Microgrid heterogeneous multi-operator shared energy storage method, system and device based on rpa technology and medium

Abstract

The present application relates to the technical field of micro-grid shared energy storage, and particularly relates to a micro-grid heterogeneous multi-operator shared energy storage method, system and device based on RPA technology and a medium. The method comprises a collection and dispatch center, a controller, a database, a gateway and a mobile APP / Web terminal. Compared with other methods that only focus on the optimal benefit obtained according to the time-of-use electricity price algorithm and ignore the resource waste caused by actual user selection and operator game, the present application pays more attention to the actual benefit of users and is closer to sharing. Compared with other algorithms that do not consider the performance and cost difference of the energy storage cabinet itself, the present application 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, allows more energy storage cabinets with special functions to participate in shared energy storage, and reduces resource waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid shared energy storage, and in particular to a micro-grid heterogeneous multi-operator shared energy storage method, system, device and medium based on RPA technology. BACKGROUND

[0002] As an important part of modern energy systems, micro-grids and shared energy storage technologies play a key role in improving energy efficiency, promoting renewable energy utilization, and optimizing power resource allocation. Micro-grids are small power systems that can self-control and manage, optimize energy utilization, and improve power quality, stability, and power supply reliability. Micro-grids can operate in parallel with the main grid or independently when needed, especially in remote areas or emergency situations. The application of micro-grid 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 business application model that combines traditional energy storage technology with the sharing economy model. This model allows users to use energy storage systems without high investment, while ensuring efficient use of energy storage systems through the flexibility of the sharing economy, enabling rapid recovery of shared energy storage station costs.

[0003] Currently, the shared energy storage mode based on micro-grids has significant differences between various energy storage cabinet products due to the lack of similar Internet standardization mechanisms, such as different heat dissipation methods, different storage capacities, and different power sizes. Therefore, when considering the actual benefits of shared energy storage, the shared energy storage system must be able to flexibly respond to the power demand and operating condition differences of multiple service objects. Once there is a conflict of interest between micro-grids, an unequal profit subject status, and information asymmetry, how to ensure the fairness, security, and rapid response of transactions in centralized control and scheduling becomes a major challenge for shared energy storage facing multiple service objects. In addition, existing energy storage cabinets mostly use local monitoring and data cloud uploading operation and management solutions. Local monitoring generally uses HMI for manual operation, while data cloud uploading also has multiple protocol methods, including direct database access and Restful API interface access. Most of them only allow data access through computer web pages, software, or mobile APP, which are not the same. This results in ignoring product differences when discussing shared energy storage, focusing only on centralized unified control of energy storage nodes. SUMMARY

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

[0005] To solve the above technical problems, the present application provides the following technical solutions: a micro-grid heterogeneous multi-operator shared energy storage method based on RPA technology, comprising,

[0006] The acquisition instruction is issued to the controllers of different energy storage cabinets of different operators by the acquisition and dispatching center, data acquisition is performed by the controller, and the local deployed energy storage cabinet obtains the displayed energy storage cabinet related information frame by frame in real time through the camera and uploads the screenshot;

[0007] The RPA technology of API access, database access, web app parsing and intelligent video parsing is used by the acquisition and dispatching center to analyze, count and summarize the statistical information uploaded by each heterogeneous energy storage cabinet;

[0008] Operator A masters heterogeneous energy storage cabinets A1, A2, the capacity of A1 is Aw1, the unit capacity leasing fee is A11, the start-up cost is AF1, the capacity of A2 is Aw2, the leasing fee is A21, and the cost is AF2; operator B masters heterogeneous energy storage cabinets B1, B2, the capacity of B1 is Bw1, the unit capacity leasing fee is B11, the start-up cost is BF1, the capacity of B2 is Bw2, the leasing fee is B21, and the cost is BF2, and A11 < B11 < A21 < B21; after all the information is summarized in the acquisition and dispatching center, the capacity optimal allocation principle of minimizing the user leasing fee is constructed, and the leasing fee is minimized through the optimization criterion.

[0009] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology, wherein:

[0010] The mathematical model based on the capacity optimal allocation principle of minimizing the user leasing fee reduces the waste of battery capacity and the increase of operation and maintenance cost; if the capacity cannot be fully used, the capacity allocation of the operator energy storage cabinet is dynamically adjusted based on the minimum user leasing fee through the optimal criterion;

[0011] The optimization criterion is represented as:

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

[0013] Wherein, E represents the capacity adjustment capacity within the range of the user acceptable price fluctuation after the capacity allocation based on the minimum user leasing fee;

[0014] The RPA technology is used to realize the data interaction of the heterogeneous energy storage cabinets of each operator, and the BMS cluster management cabinet module is used to manage the distributed heterogeneous energy storage cabinets.

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

[0016] The micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology comprises a collection and scheduling module, a control module, a database module, a gateway and a mobile APP / Web terminal.

[0017] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology, the collection and scheduling module collects the data of the heterogeneous distributed energy storage cabinet in real time through the RPA technology in each dimension, schedules according to the capacity optimization allocation principle of minimizing the user rental cost, and minimizes the rental cost through the optimization criterion.

[0018] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology, the control module collects the voltage, current and temperature parameters of each battery pack in real time and manages the battery pack.

[0019] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology, the database module cleans and stores the collected data, and provides a service port to the outside so that the RPA can access through the port.

[0020] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology, the gateway comprises a controller that collects the data of the energy storage cabinet and transmits the data to the cloud.

[0021] As a preferred scheme of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology, the mobile APP / Web terminal comprises a controller that collects the data of the energy storage cabinet and transmits the data to the cloud.

[0022] A computer device comprises a memory and a processor, and the memory stores a computer program, wherein the processor implements the steps of the method according to any one of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology when executing the computer program.

[0023] A computer readable storage medium stores a computer program, wherein the computer program is executed by a processor to implement the steps of the method according to any one of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology.

[0024] The beneficial effects of the present application are as follows: first, compared with other algorithms that only focus on the optimal benefit obtained according to the time-of-use pricing algorithm and ignore the actual user selection and the resource waste caused by the operator game, the present application pays more attention to the actual benefit of the user and is closer to sharing. Second, compared with other algorithms that do not consider the performance and cost difference of the energy storage cabinet, the present application pays more attention to the loss of the energy storage cabinet itself in the sharing mode and realizes the interaction between the multiple heterogeneous energy storage cabinets through the RPA technology, allowing more energy storage cabinets with special functions to participate in the shared energy storage, thereby reducing the resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0026] Figure 1 The flowchart of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology according to the present application.

[0027] Figure 2 The simulation experiment of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology according to the present application Figure 1 .

[0028] Figure 3 The simulation experiment of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology according to the present application Figure 2 .

[0029] Figure 4 The architecture diagram of the micro-grid heterogeneous multi-operator shared energy storage system based on the RPA technology according to the present application. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0031] Embodiment 1

[0032] Reference Figures 1-3 For the first embodiment of the present application, a micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology is provided, as shown in Figure 1 .

[0033] S1: Collect data 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 acquisition. 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: The acquisition and scheduling module summarizes the information.

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

[0037] S3: Build 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 fees 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 summarizes all the information, a mathematical model based on the principle of minimizing the user's rental cost is built. It will rent in turn from A11, B11, A21, B21 according to the capacity until the capacity is satisfied. In this way, the user's rental cost is the lowest. According to the rental fees 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 for being multi-stage means that if the user's required capacity cannot be satisfied by a single energy storage cabinet, the acquisition and scheduling center will arrange the capacity allocation according to the aforementioned rental fee order. 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. It can be seen that renting according to the minimum principle, regardless of the required capacity, the rental cost is the lowest.

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

[0040] The mathematical model based on the capacity optimal allocation principle of minimizing user rental cost can reduce the waste of battery capacity and the increase of operation and maintenance cost. However, some energy storage cabinets have large capacity and large start-up cost. If the capacity cannot be fully used, the operator will suffer losses. Therefore, based on the minimum user rental cost, the capacity allocation of the operator's energy storage cabinet is dynamically adjusted to avoid the emergence of most idle energy storage cabinets as much as possible, and the remaining unrented energy storage cabinets are used as emergency. Figure 3 As shown, for the required capacity of the user being 5, if A21 is used to meet the extra capacity, although the user rental cost will increase, the operator's profit can be greatly saved, so this optimization scheme can be selected.

[0041] The 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 Bw1 6 B11 3 BF1 15 Bw2 8 B21 5 BF2 20

[0043] In the case of the required capacity of the user being 5, the first four capacities are still selected as A1, and the extra capacity should be selected as B1 based on the minimum rental principle. However, since the start-up cost of B1 is too high, the user rental cost can be slightly sacrificed to reduce the loss of the operator. The optimization criterion is:

[0044] Max((A21 x E-AF2)-(B11 x E-BF1))

[0045] Wherein, E represents the capacity that can be further adjusted within the range of the user's acceptable price fluctuation after the capacity is allocated based on the minimum user rental cost. The range of price fluctuation can be obtained according to the market feedback of each place, such as -5% to 5%.

[0046] The optimization point is crucial here. Generally, the optimized energy storage cabinet is selected between the two that can meet the capacity and have little difference, but the cost difference is large. The optimization point should be selected in the case where the capacity is not met only with slight redundancy to ensure the minimum rental cost of the user as much as possible, thereby attracting more users to participate in use and further improving the profit of the operator.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application 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 application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application

[0048] Embodiment 2

[0049] Referring to Figure 4For the first embodiment of the application, the embodiment provides a micro-grid heterogeneous multi-operator shared energy storage system based on RPA technology, comprising:

[0050] In the multi-user multi-energy storage cabinet product scene of the micro-grid, the data intercommunication between the multi-user multi-energy storage cabinets cannot be guaranteed, and the so-called shared energy storage is also shared between single users and the same technical products, and it is almost impossible to share between multi-user heterogeneous energy storage cabinets.Secondly, how to coordinate the differences between multi-user heterogeneous energy storage cabinet products themselves to affect the income distribution of the shared energy storage mode is ignored by many current algorithms, such as the different heat dissipation methods resulting in different energy storage cabinet cost prices, different capacities making the large-capacity energy storage cabinet not reaching the optimal benefit when receiving small-capacity electric energy, and the small-capacity energy storage cabinet being unable to bear a large capacity at once and being idle all the time, etc.

[0051] The purpose of the application is to provide a master-slave shared energy storage architecture between multi-user heterogeneous multi-energy storage cabinets, on the one hand, through RPA technology, to ensure that heterogeneous energy storage cabinets can obtain real-time operating cost, real-time capacity, fault switching and real-time yield rate through information interaction, and on the other hand, to realize the master-slave shared architecture of heterogeneous energy storage cabinets according to the above information, so that in the same micro-grid, based on the capacity allocation optimization principle, the multi-user is taken as the main body, and at the same time, multiple operators are taken as the slave to carry out lease price negotiation to reduce the operator game phenomenon, so as to obtain more benefits in the heterogeneous shared energy storage.

[0052] As shown in Figure 4 S5: The collection and dispatch center 100 collects the data of the heterogeneous distributed energy storage cabinets collected through the RPA technology in each dimension in real time and dispatches according to the algorithm.

[0053] The collection and dispatch module sends collection instructions to the controllers of different energy storage cabinets of different operators, and the controllers collect data. The locally deployed energy storage cabinets have no data upload channel of their own, so they acquire the displayed energy storage cabinet related information through the camera in real time and frame by frame and upload the screenshots; others upload data according to their own Internet of Things protocol;

[0054] It should be noted that for the electricity strategy, the operators and users lease services during the electricity peak period and the operators store energy in their own equipment after the trough. The relationship between multiple operators in the unified micro-grid is a game relationship, and the users as buyers can choose the lowest price to purchase, but the capacity of the energy storage cabinet held by a single operator is limited. Based on this, the capacity optimization allocation principle is considered from the user side to minimize the rental cost of the user, and at the same time, to reduce the waste of battery capacity and the increase of operation and maintenance cost.

[0055] Further, the user-side capacity optimal allocation principle as the main body will not lead to the game problem of operators, but also needs to consider the maximum rental income of multiple operators. Based on the maximum unit capacity, the rental income of multiple operators can be balanced, so that the income of the operator with more capacity usage is not too small, and the operator with less capacity provides the maximum benefit, and if multiple users participate in capacity allocation, it can further maximize the income of multiple operators.

[0056] In addition, while considering the income, for both operators and users, fault switching is essential, and emergency energy storage needs to be selected based on the charging and discharging efficiency of the energy storage cabinet (i.e. real-time current and voltage values) and real-time income. Two factors, normalize (current* voltage / income)*(emergency required capacity / storage cabinet capacity), get the maximum value of the energy storage cabinet as tradeoff.

[0057] S6: Control module 200: Real-time acquisition of voltage, current and temperature parameters of each battery pack and management of battery pack.

[0058] It should be noted that the distributed energy storage cabinets controlled by each operator have different capacities, different cooling methods, and different operation and maintenance costs. First, through RPA technology, the heterogeneous energy storage cabinets of each operator are realized to interact with data, and a set of BMS clusters are used to manage the large cabinet module 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 preprocessed and stored by the database module 300; only the energy storage cabinet with local deployment of HMI will upload the HMI page to the collection and dispatching center through RPA real-time intelligent video recognition technology, and for the energy storage cabinet with control module 200+gateway module 400 combination data uploading to the cloud, it is divided into web segment and APP end. If restful api access is provided directly, requests open source library can be used, if direct api access is not supported, selenium and appium open source libraries can be used to crawl page web and mobile app page data, and upload the data to the collection and dispatching center. For the energy storage cabinet that uses a database to store data, RPA can directly access data through the port to obtain it. RPA can be implemented in multiple languages such as JAVA, PYTHON, etc.

[0059] S7: Database module 300: clean and store the collected data, and then provide a service port to allow RPA to access through the port.

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

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

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

[0063] It should be noted that the energy storage cabinet generally has three means of local deployment HMI, web and app to display statistical information. For the energy storage cabinet with only local deployment HMI, the page of the HMI can be uploaded to the collection and scheduling center through real-time video recognition RPA technology, and open source video recognition software is called to parse the information, so that the detailed information of the local deployment energy storage cabinet is obtained remotely. For the energy storage cabinet with control module 200+gateway module 400 combination data cloud, from the application of display, it can be divided into web and app, and the underlying is generally restful API interface or 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, page data parsing can be performed on the web and mobile app based on selenium and appium open source library. The current mainstream RPA technology can be implemented by using JAVA, PYTHON and other languages.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application 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 application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

[0065] Embodiment 3

[0066] One embodiment of the present application is different from the first two embodiments:

[0067] When the function is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Specifically, the technical solutions of the present application can be embodied in the form of a software product, stored in a computer readable storage medium. The storage medium includes but is not limited to a U disk, 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 product in these storage media contains a plurality of instructions for making a computer device (such as a personal computer, a server or a network device, etc.) execute the steps of various methods described in the embodiments of the present application.

[0068] In this embodiment, the use of storage media also involves the execution of logical functions, i.e. the logical steps mentioned in the flowchart or other descriptions, which can be realized by instructions stored in the computer readable medium. These instructions can be used by an instruction execution system (such as a computer system, a device containing a processor, or other devices capable of reading and executing instructions from a storage medium). It is worth noting that the computer readable medium 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 through optical scanning and stored in a computer.

[0069] In addition, various parts of the present application can be realized by hardware, software, firmware or their combination. In this context, if realized by hardware, the logical functions can be realized by using logical gate circuits, application specific integrated circuits (ASIC), programmable gate arrays (PGA), field programmable gate arrays (FPGA) and the like.

[0070] In combination with actual applications, the present application combines RPA technology to closely combine the functions of the collection and dispatch center with the software system, so that the operation process is more automated and efficient. Specifically, the collection and dispatch 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 cabinet in real time through data collection and HMI (human-machine interface) image acquisition technology, and upload it to the dispatch center. Through RPA technology, the collection center can analyze, count and summarize the statistical information uploaded by each heterogeneous energy storage cabinet, thereby obtaining detailed data such as the capacity, rental fee and start-up cost of different energy storage cabinets.

[0071] These data are used for further optimization processing, specifically, optimal allocation of capacity according to the principle of minimizing user rental fees. At this time, based on the capacity, rental fee and start-up cost differences of each operator's energy storage cabinet, the collection center dynamically adjusts the capacity allocation of the energy storage cabinet through optimization criteria to ensure that the user's demand is met with the smallest rental fee. The optimization criteria include the user-acceptable capacity adjustment range, i.e. E, which means that if the capacity allocation cannot fully meet the demand, it can be flexibly adjusted within the user-acceptable range. The core idea in this process is to allocate capacity reasonably, which not only avoids waste of battery capacity, but also reduces the increase of operation and maintenance cost, and improves 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 BMS (Battery Management System) cluster, ensuring efficient and accurate capacity allocation and operation and maintenance management of the energy storage cabinet. The close cooperation between the BMS cluster and the dispatch center forms a high-efficiency closed loop, which not only optimizes the use of energy storage resources, but also monitors in real time and dynamically adjusts according to user demand. In this shared energy storage mode, the user is the main body and the operator is the subordinate structure, so that the entire system can maximize resource sharing and economic benefits.

[0073] In summary, the present application combines computer software and hardware, and through precise capacity allocation and scheduling optimization, ensures efficient operation of the energy storage system. Based on the capacity optimal allocation principle of minimizing rental costs, the operation and maintenance costs are effectively reduced and the resource utilization is improved. Through the combination of RPA technology and BMS cluster management, seamless data interaction between different heterogeneous energy storage cabinets is realized, and an efficient shared energy storage system that dynamically adjusts and flexibly responds to demand changes is formed.

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

Claims

1. A method for heterogeneous multi-operator sharing energy storage in microgrid based on RPA technology, characterized in that: comprising, The acquisition and dispatch module sends acquisition instructions to the controllers of different energy storage cabinets of different operators, and the controllers perform data acquisition. The locally deployed energy storage cabinet obtains the displayed information of the energy storage cabinet through the camera in real time and uploads the screenshots frame by frame. The acquisition and dispatch module uses RPA technology such as API access, database access, web app parsing, and intelligent video parsing to analyze, count, and summarize the statistical information uploaded by each heterogeneous energy storage cabinet. Operator A has heterogeneous energy storage cabinets A1, A2, A1, 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 has heterogeneous energy storage cabinets B1, 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. A11 < B11 < A21 < B21. After collecting and dispatching all information in the center, the capacity optimal allocation principle of minimizing user rental cost is constructed, and the rental cost is minimized through the optimization criterion. Based on the mathematical model of the capacity optimal allocation principle of minimizing user rental cost, the waste of battery capacity and the increase of operation and maintenance cost are reduced. If the capacity cannot be fully utilized, the capacity allocation of the operator's energy storage cabinet is dynamically adjusted based on the minimum user rental cost. The optimization criterion includes that when the user required capacity exceeds the range that can be met by energy storage cabinet A1, energy storage cabinet B1 should be selected based on the minimum rental cost principle. However, due to the high start-up cost of B1, if the capacity cannot be fully utilized, it will cause the operator to generate idle loss. Under the premise of ensuring user capacity demand, energy storage cabinet A2 is introduced to adjust capacity allocation, which increases user rental cost while reducing the loss of the operator due to idling. The optimization criterion selects between energy storage cabinets A2 and B1, which is represented as: Max((A21×E-AF2)-(B11×E-BF1)) Where E represents the capacity adjustment range within the user's acceptable price fluctuation range after allocating capacity based on the minimum user rental cost. Through RPA technology, the data of each operator's heterogeneous energy storage cabinet is interacted, and the BMS cluster management cabinet module distributes the heterogeneous energy storage cabinet. Based on the multi-user capacity optimal principle, the user is the main body and the operator is the subordinate shared energy storage mode.

2. The system of the method for sharing energy storage of heterogeneous multi-operators of micro-grid based on RPA technology, adopts the method for sharing energy storage of heterogeneous multi-operators of micro-grid based on RPA technology as claimed in claim 1, characterized in that: comprising, an acquisition and dispatch module (100), a control module (200), a database module (300), a gateway module (400), and a mobile APP / Web terminal (500).

3. The system for microgrid heterogeneous multi-operator sharing energy storage based on RPA technology according to claim 2, wherein: The acquisition and dispatch module (100) collects the data of the heterogeneous distributed energy storage cabinet in each dimension through RPA technology in real time, dispatches according to the capacity optimal allocation principle of minimizing user rental cost, and minimizes the rental cost through the optimization criterion.

4. The system for microgrid heterogeneous multi-operator sharing energy storage based on RPA technology according to claim 2, wherein: The control module (200) collects the voltage, current, and temperature parameters of each battery pack in real time and manages the battery pack.

5. The system for RPA technology based microgrid heterogeneous multi-operator sharing energy storage method of claim 2, 3 or 4, wherein: The database module (300) cleans and stores the data collected by the collection and scheduling module (100) and the control module (200), and provides a service port for RPA to access through the port.

6. The system for microgrid heterogeneous multi-operator sharing energy storage based on RPA technology according to claim 4, wherein: The gateway module (400) transmits the energy storage cabinet related data collected by the control module (200) to the cloud.

7. The system for RPA technology based microgrid heterogeneous multi-operator sharing energy storage method of claim 2, 3 or 4, wherein: 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 provides an API interface or only an access page for RPA. 8.An electronic device, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, realize the steps of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology in claim 1. 9.A computer readable storage medium storing computer executable instructions, the computer executable instructions, when executed by a processor, realize the steps of the micro-grid heterogeneous multi-operator shared energy storage method based on the RPA technology in claim 1. ​

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