Method for optimizing operation of distributed energy storage system

Abstract

The application discloses a distributed energy storage system optimal operation method and relates to the technical field of distributed energy storage, which comprises the following steps: obtaining the high-power operation characteristics of the energy storage operation data of an energy storage node by using a high-power analysis method; building a system optimization model and optimizing the system optimization model to obtain a distributed optimization model; and optimizing the distributed energy storage system by using the distributed optimization model; and the application is used to solve the problem that, in the prior art, when the energy storage system is in a high-power operation state, the high-frequency operation interval and operation expectation of the subsequent work of the energy storage system cannot be effectively analyzed based on the high-power operation characteristics of the energy storage system, so that the operation of the energy storage system cannot be effectively optimized after the energy storage system ends the high-power operation state.

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, which can only optimize the energy storage system in the conventional operation state, and cannot effectively analyze the high-frequency operation interval and operation expectation of the energy storage system in the subsequent work based on the high-power operation characteristics of the energy storage system when the energy storage system is in a high-power operation state, 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:

[0006] 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;

[0007] 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;

[0008] 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.

[0009] 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:

[0010] 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 sequentially recorded as the high-frequency operation data GY1 to the high-frequency operation data GYn of the energy storage node from the time of acquisition to the time of acquisition. m ;

[0011] 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.

[0012] Further, the high-power analysis method comprises:

[0013] For any one energy storage node: respectively acquire the date of all high-frequency operation data corresponding to the energy storage node when acquiring, and record it as the high-frequency operation date;

[0014] A high-frequency operation date in which the number of high-frequency operation data existing in the high-frequency operation date is greater than or equal to 2 is recorded as a multi-high-frequency date; a high-frequency operation date in which only one high-frequency operation data exists in the high-frequency operation date is recorded as a single-high-frequency date;

[0015] All multi-high-frequency dates and all single-high-frequency dates are analyzed using the multi-frequency sub-method and the single-frequency sub-method respectively, and multi-high-frequency features and single-high-frequency features are obtained based on the analysis results;

[0016] The multi-high-frequency features and the single-high-frequency features are recorded as high-power features obtained by the high-power analysis method.

[0017] Further, the multi-frequency sub-method comprises:

[0018] For any one multi-high-frequency date: the number of high-frequency operation data existing in the multi-high-frequency date is recorded as n; a plane rectangular coordinate system is established and recorded 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, 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 is drawn between X=0 to X=24 in the multi-frequency analysis coordinate system, and is recorded as a multi-frequency analysis curve;

[0019] The n points with different and larger Y-coordinates in the multi-frequency analysis curve are recorded as high-frequency operation points GY1 to GYn in turn 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

[0020] For any one high-frequency operation point α (X1, Y1): a line segment composed of all points with Y-coordinate Y1 in the multi-frequency analysis curve is recorded 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, 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 is recorded as the same-frequency operation span of Y1.

[0021] Further, the multi-frequency sub-method further comprises:

[0022] ​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 by 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 by 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;

[0023] When the rightmost high-frequency operation point of the high-frequency operation point α is the high-frequency operation point β (X2, Y2) which is not equal to the vertical coordinate 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 by 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 about 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 about Y2.

[0024] Further, the multi-frequency sub-method further comprises:

[0025] The same-frequency operation span, the same-frequency operation interval, the high-frequency interval and the interval power of all the high-frequency operation points obtained by all the multi-high-frequency dates are obtained, and the vertical coordinates of all the high-frequency operation points obtained by all the multi-high-frequency dates are recorded as recorded power, and the same-frequency operation span, the same-frequency operation interval, the high-frequency interval and the interval power corresponding to all the recorded power are recorded as multi-high-frequency characteristics;

[0026] When there are multiple same-frequency operation spans for the recorded power, the maximum value in 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 in 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 about the same power, the minimum value in the multiple high-frequency intervals is recorded as the high-frequency interval of the recorded power about the power; when there are multiple interval powers for the recorded power about the same power, the minimum value in the multiple interval powers is recorded as the interval power of the recorded power about the power.

[0027] Further, the single-frequency sub-method comprises:

[0028] 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 the recorded power, the analysis of the single high-frequency date γ is stopped;

[0029] When there is a power in the power of the energy storage device recorded in the high-frequency operation data in the single high-frequency date which is not recorded as the recorded power, the data corresponding to the power not recorded as the recorded power in the high-frequency operation data is intercepted and recorded as available single data;

[0030] Let the power available to the energy storage device in the single data be K, the length of time that the power available to the energy storage device in the single data is K be the same frequency operation span of K, and K be the recorded power;

[0031] The same frequency operation span corresponding to the recorded power obtained by all single high frequency dates is recorded as the single high frequency feature.

[0032] Further, based on the high power operation feature 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 includes:

[0033] The system optimization model is built, wherein the high power operation features of all energy storage nodes are stored in the system optimization model, and a system optimization method is built in the system optimization model;

[0034] The system optimization method includes: when the distributed energy storage system is in a running state, the energy storage node in the running state is recorded as an optimizable node; 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;

[0035] When the optimizable power only has a same frequency operation span, power judgment method 1 is executed;

[0036] 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.

[0037] Further, power judgment methods 1 to 4 include:

[0038] Power judgment method 1: record the length of time that the energy storage device in the optimizable node is at the optimizable power, and when the length of time is greater than the same frequency operation span of the optimizable power, send a high power operation time too long warning;

[0039] 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, record the end time of the previous high power operation state of the optimizable node as t1, and record 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, send a same power operation interval too short warning;

[0040] Power determination method 3: based on the working log of the energy storage device, the starting 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 optimized power exists high-frequency interval and interval power about P, t4 is subtracted by the value of the high-frequency interval of the optimized power existing about P, denoted as the latest end time of the high-power operation state of the optimized node, and the interval power of the optimized power existing about P is denoted as the maximum power allowed to continue to maintain after the high-frequency operation state of the optimized node ends;

[0041] Power determination method 4: when the optimized power does not exist high-frequency interval and interval power about P, the device parameters of the energy storage device are monitored and warned in real time from the end of the high-power operation state to the start of the next high-power operation state of the energy storage node, and the end date of the high-power operation state of the energy storage node is denoted as the model optimization date.

[0042] Further, the system optimization model is optimized, and the optimized system optimization model is denoted as a distributed optimization model, which includes:

[0043] 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;

[0044] The model optimization method includes: based on the high-power analysis method, the high-frequency running data of the energy storage node corresponding to the model optimization date within the model optimization date is analyzed, and the high-power running characteristics of all energy storage nodes are updated based on the analysis result;

[0045] When the system optimization model that stores the latest high-power running characteristics of all energy storage nodes is denoted as a distributed optimization model.

[0046] The beneficial effects of the present application are: firstly, the energy storage running data of each energy storage node in the distributed energy storage system is obtained, and the high-power running characteristics of each energy storage node are obtained based on the energy storage running data of each energy storage node using the high-power analysis method, which has the advantages that by obtaining the corresponding high-power running characteristics based on the energy storage running data of each energy storage node, the characteristic relationship between the energy storage node running in each high-power state and the subsequent high-power operation state can be obtained, so that in subsequent analysis, the high-frequency running interval and running expectation of the subsequent work of the energy storage system can be effectively analyzed based on the high-power running characteristics of each energy storage node, thereby effectively optimizing the energy storage system;

[0047] The application also builds a system optimization model, and uses the system optimization model 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, which has the advantages that by establishing the system optimization model and optimizing the system optimization model to obtain the distributed optimization model, the real-time operation input can be directly imported into the distributed optimization model when the distributed energy storage system is running, so as to obtain 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

[0048] Figure 1 A flowchart of the steps of the method of the application;

[0049] Figure 2 A schematic diagram of the multi-frequency analysis coordinate system of the application

[0050] Figure 3 A schematic diagram of the acquisition of the same-frequency operation span, the same-frequency operation interval, the high-frequency interval and the interval power of the application;

[0051] Figure 4 A structural schematic diagram of the electronic device of the application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0053] Embodiment 1, please refer to Figure 1 The application provides a distributed energy storage system optimization operation method, which comprises the following steps:

[0054] Step S1, the energy storage operation data of each energy storage node in the distributed energy storage system is acquired respectively, and the high-power operation characteristics of each energy storage node are acquired based on the energy storage operation data of each energy storage node by using a high-power analysis method;

[0055] Step S1 comprises: step S101, acquiring 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 in chronological order as the high-frequency operation data GY1 to GY m ;

[0056] In the specific implementation process, according to the judgment standard of each energy storage node for high-power operation, the data corresponding to the energy storage node determined as 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;

[0057] 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 turn, and acquiring the high-power operation characteristics of each energy storage node based on the analysis result.

[0058] Step S103, the high-power analysis method comprises: step S1031, for any one energy storage node: acquiring the dates of all high-frequency operation data corresponding to the energy storage node when the data is acquired, and recording the dates as high-frequency operation dates;

[0059] 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;

[0060] In the specific implementation process, if the high-frequency operation date is December 1, and there are three high-frequency operation data records on December 1 in the high-frequency operation data of the energy storage node, it indicates 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;

[0061] Step S1033, using the multi-frequency sub-method and the single-frequency sub-method to analyze all multi-high-frequency dates and all single-high-frequency dates respectively, and acquiring multi-high-frequency characteristics and single-high-frequency characteristics based on the analysis result;

[0062] Step S1034, recording the multi-high-frequency characteristics and the single-high-frequency characteristics as the high-power characteristics obtained by the high-power analysis method.

[0063] The multi-frequency sub-method comprises the following steps: V1, for any multi-high-frequency date, the number of high-frequency operation data existing in the multi-high-frequency date is recorded as n; a plane rectangular coordinate system is established and recorded as a multi-frequency analysis coordinate system, wherein the unit of the X-axis and the Y-axis of the multi-frequency analysis coordinate system is h and W respectively; based on the energy storage operation data of the energy storage node, 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 is drawn between X=0 and X=24 of the multi-frequency analysis coordinate system, and the curve is recorded as a multi-frequency analysis curve;

[0064] V2, n points with different and larger Y coordinates in the multi-frequency analysis curve are recorded as high-frequency operation points GY1 to GYn in turn based on the X coordinates from small to large; n wherein, for any high-frequency operation point with a coordinate (X0, Y0), when there is only one point with a Y coordinate of Y0 in the multi-frequency analysis curve, the high-frequency operation point is excluded from the high-frequency operation points GY1 to GYn; n

[0065] 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, and therefore 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;

[0066] V3, for any high-frequency operation point α (X1, Y1), a line segment composed of all points with a Y coordinate of Y1 in the multi-frequency analysis curve is recorded 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 a Y coordinate of 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, 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 is recorded as the same-frequency operation span of Y1.

[0067] The multi-frequency sub-method further comprises the following steps: V4, when the high-frequency operation line segment is multiple independent line segments, the value obtained by subtracting the X coordinate of the leftmost point from the X coordinate of the rightmost point in each high-frequency operation line segment is recorded as the same-frequency operation span of Y1; for any two adjacent high-frequency operation line segments, the value obtained by subtracting the X coordinate of the rightmost point of the left high-frequency operation line segment from the X coordinate of the leftmost point of the right high-frequency operation line segment is recorded as the same-frequency operation interval of Y1.

[0068] ​In the process of implementation, such as in a data analysis, the obtained multi-frequency analysis curve is as shown in the curve DF of Figure 2 When analyzing the high-frequency operating point GY1 in the multi-frequency analysis curve, it is obtained through analysis that the high-frequency operating line corresponding to GY1 is a plurality of independent line segments, i.e., GX1 to GX3 in Figure 3 Therefore, the values of XX2 minus XX1, XX4 minus XX3 and XX6 minus XX5 in Figure 3 are recorded as the same-frequency operating span of YY1, and the values of XX3 minus XX2 and XX5 minus XX4 are recorded as the same-frequency operating interval of YY1; through the combined analysis of Figure 2 and Figure 3 , it is obtained that the high-frequency operating point GY2 in Figure 2 is the closest to the right side of the high-frequency operating point GY1 and is not on the high-frequency operating line segment where GY1 is located, and the ordinate of GY1 is not equal to that of GY2, so that the value of XX7 minus XX2 in Figure 3 is recorded as the high-frequency interval of YY1 with respect to YY2, and YY3 is recorded as the interval power of YY1 with respect to YY2;

[0069] Step V5, when the high-frequency operating point β (X2, Y2) closest to the right side of the high-frequency operating point α and not on the high-frequency operating line segment where the high-frequency operating point α is located is a high-frequency operating point with an ordinate different from that of the high-frequency operating point α, the value of the abscissa of the leftmost point of the high-frequency operating line segment where the high-frequency operating point β is located minus the abscissa of the rightmost point of the high-frequency operating line segment where the high-frequency operating point α is located is recorded as the high-frequency interval of Y1 with respect to Y2, and the ordinate 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.

[0070] The multi-frequency sub-method further comprises: step V6, obtaining the same-frequency operating span, the same-frequency operating interval, the high-frequency interval and the interval power of all high-frequency operating points obtained on all multi-high-frequency dates, and recording the ordinates of all high-frequency operating points obtained on all 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 recorded power as multi-high-frequency characteristics.

[0071] Step V7, 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;

[0072] In the specific 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.

[0073] The single-frequency sub-method includes: 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;

[0074] 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, for example, the recorded power is 12KW, 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;

[0075] 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, the data corresponding to the power not recorded as the recorded power in the high-frequency operation data is intercepted and recorded as the available single data;

[0076] 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;

[0077] Step C4, record the single high frequency feature corresponding to the recorded power of all single high frequency dates as the same frequency operation span.

[0078] Step S2, based on the high power operation feature of each energy storage node, build a system optimization model, and use the system optimization model to optimize the operation of the distributed energy storage system when the distributed energy storage system is running; optimize the system optimization model, and record the optimized system optimization model as a distributed optimization model;

[0079] Step S2 includes: step S201, building a system optimization model, wherein the high power operation features of all energy storage nodes are stored in the system optimization model, and a system optimization method is built in the system optimization model;

[0080] 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 features 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;

[0081] Step S202, the system optimization method includes: step S2021, when the distributed energy storage system is in a running state, record the energy storage nodes in the running state as the optimizable nodes; for any one optimizable node: when the optimizable node is in a high power operation state, record the real-time power of the energy storage device in the optimizable node as the optimizable power;

[0082] Step S2022, when the optimizable power only exists in the same frequency operation span, execute the power judgment method 1;

[0083] 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, execute the power judgment method 1 to the power judgment method 4 in turn.

[0084] 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;

[0085] Power determination method 2: when the power corresponding to the previous high-power operation state of the optimizable node is equal to the optimizable power, record the end time of the previous high-power operation state of the optimizable node as t1, and record 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, send a shorter same power operation interval warning;

[0086] In the specific implementation process, for example, in a data analysis, t1 and t2 obtained are 12:00 and 12:30 respectively, and by calculation, the value of t2 minus t1 is 0.5h, 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 0.5h as the interval, it may cause damage to the energy storage device of the optimizable node, therefore, a shorter same power operation interval warning should be sent to remind the staff to intervene;

[0087] Power determination method 3: based on the working log of the energy storage device, obtain the start time of the next high-power operation state of the energy storage device and the corresponding power, 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 of the optimizable node, 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 frequency operation state;

[0088] In the specific implementation process, for example, in a data analysis, the start time of the next high-power operation state of the energy storage device and the corresponding power obtained 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, so as 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, which is greater than 0.5KW, the task can be modified in advance; in summary, based on the latest termination 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;

[0089] Power determination method 4: when the optimal power does not exist for the high-frequency interval of P and the interval power, the device parameters of the energy storage device are monitored and warned in real time from the end of the high-power operation state of the energy storage node to the start of the next high-power operation state, and the end date of the high-power operation state of the energy storage node is recorded as the model optimization date.

[0090] Step S2 further comprises: step S203, when there is a model optimization date that has not been analyzed, optimizing the system optimization model using the model optimization method, and recording the model optimization date that has not been analyzed as an analyzed date;

[0091] The model optimization method comprises: step S2031, analyzing the high-frequency operation data of the energy storage node corresponding to the model optimization date within the model optimization date based on the high-power analysis method, and updating the high-power operation characteristics of all energy storage nodes based on the analysis result;

[0092] 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, thereby updating the original high-power operation characteristics;

[0093] Step S2032, record the system optimization model storing the latest high-power operation characteristics of all energy storage nodes as a distributed optimization model.

[0094] Step S3, 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.

[0095] Embodiment 2, please refer to Figure 4 shown, Figure 4An example is shown in a structural diagram of an electronic device, 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 executed 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 distributed energy storage system is optimized using the system optimization model 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.

[0096] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part 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, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing 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 program code storage media.

[0097] Embodiment 3, the application further provides a computer program product, the computer program product comprises a computer program stored on a computer readable storage medium, the computer program comprises 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 each method, the method comprises the following steps: first, 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; 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.

[0098] Embodiment 4, the application further provides a computer readable storage medium, the application provides a storage medium, the storage medium stores a computer program, when the computer program is executed by a processor, the steps of the distributed energy storage system optimization operation method are executed 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 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; 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.

[0099] Through the description of the above embodiments, the embodiments of the 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 ROM / RAM, magnetic disk, 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 method described in each embodiment or some parts of the embodiment.

[0100] In the embodiments of 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 other division manners can be used in actual implementation, 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, the indirect coupling or communication connection between the system, the module and the unit can be electrical, mechanical or other forms.

[0101] 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: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the 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 the operation of a distributed energy storage system, characterized in that, Includes the following steps: The energy storage operation data of each energy storage node in the distributed energy storage system is obtained separately, 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. 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 operation. The system optimization model is optimized, and the optimized system optimization model is denoted as the distributed optimization model; During the operation of a distributed energy storage system, a distributed optimization model is used to optimize the distributed energy storage system based on the energy storage nodes that are in operation. The energy storage operation data of each energy storage node in the distributed energy storage system is obtained separately, 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, including: Acquire the energy storage operation data of all energy storage nodes in the distributed energy storage; for any energy storage node, the energy storage operation data when the energy storage node is in a high-power operation state are recorded in order of acquisition time from first to last as the high-frequency operation data GY1 to high-frequency operation data GYm of the energy storage node. The high-power analysis method is used to analyze the energy storage operation data and all high-frequency operation data of each energy storage node in turn, and the high-power operation characteristics of each energy storage node are obtained based on the analysis results. High-power analysis methods include: For any energy storage node: obtain the date of acquisition for all high-frequency operation data corresponding to the energy storage node, and record it as the high-frequency operation date; High-frequency running dates containing two or more high-frequency running data points are categorized as "multiple high-frequency dates"; high-frequency running dates containing only one high-frequency running data point are categorized as "single high-frequency dates". All high-frequency dates and all high-frequency dates were analyzed using both multi-frequency and single-frequency sub-methods, and high-frequency and single-frequency features were obtained based on the analysis results. Multi-frequency features and single-frequency features are denoted as high-power features obtained by high-power analysis; Multi-frequency sub-methods include: For any given high-frequency date: denote the number of high-frequency operational data points within that high-frequency date as n; establish a Cartesian coordinate system, denoted as the high-frequency analysis coordinate system, where the units of the X-axis and Y-axis are h and W, respectively; based on the energy storage operational data of the energy storage nodes, plot the power curves corresponding to the energy storage devices within the energy storage nodes from 0h to 24h within the high-frequency analysis coordinate system between X=0 and X=24, and denote them as the high-frequency analysis curves; The n points with large and distinct ordinates in the multi-frequency analysis curve are denoted as high-frequency operating points GY1 to GYn based on their abscissas from smallest to largest. For any high-frequency operating point with coordinates (X0, Y0), if there is only one point with ordinate Y0 in the multi-frequency analysis curve, the high-frequency operating point is removed from the high-frequency operating points GY1 to GYn. For any high-frequency operating point α(X1, Y1): the line segment formed by all points with ordinate Y1 within the multi-frequency analysis curve is denoted as the high-frequency operating line segment. There can be multiple high-frequency operating line segments, that is, there are multiple line segments with ordinate Y1 within the multi-frequency analysis curve that are parallel to the X-axis. When the high-frequency operating line segment is a continuous line segment, the value of subtracting the x-coordinate of the leftmost point from the x-coordinate of the rightmost point in the high-frequency operating line segment is denoted as the same-frequency operating span of Y1. When the high-frequency operating segment is composed of multiple independent segments, the difference between the x-coordinate of the rightmost point and the x-coordinate of the leftmost point in each high-frequency operating segment is recorded as the same-frequency operating span of Y1; for any two adjacent high-frequency operating segments, the difference between the x-coordinate of the leftmost point of the right high-frequency operating segment and the x-coordinate of the rightmost point of the left high-frequency operating segment is recorded as the same-frequency operating interval of Y1. When the high-frequency operating point β(X2, Y2) that is closest to the right of the high-frequency operating point α and is not located in the high-frequency operating line segment where the high-frequency operating point α is located is a high-frequency operating point β with a ordinate that is not equal to that of the high-frequency operating point α, the value of subtracting the abscissa of the rightmost point of the high-frequency operating line segment where the high-frequency operating point α is located from the abscissa of the leftmost point of the high-frequency operating line segment where the high-frequency operating point β is located is recorded as the high-frequency interval of Y1 with respect to Y2, and the ordinate of the lowest point in the curve between X=X1 and X=X2 in the multi-frequency analysis curve is marked as the interval power of Y1 with respect to Y2.

2. The method for optimizing the operation of a distributed energy storage system according to claim 1, characterized in that, The multi-frequency sub-method also includes: Obtain the same-frequency operating span, same-frequency operating interval, high-frequency interval, and interval power of all high-frequency operating points obtained from all multi-frequency dates, and record the ordinate of all high-frequency operating points obtained from all multi-frequency dates as the recorded power, and record the same-frequency operating span, same-frequency operating interval, high-frequency interval, and interval power corresponding to all recorded power as multi-frequency features; When the recorded power has multiple same-frequency operating spans, the maximum value among the multiple same-frequency operating spans is recorded as the same-frequency operating span corresponding to the recorded power; when the recorded power has multiple same-frequency operating intervals, the minimum value among the multiple same-frequency operating intervals is recorded as the same-frequency operating interval corresponding to the recorded power; when the recorded power has multiple high-frequency intervals for the same power, the minimum value of the multiple high-frequency intervals is recorded as the high-frequency interval of the recorded power for that power; when the recorded power has multiple interval power for the same power, the minimum value of the multiple interval power is recorded as the interval power of the recorded power for that power.

3. The method for optimizing the operation of a distributed energy storage system according to claim 2, characterized in that, Single-frequency sub-methods include: 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 the analysis of the single high-frequency date γ; When there are power values ​​in the high-frequency operation data of energy storage devices that have not been recorded as recorded power within a single high-frequency date, extract the data corresponding to the power values ​​that have not been recorded as recorded power in the high-frequency operation data and record them as available single data. The power of the energy storage device in the available single data is denoted as K, the time length during which the power of the energy storage device in the available single data is at K is denoted as the same frequency operation span of K, and K is denoted as the recorded power; The same frequency operating span corresponding to the recorded power obtained from all single high-frequency dates is denoted as a single high-frequency feature.

4. The method for optimizing the operation of a distributed energy storage system according to claim 3, characterized in that, 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 operation, including: A 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 within the system optimization model; The system optimization method includes: when the distributed energy storage system is in operation, the energy storage nodes in operation are recorded as optimizable nodes; for any optimizable node: when the optimizable node is in a high-power operation state, the real-time power of the energy storage devices within the optimizable node is recorded as the optimizable power; When the optimizable power only exists within the same frequency operating span, execute power judgment method 1; When there are same-frequency operation span, same-frequency operation interval, high-frequency interval, and interval power, power judgment method 1 to power judgment method 4 are executed in sequence.

5. The method for optimizing the operation of a distributed energy storage system according to claim 4, characterized in that, Power determination methods 1 to 4 include: Power judgment method 1: Record the duration of energy storage devices in the optimizable node at the optimizable power, and send a warning of excessive high power operation time when the duration exceeds 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 warning of short same frequency operation interval is sent. Power determination method 3: Based on the working log of the energy storage device, obtain the start time and corresponding power of the next high-power operation state of the energy storage device, which are denoted as t4 and P respectively; when there is a high-frequency interval and interval power of the optimizable power with respect to P, subtract the value of the high-frequency interval of the optimizable power with respect to P from t4, and record it as the latest termination time of the high-power operation state of the optimizable node, and record the interval power of the optimizable power with respect to P as the maximum power that the optimizable node can continue to maintain after the high-frequency operation state ends; Power Judgment Method 4: When there is no high-frequency interval or interval power related to P in the optimizable power, the equipment parameters of the energy storage device are monitored and warned in real time from the end of the high-power operation state of the energy storage node to the start of the next high-power operation state, and the end date of the high-power operation state of the energy storage node is recorded as the model optimization date.

6. The method for optimizing the operation of a distributed energy storage system according to claim 5, characterized in that, The system optimization model is optimized, and the optimized system optimization model is denoted as the distributed optimization model, which includes: When there are unanalyzed model optimization dates, the system optimization model is optimized using model optimization methods, and the unanalyzed model optimization dates are recorded as analyzed dates. The model optimization method includes: analyzing the high-frequency operating data of the energy storage nodes corresponding to the model optimization date based on the high-power analysis method, and updating the high-power operating characteristics of all energy storage nodes based on the analysis results; The system optimization model that stores the latest high-power operating characteristics of all energy storage nodes is denoted as the distributed optimization model.

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