Optimized operation method of distributed energy storage system

Abstract

The invention discloses a distributed energy storage system optimization operation method, and relates to the technical field of distributed energy storage, and the method comprises the steps: obtaining the high-power operation characteristics of the energy storage operation data of an energy storage node through a high-power analysis method; building a system optimization model, and optimizing the system optimization model to obtain a distributed optimization model; using the distributed optimization model to optimize the distributed energy storage system; the method is used for solving the problem that in an existing distributed energy storage system optimization operation method, when an energy storage system is in a high-power operation state, the high-frequency operation interval and the operation expectation of the energy storage system during follow-up work cannot be effectively analyzed based on the high-power operation characteristics of the energy storage system, and the energy storage system cannot be optimized. Therefore, the problem that the operation of the energy storage system cannot be effectively optimized after the high-power operation state of the energy storage system is ended can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed storage, and particularly to a distributed energy storage system optimization operation method. BACKGROUND

[0002] A distributed energy storage system is a system that disperses multiple small energy storage units at the user side or near the distribution network, and cooperatively operates through communication and control systems to achieve power storage, distribution and utilization. The core is "dispersed deployment and centralized control", which is different from centralized large-scale energy storage power stations. The system is mainly used in industrial parks, communities and microgrids, etc., and can improve energy utilization efficiency and reduce electricity costs.

[0003] The existing method for optimizing the operation of a distributed energy storage system usually predicts the load by using power load data and predicted demand, and extracts parameters corresponding to key indicators to optimize the operation of the distributed storage system based on the operating characteristics of the energy storage device and the load prediction results. Although this improved method can adjust and control the energy storage system, it can only optimize the energy storage system in a normal operating state. When the energy storage system is in a high-power operating state, it cannot effectively analyze the high-frequency operating interval and operating expectations of the energy storage system in the subsequent work based on the high-power operating characteristics of the energy storage system, resulting in the problem that the operation of the energy storage system cannot be effectively optimized after the energy storage system ends the high-power operating state. For example, in the patent application with the publication number CN119891318A, a device management and control method for a distributed energy storage system cluster based on energy storage EMS is disclosed. The method is to obtain power load data and predicted demand of the slave control end by the master control end EMS through the master-slave communication network, use the load automatic prediction model to predict the load, analyze the operating characteristics of the slave control end, and optimize the operating strategy combined with the prediction information. Other improvements to the method for optimizing the operation of a distributed energy storage system usually focus on extending the service life of energy storage batteries, but still cannot solve the problem that when the energy storage system is in a high-power operating state, it cannot effectively analyze the high-frequency operating interval and operating expectations of the energy storage system in the subsequent work based on the high-power operating characteristics of the energy storage system, resulting in the problem that the operation of the energy storage system cannot be effectively optimized after the energy storage system ends the high-power operating state. Therefore, it is necessary to improve the existing method for optimizing the operation of a distributed energy storage system. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art, by proposing a distributed energy storage system optimization operation method, for solving the problem that in the prior art distributed energy storage system optimization operation method, only the energy storage system in the normal operation state can be optimized, when the energy storage system is in a high-power operation state, the high-frequency operation interval and operation expectation of the energy storage system in the subsequent work cannot be effectively analyzed based on the high-power operation characteristics of the energy storage system, resulting in the problem that the operation of the energy storage system cannot be effectively optimized after the energy storage system ends the high-power operation state.

[0005] To achieve the above-mentioned purpose, the present application provides a distributed energy storage system optimization operation method, comprising the following steps: Respectively acquire the energy storage operation data of each energy storage node in the distributed energy storage system, and use a high-power analysis method to acquire the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node; Based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system when the distributed energy storage system is running; the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model; When the distributed energy storage system is running, the distributed optimization model is used to optimize the distributed energy storage system based on the energy storage nodes in the running state.

[0006] Further, respectively acquiring the energy storage operation data of each energy storage node in the distributed energy storage system, and using a high-power analysis method to acquire the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node comprises: Respectively acquire the energy storage operation data of all energy storage nodes in the distributed energy storage system; for any one energy storage node, the energy storage operation data of the energy storage node in the high-power operation state in the energy storage operation data of the energy storage node is recorded as high-frequency operation data GY1 to high-frequency operation data GYn from the time of acquisition to the time of acquisition in sequence. m ; Using a high-power analysis method to analyze the energy storage operation data of each energy storage node and all high-frequency operation data in sequence, and acquiring the high-power operation characteristics of each energy storage node based on the analysis results.

[0007] Further, the high-power analysis method comprises: For any one energy storage node: respectively acquire the date of all high-frequency operation data corresponding to the energy storage node when the high-frequency operation data is acquired, and record the date as the high-frequency operation date; Record the high-frequency operation date in which the number of high-frequency operation data is greater than or equal to 2 as a multi-high-frequency date; record the high-frequency operation date in which there is only one high-frequency operation data as a single high-frequency date; respectively, and obtain multi-high-frequency features and single-high-frequency features based on the analysis results; The multi-high-frequency features and the single-high-frequency features are recorded as high-power features obtained by the high-power analysis method.

[0008] Further, the multi-frequency sub-method comprises: For any one multi-high-frequency date: record the number of high-frequency operation data existing in the multi-high-frequency date as n; establish a plane rectangular coordinate system and record it as a multi-frequency analysis coordinate system, wherein the units of the X-axis and the Y-axis of the multi-frequency analysis coordinate system are h and W respectively; based on the energy storage operation data of the energy storage node, draw a curve corresponding to the power of the storage device in the energy storage node from 0h to 24h in the multi-high-frequency date between X=0 to X=24 in the multi-frequency analysis coordinate system, and record it as a multi-frequency analysis curve; Record the n points with larger and different vertical coordinates in the multi-frequency analysis curve as high-frequency operation points GY1 to GYn in turn based on the horizontal coordinates from small to large; n , wherein for any one high-frequency operation point with coordinates (X0, Y0), when there is only one point with vertical coordinate Y0 in the multi-frequency analysis curve, the high-frequency operation point is excluded from the high-frequency operation points GY1 to GYn; n For any one high-frequency operation point α (X1, Y1): record the line segment composed of all points with vertical coordinate Y1 in the multi-frequency analysis curve as a high-frequency operation line segment, wherein the number of high-frequency operation line segments can be multiple, that is, there are multiple line segments with vertical coordinate Y1 and parallel to the X-axis in the multi-frequency analysis curve; when the high-frequency operation line segment is a continuous line segment, record the value obtained by subtracting the horizontal coordinate of the leftmost point from the horizontal coordinate of the rightmost point in the high-frequency operation line segment as the same-frequency operation span of Y1.

[0009] Further, the multi-frequency sub-method further comprises: When the high-frequency operation line segment is multiple independent line segments, record the value obtained by subtracting the horizontal coordinate of the leftmost point from the horizontal coordinate of the rightmost point in each high-frequency operation line segment as the same-frequency operation span of Y1; for any two adjacent high-frequency operation line segments, record the value obtained by subtracting the horizontal coordinate of the rightmost point of the left high-frequency operation line segment from the horizontal coordinate of the leftmost point of the right high-frequency operation line segment as the same-frequency operation interval of Y1; ​When the high-frequency operating point β (X2, Y2) is the nearest high-frequency operating point to the right of the high-frequency operating point α and is not on the high-frequency operating line segment where the high-frequency operating point α is located, the horizontal coordinate of the leftmost point of the high-frequency operating line segment where the high-frequency operating point β is located is subtracted from the horizontal coordinate of the rightmost point of the high-frequency operating line segment where the high-frequency operating point α is located, and the value is recorded as Y1, the high-frequency interval of Y2, and the vertical coordinate of the lowest point in the curve between X=X1 and X=X2 in the multi-frequency analysis curve is recorded as the interval power of Y1 with respect to Y2.

[0010] Further, the multi-frequency sub-method further comprises: obtaining the same-frequency operating span, the same-frequency operating interval, the high-frequency interval, and the interval power of all the high-frequency operating points obtained by all the multi-high-frequency dates, recording all the vertical coordinates of all the high-frequency operating points obtained by all the multi-high-frequency dates as recorded power, and recording the same-frequency operating span, the same-frequency operating interval, the high-frequency interval, and the interval power corresponding to all the recorded power as multi-high-frequency characteristics; when there are multiple same-frequency operating spans for the recorded power, recording the maximum value in the multiple same-frequency operating spans as the same-frequency operating span corresponding to the recorded power; when there are multiple same-frequency operating intervals for the recorded power, recording the minimum value in the multiple same-frequency operating intervals as the same-frequency operating interval corresponding to the recorded power; when there are multiple high-frequency intervals for the recorded power with respect to the same power, recording the minimum value in the multiple high-frequency intervals as the high-frequency interval of the recorded power with respect to the power; and when there are multiple interval powers for the recorded power with respect to the same power, recording the minimum value in the multiple interval powers as the interval power of the recorded power with respect to the power.

[0011] Further, the single-frequency sub-method comprises: for any single high-frequency date γ: when the power of the energy storage device recorded in the high-frequency operating data within the single high-frequency date is all recorded power, stop analyzing the single high-frequency date γ; when there is power in the power of the energy storage device recorded in the high-frequency operating data within the single high-frequency date that is not recorded power, intercept the data corresponding to the power that is not recorded power in the high-frequency operating data and record it as available single data; record the power at which the energy storage device is located in the available single data as K, record the time length during which the power of the energy storage device in the available single data is at K as the same-frequency operating span of K, and record K as recorded power; record the same-frequency operating span corresponding to the recorded power obtained by all the single high-frequency dates as single high-frequency characteristics.

[0012] Further, based on the high-power operating characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system when the distributed energy storage system is running, which comprises: The system optimization model is built, in which the high-power operation characteristics of all energy storage nodes are stored in the system optimization model, and a system optimization method is built in the system optimization model; The system optimization method comprises: when the distributed energy storage system is in an operation state, recording the energy storage nodes in the operation state as optimizable nodes; for any one of the optimizable nodes: when the optimizable node is in a high-power operation state, recording the real-time power of the energy storage device in the optimizable node as an optimizable power; When the optimizable power only has a same-frequency operation span, the power judgment method 1 is executed; When the optimizable power has a same-frequency operation span, a same-frequency operation interval, a high-frequency interval and an interval power, the power judgment methods 1 to 4 are executed in sequence.

[0013] Further, the power judgment methods 1 to 4 comprise: The power judgment method 1: recording the time length of the energy storage device in the optimizable node at the optimizable power, and when the time length is greater than the same-frequency operation span of the optimizable power, sending a high-power operation time too long warning; The power judgment method 2: when the power corresponding to the previous high-power operation state of the optimizable node is equal to the optimizable power, recording the end time of the previous high-power operation state of the optimizable node as t1, and recording the start time of the high-power operation state of the optimizable node as t2; when the value of t2 minus t1 is less than the same-frequency operation interval corresponding to the optimizable power, sending a same-power operation interval too short warning; The power judgment method 3: based on the working log of the energy storage device, obtaining the start time and the corresponding power of the next high-power operation state of the energy storage device, which are recorded as t4 and P respectively; when the optimizable power has a high-frequency interval and an interval power about P, recording t4 minus the value of the high-frequency interval of the optimizable power about P as the latest termination time of the high-power operation state of the optimizable node, and recording the interval power of the optimizable power about P as the maximum power allowed to be maintained after the high-power operation state of the optimizable node ends; The power judgment method 4: when the optimizable power does not have a high-frequency interval and an interval power about P, real-time monitoring and warning the device parameters of the energy storage device between the end of the high-power operation state and the start of the next high-power operation state of the energy storage node, and recording the end date of the high-power operation state of the energy storage node as the model optimization date.

[0014] Further, the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model, which comprises: When there is a model optimization date that has not been analyzed, the system optimization model is optimized using the model optimization method, and the model optimization date that has not been analyzed is recorded as an analyzed date; The model optimization method comprises: based on the high-power analysis method, analyzing high-frequency operation data of the energy storage node corresponding to the model optimization date within the model optimization date, and updating high-power operation characteristics of all energy storage nodes based on the analysis result; When the system optimization model storing the latest high-power operation characteristics of all energy storage nodes is recorded as a distributed optimization model.

[0015] The application first obtains the energy storage operation data of each energy storage node in the distributed energy storage system, and obtains the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node using the high-power analysis method. The advantage of this is that by obtaining the corresponding high-power operation characteristics based on the energy storage operation data of each energy storage node, the characteristic relationship between the energy storage node after running in each high-power state and the subsequent high-power operation state can be obtained, so that the high-frequency operation interval and operation expectation of the energy storage system during subsequent operation can be effectively analyzed based on the high-power operation characteristics of each energy storage node during subsequent analysis, thereby effectively optimizing the energy storage system. The application also builds a system optimization model, and uses the system optimization model to optimize the distributed energy storage system during operation of the distributed energy storage system. The system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model. Finally, based on the energy storage nodes in the running state, the distributed optimization model is used to optimize the distributed energy storage system during operation of the distributed energy storage system. The advantage of this is that by establishing the system optimization model and optimizing the system optimization model to obtain the distributed optimization model, real-time operation input can be directly imported into the distributed optimization model during operation of the distributed energy storage system, thereby obtaining the operation optimization result of the distributed energy storage system, which helps to improve the analysis efficiency of real-time data and realizes efficient operation optimization of the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the steps of the method of the application; Figure 2 The schematic diagram of the multi-frequency analysis coordinate system of the application Figure 3 The schematic diagram of the acquisition of the same-frequency operation span, same-frequency operation interval, high-frequency interval and interval power of the application; Figure 4 The structural schematic diagram of the electronic device of the application. DETAILED DESCRIPTION

[0017] With reference to the accompanying drawings on which embodiments of the application are illustrated, the technical solutions in the embodiments of the application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the application.

[0018] Embodiment 1, please refer to Figure 1 As shown in the figure, the application provides a distributed energy storage system optimization operation method, comprising the following steps: Step S1, respectively acquiring the energy storage operation data of each energy storage node in the distributed energy storage system, and using a high-power analysis method to acquire the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node; Step S1 comprises: step S101, respectively acquiring the energy storage operation data of all energy storage nodes in the distributed energy storage; for any one energy storage node, the energy storage operation data of the energy storage node in the high-power operation state in the energy storage operation data of the energy storage node is sequentially recorded as the high-frequency operation data GY1 to the high-frequency operation data GY m from the first to the last based on the acquisition time; In the specific implementation process, according to the determination standard of each energy storage node for high-power operation, the data corresponding to the time when the energy storage node is determined to be high-power operation in the energy storage operation data of the energy storage node is acquired as the high-frequency operation data, and subsequent analysis is performed; Step S102, using the high-power analysis method to analyze the energy storage operation data of each energy storage node and all high-frequency operation data in sequence, and acquiring the high-power operation characteristics of each energy storage node based on the analysis result.

[0019] Step S103, the high-power analysis method comprises: step S1031, for any one energy storage node: respectively acquiring the dates of all high-frequency operation data corresponding to the energy storage node when the high-frequency operation data is acquired, and recording the dates as high-frequency operation dates; Step S1032, recording the high-frequency operation dates in which the number of high-frequency operation data is greater than or equal to 2 as multi-high-frequency dates; recording the high-frequency operation dates in which there is only one high-frequency operation data as single-high-frequency dates; In the specific implementation process, if the high-frequency operation date is December 1, and the recording dates of the three high-frequency operation data in the high-frequency operation data of the energy storage node are December 1, it is indicated that there are three high-frequency operation states of the energy storage node on December 1, so December 1 can be recorded as a multi-high-frequency date; in addition, for the date with only one high-frequency operation state, in order to consider the particularity of the corresponding high-frequency operation data, the date can be recorded as a single-high-frequency date, so as to be distinguished from the multi-high-frequency date; Step S1033, analyze all multi-high-frequency dates and all single-high-frequency dates using the multi-frequency sub-method and the single-frequency sub-method respectively, and obtain multi-high-frequency features and single-high-frequency features based on the analysis results; Step S1034, record the multi-high-frequency features and the single-high-frequency features as high-power features obtained by the high-power analysis method.

[0020] The multi-frequency sub-method includes: Step V1, for any one multi-high-frequency date: record the number of high-frequency operation data existing in the multi-high-frequency date as n; establish a plane rectangular coordinate system and record it as a multi-frequency analysis coordinate system, wherein the units of the X-axis and the Y-axis of the multi-frequency analysis coordinate system are h and W respectively; based on the energy storage operation data of the energy storage node, draw a curve corresponding to the power of the storage device in the energy storage node from 0h to 24h in the energy storage node in the multi-high-frequency date between X=0 to X=24 in the multi-frequency analysis coordinate system, and record it as a multi-frequency analysis curve; Step V2, record the n points with different and larger Y coordinates in the multi-frequency analysis curve in order as high-frequency operation points GY1 to GYn based on the X coordinates from small to large; n wherein, for any one high-frequency operation point with coordinates (X0, Y0), when there is only one point with Y coordinate Y0 in the multi-frequency analysis curve, the high-frequency operation point is excluded from the high-frequency operation points GY1 to GYn; n In the specific implementation process, for example, in one data analysis, the multi-frequency analysis coordinate system obtained based on one multi-high-frequency date December 1 is as shown in Figure 2 , and the high-frequency operation points obtained based on the number of high-frequency operation data corresponding to the multi-high-frequency date are GY1 to GY3, wherein in this embodiment, the points with larger Y coordinates in the multi-frequency analysis curve are the points corresponding to the power of the energy storage device in the high-frequency operation data, so the points GY1 to GY3 are the points corresponding to the power of the energy storage device in the three high-frequency operation data on December 1, that is, the Y coordinates of the points GY1 to GY3 reflect the power of the energy storage device in the three high-frequency operation data on December 1; Step V3, for any one high-frequency operation point α (X1, Y1): record the line segment composed of all points with Y coordinate Y1 in the multi-frequency analysis curve as a high-frequency operation line segment, wherein the number of high-frequency operation line segments can be multiple, that is, there are multiple line segments with Y coordinate Y1 and parallel to the X-axis in the multi-frequency analysis curve; when the high-frequency operation line segment is a continuous line segment, record the value obtained by subtracting the X coordinate of the leftmost point from the X coordinate of the rightmost point in the high-frequency operation line segment as the same-frequency operation span of Y1.

[0021] ​The multi-frequency sub-method further comprises: step V4, when the high-frequency operation line segment is a plurality of independent line segments, the horizontal coordinate of the rightmost point in each high-frequency operation line segment is subtracted from the horizontal coordinate of the leftmost point, and the value is recorded as a same-frequency operation span of Y1; for any two adjacent high-frequency operation line segments, the horizontal coordinate of the leftmost point of the right high-frequency operation line segment is subtracted from the horizontal coordinate of the rightmost point of the left high-frequency operation line segment, and the value is recorded as a same-frequency operation interval of Y1; In the specific implementation process, for example, in a data analysis, the obtained multi-frequency analysis curve is as shown in the curve DF in Figure 2 When the high-frequency operation point GY1 in the multi-frequency analysis curve is analyzed, it can be obtained through analysis that the high-frequency operation line corresponding to GY1 is a plurality of independent line segments, that is, GX1 to GX3 in Figure 3 Therefore, the value of XX2 minus XX1, the value of XX4 minus XX3, and the value of XX6 minus XX5 in Figure 3 are recorded as a same-frequency operation span of YY1, the value of XX3 minus XX2 and the value of XX5 minus XX4 are recorded as a same-frequency operation interval of YY1; through combined analysis of Figure 2 and Figure 3 , it can be obtained that Figure 2 the high-frequency operation point GY2 in is the closest high-frequency operation point to the right of the high-frequency operation point GY1 and not on the high-frequency operation line segment where GY1 is located, and the vertical coordinate of GY1 is not equal to that of GY2, so that the value of XX7 minus XX2 in Figure 3 is recorded as a high-frequency interval of YY1 with respect to YY2, and YY3 is recorded as an interval power of YY1 with respect to YY2; Step V5, when the closest high-frequency operation point to the right of the high-frequency operation point α and not on the high-frequency operation line segment where the high-frequency operation point α is located is a high-frequency operation point β (X2, Y2) with a vertical coordinate different from that of the high-frequency operation point α, the horizontal coordinate of the leftmost point of the high-frequency operation line segment where the high-frequency operation point β is located is subtracted from the horizontal coordinate of the rightmost point of the high-frequency operation line segment where the high-frequency operation point α is located, and the value is recorded as a high-frequency interval of Y1 with respect to Y2, and the vertical coordinate of the lowest point in the curve between X=X1 and X=X2 in the multi-frequency analysis curve is recorded as an interval power of Y1 with respect to Y2.

[0022] The multi-frequency sub-method further comprises: step V6, obtaining the same-frequency operation span, the same-frequency operation interval, the high-frequency interval, and the interval power of all high-frequency operation points obtained on all multi-high-frequency dates, and recording the vertical coordinates of all high-frequency operation points obtained on all multi-high-frequency dates as recorded powers, and recording the same-frequency operation span, the same-frequency operation interval, the high-frequency interval, and the interval power corresponding to all recorded powers as multi-high-frequency characteristics. Step V7, when there are multiple same-frequency operation spans for the recorded power, record the maximum value in the multiple same-frequency operation spans as the same-frequency operation span corresponding to the recorded power; when there are multiple same-frequency operation intervals for the recorded power, record the minimum value in the multiple same-frequency operation intervals as the same-frequency operation interval corresponding to the recorded power; when there are multiple high-frequency intervals for the recorded power with respect to the same power, record the minimum value in the multiple high-frequency intervals as the high-frequency interval of the recorded power with respect to the power; when there are multiple interval powers for the recorded power with respect to the same power, record the minimum value in the multiple interval powers as the interval power of the recorded power with respect to the power; In the implementation process, such as in a data analysis, the multiple same-frequency operation spans corresponding to the recorded power obtained are 1h, 2h and 0.5h respectively, then 0.5h is recorded as the same-frequency operation span corresponding to the recorded power; in this embodiment, the purpose of obtaining the same-frequency operation span, the same-frequency operation interval, the high-frequency interval and the interval power is to obtain the maximum operation time of the storage device under the recorded power, the minimum operation interval when continuously operating with two same recorded powers, the minimum operation interval when continuously operating with two different recorded powers and the minimum power that the storage device can maintain in the interval when continuously operating with two different recorded powers, so as to optimize the operation of the distributed energy storage system when the distributed energy storage system operates.

[0023] The single-frequency sub-method comprises: Step C1, for any single high-frequency date γ: when the power of the energy storage device recorded in the high-frequency operation data in the single high-frequency date is all the recorded power, stop analyzing the single high-frequency date γ; In this embodiment, when obtaining the high-frequency operation data, for the data corresponding to the recorded power in the high-frequency operation data, a slight error should be calibrated to the target power corresponding to the recorded power, such as the recorded power is 12KW, then if there is 11.999KW data in the high-frequency operation data, 11.999KW should be calibrated to 12KW, and the high-frequency operation line segment is drawn; Step C2, when there is a power not recorded as the recorded power in the power of the energy storage device recorded in the high-frequency operation data in the single high-frequency date, intercept the data corresponding to the power not recorded as the recorded power in the high-frequency operation data and record it as the available single data; Step C3, record the power of the energy storage device in the available single data as K, record the time length of the power of the energy storage device in the available single data at K as the same-frequency operation span of K, and record K as the recorded power; Step C4, record the same-frequency operation span corresponding to the recorded power obtained from all single high-frequency dates as the single high-frequency feature.

[0024] Step S2, based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system when the distributed energy storage system is running; the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model; Step S2 includes: step S201, building a system optimization model, wherein the high-power operation characteristics of all energy storage nodes are stored in the system optimization model, and a system optimization method is built in the system optimization model; In this embodiment, the system optimization model is a logical execution model, that is, the purpose of building the system optimization model is to execute the system optimization method; in addition, after building the system optimization model, the high-power operation characteristics of all energy storage nodes can be stored in the system optimization model to improve the execution efficiency when executing the system optimization method, and then when the distributed energy storage system is running, based on the energy storage nodes in the running state, the distributed optimization model is used to optimize the distributed energy storage system, wherein the distributed optimization model is obtained by optimizing the system optimization model; Step S202, the system optimization method includes: step S2021, when the distributed energy storage system is in a running state, the energy storage nodes in the running state are recorded as optimizable nodes; for any one optimizable node: when the optimizable node is in a high-power operation state, the real-time power of the energy storage device in the optimizable node is recorded as an optimizable power; Step S2022, when the optimizable power only exists in the same frequency operation span, power judgment method 1 is executed; Step S2023, when the optimizable power exists in the same frequency operation span, the same frequency operation interval, the high frequency interval and the interval power, power judgment method 1 to power judgment method 4 are executed in turn.

[0025] Power judgment method 1: record the time length of the energy storage device in the optimizable node at the optimizable power, and send a high-power operation time too long warning when the time length is greater than the same frequency operation span of the optimizable power; Power judgment method 2: when the power corresponding to the previous high-power operation state of the optimizable node is equal to the optimizable power, the end time of the previous high-power operation state of the optimizable node is recorded as t1, and the start time of the high-power operation state of the optimizable node is recorded as t2; when the value of t2 minus t1 is less than the same frequency operation interval corresponding to the optimizable power, a same power operation interval is sent. Short warning; In the implementation process, for example, in one data analysis, t1 and t2 are 12:00 and 12:30 respectively, and the value of t2 minus t1 is 0.5h through calculation, if the same frequency operation interval corresponding to the optimizable power is 1h, then because 0.5h is less than 1h, if the optimizable node continues to enter the high-power operation state at the optimizable power with an interval of 0.5h, it may cause damage to the energy storage device of the optimizable node, so a shorter power operation interval warning should be sent to remind the staff to intervene and handle; Power determination method 3: based on the working log of the energy storage device, the start time of the next high-power operation state of the energy storage device and the corresponding power are obtained, denoted as t4 and P respectively; when the optimizable power has a high-frequency interval and an interval power about P, t4 is subtracted from the value of the high-frequency interval of the optimizable power about P, denoted as the latest end time of the high-power operation state of the optimizable node, and the interval power of the optimizable power about P is denoted as the maximum power allowed to be maintained after the optimizable node ends the high-frequency operation state; In the implementation process, for example, in one data analysis, the start time of the next high-power operation state of the energy storage device and the corresponding power are 13:00 and 3KW respectively, and the high-frequency interval and the interval power of the optimizable power about 3KW are 2h and 0.5KW respectively, in order to ensure the stable operation of the energy storage device, the high-power operation state of the optimizable node should end before 11:00 to ensure that the energy storage device can stably enter the next high-power operation state at 13:00, in addition, during the period from 11:00 to 13:00, the maximum power that the energy storage device can maintain is 0.5KW, if there is a task in the task log that allocates power to the energy storage device during the period from 11:00 to 13:00 greater than 0.5KW, the task can be modified in advance; in summary, based on the latest end time of the high-power operation state of the optimizable node and the maximum power allowed to be maintained after the optimizable node ends the high-frequency operation state obtained by the power determination method 3, the operation of the multi-node energy storage system can be optimized; Power determination method 4: when the optimizable power does not have a high-frequency interval and an interval power about P, the device parameters of the energy storage device between the end of the high-power operation state and the start of the next high-power operation state are monitored and warned in real time, and the end date of the high-power operation state of the energy storage node is denoted as the model optimization date.

[0026] Step S2 also includes step S203: when there is a model optimization date that has not been analyzed, the system optimization model is optimized using the model optimization method, and the model optimization date that has not been analyzed is denoted as an analyzed date; The model optimization method comprises: in step S2031, based on the high-power analysis method, the high-frequency operation data of the energy storage nodes corresponding to the model optimization date is analyzed within the model optimization date, and the high-power operation characteristics of all energy storage nodes are updated based on the analysis result; In the specific implementation process, the high-power operation characteristics of all energy storage nodes are updated based on the analysis result, that is, the high-power operation characteristics obtained from the model optimization date are taken as the latest high-power operation characteristics, so that the original high-power operation characteristics are updated; In step S2032, the system optimization model in which the latest high-power operation characteristics of all energy storage nodes are stored is recorded as a distributed optimization model.

[0027] In step S3, during the operation of the distributed energy storage system, the distributed optimization model is used to optimize the distributed energy storage system based on the energy storage nodes in the running state.

[0028] Embodiment 2, please refer to Figure 4 as shown, Figure 4 An example of a structural diagram of an electronic device is shown, which can include a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus. The memory stores computer readable instructions, and the processor can call the instructions in the memory, when the computer readable instructions are executed by the processor, the steps in the distributed energy storage system optimization operation method are run to realize the following functions: first, the energy storage operation data of each energy storage node in the distributed energy storage system is obtained respectively, and the high-power operation characteristics of each energy storage node are obtained based on the energy storage operation data of each energy storage node using the high-power analysis method; then, based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system during the operation of the distributed energy storage system; the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model; finally, during the operation of the distributed energy storage system, the distributed optimization model is used to optimize the distributed energy storage system based on the energy storage nodes in the running state.

[0029] Moreover, the logic instructions in the above-mentioned memory can be realized in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0030] Embodiment 3, the present application also provides a computer program product, the computer program product includes a computer program stored on a computer readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, the computer can execute the distributed energy storage system optimization operation method provided by the above-mentioned method, the method includes: first, respectively acquiring the energy storage operation data of each energy storage node in the distributed energy storage system, and using the high-power analysis method to acquire the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node; then, based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system when the distributed energy storage system is running; the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model; finally, based on the energy storage nodes in the running state, the distributed optimization model is used to optimize the distributed energy storage system when the distributed energy storage system is running.

[0031] Embodiment 4, the present application also provides a computer readable storage medium, the present application provides a storage medium, which stores a computer program, and the computer program is executed by a processor to run the steps in the above distributed energy storage system optimization operation method to realize the following functions: first, respectively acquiring the energy storage operation data of each energy storage node in the distributed energy storage system, and using the high-power analysis method to acquire the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node; then, based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system when the distributed energy storage system is running; the system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model; finally, based on the energy storage nodes in the running state, the distributed optimization model is used to optimize the distributed energy storage system when the distributed energy storage system is running.

[0032] Through the description of the above embodiments, the embodiments of the present application can be provided as a method, a system or a computer program product. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

[0033] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are only illustrative, for example, the division of modules or units is only a logical function division, and in actual implementation, there can be another division manner, for example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some communication interface, indirect coupling or communication connection between the systems, modules and units can be electrical, mechanical or other forms.

[0034] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing operation of a distributed energy storage system, characterized in that, The method comprises the following steps: acquiring the energy storage operation data of each energy storage node in the distributed energy storage system respectively, and acquiring the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node by using a high-power analysis method; building a system optimization model based on the high-power operation characteristics of each energy storage node, and optimizing the distributed energy storage system by using the system optimization model during the operation of the distributed energy storage system; optimizing the system optimization model, and recording the optimized system optimization model as a distributed optimization model; optimizing the distributed energy storage system by using the distributed optimization model based on the energy storage nodes in the running state during the operation of the distributed energy storage system.

2. The method of claim 1, wherein, The method of acquiring the energy storage operation data of each energy storage node in the distributed energy storage system respectively, and acquiring the high-power operation characteristics of each energy storage node based on the energy storage operation data of each energy storage node by using a high-power analysis method comprises: Obtain the energy storage operation data of all energy storage nodes in the distributed energy storage respectively; for any one energy storage node, the energy storage operation data of the energy storage node in the high-power operation state is recorded as the high-frequency operation data GY1 to the high-frequency operation data GYn of the energy storage node in chronological order according to the obtained time m ; analyzing the energy storage operation data of each energy storage node and all the high-frequency operation data in sequence by using the high-power analysis method, and acquiring the high-power operation characteristics of each energy storage node based on the analysis results.

3. The method of claim 2, wherein, The high-power analysis method comprises: for any one energy storage node, acquiring the dates when all the high-frequency operation data corresponding to the energy storage node are acquired, and recording the dates as high-frequency operation dates; recording the high-frequency operation dates in which the number of high-frequency operation data is greater than or equal to 2 as multi-high-frequency dates, and recording the high-frequency operation dates in which there is only one high-frequency operation data as single-high-frequency dates; analyzing all the multi-high-frequency dates and all the single-high-frequency dates by using a multi-frequency sub-method and a single-frequency sub-method respectively, and acquiring multi-high-frequency characteristics and single-high-frequency characteristics based on the analysis results; recording the multi-high-frequency characteristics and the single-high-frequency characteristics as high-power characteristics obtained by the high-power analysis method.

4. The method of claim 3, wherein, The multi-frequency sub-method comprises: for any one multi-high-frequency date, recording the number of high-frequency operation data existing in the multi-high-frequency date as n; establishing a plane rectangular coordinate system, and recording the plane rectangular coordinate system as a multi-frequency analysis coordinate system, wherein the units of the X-axis and the Y-axis of the multi-frequency analysis coordinate system are h and W respectively; based on the energy storage operation data of the energy storage node, drawing a curve corresponding to the power of the storage device of the energy storage node from 0h to 24h between X=0 and X=24 of the multi-frequency analysis coordinate system, and recording the curve as a multi-frequency analysis curve; The n points with different and larger longitudinal coordinates in the multi-frequency analysis curve are sequentially recorded as high-frequency operating points GY1 to GYn based on the horizontal coordinates from small to large n , wherein, for any high-frequency operating point with coordinates (X0, Y0), when there is only one point with the longitudinal coordinate Y0 in the multi-frequency analysis curve, the high-frequency operating point is removed from the high-frequency operating points GY1 to GYn n . for any one high-frequency operation point α (X1, Y1), recording a line segment composed of all the points with the same Y1 in the multi-frequency analysis curve as a high-frequency operation line segment, wherein the number of the high-frequency operation line segments can be multiple, that is, there are multiple line segments with the same Y1 and parallel to the X-axis in the multi-frequency analysis curve; when the high-frequency operation line segment is a continuous line segment, recording the value obtained by subtracting the horizontal coordinate of the leftmost point from the horizontal coordinate of the rightmost point in the high-frequency operation line segment as the same-frequency operation span of Y1.

5. The method of claim 4, wherein, The multi-frequency sub-method further comprises: When the high-frequency operation line segment is multiple independent line segments, the horizontal coordinate of the rightmost point in each high-frequency operation line segment is subtracted from the horizontal coordinate of the leftmost point, and the value is recorded as the same-frequency operation span of Y1; for any two adjacent high-frequency operation line segments, the horizontal coordinate of the leftmost point of the right high-frequency operation line segment is subtracted from the horizontal coordinate of the rightmost point of the left high-frequency operation line segment, and the value is recorded as the same-frequency operation interval of Y1; When the nearest high-frequency operation point to the right of the high-frequency operation point α is a high-frequency operation point β (X2, Y2) with a vertical coordinate different from that of the high-frequency operation point α, the horizontal coordinate of the leftmost point of the high-frequency operation line segment where the high-frequency operation point β is located is subtracted from the horizontal coordinate of the rightmost point of the high-frequency operation line segment where the high-frequency operation point α is located, and the value is recorded as the high-frequency interval of Y1 with respect to Y2, and the vertical coordinate of the lowest point in the curve between X=X1 and X=X2 in the multi-frequency analysis curve is recorded as the interval power of Y1 with respect to Y2.

6. The method of claim 5, wherein, The multi-frequency sub-method further comprises: Obtaining the same-frequency operation span, same-frequency operation interval, high-frequency interval, and interval power of all high-frequency operation points obtained from all multi-high-frequency dates, and recording the vertical coordinates of all high-frequency operation points obtained from all multi-high-frequency dates as recorded power, and recording the same-frequency operation span, same-frequency operation interval, high-frequency interval, and interval power corresponding to all recorded power as multi-high-frequency characteristics; When there are multiple same-frequency operation spans for the recorded power, the maximum value of the multiple same-frequency operation spans is recorded as the same-frequency operation span corresponding to the recorded power; when there are multiple same-frequency operation intervals for the recorded power, the minimum value of the multiple same-frequency operation intervals is recorded as the same-frequency operation interval corresponding to the recorded power; when there are multiple high-frequency intervals for the recorded power with respect to the same power, the minimum value of the multiple high-frequency intervals is recorded as the high-frequency interval of the recorded power with respect to the power; when there are multiple interval powers for the recorded power with respect to the same power, the minimum value of the multiple interval powers is recorded as the interval power of the recorded power with respect to the power.

7. The method of claim 6, wherein, The single-frequency sub-method comprises: For any single high-frequency date γ: when the power of the energy storage device recorded in the high-frequency operation data within the single high-frequency date is the recorded power, stop analyzing the single high-frequency date γ; When there is a power in the power of the energy storage device recorded in the high-frequency operation data within the single high-frequency date that is not recorded as the recorded power, intercept the data corresponding to the power not recorded as the recorded power in the high-frequency operation data and record it as available single data; Record the power of the energy storage device in the available single data as K, record the time length of the power of the energy storage device in the available single data as the same-frequency operation span of K, and record K as the recorded power; Record the same-frequency operation span corresponding to the recorded power obtained from all single high-frequency dates as single high-frequency characteristics.

8. The method of claim 7, wherein, Based on the high-power operation characteristics of each energy storage node, a system optimization model is built, and the system optimization model is used to optimize the operation of the distributed energy storage system during the operation of the distributed energy storage system, comprising: The system optimization model is built, in which the high-power operation characteristics of all energy storage nodes are stored in the system optimization model, and a system optimization method is built in the system optimization model; The system optimization method includes: when the distributed energy storage system is in an operating state, the energy storage node in the operating state is recorded as an optimizable node; for any optimizable node: when the optimizable node is in a high-power operation state, the real-time power of the energy storage device in the optimizable node is recorded as an optimizable power; When the optimizable power only has a same-frequency operation span, power judgment method 1 is executed; When the optimizable power has a same-frequency operation span, a same-frequency operation interval, a high-frequency interval, and an interval power, power judgment methods 1 to 4 are executed in turn.

9. The method of claim 8, wherein, Power judgment methods 1 to 4 include: Power judgment method 1: the time length during which the energy storage device in the optimizable node is in the optimizable power is recorded, and when the time length is greater than the same-frequency operation span of the optimizable power, a high-power operation time too long warning is sent; Power judgment method 2: when the power corresponding to the previous high-power operation state of the optimizable node is equal to the optimizable power, the end time of the previous high-power operation state of the optimizable node is recorded as t1, and the start time of the high-power operation state in which the optimizable node is located is recorded as t2; when the value of t2 minus t1 is less than the same-frequency operation interval corresponding to the optimizable power, a same-power operation interval too short warning is sent; Power judgment method 3: based on the working log of the energy storage device, the start time and the corresponding power of the next time the energy storage device is in a high-power operation state are obtained, which are recorded as t4 and P respectively; when the optimizable power has a high-frequency interval and an interval power about P, t4 minus the value of the high-frequency interval of the optimizable power about P is recorded as the latest termination time of the high-power operation state in which the optimizable node is located, and the interval power of the optimizable power about P is recorded as the maximum power allowed to be maintained after the optimizable node ends the high-power operation state; Power judgment method 4: when the optimizable power does not have a high-frequency interval and an interval power about P, the device parameters of the energy storage device between the end of the high-power operation state and the start of the next high-power operation state of the energy storage node are monitored and warned in real time, and the end date of the high-power operation state of the energy storage node is recorded as the model optimization date.

10. The method of claim 9, wherein, The system optimization model is optimized, and the optimized system optimization model is recorded as a distributed optimization model, which includes: When there is a model optimization date that has not been analyzed, the system optimization model is optimized using a model optimization method, and the model optimization date that has not been analyzed is recorded as an analyzed date; The model optimization method includes: based on the high-power analysis method, the high-frequency operation data of the energy storage node corresponding to the model optimization date in the model optimization date is analyzed, and the high-power operation characteristics of all energy storage nodes are updated based on the analysis result; When the system optimization model storing the latest high-power operation characteristics of all energy storage nodes is recorded as a distributed optimization model.

Images