A method and system for intelligent regulation of charging and discharging of energy storage devices based on multiple regions

Abstract

This invention relates to the field of intelligent charging and discharging regulation technology, specifically to a method and system for intelligent charging and discharging regulation of energy storage devices based on multiple regions. The method includes the following steps: analyzing power trends over time periods based on energy user data from each region, identifying and marking abnormal nodes, judging load trend deviations by combining monitoring and early warning, optimizing the regulation sequence, dynamically balancing capacity allocation, and generating charging and discharging commands. This invention relies on periodic factors and holiday corrections for real-time data fusion, enabling dynamic identification of abnormal fluctuations and changes in energy consumption trends within each time period. By constructing trend monitoring and early warning features to address load differences between nodes, it achieves early identification and linkage of load anomalies. Energy support allocation is sorted according to energy storage status and output capacity, promoting precise matching of task objects and command schemes, dynamically optimizing the participation sequence of energy storage units and energy release windows, and realizing coordinated regional energy regulation actions.

Description

Technical Field

[0001] This invention relates to the field of intelligent charging and discharging regulation technology, and in particular to a method and system for intelligent charging and discharging regulation of energy storage devices based on multiple regions. Background Technology

[0002] The field of intelligent charging and discharging regulation mainly involves the efficient management and optimized scheduling of energy flow in energy storage devices during the charging and discharging process. This includes the formulation of operating strategies for energy storage devices, the design of energy allocation schemes, and the adjustment of charging and discharging time and power. Overall, it uses intelligent methods to scientifically manage the charging and discharging behavior of energy storage systems to improve energy storage efficiency, ensure the safe and stable operation of the power system, and adapt to changing electricity demands. Traditional intelligent charging and discharging regulation methods for energy storage devices refer to determining the charging and discharging sequence, time periods, and specific charging and discharging power of energy storage devices in each region through centralized calculation or distributed rules for multiple energy storage units distributed in different geographical areas or power network nodes. This typically involves using historical load data statistical analysis and fixed threshold methods to judge the load status, and then presetting the charging and discharging priorities and working time allocations for energy storage units in different regions.

[0003] Existing technologies are limited to historical load data and fixed judgment rules, and are not sensitive enough to abnormal fluctuations and sudden behaviors in time-series data. They cannot track load trends and dynamic changes between regions in a timely manner. During special periods such as holidays, there may be discrepancies between the actual load and the preset mode. When load shifts occur, early warning and adjustment methods rely on preset thresholds, resulting in delayed response actions. They are unable to cope with rapid changes in energy storage status and energy flow between nodes, affecting the timeliness and accuracy of the dispatch system in managing energy balance and emergency response between regions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a method and system for intelligent regulation of charging and discharging of energy storage devices based on multiple regions.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent regulation of charging and discharging of a multi-regional energy storage device, comprising the following steps:

[0006] S1: Based on energy users in each region, analyze the daily power changes during different time periods, compare the load optimal item, the periodic factor and the holiday correction parameters, determine the stability of the periodic factor, screen out periods of abnormal fluctuations, mark them as adjustment nodes, and obtain a time-series anomaly marker set;

[0007] S2: Based on the time-series anomaly marker set, analyze the difference between the load forecast and real-time collected data of the adjustment node, determine the trend of difference between consecutive time periods, and establish trend anomaly early warning features by combining the early warning information of the load monitoring equipment.

[0008] S3: Based on the aforementioned trend anomaly warning characteristics, analyze the remaining power and output power of the energy storage unit, calculate its load support capacity, sort and select the energy storage unit with the best capacity, and obtain the dynamic load support sequence.

[0009] S4: Based on the dynamic load support sequence, analyze the capacity utilization ratio and change rate of the energy storage units, determine the amplitude difference, identify energy storage units with low change rate as charging targets and energy storage units with high change rate as discharging targets, and obtain a capacity balance instruction list.

[0010] The present invention is improved in that the time-series anomaly marker set includes time period labels, anomaly fluctuation levels, and adjustment reference indexes; the trend anomaly early warning features include offset trend factors, anomaly type classifications, and early warning level parameters; the dynamic load support sequence includes support capacity sorting, energy allocation identifiers, and node response attributes; and the capacity balancing instruction list includes charging object number, discharging object number, and capacity balancing level.

[0011] The present invention is improved in that the step of obtaining the time-series anomaly marker set is specifically as follows:

[0012] S111: Based on energy users in each region, analyze the power change trend of users in each time period of the day, compare the relationship between the maximum load item in each time period and the cycle factor and holiday correction parameters, determine the synchronicity and difference in the change process, and obtain the correlation trend characteristic sequence.

[0013] S112: Based on the correlation trend feature sequence, determine whether the change of the periodic factor is stable in a continuous period, calculate the fluctuation of the periodic factor in each continuous period, screen out the period with abnormal fluctuation, optimize the period division, and obtain the periodic continuous change pattern.

[0014] S113: Based on the aforementioned periodic continuous change pattern, screen time nodes with abnormal fluctuation amplitudes, compare the distribution and change characteristics of each node in the sequence, calculate the abnormal fluctuation aggregation index, and combine the index with the node trajectory to obtain a time-series abnormality marker set.

[0015] The present invention is improved in that the step of obtaining the trend anomaly early warning feature is specifically as follows:

[0016] S211: Based on the time-series anomaly marker set, analyze and adjust the changes in load forecast data and real-time collected data of the adjustment nodes, calculate the development trend of forecast offset between continuous time periods, compare the change rate of each node with the change of regional load variation boundary in continuous time, filter the trend change of each node, and obtain the trend change response sequence.

[0017] S212: Based on the trend change response sequence, determine the trend growth of each node, analyze the relationship between the node trend change and the upper and lower bounds of the allowable range of regional load change, filter nodes whose trend changes exceed the allowable range, optimize node classification, and obtain a set of trend deviation nodes.

[0018] S213: Based on the set of trend deviation nodes, compare the trend change of each node with the alarm status of the monitoring device, screen the nodes with key trend warning signal strength, determine the trend direction, diffusion speed and device response of the nodes, and establish trend anomaly warning features.

[0019] The present invention is improved in that the step of obtaining the dynamic load support sequence is specifically as follows:

[0020] S311: Based on the aforementioned abnormal trend warning characteristics, analyze the remaining electrical energy and sustainable output status of the regional energy storage unit, calculate its corresponding unit time support capacity and energy release limit range, adjust the parameter weights to normalize the ratio, and obtain the unit support capacity parameters.

[0021] S312: Based on the unit support capability parameters, compare the support differences of each unit at the abnormal node, calculate the adjustment capability strength of each energy storage unit, perform a sequence sorting operation, and obtain the energy storage unit sorting sequence.

[0022] S313: Based on the energy storage unit sorting sequence, select the units with priority in sorting, determine their matching order with each node, adjust the node mapping structure and rearrange the numbering correspondence, determine the dynamic linkage order of the adjustment resources, and obtain the dynamic load support sequence.

[0023] The present invention is improved in that the step of obtaining the capacity balancing instruction list is specifically as follows:

[0024] S411: Based on the dynamic load support sequence, analyze the current capacity utilization ratio and change rate of the energy storage unit, compare the change amplitude of each parameter in the same period, and judge the difference between the two by comparing their growth or decrease trends to obtain capacity parameter comparison characteristics.

[0025] S412: Based on the capacity parameter comparison characteristics, classify each energy storage unit according to the rate of change, optimize the classification results, classify those with low rate of change as charging objects, and classify those with high rate of change as discharging objects, and obtain charging and discharging group identifiers.

[0026] S413: Based on the charge / discharge group identifier, analyze the current capacity utilization ratio, rate of change, output power and regional load response of each energy storage unit, determine the charge / discharge task category and allocation relationship of each energy storage unit in the current cycle, and obtain the capacity balance instruction list.

[0027] The present invention is improved in that the steps further include:

[0028] S5: Based on the capacity balance instruction list, adjust the task category and regional allocation of each energy storage unit, optimize the charging and discharging capabilities of the energy storage units in the adjustment sequence, determine the energy release order and instruction time period, issue adjustment commands, and obtain the partition linkage execution results.

[0029] The results of the zoned linkage execution include the coordinated adjustment indicator, the regional energy allocation ratio, and the execution feedback signal.

[0030] The present invention is improved in that the step of obtaining the partition linkage execution result is specifically as follows:

[0031] S511: Based on the capacity balance instruction list, analyze the charging object number, discharging object number and capacity balance level, optimize the task category allocation of each energy storage unit, compare the area number and node distribution corresponding to each unit, determine the adaptability of task category and area division, and obtain the energy storage task partition mapping list.

[0032] S512: Based on the energy storage task partition mapping list, calculate the matching relationship between the task category of each energy storage unit and the energy storage capacity of its region, optimize the adjustment order, adjust the correspondence between the region number and the task category, and obtain the energy storage task scheduling sorting identifier.

[0033] S513: Call the energy storage task scheduling sorting identifier, analyze the adjustment command issued by the regional energy storage scheduling communication terminal, compare the energy storage unit feedback signal with the task category action, filter the task completion status and organize the communication results to obtain the partition linkage execution result.

[0034] A multi-regional intelligent charging and discharging regulation system for energy storage devices, the system comprising:

[0035] The time-series feature extraction module analyzes the power change trend of each time period based on energy users in each region, compares the correlation characteristics between the maximum load item and the periodic factor and holiday correction parameters in each time period, optimizes the adaptability of the periodic factor and correction parameters in time differences, judges the stability of continuous changes in the periodic factor, filters out abnormal fluctuation periods and marks them as adjustment nodes, and obtains a time-series anomaly marker set.

[0036] The load anomaly detection module analyzes the difference between the load forecast data and the real-time collected data of each adjustment node based on the time-series anomaly marker set, calculates the difference trend between consecutive time periods, compares the degree of matching between the trend change and the range of regional load changes, identifies nodes whose trend changes are greater than the range, and establishes trend anomaly early warning features in conjunction with the early warning prompts of the regional node load monitoring equipment.

[0037] Based on the aforementioned trend anomaly warning features, the energy storage optimization and ranking module optimizes the remaining power and maximum output power of energy storage units in the associated region, calculates the load support capacity index of each energy storage unit per unit time, compares the ranking of the capacity index of each energy storage unit in the node, and selects the energy storage unit with the best capacity index to obtain a dynamic load support sequence.

[0038] Based on the dynamic load support sequence, the capacity intelligent allocation module analyzes the current capacity utilization ratio and change rate of the energy storage unit, judges the difference in the amplitude of the two parameters, optimizes the dynamic balance of the capacity utilization ratio and change rate, identifies energy storage units with low change rate as charging targets and those with high change rate as discharging targets, and obtains a capacity balance instruction list.

[0039] Based on the capacity balancing instruction list, the collaborative scheduling execution module adjusts the task category and regional allocation of each energy storage unit, optimizes the charging and discharging capabilities of energy storage units within the adjustment sequence, determines the energy release order and instruction execution time period among each energy storage unit, and synchronously issues adjustment commands according to the regional energy storage scheduling communication terminal to obtain the partitioned linkage execution results.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0041] In this invention, multi-dimensional data extraction of regional energy consumption behavior time series is performed, and real-time data fusion is carried out based on periodic factors and holiday corrections. This enables dynamic identification of abnormal fluctuations and changes in energy consumption trends in each time period. By constructing trend monitoring and early warning features for load differences between nodes, early identification and linkage of load anomalies are achieved. Energy support allocation is sorted according to energy storage status and output capacity, promoting fine matching of task objects and instruction schemes, dynamically optimizing the participation order of energy storage units and energy release windows, realizing coordinated regional energy regulation actions, making the allocation of task instructions between regions more adaptive, promoting continuous optimization of energy flow paths and resource allocation relationships, and improving the flexibility and accuracy of multi-regional power network regulation. Attached Figure Description

[0042] Figure 1 This is a flowchart of the main steps of the present invention;

[0043] Figure 2 This is a flowchart illustrating the process of obtaining the timing anomaly marker set in this invention.

[0044] Figure 3 This is a flowchart illustrating the acquisition of trend anomaly warning features in this invention;

[0045] Figure 4 This is a flowchart illustrating the process of obtaining the dynamic load support sequence in this invention.

[0046] Figure 5 This is a flowchart illustrating the process of obtaining the capacity balancing instruction list in this invention.

[0047] Figure 6 This is a flowchart illustrating the process of obtaining the execution results of the partitioned linkage in this invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0050] Example:

[0051] Please see Figure 1 This invention provides a technical solution: a method for intelligent regulation of charging and discharging of a multi-region energy storage device, comprising the following steps:

[0052] S1: Based on energy users in each region, analyze the power change trend of each time period every day, compare the correlation characteristics between the maximum load item and the periodic factor and holiday correction parameters in each time period, optimize the adaptability of the periodic factor and correction parameters in different times, judge the stability of continuous change of the periodic factor, screen the abnormal fluctuation period and mark it as adjustment node, and obtain the time series abnormal mark set.

[0053] S2: Based on the time-series anomaly marker set, analyze the difference between the load forecast data and the real-time collected data of each adjustment node, calculate the difference trend between consecutive time periods, compare the degree of matching between the trend change and the range of regional load changes, identify nodes whose trend changes are greater than the range, and combine the early warning prompts of regional node load monitoring equipment to establish trend anomaly early warning features.

[0054] S3: Based on the trend anomaly early warning characteristics, optimize the remaining power and maximum output power of energy storage units in the associated area, calculate the load support capacity index of each energy storage unit per unit time, compare the ranking of the capacity index of each energy storage unit in the node, select the energy storage unit with the best capacity index, form the current adjustment sequence, and obtain the dynamic load support sequence.

[0055] S4: Based on the dynamic load support sequence, analyze the current capacity utilization ratio and change rate of the energy storage unit, determine the difference in the amplitude of the two parameters, optimize the dynamic balance of the capacity utilization ratio and change rate, identify energy storage units with low change rate as charging targets and those with high change rate as discharging targets, and obtain a capacity balance instruction list.

[0056] S5: Based on the capacity balance instruction list, adjust the task category and regional allocation of each energy storage unit, optimize the charging and discharging capacity of the energy storage units within the adjustment sequence, determine the energy release order and instruction execution time between each energy storage unit, and synchronously issue adjustment commands according to the regional energy storage scheduling communication terminal to obtain the partition linkage execution results.

[0057] The time-series anomaly marker set includes time period labels, anomaly fluctuation levels, and adjustment reference indexes. The trend anomaly early warning features include offset trend factors, anomaly type classifications, and early warning level parameters. The dynamic load support sequence includes support capacity ranking, energy allocation identifiers, and node response attributes. The capacity balance instruction list includes charging object number, discharging object number, and capacity balance level. The zonal linkage execution results include coordinated adjustment identifiers, regional energy allocation ratios, and execution feedback signals.

[0058] In S1, the maximum load item refers to the item with the largest value in the user's electricity (or energy) load data within a certain period, reflecting the peak energy consumption during that period, and is usually used for load characteristic analysis; the periodic factor refers to the parameter that affects the change of energy load in a periodic manner (such as daily, weekly, monthly), and commonly includes factors such as weekday / non-working day, time of day (morning / evening), and temperature; the holiday correction parameter is a parameter used to correct energy consumption anomalies caused by special times such as statutory holidays, and is usually set based on historical holiday data and social habits; the adaptability of the difference time refers to the degree of matching and adjustment flexibility between the periodic factor and the holiday correction parameter and the actual energy consumption data in different time periods (or different types of time periods); the stability of continuous change refers to whether the change trend of the periodic factor is stable (i.e., small fluctuation) and does not have drastic jumps in multiple consecutive time periods; the period of abnormal fluctuation refers to the period when parameters such as the periodic factor show sudden changes or deviate from the normal pattern, which usually indicates abnormal energy consumption patterns or load anomalies; the adjustment node refers to the time point / data node that is identified as a period of abnormal fluctuation and is designated as a key time point / data node for subsequent analysis.

[0059] In S2, the difference trend refers to the continuous change in the deviation between the load forecast data and the real-time collected data over time, reflecting the accuracy of the forecast and its change over time; the regional load variation range refers to the range of allowable fluctuations in energy load under normal conditions within a specific region, which can be determined by historical data statistics and used to determine whether abnormal deviations have occurred; nodes exceeding this range refer to time nodes where the difference trend exceeds the above-mentioned normal range of regional load variation, and are identified as abnormal nodes; load monitoring equipment refers to instruments and equipment installed at each regional node that can collect and record energy data in real time and have alarm / early warning functions, such as smart meters, sensors, etc.

[0060] In S3, an energy storage unit refers to an energy storage system module that can be independently charged and discharged and can receive dispatch commands, such as an energy storage device, a group of batteries, or a regional energy storage system; the supporting load refers to the power that the energy storage unit can release to provide supplementary power to the grid or energy consumers and buffer load fluctuations; the capacity index is a parameter that comprehensively evaluates the energy storage unit's ability to support the load over a period of time, generally considering remaining power, output power, response speed, etc.; the ranking in the node refers to ranking the capacity indexes of multiple energy storage units according to their superiority or inferiority, which is used for subsequent dispatch priority allocation; the adjustment sequence refers to the queue of energy storage units that can participate in this round of dispatch after being ranked by capacity indexes.

[0061] In S4, the utilization ratio refers to the ratio between the current available capacity of an energy storage unit and its rated maximum capacity, reflecting the real-time availability of energy storage resources; the rate of change refers to the speed at which the utilization ratio changes over time, used to judge the charging and discharging activity of the energy storage unit; the parameter amplitude difference refers to the absolute amount of the difference between the utilization ratio and the rate of change, used to analyze the fluctuation characteristics of the energy storage unit's state; dynamic balance refers to coordinating the charging and discharging division of each energy storage unit during the scheduling process, so that the overall capacity state is kept in a reasonable distribution, avoiding overload or idleness of some energy storage units.

[0062] In S5, task category refers to the functional tasks undertaken by the energy storage unit according to the dispatch instructions, such as charging, discharging, and standby; area allocation refers to assigning specific service areas to different energy storage units and determining their supported objects (such as a regional power grid, load nodes, etc.); energy release sequence refers to determining the order of actions of multiple energy storage units according to priority when they need to release energy in sequence, so as to avoid energy fluctuations caused by simultaneous actions; instruction execution period refers to the specific time window for the dispatch instructions to take effect and be executed, ensuring that the operation sequence is synchronized with the system requirements; dispatch communication terminal refers to the terminal equipment used for real-time communication with each energy storage unit and the issuance of instructions, including the background dispatch host, remote communication module, etc.

[0063] Please see Figure 2 The specific steps for obtaining the time-series anomaly marker set are as follows:

[0064] S111: Based on energy users in each region, analyze the power change trend of users in each time period of the day, compare the relationship between the maximum load item in each time period and the cycle factor and holiday correction parameters, determine the synchronicity and difference in the change process, and obtain the correlation trend characteristic sequence.

[0065] First, historical power data for all electricity users in each area is collected. Each day is divided into several fixed-length time periods, such as one hourly period. Then, the power variation for each user within each time period is statistically analyzed. The historical power within the same time period each day is averaged to obtain a typical power curve for each user during that period. Next, the maximum power value among all power data within that time period is identified as the maximum power item. This data point is then labeled with its corresponding time label, date category (e.g., weekday or weekend), and whether it is a holiday. Based on the date category corresponding to the time label, relevant labels are extracted from the periodic factor parameter library, such as "Monday morning" or "Friday afternoon." These labels are compared with the maximum power item for that time period. Simultaneously, the corresponding correction factor is extracted from the holiday correction parameter table and associated with the current time period. For example, if the average user load drops to about 70% of the weekday load during a statutory holiday, then that time period is considered a holiday. The correction factor is set to a negative value, indicating a deviation from the normal cyclical energy consumption level. Then, the maximum power of each user during the period is combined with the cycle factor and the holiday correction factor to form a combined data item. All users in each region are collected for this combination during the period, and the numerical difference between the maximum power values ​​is compared item by item. If the difference exceeds a certain set threshold, such as the difference in maximum power between different users exceeding 3 kilowatts, the period is marked as having a synchronization deviation. Then, the change process of the maximum power under different cycle factor labels is checked. For example, if the difference in the maximum power value between "Wednesday morning" and "Friday morning" in the same period also reaches a certain magnitude, it is determined that the influence of the cycle factor is volatile. Finally, the linkage trend between this maximum power item and the cycle factor and holiday correction item in all time periods is summarized. Through the observation of continuously changing data, the power change trend of each time period under different cycles is extracted to form a correlation trend feature sequence.

[0066] S112: Based on the correlation trend feature sequence, determine whether the change of the periodic factor is stable in a continuous period, calculate the fluctuation of the periodic factor in each continuous period, screen the period with abnormal fluctuation, optimize the period division, and obtain the periodic continuous change pattern.

[0067] The system divides each day into multiple smaller segments, using three consecutive time periods as units. The maximum and minimum differences between power trend values ​​within each segment are calculated and recorded as the fluctuation amplitude of that segment. This amplitude is then checked to see if it exceeds a preset fluctuation threshold. For example, if the threshold is set at 1.5 kW, a segment is considered an abnormal fluctuation segment if the difference between the maximum and minimum power trend values ​​reaches 2.2 kW. All consecutive time periods are then iterated over, and the difference in the rate of change of the trend before and after each period is analyzed. If the rate of change between two adjacent time periods is greater than a set percentage threshold, such as 25%, it is considered an unstable continuous change in the periodic factor, and this time period is further recorded. Unstable segments are identified by compiling all time periods deemed to have abnormal fluctuations or drastic changes, forming a set of abnormal time periods. These abnormal time periods are then further subdivided or reorganized based on the original time period divisions. For example, if an abnormal fluctuation occurs within a 45-minute period in a previously defined four-hour time period, the four-hour time period is divided into three smaller time periods to ensure that the fluctuation characteristics within each division unit remain relatively consistent. Finally, based on the trend changes of all time periods and the results of the re-division, a fluctuation stability map of the entire cycle factor over continuous time periods is drawn, identifying the trajectory and continuity of the cycle factor value in each time period, thus completing the construction of the cycle continuity change pattern.

[0068] S113: Based on the periodic continuous change pattern, time nodes with abnormal fluctuation amplitudes are screened, and the distribution and change characteristics of each node in the sequence are compared using the following formula:

[0069] ;

[0070] Calculate the anomalous fluctuation aggregation index, and combine this index with the node trajectory to obtain a time-series anomaly marker set, where, This represents the aggregated index of abnormal fluctuations in the i-th region during the j-th time period. For the period k in the i-th region, the maximum load item is... Let be the fluctuation of the periodic factor in the k-th time period. Let k be the time span of continuous change of the periodic factor corresponding to the k-th time period. For the holiday correction parameter in the i-th region during the k-th time period, there is the variation term within the time period, where n is the total number of consecutive time periods.

[0071] The abnormal fluctuation aggregation index is a comprehensive quantitative indicator that reflects the intensity of abnormal fluctuations in energy load within a specific period of time for each region. It is obtained by aggregating and analyzing multiple data such as the maximum load item, the fluctuation of the periodic factor, the periodic change span, and the holiday correction parameters. It is used to measure the coupling fluctuation between load changes and periodic patterns and special time correction parameters in a region during a certain period of time, and to highlight the time nodes when obvious abnormalities or sudden changes occur in load behavior.

[0072] If the sum of the squares of the periodic factor fluctuations exceeds a set reference range over three consecutive time periods, the node is identified as having an abnormal fluctuation amplitude and is designated as j. The region number i and its combination serve as the unique identifier. Subsequently, further data aggregation calculations are performed on such nodes within each region, calling the maximum load item for each region. cyclical factor volatility Periodic span Holiday changes Calculate its abnormal fluctuation aggregation index;

[0073] In a certain region i=1, a set of continuous time periods k=1,2,3 is selected, and the data is as follows:

[0074] Time period 1: After normalization, it becomes 0.83; After normalization, it becomes 0.75; After normalization, it becomes 0.60; After normalization, it becomes 0.50;

[0075] Second period: After normalization, it becomes 0.88; After normalization, it becomes 0.45; After normalization, it becomes 0.60; After normalization, it becomes 0.75;

[0076] Third period: After normalization, it becomes 0.95; After normalization, it becomes 0.35; After normalization, it becomes 0.60; After normalization, it becomes 0.25;

[0077] After substituting the normalized data, the numerator is calculated as follows:

[0078] ;

[0079] Calculation of the denominator:

[0080] ;

[0081] Calculation results:

[0082] ;

[0083] at this time This indicates that a strong anomalous fluctuation aggregation occurred in the first region during the period of continuous cyclical change. If 0.50 is defined as a medium-to-high fluctuation level threshold within the set fluctuation response identification interval, then the node will be identified as an anomalous node and included in the subsequent adjustment strategy identification scope. The trajectory number, location index, and indicator record of the node will be constructed and finally written into the time-series anomaly marker set as the node information source for subsequent energy storage adjustment judgment. This formula introduces the fusion effect of load and rhythm change dual factors by multiplying the maximum load term with the square root of the cyclic factor fluctuation. Combined with the superposition factors of cycle span and holiday parameters, it quantifies the anomalous intensity level of the node within the overall scheduling cycle, improving the fine-grainedness of node status identification during the scheduling process.

[0084] Please see Figure 3 The specific steps for obtaining the trend anomaly warning features are as follows:

[0085] S211: Based on the time-series anomaly marker set, analyze the changes in load forecast data and real-time collected data of adjustment nodes, calculate the development trend of forecast offset between continuous time periods, compare the change rate of each node with the change of regional load variation boundary in continuous time, filter the trend change of each node, and obtain the trend change response sequence.

[0086] The location of each marked node within a specific time period of the day is determined, and the load forecast data for that node within the corresponding time period is extracted. This forecast data comes from the historical average load curve data of the previous period, and a dynamically updated forecast curve is formed through multiple daily collections. Simultaneously, the real-time load data for that node within the corresponding time period is extracted. This data comes from smart meter collection modules deployed in the area, recording each load value at the minute level. The forecast data and real-time data are arranged sequentially by time, and the difference between the forecast value and the actual value at each adjacent time point is compared. The overall forecast offset trend is determined by calculating the continuous increase or decrease direction of this difference sequence point by point. For example, between 6:00 AM and 7:00 AM, the forecast data is 4.8, 5.1, 5.3, 5.6, and 6.0 kW, while the real-time data is 5.0, 5.5, 5.9, 6.3, and 6.8 kW, corresponding to offset values ​​of 0.2, 0.4, 0.6, 0.7, and 0. For an 8 kW load, the offset value shows an increasing trend over a continuous time period. The growth rate of this trend is recorded as the rate of change. Then, the upper and lower bounds are extracted based on the load fluctuation limits set for the region. For example, the normal fluctuation range of the historical load in this region during this time period is set to ±0.5 kW. Exceeding this range is considered abnormal fluctuation. The relationship between the rate of change and the upper and lower bounds of the fluctuation limit is compared. If the offset value growth rate exceeds 0.5 kW every 15 minutes, it is marked as a node with an excessively fast trend change. The rate of change of all nodes is horizontally sorted, and nodes with significant changes in growth rate are selected. For example, the dividing range is set to three levels: below 0.3 kW / 15 minutes is the stable segment, between 0.3 and 0.5 is the acceptable segment, and above 0.5 is the drastic segment. All nodes are classified accordingly, and parameters such as the time period, change value, and growth direction are recorded. Finally, the selected nodes are arranged in order to form a trend change response sequence.

[0087] S212: Based on the trend change response sequence, determine the trend growth of each node, analyze the relationship between the node trend change and the upper and lower bounds of the allowable range of regional load change, screen nodes whose trend changes exceed the allowable range, optimize node classification, and obtain a set of trend deviation nodes.

[0088] The trend direction and rate of change for each node are extracted to determine whether the node represents an increasing trend within its time period. This is done by comparing the load value at the current time point with that at the previous time point to see if the increase is continuous. For example, if the load at a node increases from 5.2 kW to 7.1 kW between 8:00 AM and 9:00 AM, it is identified as a node with a trending increase. Then, the trend change value of this node is correlated with the upper and lower limits of the allowable load fluctuation range for the region. The allowable fluctuation range for the region's load is determined based on historical seasonal load changes, such as the historical maximum fluctuation range between 8:00 AM and 9:00 AM during the summer peak season. The limit is 1.5 kilowatts. If the change value of a node reaches 2.2 kilowatts, it exceeds the upper limit and is judged as a trend deviation node. Then, all nodes that exceed the upper and lower limits are classified and labeled with multi-dimensional tags according to the trend growth rate, duration, and region. For example, the growth rate level of trend deviation nodes is set as 1.5-2.0 kilowatts as Level 1 deviation, 2.0-3.0 kilowatts as Level 2 deviation, and more than 3.0 kilowatts as Level 3 deviation. Then, the set of deviation nodes is organized according to the classification tags, and the node number, occurrence time, deviation direction, deviation magnitude and region information are summarized to form a trend deviation node set.

[0089] S213: Based on the set of trend deviation nodes, compare the trend change of each node with the alarm status of the monitoring equipment, using the following formula:

[0090] ;

[0091] By screening key nodes based on trend warning signal strength, determining the trend direction, diffusion speed, and device response of these nodes, and establishing trend anomaly warning characteristics, among which... This indicates the strength of the trend warning signal at node z. This represents the magnitude of the trend change at node z. This represents the total load offset of node z. This represents the variance of the prediction difference at node z. This represents the upper bound of load variation in the z-region of node. This represents the lower bound of load variation in the z-region of node z. This represents the alarm level factor between node z and monitoring device j. This represents the trend coupling magnitude between node z and monitoring device j. This indicates the number of monitoring devices associated with node z. This represents the total number of monitoring devices associated with node z, summed for each j.

[0092] Trend warning signal strength refers to a quantitative indicator used to measure the risk of abnormal load trends at nodes during the intelligent charging and discharging adjustment of multi-regional nodes in energy storage devices. This indicator is calculated by considering multiple factors, including node trend changes, load shifts, prediction errors, and alarm status of monitoring equipment. It reflects the degree of deviation between the node's load change trend and multi-dimensional data such as actual collected data, predicted data, and equipment status within a certain period, as well as its abnormal response capability. A higher value indicates a greater deviation of the node's trend change from the normal range of the region, and a higher correlation with alarm signals from monitoring equipment, suggesting the occurrence of abnormal loads or warnings. This indicator facilitates rapid system identification and priority response to risky nodes, enabling proactive early warning and adjustment of the zoned energy storage system.

[0093] Taking node Z01 as an example, its original data is:

[0094] Alarm level factor The array is [1.2, 0.9, 1.1], and the trend coupling amplitude is... Given the array [1.0, 0.8, 1.2], after normalizing each parameter according to its category, we get: The alarm factor group is normalized to [0.80, 0.66, 0.72], and the coupling amplitude is normalized to [0.71, 0.59, 0.83].

[0095] Substitute the normalized values ​​above into the formula for calculation:

[0096] Part One: Molecules

[0097] ;

[0098] First part, denominator:

[0099] ;

[0100] Results in Part 1:

[0101] ;

[0102] Part Two Summation Terms:

[0103] ;

[0104] Part Two: Overall Value

[0105] ;

[0106] The final trend warning signal strength is obtained through combined calculation:

[0107] ;

[0108] The results indicate that the multi-parameter composite trend change characteristics of node Z01 in the current period have reached a high alarm response level. Its trend change value has exceeded the set warning response limit. In the subsequent adjustment process, the energy storage resource coordinated response priority of this node should be given priority. This value is used as the core input basis for constructing the trend anomaly early warning characteristics. The formula incorporates the trend amplitude, error factor and multi-device coupled data at the same time, and integrates the temporal trend evolution and spatial response coordination into a unified measurement framework, thereby improving the stability and difference identification accuracy of the distributed trend alarm mechanism.

[0109] Please see Figure 4 The specific steps for obtaining the dynamic load support sequence are as follows:

[0110] S311: Based on the trend anomaly early warning characteristics, analyze the remaining power status and sustainable output status of the regional energy storage unit, calculate its corresponding unit time support capacity and energy release limit range, adjust the parameter weights to normalize the ratio, and obtain the unit support capacity parameters.

[0111] Extract the unit numbers of all energy storage units currently under abnormal warning status, and obtain their latest remaining energy data for each unit. Remaining energy is calculated by multiplying the current energy reading by the unit's rated maximum capacity and then by its rated capacity. For example, if energy storage unit A101 currently has 42 kWh of energy and a rated capacity of 100 kWh, its remaining energy is 42 kWh. Then, obtain the unit's average output power and maximum continuous discharge duration over the past hour to calculate the amount of electricity that can be released per unit time and determine whether it is in a sustainable output state. For example, if the output power is 7 kW and the discharge duration is 3 hours, the total sustainable output is 21 kWh, corresponding to a support capacity of 7 kW per hour. Repeat this operation for all energy storage units and record their support capacity per unit time. Then, combine this with each unit's rated output power limit and minimum release power to establish... The energy release limit range of this unit is defined as follows: if the rated power of unit A101 is 10 kW and the minimum release power is 3 kW, then the limit range is 3 to 10 kW. Then, the remaining energy value, unit time support capacity value, and upper and lower limits of the limit range for each unit are multiplied by an adjustment weighting factor. This factor is set based on the importance level of the region to which the energy storage unit belongs and its previous scheduling participation frequency. For example, if the importance level is level three and the unit has participated 3 times in the last five scheduling rounds, the corresponding weight is set to 0.85. The three dimensions of data are multiplied by this factor and then proportionally normalized so that the three parameters can be compared within the same range. A linear normalization method is used to map the parameter values ​​of all energy storage units to between 0 and 1. Finally, the normalized support capacity value of each energy storage unit is recorded, and this value is used as the basis for subsequent scheduling priority ranking to generate unit support capacity parameters.

[0112] S312: Based on the element support capability parameters, compare the support differences of each element at abnormal nodes using the following formula:

[0113] ;

[0114] Calculate the regulation capacity of each energy storage unit, perform a sequence sorting operation, and obtain the energy storage unit sorting sequence, where, This indicates the regulation capability strength of the energy storage unit numbered 'o'. This represents the standard support power of the energy storage unit numbered 'o' per unit time. This indicates the current remaining available electrical energy of the energy storage unit numbered 'o'. This indicates the degree of support capability matching between the energy storage unit numbered 'o' and the target node. This represents the amount of fluctuation disturbance generated by the energy storage unit numbered 'o' during node load regulation. This represents the response time of the energy storage unit numbered 0 to the current node task.

[0115] Regulation capacity strength refers to the core indicator that quantitatively measures the ability of an energy storage unit to make an immediate contribution to regional energy regulation tasks under specific time periods and node environments, after considering all relevant state parameters. It is the direct decision-making basis for the energy storage scheduling system to intelligently screen, allocate, and prioritize energy storage resources.

[0116] The energy storage unit, designated E01, has the following original parameters: standard supporting power 85.5kW, remaining usable energy 180.0kWh, matching degree 0.92, fluctuation interference 5.6, and response time 2.0. Its normalized parameters are as follows: , , , , Substituting into the formula, the calculation is as follows:

[0117] Addition term calculation:

[0118] ;

[0119] Multiply by the degree of matching:

[0120] ;

[0121] Subtract interference:

[0122] ;

[0123] Denominator processing:

[0124] ;

[0125] Regulatory capacity strength:

[0126] ;

[0127] Similarly, for energy storage unit E02, with the following original parameters: standard supporting power 92.0kW, remaining usable energy 150.0kWh, matching degree 0.87, fluctuation interference 7.2, and response time 2.5, its normalized parameters are: , , , , The calculation steps are as follows:

[0128] Addition term calculation:

[0129] ;

[0130] Multiply by the degree of matching:

[0131] ;

[0132] Subtract interference:

[0133] ;

[0134] Denominator processing:

[0135] ;

[0136] Final regulatory capacity strength:

[0137] ;

[0138] For energy storage unit E03, the original parameters are: standard supporting power 88.3kW, remaining usable energy 210.0kWh, matching degree 0.89, fluctuation interference 4.9, response time 1.8, and its normalized parameters are: , , , , The calculation is as follows:

[0139] Addition term calculation:

[0140] ;

[0141] Multiply by the degree of matching:

[0142] ;

[0143] Subtract interference:

[0144] ;

[0145] Denominator processing:

[0146] ;

[0147] Final regulatory capacity strength:

[0148] ;

[0149] The results show that E03 has the highest regulation capacity, followed by E01, and then E02. The ranking result is: E03>E01>E02. This result is the energy storage unit ranking sequence generated in this step, which can provide a basis for issuing priority instructions for subsequent node support linkage. The results show that the larger the value of the regulation capacity, the higher the support response capability of the energy storage unit to abnormal nodes in the current cycle.

[0150] S313: Based on the energy storage unit sorting sequence, select the units with priority in sorting, determine their matching order with each node, adjust the node mapping structure and rearrange the numbering correspondence, determine the dynamic linkage order of the regulating resources, and obtain the dynamic load support sequence.

[0151] Select the top-ranked energy storage units and extract their numbers as the current candidate set of regulation resources. Read the load response parameters of each abnormal node sequentially according to scheduling time. These parameters include the node's load gap value, response delay, and region number within a certain time period. Compare the currently ranked best energy storage unit with each node pairwise, checking whether its support capacity parameter is greater than the node's gap value, whether it is located in the node's region or can be scheduled to that region, and whether the scheduling response time does not exceed the node's maximum tolerable delay time. If all conditions are met, establish a preliminary matching relationship between the energy storage unit and the node, and record the matching relationship as a node mapping record. After each successful matching, adjust the remaining power of the current energy storage unit and the expected dispatch time. The number of times is determined, and the result is inserted at the end of the next round of scheduling queue. If energy storage unit A101 matches node N005, then "node N005-energy storage unit A101" is recorded. Then, A101 is moved from the current sequence to the temporary task list. The above process is repeated until all nodes are matched or there are no energy storage units that meet the matching conditions. Then, the final mapping relationship of all nodes is calculated, and the energy storage unit number corresponding to each node is renumbered to generate a mapping table. For example, the original number of node N005 corresponds to A101. After rearrangement, it is marked as number S01. Finally, according to the order of energy storage units matched by each node, the order in which the regulation resources should be activated on the time axis is arranged. The time and sequence number of the energy storage unit scheduling actions are listed in sequence to obtain the dynamic load support sequence.

[0152] Please see Figure 5The specific steps for obtaining the capacity balancing instruction list are as follows:

[0153] S411: Based on the dynamic load support sequence, analyze the current capacity utilization ratio and rate of change of the energy storage unit, compare the change amplitude of each parameter in the same period, and judge the difference between the two by comparing their growth or decrease trends, and obtain the capacity parameter comparison characteristics.

[0154] Extract the unit numbers of all energy storage units currently in the adjustment task state. For each unit, obtain its current capacity utilization ratio, which is calculated by converting the current remaining power to the rated maximum capacity to obtain a percentage utilization ratio. For example, if an energy storage unit currently has 52 kWh of remaining power and a rated maximum capacity of 100 kWh, its capacity utilization ratio is 52%. Then, retrieve the capacity utilization ratio change records for the unit over the past two consecutive periods and calculate the rate of change of this ratio per unit time. The rate of change is calculated by dividing the difference in capacity utilization ratio between the two time points by the time interval. For example, if the ratio is 48% in the previous period and 52% in the next period, with a time interval of 1 hour, the rate of change is 4% per hour. Calculate the utilization ratio and rate of change for all energy storage units in this way, and then combine these two parameters within the same period. The absolute difference is compared. The comparison process involves subtracting the two parameters within each energy storage unit and recording the difference. The larger the difference, the more obvious the growth or decline trend. The above differences of all energy storage units are sorted from largest to smallest to determine whether the trend difference is in a drastic change range. The threshold for the drastic range is set at 15%, the intermediate fluctuation range is 5%-15%, and the low fluctuation range is less than 5%. Energy storage units with differences falling into different ranges are marked. When comparing the direction of growth or decline, if the current utilization ratio is higher than the previous period and the rate of change is positive, it is judged as an growth trend; otherwise, it is a decline trend. The above judgment results are sorted out, and four characteristic data of each energy storage unit are recorded: capacity ratio, rate of change, difference, and trend direction. Finally, the data are combined to form a capacity parameter comparison feature.

[0155] S412: Based on the comparison characteristics of capacity parameters, classify each energy storage unit according to the rate of change, optimize the classification results, classify those with low rate of change as charging objects, and classify those with high rate of change as discharging objects, and obtain charging and discharging group identifiers.

[0156] The rate of change values ​​for each energy storage unit are retrieved and sorted by value. Three ranges are initially defined for the rate of change: low rate (less than 2% per hour), medium rate (2% to 5% per hour), and high rate (greater than 5% per hour). All energy storage units are then initially categorized according to this standard: units with a rate of change less than 2% are classified into the low-rate group, those with a rate of change greater than 5% into the high-rate group, and those in the middle range into the medium-rate group. The categorization results are then cross-checked. If a unit is in the low-rate group but its capacity utilization rate is higher than 80%, the classification is not performed. If a unit is reclassified and marked as a standby unit instead of a charging unit, and if a unit is in the high-rate group but its current capacity utilization rate is less than 20%, it is marked as a discharge warning unit again. This optimizes the classification results and obtains an initial set of charging and discharging objects. Then, all units marked as abnormal or standby are removed, and only energy storage units that meet the adjustment conditions are retained. The remaining low-rate units are then assigned to the charging object set, and the high-rate units are assigned to the discharging object set. A current time period identifier, number identifier, and classification identifier are added to each member of the set to form a standardized charging and discharging group identifier.

[0157] S413: Based on the charging and discharging group identifier, analyze the current capacity utilization ratio, rate of change, output power and regional load response of each energy storage unit, determine the charging and discharging task category and allocation relationship of each energy storage unit in the current cycle, and obtain the capacity balance instruction list.

[0158] For each energy storage unit, extract its capacity utilization rate, rate of change, output power, and the regional load response record for the current cycle. Assess the utilization rate to determine if it is within the discharge range (e.g., exceeding 60%) or must enter the charging protection range (e.g., below 20%). Compare the rate of change with the previous cycle to confirm its stability. Retrieve the output power to verify if it is within the rated release range. Compare the regional load response record to analyze whether the regional load is in a deficit or redundancy state. For example, if the current load in a region is 23 kW, the current output capacity of this energy storage unit is 6 kW, and the regional deficit is 4 kW, then this energy storage unit can handle... To handle a portion of the load, a comprehensive judgment is made based on the above four types of data parameters. If the utilization rate of a unit is higher than 60%, the rate of change is greater than 5%, the output power is in the middle to upper range, and the regional response is a load gap, it is determined to be a discharge task unit. Conversely, if the utilization rate is lower than 30%, the rate of change is less than 2%, the current output is zero, and the regional load is in a redundant state, it is classified as a charging task unit. After determining the task category of each energy storage unit, a task category instruction code is generated and attached to the allocation record of the energy storage unit. At the same time, its service area number and scheduling time period are recorded. Finally, the task category, allocation area, response object, and scheduling sequence of all energy storage units are summarized to form a capacity balancing instruction list.

[0159] Please see Figure 6 The specific steps for obtaining the results of the partitioned linkage execution are as follows:

[0160] S511: Based on the capacity balance instruction list, analyze the charging object number, discharging object number and capacity balance level, optimize the task category allocation of each energy storage unit, compare the area number and node distribution corresponding to each unit, judge the adaptability of task category and area division, and obtain the energy storage task partition mapping list.

[0161] Read the list of charging and discharging object numbers marked in the inventory, and extract the basic parameters of the energy storage unit corresponding to each number, including its rated capacity, current utilization rate, rate of change, and region number. Then extract the capacity balance level parameter, which is divided according to the difference between the remaining capacity of the energy storage unit and the target balance ratio. For example, if the balance target is set to 60%, and the current capacity of a unit is 78%, then its deviation is 18%, which can be divided into three capacity deviation levels. After completing the above basic data extraction, classify the current task of each energy storage unit according to the task category label, and determine whether it is a task unit to be adjusted, adjusted, or standby. Then match the task category with its region number one by one. If there are multiple region numbers, The node configuration is expanded and listed, and compared with the energy storage unit number to check whether the current task category is suitable for the current node location. For example, a discharge task assigned to an area with high node density and frequent voltage fluctuations is suitable, and the opposite is not suitable. For unsuitable configurations, they are marked as task adjustment units. Then, the relationship between all current energy storage units and their task categories and the node areas they serve is reorganized. Units within the same task category are re-matched according to the regional centralized distribution method. For example, three discharge task units located in areas A1, A2, and A3 respectively are re-integrated into nodes in area A1 and other area configurations are released. Finally, a new correspondence between task category, energy storage unit number, area number, and node number is output, generating an energy storage task partition mapping list.

[0162] S512: Based on the energy storage task partition mapping list, calculate the matching relationship between the task category of each energy storage unit and the energy storage capacity of its region, optimize the adjustment order, adjust the correspondence between the region number and the task category, and obtain the energy storage task scheduling sorting identifier.

[0163] The task category of each energy storage unit is compared and matched with the energy storage capacity of its region. First, the regional energy storage capacity data is extracted, based on the total dispatchable capacity of all energy storage units within the region. Simultaneously, the maximum dispatch demand value corresponding to each region is extracted to calculate the types of tasks that the region can handle. For example, if the total energy storage capacity in region A1 is 240 kWh and the dispatch demand is 80 kWh, the allocation ratio is 3.0, classifying it as a high-load region, allowing for the configuration of multiple discharge task units. The task categories currently allocated to this region are compared to check if they exceed the regional dispatch capacity boundary. For instance, if region A1 already has four discharge task units with a total discharge capacity of 120 kW, the allocation ratio is 3.0, indicating a high-load region that allows for the configuration of multiple discharge task units. If the scheduling demand exceeds 80 kilowatts, it is judged as an over-sizing state and marked as an optimization adjustment area. Then, the allocation of all areas is sorted, and the adjustment order of energy storage units is rearranged according to the priority of regional energy storage capacity. High-capacity areas are given priority to discharge task units, and low-capacity areas are given priority to charging task units. At the same time, the correspondence between task category and area number is adjusted. For example, the unit number D101 is moved from area A1 to area B2. Area B2 currently has a task load of less than 50%, which is more suitable for undertaking charging tasks. A new mapping list is formed for all updated area numbers and task categories, with timestamps and task numbers attached. After sorting, the energy storage task scheduling sorting identifier is output.

[0164] S513: Call the energy storage task scheduling sorting identifier, analyze the adjustment commands issued by the regional energy storage scheduling communication terminal, compare the feedback signals of the energy storage unit with the task category actions, filter the task completion status and organize the communication results to obtain the partition linkage execution results;

[0165] The task number of each energy storage unit is sequentially matched with the dispatch communication terminal of its region. The adjustment command records of the communication terminal in the current cycle are read. For each command record, the issuance time, target unit number, adjustment command type (charging or discharging), and target parameter value are extracted. Then, the actual execution signal fed back by the energy storage unit is compared. This signal includes the execution start time, execution power, duration, and status flag. The issued command and feedback signal are compared item by item to see if they are consistent. For example, if the command requires unit number C206 to discharge at a power of 6 kW for 30 minutes, if the feedback from C206 is a discharge power of 6 kW, a discharge duration of 28 minutes, and a status of completion, it is determined to be a successful execution. If the feedback shows a power of only 4 kW or a status of interruption, it is recorded as an execution failure. The completion rate of each type of task is then calculated, and the ratio between the number of successful matches and the total number of tasks is selected. A completion status table is generated by task category. All communication records and feedback results are organized to generate a multi-dimensional data list containing region number, energy storage unit number, task category, execution status, timestamp, and feedback indicators, thus obtaining the regional linkage execution result.

[0166] A multi-regional intelligent charging and discharging regulation system for energy storage devices, the system comprising:

[0167] The time-series feature extraction module analyzes the power change trend of each time period based on energy users in each region, compares the correlation characteristics between the maximum load item and the periodic factor and holiday correction parameters in each time period, optimizes the adaptability of the periodic factor and correction parameters in time differences, judges the stability of continuous changes in the periodic factor, filters out abnormal fluctuation periods and marks them as adjustment nodes, and obtains a time-series anomaly marker set.

[0168] The load anomaly detection module analyzes the difference between the load forecast data and the real-time collected data of each adjustment node based on the time-series anomaly marker set, calculates the difference trend between consecutive time periods, compares the degree of matching between the trend change and the range of regional load changes, identifies nodes whose trend changes are greater than the range, and establishes trend anomaly early warning features in combination with the early warning prompts of regional node load monitoring equipment.

[0169] Based on the trend anomaly early warning characteristics, the energy storage optimization and ranking module optimizes the remaining power and maximum output power of energy storage units in the associated area, calculates the load support capacity index of each energy storage unit per unit time, compares the ranking of the capacity index of each energy storage unit in the node, and selects the energy storage unit with the best capacity index to obtain the dynamic load support sequence.

[0170] The capacity intelligent allocation module analyzes the current capacity utilization ratio and change rate of energy storage units based on the dynamic load support sequence, judges the difference in the amplitude of the two parameters, optimizes the dynamic balance of capacity utilization ratio and change rate, identifies energy storage units with low change rate as charging targets and those with high change rate as discharging targets, and obtains a capacity balance instruction list.

[0171] Based on the capacity balancing instruction list, the collaborative scheduling execution module adjusts the task categories and regional allocation of each energy storage unit, optimizes the charging and discharging capabilities of energy storage units within the adjustment sequence, determines the energy release order and instruction execution time between each energy storage unit, and synchronously issues adjustment commands according to the regional energy storage scheduling communication terminal to obtain the partitioned linkage execution results.

[0172] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent regulation of charging and discharging of a multi-region energy storage device, characterized in that, Includes the following steps: S1: Based on energy users in each region, analyze the daily power changes, compare the load optimal item, the cycle factor and the holiday correction parameters, judge the stability of the cycle factor, screen out the periods of abnormal fluctuations, mark them as adjustment nodes, and obtain the time series anomaly marker set; S2: Based on the time-series anomaly marker set, analyze the difference between the load forecast and real-time collected data of the adjustment node, determine the trend of difference between consecutive time periods, and establish trend anomaly early warning features by combining the early warning information of the load monitoring equipment. S3: Based on the aforementioned trend anomaly warning characteristics, analyze the remaining power and output power of the energy storage unit, calculate its load support capacity, sort and select the energy storage unit with the best capacity, and obtain the dynamic load support sequence. S4: Based on the dynamic load support sequence, analyze the capacity utilization ratio and change rate of the energy storage units, determine the amplitude difference, identify energy storage units with low change rate as charging targets and energy storage units with high change rate as discharging targets, and obtain a capacity balance instruction list.

2. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The time-series anomaly tag set includes time period labels, anomaly fluctuation levels, and adjustment reference indexes. The trend anomaly early warning features include offset trend factors, anomaly type classifications, and early warning level parameters. The dynamic load support sequence includes support capacity sorting, energy allocation identifiers, and node response attributes. The capacity balancing instruction list includes charging object number, discharging object number, and capacity balancing level.

3. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The specific steps for obtaining the time-series anomaly marker set are as follows: S111: Based on energy users in each region, analyze the power change trend of users in each time period of the day, compare the relationship between the maximum load item in each time period and the cycle factor and holiday correction parameters, determine the synchronicity and difference in the change process, and obtain the correlation trend characteristic sequence. S112: Based on the correlation trend feature sequence, determine whether the change of the periodic factor is stable in a continuous period, calculate the fluctuation of the periodic factor in each continuous period, screen out the period with abnormal fluctuation, optimize the period division, and obtain the periodic continuous change pattern. S113: Based on the aforementioned periodic continuous change pattern, screen time nodes with abnormal fluctuation amplitudes, compare the distribution and change characteristics of each node in the sequence, calculate the abnormal fluctuation aggregation index, and combine the index with the node trajectory to obtain a time-series abnormality marker set.

4. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The specific steps for obtaining the trend anomaly early warning features are as follows: S211: Based on the time-series anomaly marker set, analyze and adjust the changes in load forecast data and real-time collected data of the adjustment nodes, calculate the development trend of forecast offset between continuous time periods, compare the change rate of each node with the change of regional load variation boundary in continuous time, filter the trend change of each node, and obtain the trend change response sequence. S212: Based on the trend change response sequence, determine the trend growth of each node, analyze the relationship between the node trend change and the upper and lower bounds of the allowable range of regional load change, filter nodes whose trend changes exceed the allowable range, optimize node classification, and obtain a set of trend deviation nodes. S213: Based on the set of trend deviation nodes, compare the trend change of each node with the alarm status of the monitoring device, screen the nodes with key trend warning signal strength, determine the trend direction, diffusion speed and device response of the nodes, and establish trend anomaly warning features.

5. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The specific steps for obtaining the dynamic load support sequence are as follows: S311: Based on the aforementioned abnormal trend warning characteristics, analyze the remaining electrical energy and sustainable output status of the regional energy storage unit, calculate its corresponding unit time support capacity and energy release limit range, adjust the parameter weights to normalize the ratio, and obtain the unit support capacity parameters. S312: Based on the unit support capability parameters, compare the support differences of each unit at the abnormal node, calculate the adjustment capability strength of each energy storage unit, perform a sequence sorting operation, and obtain the energy storage unit sorting sequence. S313: Based on the energy storage unit sorting sequence, select the units with priority in sorting, determine their matching order with each node, adjust the node mapping structure and rearrange the numbering correspondence, determine the dynamic linkage order of the adjustment resources, and obtain the dynamic load support sequence.

6. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The specific steps for obtaining the capacity balancing instruction list are as follows: S411: Based on the dynamic load support sequence, analyze the current capacity utilization ratio and change rate of the energy storage unit, compare the change amplitude of each parameter in the same period, and judge the difference between the two by comparing their growth or decrease trends to obtain capacity parameter comparison characteristics. S412: Based on the capacity parameter comparison characteristics, classify each energy storage unit according to the rate of change, optimize the classification results, classify those with low rate of change as charging objects, and classify those with high rate of change as discharging objects, and obtain charging and discharging group identifiers. S413: Based on the charge / discharge group identifier, analyze the current capacity utilization ratio, rate of change, output power and regional load response of each energy storage unit, determine the charge / discharge task category and allocation relationship of each energy storage unit in the current cycle, and obtain the capacity balance instruction list.

7. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 1, characterized in that, The steps also include: S5: Based on the capacity balance instruction list, adjust the task category and regional allocation of each energy storage unit, optimize the charging and discharging capabilities of the energy storage units in the adjustment sequence, determine the energy release order and instruction time period, issue adjustment commands, and obtain the partition linkage execution results. The results of the zoned linkage execution include the coordinated adjustment indicator, the regional energy allocation ratio, and the execution feedback signal.

8. The intelligent charging and discharging regulation method for a multi-regional energy storage device according to claim 7, characterized in that, The specific steps for obtaining the partition linkage execution result are as follows: S511: Based on the capacity balance instruction list, analyze the charging object number, discharging object number and capacity balance level, optimize the task category allocation of each energy storage unit, compare the area number and node distribution corresponding to each unit, determine the adaptability of task category and area division, and obtain the energy storage task partition mapping list. S512: Based on the energy storage task partition mapping list, calculate the matching relationship between the task category of each energy storage unit and the energy storage capacity of its region, optimize the adjustment order, adjust the correspondence between the region number and the task category, and obtain the energy storage task scheduling sorting identifier. S513: Call the energy storage task scheduling sorting identifier, analyze the adjustment command issued by the regional energy storage scheduling communication terminal, compare the energy storage unit feedback signal with the task category action, filter the task completion status and organize the communication results to obtain the partition linkage execution result.

9. A smart charging and discharging regulation system for a multi-regional energy storage device, characterized in that, The system is used to implement the intelligent charging and discharging regulation method for a multi-regional energy storage device as described in any one of claims 1-8, and the system includes: The time-series feature extraction module analyzes the power change trend of each time period based on energy users in each region, compares the correlation characteristics between the maximum load item and the periodic factor and holiday correction parameters in each time period, optimizes the adaptability of the periodic factor and correction parameters in time differences, judges the stability of continuous changes in the periodic factor, filters out abnormal fluctuation periods and marks them as adjustment nodes, and obtains a time-series anomaly marker set. The load anomaly detection module analyzes the difference between the load forecast data and the real-time collected data of each adjustment node based on the time-series anomaly marker set, calculates the difference trend between consecutive time periods, compares the degree of matching between the trend change and the range of regional load changes, identifies nodes whose trend changes are greater than the range, and establishes trend anomaly early warning features in conjunction with the early warning prompts of the regional node load monitoring equipment. Based on the aforementioned trend anomaly warning features, the energy storage optimization and ranking module optimizes the remaining power and maximum output power of energy storage units in the associated region, calculates the load support capacity index of each energy storage unit per unit time, compares the ranking of the capacity index of each energy storage unit in the node, and selects the energy storage unit with the best capacity index to obtain a dynamic load support sequence. Based on the dynamic load support sequence, the capacity intelligent allocation module analyzes the current capacity utilization ratio and change rate of the energy storage unit, judges the difference in the amplitude of the two parameters, optimizes the dynamic balance of the capacity utilization ratio and change rate, identifies energy storage units with low change rate as charging targets and those with high change rate as discharging targets, and obtains a capacity balance instruction list. Based on the capacity balancing instruction list, the collaborative scheduling execution module adjusts the task category and regional allocation of each energy storage unit, optimizes the charging and discharging capabilities of energy storage units within the adjustment sequence, determines the energy release order and instruction execution time period among each energy storage unit, and synchronously issues adjustment commands according to the regional energy storage scheduling communication terminal to obtain the partitioned linkage execution results.

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