Micro-grid heterogeneous multi-operator shared energy storage method, system and device based on RPA technology and medium

Abstract

The invention relates to the technical field of micro-grid shared energy storage, in particular to a micro-grid heterogeneous multi-operator shared energy storage method, system and device based on an RPA technology and a medium. The method comprises an acquisition and dispatching center, a controller, a database, a gateway and a mobile APP / Web end. Compared with other methods which only pay attention to the optimal benefit obtained according to the time-of-use electricity price algorithm and ignore resource waste caused by actual user selection and operator game, the method pays more attention to the actual benefit of the user and is closer to sharing. Compared with other algorithms which do not consider the performance and cost difference of the energy storage cabinets, the method pays more attention to the loss of the energy storage cabinets in the sharing mode, interaction among the heterogeneous energy storage cabinets is achieved through the RPA technology, more energy storage cabinets with special functions are allowed to participate in sharing energy storage, and resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the field of microgrid shared energy storage technology, and in particular to a microgrid heterogeneous multi-operator shared energy storage method, system, device and medium based on RPA technology. Background Art

[0002] As an important part of modern energy systems, microgrids and shared energy storage technologies play a key role in improving energy efficiency, promoting the use of renewable energy, and optimizing the allocation of power resources. A microgrid is a small power system that can self-control and manage, optimize energy utilization, and improve power quality, stability, and power supply reliability. A microgrid can be connected to the main grid or operate independently when needed, especially in remote areas or emergency situations. The application of microgrid technology includes the integration of distributed power sources, energy storage systems, energy conversion devices, related loads, and monitoring and protection devices. Shared energy storage is a commercial application model that combines traditional energy storage technology with a sharing economy model. This model allows users to use energy storage systems without high investments, while ensuring the efficient use of energy storage systems with the flexibility of the sharing economy, and realizing the rapid recovery of the cost of shared energy storage power stations.

[0003] At present, the shared energy storage model based on microgrids has no constraints similar to the Internet standardization mechanism, and there are great differences between the energy storage cabinet products of various companies, such as different heat dissipation methods, different storage capacities of energy storage cabinets, different stored power sizes, etc. Therefore, when considering the actual benefits of shared energy storage, it is required that the shared energy storage system can flexibly respond to the power demand and operating conditions of diversified service objects. Once there are conflicts of interest between microgrids, unequal status of profit entities, and information asymmetry, how to ensure the fairness and security of transactions in centralized control and scheduling, and ensure the rapidity of response, becomes a major challenge for shared energy storage facing multiple service objects. In addition, the existing energy storage cabinets mostly adopt two operation and maintenance management solutions: local monitoring and data cloud. Local monitoring generally uses HMI for manual operation, and data cloud is also divided into multiple protocols. Some can directly access the database, and some allow access through the RestfulAPI interface. Most of them only allow data access through computer web pages, software or mobile APPs, which are different. This results in ignoring product differences when talking about shared energy storage, and only focusing on energy storage nodes that can be centrally and uniformly controlled. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a microgrid heterogeneous multi-operator shared energy storage method based on RPA technology, comprising:

[0006] The acquisition and dispatching center issues acquisition instructions to the controllers of different energy storage cabinets of different operators. The controllers perform data acquisition. The locally deployed energy storage cabinets use cameras to obtain and upload screenshots of the relevant information of the energy storage cabinets displayed frame by frame in real time for the HMI.

[0007] The acquisition and dispatching center uses RPA technology of API access, database access, web app parsing, and intelligent video parsing to parse, statistically summarize, and aggregate the statistical information uploaded by different heterogeneous energy storage cabinets.

[0008] Operator A controls heterogeneous energy storage cabinets A1 and A2. The capacity of A1 is Aw1, the unit capacity rental fee is A11, and the start-up cost is AF1. The capacity of A2 is Aw2, the rental fee is A21, and the cost is AF2. Operator B controls heterogeneous energy storage cabinets B1 and B2. The capacity of B1 is Bw1, the unit capacity rental fee is B11, and the start-up cost is BF1. The capacity of B2 is Bw2, the rental fee is B21, and the cost is BF2. And A11 < B11 < A21 < B21. After the acquisition and dispatching center summarizes all the information, a capacity optimal allocation principle for minimizing the user's rental cost is constructed, and the rental cost is minimized through the optimization criterion.

[0009] As a preferred solution of the method for sharing heterogeneous multi-operator energy storage in a microgrid based on RPA technology described in the present invention, wherein:

[0010] The mathematical model based on the capacity optimal allocation principle for minimizing the user's rental cost reduces the waste of battery capacity and the increase in operation and maintenance costs. If the capacity cannot be fully utilized, based on the minimum user rental cost, the capacity allocation of the operator's energy storage cabinets is dynamically adjusted through the optimal criterion.

[0011] The optimization criterion is expressed as:

[0012] Max((A21×E - AF2)-(B11×E - BF1))

[0013] Wherein, E represents the capacity for adjusting the capacity allocation within the acceptable range of increase and decrease of the user after allocating the capacity based on the minimum user rental cost.

[0014] Through RPA technology, data interaction of heterogeneous energy storage cabinets of each operator is realized, and the BMS cluster manages large cabinet module distributed heterogeneous energy storage cabinets.

[0015] Based on the multi-user capacity optimal principle, a shared energy storage mode with the user as the main body and the operator as the subordinate is adopted.

[0016] The microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention includes a collection and scheduling module, a control module, a database module, a gateway, and a mobile APP / Web terminal.

[0017] As an optimal solution for the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention, the collection and scheduling module collects data of heterogeneous distributed energy storage cabinets collected by RPA technology in real time in various dimensions, and schedules according to the principle of optimal capacity allocation that minimizes user rental costs, and minimizes rental costs through optimization criteria.

[0018] As a preferred solution of the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention, the control module collects the voltage, current and temperature parameters of each battery group in real time and manages the battery group.

[0019] As a preferred solution for the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention, the database module cleans and stores the collected data, and provides a service port to the outside so that RPA can access it through the port.

[0020] As a preferred solution of the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention, the gateway includes transmitting the energy storage cabinet related data collected by the controller to the cloud.

[0021] As a preferred solution for the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention, the mobile APP / Web terminal includes remotely obtaining real-time data of the corresponding distributed energy storage cabinet by accessing the cloud, and providing an API interface for RPA or only providing an access page.

[0022] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods for sharing energy storage among heterogeneous multi-operators in a microgrid based on RPA technology are implemented.

[0023] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods for sharing energy storage among heterogeneous multi-operators in a microgrid based on RPA technology are implemented.

[0024] The beneficial effects of the present invention are as follows: First, compared with other algorithms that only focus on the best benefits obtained according to the time-of-use electricity price algorithm and ignore the actual user selection and the resource waste caused by the operator's game, the present invention pays more attention to the actual benefits of users and is closer to sharing. Second, compared with other algorithms that do not consider the performance and cost differences of the energy storage cabinet itself, the present invention pays more attention to the loss of the energy storage cabinet itself in the sharing mode, and realizes the interaction between multiple heterogeneous energy storage cabinets through RPA technology, allowing more energy storage cabinets with special functions to participate in shared energy storage and reduce resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 This is a flow chart of the microgrid heterogeneous multi-operator shared energy storage method based on RPA technology described in the present invention.

[0027] Figure 2 This is a simulation experiment of the RPA-based microgrid heterogeneous multi-operator shared energy storage method described in the present invention. Figure 1 .

[0028] Figure 3 This is a simulation experiment of the RPA-based microgrid heterogeneous multi-operator shared energy storage method described in the present invention. Figure 2 .

[0029] Figure 4 This is an architecture diagram of the microgrid heterogeneous multi-operator shared energy storage system based on RPA technology described in the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0031] Example 1

[0032] refer to Figure 1-Figure 3 , which is the first embodiment of the present invention, provides a microgrid heterogeneous multi-operator shared energy storage method based on RPA technology, such as Figure 1 shown.

[0033] S1: Data collection is performed through the acquisition and scheduling module.

[0034] The acquisition and scheduling module sends acquisition instructions to the controllers of different energy storage cabinets of different operators, and the controllers perform data collection. Since the locally deployed energy storage cabinets do not have their own data upload channels, the relevant information of the energy storage cabinets displayed in real-time frame by frame is obtained through the camera and the screenshots are uploaded; others upload the data according to their own Internet of Things protocols;

[0035] S2: Information aggregation is performed by the acquisition and scheduling module.

[0036] The acquisition and scheduling module uses the RPA technology of API access, database access, web app parsing and intelligent video parsing to parse, statistically analyze and aggregate the statistical information uploaded by different types of energy storage cabinets;

[0037] S3: Construct a mathematical model based on the principle of minimizing the user's rental cost.

[0038] Suppose operator A has heterogeneous energy storage cabinets A1, A2, with capacities Aw1 and Aw2 respectively, unit capacity rental costs A11 and A21 respectively, start-up costs AF1 and AF2 respectively, and operator B has heterogeneous energy storage cabinets B1, B2, and so on, and A11 < B11 < A21 < B21. After the acquisition and scheduling center aggregates all the information, a mathematical model based on the principle of minimizing the user's rental cost is constructed. It will rent in the order of A11, B11, A21, B21 according to the capacity until the capacity is satisfied, so that the user's rental cost is minimized. According to the rental costs of each type of energy storage cabinet under each operator, the construction of the algorithm for minimizing the user's rental cost is completed, that is, the multi-stage allocation model. The user rental cost of the mathematical model = ∑Min(A11, B11, A21, B21) * the user's single-stage required capacity. The reason why it is multi-stage means that the user's required capacity cannot be satisfied by a single energy storage cabinet, then the acquisition and scheduling center will arrange the capacity allocation according to the rental cost order described above. Suppose the user's required capacity needs three energy storage cabinets to be satisfied. In the first stage, the result of Min is A11, and the user rental cost in the first stage is A11 * Aw1. And so on, the final user rental cost is A11 * Aw1 + B11 * Bw1 + A21 * (the user's required capacity - Aw1 - Bw1). As Figure 2 shown, the x-axis is the user's required capacity, and it can be seen that renting according to the minimum principle, no matter how much the required capacity is, the rental cost is the minimum.

[0039] S4: Minimize the rental cost through the optimization criterion.

[0040] The mathematical model based on the principle of optimal capacity allocation to minimize user rental costs can reduce the waste of battery capacity and the increase in operation and maintenance costs. However, some energy storage cabinets have large capacity and high startup costs. If the capacity cannot be fully used, the operator will suffer losses. Therefore, based on the minimum user rental fee, the capacity allocation of the operator's energy storage cabinets is dynamically adjusted to avoid the occurrence of mostly idle energy storage cabinets as much as possible, and the remaining unrented energy storage cabinets are just used as emergency measures, such as Figure 3 As shown, when the user's required capacity is 5, if A21 is used to meet the extra capacity, although it will increase some user rental fees, it can save more operator profits, so this optimization solution can be selected.

[0041] Dynamic adjustment needs to meet the following conditions:

[0042] Variable Name value Variable Name value Variable Name value Aw1 4 A11 2 AF1 5 Aw2 6 A21 4 AF2 8 Bw 6 B11 3 BF1 15 Bw 8 B21 5 BF2 20

[0043] When the user's required capacity is 5, the first 4 capacities are still selected from A1. The extra capacity should be selected from B1 based on the minimum rental principle. However, since the startup cost of B1 is too high, the user rental fee can be slightly sacrificed to ensure that the operator's losses are reduced. The optimization criterion is:

[0044] Max((A21×E-AF2)-(B11×E-BF1))

[0045] Among them, E means that after allocating capacity based on the minimum user rental fee, the capacity allocation can be further adjusted within the range of increase or decrease acceptable to users. This range of increase or decrease can be obtained based on feedback from local markets, such as -5% to 5%.

[0046] The selection of optimization points is crucial here. Generally, the optimized energy storage cabinets are selected when the capacity can be met and the gap is not large, but the cost gap is large. The optimization point should be selected when the capacity is not met and there is only a slight redundancy, so as to ensure the minimum rental fee for users as much as possible, thereby attracting more users to participate in the use and further improving the profitability of operators.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

[0048] Example 2

[0049] Reference Figure 4, which is the first embodiment of the present invention, and provides a microgrid heterogeneous multi-operator shared energy storage system based on RPA technology, including:

[0050] In the scenario of multiple energy storage cabinets for multiple users in a microgrid, data intercommunication between multiple energy storage cabinets cannot be guaranteed. The so-called shared energy storage is also relative to the sharing between single-user, same-technology products, and it is almost impossible to share between heterogeneous energy storage cabinets for multiple users. Secondly, how to coordinate the impact of the differences in the products of heterogeneous energy storage cabinets for multiple users on the distribution of benefits in the shared energy storage model is ignored by many current algorithms. For example, different heat dissipation methods lead to different cost prices of energy storage cabinets, and different capacities make large-capacity energy storage cabinets unable to achieve the best benefits when receiving small-capacity electricity, while small-capacity energy storage cabinets cannot bear large capacities at once and remain idle, etc.

[0051] The purpose of the present invention is to provide a master-slave shared energy storage architecture between multi-user heterogeneous multi-energy storage cabinets. On the one hand, the RPA technology is used to ensure that the real-time operating cost, real-time capacity, fault switching and real-time rate of return can be obtained through information interaction between heterogeneous energy storage cabinets. On the other hand, the master-slave shared architecture of heterogeneous energy storage cabinets can be realized based on the above information. In the same microgrid, based on the optimal principle of capacity allocation, multiple users are the main body, and multiple operators are subordinates to negotiate the rental price to reduce the operator game phenomenon, thereby obtaining more benefits in heterogeneous shared energy storage.

[0052] like Figure 4 As shown, S5: the collection and dispatching center 100: collects data of heterogeneous distributed energy storage cabinets collected by RPA technology in various dimensions in real time and dispatches according to the algorithm.

[0053] The collection and dispatching module sends the collection instructions to the controllers of different energy storage cabinets of different operators, and the controllers collect data. Since the locally deployed energy storage cabinets do not have their own data upload channels, they use cameras to obtain the relevant information of the energy storage cabinets displayed on the HMI frame by frame in real time and upload screenshots; others upload data according to their own IoT protocols;

[0054] It should be noted that for the electricity consumption strategy, operators and users rent services during peak hours, and operators store energy for their own equipment after the trough. Multiple operators in the unified microgrid are in a game relationship, and users as buyers can choose to buy at the lowest price, but the capacity of energy storage cabinets held by a single operator is limited. Based on this, from the user side, the optimal capacity allocation principle is considered to minimize the user's rental costs, while also reducing the waste of battery capacity and the increase in operation and maintenance costs.

[0055] Furthermore, the principle of optimal allocation of user-side capacity will not lead to the problem of operators' bargaining, but it is also necessary to consider the maximization of the rental income of multiple operators. Based on the maximization of unit capacity, the rental income of multiple operators can be balanced, which will not lead to too small income for operators with more capacity usage, and also maximize the benefits of operators with less capacity. At the same time, if multiple users participate in the allocation of capacity, the income of multiple operators can be further increased.

[0056] In addition, while considering the benefits, fault switching is an indispensable issue for both operators and users. It is necessary to select emergency energy storage. Based on the charging and discharging efficiency of the energy storage cabinet (i.e., the real-time current and voltage values) and the real-time benefits, the energy storage cabinet with the maximum value is normalized (current * voltage / benefits) * (emergency required capacity / energy storage cabinet capacity) to be selected as the tradeoff.

[0057] S6: Control module 200: collect voltage, current and temperature parameters of each battery pack in real time and manage the battery pack.

[0058] It should be noted that the distributed energy storage cabinets controlled by various operators have different capacities and different heat dissipation methods, which also means that their operation and maintenance costs are different. First, the RPA technology is used to realize data interaction between the heterogeneous energy storage cabinets of various operators. A set of BMS clusters is used to manage large cabinet distributed heterogeneous energy storage cabinets. The parameters that need to be collected in real time include but are not limited to current, voltage, temperature, remaining capacity, etc. The collected information is pre-processed and stored through the database module 300; only the energy storage cabinet with HMI deployed locally will upload the HMI page to the collection and dispatch center through the RPA real-time intelligent video recognition technology. For the energy storage cabinet with control module 200 + gateway module 400 combined data on the cloud, it is divided into web segment and APP end. If the restful api access is directly provided, the requests open source library can be used. If direct api access is not supported, the selenium and appium open source libraries can be used to crawl the web and mobile app page data and upload the data to the collection and dispatch center. For the energy storage cabinet that uses the database to store data, RPA can directly access the data through the port. RPA can be implemented in multiple languages ​​such as JAVA and PYTHON.

[0059] S7: Database module 300: cleans and stores the collected data, and then provides a service port to the outside so that the RPA can access it through the port.

[0060] It should be noted that the data collected by S5 and S6 are preprocessed and stored through the database module to ensure the integrity and reliability of the data.

[0061] S8: Gateway module 400: transmits the energy storage cabinet related data collected by the control module 200 to the cloud.

[0062] S9: Mobile APP / Web 500: Remotely obtain the real-time data of the corresponding distributed energy storage cabinet by accessing the cloud, and provide an API interface or only an access page for RPA.

[0063] It should be noted that energy storage cabinets generally have three means of displaying statistical information, namely, locally deployed HMI, web end and app end. For energy storage cabinets with only locally deployed HMI, the HMI page can be uploaded to the collection and dispatch center through real-time video recognition RPA technology, and the open source video recognition software can be called for information analysis, so as to obtain detailed information of the locally deployed energy storage cabinet remotely. For energy storage cabinets with a combination of control module 200 + gateway module 400 data on the cloud, they can be divided into web end and app end from the display application. In fact, the bottom layer is generally a restful API interface or a mysql database. If the energy storage cabinet operator can directly provide API access or database access port, various open source connection software can be used to obtain statistical information; if direct access is not supported, the page data can be analyzed for the web end and mobile app end respectively based on the selenium and appium open source libraries. The current mainstream RPA technology can be implemented in multiple languages ​​such as JAVA and PYTHON.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0065] Example 3

[0066] An embodiment of the present invention is different from the first two embodiments in that:

[0067] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Specifically, the technical solution of the present invention can be embodied in the form of a software product and stored in a computer-readable storage medium. The storage medium includes but is not limited to a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The computer software products in these storage media contain several instructions for enabling a computer device (such as a personal computer, a server or a network device, etc.) to perform the steps of each method described in the embodiments of the present invention.

[0068] In this embodiment, the use of storage media also involves the execution of logical functions, that is, the logical steps mentioned in the flowchart or other descriptions can be implemented by instructions stored in computer-readable media. These instructions can be used by an instruction execution system (such as a computer system, a device containing a processor, or other device capable of reading and executing instructions from a storage medium). It is worth noting that computer-readable media is not limited to traditional electronic storage devices, but can also be paper media, provided that the program content can be converted into electronic data and stored in a computer by optical scanning or the like.

[0069] In addition, various parts of the present invention may be implemented by hardware, software, firmware or a combination thereof. In this context, if implemented by hardware, logic gate circuits, application specific integrated circuits (ASICs), programmable gate arrays (PGAs), field programmable gate arrays (FPGAs) and other technologies may be used to implement logic functions.

[0070] In combination with practical applications, the present invention combines the RPA technology to closely integrate the functions of the collection and dispatching center with the software system, making the operation process more automated and efficient. Specifically, the collection and dispatching center is responsible for issuing collection instructions to the energy storage cabinet controllers of different operators. These controllers obtain relevant information of the energy storage cabinets in real time through data collection and HMI (human-machine interface) image acquisition technology, and upload it to the dispatching center. Through RPA technology, the collection center can parse, count and summarize the statistical information uploaded by each heterogeneous energy storage cabinet, so as to obtain detailed data such as the capacity, rental fee, and startup cost of different energy storage cabinets.

[0071] These data are used for further optimization processing, specifically the optimal allocation of capacity based on the principle of minimizing user rental costs. At this time, based on the differences in capacity, rental fees, and startup costs of the energy storage cabinets of each operator, the collection center dynamically adjusts the capacity allocation of the energy storage cabinets through optimization criteria to ensure that user needs are met with the lowest rental costs. The optimization criteria include the range of capacity adjustment accepted by users, that is, E, which means that if the capacity allocation cannot fully meet the needs, it can be flexibly adjusted within the range acceptable to users. The core idea of ​​this process is to avoid the waste of battery capacity through reasonable capacity allocation, reduce the increase in operation and maintenance costs, and improve the overall economy of the system.

[0072] More importantly, through RPA technology, the collection and dispatch center can not only realize data interaction between different operators, but also manage multiple heterogeneous energy storage cabinets through the BMS (battery management system) cluster to ensure efficient and accurate capacity allocation and operation and maintenance management of the energy storage cabinets. The close collaboration between the BMS cluster and the dispatch center forms an efficient closed loop, which not only optimizes the use of energy storage resources, but also enables real-time monitoring and dynamic adjustment according to user needs. In this shared energy storage model, the structure with users as the main body and operators as subordinates enables the entire system to maximize resource sharing and economic benefits.

[0073] In summary, the present invention combines computer software with hardware, and ensures the efficient operation of the energy storage system through precise capacity allocation and scheduling optimization. The principle of optimal capacity allocation based on minimizing rental costs effectively reduces operation and maintenance costs and improves resource utilization. Through the combination of RPA technology and BMS cluster management, not only seamless data interaction between heterogeneous energy storage cabinets is achieved, but also an efficient shared energy storage system that can dynamically adjust and flexibly respond to changes in demand is formed.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A microgrid heterogeneous multi-operator shared energy storage method based on RPA technology, characterized by: including The acquisition and scheduling module issues acquisition instructions to the controllers of different energy storage cabinets of different operators. The controllers perform data acquisition. The locally deployed energy storage cabinets obtain and upload screenshots of the relevant information of the energy storage cabinets displayed frame by frame in real time through a camera for the HMI. The acquisition and scheduling module uses RPA technology of API access, database access, web app parsing, and intelligent video parsing to parse, statistically summarize, and aggregate the statistical information uploaded by different heterogeneous energy storage cabinets. Operator A controls heterogeneous energy storage cabinets A1 and A2. The capacity of A1 is Aw1, the rental cost per unit capacity is A11, and the start-up cost is AF1. The capacity of A2 is Aw2, the rental cost is A21, and the cost is AF2. Operator B controls heterogeneous energy storage cabinets B1 and B2. The capacity of B1 is Bw1, the rental cost per unit capacity is B11, and the start-up cost is BF1. The capacity of B2 is Bw2, the rental cost is B21, and the cost is BF2. And A11 < B11 < A21 < B21. After the acquisition and scheduling center summarizes all information, a capacity optimal allocation principle that minimizes the user's rental cost is constructed, and the rental cost is minimized through the optimization criterion.

2. The method for sharing heterogeneous multi-operator energy storage in a microgrid based on RPA technology according to claim 1, wherein: The mathematical model based on the capacity optimal allocation principle that minimizes the user's rental cost reduces the waste of battery capacity and the increase in operation and maintenance costs. If the capacity cannot be fully utilized, based on the minimum user rental cost, the capacity allocation of the operator's energy storage cabinets is dynamically adjusted through the optimal criterion. The optimization criterion is expressed as: Max((A21×E - AF2)-(B11×E - BF1)) where E represents the capacity for adjusting the capacity allocation within the range of increase and decrease acceptable to the user after allocating the capacity based on the minimum user rental cost. Through RPA technology, data interaction of heterogeneous energy storage cabinets of each operator is realized, and the BMS cluster manages large cabinet model distributed heterogeneous energy storage cabinets. A shared energy storage mode with users as the main body and operators as the subordinate based on the multi-user capacity optimal principle.

3. A system for sharing energy storage in heterogeneous microgrids with multiple operators based on RPA technology, characterized by: including an acquisition and scheduling module (100), a control module (200), a database module (300), a gateway module (400), and a mobile APP / Web terminal (500).

4. The system for the RPA-based microgrid heterogeneous multi-operator shared energy storage method as claimed in claim 3, characterized in that: The acquisition and scheduling module (100) collects data of heterogeneous distributed energy storage cabinets collected through RPA technology in real time in each dimension, schedules according to the capacity optimal allocation principle that minimizes the user's rental cost, and minimizes the rental cost through the optimization criterion.

5. The system for the RPA-based microgrid heterogeneous multi-operator shared energy storage method as claimed in claim 3, characterized in that: The control module (200) collects voltage, current, and temperature parameters of each battery pack in real time and manages the battery pack.

6. The system for the RPA-based microgrid heterogeneous multi-operator shared energy storage method as claimed in claim 3, 4 or 5, characterized in that: The database module (300) cleans and stores the data collected by the acquisition and scheduling module (100) and the control module (200), and provides an external service port so that RPA can access through the port.

7. The system for the RPA-based microgrid heterogeneous multi-operator energy storage sharing method as claimed in claims 3 and 5 is characterized in that: The gateway module (400) transmits the relevant data of the energy storage cabinet collected by the control module (200) to the cloud.

8. The system for the RPA-based microgrid heterogeneous multi-operator energy storage sharing method as claimed in claim 3, 4 or 5, characterized in that: The mobile APP / Web terminal (500) remotely obtains the real-time data of the distributed energy storage cabinet collected by the collection and scheduling module (100) and the control module (200) by accessing the cloud, and at the same time provides an API interface or only an access page to the RPA.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the microgrid heterogeneous multi-operator shared energy storage method based on RPA technology as described in any one of claims 1 to 2 are implemented.

10. A computer-readable storage medium storing computer-executable instructions. When the execution instruction is executed by the processor, the steps of the microgrid heterogeneous multi-operator shared energy storage method based on RPA technology as described in any one of claims 1 to 2 are implemented.

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