the energy storage monitoring method based on artificial intelligence provided by an embodiment of the present invention is shown, and S200 is described in detail below, as follows: S201: Embed a coordination strategy into the node, generate a scheduling framework, and transfer the node into the scheduling framework to build a microgrid.

[0041] Embed coordination strategies into nodes, where the coordination strategy is the flow of redundant electricity among different energy-consuming units, generate a scheduling framework for redundant electricity, transfer the nodes into the scheduling framework, and build a microgrid. In other words, by utilizing microgrids, energy-consuming units can transfer the saved electricity to other units for a fee or free of charge, thereby achieving self-sufficiency in electricity consumption for each energy-consuming unit in the region.

[0042] S202: Determine an interface in the microgrid, wherein the interface includes an incoming line and an outgoing line, wherein the incoming line points to the power system, and the outgoing line points to the node.

[0043] Determine the incoming and outgoing lines in the microgrid, where the incoming lines are connected to the power system. When the redundant power in the microgrid is not enough for self-sufficiency, it can be supplemented by the power system. In other words, by utilizing the microgrid, the power system can be peak-smoothed and the load of the power system can be stabilized. The outgoing lines of the microgrid point to the nodes, and the nodes are used as the source of power in the microgrid.

[0044] In Example 6, Figure 4 The implementation process of the energy storage monitoring method based on artificial intelligence provided by the embodiment of the present invention is shown, and S300 is described in detail below, as follows: S301: Divide the microgrid, construct an off-grid encirclement circle, and determine the amount of feedback power, wherein there is no power shortage at the nodes in the off-grid encirclement circle.

[0045] The microgrid is divided into multiple areas, and the areas are used to construct an off-grid enclosure circle. That is to say, the redundant power within the off-grid enclosure circle is greater than the load, that is, the energy-consuming units within the off-grid enclosure circle can achieve self-sufficiency and even generate excess power, which is feedback power.

[0046] S302: Based on the off-grid encirclement circle, a transmission link for feedback electricity is established.

[0047] The feedback electricity is transmitted to the power system through the external transmission link, thus realizing electricity consumption feedback.

[0048] In Example 7, different from Example 1, in this embodiment of the present invention, the method further includes: Integrate the nodes and loads into a virtual power plant, and based on the change in electricity price, use the virtual power plant to smooth the peak and valley of the power system; The nodes are expanded, an energy sharing platform is established, and a pre-built redundant electricity trading mechanism is integrated.

[0049] Nodes and loads are used to generate virtual power plants, and the power system is smoothed according to changes in electricity prices and power loads. At the same time, nodes are used to build an energy sharing platform, and a redundant electricity trading mechanism is embedded in the energy sharing platform so that redundant electricity between nodes can be traded between energy-consuming units. The redundant electricity trading mechanism is the specific way to trade redundant electricity.

[0050] In this application, energy-saving equipment suppliers provide energy-saving equipment to energy-consuming units, where the energy-saving equipment should include: photovoltaic power generation systems, cogeneration systems; node devices can also be old equipment in energy-consuming units; energy-saving equipment suppliers replace old equipment or use the energy-consuming unit's site to build a new photovoltaic power generation system, etc., and the electricity saved or generated is called redundant electricity, and this part of the redundant electricity is transmitted to the energy storage device; if the redundant electricity is sufficient for the energy-consuming unit to produce and use, the nodes (energy storage devices) are used to build a microgrid, and the energy-consuming units in the microgrid that can achieve self-sufficiency in electricity consumption are cut out to build an off-grid enclosure circle; the excess electricity in the off-grid enclosure circle can also be sold and traded through a sharing mechanism or an energy sharing platform, and the generated income is shared by the energy-consuming unit and the energy-saving equipment supplier; if the redundant electricity is not enough for the energy-consuming unit to use, it is necessary to connect the power system to the microgrid, and while supplying energy to the energy-consuming unit, the power system is peak-shaving and valley-flattening, and the energy-consuming unit takes out a corresponding proportion of the saved electricity bill as a subsidy and distributes it to the energy-saving equipment supplier.

[0051] Figure 5 The structure diagram of the energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention is shown. The energy storage monitoring system based on artificial intelligence 1 includes: Establishing module 11, for establishing an energy contract template, embedding the use rights of energy-saving equipment, determining the energy-consuming units in the area, generating an energy management agreement, issuing the use rights of energy-saving equipment to the energy-consuming units, and establishing a revenue sharing mechanism; The marking module 12 is used to transmit the redundant power flowing in the energy-saving device to the energy storage device, configure a one-to-one correspondence between the energy storage device and the energy-consuming unit, define the energy storage device as a node, extract the attributes of the node, determine the coverage of each node, summarize all the nodes, construct a microgrid, and mark the interface of the microgrid; The judgment module 13 is used to traverse the load in the node, where the load is used to characterize the equipment that is using electricity, use a machine learning algorithm to predict the load change of the node, compare the changing load and redundant power, and judge whether there is a power gap in the microgrid. If so, open the interface to the power system.

[0052] Figure 6 The structure diagram of the energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention is shown, and the establishment module 11 includes: A generating unit 111, configured to insert a label generated by an energy management protocol into the node; A correction unit 112, configured to determine a reward coefficient according to the power consumption of the energy-consuming unit and to correct the revenue sharing mechanism; The acquisition unit 113 is used to calculate the current electricity consumption of the energy-consuming unit, and perform energy consumption evaluation to obtain the power consumption of the load; A determination unit 114, configured to find improvement points according to the evaluation results, deploy the energy-saving equipment, and determine redundant power; The issuing unit 115 is used to integrate the redundant power and the preset exchange mechanism, calculate the income of the energy-consuming unit, and issue the income using the income sharing mechanism; The refreshing unit 116 is used to refresh the revenue according to the change of the electricity price in the power system.

[0053] Figure 7 The structure diagram of the energy storage monitoring system based on artificial intelligence provided by an embodiment of the present invention is shown, and the marking module 12 includes: A construction unit 121 is used to generate a scheduling framework according to the power coordination strategy, and transfer the node into the scheduling framework to construct a microgrid; The pointing unit 122 is used to determine an interface in the microgrid, wherein the interface includes an incoming line and an outgoing line, wherein the incoming line points to the power system, and the outgoing line points to the node.

[0054] Figure 8The structure diagram of the energy storage monitoring system based on artificial intelligence provided by the embodiment of the present invention is shown, and the judgment module 13 includes: A feedback unit 131 is used to divide the microgrid, construct an off-grid encirclement circle, and determine the feedback power, wherein there is no power shortage at the nodes in the off-grid encirclement circle; The building unit 132 is used to build a transmission link for feedback power according to the off-grid encirclement circle.

[0055] The establishment module 11 is mainly used to complete step S100, the marking module 12 is mainly used to complete step S200, and the judgment module 13 is mainly used to complete step S300; The generating unit 111 is mainly used to complete step S101, the correcting unit 112 is mainly used to complete step S102, the acquiring unit 113 is mainly used to complete step S103, the determining unit 114 is mainly used to complete step S104, the issuing unit 115 is mainly used to complete step S105, and the refreshing unit 116 is mainly used to complete step S106; The construction unit 121 is mainly used to complete step S201, and the construction unit 121 is mainly used to complete step S202; The feedback unit 131 is mainly used to complete step S301, and the construction unit 132 is mainly used to complete step S302.

[0056] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy storage monitoring method based on artificial intelligence, characterized in that: The method comprises: S100: Establish an energy contract template, embed the use rights of energy-saving equipment, determine the energy-consuming units in the area, generate an energy management agreement, issue the use rights of energy-saving equipment to the energy-consuming units, and establish a profit sharing mechanism; S200: The redundant power flowing in the energy-saving device is transmitted to the energy storage device, a one-to-one correspondence between the energy storage device and the energy-consuming unit is configured, and the energy storage device is defined as a node, the attributes of the node are extracted, the coverage of each node is determined, all nodes are aggregated, a microgrid is constructed, and the interface of the microgrid is marked; S300: traverse the loads in the node, where the loads are used to characterize the devices that are using electricity, use a machine learning algorithm to predict the load changes of the node, compare the changing loads with the redundant power, and determine whether there is a power gap in the microgrid. If so, open the interface to the power system.

2. The energy storage monitoring method based on artificial intelligence according to claim 1 is characterized in that: The S100 includes: inserting a tag generated by the energy management protocol into the node; Based on the electricity consumption of the energy-consuming unit, a reward coefficient is determined and the profit sharing mechanism is modified.

3. The energy storage monitoring method based on artificial intelligence according to claim 2 is characterized in that: The S100 further includes: Calculate the current electricity consumption of the energy-consuming units, conduct energy consumption evaluation, and obtain the electricity consumption of the loads; Based on the evaluation results, improvement points are found, and the energy-saving equipment is deployed to determine the redundant power.

4. The energy storage monitoring method based on artificial intelligence according to claim 3 is characterized in that: The S100 further includes: Integrate the redundant electricity and the preset exchange mechanism, calculate the income of the energy-consuming unit, and distribute the income using the income sharing mechanism; The revenue is refreshed according to the change of the electricity price in the power system.

5. The energy storage monitoring method based on artificial intelligence according to claim 2 is characterized in that: The S200 includes: Embed a coordination strategy into the node, generate a scheduling framework, and transfer the node into the scheduling framework to build a microgrid; An interface in the microgrid is determined, wherein the interface includes an incoming line and an outgoing line, the incoming line points to the power system, and the outgoing line points to the node.

6. The energy storage monitoring method based on artificial intelligence according to claim 5 is characterized in that: The S300 includes: The microgrid is divided into two parts, an off-grid encirclement circle is constructed, and a feedback amount of electricity is determined, wherein there is no electricity consumption gap at the nodes in the off-grid encirclement circle; Based on the off-grid encirclement circle, a transmission link for feedback electricity is established.

7. The energy storage monitoring method based on artificial intelligence according to claim 4 is characterized in that: The method further comprises: Integrate the nodes and loads into a virtual power plant, and based on the change in electricity price, use the virtual power plant to smooth the peak and valley of the power system; The nodes are expanded, an energy sharing platform is established, and a pre-built redundant electricity trading mechanism is integrated.

8. An energy storage monitoring system based on artificial intelligence, characterized in that: The system comprises: Establish a module for establishing energy contract templates, embedding the use rights of energy-saving equipment, determining energy-consuming units in the region, generating energy management agreements, issuing the use rights of energy-saving equipment to the energy-consuming units, and establishing a revenue sharing mechanism; A marking module is used to transmit the redundant power flowing in the energy-saving device to the energy storage device, configure a one-to-one correspondence between the energy storage device and the energy-consuming unit, define the energy storage device as a node, extract the attributes of the node, determine the coverage of each node, aggregate all nodes, construct a microgrid, and mark the interface of the microgrid; The judgment module is used to traverse the load in the node, wherein the load is used to characterize the equipment that is using electricity, use a machine learning algorithm to predict the load change of the node, compare the changing load and redundant power, and judge whether there is a power gap in the microgrid. If so, open the interface to the power system.

9. According to the artificial intelligence-based energy storage monitoring system of claim 8, the establishment module comprises: a generating unit, configured to insert a label generated by an energy management protocol into the node; A correction unit, used to determine a reward coefficient according to the power consumption of the energy-consuming unit and to correct the revenue sharing mechanism; An acquisition unit is used to calculate the current electricity consumption of the energy-consuming unit, conduct energy consumption evaluation, and obtain the electricity consumption of the load; A determination unit, configured to find improvement points according to the evaluation results, deploy the energy-saving equipment, and determine redundant power; A sending unit, used for integrating the redundant power and the preset exchange mechanism, calculating the income of the energy-consuming unit, and sending the income by using the income sharing mechanism; A refreshing unit is used to refresh the revenue according to the change of electricity price in the power system.

10. According to the artificial intelligence-based energy storage monitoring system of claim 8, the marking module comprises: A construction unit, used to generate a scheduling framework according to the power coordination strategy, and transfer the nodes into the scheduling framework to construct a microgrid; The pointing unit is used to determine an interface in the microgrid, wherein the interface includes an incoming line and an outgoing line, wherein the incoming line points to the power system, and the outgoing line points to the node.

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