Intelligent low-voltage cabinet energy storage management method and system

Abstract

The application relates to the technical field of low-voltage cabinet energy storage management, in particular to an intelligent low-voltage cabinet energy storage management method and system. The intelligent low-voltage cabinet energy storage management method comprises the following steps: S1: taking a low-voltage cabinet as a central equipment, acquiring power supply equipment and power transmission equipment and marking the same to obtain a marking result; according to the marking result, determining a stage sequence, a forward direction and a reverse direction, and determining an overall sequence according to the stage sequence, the forward direction and the reverse direction; S2: sorting the overall sequence to obtain a sorting result; according to the sorting result, determining a time overlap event, and according to the time overlap event, acquiring a starting time difference, a duration difference and an electric quantity difference. Through dynamic attribute marking and sequence construction, the application can accurately extract power supply and power transmission characteristics, weight calculates a period correlation degree, realizes intelligent classification of energy storage equipment and hierarchical matching of energy storage demand, and significantly improves recognition accuracy and energy storage efficiency.

Description

Technical Field

[0001] This invention relates to the field of low-voltage switchgear energy storage management technology, and in particular to an intelligent low-voltage switchgear energy storage management method and system. Background Technology

[0002] Low-voltage switchgear is a core device in power systems used for distributing, controlling, and protecting low-voltage electrical energy, and is widely used in industrial, commercial, and residential power distribution networks. Its importance lies in ensuring power safety, optimizing power distribution, and supporting grid stability. Current low-voltage switchgear energy storage management mainly adopts static strategies: triggering charging and discharging through preset thresholds, or scheduling energy storage devices at fixed time periods based on historical electricity consumption curves. Some technologies attempt to adjust charging and discharging modes in conjunction with real-time electricity prices, but rely on manual configuration of power supply / transmission equipment priorities and cannot dynamically identify equipment attribute transitions, such as from power supply to transmission.

[0003] The existing technology has significant drawbacks: First, manual labeling of equipment conversion attributes is inefficient and prone to errors; second, static strategies cannot capture the fluctuating characteristics of equipment, and intermittent power supply to equipment leads to insufficient energy storage matching accuracy; third, there is a lack of quantitative analysis of the temporal behavior of equipment (such as overlapping reverse events), which causes mismatch of unstable equipment and reduces overall energy efficiency.

[0004] Therefore, there is an urgent need for an energy storage management solution that can automatically identify the dynamic characteristics of equipment, accurately distinguish stability levels, and achieve intelligent matching, so as to improve the adaptability of low-voltage switchgear in complex power consumption scenarios. Summary of the Invention

[0005] To overcome the shortcomings of low accuracy in identifying stable equipment and poor matching efficiency, this invention provides an intelligent low-voltage switchgear energy storage management method and system.

[0006] The technical implementation scheme of the present invention is: an intelligent low-voltage switchgear energy storage management method, comprising the following steps:

[0007] S1: Using the low-voltage switchgear as the central device, acquire and mark the power supply equipment and transmission equipment to obtain the marking results; based on the marking results, determine the stage sequence, forward direction and reverse direction, and determine the overall sequence based on the stage sequence, forward direction and reverse direction;

[0008] S2: Sort the overall sequence to obtain a sorting result; determine the time overlap events based on the sorting result, and obtain the start time difference, duration difference, and battery difference based on the time overlap events;

[0009] S3: Calculate the starting time difference, duration difference, and power difference to obtain a comprehensive correlation, and obtain a periodic correlation based on the comprehensive correlation; determine stable energy storage devices and unstable energy storage devices based on the periodic correlation.

[0010] S4: Obtain data on the fixed power supply equipment, the fixed power transmission equipment, and the stored power values ​​regulated by the low-voltage switchgear; match the fixed power supply equipment, the fixed power transmission equipment, and the power values ​​with the stable energy storage equipment and the unstable energy storage equipment to obtain a matching result.

[0011] Preferably, the step of using a low-voltage switchgear as the central device to acquire and mark power supply and transmission equipment, and obtaining the marking results, includes:

[0012] The power supply equipment is a device that transmits power to the central device;

[0013] The power transmission equipment refers to the equipment that transmits power via the central equipment;

[0014] The conversion attributes of the power supply equipment and the power transmission equipment are marked to obtain the marking results;

[0015] If the power supply equipment is converted to the power transmission equipment, then the conversion time, the power supply duration before conversion, and the input power are marked.

[0016] If the power transmission equipment is converted to the power supply equipment, then the conversion time and the attributes before the conversion, such as the power transmission duration and the output power, are marked.

[0017] The conversion attribute refers to the power supply equipment being converted into a power transmission equipment or the power transmission equipment being converted into a power supply equipment.

[0018] Preferably, determining the stage sequence, forward direction, and reverse direction based on the marking results, and determining the overall sequence based on the stage sequence, forward direction, and reverse direction, includes:

[0019] Based on the marking results, the power supply equipment and the power transmission equipment are traversed according to the conversion frequency and conversion time of the conversion attributes;

[0020] Based on the initial attributes of a single power supply device or power transmission device, the interaction process between the power supply device or power transmission device and the central device is constituted as a stage sequence;

[0021] If the initial attribute is the power supply device, then the direction from the original attribute where the conversion attribute has not changed to the central device is taken as the positive direction, and the direction from the original attribute where the conversion attribute has changed to the central device is taken as the negative direction.

[0022] If the initial attribute is the power transmission equipment, then the direction from the central equipment to the original attribute where the conversion attribute has not changed is taken as the positive direction, and the direction from the central equipment to the original attribute where the conversion attribute has changed is taken as the negative direction.

[0023] The overall sequence is constructed based on the stage sequence, the forward direction, and the reverse direction, and the overall sequence includes the marking result.

[0024] Preferably, sorting the overall sequence to obtain a sorting result includes:

[0025] Using the overall sequence as a single overall period, N overall periods are obtained;

[0026] Sort the N overall cycles in chronological order to obtain the sorting result;

[0027] The reverse directions in the N overall cycles are marked, and the marking results of the reverse directions are extracted;

[0028] Based on the sorting results, the correlation of the labeling results in the reverse direction of the N overall cycles is calculated one by one to obtain the correlation calculation results.

[0029] Preferably, the step of determining time overlap events based on the sorting results, and obtaining the start time difference, duration difference, and battery level difference based on the time overlap events, includes:

[0030] Obtain the reverse direction marking results of all the complete cycles, select the first complete cycle as the reference cycle, and extract all reverse events within the reference cycle to form a reference event set;

[0031] Take the next unprocessed complete cycle as the comparison cycle, and extract all reverse events within the comparison cycle to form a comparison event set;

[0032] Perform a matching operation on each reverse event in the benchmark event set: detect reverse events in the comparison event set that overlap with the current benchmark event in time, where time overlap means that the event time intervals have a common period;

[0033] If there are overlapping events, calculate the absolute value of the start time difference, the absolute value of the duration difference, and the absolute value of the battery difference between each overlapping event and the baseline event, and select the overlapping event with the smallest absolute value of the start time difference as the best matching event.

[0034] If there are no time-overlapping events, the absolute value of the starting time difference between all events in the event set and the baseline event is calculated and compared. The event with the smallest absolute value of the starting time difference is selected as the best matching event. The best matching event refers to the candidate event selected by the principle of minimum starting time difference.

[0035] Preferably, the step of calculating the start time difference, duration difference, and power difference to obtain a comprehensive correlation, and obtaining a periodic correlation based on the comprehensive correlation, includes:

[0036] Calculate the comprehensive correlation between the current benchmark event and the best matching event to obtain the comprehensive correlation calculation result. The comprehensive correlation is generated based on a weighted evaluation of the start time difference, duration difference, and power difference. Store the comprehensive correlation calculation result in a temporary storage pool.

[0037] Repeat the matching operation until all reverse events in the baseline event set have been processed. Calculate the arithmetic mean of all comprehensive correlations in the temporary storage pool, and record it as the periodic correlation between the current baseline period and the comparison period. The periodic correlation refers to the arithmetic mean of the correlations of all matching events during a single baseline period and comparison period.

[0038] Preferably, determining stable and unstable energy storage devices based on the periodic correlation includes:

[0039] Set the current comparison period as the new baseline period, and repeat the process from setting the baseline period to calculating the period correlation until all complete periods have been processed.

[0040] Collect all periodic correlations and calculate the total average value. Compare the total average value with a preset energy storage threshold: if the total average value is greater than or equal to the preset energy storage threshold, mark the corresponding device as a stable energy storage device; if the total average value is less than the preset energy storage threshold, mark the corresponding device as an unstable energy storage device. A stable energy storage device is a device whose periodic correlation average value reaches the preset energy storage threshold.

[0041] Preferably, the acquisition of power value data stored in the fixed power supply equipment, fixed power transmission equipment, and controlled by the low-voltage switchgear includes:

[0042] Based on the fixed power supply equipment and the fixed power transmission equipment, calculate the maximum net difference between the power supply and the power transmission after regulation. The maximum net difference refers to the result after regulation by the low-voltage switch. Sort the maximum net difference from largest to smallest to obtain the first sorting result.

[0043] Based on the power consumption data, the maximum net difference between the power consumption of fixed transmission equipment and the power transmission of fixed power supply equipment is selected from the first sorting results to obtain the filtering results; the filtering results are re-sorted according to the power consumption value; if the sum of the power consumption value and the power transmission of fixed power supply equipment is greater than the power consumption of fixed transmission equipment, then the results are re-sorted to obtain the second sorting results.

[0044] Preferably, the step of matching the stable energy storage device and the unstable energy storage device with the fixed power supply equipment, the fixed power transmission equipment, and the power value data to obtain a matching result includes:

[0045] The periodic correlation of the stable energy storage devices is sorted from largest to smallest to obtain a third sorting result;

[0046] Based on the third sorting result and the second sorting result, the period correlation in the third sorting result is matched with the second sorting result from largest to smallest. If the matching is completed and there are remaining items in the second sorting result, the period correlation in the unstable energy storage device is matched with the remaining devices in the second sorting result from smallest to largest to obtain the final matching result.

[0047] Preferably, an intelligent low-voltage switchgear energy storage management system includes:

[0048] Equipment labeling and sequence construction module: Taking the low-voltage switchgear as the central device, it acquires power supply equipment and transmission equipment and labels their conversion attributes to obtain labeling results; it determines the stage sequence, forward direction, and reverse direction based on the labeling results; and it constructs an overall sequence containing the labeling results based on the stage sequence, forward direction, and reverse direction.

[0049] Event sorting and overlap detection module: sorts the overall sequence into multiple complete cycles to obtain sorting results; detects time overlap of events in the opposite direction based on the sorting results, and obtains the start time difference, duration difference, and power difference; determines the best matching event based on the principle of time overlap or minimum start time distance.

[0050] The correlation calculation and stability classification module performs weighted calculations on the start time difference, duration difference, and power difference to obtain a comprehensive correlation calculation result; it calculates the periodic correlation based on all events; and it classifies stable energy storage devices into unstable energy storage devices by comparing the average periodic correlation with a preset energy storage threshold.

[0051] Equipment matching and optimization module: acquires power value data of fixed power supply equipment, fixed power transmission equipment, and power consumption data regulated by low-voltage switchgear; sorts the power value data and equipment attributes based on the maximum net difference; prioritizes matching the periodic correlation results of stable energy storage equipment and unstable energy storage equipment to obtain the final matching result.

[0052] Beneficial Effects: This invention automatically marks the dynamic conversion attributes of power supply and transmission equipment, constructing stage sequences and overall sequences. Based on time-overlapping events, it accurately extracts features such as start time difference, duration difference, and power difference. Through weighted calculation, it generates periodic correlation, achieving intelligent classification of stable and unstable energy storage devices. Combining the power data after regulation of fixed equipment, it sorts by net load gap and matches energy storage demand according to stability levels. This significantly improves the accuracy of equipment dynamic characteristic identification, solving the problem of manual marking errors. Based on behavioral regularity judgment, it achieves accurate stability classification, effectively avoiding the risk of mismatching unstable equipment. Furthermore, through a collaborative mechanism where stable equipment prioritizes covering large load gaps and unstable equipment absorbs secondary demands, it greatly optimizes the efficiency of energy storage resource allocation, comprehensively enhancing the adaptability and energy utilization efficiency of low-voltage switchgear in intermittent power consumption scenarios. Attached Figure Description

[0053] Figure 1 This is a flowchart of the intelligent low-voltage switchgear energy storage management method of the present invention;

[0054] Figure 2 This is a structural diagram of the intelligent low-voltage switchgear energy storage management system of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Currently, low-voltage switchgear energy storage management relies on manually marking equipment attributes and static strategies, making it difficult to dynamically identify the state transitions of power supply / transmission equipment. This results in delayed response to intermittent equipment fluctuations, insufficient energy storage matching accuracy, and a lack of quantitative analysis of time-series behaviors (such as overlapping reverse events), which can easily lead to mismatching of unstable equipment and reduce system energy efficiency.

[0057] This solution automatically tags device conversion attributes, constructs stage sequences and overall sequences, and extracts start time difference, duration difference and power difference based on time overlap event analysis. Then, it calculates cycle correlation to distinguish between stable and unstable energy storage devices. Finally, it combines the power value data of fixed power supply / transmission equipment to prioritize matching energy storage demand according to stability level.

[0058] This enables precise identification of equipment dynamic characteristics, intelligent classification of stability, and optimized scheduling of energy storage resources, significantly improving the adaptability and energy utilization efficiency of low-voltage switchgear in complex power consumption scenarios.

[0059] Example 1: An intelligent low-voltage switchgear energy storage management method, such as Figure 1 As shown, it includes the following steps:

[0060] S1-1: Using the low-voltage switchgear as the central device, acquire and mark the power supply equipment and transmission equipment, and obtain the marking results;

[0061] It should be noted that power supply equipment is identified in real time through a current direction sensor and an energy metering device connected to the low-voltage switchgear: when a continuous current flow to the central node of the low-voltage switchgear is detected, and the voltage is higher than the central reference, it is marked as power supply equipment. Example: A photovoltaic inverter supplying power to the low-voltage switchgear during sunshine is identified as power supply equipment. Transmission equipment is identified by monitoring voltage difference and load current path: when current continuously flows from the low-voltage switchgear to external equipment, and the equipment port voltage is lower than the output of the low-voltage switchgear, it is marked as transmission equipment. Example: A machine tool in a production workshop drawing power from the low-voltage switchgear is identified as transmission equipment. Equipment role conversion scenarios: Power supply to transmission conversion: when the original power supply equipment itself needs to consume energy and stops supplying power. Example: A photovoltaic panel stops generating electricity at night and needs to draw power from the low-voltage switchgear to maintain the operation of the control system; its attribute changes to transmission equipment. Transmission to power supply conversion: when the original transmission equipment generates excess energy and feeds it back to the grid. Example: When an elevator descends, regenerative braking converts kinetic energy into electrical energy and transmits it in the reverse direction to the low-voltage switchgear; its attribute changes to power supply equipment.

[0062] A1: The power supply equipment refers to the equipment that transmits power to the central equipment;

[0063] A2: The power transmission equipment refers to the equipment that transmits power via the central equipment;

[0064] A3: Mark the conversion attributes of the power supply equipment and the power transmission equipment to obtain the marking results;

[0065] A4: If the power supply equipment is converted to the power transmission equipment, then mark the conversion time, the power supply duration before conversion, and the input power.

[0066] A5: If the power transmission equipment is converted to the power supply equipment, then mark the conversion time and the power transmission duration and output power before the conversion.

[0067] A6: The conversion attribute refers to the power supply equipment being converted into a power transmission equipment or the power transmission equipment being converted into a power supply equipment.

[0068] It should be noted that definitions A1-A2 define power supply equipment as: equipment that transmits power to the low-voltage switchgear, such as photovoltaic panels generating electricity. Transmission equipment refers to equipment that draws power from the low-voltage switchgear, such as machine tools in a factory. A3 marks conversion attributes, continuously monitoring changes in equipment roles and recording conversion types. Example A4 shows a power supply to transmission conversion: photovoltaic panels stop generating power at night and switch to power consumption, marking the conversion time, the power supply duration before shutdown (8 hours), and the input power (32kWh). Example A5 shows a transmission to power supply conversion: elevator braking generates electricity to feed back to the grid, marking the conversion time, the transmission duration before operation (0.5 hours), and the output power (1.2kWh). The essence of conversion attributes in A6 is only two types: power supply equipment to transmission equipment, or transmission equipment to power supply equipment.

[0069] It should be further explained that existing low-voltage switchgear energy storage management neglects the dynamic attribute transitions between power supply and transmission equipment, resulting in the inability to accurately schedule intermittent energy and feedback equipment. This embodiment 1, by real-time monitoring of current direction and voltage difference, clearly defines power supply and transmission equipment and fully records key parameters of equipment role transitions, such as photovoltaic panels switching from power generation to power consumption and elevator braking to feed back to the grid, laying a data foundation for subsequent stability analysis and energy storage matching.

[0070] Example 2, S1-2: Based on the marking results, determine the stage sequence, forward direction, and reverse direction, and determine the overall sequence based on the stage sequence, forward direction, and reverse direction;

[0071] It should be noted that photovoltaic (PV) power generation equipment switches from power supply to power transmission during cloudy or rainy weather or when sunlight suddenly decreases. Elevator braking feedback scenarios also trigger a shift from power transmission to power supply. These frequent attribute changes require structured recording. This step defines the forward direction based on the initial equipment attributes. For example, when PV initially supplies power, the direction to the low-voltage switchgear is considered forward. The direction after attribute conversion is recorded as the reverse direction. For example, when PV switches to power consumption, the direction remains the same but is marked as reverse. All stage sequences are integrated to form a holistic sequence. This clarifies the stage conversion nodes and grasps the overall change pattern, laying the foundation for subsequent periodic sequencing and overlapping event analysis.

[0072] Example illustration: Photovoltaic equipment sequence: Phase 1 (forward): 8 hours of power supply under sunlight; Phase 2 (reverse): 2 hours of power consumption during cloudy / rainy weather; Overall sequence: 8 hours forward + 2 hours reverse. Elevator equipment sequence: Phase 1 (forward): 0.5 hours of power extraction; Phase 2 (reverse): 0.1 hours of regenerative braking power supply; Overall sequence: 0.5 hours forward + 0.1 hours reverse.

[0073] B1: Based on the marking results, the power supply equipment and the power transmission equipment are traversed according to the conversion frequency and conversion time of the conversion attributes;

[0074] B2: Based on the initial attributes of a single power supply device or power transmission device, the interaction process between the power supply device or power transmission device and the central device is constituted as a stage sequence;

[0075] B3: If the initial attribute is the power supply device, then the direction from the original attribute where the conversion attribute has not changed to the central device is taken as the positive direction, and the direction from the original attribute where the conversion attribute has changed to the central device is taken as the negative direction.

[0076] B4: If the initial attribute is the power transmission equipment, then the direction from the central equipment to the original attribute where the conversion attribute has not changed is taken as the positive direction, and the direction from the central equipment to the original attribute where the conversion attribute has changed is taken as the negative direction.

[0077] B5: The overall sequence is formed by the stage sequence, the forward direction and the reverse direction, and the overall sequence includes the marking result.

[0078] It should be noted that, Example 1: Photovoltaic equipment (initial attribute is power supply), stage sequence: initial power supply (forward), rainy weather to power consumption (reverse), forward direction: the direction of power transmission from the photovoltaic panel to the low-voltage switchgear (before conversion), reverse direction: the same power transmission path but marked in reverse (power consumption state after conversion), overall sequence: 6 hours of forward power supply + 1 hour of reverse power consumption; Example 2: Elevator equipment (initial attribute is power transmission), stage sequence: initial power intake (forward), braking feedback (reverse), forward direction: the direction of power transmission from the low-voltage switchgear to the elevator (before conversion), reverse direction: the direction of power feedback from the elevator to the low-voltage switchgear (after conversion), overall sequence: 0.4 hours of forward power intake + 0.05 hours of reverse power supply.

[0079] Traversal criteria: Equipment is sorted by conversion frequency, e.g., photovoltaic systems convert 2 times per day > elevator systems convert 10 times per day; Core direction definition: Initial power supply equipment: The power supply path is always forward, and the path remains unchanged after attribute conversion but the mark is reversed; Initial power transmission equipment: The power transmission path is always forward, and the path is reversed and the mark is reversed after attribute conversion. Overall sequence value: Aggregating the phase sequence and forward / reverse marks forms the full-cycle behavior trajectory of the equipment, e.g., photovoltaic systems supply 6 hours per day + consume 1 hour per night, supporting subsequent cycle overlap analysis.

[0080] S2-1: Sort the entire sequence to obtain the sorting result;

[0081] C1: Taking the overall sequence as a whole period, obtain N whole periods;

[0082] C2: Sort N overall cycles in chronological order to obtain the sorting result;

[0083] C3: Mark the reverse direction in the N overall cycles, and extract the marking results of the reverse direction;

[0084] C4: Based on the sorting results, the correlation of the labeling results in the reverse direction of the N overall cycles is calculated one by one to obtain the correlation calculation results.

[0085] It should be noted that the complete operating trajectory of a single device (such as a photovoltaic system) is divided into overall cycles of fixed duration (e.g., 24 hours). This ensures that each cycle includes the entire process from its initial attributes to its final state transition; for example, 6 hours of daytime power supply + 1 hour of nighttime power consumption. The consecutive cycles of the same device are ordered according to natural time sequence: Cycle 1: 06:00 on the first day - 06:00 on the next day; Cycle 2: 06:00 on the next day - 06:00 on the third day. Within each cycle, reverse event segments (such as the nighttime power consumption period of the photovoltaic system) are marked, and key parameters (start time, duration, power consumption) are extracted. Finally, the correlation between reverse events in adjacent time-series cycles is calculated. For example, comparing the difference in start time, duration fluctuation, and power consumption between Cycle 1 and Cycle 2 at nighttime power consumption quantifies the continuity characteristics of the device's behavior, providing a basis for stability assessment.

[0086] Example (photovoltaic equipment), cycle division: Cycle 1: Power supply at 06:00 on the first day, power consumption starts at 19:30, and ends at 06:00 on the second day; Cycle 2: Power supply at 06:00 on the second day, power consumption starts at 20:15, and ends at 06:00 on the third day; Reverse event marking: Cycle 1 reverse segment: 19:30-06:00, power consumption 1.5kWh; Cycle 2 reverse segment: 20:15-06:00, power consumption 1.8kWh; Correlation calculation: starting time difference = 45 minutes, power consumption difference = 0.3kWh, obtain the correlation calculation results for each item.

[0087] It should be further explained that existing technologies cannot structurally record the dynamic transformation of equipment attributes, such as the conversion of photovoltaic power consumption to electricity consumption during cloudy / rainy weather or elevator braking feedback, resulting in fragmented behavioral trajectories that are difficult to support subsequent analysis. This embodiment 2 defines an initial attribute baseline direction, with photovoltaic power supply and elevator power intake as positive. The transformed paths are marked as reversed, while the photovoltaic power consumption direction remains unchanged but is marked as reversed. The elevator feedback path is reversed, integrating these elements to form a complete sequence of the equipment's behavioral trajectory, such as 6 hours of photovoltaic power supply per day + 1 hour of power consumption per night. Then, fixed periods are defined, such as 24 hours, and reverse event parameters are extracted and the correlation between adjacent periods is calculated. For example, the difference in the start time of photovoltaic power consumption between two days is 45 minutes, laying the foundation for quantitative judgment of equipment stability.

[0088] Example 3, S2-2: Based on the sorting results, determine the time overlap events, and based on the time overlap events, obtain the start time difference, duration difference, and power difference;

[0089] It should be noted that time overlap events refer to two opposite events in different cycles sharing a common time period. For example, the nighttime power consumption period (19:30-06:00) of photovoltaic equipment in cycle 1 overlaps with the nighttime power consumption period (20:15-06:00) of cycle 2 (20:15-06:00). Start time difference: This refers to the absolute time difference between the start time of the reference event and the overlap event. For example, the start time difference between photovoltaic power consumption in cycle 1 and cycle 2 is 45 minutes. Duration difference: This refers to the absolute duration difference between the reference event and the overlap event. For example, if power consumption in cycle 1 lasts 10.5 hours while in cycle 2 it lasts 9.75 hours, the duration difference is 0.75 hours. Energy difference: This refers to the absolute value difference between the energy consumed or fed back by the reference event and the overlap event. For example, if power consumption in cycle 1 is 1.5 kWh while in cycle 2 it is 1.8 kWh, the energy difference is 0.3 kWh.

[0090] D1: Obtain the reverse direction marking results of all the complete cycles, select the first complete cycle as the reference cycle, and extract all reverse events within the reference cycle to form a reference event set;

[0091] D2: Take the next unprocessed complete cycle as the comparison cycle, and extract all reverse events within the comparison cycle to form a comparison event set;

[0092] D3: Traverse each reverse event in the baseline event set and perform a matching operation: Detect reverse events in the comparison event set that overlap with the current baseline event in time. Time overlap means that the event time intervals have a common period.

[0093] D4: If there are overlapping events, calculate the absolute value of the start time difference, the absolute value of the duration difference, and the absolute value of the battery difference between each overlapping event and the baseline event, and select the overlapping event with the smallest absolute value of the start time difference as the best matching event;

[0094] D5: If there are no time-overlapping events, calculate the absolute value of the starting time difference between all events in the event set and the baseline event, and select the event with the smallest absolute value of the starting time difference as the best matching event. The best matching event refers to the candidate event selected by the principle of minimum starting time difference.

[0095] It should be noted that this step establishes a set of reverse events for the baseline and comparison periods, such as nighttime power consumption events of photovoltaic equipment, to detect time overlap (i.e., common time periods) of cross-period events. If overlap exists, such as partial overlap of power consumption periods on two days, the absolute values ​​of the start time difference, duration difference, and power difference between the overlapping event and the baseline event are calculated, and the event with the smallest start time difference is selected as the best match. If there is no overlap, such as no power consumption event on a certain day, the event with the closest start time is directly selected for matching. This mechanism ensures the consistency of quantification of equipment fluctuation behavior and avoids misallocation of energy storage resources by low-voltage switchgear due to misjudgment of event timing, such as premature discharge to unstable equipment. When multiple events simultaneously meet the minimum time difference requirement, the event with the smallest power difference is selected first. Reverse event: an event involving the interaction of electrical energy between the equipment and the low-voltage switchgear that occurs within the marked time period in the reverse direction.

[0096] Example (PV equipment): Baseline event set: Cycle 1 reverse event, power consumption period 19:30-06:00, power consumption 1.5kWh; Comparison event set: Cycle 2 reverse event, power consumption period 20:15-06:00, power consumption 1.8kWh; Time overlap: Common period 20:15-06:00; Best match: Selected because the start time difference is the smallest (45 minutes); No overlap scenario: If there is no power consumption event in Cycle 2, then select other events in Cycle 2 with the closest start time (such as equipment startup at 21:00) for matching.

[0097] S3-1: Calculate the starting time difference, duration difference, and power difference to obtain the comprehensive correlation, and obtain the periodic correlation based on the comprehensive correlation.

[0098] E1: Calculate the comprehensive correlation between the current benchmark event and the best matching event, and obtain the comprehensive correlation calculation result. The comprehensive correlation is generated based on the weighted evaluation of the start time difference, duration difference, and power difference. Store the comprehensive correlation calculation result in a temporary storage pool.

[0099] E2: Repeat the matching operation until all reverse events in the baseline event set have been processed. Calculate the arithmetic mean of all comprehensive correlations in the temporary storage pool, and record it as the periodic correlation between the current baseline period and the comparison period. The periodic correlation refers to the arithmetic mean of the correlations of all matching events during a single baseline period and comparison period.

[0100] It should be noted that a comprehensive correlation score for a single reverse event is generated by weighting the start time difference, duration difference, and power difference (e.g., 40% for time difference, 30% for duration difference, and 30% for power difference), quantifying the consistency of fluctuations in similar events within the benchmark and comparison periods. After repeating the calculation for all matching events, the arithmetic mean of the correlation scores is taken as the periodic correlation score (e.g., 0.85). This value reflects the periodic stability of equipment behavior—a high score indicates that the equipment exhibits similar behavior patterns in adjacent periods; for example, if the daily power consumption periods of photovoltaic systems are similar, the equipment is considered stable, thus guiding the low-voltage switchgear to prioritize the scheduling of energy storage resources for it. Temporary storage pool: A temporary buffer area used to temporarily store the comprehensive correlation calculation results between the benchmark event and the best matching event.

[0101] Example (PV equipment), Baseline event: Cycle 1 power consumption, starting at 19:30, duration 10.5h, power consumption 1.5kWh; Optimal match: Cycle 2 power consumption, starting at 20:15, duration 9.75h, power consumption 1.8kWh; Weighted calculation: starting time difference 45min (deduction 0.2), duration difference 0.75h (deduction 0.1), power difference 0.3kWh (deduction 0.1); Overall correlation 0.6 (out of 1.0); Cycle correlation: if the average correlation of other events for this equipment is 0.75, then the cycle correlation is 0.75; Energy storage impact: High-correlation equipment has strong predictability, and low-voltage switchgear stores energy in advance to match its peak power consumption.

[0102] S3-2: Based on the aforementioned periodic correlation, determine stable energy storage devices and unstable energy storage devices;

[0103] F1: Set the current comparison period as the new baseline period, and repeat the process from setting the baseline period to calculating the period correlation until all complete periods have been processed.

[0104] F2: Collect all periodic correlations and calculate the total average value, and compare the total average value with the preset energy storage threshold: if the total average value is greater than or equal to the preset energy storage threshold, mark the corresponding device as a stable energy storage device; if the total average value is less than the preset energy storage threshold, mark the corresponding device as an unstable energy storage device. The stable energy storage device refers to the device whose average periodic correlation value reaches the preset energy storage threshold.

[0105] It should be noted that the threshold setting principle is based on the statistical analysis of historical device behavior data, such as taking the median of the historical values ​​of the cycle correlation of all devices. The core principle is to calculate the average value of the device cycle correlation (e.g., the 5-day cycle correlation of photovoltaic equipment: 0.82, 0.79, 0.85, 0.80, 0.83, with a total average of 0.818) and compare it with a threshold (e.g., 0.80). If the total average value is greater than or equal to the threshold, it indicates that the device behavior is highly regular, for example, the daily power consumption period deviation is less than 10%, and it is marked as a stable energy storage device; otherwise, it is marked as an unstable device.

[0106] The significance of energy storage optimization lies in the fact that stable equipment has strong predictive capabilities, and low-voltage switchgear can pre-store electrical energy in a targeted manner, such as charging before the photovoltaic power consumption period; while unstable equipment adopts a dynamic adjustment strategy to avoid resource waste caused by ineffective energy storage.

[0107] For example, photovoltaic equipment: the 5-day total average correlation is 0.818, which is greater than the threshold of 0.80, so it is marked as stable equipment. Elevator equipment: the 5-day total average correlation is 0.75, which is less than the threshold of 0.80, so it is marked as unstable equipment. Scheduling strategy: photovoltaic equipment discharges at fixed time periods, and elevator equipment responds to electricity price fluctuations in real time.

[0108] It should be further explained that existing technologies suffer from rigid energy storage scheduling due to a lack of quantitative analysis of dynamic changes in equipment attributes. This embodiment 3 detects the temporal overlap of cross-cycle reverse events, such as overlapping nighttime power consumption periods for photovoltaic equipment. It calculates the start time difference, duration difference, and power difference, and generates a weighted comprehensive correlation score to obtain the comprehensive correlation score calculation result. Then, it analyzes the overall average value of the cycle correlation score and compares it with a preset threshold to distinguish the equipment stability level; for example, photovoltaic equipment is marked as stable, while elevator equipment is marked as unstable. This achieves elastic energy storage management based on behavioral similarity, avoiding resource misallocation caused by static strategies.

[0109] Example 4, S4-1: Acquire the power value data of the fixed power supply equipment, the fixed power transmission equipment, and the power value stored by the low-voltage switchgear;

[0110] G1: Based on the fixed power supply equipment and the fixed power transmission equipment, calculate the maximum net difference between the power supply and the power transmission after regulation. The maximum net difference refers to the result after regulation by the low-voltage switch. Sort the maximum net difference from largest to smallest to obtain the first sorting result.

[0111] G2: Based on the power consumption data, filter out the maximum net difference between the power consumption of fixed transmission equipment and the power transmission of fixed power supply equipment from the first sorting results to obtain the filtering results; re-sort the filtering results according to the power consumption value; if the sum of the power consumption value and the power transmission of fixed power supply equipment is greater than the power consumption of fixed transmission equipment, then re-sort to obtain the second sorting results.

[0112] It should be noted that fixed power supply equipment and fixed transmission equipment are the primary control targets because their energy consumption behavior is stable and they account for the majority of the load, making them the core objectives of low-voltage switchgear energy storage scheduling. Equipment with dynamic conversion attributes (such as photovoltaics and elevators) are secondary variables, requiring flexibility to be added in subsequent matching. The extraction of the maximum net difference needs to be based on the low-voltage switchgear after control. Since the power supply / transmission volume in actual scenarios needs to be buffered and adjusted by the energy storage within the switchgear, this difference directly reflects the actual energy storage demand: the larger the difference, the greater the supply-demand gap and the higher the urgency of energy storage. The key to the secondary sorting of energy values ​​(G2) is to exclude ineffective energy storage scenarios: when the fixed power supply already exceeds the transmission demand, such as when the grid supply is sufficient, additional energy storage is unnecessary; only when the "fixed power supply + energy storage volume" is still insufficient for the transmission demand is the energy value re-sorted according to the difference, ensuring that energy storage resources are accurately allocated to the actual equipment with the shortage and avoiding energy waste.

[0113] S4-2: Based on the fixed power supply equipment, fixed power transmission equipment, and power value data, match them with the stable energy storage equipment and the unstable energy storage equipment to obtain the matching result.

[0114] H1: Sort the periodic correlation of the stable energy storage devices from largest to smallest to obtain a third sorting result;

[0115] H2: Based on the third sorting result and the second sorting result, the period correlation in the third sorting result is matched with the second sorting result from largest to smallest. If the matching is completed and there are remaining items in the second sorting result, the period correlation in the unstable energy storage device is matched with the remaining devices in the second sorting result from smallest to largest to obtain the final matching result.

[0116] It should be noted that this step achieves optimal allocation of energy storage resources through a tiered strategy. First, stable energy storage devices are sorted from highest to lowest based on their periodic relevance (third sorting result), ensuring that highly predictable stable devices participate in scheduling first. Then, the third sorting result (stable devices in descending order of relevance) is matched with the second sorting result (net load gap after fixed equipment regulation in descending order): the stable devices with the highest relevance are matched to the fixed transmission equipment with the largest gap, and so on, ensuring that the most stable devices cover the most urgent energy storage needs. If, after matching all stable devices, there are still unmet load gaps in the second sorting result, unstable energy storage devices are sorted from lowest to highest based on their relevance, prioritizing the matching of the most volatile unstable devices to the remaining devices with smaller gaps. This design ensures a stable supply of critical loads while utilizing the flexibility of unstable devices to absorb secondary demands, maximizing energy storage efficiency.

[0117] Example 5: Based on Examples 1-4, an intelligent low-voltage switchgear energy storage management system, such as... Figure 2 As shown, it includes:

[0118] Equipment labeling and sequence construction module: Taking the low-voltage switchgear as the central device, it acquires power supply equipment and transmission equipment and labels their conversion attributes to obtain labeling results; it determines the stage sequence, forward direction, and reverse direction based on the labeling results; and it constructs an overall sequence containing the labeling results based on the stage sequence, forward direction, and reverse direction.

[0119] Event sorting and overlap detection module: sorts the overall sequence into multiple complete cycles to obtain sorting results; detects time overlap of events in the opposite direction based on the sorting results, and obtains the start time difference, duration difference, and power difference; determines the best matching event based on the principle of time overlap or minimum start time distance.

[0120] The correlation calculation and stability classification module performs weighted calculations on the start time difference, duration difference, and power difference to obtain a comprehensive correlation calculation result; it calculates the periodic correlation based on all events; and it classifies stable energy storage devices into unstable energy storage devices by comparing the average periodic correlation with a preset energy storage threshold.

[0121] Equipment matching and optimization module: acquires power value data of fixed power supply equipment, fixed power transmission equipment, and power consumption data regulated by low-voltage switchgear; sorts the power value data and equipment attributes based on the maximum net difference; prioritizes matching the periodic correlation results of stable energy storage equipment and unstable energy storage equipment to obtain the final matching result.

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent low-voltage switchgear energy storage management method, characterized in that, Includes the following steps: S1: Using the low-voltage switchgear as the central device, acquire and mark the power supply equipment and transmission equipment to obtain the marking results; Based on the marking results, a stage sequence, a forward direction, and a reverse direction are determined, and an overall sequence is determined based on the stage sequence, forward direction, and reverse direction. This includes: traversing the power supply equipment and the power transmission equipment according to the conversion frequency and conversion time of their conversion attributes based on the marking results; using the initial attributes of a single power supply equipment or power transmission equipment as a reference, constructing a stage sequence from the interaction process between the power supply equipment or power transmission equipment and the central equipment; if the initial attribute is the power supply equipment, then the direction from the original attribute (where the conversion attribute has not changed) to the central equipment is taken as the forward direction, and the direction from the original attribute (where the conversion attribute has changed) to the central equipment is taken as the reverse direction; if the initial attribute is the power transmission equipment, then the direction from the central equipment to the original attribute (where the conversion attribute has not changed) is taken as the forward direction, and the direction from the central equipment to the original attribute (where the conversion attribute has changed) is taken as the reverse direction; and constructing an overall sequence based on the stage sequence, the forward direction, and the reverse direction, wherein the overall sequence includes the marking results. S2: Sort the overall sequence to obtain a sorting result; determine the time overlap events based on the sorting result, and obtain the start time difference, duration difference, and battery difference based on the time overlap events; S3: Calculate the starting time difference, duration difference, and power difference to obtain a comprehensive correlation, and obtain a periodic correlation based on the comprehensive correlation; determine stable and unstable energy storage devices based on the periodic correlation, including: setting the current comparison period as a new reference period, repeating the process from setting the reference period to calculating the periodic correlation until all complete periods are processed; collecting all periodic correlations and calculating the total average value, comparing the total average value with a preset energy storage threshold: if the total average value is greater than or equal to the preset energy storage threshold, mark the corresponding device as a stable energy storage device; if the total average value is less than the preset energy storage threshold, mark the corresponding device as an unstable energy storage device, wherein a stable energy storage device is a device whose average periodic correlation value reaches the preset energy storage threshold; S4: Obtain data on the fixed power supply equipment, the fixed power transmission equipment, and the stored power values ​​regulated by the low-voltage switchgear; match the fixed power supply equipment, the fixed power transmission equipment, and the power values ​​with the stable energy storage equipment and the unstable energy storage equipment to obtain a matching result.

2. The intelligent low-voltage switchgear energy storage management method according to claim 1, characterized in that, The process of using a low-voltage switchgear as the central device to acquire and mark power supply and transmission equipment, and obtaining marking results, includes: The power supply equipment is a device that transmits power to the central device; The power transmission equipment refers to the equipment that transmits power via the central equipment; The conversion attributes of the power supply equipment and the power transmission equipment are marked to obtain the marking results; If the power supply equipment is converted to the power transmission equipment, then the conversion time, the power supply duration before conversion, and the input power are marked. If the power transmission equipment is converted to the power supply equipment, then the conversion time and the attributes before the conversion, such as the power transmission duration and the output power, are marked. The conversion attribute refers to the power supply equipment being converted into a power transmission equipment or the power transmission equipment being converted into a power supply equipment.

3. The intelligent low-voltage switchgear energy storage management method according to claim 1, characterized in that, The step of sorting the overall sequence to obtain the sorting result includes: Using the overall sequence as a single overall period, N overall periods are obtained; Sort the N overall cycles in chronological order to obtain the sorting result; The reverse directions in the N overall cycles are marked, and the marking results of the reverse directions are extracted; Based on the sorting results, the correlation of the labeling results in the reverse direction of the N overall cycles is calculated one by one to obtain the correlation calculation results.

4. The intelligent low-voltage switchgear energy storage management method according to claim 1, characterized in that, The step of determining time overlap events based on the sorting results, and obtaining the start time difference, duration difference, and battery level difference based on the time overlap events, includes: Obtain the reverse direction marking results of all complete cycles, select the first complete cycle as the reference cycle, and extract all reverse events within the reference cycle to form a reference event set; Take the next unprocessed complete cycle as the comparison cycle, and extract all reverse events within the comparison cycle to form a comparison event set; Perform a matching operation on each reverse event in the benchmark event set: detect reverse events in the comparison event set that overlap with the current benchmark event in time, where time overlap means that the event time intervals have a common period; If there are overlapping events, calculate the absolute value of the start time difference, the absolute value of the duration difference, and the absolute value of the battery difference between each overlapping event and the baseline event, and select the overlapping event with the smallest absolute value of the start time difference as the best matching event. If there are no time-overlapping events, the absolute value of the starting time difference between all events in the event set and the baseline event is calculated and compared. The event with the smallest absolute value of the starting time difference is selected as the best matching event. The best matching event refers to the candidate event selected by the principle of minimum starting time difference.

5. The intelligent low-voltage switchgear energy storage management method according to claim 4, characterized in that, The calculation of the start time difference, duration difference, and power difference to obtain a comprehensive correlation, and the obtaining of a periodic correlation based on the comprehensive correlation, includes: Calculate the comprehensive correlation between the current benchmark event and the best matching event to obtain the comprehensive correlation calculation result. The comprehensive correlation is generated based on a weighted evaluation of the start time difference, duration difference, and power difference. Store the comprehensive correlation calculation result in a temporary storage pool. Repeat the matching operation until all reverse events in the baseline event set have been processed. Calculate the arithmetic mean of all comprehensive correlations in the temporary storage pool, and record it as the periodic correlation between the current baseline period and the comparison period. The periodic correlation refers to the arithmetic mean of the correlations of all matching events during a single baseline period and comparison period.

6. The intelligent low-voltage switchgear energy storage management method according to claim 1, characterized in that, The acquisition of power value data from fixed power supply equipment, fixed power transmission equipment, and the low-voltage switchgear includes: Based on the fixed power supply equipment and the fixed power transmission equipment, calculate the maximum net difference between the power supply and the power transmission after regulation. The maximum net difference refers to the result after regulation by the low-voltage switch. Sort the maximum net difference from largest to smallest to obtain the first sorting result. Based on the power consumption data, the maximum net difference between the power consumption of fixed transmission equipment and the power transmission of fixed power supply equipment is selected from the first sorting results to obtain the filtering results; the filtering results are re-sorted according to the power consumption value; if the sum of the power consumption value and the power transmission of fixed power supply equipment is greater than the power consumption of fixed transmission equipment, then the results are re-sorted to obtain the second sorting results.

7. The intelligent low-voltage switchgear energy storage management method according to claim 6, characterized in that, The step of matching the stable energy storage device and the unstable energy storage device with the fixed power supply equipment, fixed power transmission equipment and power value data to obtain the matching result includes: The periodic correlation of the stable energy storage devices is sorted from largest to smallest to obtain a third sorting result; Based on the third sorting result and the second sorting result, the period correlation in the third sorting result is matched with the second sorting result from largest to smallest. If the matching is completed and there are remaining items in the second sorting result, the period correlation in the unstable energy storage device is matched with the remaining devices in the second sorting result from smallest to largest to obtain the final matching result.

8. An intelligent low-voltage switchgear energy storage management system, used to implement the intelligent low-voltage switchgear energy storage management method according to any one of claims 1-7, characterized in that, include: Equipment tagging and sequence construction module: Taking the low-voltage switchgear as the central device, it acquires power supply equipment and power transmission equipment, tags and transforms their attributes, and obtains the tagging results; The phase sequence, forward direction, and reverse direction are determined based on the marking results; Construct an overall sequence containing the labeled results based on the stage sequence, the forward direction, and the reverse direction; Event sorting and overlap detection module: sorts the overall sequence into multiple complete cycles to obtain sorting results; detects time overlap of events in the opposite direction based on the sorting results, and obtains the start time difference, duration difference, and power difference; determines the best matching event based on the principle of time overlap or minimum start time distance. The correlation calculation and stability classification module performs weighted calculations on the start time difference, duration difference, and power difference to obtain a comprehensive correlation calculation result; it calculates the periodic correlation based on all events; and it classifies stable energy storage devices into unstable energy storage devices by comparing the average periodic correlation with a preset energy storage threshold. Equipment matching and optimization module: acquires power value data of fixed power supply equipment, fixed power transmission equipment, and power consumption data regulated by low-voltage switchgear; sorts the power value data and equipment attributes based on the maximum net difference; prioritizes matching the periodic correlation results of stable energy storage equipment and unstable energy storage equipment to obtain the final matching result.

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