Distributed energy storage multi-scene application system and method

Abstract

The invention discloses a distributed energy storage multi-scene application system and method, and relates to the technical field of distributed energy storage. Historical data of distributed energy storage scenes are acquired; analyzing the correlation between the distributed energy storage scene and the energy storage site; based on the correlation between the distributed energy storage scene and the energy storage site, the distributed energy storage scene is used as a reference, and distributed energy storage scene data is stored on data nodes in a scattered mode; when the distributed energy storage scene is triggered, monitoring and analysis of all related energy storage power stations can be completed at one energy storage station instead of utilizing the energy storage stations corresponding to the energy storage power stations, so that the processing efficiency of the distributed energy storage scene is improved; the response speed is improved by storing the data on the data nodes in a scattered manner; dispersed distributed energy storage data are stored in a data center, so that sharing and collaboration of the distributed energy storage data among data nodes are realized.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy storage technology, and specifically to a distributed energy storage multi-scenario application system and method. Background Art

[0002] A distributed energy storage system is an energy management system that distributes energy storage devices on the user side. It achieves the storage, management and utilization of energy by installing energy storage devices such as batteries and supercapacitors on the user side. Distributed energy storage systems can cooperate with renewable energy, power grids, etc. to optimize the scheduling and utilization of energy, improve energy efficiency and reduce energy costs. The system has the characteristics of high flexibility, strong scalability, and the ability to adapt to different scenarios and needs. It can be widely used in many fields such as home, industry, and commerce. Through the application of distributed energy storage systems, efficient utilization and conservation of energy can be achieved, energy costs can be reduced, and the stability and reliability of the power system can also be improved.

[0003] Since the installation locations of energy storage power stations in distributed energy storage systems are relatively scattered, it brings certain challenges to data management and application. Distributed energy storage scenarios have high requirements for the real-time nature of data. Therefore, how to centrally manage the data of energy storage power stations and improve the processing efficiency of distributed energy storage scenarios has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed energy storage multi-scenario application system and method to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a distributed energy storage multi-scenario application system, comprising a data center, a data node, an energy storage site and a data transmission module; the data center is interconnected with the data node and is used to store data generated by all energy storage power stations; the data node is interconnected with the energy storage site and stores distributed scene data in units of distributed scenes; the energy storage site is used to obtain distributed scene data from the data node and process the distributed scene; the data transmission module is used to synchronize data stored in the data node and the data center.

[0006] The data center also includes a data storage unit, a scene analysis unit, a scene node analysis unit, a planning unit and a judgment unit; the data storage unit is used to store data generated by all energy storage power stations; the scene analysis unit is used to analyze the probability of occurrence of distributed scenes; the scene node analysis unit is used to determine the correlation between energy storage sites and scenes; the planning unit is used to determine the data node location of each distributed scene data storage; the judgment unit is used to determine the load condition of the data node. The data node also includes an update unit, a response unit and a scene storage unit; the update unit is used to manage the life cycle of the data; the response unit is used to respond to the request of the energy storage site; the scene storage unit is used to store distributed scene data. The energy storage site also includes an interaction unit and a request unit; the request unit is used to send a distributed scene data request to the data node; the interaction unit is used to present the distributed scene data to the user, and the user processes the distributed scene according to the distributed scene data.

[0007] To achieve the above object, the present invention provides the following technical solution: a distributed energy storage multi-scenario application method, comprising the following steps: Obtain historical data of distributed energy storage scenarios; Analyze the correlation between distributed energy storage scenarios and energy storage sites; Based on the correlation between distributed energy storage scenarios and energy storage sites, distributed energy storage scenario data is dispersedly stored on data nodes with distributed energy storage scenarios as the benchmark; The energy storage site accesses the data nodes and processes the distributed energy storage scenarios.

[0008] The analysis of the correlation between the distributed energy storage scenario and the energy storage site also includes the following steps: Let A represent the random variable of the distributed energy storage scenario, and B represent the random variable of the energy storage site. Calculate the correlation between the j-th energy storage site and the i-th distributed energy storage scenario: P{B=j|A=i}=P{B=j∩A=i} / P{A=i}=n(i,j) / n(i), where P represents the probability of the event, A=i represents the occurrence of the i-th distributed energy storage scenario, B=j represents the distributed energy storage scenario involving the j-th energy storage site, n(i) represents the number of occurrences of the i-th distributed energy storage scenario, and n(i,j) represents the number of times the i-th distributed energy storage scenario involves the j-th energy storage site. Store the correlation between the j-th energy storage site and the i-th distributed energy storage scenario.

[0009] Distributed energy storage scenarios are triggered at energy storage sites and involve other energy storage sites. Energy storage sites are responsible for different energy storage power stations, and there are differences between the equipment of energy storage power stations. Energy storage power stations may not have energy storage equipment corresponding to distributed energy storage scenarios. When energy storage sites trigger distributed energy storage scenarios, not all energy storage power stations are involved, and only the data of some energy storage power stations need to be monitored and analyzed. At the same time, when a distributed energy storage scenario is triggered at an energy storage site, the energy storage site can complete the monitoring and analysis of all involved energy storage power stations by accessing the data node, instead of having to use the energy storage site corresponding to the energy storage power station, which improves the processing efficiency of distributed energy storage scenarios.

[0010] The method of distributing and storing the distributed energy storage scenario data on the data nodes based on the distributed energy storage scenario also includes the following steps: S10, for the i-th distributed energy storage scenario, analyze the optimization value Prio(i,x1,xk) brought about when the i-th distributed energy storage scenario data is stored on the first data node x1, where xk is the k-th data node. When k is 1, the optimization value Prio(i,x1,xk) is 0; S20, calculate the total response time T(i,x1) when storing the i-th distributed energy storage scenario data on the first data node x1, , where t(x1,j) represents the response time of the j-th energy storage site to obtain the distributed energy storage scenario data from the data node x1; the total response time T(i,xk) when storing the i-th distributed energy storage scenario data on the k-th data node xk is calculated, , where t(xk,j) represents the response time of the jth energy storage site to obtain the distributed energy storage scenario data from the data node xk; the response time T(i,x1) and T(i,xk) are subtracted to obtain the optimized value Prio(i,x1,xk) of data node x1 to data node xk, Prio(i,x1,xk)=-[T(i,x1)-T(i,xk)]; for other data nodes except x1, perform the same operation to obtain the optimized values ​​of other data nodes to data node xk and store them.

[0011] Due to differences in the location and performance of data nodes, the time it takes for data nodes to respond to energy storage sites is usually not exactly the same, that is, there is a data node with the shortest response time. Under each value of k, the data node with the shortest response time has an optimized value for the data node xk. The minimum optimized value is 0, and it is only generated when xk is the same as the data node with the shortest response time.

[0012] The method of distributing and storing the distributed energy storage scenario data on the data nodes based on the distributed energy storage scenario also includes the following steps: S100, obtaining the probability P{A=i} of the i-th distributed scenario occurring, and arranging all distributed scenarios in descending order of probability of occurrence; performing steps S200 and S300 for r=1, 2, ..., m, where m is the number of distributed scenarios; S200, for the rth distributed scenario in the arrangement, obtain the optimization values ​​of other data nodes for the data node xk in the rth distributed scenario, find out the data node whose optimization value for the data node xk is not less than 0, record it as the target data node, store the rth distributed scenario data on the target data node, and obtain the minimum optimization value of the target data node for the data node xk; Under each value of k, the data node with the shortest response time has an optimal value for data node xk. When data node xk is different from the data node with the shortest response time, the optimal value of the data node with the shortest response time for all data nodes xk is not less than 0. S300, determining whether the target data node is overloaded, if not, completing the storage of the rth distributed scene data; if overloaded, removing the change corresponding to the target data node from the value range of k, and returning to step S200; The larger the optimization value, the more response time is saved. For this reason, priority is given to scenes with a higher probability of occurrence, and data nodes for storage are arranged according to the probability of occurrence for distributed scene data. Since the performance of data nodes is limited and the data that can be stored is limited, this brings up a problem. For example, for the 1st, 2nd, and 3rd distributed scenes in the arrangement, the corresponding target data nodes are the same, but the storage space of the target data nodes is limited. The initial solution can be to arrange the 1st distributed scene data in the arrangement at the target data node. However, there is duplicate data between different distributed scene data. When the 2nd and 3rd distributed scenes are energy storage power station inspection scenes and energy storage power station monitoring scenes, there is duplicate data in the two scenes, and the storage space occupied by the two will be reduced. When the two are stored in the same data node, the duplicate data only needs to be stored once; when the two are stored in different data nodes, the duplicate data is stored repeatedly, which wastes both storage space and data node performance.

[0013] S400, taking the data node positions of all distributed scene data storages as the current solution, determining the estimated value f0 corresponding to the current solution; and taking the initial solution as the current solution, setting the initial value of the current temperature T; S500, repeat steps S600 to S700 for variable L=1, 2, ..., c; c is the number of inner loops; S600, by generating a disturbance on the basis of the current solution, changing the data node position of the distributed scene data storage, taking the data node position of the distributed scene data storage after the disturbance as a new solution, and determining the score value f corresponding to the new solution; judging whether there is an overloaded data node under the new solution, if so, returning to step S600, if not, proceeding to step S700; S700, calculate the increment Δf of the score estimate brought by the new solution. If the increment Δf is less than 0, accept the new solution as the current solution. If the increment is not less than 0, Accept the new solution as the new current solution; S800, lower the current temperature according to the cooling plan. If the current temperature is greater than or equal to the temperature threshold, proceed to step S500; if the current temperature is less than the temperature threshold, determine the data node location of the distributed scene data storage according to the current solution.

[0014] The sub-estimate is determined by the following steps: Get the data node location of the distributed scene data storage, calculate the sum of the optimization values ​​SPrio of all distributed scene data, , where Prio(i,min) represents the minimum optimized value of the data node of the i-th distributed scene data storage for the data node xk; SPrio is processed in reverse to obtain the estimated value f, f=con-SPrio, where con is a constant, which is taken as the maximum value among all SPrio.

[0015] Determining whether a data node is overloaded also includes the following steps: Obtain the data update speed V of data node X and the generation speed of each distributed scene data; S61, according to the data node position of the distributed scene data storage, if only one distributed scene data is stored in the data node X, then compare the data update speed V and the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S62: If there are two distributed scene data stored in the data node X, the repeated parts of the two distributed scene data stored in the data node X are obtained to obtain the generation speed V of the two distributed scene data. z+b , V z+b =(1-sim(z,b) / 2)×V b +(1-sim(z,b) / 2)×V z , where z and b are used as identifiers to represent the zth and bth distributed scenarios, V b and V zrepresents the data generation speed of the zth and bth distributed scenes, sim(z,b) represents the repeated part of the zth and bth distributed scene data, compares the data update speed V with the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S63, if there are more than two distributed scene data stored in data node X, the distributed scene data are processed in pairs according to step S62 to obtain the generation speed of more than two distributed scene data, and the data update speed V is compared with the generation speed of the distributed scene data stored in data node X. If the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: when a distributed energy storage scenario is triggered, monitoring and analysis of all involved energy storage power stations can be completed at one energy storage site, rather than having to use the energy storage site corresponding to the energy storage power station, thereby improving the processing efficiency of distributed energy storage scenarios; by distributing and storing data on data nodes, the response speed is improved; the distributed distributed energy storage data is stored in the data center, thereby realizing the sharing and coordination of distributed energy storage data among data nodes; combining distributed energy storage and distributed storage, remote monitoring of distributed energy storage power stations is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of a distributed energy storage multi-scenario application system of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example: Figure 1 As shown, the present invention provides a technical solution, a distributed energy storage multi-scenario application system, including a data center, a data node, an energy storage site and a data transmission module; the data center is interconnected with the data node and is used to store data generated by all energy storage power stations; the data node and the energy storage site are interconnected, and distributed scene data is stored in units of distributed scenes; the energy storage site is used to obtain distributed scene data from the data node and process the distributed scene; the data transmission module is used to synchronize data stored in the data node and the data center.

[0020] The data center also includes a data storage unit, a scene analysis unit, a scene node analysis unit, a planning unit and a judgment unit; the data storage unit is used to store data generated by all energy storage power stations; the scene analysis unit is used to analyze the probability of occurrence of distributed scenes; the scene node analysis unit is used to determine the correlation between energy storage sites and scenes; the planning unit is used to determine the data node location of each distributed scene data storage; the judgment unit is used to determine the load condition of the data node. The data node also includes an update unit, a response unit and a scene storage unit; the update unit is used to manage the life cycle of the data; the response unit is used to respond to the request of the energy storage site; the scene storage unit is used to store distributed scene data. The energy storage site also includes an interaction unit and a request unit; the request unit is used to send a distributed scene data request to the data node; the interaction unit is used to present the distributed scene data to the user, and the user processes the distributed scene according to the distributed scene data.

[0021] The data center can be a cloud platform. In this case, the data node is a node of the cloud platform. The existence of the cloud platform can effectively avoid single point failures and improve the reliability and availability of data. At the same time, distributed storage technology can handle large amounts of data and ensure the real-time nature of data, which is very suitable for processing large amounts of data generated by distributed energy storage power stations.

[0022] Embodiment: The present invention provides a technical solution, a distributed energy storage multi-scenario application method, comprising the following steps: Obtain historical data of distributed energy storage scenarios; Analyze the correlation between distributed energy storage scenarios and energy storage sites: Let A represent the random variable of the distributed energy storage scenario, and B represent the random variable of the energy storage site. Calculate the correlation between the j-th energy storage site and the i-th distributed energy storage scenario: P{B=j|A=i}=P{B=j∩A=i} / P{A=i}=n(i,j) / n(i), where P represents the probability of the event, A=i represents the occurrence of the i-th distributed energy storage scenario, B=j represents the distributed energy storage scenario involving the j-th energy storage site, n(i) represents the number of occurrences of the i-th distributed energy storage scenario, and n(i,j) represents the number of times the i-th distributed energy storage scenario involves the j-th energy storage site. Store the correlation between the j-th energy storage site and the i-th distributed energy storage scenario.

[0023] Based on the correlation between distributed energy storage scenarios and energy storage sites, the distributed energy storage scenario data is dispersedly stored on data nodes with the distributed energy storage scenario as the benchmark.

[0024] Distributed energy storage scenarios include but are not limited to: energy storage power station information management, energy storage power station equipment information management, energy storage power station alarm information monitoring and real-time operation monitoring.

[0025] Energy storage power station information management: add, delete, modify, share and export energy storage power station information; Energy storage power station equipment information management: according to the energy storage equipment connected to the energy storage power station, information can be entered, modified, deleted, and data can be exported; Energy storage power station inspection task management: query energy storage power station inspection task data; Real-time operation monitoring: Based on the data of the current energy storage power station, the energy storage power station operation status and alarm information are prompted.

[0026] S10, for the i-th distributed energy storage scenario, analyze the optimization value Prio(i,x1,xk) brought about when the i-th distributed energy storage scenario data is stored on the first data node x1, where xk is the k-th data node. When k is 1, the optimization value Prio(i,x1,xk) is 0; S20, calculate the total response time T(i,x1) when storing the i-th distributed energy storage scenario data on the first data node x1, , where t(x1,j) represents the response time of the j-th energy storage site to obtain the distributed energy storage scenario data from the data node x1; the total response time T(i,xk) when storing the i-th distributed energy storage scenario data on the k-th data node xk is calculated, , where t(xk,j) represents the response time of the j-th energy storage site to obtain the distributed energy storage scenario data from the data node xk; subtract the response time T(i,x1) and T(i,xk) to obtain the optimized value Prio(i,x1,xk) of data node x1 to data node xk, Prio(i,x1,xk)==-[T(i,x1)-T(i,xk)]; perform the same operation on other data nodes except x1 to obtain and store the optimized values ​​of other data nodes to data node xk.

[0027] S100, obtaining the probability P{A=i} of the i-th distributed scenario occurring, and arranging all distributed scenarios in descending order of probability of occurrence; performing steps S200 and S300 for r=1, 2, ..., m, where m is the number of distributed scenarios; S200, for the rth distributed scenario in the arrangement, obtain the optimization values ​​of other data nodes for the data node xk in the rth distributed scenario, find out the data node whose optimization value for the data node xk is not less than 0, record it as the target data node, store the rth distributed scenario data on the target data node, and obtain the minimum optimization value of the target data node for the data node xk; S300, determining whether the target data node is overloaded, if not, completing the storage of the rth distributed scene data; if overloaded, removing the change corresponding to the target data node from the value range of k, and returning to step S200; Determining whether a data node is overloaded also includes the following steps: Obtain the data update speed V of data node X and the generation speed of each distributed scene data; S61, according to the data node position of the distributed scene data storage, if only one distributed scene data is stored in the data node X, then compare the data update speed V and the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S62: If there are two distributed scene data stored in the data node X, the repeated parts of the two distributed scene data stored in the data node X are obtained to obtain the generation speed V of the two distributed scene data. z+b , V z+b =(1-sim(z,b) / 2)×V b +(1-sim(z,b) / 2)×V z , where z and b are used as identifiers to represent the zth and bth distributed scenarios, V b and V z represents the data generation speed of the zth and bth distributed scenes, sim(z,b) represents the repeated part of the zth and bth distributed scene data, compares the data update speed V with the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S63, if there are more than two distributed scene data stored in the data node X, the distributed scene data are processed in pairs according to step S62 to obtain the generation speed of more than two distributed scene data, and the data update speed V is compared with the generation speed of the distributed scene data stored in the data node X. If the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; If there are three distributed scene data, first determine the generation speed of the first two distributed scene data according to step S62, then analyze the repeated parts of the first two distributed scene data and the third distributed scene data, and obtain the generation speed of the three distributed scene data according to step S62.

[0028] S400, taking the data node positions of all distributed scene data storages as the current solution, determining the estimated value f0 corresponding to the current solution; and taking the initial solution as the current solution, setting the initial value of the current temperature T; S500, repeat steps S600 to S700 for variable L=1, 2, ..., c; c is the number of inner loops; S600, by generating a disturbance on the basis of the current solution, changing the data node position of the distributed scene data storage, taking the data node position of the distributed scene data storage after the disturbance as a new solution, and determining the score value f corresponding to the new solution; judging whether there is an overloaded data node under the new solution, if so, returning to step S600, if not, proceeding to step S700; The sub-estimate is determined by the following steps: Get the data node location of the distributed scene data storage, calculate the sum of the optimization values ​​SPrio of all distributed scene data, , where Prio(i,min) represents the minimum optimized value of the data node of the i-th distributed scene data storage for the data node xk; SPrio is processed in reverse to obtain the estimated value f, f=con-SPrio, where con is a constant and is taken as the maximum value among all SPrios; S700, calculate the increment Δf of the score estimate brought by the new solution. If the increment Δf is less than 0, accept the new solution as the current solution. If the increment is not less than 0, Accept the new solution as the new current solution; S800, lower the current temperature according to the cooling plan. If the current temperature is greater than or equal to the temperature threshold, proceed to step S500; if the current temperature is less than the temperature threshold, determine the data node location of the distributed scene data storage according to the current solution.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A distributed energy storage multi-scenario application method, characterized in that: The following steps are involved: Obtain historical data of distributed energy storage scenarios; Analyze the correlation between distributed energy storage scenarios and energy storage sites; Based on the correlation between distributed energy storage scenarios and energy storage sites, distributed energy storage scenario data is dispersedly stored on data nodes with distributed energy storage scenarios as the benchmark; The energy storage site accesses the data nodes and processes the distributed energy storage scenarios.

2. The distributed energy storage multi-scenario application method according to claim 1 is characterized in that: The analysis of the correlation between the distributed energy storage scenario and the energy storage site also includes the following steps: Let A represent the random variable of the distributed energy storage scenario, and B represent the random variable of the energy storage site. Calculate the correlation between the j-th energy storage site and the i-th distributed energy storage scenario: P{B=j|A=i}=P{B=j∩A=i} / P{A=i}=n(i,j) / n(i), where P represents the probability of the event, A=i represents the occurrence of the i-th distributed energy storage scenario, B=j represents the distributed energy storage scenario involving the j-th energy storage site, n(i) represents the number of occurrences of the i-th distributed energy storage scenario, and n(i,j) represents the number of times the i-th distributed energy storage scenario involves the j-th energy storage site. Store the correlation between the j-th energy storage site and the i-th distributed energy storage scenario.

3. The distributed energy storage multi-scenario application method according to claim 2 is characterized in that: The method of distributing and storing the distributed energy storage scenario data on the data nodes based on the distributed energy storage scenario also includes the following steps: S10, for the i-th distributed energy storage scenario, analyze the optimization value Prio(i,x1,xk) brought about when the i-th distributed energy storage scenario data is stored on the first data node x1, where xk is the k-th data node. When k is 1, the optimization value Prio(i,x1,xk) is 0; S20, calculate the total response time T(i,x1) when storing the i-th distributed energy storage scenario data on the first data node x1, , where t(x1,j) represents the response time of the j-th energy storage site to obtain the distributed energy storage scenario data from the data node x1; the total response time T(i,xk) when storing the i-th distributed energy storage scenario data on the k-th data node xk is calculated, , where t(xk,j) represents the response time of the j-th energy storage site to obtain the distributed energy storage scenario data from the data node xk; subtract the response time T(i,x1) and T(i,xk) to obtain the optimized value Prio(i,x1,xk) of data node x1 to data node xk, Prio(i,x1,xk)==-[T(i,x1)-T(i,xk)]; perform the same operation on other data nodes except x1 to obtain and store the optimized values ​​of other data nodes to data node xk.

4. The distributed energy storage multi-scenario application method according to claim 3 is characterized in that: The method of distributing and storing the distributed energy storage scenario data on the data nodes based on the distributed energy storage scenario also includes the following steps: S100, obtaining the probability P{A=i} of the i-th distributed scenario occurring, and arranging all distributed scenarios in descending order of probability of occurrence; performing steps S200 and S300 for r=1, 2, ..., m, where m is the number of distributed scenarios; S200, for the rth distributed scenario in the arrangement, obtain the optimization values ​​of other data nodes for the data node xk in the rth distributed scenario, find out the data node whose optimization value for the data node xk is not less than 0, record it as the target data node, store the rth distributed scenario data on the target data node, and obtain the minimum optimization value of the target data node for the data node xk; S300, determining whether the target data node is overloaded, if not, completing the storage of the rth distributed scene data; if overloaded, removing the change corresponding to the target data node from the value range of k, and returning to step S200; S400, taking the data node positions of all distributed scene data storages as the current solution, determining the estimated value f0 corresponding to the current solution; and taking the initial solution as the current solution, setting the initial value of the current temperature T; S500, repeat steps S600 to S700 for variable L=1, 2, ..., c; c is the number of inner loops; S600, by generating a disturbance on the basis of the current solution, changing the data node position of the distributed scene data storage, taking the data node position of the distributed scene data storage after the disturbance as a new solution, and determining the score value f corresponding to the new solution; judging whether there is an overloaded data node under the new solution, if so, returning to step S600, if not, proceeding to step S700; S700, calculate the increment Δf of the score estimate brought by the new solution. If the increment Δf is less than 0, accept the new solution as the current solution. If the increment is not less than 0, Accept the new solution as the new current solution; S800, lower the current temperature according to the cooling plan. If the current temperature is greater than or equal to the temperature threshold, proceed to step S500; if the current temperature is less than the temperature threshold, determine the data node location of the distributed scene data storage according to the current solution.

5. The distributed energy storage multi-scenario application method according to claim 4 is characterized in that: The sub-estimate is determined by the following steps: Get the data node location of the distributed scene data storage, calculate the sum of the optimization values ​​SPrio of all distributed scene data, , where Prio(i,min) represents the minimum optimized value of the data node of the i-th distributed scene data storage for the data node xk; SPrio is processed in reverse to obtain the estimated value f, f=con-SPrio, where con is a constant, which is taken as the maximum value among all SPrio.

6. The distributed energy storage multi-scenario application method according to claim 5 is characterized in that: Determining whether a data node is overloaded also includes the following steps: Obtain the data update speed V of data node X and the generation speed of each distributed scene data; S61, according to the data node position of the distributed scene data storage, if only one distributed scene data is stored in the data node X, then compare the data update speed V and the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S62: If there are two distributed scene data stored in the data node X, the repeated parts of the two distributed scene data stored in the data node X are obtained to obtain the generation speed V of the two distributed scene data. z+b , V z+b =(1-sim(z,b) / 2)×V b +(1-sim(z,b) / 2)×V z , where z and b are used as identifiers to represent the zth and bth distributed scenarios, V b and V z represents the data generation speed of the zth and bth distributed scenes, sim(z,b) represents the repeated part of the zth and bth distributed scene data, compares the data update speed V with the generation speed of the distributed scene data stored in the data node X, if the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded; S63, if there are more than two distributed scene data stored in data node X, the distributed scene data are processed in pairs according to step S62 to obtain the generation speed of more than two distributed scene data, and the data update speed V is compared with the generation speed of the distributed scene data stored in data node X. If the data update speed V is not less than the generation speed, the data node is not overloaded, otherwise the data node is overloaded.

7. Distributed energy storage multi-scenario application system, characterized by: It includes a data center, data nodes, energy storage sites and a data transmission module; the data center is interconnected with the data nodes and is used to store data generated by all energy storage power stations; the data nodes are interconnected with the energy storage sites and store distributed scene data in units of distributed scenes; the energy storage sites are used to obtain distributed scene data from the data nodes and process the distributed scenes; the data transmission module is used to synchronize the data stored in the data nodes and the data center.

8. The distributed energy storage multi-scenario application system according to claim 7 is characterized in that: The data center also includes a data storage unit, a scene analysis unit, a scene node analysis unit, a planning unit and a judgment unit; the data storage unit is used to store data generated by all energy storage power stations; The scenario analysis unit is used to analyze the probability of occurrence of distributed scenarios; The scene node analysis unit is used to determine the correlation between the energy storage site and the scene; The planning unit is used to determine the data node location of each distributed scene data storage; The determination unit is used to determine the load condition of the data node.

9. The distributed energy storage multi-scenario application system according to claim 7, characterized in that: The data node also includes an update unit, a response unit and a scene storage unit; the update unit is used to manage the life cycle of data; the response unit is used to respond to the request of the energy storage site; and the scene storage unit is used to store distributed scene data.

10. The distributed energy storage multi-scenario application system according to claim 7, characterized in that: The energy storage site also includes an interaction unit and a request unit; the request unit is used to send a distributed scene data request to the data node; the interaction unit is used to present the distributed scene data to the user, and the user processes the distributed scene according to the distributed scene data.

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