Multi-mode switching mobile energy storage control method and system

Abstract

The invention relates to the technical field of mobile energy storage, in particular to a multi-mode switching mobile energy storage control method and system, and the method comprises the following steps: obtaining the electric quantity state of energy storage equipment, extracting the state of a parallel cabinet access signal, marking electric quantity load matching, screening adaptive load modes, calculating deviation classification, extracting PCS efficiency data, and screening non-interference nodes. And extracting task record operation data, mapping a task distribution amount and an operation time period, calculating a ratio of resource input to tasks, recording a period difference, and generating a monitoring index. According to the invention, by monitoring voltage and power fluctuation in real time, equipment can intelligently adjust a discharge mode, ensure continuous power supply of a load, avoid influence on system efficiency caused by reduction of equipment performance, compare a PCS operation period with voltage fluctuation, screen interference fluctuation, dynamically adjust task execution of a power distribution cabinet, avoid operation imbalance, optimize energy management and improve power dispatching intelligence; the service life of energy storage equipment is prolonged, and adaptability and stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mobile energy storage technology, and in particular to a mobile energy storage control method and system with multi-mode switching. BACKGROUND

[0002] The field of mobile energy storage technology includes aspects such as energy storage, conversion, and scheduling. In particular, under the background of global energy structure transformation towards low carbonization, mobile energy storage has rapidly developed. The core content of this field includes the construction of battery clusters, the integration of power control systems (PCS), the intelligent control of energy management and power scheduling, and other technologies. Mobile energy storage devices have strong environmental adaptability, flexible deployment, and the advantage of adapting to a wide range of scenarios. They can play an important role in emergency support, support for remote areas and outdoor operations, micro-grid construction, and power stability protection. With the development of technology, a variety of different types of mobile energy storage solutions have emerged in the market and are constantly being optimized and updated.

[0003] Among them, the mobile energy storage control method refers to a technical solution that solves the problem that traditional mobile energy storage cannot continue to supply power to the load when the battery power is depleted. The theme focuses on switching to multiple modes when the battery power is insufficient to ensure the continuous power supply of the load. Specifically, a multi-mode switching mechanism is adopted. When the power is depleted, it is automatically or manually switched to the appropriate working mode, avoiding the problem of power outage and inability to work due to battery power depletion. This switching method ensures that when the battery power is below the set threshold, effective measures can be taken to extend the use time and maintain the continuity of the load power supply.

[0004] The existing technology has certain limitations in the power management of mobile energy storage devices, especially when the battery power is close to depletion. There is a lack of sufficient flexibility and early warning mechanism. Traditional methods cannot accurately identify the dynamic relationship between battery power and load demand, and rely on preset power thresholds to switch working modes, which cannot adapt to the instantaneous changes of load demand and the slight fluctuations of battery state to some extent. Therefore, when the battery power is low, the device fails to switch to the appropriate working mode in time, resulting in power interruption or power waste. The existing technology also lacks real-time monitoring and fluctuation identification of PCS operation efficiency, and cannot accurately analyze the impact of voltage and power fluctuations on device operation, resulting in a decrease in device operation efficiency when voltage fluctuation is large or load increases suddenly. The existing technology is relatively extensive in task allocation and cannot timely identify and adjust the imbalance of task allocation, causing unreasonable resource allocation and imbalance of partial task execution, directly affecting the stability of the device and the accuracy of power scheduling, and thus reducing the reliability and efficiency of mobile energy storage devices in actual application. SUMMARY

[0005] The object of the present application is to solve the drawbacks existing in the prior art and propose a mobile energy storage control method and system with multi-mode switching.

[0006] In order to achieve the above object, the present application adopts the following technical scheme: a mobile energy storage control method with multi-mode switching, comprising the following steps: S1: obtaining the battery cluster assembly power state, PCS assembly operating parameters and transformer assembly output data of the mobile energy storage device, extracting the cabinet access signal state, marking the matching condition of the power state and load demand, and generating a discharge mode switching list of the energy storage device; S2: based on the discharge mode switching list of the energy storage device, screening the working mode suitable for the current load demand, combining the power dispatching strategy to extract the real-time voltage and power fluctuation interval, and performing deviation calculation and classification processing with the reference threshold to obtain a discharge mode adaptability label; S3: calling the discharge mode adaptability label, extracting the voltage fluctuation section number, identifying the PCS operating efficiency data in the section, comparing the PCS operating period and the voltage fluctuation section, recording the number of coincidence time periods, and generating a PCS operating efficiency influence section list; S4: based on the PCS operating efficiency influence section list, screening the discharge task nodes not disturbed, extracting the power distribution cabinet segmented task record and operation data, mapping the task allocation amount and operation period distribution, judging whether the task exists operation allocation imbalance, and obtaining a power distribution cabinet node task execution fluctuation group.

[0007] As a further scheme of the present application, the discharge mode switching list of the energy storage device includes mode switching number, power state label, voltage deviation, mode classification, the discharge mode adaptability label includes adaptability level, fluctuation type, reference comparison result, task association number, the PCS operating efficiency influence section list includes device type, influence time section, coincidence time period number, affected task number, and the power distribution cabinet node task execution fluctuation group includes task distribution uneven number, operation time record, task completion deviation, and operation matching degree.

[0008] As a further scheme of the present application, the acquisition step of the discharge mode switching list of the energy storage device is specifically: S111: obtaining the battery cluster assembly power state, PCS assembly operating parameters and transformer assembly output data of the mobile energy storage device, extracting the cabinet access signal state, matching the power state acquisition time and load demand time, and comparing the time range and power state demand to generate a partition node discharge record period; S112: Extract the overlap period of the power state and the load demand time interval based on the partition node discharge record period, and calculate the ratio of the overlap time to the total length of the load demand, screen the nodes with a ratio below the benchmark value, and obtain the partition node power coverage deviation rate according to the power state label quantity; S113: According to the partition node power coverage deviation rate, the node number is judged in the deviation state, the node number whose deviation rate exceeds the node synchronization threshold is identified, the node number, power coverage information and deviation rate value are integrated, and the energy storage equipment discharge mode switching list is generated.

[0009] As a further scheme of the application, the obtaining step of the discharge mode adaptability label is specifically: S211: Based on the energy storage equipment discharge mode switching list, identify the working mode and power dispatching strategy that adapt to the current load demand, extract the real-time voltage start and end fluctuation interval, calculate the start and end fluctuation difference value of the voltage section, compare with the benchmark voltage fluctuation interval, and obtain the voltage fluctuation deviation value; S212: Call the voltage fluctuation deviation value, combine the section distribution, fluctuation trend and adjustment frequency, and integrate the working mode deviation data, identify and calculate the adaptability deviation degree according to the section number, judge the fluctuation direction according to the adjustment frequency, and obtain the discharge mode adaptability label.

[0010] As a further scheme of the application, the obtaining step of the PCS operation efficiency influence section list is specifically: S311: Call the discharge mode adaptability label, screen the voltage fluctuation deviation task section number, extract the voltage fluctuation time period according to the node association table, process the section voltage period according to the time dimension, identify the voltage fluctuation time period index table, and obtain the voltage fluctuation task time period set; S312: According to the voltage fluctuation task time period set, collect the PCS operation efficiency data of the same period section, identify the PCS influence table, judge the daily PCS interference according to the interference threshold, and match with the voltage fluctuation task time period, judge whether there is voltage fluctuation abnormal association, and generate the PCS operation efficiency influence section list.

[0011] As a further scheme of the application, the obtaining step of the distribution cabinet node task execution fluctuation group is specifically: S411: Based on the PCS operation efficiency influence section list, screen the unmarked discharge task node, extract the task list, and obtain the uninterfered node task set; S412: Call the uninterfered node task set, match the daily segmented task record and operation data of the distribution cabinet, extract the planned task quantity according to the task number, count the number of operators and real-time operation period, and generate the distribution cabinet cooperation execution situation matching data set; S413: According to the power distribution cabinet cooperation execution condition matching data set, the matching degree of task execution efficiency and operation distribution is evaluated, the efficiency fluctuation node is identified, the tasks with fluctuation exceeding the reference value are marked as abnormal nodes, the power distribution cabinet task execution matching deviation value is calculated, and the power distribution cabinet node task execution fluctuation group is obtained.

[0012] As a further scheme of the present application, the method further comprises a step S5: S5: The power distribution cabinet node task execution fluctuation group is called, the abnormal fluctuation task group is extracted, the resource input quantity and task quantity ratio in the energy storage model are calculated, the difference distribution of resource deployment period and task execution period is recorded, and the energy storage progress monitoring structure index is generated. The energy storage progress monitoring structure index includes resource allocation ratio, task intensity level, execution period difference, and energy storage efficiency index.

[0013] As a further scheme of the present application, the step of obtaining the energy storage progress monitoring structure index is specifically: S511: The power distribution cabinet node task execution fluctuation group is called, the nodes exceeding the threshold value are screened, the time interval and task quantity change amplitude are recorded, and the progress fluctuation abnormality identification set is obtained. S512: Based on the energy storage model resources corresponding to the nodes in the progress fluctuation abnormality identification set, the node resource input ratio sequence is identified, the abnormal distribution interval is extracted and compared with the critical coefficient, the ratio deviation direction and node number are recorded, and the energy storage resource matching deviation index group is formed. S513: According to the energy storage resource matching deviation index group, the energy storage model resource deployment and task execution time period are extracted, the difference between the resource deployment period and the operation period is identified, and the progress benchmark is sorted and labeled according to the progress benchmark, and the energy storage progress monitoring structure index is generated.

[0014] The multi-mode switching mobile energy storage control system is used to execute the multi-mode switching mobile energy storage control method described above, and the system comprises: The power state extraction module obtains the battery cluster assembly power state, the PCS assembly operating parameter and the transformer assembly output data of the mobile energy storage device, extracts the parallel cabinet access signal state, compares the power state and the load demand matching condition, marks the discharge tasks with inconsistent time, and generates the node state deviation label group. The mode adaptability classification module positions the working mode adapted to the current load demand based on the node state deviation label group, extracts the discharge node identification, process node and reference voltage, calculates the difference between the field voltage and the reference voltage, classifies and labels the difference type, and generates the node mode adaptability identification. The PCS efficiency identification module identifies adaptability based on the node mode, screens a voltage fluctuation lag task component, locates a corresponding section, extracts a PCS operation efficiency interference period, determines coincidence with an operation time period, screens an interference frequent section, and generates an energy storage progress PCS interference mapping set; The operation efficiency diagnosis module removes an interference section task based on the energy storage progress PCS interference mapping set, extracts a power distribution cabinet task record and operation data, matches task assignment and operation time periods, calculates an operation amount and task ratio, identifies a task-intensive and inefficiently executed component task, and obtains a node operation execution deviation set. The resource configuration analysis module locates a resource input record of a task in an energy storage model based on the node operation execution deviation set, extracts a task amount and resource amount configuration ratio, compares operation cycle and input cycle differences, maps task resource use and progress state, and forms an energy storage progress monitoring structure index.

[0015] Compared with the prior art, the application has the advantages and positive effects that: In the application, the mentioned multiple operations can effectively realize adaptive adjustment of the mobile energy storage device when the power is insufficient, ensuring continuous power supply of the load. The matching between the battery cluster power state and the load demand is marked, so that the device can intelligently switch the discharge mode and quickly respond when the power is exhausted. Real-time voltage and power fluctuation monitoring is adopted, combined with power dispatching strategy, to optimize the adaptability of the discharge mode, ensuring that the energy storage device always works stably within the load demand range. By comparing the PCS operation period and the voltage fluctuation section, the running efficiency influence section can be effectively identified, avoiding the influence of device performance decline on the overall system efficiency. The interference fluctuation in task allocation is effectively screened, the power distribution cabinet node task execution can be dynamically adjusted, the efficiency reduction caused by operation imbalance is avoided, and the resources are reasonably allocated. The measures comprehensively optimize the energy management process, enhance the intelligence of power dispatching, enable the device to realize efficient and reliable power management in a variable working environment, thereby prolonging the service life of the energy storage device and improving its adaptability and stability. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The figure is a workflow schematic diagram of the application; Figure 2 The figure is a flowchart of obtaining a discharge mode switching list of the energy storage device in the application; Figure 3 The figure is a flowchart of obtaining a discharge mode adaptability label in the application; Figure 4 The figure is a flowchart of obtaining a PCS running efficiency influence section list in the application; Figure 5The flow chart for obtaining the power distribution cabinet node task execution fluctuation group in the application; Figure 6 The flow chart for obtaining the energy storage progress monitoring structure index in the application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the application more clear, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and not to limit the application.

[0018] In the description of the application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, in the description of the application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0019] Example one Please refer to Figure 1 The application provides a technical scheme: a multi-mode switching mobile energy storage control method, comprising the following steps: S1: obtaining the battery cluster assembly power state, PCS assembly operating parameters and transformer assembly output data of the mobile energy storage device, extracting the cabinet access signal state, marking the matching condition of the power state and load demand, and generating a discharge mode switching list of the energy storage device; S2: based on the discharge mode switching list of the energy storage device, screening the working mode suitable for the current load demand, extracting the real-time voltage and power fluctuation interval in combination with the power dispatching strategy, and performing deviation calculation and classification processing with the reference threshold to obtain a discharge mode adaptability label; S3: calling the discharge mode adaptability label, extracting the voltage fluctuation section number, identifying the PCS operating efficiency data in the section, comparing the PCS operating period and the voltage fluctuation section, recording the number of coincidence time periods, and generating a PCS operating efficiency influence section list; S4: based on the PCS operating efficiency influence section list, screening the discharge task nodes not disturbed, extracting the power distribution cabinet segmented task record and operation data, mapping the task allocation amount and operation period distribution, judging whether the task exists operation allocation imbalance, and obtaining a power distribution cabinet node task execution fluctuation group; S5: Call the power distribution cabinet node task execution fluctuation group, extract the abnormal fluctuation task group, calculate the ratio of resource input to task quantity in the energy storage model, record the difference distribution of resource deployment period and task execution period, and generate the energy storage progress monitoring structure index.

[0020] The energy storage equipment discharge mode switching list includes mode switching number, power state label, voltage deviation, mode classification, and discharge mode adaptability label includes adaptability level, fluctuation type, reference comparison result, and task association number. The PCS operation efficiency influence section list includes device type, influence time section, coincidence period number, and affected task number. The power distribution cabinet node task execution fluctuation group includes task distribution uneven number, operation time length record, task completion deviation, and operation matching degree. The energy storage progress monitoring structure index includes resource configuration ratio, task intensity level, execution period difference, and energy storage efficiency index.

[0021] Please refer to Figure 2 The acquisition steps of the energy storage equipment discharge mode switching list are as follows: S111: Acquire the battery cluster assembly power state, PCS assembly operation parameters, and transformer assembly output data of the mobile energy storage equipment, extract the cabinet access signal state, match the power state collection time and load demand time, and compare the time range and power state demand to generate a partition node discharge record period. The battery cluster assembly of the energy storage device has an electricity level of 80% at 09:00, the PCS assembly has an active power output of 50kW at 09:00 and no alarm information, the transformer assembly has three-phase currents of 20A, 21A and 22A at 09:00 and an output voltage of 400V, the signal of the grid-connected cabinet A is extracted at 09:00, the electricity level state collection time at 09:00 is matched with the load demand time planned to start at 09:15, the time range from 09:00 to 09:05 is less than the set 5-minute threshold and the electricity level state of 80% is higher than the set 70% lower limit of demand, and the sub-node discharge record period of the grid-connected cabinet A from 09:00 to 09:05 is preliminarily generated; for another mobile energy storage device, the battery cluster assembly has an electricity level of 65% at 10:00, the PCS assembly has an active power output of 10kW at 10:00 and has an over-temperature alarm, the transformer assembly has three-phase currents of 5A, 6A and 5A at 10:00 and an output voltage of 390V, the signal of the grid-connected cabinet B is extracted at 10:00, the electricity level state collection time at 10:00 is matched with the load demand time planned to start at 10:10, the time range from 10:00 to 10:05 is greater than the 5-minute threshold or the electricity level state of 65% is lower than the 70% lower limit of demand, and the sub-node discharge record period of the grid-connected cabinet B is not generated, and the sub-node discharge record period of the grid-connected cabinet A from 09:00 to 09:05 is the first step result.

[0022] S112: Based on the sub-node discharge record period, the overlapping period of the electricity level state and the load demand time interval is extracted, and the ratio of the overlapping time to the total load demand time is calculated, the nodes with a lower ratio than the reference value are screened, and the sub-node electricity coverage deviation rate is obtained according to the number of electricity level state annotations; Based on the sub-node discharge record period of the grid-connected cabinet A from 09:00 to 09:05, the overlapping period of the time interval of the battery cluster electricity level from 78% to 80% and the load demand time at 09:15 with the record period from 09:00 to 09:05 is 09:00 to 09:05, the ratio of the overlapping time 5 minutes to the total load demand time 10 minutes is 50%, if the set overlapping ratio reference value is 60%, the grid-connected cabinet A node with a lower overlapping ratio than 60% is screened, and the sub-node electricity coverage deviation rate of the grid-connected cabinet A is 25% according to the number of electricity level state annotations at 09:00, 09:02 and 09:04, if the reference electricity coverage number is 4 annotation points.

[0023] S113: According to the deviation rate of the power coverage of the partition node, the deviation state of the node number is determined, the node number whose deviation rate exceeds the node synchronization threshold is identified, and the node number, power coverage information and deviation rate value are integrated to generate a discharge mode switching list of the energy storage device; According to the 25% partition node power coverage deviation rate of grid-connected cabinet A, if the node synchronization threshold is set to 20%, it is determined that the deviation state of grid-connected cabinet A exceeds the node synchronization threshold, the node number whose deviation rate exceeds 20% is identified as A, and the node number A, power coverage information (power 80% at 09:00, power 79% at 09:02, power 78% at 09:04) and deviation rate value 25% are integrated to generate a discharge mode switching list of the energy storage device, which contains node number A, power coverage information (80%, 79%, 78%), deviation rate 25%.

[0024] Please refer to Figure 3 The acquisition step of the discharge mode adaptability label is specifically: S211: Based on the discharge mode switching list of the energy storage device, the working mode and power dispatching strategy suitable for the current load demand are identified, the real-time voltage starting and ending fluctuation interval is extracted, and the formula: ; The starting and ending fluctuation difference value of the voltage section is calculated, compared with the reference voltage fluctuation interval, and the voltage fluctuation deviation value is obtained; Among them, represents the starting and ending fluctuation difference value of the voltage section, represents the starting voltage value of the mth voltage interval, represents the ending voltage value of the mth voltage interval, and N represents the total number of voltage intervals; The starting and ending fluctuation difference value of the voltage section is an index for measuring the voltage stability of the energy storage device during discharge. By calculating the difference between the starting voltage and the ending voltage in each interval within multiple voltage intervals, and further calculating the square root of the average value of the sum of the squares of the differences, the overall fluctuation amplitude of the voltage is reflected. Specifically, the larger the starting and ending fluctuation difference value, the more intense the voltage fluctuation of the energy storage device during discharge, which adversely affects the stable operation of the power system. Through the index, the degree of voltage fluctuation can be quantified to help evaluate the performance of the device and guide the subsequent discharge mode optimization; Based on the energy storage device discharge mode switching list containing node number A, power coverage information (80%, 79%, 78%), and deviation rate 25%, it is identified that the current adaptive working mode of node A is constant power discharge mode and power grid frequency priority power dispatching strategy, the real-time voltage of node A at the start of discharge 09:15 is 398V, the real-time voltage at the end of 09:20 is 402V, and the real-time voltage at the end of 09:25 is 399V, a total of N=3 voltage intervals, the starting voltage value , the end voltage value , the starting voltage value , the end voltage value , the starting voltage value , the end voltage value , the starting voltage value , the end voltage value ; If the set reference voltage fluctuation interval is ±2V, the calculated start and end fluctuation difference value of the voltage section is 3.109V, and the voltage fluctuation deviation value is 3.109-2=1.109V, and the voltage fluctuation deviation value 1.109V is the result content of this paragraph. The beneficial effect of the formula is that by calculating the start and end fluctuation difference value of the voltage section, the stability of the voltage of the energy storage device during discharge can be quantitatively evaluated, which provides data support for the optimization of the subsequent discharge mode. The current voltage fluctuation deviation value exceeds the reference range, affecting the stable operation of the power system.

[0025] S212: Call voltage fluctuation deviation value, combine section distribution, fluctuation trend and adjustment frequency, uniformly collect working mode deviation data, identify and calculate adaptive deviation degree according to section number, judge fluctuation direction according to adjustment frequency, and get discharge mode adaptability label; Call voltage fluctuation deviation value 1.109V, combine section A distribution, fluctuation trend first increases and then decreases, and adjustment frequency is 2 times, uniformly collect working mode deviation data, identify and calculate adaptive deviation degree according to section number A, if voltage fluctuation deviation is between 0V and 1V, adaptive deviation degree is low, between 1V and 3V, adaptive deviation degree is medium, and greater than 3V, adaptive deviation degree is high, then the adaptive deviation degree of node A is medium, according to the adjustment frequency 2 times, if the adjustment frequency is less than or equal to 1 time, the fluctuation direction is stable, if the adjustment frequency is greater than 1 time and less than or equal to 3 times, the fluctuation direction is slight fluctuation, and if the adjustment frequency is greater than 3 times, the fluctuation direction is significant fluctuation, then it is judged that the fluctuation direction of node A is slight fluctuation, and the discharge mode adaptability label of node A is "medium deviation-light fluctuation".

[0026] Please refer toFigure 4 The step of obtaining the PCS operation efficiency influence section list is specifically as follows: S311: Call the discharge mode adaptability label, filter the voltage fluctuation deviation task section number, extract the voltage fluctuation time period according to the node association table, process the section voltage period in the time dimension, identify the voltage fluctuation time period index table, and obtain the voltage fluctuation task period set; Call the discharge mode adaptability label “medium deviation-mild fluctuation”, filter the voltage fluctuation deviation task section number A, extract the voltage fluctuation time period of node A as 09:15-09:30 according to the node association table, process the section voltage period in the time dimension, identify the voltage fluctuation time period index table, and obtain the voltage fluctuation task period set {09:15-09:20, 09:20-09:25, 09:25-09:30}.

[0027] S312: According to the voltage fluctuation task period set, collect the PCS operation efficiency data of the same period section, identify the PCS influence table, judge the daily PCS interference according to the interference threshold, and match with the voltage fluctuation task period to judge whether there is voltage fluctuation abnormal association, and generate the PCS operation efficiency influence section list; According to the voltage fluctuation task period set {09:15-09:20, 09:20-09:25, 09:25-09:30}, the PCS operation efficiency data of the same period section A is 95%, 93%, and 94% respectively, the PCS influence table is identified, and if the interference threshold is set to be lower than 90% to determine the daily PCS interference, it is determined that there is no daily PCS interference. Match the non-interference condition with the voltage fluctuation task period to judge whether there is voltage fluctuation abnormal association, if the voltage fluctuation deviation is greater than 0.5V and the PCS efficiency is lower than 96%, it is determined that there is abnormal association. Since the voltage fluctuation deviation is 1.109V and the PCS efficiency is lower than 96%, the PCS operation efficiency influence section list is generated as {section A: 09:15-09:30, there is voltage fluctuation abnormal association}.

[0028] Please refer to Figure 5 The step of obtaining the distribution cabinet node task execution fluctuation group is specifically as follows: S411: Based on the PCS operation efficiency influence section list, filter the unmarked discharge task node, extract the task list, and obtain the uninterfered node task set; Based on the PCS operation efficiency influence section list {section A: 09:15-09:30, there is voltage fluctuation abnormal association}, filter the unmarked discharge task node, extract the task list, and obtain the uninterfered node task set. Assuming that the tasks of nodes B and C are not interfered by the PCS, the uninterfered node task set is {node B task list, node C task list}.

[0029] S412: Call the PCS-undisturbed node task set, match the power distribution cabinet daily segmented task record with the operation data, extract the planned task amount according to the task number, count the number of operators and the real-time operation period, and generate the power distribution cabinet cooperation execution matching data set; Call the PCS-undisturbed node task set {node B task list, node C task list}, match the power distribution cabinet daily segmented task record with the operation data, extract the planned task amount of node B as 100 kWh according to the task number, count the number of operators as 2 and the real-time operation period as 30 minutes, extract the planned task amount of node C as 120 kWh, count the number of operators as 3 and the real-time operation period as 40 minutes, and generate the power distribution cabinet cooperation execution matching data set {(node B, 100 kWh, 2, 30 min), (node C, 120 kWh, 3, 40 min)}; Table 1: Power distribution cabinet cooperation execution matching data set Node Scheduled task volume (kWh) Number of operators Real-time operating period (min) Node B 100 2 30 Node C 120 3 40 As shown in Table 1, the planned task amount, the number of operators and the operation period of node B and node C are listed for matching the cooperation execution of the power distribution cabinet.

[0030] S413: According to the power distribution cabinet cooperation execution matching data set, evaluate the matching degree of task execution efficiency and operation distribution, identify the efficiency fluctuation node, mark the task with fluctuation exceeding the benchmark value as an abnormal node, and use the formula: ; Calculate the power distribution cabinet task execution matching deviation value to obtain the power distribution cabinet node task execution fluctuation group; Wherein, represents the power distribution cabinet task execution matching deviation value, n represents the total number of power distribution cabinet task nodes, represents the actual execution time of the i-th task k-th node, represents the average execution time of the i-th task, represents the weight factor of the k-th node, represents the total weight of the i-th task; Based on the cooperative execution status of the distribution cabinets, a dataset {(Node B, 100kWh, 2 people, 30min)} is matched to evaluate the matching degree between task execution efficiency and operation distribution, and nodes with efficiency fluctuations are identified. If the baseline value for task execution efficiency is set at 3kWh / min, the execution efficiency of node B is approximately 3.33kWh / min (100kWh / 30min), and the execution efficiency of node C is approximately 3kWh / min (120kWh / 40min). If the efficiency fluctuation exceeds ±0.2kWh / min of the baseline value, it is marked as an abnormal node. Therefore, node B is marked as an abnormal node, using the formula... The task execution matching deviation value for the power distribution cabinet is calculated using a formula. This formula calculates the absolute value of the difference between the actual execution time and the average execution time for each of the n power distribution cabinet task nodes. This difference is multiplied by the ratio of the node's weight factor to the total weight. The sum of these products for all nodes is then taken as the average value. This reflects the dispersion of task execution time. For example, assuming a task i contains two nodes B and C, the actual execution time... , Average execution time Weight factor of node B The weight factor of node C Total weight ; The calculated matching deviation value for the power distribution cabinet task execution is: ; The resulting task execution fluctuation group for the power distribution cabinet nodes is {(Node B, Abnormal), (Node C, Normal), Deviation 2.5}, which is the content of this paragraph. The advantage of this formula is that it can quantitatively evaluate the matching degree of task execution time for each node in the power distribution cabinet, identify nodes with abnormal execution efficiency, and provide a basis for subsequent task scheduling optimization. The task execution of node B exhibits abnormal fluctuations, requiring further analysis of the reasons. Table 2: Fluctuation Group of Task Execution at Distribution Cabinet Nodes Node Performance Deviation value (kWh / min) Node B Abnormal 2.5 Node C Normal 2.5 As shown in Table 2, node B has an abnormal task execution with a deviation value of 2.5, while node C executes the task normally, and the deviation values ​​of the two are the same.

[0031] Please see Figure 6 The specific steps for obtaining structural indicators for energy storage progress monitoring are as follows: S511: Call the power distribution cabinet node task execution fluctuation group, filter nodes that exceed the threshold, record the time interval and task volume change range, and obtain the progress fluctuation anomaly identifier set; The power distribution cabinet node task execution fluctuation group {(node B, abnormal), (node C, normal)} with a deviation value of 2.5 is called, and the node B exceeding the threshold is screened out, and the time interval is recorded as 09:00-09:30 and the task quantity change amplitude, assuming that the planned task quantity of node B is 100 kWh, and the actual completion is 90 kWh, then the change amplitude is (90-100) / 100*100%=-10%, and the progress fluctuation abnormality identification set is {(node B, 09:00-09:30, -10%)}.

[0032] S512: Based on the energy storage model resources corresponding to the nodes in the progress fluctuation abnormality identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and node number, and form the energy storage resource matching deviation index group; Based on the energy storage model resources corresponding to node B in the progress fluctuation abnormality identification set {(node B, 09:00-09:30, -10%)}, identify the battery resource input ratio of node B as battery capacity / planned task quantity, assuming that the battery capacity is 120 kWh and the planned task quantity is 100 kWh, then the ratio is 120 / 100=1.2, and the cable resource input ratio is cable length / coverage distance, assuming that the cable length is 50 m and the coverage distance is 40 m, then the ratio is 50 / 40=1.25, extract the abnormal distribution interval 09:00-09:30 and compare the critical coefficient, if the set battery resource input ratio critical coefficient is 1.1 and the cable resource input ratio critical coefficient is 1.2, then the battery resource input ratio 1.2 is greater than 1.1, and the cable resource input ratio 1.25 is greater than 1.2, record the ratio deviation direction as positive deviation and node number B, form the energy storage resource matching deviation index group {(node B, battery resource ratio 1.2, positive deviation), (node B, cable resource ratio 1.25, positive deviation)}.

[0033] S513: According to the energy storage resource matching deviation index group, extract the energy storage model resource deployment and task execution time period, identify the difference between the resource deployment period and the operation period, and sort and mark according to the progress benchmark, generate the energy storage progress monitoring structure index; According to the energy storage resource matching offset index group {(node B, battery resource ratio 1.2, positive offset), (node B, cable resource ratio 1.25, positive offset)}, the energy storage model resource deployment time period of node B is extracted as 08:30-08:45 and the task execution time period is 09:00-09:30, the difference between the resource deployment period 15 minutes and the operation period 30 minutes is 15 minutes, and the progress benchmark is sorted and labeled, if the resource deployment period is less than the operation period, it is normal, and greater than or equal to is abnormal, then label node B as abnormal, generate energy storage progress monitoring structure index {(node B, resource deployment period 15min, operation period 30min, difference 15min, label: abnormal)}.

[0034] The multi-mode switching mobile energy storage control system is used to execute the multi-mode switching mobile energy storage control method described above, and the system comprises: The power state extraction module obtains the battery cluster assembly power state, PCS assembly operating parameters and transformer assembly output data of the mobile energy storage device, extracts the cabinet access signal state, compares the power state and load demand matching situation, marks the discharge task with inconsistent time, and generates a node state deviation label group; The mode adaptability classification module is based on the node state deviation label group, locates the working mode that adapts to the current load demand, extracts the discharge node identification, process node and reference voltage, calculates the difference between the field voltage and the reference voltage, and classifies and labels the difference type, and generates a node mode adaptability identification; The PCS efficiency identification module is based on the node mode adaptability identification, screens the voltage fluctuation lag task components, locates the corresponding section, extracts the PCS operation efficiency interference period, and determines the coincidence with the operation time period, screens the frequent interference section, and generates a storage progress PCS interference mapping set; The operation efficiency diagnosis module is based on the storage progress PCS interference mapping set, eliminates the interference section task, extracts the power distribution cabinet task record and operation data, matches the task dispatch and operation time period, calculates the operation amount and task ratio, identifies the task-intensive and low-efficiency component task, and obtains the node operation execution deviation set; The resource configuration analysis module is based on the node operation execution deviation set, locates the task resource input record in the energy storage model, extracts the task amount and resource amount configuration ratio, and compares the operation period and input period difference, maps the task resource use and progress state, and forms the energy storage progress monitoring structure index.

[0035] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in other forms. Any skilled person in the art can modify or change the disclosed technical content into equivalent embodiments with equivalent changes, and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution content of the present application, still falls within the protection scope of the present application.

Claims

1. A multi-mode switched mobile energy storage control method, characterized in that, The method comprises the following steps: S1: obtaining the battery cluster assembly power state, PCS assembly operating parameter and transformer assembly output data of the mobile energy storage device, extracting the cabinet access signal state, marking the matching condition of the power state and load demand, and generating a discharge mode switching list of the energy storage device; S2: based on the discharge mode switching list of the energy storage device, screening the working mode suitable for the current load demand, extracting the real-time voltage and power fluctuation interval in combination with the power dispatching strategy, and performing deviation calculation and classification processing with the reference threshold to obtain a discharge mode adaptability label; S3: calling the discharge mode adaptability label, extracting the voltage fluctuation section number, identifying the PCS operating efficiency data in the section, comparing the PCS operating period and the voltage fluctuation section, recording the number of coincidence time periods, and generating a PCS operating efficiency influence section list; S4: based on the PCS operating efficiency influence section list, screening the discharge task nodes not disturbed, extracting the power distribution cabinet segmented task record and operation data, mapping the task allocation amount and operation period distribution, judging whether the task exists operation allocation imbalance, and obtaining a power distribution cabinet node task execution fluctuation group.

2. The multi-mode switched mobile energy storage control method of claim 1, wherein, The discharge mode switching list of the energy storage device includes mode switching number, power state label, voltage deviation, mode classification, the discharge mode adaptability label includes adaptability level, fluctuation type, reference comparison result, task association number, the PCS operating efficiency influence section list includes device type, influence time section, coincidence time period number, affected task number, and the power distribution cabinet node task execution fluctuation group includes task distribution uneven number, operation time record, task completion deviation, and operation matching degree.

3. The multi-mode switched mobile energy storage control method of claim 1, wherein, The acquisition step of the discharge mode switching list of the energy storage device is specifically: S111: obtaining the battery cluster assembly power state, PCS assembly operating parameter and transformer assembly output data of the mobile energy storage device, extracting the cabinet access signal state, matching the power state acquisition time and load demand time, and comparing the time range and power state demand to generate a partition node discharge record period; S112: based on the partition node discharge record period, extracting the coincidence time period of the power state and load demand time interval, calculating the ratio of the coincidence time and the total length of the load demand, screening the nodes with a coincidence ratio lower than the reference value, obtaining the partition node power coverage deviation rate according to the power state label number; S113: according to the partition node power coverage deviation rate, judging the deviation state of the node number, identifying the node number with a deviation rate exceeding the node synchronization threshold, integrating the node number, power coverage information and deviation rate value, and generating a discharge mode switching list of the energy storage device.

4. The multi-mode switched mobile energy storage control method of claim 3, wherein, The acquisition step of the discharge mode adaptability label is specifically: S211: based on the discharge mode switching list of the energy storage device, identifying the working mode suitable for the current load demand and the power dispatching strategy, extracting the real-time voltage start and end fluctuation interval, calculating the start and end fluctuation difference value of the voltage section, comparing with the reference voltage fluctuation interval, and obtaining the voltage fluctuation deviation value; S212: Call the voltage fluctuation deviation value, combine the section distribution, fluctuation trend and adjustment frequency, and uniformly collect the working mode deviation data. Identify and calculate the adaptive deviation degree according to the section number, judge the fluctuation direction according to the adjustment frequency, and get the discharge mode adaptability label.

5. The multi-mode switched mobile energy storage control method of claim 4, wherein, The acquisition step of the PCS operation efficiency influence section list is specifically: S311: Call the discharge mode adaptability label, filter the voltage fluctuation deviation task section number, extract the voltage fluctuation time period according to the node association table, process the section voltage time period according to the time dimension, identify the voltage fluctuation time period index table, and obtain the voltage fluctuation task time period set; S312: According to the voltage fluctuation task time period set, collect the PCS operation efficiency data of the same period section, identify the PCS influence table, judge the daily PCS interference according to the interference threshold, and match with the voltage fluctuation task time period, judge whether there is voltage fluctuation abnormal association, and generate the PCS operation efficiency influence section list.

6. The multi-mode switched mobile energy storage control method of claim 5, wherein, The acquisition step of the distribution cabinet node task execution fluctuation group is specifically: S411: Based on the PCS operation efficiency influence section list, filter the discharge task node not marked, extract the task list, and obtain the node task set not interfered by the PCS; S412: Call the node task set not interfered by the PCS, match the daily segmented task record and operation data of the distribution cabinet, extract the planned task amount according to the task number, count the number of operators and real-time operation period, and generate the matching data set of the distribution cabinet cooperation execution situation; S413: According to the matching data set of the distribution cabinet cooperation execution situation, evaluate the matching degree of task execution efficiency and operation distribution, identify the efficiency fluctuation node, mark the task with fluctuation exceeding the reference value as an abnormal node, calculate the distribution cabinet task execution matching deviation value, and get the distribution cabinet node task execution fluctuation group.

7. The multi-mode switched mobile energy storage control method of claim 1, wherein, The method further comprises the S5 step: S5: Call the distribution cabinet node task execution fluctuation group, extract the abnormal fluctuation task group, calculate the resource input amount and task amount ratio in the energy storage model, record the difference distribution of resource deployment period and task execution period, and generate the energy storage progress monitoring structure index; The energy storage progress monitoring structure index includes resource allocation ratio, task intensity level, execution period difference, and energy storage efficiency index.

8. The multi-mode switched mobile energy storage control method of claim 7, wherein, The acquisition step of the energy storage progress monitoring structure index is specifically: S511: Call the distribution cabinet node task execution fluctuation group, filter the nodes exceeding the threshold, record the time interval and task amount change amplitude, and obtain the progress fluctuation abnormal identification set; S512: Based on the energy storage model resources corresponding to the nodes in the progress fluctuation abnormal identification set, identify the node resource input ratio sequence, extract the abnormal distribution interval and compare the critical coefficient, record the ratio deviation direction and node number, and form the energy storage resource matching deviation index group; S513: According to the energy storage resource matching deviation index group, extract the energy storage model resource deployment and task execution time period, identify the difference between the resource deployment period and the operation period, and sort and mark according to the progress reference, and generate the energy storage progress monitoring structure index.

9. A multi-mode switched mobile energy storage control system, characterized by, The system is used for realizing the multi-mode switching mobile energy storage control method of any one of claims 1-8, and the system comprises: The power state extraction module acquires the battery cluster assembly power state, PCS assembly operating parameters and transformer assembly output data of the mobile energy storage device, extracts the cabinet access signal state, compares the power state and load demand matching condition, marks the time inconsistent discharge task, and generates the node state deviation label group; The mode adaptability classification module is based on the node state deviation label group, locates the working mode adapted to the current load demand, extracts the discharge node identification, process node and reference voltage, calculates the difference between the field voltage and the reference voltage, classifies and labels the difference type, and generates the node mode adaptability identification; The PCS efficiency identification module is based on the node mode adaptability identification, screens the voltage fluctuation lag task component, locates the corresponding section, extracts the PCS operation efficiency interference period, determines the coincidence with the operation time period, screens the frequent interference section, and generates the energy storage progress PCS interference mapping set; The operation efficiency diagnosis module is based on the energy storage progress PCS interference mapping set, eliminates the interference section task, extracts the power distribution cabinet task record and operation data, matches the task assignment and operation time period, calculates the operation amount and task ratio, identifies the component task with intensive task and low execution efficiency, and acquires the node operation execution deviation set; The resource configuration analysis module is based on the node operation execution deviation set, locates the resource input record of the task in the energy storage model, extracts the task amount and resource amount configuration ratio, compares the operation period and input period difference, maps the task resource use and progress state, and forms the energy storage progress monitoring structure index.

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