Micro-grid energy storage energy intelligent optimization method

Abstract

The present application relates to the field of wind power generation technology, specifically to a micro-grid energy storage energy intelligent optimization method, real-time sampling data of wind power output is collected, and the wind power output instantaneous fluctuation amplitude is obtained through the difference value calculation of adjacent sampling points; then the fluctuation amplitude is compared with the preset threshold value, the time node exceeding the limit is marked to form the interval nesting division point; the charging and discharging time sequence single interval is split relying on the division point and the fluctuation duration is matched, the wind power output fluctuation increment amplitude and the falling amplitude are respectively obtained; the nested sub-interval charging and discharging power setting value is obtained through forward and reverse correlation operation; the sub-interval charging and discharging action time sequence is matched with the wind power output fluctuation rising edge and falling edge, and the pre and post sequence charging and discharging actions are triggered according to the characteristic node and threshold condition. The present application can finely suppress the wind power instantaneous fluctuation, effectively weaken the impact of wind power fluctuation on the micro-grid power balance, improve the adaptation degree of energy storage charging and discharging action and wind power fluctuation, and avoid and reduce the invalid energy storage charging and discharging cycle.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to a smart optimization method for microgrid energy storage. Background Technology

[0002] The field of wind power technology encompasses wind energy resource surveying, wind turbine research and development, manufacturing, wind farm construction and operation maintenance, wind power grid connection control, and supporting energy storage system scheduling. Its core content is the development and utilization of renewable energy through the conversion of wind energy into electricity. It focuses on the reliable operation of wind turbines, output regulation of wind farms, grid power balance after wind power grid connection, and power quality assurance through technological research and engineering implementation. Simultaneously, it integrates the configuration and operation regulation of energy storage systems to address the intermittency and volatility of wind power output, adapting to the safe operation requirements of the power grid, and supporting the large-scale development of the wind power industry and the high-proportion consumption of renewable energy.

[0003] One of the microgrid energy storage intelligent optimization methods refers to a method applied to microgrid scenarios with wind power access. This method addresses technical matters related to the power interaction and regulation between the microgrid and the public grid, including the timing of energy storage system charging and discharging. It utilizes real-time sampling data of wind power output within the microgrid, real-time electricity price data from the grid side, and the current state of charge data of the energy storage batteries. The method calculates the energy storage charging and discharging power according to a preset time step, determines the timing of the energy storage system's charging and discharging actions within the corresponding time interval, and completes the matching and scheduling of wind power output and energy storage charging and discharging behavior. Simultaneously, it sets the power interaction values ​​between the microgrid and the public grid based on grid power threshold constraints.

[0004] Existing technologies calculate and schedule energy storage charging and discharging power according to a preset fixed time step. They rely solely on overall wind power output sampling data, grid electricity prices, and energy storage state of charge for global scheduling. This approach fails to capture the millisecond-level instantaneous fluctuations in wind power output and cannot match differentiated charging and discharging strategies for different stages of fluctuations. The timing of charging and discharging actions is not precisely linked to the wind power fluctuations, which can easily lead to delayed or premature charging and discharging actions. This approach cannot smooth out instantaneous fluctuations in wind power, exacerbates power fluctuations on the grid side, affects grid power quality and operational safety, and increases unnecessary charging and discharging losses in the energy storage system. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a smart optimization method for microgrid energy storage. The technical solution is as follows: A method for intelligent optimization of energy storage in microgrids includes the following steps: S1: Collect real-time sampling data of wind power output, calculate the difference in wind power output between adjacent sampling points, and obtain the instantaneous fluctuation amplitude of wind power output; S2: Compare the instantaneous fluctuation amplitude of the wind power output with the preset wind power output fluctuation threshold, mark the time node corresponding to the threshold, and obtain the nested interval boundary point; S3: Based on the nested boundary point of the interval, split the preset charging and discharging time sequence single interval into two nested sub-time sequence intervals, match the time length of the sub-time sequence interval with the duration of the instantaneous fluctuation of wind power output, and obtain the wind power output fluctuation increment amplitude corresponding to the preceding sub-time sequence interval and the wind power output fluctuation drop amplitude corresponding to the following sub-time sequence interval. S4: Perform a positive correlation operation between the charging and discharging power setting value of the preceding sub-interval and the incremental amplitude, and a negative correlation operation between the charging and discharging power setting value of the subsequent sub-interval and the falling amplitude to obtain the nested sub-interval charging and discharging power setting value. S5: Match the timing of the charging and discharging actions of the two sub-intervals with the rising and falling edges of the wind power output fluctuation. When the rising edge reaches the nested boundary point of the interval, the preceding action is triggered. When the falling edge falls back to within the wind power output fluctuation threshold, the subsequent action is triggered.

[0006] As a further aspect of the present invention, the step of obtaining S1 is as follows: S101: Collect real-time sampling data of wind power output, arrange sampling points in chronological order, record the timestamp and output value of each sampling point, verify the consistency of sampling point time intervals, remove invalid sampling entries, and generate wind power output time-series sampling dataset.

[0007] S102: Based on the wind power output time series sampling dataset, all sampling points are traversed in time order, the output values ​​corresponding to two adjacent sampling points are taken, the difference operation is performed, and the output content is arranged in the order of sampling points to obtain the wind power output adjacent sampling difference sequence.

[0008] S103: For the adjacent sampling difference sequence of wind power output, take the absolute value of the output of each difference operation, match the time interval of the corresponding sampling point, and complete the extraction of the full sequence values ​​in time order to obtain the instantaneous fluctuation amplitude of wind power output.

[0009] As a further aspect of the present invention, the step of obtaining S2 is as follows: S201: Compare the instantaneous fluctuation amplitude of wind power output with the preset wind power output fluctuation threshold point by point, traverse the full amplitude data in chronological order, record the comparison status of each set of data, match the corresponding amplitude data timestamp, complete the arrangement of all comparison contents according to the time axis, distinguish between the two types of comparison status, and generate a wind power output fluctuation threshold comparison sequence. S202: Based on the wind power output fluctuation threshold comparison sequence, complete the full traversal of entries along the time axis, filter the corresponding entries whose amplitude exceeds the preset wind power output fluctuation threshold, extract the timestamps matching the entries, arrange the extracted timestamp data in chronological order, complete the removal of duplicate and invalid timestamps, and obtain the interval nesting boundary point.

[0010] As a further aspect of the present invention, the step of obtaining S3 is as follows: S301: Based on the interval nesting boundary point, call the preset charging and discharging time sequence single interval, use the boundary point as the time axis segmentation node, complete the interval boundary delineation according to the time axis sequence, perform the splitting operation on the time sequence single interval, generate two time sequence intervals connected before and after the time axis, verify the continuity of the interval time axis, remove invalid interval segments, confirm that the intervals have no time overlap, record the start and end timestamps of the intervals, and generate a set of charging and discharging nested sub-time sequence intervals;

[0011] S302: For the set of nested sub-time series intervals for charging and discharging, extract the matching time length of two sub-time series intervals, call the matching duration of instantaneous fluctuations in wind power output, perform the duration matching operation in the order of the time axis, adjust the start and end timestamps of the sub-time series intervals, complete the equal matching between the interval duration and the fluctuation duration, verify the integrity of the time axis of the matched interval, and generate the fluctuation duration matching sub-time series interval. S303: Match sub-time series intervals based on fluctuation duration, divide them into preceding and subsequent intervals according to the time axis, match the time range of the intervals, extract the wind power output fluctuation data matched in the preceding interval, extract the wind power output fluctuation data matched in the subsequent interval, complete the alignment of data with the time axis of the intervals, remove redundant data outside the intervals, complete the amplitude data statistics within the intervals respectively, and obtain the incremental amplitude of wind power output fluctuation and the decline amplitude of wind power output fluctuation.

[0012] As a further aspect of the present invention, the step of obtaining S4 is as follows: S401: Perform a positive correlation calculation between the charging and discharging power setting value of the preceding sub-interval and the wind power output fluctuation increment amplitude, call the matching time axis data of the preceding sub-time interval, align the time dimensions of the two sets of parameters, complete the point-by-point calculation in the order of the time axis, verify the numerical range of the calculation result, remove invalid calculation entries, arrange the calculation results in the time axis, and generate the charging and discharging power calculation value of the preceding sub-interval. S402: Based on the calculated charging and discharging power value of the preceding sub-interval, perform reverse correlation calculation between the set value of the charging and discharging power of the following sub-interval and the amplitude of the wind power output fluctuation, align the time dimension of the following sub-time interval, complete the point-by-point calculation, verify the numerical range of the calculation result, merge the two sets of calculation results according to the time axis, complete the time interval matching verification, and obtain the set value of the charging and discharging power of the nested sub-interval.

[0013] As a further aspect of the present invention, the step of obtaining S5 is as follows: S501: Perform a matching operation between the charging and discharging action timing of the two nested sub-time intervals and the rising and falling edges of the wind power output fluctuation. Call the corresponding timing node of the sub-time interval for fluctuation duration matching, extract the timestamps corresponding to the rising and falling edges of the wind power output fluctuation, complete the timing node alignment according to the time axis sequence, verify the consistency of the time dimension of the timing node, remove invalid timing nodes, complete the binding of the matching relationship according to the time axis, and generate a set of matching relationships for the charging and discharging action fluctuation edges.

[0014] S502: Based on the matching relationship set of the fluctuation edge of the charging and discharging action, call the timestamp corresponding to the nested boundary point of the interval, traverse the full data of the rising edge time axis of the wind power output fluctuation, determine the overlap state between the rising edge time node and the boundary point timestamp, record the time node corresponding to the overlap state, bind the charging and discharging action of the preceding sub-time interval, verify the temporal continuity of the binding relationship, and generate the trigger node of the preceding charging and discharging action. S503: Based on the preceding charging and discharging action trigger node, call the corresponding value of the wind power output fluctuation threshold, traverse the full data of the wind power output fluctuation falling edge time axis, determine the corresponding time node when the amplitude of the falling edge falls back to within the threshold, bind the subsequent sub-time interval charging and discharging action, merge the two sets of trigger nodes according to the time axis, verify the trigger timing matching relationship, and obtain the charging and discharging action timing trigger execution sequence.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, the instantaneous fluctuation amplitude is calculated based on the difference between adjacent sampling points of wind power output. The charging and discharging time sequence interval is split at the node where the fluctuation exceeds the threshold, so that the interval duration is accurately matched with the duration of the fluctuation. The incremental amplitude of the rising phase and the falling amplitude of the falling phase of the fluctuation are extracted respectively. The charging and discharging power setpoint and the corresponding amplitude are correlated in a positive and negative direction, so that the timing of the charging and discharging action is accurately aligned with the rising and falling edges of the wind power fluctuation. The corresponding charging and discharging action is triggered at the fluctuation characteristic node, so as to realize the fine-grained smoothing of instantaneous fluctuations in wind power output, reduce the impact of wind power fluctuations on the power balance of the grid, improve the adaptability of energy storage charging and discharging actions to wind power fluctuations, and reduce ineffective charging and discharging cycles of energy storage. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the invention, terms such as "exemplarily," "for example," etc., are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as an example in the invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the invention, the meaning expressed and / or may be both, or either one may be preferred.

[0019] In this embodiment of the invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing their distinction, their intended meanings are consistent. Similarly, the terms "of," "corresponding," and "relevant" may sometimes be used interchangeably. It should be noted that, without emphasizing their distinction, their intended meanings are consistent.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a technical solution: a smart optimization method for microgrid energy storage, comprising the following steps: S1: Collect real-time sampling data of wind power output, calculate the difference in wind power output between adjacent sampling points, and obtain the instantaneous fluctuation amplitude of wind power output; S2: Compare the instantaneous fluctuation amplitude of the wind power output with the preset wind power output fluctuation threshold, mark the time node corresponding to the wind power output fluctuation threshold, and obtain the interval nesting boundary point; S3: Based on the nested boundary point of the interval, split the preset charging and discharging time sequence single interval into two nested sub-time sequence intervals, match the time length of the sub-time sequence interval with the duration of the instantaneous fluctuation of wind power output, and obtain the wind power output fluctuation increment amplitude corresponding to the preceding sub-time sequence interval and the wind power output fluctuation drop amplitude corresponding to the following sub-time sequence interval. S4: Perform a positive correlation operation between the charging and discharging power setting value of the preceding sub-time interval and the wind power output fluctuation increment amplitude, and perform a negative correlation operation between the charging and discharging power setting value of the following sub-time interval and the wind power output fluctuation decline amplitude to obtain the nested sub-interval charging and discharging power setting value. S5: Match the charging and discharging actions of the two nested sub-time intervals one-to-one with the rising and falling edges of the wind power output fluctuation. When the rising edge of the wind power output fluctuation reaches the nested interval boundary, the charging and discharging actions of the preceding sub-time interval are triggered synchronously. When the falling edge of the wind power output fluctuation falls back to the wind power output fluctuation threshold, the charging and discharging actions of the following sub-time interval are triggered synchronously.

[0023] The steps for obtaining S1 are as follows: S101: Collect real-time sampling data of wind power output, arrange sampling points in chronological order, record the timestamp and output value of each sampling point, verify the consistency of sampling point time intervals, remove invalid sampling entries, and generate wind power output time-series sampling dataset. The data sources for real-time wind power output sampling are clearly defined as active power transmitters at the wind power grid connection point, wind farm SCADA systems, or wind turbine main control systems, with a preset standard sampling frequency of 100ms / time. The sampling points are arranged in ascending order of UTC timestamps, and each sampling entry is bound to a unique sampling ID, a UTC format timestamp, and a wind power output value. The time interval consistency verification rule is: the absolute value of the deviation between the timestamp difference of adjacent sampling points and the standard sampling interval shall not be greater than 0.05 times the standard sampling interval; if it exceeds this value, it shall be judged as an abnormal interval entry. The criteria for determining invalid sampling entries are: output values ​​exceeding the rated active power range of wind turbines / stations, null values, out-of-order timestamps, and abnormal intervals. After these entries are removed, a wind power output time-series sampling dataset with verification status labels and continuous time is generated.

[0024] S102: Based on the wind power output time series sampling dataset, all sampling points are traversed in time order, the output values ​​corresponding to two adjacent sampling points are taken, the difference operation is performed, and the output content is arranged in the order of sampling points to obtain the wind power output adjacent sampling difference sequence. The calculation rules for the output difference between adjacent sampling points are clearly defined, and the core formula is as follows: in, For the time-series sampling dataset, the first Wind power output values ​​at each valid sampling point For the first The wind power output values ​​of each adjacent valid sampling point, and satisfying ; The traversal operation starts at the second valid sampling point of the dataset and ends at the last valid sampling point, ensuring that the traversal is complete and without repetition. Each difference operation result is bound to the start and end timestamps of the corresponding sampling interval (i.e., the first...). The and the first (Time stamps of each sampling point), arranged in ascending order of time, to generate a sequence of adjacent sampling differences of wind power output that corresponds one-to-one with the effective sampling interval.

[0025] S103: For the adjacent sampling difference sequence of wind power output, take the absolute value of the output of each difference operation, match the time interval of the corresponding sampling point, and complete the extraction of the full sequence values ​​in time order to obtain the instantaneous fluctuation amplitude of wind power output.

[0026] The physical definition of the instantaneous fluctuation amplitude of wind power output is the absolute change in wind power output within a single sampling interval. The core formula is as follows: (The formula is repeated for each difference in the sequence of adjacent sampling differences.) Calculation result This refers to the instantaneous fluctuation amplitude of wind power output in the corresponding sampling interval. Each amplitude data is uniquely bound to the start and end timestamps and sampling ID of the corresponding sampling interval. The entire sequence is arranged in ascending order of time, and invalid entries with non-numerical results are removed. Finally, a time series sequence of instantaneous fluctuation amplitude of wind power output is generated that is completely matched with the length of the original valid sampling time series and can be traced back to the original sampling data.

[0027] The steps for obtaining S2 are as follows: S201: Compare the instantaneous fluctuation amplitude of wind power output with the preset wind power output fluctuation threshold point by point, traverse the full amplitude data in chronological order, record the comparison status of each set of data, match the corresponding amplitude data timestamp, complete the arrangement of all comparison contents according to the time axis, distinguish between the two types of comparison status, and generate a wind power output fluctuation threshold comparison sequence. The threshold value for the preset wind power output fluctuation is determined based on 0.03 to 0.08 times the rated active power of a single wind turbine, or a preset fixed fluctuation limit at the wind farm / station level. This threshold is a non-negative constant value, denoted as [insert value here]. The point-by-point comparison rule is as follows: sort by time in ascending order, and compare the instantaneous fluctuation amplitude corresponding to each sampling interval. With preset threshold Perform magnitude comparison and divide into two mutually exclusive comparison states: State 1 is when the magnitude exceeds the threshold (satisfying...). State 2 is when the amplitude does not exceed the threshold (satisfies) ); Each comparison result is bound to the corresponding amplitude time interval, original amplitude value, and status label, and arranged in ascending order of the time axis to generate a wind power output fluctuation threshold comparison sequence that corresponds one-to-one with the instantaneous fluctuation amplitude sequence and has no time misalignment.

[0028] S202: Based on the wind power output fluctuation threshold comparison sequence, complete the full traversal of entries along the time axis, filter the corresponding entries whose amplitude exceeds the preset wind power output fluctuation threshold, extract the timestamps matching the entries, arrange the extracted timestamp data in chronological order, complete the removal of duplicate and invalid timestamps, and obtain the interval nesting boundary point; When traversing the threshold comparison sequence, only the entries with the state of amplitude exceeding the threshold are retained, and the start timestamp of the sampling interval corresponding to the entry is extracted as the candidate interval nesting boundary point; After sorting the candidate boundary points in ascending order of time, deduplication and invalid value removal operations are performed: if the time interval between two adjacent candidate boundary points is less than the preset minimum fluctuation judgment time of 200ms, they are merged into one boundary point, and the earliest candidate boundary point is retained. At the same time, invalid boundary points whose timestamps exceed the preset charging and discharging time sequence single interval time range are removed; The final effective dividing point is the starting time node when the wind power output fluctuation changes from the normal range to the over-threshold range, denoted as . Each dividing point corresponds to an independent wind power output over-limit fluctuation event.

[0029] The steps for obtaining S3 are as follows: S301: Based on the interval nesting boundary point, call the preset charging and discharging time sequence single interval, use the boundary point as the time axis segmentation node, complete the interval boundary delineation according to the time axis sequence, perform the splitting operation on the time sequence single interval, generate two time sequence intervals connected before and after the time axis, verify the continuity of the interval time axis, remove invalid interval segments, confirm that the intervals have no time overlap, record the start and end timestamps of the intervals, and generate a set of charging and discharging nested sub-time sequence intervals; The preset charging / discharging timing interval is defined as a fixed-duration timing interval that includes a complete charging / discharging cycle, and each interval is bound to a unique start timestamp. and end timestamp ; Nested interval boundary points As a timeline segmentation node, the original single interval is split into two consecutive sub-time series intervals, with the time range of the intervals defined as follows: - Preceding sub-time series interval: -Subsequent sub-time intervals: The interval continuity check rule is: the end timestamp of the preceding sub-interval is completely consistent with the start timestamp of the following sub-interval, with no time interval and no time overlap; At the same time, invalid sub-interval segments with a time length less than the preset minimum time interval length of 100ms are removed, and finally a set of charging and discharging nested sub-time intervals is generated, which includes two valid sub-intervals, namely the preceding and following sequence, and has interval ID, start and end timestamps, and interval duration attributes.

[0030] S302: For the set of nested sub-time series intervals for charging and discharging, extract the matching time length of two sub-time series intervals, call the matching duration of instantaneous fluctuations in wind power output, perform the duration matching operation in the order of the time axis, adjust the start and end timestamps of the sub-time series intervals, complete the equal matching between the interval duration and the fluctuation duration, verify the integrity of the time axis of the matched interval, and generate the fluctuation duration matching sub-time series interval. The duration of instantaneous fluctuation matching in wind power output is defined as the total time from the start of the fluctuation corresponding to the boundary point of the nested interval to the end time when the fluctuation amplitude falls back to within the preset threshold, denoted as . ; The duration of the upward fluctuation phase is: The duration of the downward fluctuation phase is The three satisfy the following formula: The duration matching operation rule is: the duration of the preceding sub-interval and... Completely equal, the duration of the subsequent subinterval is equal to They are completely equal; Based on this rule, the starting timestamp of the preceding sub-interval is used as the benchmark to adjust the ending timestamp (i.e., the boundary point) of the preceding sub-interval. ), so that the duration of the preceding interval is equal to ; Synchronously adjust the start and end timestamps of the subsequent sub-intervals so that the duration of the subsequent interval is equal to... ; After adjustment, verify the total duration of the two sub-intervals. Completely equal, with no breaks or overlaps in the time axis, generating a fluctuation duration matching sub-time series interval that precisely matches the full cycle duration of the fluctuation.

[0031] S303: Match sub-time intervals based on fluctuation duration, divide the time axis into preceding and subsequent intervals, match the time range of the intervals, extract the wind power output fluctuation data matched in the preceding interval, extract the wind power output fluctuation data matched in the subsequent interval, complete the alignment of data with the time axis of the intervals, remove redundant data outside the intervals, complete the amplitude data statistics within the intervals respectively, and obtain the incremental amplitude of wind power output fluctuation and the falling amplitude of wind power output fluctuation. Based on the chronological order of the timeline, the fluctuation duration matching sub-time series intervals are divided into the preceding interval corresponding to the rising phase of fluctuation and the following interval corresponding to the falling phase of fluctuation. The data extraction and timeline alignment rules are as follows: Based on the start and end timestamps of the preceding interval, extract the amplitude data from the instantaneous fluctuation amplitude time series where all time intervals fall completely within the range of the preceding interval. Perform a cumulative summation operation on this set of data, and the result is the incremental amplitude of wind power output fluctuation. The total change in output power during the upward phase of the fluctuation is represented by the following core formula: in, This is the sequence number of the first valid sampling point within the preceding interval. The first valid sampling point number is the sequence number of the last valid sampling point in the preceding interval. Based on the start and end timestamps of the following interval, all amplitude data within the corresponding time range are extracted, and the summation operation is performed on this set of data. The result is the amplitude of the wind power output fluctuation decline. The total change in output during the downward phase of the fluctuation is represented by the following core formula: in, This is the sequence number of the first valid sampling point within the subsequent interval. This is the sequence number of the last valid sampling point in the subsequent interval; at the same time, redundant data that does not completely match the interval boundary are removed to ensure that the two amplitude data are completely aligned with the time range of the corresponding sub-interval.

[0032] The steps for obtaining S4 are as follows: S401: Perform a positive correlation calculation between the charging and discharging power setting value of the preceding sub-interval and the wind power output fluctuation increment amplitude, call the matching time axis data of the preceding sub-time interval, align the time dimensions of the two sets of parameters, complete the point-by-point calculation in the order of the time axis, verify the numerical range of the calculation result, remove invalid calculation entries, arrange the calculation results in the time axis, and generate the charging and discharging power calculation value of the preceding sub-interval. The positive correlation operation explicitly uses a linear proportional correlation algorithm, and the core formula is as follows: in, This is the calculated value of the charging and discharging power of the preceding sub-interval. The preset positive correlation coefficient (with values ​​ranging from 0.8 to 1.2, non-negative constant values, used to match the charge and discharge response characteristics of the energy storage system). This represents the amplitude of the fluctuation in wind power output. The time dimension alignment rule is: The result of the calculation... The power setting value of each sampling point is evenly distributed to all sampling time points within the preceding sub-time interval, and the power setting value of each sampling point is bound to the corresponding timestamp. The numerical range verification rule is as follows: discard calculated values ​​that exceed the rated charging and discharging power range of the energy storage system, clamp the values ​​that exceed the limit to the upper or lower limit of the rated power of the energy storage system, and finally arrange them in ascending order of time to generate a time sequence of calculated charging and discharging power values ​​of the preceding sub-interval that is completely matched with the time axis of the preceding sub-interval.

[0033] S402: Based on the calculated charging and discharging power value of the preceding sub-interval, perform reverse correlation calculation between the set value of the charging and discharging power of the following sub-interval and the amplitude of the wind power output fluctuation, align the time dimension of the following sub-time interval, complete the point-by-point calculation, verify the numerical range of the calculation result, merge the two sets of calculation results according to the time axis, complete the time interval matching verification, and obtain the set value of the charging and discharging power of the nested sub-interval. The reverse correlation operation explicitly uses the linear inverse proportional correlation algorithm, and the core formula is as follows: in, This is the calculated value of the charging and discharging power for the subsequent sub-intervals. The preset subsequent reverse correlation coefficient (with a value range of 0.8 to 1.2, a non-negative constant value, which works with the preceding correlation coefficient to achieve power balance and smooth out fluctuations). This represents the amplitude of wind power output fluctuations; the negative sign in the formula establishes a reverse correlation, ensuring that subsequent power actions offset the fluctuation decline trend; the time dimension alignment rule is: the calculated... All sampling time points are evenly distributed to the subsequent sub-time intervals and bound to the corresponding timestamps. After the numerical range verification and limit clamping of the subsequent operation values ​​are completed, the preceding and subsequent power operation value sequences are merged in ascending time order. The time axis of the merged sequence completely coincides with the total time range of the two nested sub-intervals, with no breaks or overlaps, and finally a complete nested sub-interval charging and discharging power setting value time sequence is generated.

[0034] The steps for obtaining S5 are as follows: S501: Perform a matching operation between the charging and discharging action timing of the two nested sub-time intervals and the rising and falling edges of the wind power output fluctuation. Call the corresponding timing node of the sub-time interval for fluctuation duration matching, extract the timestamps corresponding to the rising and falling edges of the wind power output fluctuation, complete the timing node alignment according to the time axis sequence, verify the consistency of the time dimension of the timing node, remove invalid timing nodes, complete the binding of the matching relationship according to the time axis, and generate a set of matching relationships for the charging and discharging action fluctuation edges. The rising edge of wind power output fluctuation is clearly defined as the instantaneous fluctuation amplitude falling below a preset threshold. The time interval during which the value rises above the threshold is defined by the starting timestamp of the nested interval boundary. ; The falling edge of wind power output fluctuation is the time interval during which the instantaneous fluctuation amplitude falls from exceeding the threshold to below or equal to the preset threshold, and its termination timestamp is the effective time node when the fluctuation amplitude falls back to within the threshold. The core rule of matching operation is: the charging and discharging action sequence of the preceding sub-sequence interval is completely bound to the rising edge time interval of the fluctuation, and the charging and discharging action sequence of the subsequent sub-sequence interval is completely bound to the falling edge time interval of the fluctuation. The timing node alignment verification rule is: the deviation between the start and end timestamps of the sub-interval and the start and end timestamps of the corresponding fluctuation edge shall not exceed 1 sampling interval; otherwise, it shall be judged as an invalid timing node. For each valid binding relationship, a unique action ID, interval ID, and fluctuation edge ID are matched and sorted in ascending order of time to generate a set of charging and discharging action fluctuation edge matching relationships.

[0035] S502: Based on the matching relationship set of the fluctuation edge of the charging and discharging action, call the timestamp corresponding to the nested boundary point of the interval, traverse the full data of the rising edge time axis of the wind power output fluctuation, determine the overlap state between the rising edge time node and the boundary point timestamp, record the time node corresponding to the overlap state, bind the charging and discharging action of the preceding sub-time interval, verify the temporal continuity of the binding relationship, and generate the trigger node of the preceding charging and discharging action. The rule for determining the coincidence of the rising edge and the boundary point is as follows: the starting timestamp of the rising edge of the wind power output fluctuation and the nested boundary point of the interval. If the absolute value of the timestamp difference is less than or equal to one preset sampling interval, it is determined that the time nodes coincide. When an overlap condition is determined, the overlap time node is set as the trigger node for the charging and discharging action of the preceding sub-sequence interval. The triggering logic is as follows: when the rising edge reaches the time node, the charging and discharging power setting value instruction corresponding to the preceding sub-sequence interval is immediately issued and executed; at the same time, it is verified that the trigger node is completely consistent with the start timestamp of the preceding sub-sequence interval to ensure the continuity of the trigger timing and the timing of the preceding action, and invalid binding relationships of the trigger node that exceed the time range of the preceding sub-sequence interval are eliminated. Finally, a preceding charging and discharging action trigger node with timestamp, power instruction, and action type label is generated.

[0036] S503: Based on the preceding charging and discharging action trigger node, call the corresponding value of the wind power output fluctuation threshold, traverse the full data of the wind power output fluctuation falling edge time axis, determine the corresponding time node when the amplitude of the falling edge falls back to within the threshold, bind the subsequent sub-time interval charging and discharging action, merge the two sets of trigger nodes according to the time axis, verify the trigger timing matching relationship, and obtain the charging and discharging action timing trigger execution sequence. The determination rule for the falling edge amplitude to fall back to within the threshold is as follows: the instantaneous fluctuation amplitude of wind power output meets the threshold for two or more consecutive sampling intervals. The timestamp of the first sampling point in the continuous sampling interval is determined as the effective fallback time node; the effective fallback time node is set as the trigger node for the charging and discharging action of the subsequent sub-sequence interval. The triggering logic is: when the falling edge falls back to this time node, the corresponding charging and discharging power setting value instruction for the subsequent sub-sequence interval is immediately issued and executed. Merge the preceding and following charge / discharge action trigger nodes in ascending time order, verify that the time order of the two trigger nodes is completely consistent with the time order of the nested sub-intervals, with no trigger timing reversal or time overlap, and finally generate a charge / discharge action timing trigger execution sequence containing trigger timestamps, corresponding charge / discharge power settings, action execution intervals, and trigger condition tags, which can be directly sent to the energy storage converter (PCS) for execution.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A micro-grid energy storage energy intelligent optimization method, characterized in that, Includes the following steps: S1: Collect real-time sampling data of wind power output, calculate the difference in wind power output between adjacent sampling points, and obtain the instantaneous fluctuation amplitude of wind power output; S2: Compare the instantaneous fluctuation amplitude of the wind power output with the preset wind power output fluctuation threshold, mark the time node corresponding to the threshold, and obtain the nested interval boundary point; S3: Based on the nested boundary point of the interval, split the preset charging and discharging time sequence single interval into two nested sub-time sequence intervals, match the time length of the sub-time sequence interval with the duration of the instantaneous fluctuation of wind power output, and obtain the wind power output fluctuation increment amplitude corresponding to the preceding sub-time sequence interval and the wind power output fluctuation drop amplitude corresponding to the following sub-time sequence interval. S4: Perform a positive correlation operation between the charging and discharging power setting value of the preceding sub-interval and the incremental amplitude, and a negative correlation operation between the charging and discharging power setting value of the subsequent sub-interval and the falling amplitude to obtain the nested sub-interval charging and discharging power setting value. S5: Match the timing of the charging and discharging actions of the two sub-intervals with the rising and falling edges of the wind power output fluctuation. When the rising edge reaches the nested boundary point of the interval, the preceding action is triggered. When the falling edge falls back to within the wind power output fluctuation threshold, the subsequent action is triggered.

2. The microgrid energy storage energy intelligent optimization method of claim 1, wherein, The steps for obtaining S1 are as follows: S101: Collect real-time sampling data of wind power output, arrange sampling points in chronological order, record the timestamp and output value of each sampling point, verify the consistency of sampling point time intervals, remove invalid sampling entries, and generate wind power output time-series sampling dataset. S102: Based on the wind power output time series sampling dataset, all sampling points are traversed in time order, the output values ​​corresponding to two adjacent sampling points are taken, the difference operation is performed, and the output content is arranged in the order of sampling points to obtain the wind power output adjacent sampling difference sequence. S103: For the adjacent sampling difference sequence of wind power output, take the absolute value of the output of each difference operation, match the time interval of the corresponding sampling point, and complete the extraction of the full sequence values ​​in time order to obtain the instantaneous fluctuation amplitude of wind power output.

3. The method of claim 1, wherein, The steps for obtaining S2 are as follows: S201: Compare the instantaneous fluctuation amplitude of wind power output with the preset wind power output fluctuation threshold point by point, traverse the full amplitude data in chronological order, record the comparison status of each set of data, match the corresponding amplitude data timestamp, complete the arrangement of all comparison contents according to the time axis, distinguish between the two types of comparison status, and generate a wind power output fluctuation threshold comparison sequence. S202: Based on the wind power output fluctuation threshold comparison sequence, complete the full traversal of entries along the time axis, filter the corresponding entries whose amplitude exceeds the preset wind power output fluctuation threshold, extract the timestamps matching the entries, arrange the extracted timestamp data in chronological order, complete the removal of duplicate and invalid timestamps, and obtain the interval nesting boundary point.

4. The method of claim 1, wherein, The steps for obtaining S3 are as follows: S301: Based on the interval nesting boundary point, call the preset charging and discharging time sequence single interval, use the boundary point as the time axis segmentation node, complete the interval boundary delineation according to the time axis sequence, perform the splitting operation on the time sequence single interval, generate two time sequence intervals connected before and after the time axis, verify the continuity of the interval time axis, remove invalid interval segments, confirm that the intervals have no time overlap, record the start and end timestamps of the intervals, and generate a set of charging and discharging nested sub-time sequence intervals; S302: For the set of nested sub-time intervals for charging and discharging, extract the matching time length of two sub-time intervals, call the matching duration of instantaneous fluctuations in wind power output, perform the duration matching operation in the order of the time axis, adjust the start and end timestamps of the sub-time intervals, complete the equal matching between the interval duration and the fluctuation duration, verify the integrity of the time axis of the matched interval, and generate the fluctuation duration matching sub-time interval. S303: Match sub-time series intervals based on fluctuation duration, divide them into preceding and subsequent intervals according to the time axis, match the time range of the intervals, extract the wind power output fluctuation data matched in the preceding interval, extract the wind power output fluctuation data matched in the subsequent interval, complete the alignment of data with the time axis of the intervals, remove redundant data outside the intervals, complete the amplitude data statistics within the intervals respectively, and obtain the incremental amplitude of wind power output fluctuation and the decline amplitude of wind power output fluctuation.

5. The method of claim 1, wherein, The steps for obtaining S4 are as follows: S401: Perform a positive correlation calculation between the charging and discharging power setting value of the preceding sub-interval and the wind power output fluctuation increment amplitude, call the matching time axis data of the preceding sub-time interval, align the time dimensions of the two sets of parameters, complete the point-by-point calculation in the order of the time axis, verify the numerical range of the calculation result, remove invalid calculation entries, arrange the calculation results in the time axis, and generate the charging and discharging power calculation value of the preceding sub-interval. S402: Based on the calculated charging and discharging power value of the preceding sub-interval, perform reverse correlation calculation between the set value of the charging and discharging power of the following sub-interval and the amplitude of the wind power output fluctuation, align the time dimension of the following sub-time interval, complete the point-by-point calculation, verify the numerical range of the calculation result, merge the two sets of calculation results according to the time axis, complete the time interval matching verification, and obtain the set value of the charging and discharging power of the nested sub-interval.

6. The microgrid energy storage energy intelligence optimization method of claim 1, wherein, The steps for obtaining S5 are as follows: S501: Perform a matching operation between the charging and discharging action timing of the two nested sub-time intervals and the rising and falling edges of the wind power output fluctuation. Call the corresponding timing node of the sub-time interval for fluctuation duration matching, extract the timestamps corresponding to the rising and falling edges of the wind power output fluctuation, complete the timing node alignment according to the time axis sequence, verify the consistency of the time dimension of the timing node, remove invalid timing nodes, complete the binding of the matching relationship according to the time axis, and generate a set of matching relationships for the charging and discharging action fluctuation edges. S502: Based on the matching relationship set of the fluctuation edge of the charging and discharging action, call the timestamp corresponding to the nested boundary point of the interval, traverse the full data of the rising edge time axis of the wind power output fluctuation, determine the overlap state between the rising edge time node and the boundary point timestamp, record the time node corresponding to the overlap state, bind the charging and discharging action of the preceding sub-time interval, verify the temporal continuity of the binding relationship, and generate the trigger node of the preceding charging and discharging action. S503: According to the pre-charge and discharge action trigger node, call the wind power output fluctuation threshold corresponding value, traverse the wind power output fluctuation falling edge time axis full data, judge the falling edge corresponding amplitude back to the threshold value within the corresponding time node, bind the post-sequencing interval charge and discharge action, merge the two groups of trigger nodes according to the time axis, check the trigger timing matching relationship, and obtain the charge and discharge action timing trigger execution sequence.

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