A wind-solar-diesel-storage hybrid microgrid collaborative optimization scheduling method for renewable energy

Abstract

The application relates to the technical field of distributed power generation, in particular to a wind-solar-diesel-storage hybrid microgrid collaborative optimization scheduling method for renewable energy, which comprises the following steps: constructing a power fluctuation characteristic sequence of multi-source output through unit output and load data; connecting trend change edge point to generate a power coupling mutation time sequence position set; comparing the output points in the path with the overall trend and uniformly adjusting the inconsistent points to output an output adjustment result set; positioning the energy switching transition position, generating an adjustment abnormal area time sequence through the continuity judgment, and outputting a scheduling checking and control state result. The application establishes a numbering sequence through power measurement point difference and carries out path correction, realizes centralized indication of the adjustment imbalance area, enhances the deviation processing pertinence, and improves the microgrid scheduling response speed and the stability of local adjustment.

Description

Technical Field

[0001] This invention relates to the field of distributed generation technology, and in particular to a method for coordinated optimization scheduling of a hybrid microgrid of wind, solar, diesel and energy storage for renewable energy. Background Technology

[0002] The field of distributed generation technology encompasses power production and local consumption systems centered on small-scale generation units, as well as the access and operation control of multiple power sources. Its core focus is on researching how to improve the power supply reliability, economy, operational stability, and energy utilization efficiency of microgrids through scientific and rational resource allocation, power regulation, and energy management strategies. The generation unit component covers the research and practical application of output characteristics of wind power, photovoltaic power, and diesel generator sets, emphasizing output prediction, operational constraints, and environmental adaptability. The operation control component includes grid-connected and off-grid switching, power balance regulation, energy storage charging and discharging management, and power quality control, emphasizing real-time response and multi-source coordination during dispatching. Furthermore, it focuses on optimized dispatching and equipment technology matching throughout the entire system, often combining predictive techniques and control algorithms to achieve multi-energy system optimization.

[0003] One approach is a collaborative optimization scheduling method for hybrid microgrids combining wind, solar, diesel, and energy storage, addressing the issues of multi-energy complementarity and supply-demand balance in microgrid operation. This method proposes a unified coordination and arrangement of the output of each unit (wind, solar, diesel, and energy storage) based on energy management principles, and implements this method in conjunction with specific scheduling and control measures. The technical aspects involved include prediction deviations, load fluctuations, and equipment operational constraints caused by the randomness of meteorological conditions during microgrid operation. This is achieved by establishing output constraints and power balance models for each power generation unit, using wind speed, solar irradiance, and load forecast data, and combining multi-constraint solution methods for power allocation calculation. Simultaneously, based on the energy storage's state of charge and time-of-use demand, on-site scheduling is implemented using energy storage charging and discharging behavior arrangements, diesel engine start-stop control, and priority consumption of renewable energy. The method clearly defines the use of predicted and measured data as a foundation, employing mathematical models for collaborative calculation and operational control measures to achieve optimized power allocation within the microgrid.

[0004] Existing technologies use predictive data combined with static rules for overall scheduling, which has the problem of relying on a uniform weighting method to handle allocation. In areas with drastic weather fluctuations or instantaneous load changes, the lack of time-sharing coupling analysis of the output characteristics of multiple energy sources can easily cause local power fluctuations to be masked by the overall balance trend. In actual operation, if the output is controlled directly based on macroscopic prediction values, it often causes frequent operation of energy storage devices or inefficient operation of diesel engines. In particular, imbalances occur at energy switching transition points, affecting the coherence of overall scheduling and control and the local targeting of the response. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a collaborative optimization scheduling method for a hybrid microgrid combining wind, solar, diesel, and energy storage, oriented towards renewable energy. The technical solution is as follows: A collaborative optimization scheduling method for a hybrid microgrid of wind, solar, diesel, and energy storage for renewable energy includes the following steps: S1: Obtain time series data of power output and load of each unit of wind, solar, diesel and storage, divide the scheduling area into four time periods, compare the multi-source power output status in each area, organize the comparison results by time period number, and output the power fluctuation characteristic sequence of multi-source power output. S2: Read the power fluctuation characteristic sequence of the multi-source output, extract the time edge of the trend change between adjacent numbers, connect the power coordinates of two points with different trends, and splice them in the order of start and end to obtain the set of power coupling change time sequence positions. S3: Using the aforementioned set of power coupling mutation timing locations, extract the coordinates of the start and end points of the scheduling path, read the output points within the path in the connection order, compare the adjustment status of each point with the path trend to see if they are consistent, perform unified adjustment of the response direction for inconsistent points, and output the adjusted points as a set of output adjustment results for the scheduling path segment. S4: Call the output adjustment result set of the scheduling path segment, locate the path segment close to the energy switching transition position, make a continuous judgment on the adjustment direction, extract the middle time period of the path with inconsistent direction, and generate the time period sequence of the adjustment abnormal area in the transition scheduling zone.

[0006] As a further aspect of the present invention, the power fluctuation characteristic sequence of the multi-source output includes time period identification number, local power difference, and fluctuation change type; the power coupling mutation time sequence location set environment includes trend change point coordinates, mutation path segment index, and adjacent time period number pairs; the output adjustment result set of the scheduling path segment includes unified adjustment output point coordinates, path segment response mark, and power deviation correction value; and the time period sequence of the adjustment abnormal area in the transition scheduling zone includes the abnormal area center time point, repeated trend mutation location, and time domain connection relationship.

[0007] As a further aspect of the present invention, the step of obtaining S1 is as follows: S101: Obtain the power values ​​of the wind, solar, diesel and storage units and loads at each sampling location, and according to the sampling order of the time axis, form a scheduling unit by grouping every four adjacent time periods, call the power value parameters in each group of units, extract the power change information according to the time arrangement direction, and form a power state vector set of the scheduling unit. S102: Based on the data content of each group in the power state vector set of the scheduling unit, the power values ​​between adjacent time periods in each group of data are judged by difference. The power offset reference threshold is used to screen out the output combinations that change more than the reference standard, and they are classified into two response categories respectively. The power state classification information of the scheduling unit is output. S103: Call the power status classification information of the scheduling unit, assign numbers according to different response categories, and map the number information into the time position set of each scheduling unit. Combine the power value content contained in each group of units, and output the multi-source output fluctuation characteristic sequence through the correspondence between the number and the value.

[0008] As a further aspect of the present invention, the method of performing difference judgment on the power values ​​between adjacent time periods within each set of data specifically involves: comparing the power values ​​of the four adjacent time periods contained in each scheduling unit within the power state vector set of the scheduling unit in chronological order, and performing a closed comparison between the power value of the last time period and the power value of the first time period to form four sets of difference records. The four sets of difference records are compared one by one with the power offset reference threshold. The output combination corresponding to the difference that is greater than the power offset reference threshold is classified as a violent response, and the output combination corresponding to the difference that is less than or equal to the power offset reference threshold is classified as a linear response. When there is at least one output combination that falls under the category of severe response within the same scheduling unit, the power state classification information of the scheduling unit is recorded as having fluctuations, and the number of output combinations with severe response and the number of output combinations with linear response are recorded. The specific method of assigning numbers according to different response categories is as follows: severe responses are assigned number 1, linear responses are assigned number 0, and number 1 or number 0, along with the time location set of the corresponding scheduling unit and the corresponding power value, are written into the multi-source output fluctuation characteristic sequence.

[0009] As a further aspect of the present invention, the step of obtaining S2 is as follows: S201: Obtain the adjacent time period numbers in the multi-source power output fluctuation characteristic sequence, extract the number value item by item for the adjacent number sequence, call the power response information between adjacent numbers in the sequence, take the number pair formed by adjacent numbers as the unit, perform the difference judgment operation on the power state within the number pair, and judge whether the adjustment direction corresponding to the number pair remains continuous based on the power change benchmark value, so as to obtain the adjacent time period trend change identifier set. S202: Based on the adjacent time period trend change identifier set, for the numbered pairs with trend change identifiers, call the power data frames of the two edge points of the corresponding time period, and perform coordinate filtering operation on the time and power parameters of the two points in the data frame. Based on the positioning coordinates of the two points at the edge of the time period, extract the coordinate sequence unit to obtain the edge coordinate sequence set. S203: Call the edge coordinate sequence set, and perform time-domain position linking operation on the coordinate sequence units in sequence according to the start and end order of the coordinates in the sequence. The connection path of the coordinate sequence forms a continuous path set, and all path sets are aggregated into a power coupling mutation time-series position set to obtain the power coupling mutation time-series position set.

[0010] As a further aspect of the present invention, the step of obtaining S3 is as follows: S301: Based on each set of coordinate points in the power coupling abrupt change time sequence location set, extract the path range data interval between the two points, call the original power sampling information, retrieve all output measurement point positions in the corresponding path segment between each pair of coordinates, and construct the time domain sequence range according to the time span to obtain the path segment output position set; S302: Based on the set of output positions of the path segment, for the power vector state of all output points in each path segment, extract the trend vector parameters corresponding to the path direction, match the angle direction between the power vector of each output point and the path trend vector, determine whether the vector directions are consistent, and output the set of outputs with trend differences in the path segment. S303: Call the path segment trend difference output set, perform unified response processing on the coordinate values ​​of output points with inconsistent directions, adjust the power state of output points according to the reference parameters of the path trend vector, integrate the adjusted coordinates with other output information in the path segment, and output the scheduling path segment output adjustment result set.

[0011] As a further aspect of the present invention, the step of obtaining S4 is as follows: S401: Call all path segment position relationship data in the power output adjustment result set of the scheduling path segment, extract the set of path segments that are continuously connected to the location of the switching area in the time domain according to the time boundary information of the energy switching transition zone, and construct a time series according to the coordinate arrangement order between the path segments to obtain the continuous path segment sequence set of the transition zone. S402: Based on the continuous path segment sequence set of the transition zone, extract the trend vector of each path segment in the order of the path segments, construct trend vector pairs according to the path adjacency relationship, identify trend changes based on whether the direction of the angle change between each set of trend vectors repeats, and obtain the path trend change segment index set. S403: Call the location range of the corresponding path segment in the path trend change segment index set, extract the power data point at the center of each path segment, integrate the coordinates of the middle of all path segments in chronological order, and use these coordinate points as the basis to represent the abnormal location of the regulation in the transition area, and output the time sequence of the abnormal regulation area in the transition scheduling zone.

[0012] As a further aspect of the present invention, the step of extracting a set of path segments that are continuously connected to the location of the switching area within the time domain based on the time boundary information of the energy switching transition zone specifically involves: arranging all path segment position relationship data in the power output adjustment result set of the scheduling path segment in chronological order, reading the start time and end time of each path segment one by one, and comparing the start time or end time with the time boundary information of the energy switching transition zone for adjacency comparison, and retaining path segments whose interval with the time boundary information of the energy switching transition zone does not exceed two sampling periods; The retained path segments are connected sequentially according to the connection relationship between their termination time and the start time of adjacent path segments, and written into the transition zone continuous path segment sequence set in coordinate order. The method of identifying trend changes based on whether the angle change direction between each set of trend vectors repeats is as follows: compare the trend vector pairs formed by adjacent path segments in the continuous path segment sequence set of the transition zone one by one, record the power change direction corresponding to each set of trend vectors, retain the position where the power change direction is reversed and repeats in two consecutive sets of trend vector pairs, and write the retained position into the path trend change segment index set according to the path segment arrangement order. The extraction of power data points at the center of each path segment specifically involves: based on the location range of the corresponding path segment in the path trend change segment index set, reading the start time, end time, and corresponding central power data of each path segment, and outputting the time sequence of the adjustment abnormal area in the transition scheduling zone in ascending order of time.

[0013] As a further aspect of the present invention, the method further includes: S5: Combining the time sequence of the abnormal adjustment area in the transitional scheduling zone with the sequence of variable output fluctuation characteristics, the positions with repeated numbers are statistically analyzed, the numbers with high frequency are extracted, and the output is the microgrid multi-source collaborative scheduling verification and control status result; The results of the microgrid multi-source collaborative scheduling verification and control status include frequency statistics, key scheduling area numbers, and system operation stability levels.

[0014] As a further aspect of the present invention, the step of obtaining S5 is as follows: S501: Call the time period sequence of the transition scheduling zone adjustment abnormal area, combine it with the time period number information recorded in the multi-source power output fluctuation characteristic sequence, match the number position corresponding to each group of abnormal coordinate points, and extract all numbers into a set in time order to obtain the abnormal number position set; S502: Based on the set of abnormal number locations, group the number values ​​in the set according to the recurrence, extract the number of times each group of numbers recurs in the set, and form a paired data frame with the number and its corresponding recurrence count to obtain the number recurrence frequency data frame. S503: Call the number repetition frequency data frame, compare the number of times each number appears with the frequency filtering benchmark value, retain all number positions that meet the benchmark, perform a set extraction operation on the scheduling unit information corresponding to these position numbers, and output the multi-source collaborative scheduling verification and control status results.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, a time period numbering sequence is established by the difference in power measurement points, trend changes are introduced to extract the coordinates of abrupt change edges and form a transition trajectory, and the output state is uniformly corrected by combining the path direction to construct continuous power results. Local abnormal coordinates are obtained based on repeated trend changes and prominent areas are screened by number frequency. This enables power fluctuation characteristics to be hierarchically distinguished, coupled abrupt changes to be presented in a path-like manner, and imbalanced areas to be centrally indicated. This enhances the pertinence of multi-energy switching deviation processing and the stability of time-domain positioning, and improves the response speed and local regulation direction of microgrid scheduling and control. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of S1 in this invention; Figure 3 This is a flowchart illustrating the acquisition process of S2 in this invention; Figure 4 This is a flowchart illustrating the acquisition process of S3 in this invention; Figure 5 This is a flowchart illustrating the acquisition process of S4 in this invention; Figure 6 This is a flowchart of the acquisition process for S5 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 present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[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 collaborative optimization scheduling method for a hybrid microgrid of wind, solar, diesel, and energy storage for renewable energy, comprising the following steps: S1: Obtain time series data of power output and load of each unit of wind, solar, diesel and storage, divide the scheduling area into four time periods, compare the multi-source power output status in each area, organize the comparison results by time period number, and output the power fluctuation characteristic sequence of multi-source power output. S2: Read the power fluctuation characteristic sequence of multi-source output, extract the time edge of trend change between adjacent numbers, connect the power coordinates of two points with different trends, and splice them in the order of start and end to obtain the set of power coupling change time sequence locations. S3: Refer to the set of power coupling mutation timing locations, extract the coordinates of the start and end points of the scheduling path, read the output points in the path in the connection order, compare the adjustment status of each point with the path trend, perform unified adjustment of the response direction for inconsistent points, and output the adjusted points as a set of output adjustment results for the scheduling path segment. S4: Call the output adjustment result set of the scheduling path segment, locate the path segment close to the energy switching transition position, make a continuous judgment on the adjustment direction, extract the middle time period of the path with inconsistent direction, and generate the time period sequence of the adjustment abnormal area in the transition scheduling zone. S5: Combining the time-period sequence of the abnormal regulation area in the transitional dispatch zone with the sequence of variable output fluctuation characteristics, the positions with repeated numbers are statistically analyzed, the high-frequency numbers are extracted, and the output is the microgrid multi-source collaborative dispatch verification and control status result.

[0023] The power fluctuation characteristic sequence of multi-source output includes time period identification number, local power difference, and fluctuation change type. The power coupling mutation time sequence location set includes trend change point coordinates, mutation path segment index, and adjacent time period number pairs. The output adjustment result set of the scheduling path segment includes output point coordinates after unified adjustment, path segment response mark, and power deviation correction value. The time period sequence of the abnormal area in the transition scheduling zone includes the center time point of the abnormal area, the location of repeated trend mutation, and time domain connection relationship. The verification and control status results of the microgrid multi-source collaborative scheduling include frequency statistics indicators, key scheduling area number, and system operation stability level.

[0024] Please see Figure 2 The steps to obtain S1 are as follows: S101: Obtain the power values ​​of the wind, solar, diesel and storage units and loads at each sampling location, and according to the sampling order of the time axis, form a scheduling unit by grouping every four adjacent time periods, call the power value parameters in each group of units, extract the power change information according to the time arrangement direction, and form a power state vector set of the scheduling unit. Number all sampling points of the microgrid's operating cycle based on a pre-defined time coordinate system, such as using time... With power The constructed sequence layout assigns an index to each sampling time point. For example, in a 24-hour scheduling cycle, sampling is performed at 15-minute intervals, with the index starting from... to For each numbered point, the power values ​​of wind, solar, diesel, storage, and load are acquired through a Supervisory Control and Data Acquisition (SCADA) system. The current power value of each acquisition point is recorded in kilowatts (kW). All units acquire values ​​under a unified scheduling clock. Then, according to the sampling order, four consecutive sampling points are grouped together, for example, using... , , , This forms a scheduling unit, resulting in a total of 96 / 4 = 24 units. The power values ​​at four time points within each unit are then categorized, such as... , , , ,in for Power at a specific point in time for Power at a specific point in time for Power at a specific point in time for Point-in-time power. After acquiring the data, it is necessary to determine its trend and take appropriate action accordingly. Record the difference and then make a judgment. Is it greater than This is used to determine the fluctuation point. If Between to If it falls between these ranges, it is considered a normal adjustment range; if it falls below... This suggests that the change tends to stabilize. Similarly, for... , , Perform three-sided difference analysis to form a four-sided difference set. Further, by performing a set-average processing on the above difference set, the average power change value of the scheduling unit is obtained: This value is then used as the representative power state value for that scheduling unit. This process is repeated for all scheduling units, ultimately forming a power state vector set based on each scheduling unit. Taking 96 sampling points as an example, this results in 24 power state vectors for each scheduling unit, with each vector element accompanied by a corresponding time period number for traceability.

[0025] S102: Based on the data content of each group in the power state vector set of the scheduling unit, the power values ​​between adjacent time periods in each group of data are judged by difference. The power offset reference threshold is used to screen out the output combinations that change more than the reference standard, and they are classified into two response categories respectively. The power state classification information of the scheduling unit is output. The power values ​​of adjacent time periods within each scheduling unit in the generated power state vector set are extracted by difference, and the four sampling points in each group are rearranged in numerical order. , , , Calculate the difference for each pair, and if the calculation yields... , , , Four sets of differences were recorded. Then, each of these four sets of differences was compared with a set power offset reference threshold, which was set with reference to the microgrid grid connection guidelines and equipment constraints. According to the judgment rules, if the absolute value of a certain difference is greater than a threshold, the two time points to which the difference belongs are determined to be a "fluctuating combination"; if it is less than or equal to the threshold, it is considered a "stationary combination". For example, the power of four points obtained in a certain scheduling unit is... , , , Then the calculation yields , , , .in and All greater than ,but and Two sets of time points constitute a fluctuating combination, while the other two are stationary combinations. The scheduling unit is then marked as a "fluctuating" unit, with the fluctuating combination categorized as a "violent response" response and the stationary combination as a "linear response" response. This judgment and filtering operation is repeated for each unit; if there are 24 units in total, 24 comparisons of the four differences are performed. The filtering criterion is the absolute value. The benchmark is used to determine the size, for example, by setting the condition "when". The time marker is "fluctuation", thus completing the fluctuation identification and classification. The final output consists of a set of markers corresponding to the two response categories for the scheduling unit number, and the output marker is "unit". :fluctuation ,smooth This output serves as power status classification information for the scheduling unit and is used for subsequent processing.

[0026] S103: Call the power status classification information of the scheduling unit, assign numbers according to different response categories, and map the number information into the time position set of each scheduling unit. Combine the power value content contained in each group of units, and output the multi-source output fluctuation characteristic sequence through the correspondence between the number and the value. The response category information for each scheduling unit is read item by item, and numbers are assigned according to the two response categories. For example, "Severe Response" is assigned number 1, and "Linear Response" is assigned number 0. This numbering process ensures that each scheduling unit has a unique corresponding category number. The category number is then associated with the scheduling unit's position on the time axis. In practice, the scheduling units are first mapped to their time coordinates according to their time index order, and the number field is filled into the time position set corresponding to that unit. Then, the original power values ​​of the four sampling points within each unit are retrieved, and the number field and value field are matched item by item. For example, when unit... When marked as number 1, the power values ​​of its four sampling points need to be read. , , , Then, using number 1 and the four values ​​together, we form the fluctuation input for this unit. We perform a step-by-step comparison of the four values, subtracting each value from the minimum power value within the unit to form a power deviation sequence related to the unit's power distribution. For example, if the power value of a certain unit is... , , , The minimum value is Then the subtraction operation yields , , , This is recorded as a fluctuation deviation sequence. This sequence is then output alongside the category number, forming a "number + deviation" combination. Subsequently, the entire scheduling cycle is spliced ​​along the time axis, linking the combination items of each time period unit sequentially into a sequence. Finally, the records of all units are merged into a complete sequence in chronological order, constituting a multi-source power output fluctuation characteristic sequence.

[0027] Please see Figure 3 The steps to obtain S2 are as follows: S201: Obtain the adjacent time period numbers in the multi-source power output fluctuation characteristic sequence, extract the number value item by item for the adjacent number sequence, call the power response information between adjacent numbers in the sequence, take the number pair formed by adjacent numbers as the unit, perform the difference judgment operation on the power state within the number pair, and judge whether the adjustment direction corresponding to the number pair remains continuous based on the power change benchmark value, and obtain the trend change identifier set of adjacent time periods. Read the power fluctuation characteristic sequence data and arrange the scheduling unit numbers linearly according to the chronological order. For each adjacent number pair, extract its corresponding number value and pair it with its power deviation value. Based on this, for each unit within the number pair, call its representative power state value (average value), perform a difference operation, and calculate the power state difference. .right Perform absolute value calculation and then compare it with a preset power change reference value, which is set as follows: .like If there is a trend discontinuity between adjacent units, it is marked as a trend jump (1); If the condition is met, it is determined to be a continuous adjustment region and marked as trend continuous (0). The judgment action does not use vague descriptions, but rather relies on whether the condition is met. The standard is to perform a binary judgment. This process is repeated for all numbered pairs to generate a set of direction change identifiers of the corresponding length.

[0028] S202: Based on the trend change identifier set of adjacent time periods, for the numbered pairs with trend change identifiers, call the power data frames of the two edge points of the corresponding time period, and perform coordinate filtering operation on the time and power parameters of the two points in the data frame. Based on the positioning coordinates of the two points at the edge of the time period, extract the coordinate sequence unit to obtain the edge coordinate sequence set. Read all pairs of numbers in the direction change identifier set item by item, and filter out the pairs with an identifier value of 1. For each selected pair, retrieve the edge position coordinates of its two corresponding cells in the time grid. Each cell consists of a start time point and an end time point. For the identification of shared time points at the edges between adjacent cells, perform a coordinate intersection extraction operation to obtain the coordinates of two common points (time) on the connection boundary between the two cells. With power For example, the range of unit A is... Unit B has a range of Then the shared edge points are Power records at specific times are used to extract power data frames from edge points that form the corresponding number pairs. For coordinate points within the data frames, position parameter determination is performed, based on whether the point is the latest time point of the current scheduling unit or the earliest time point of another unit. Subsequently, the determined edge points are arranged into coordinate sequence units in chronological order. And store it in the edge coordinate sequence set.

[0029] S203: Call the edge coordinate sequence set, and perform temporal position linking operation on the coordinate sequence units in sequence according to the start and end order of the coordinates in the sequence. The connection path of the coordinate sequence forms a continuous path set, and all path sets are aggregated into a power coupling abrupt change temporal position set to obtain the power coupling abrupt change temporal position set. For each coordinate sequence unit in the sequence set, its start and end coordinate values ​​are extracted and represented as line segment units. A time-domain concatenation operation is performed on all line segments: the time of the end point of each line segment is found. Is it related to the time of the starting point of another line segment? If they overlap, the two segments are merged into a single path segment until no further connections can be made, forming a complete, continuous mutation path. During the connection operation, coordinate comparisons are performed on the endpoints, calling their time and power axis values ​​and checking for consistency. If the starting point... With another finish line satisfy and If a path is found to be a continuous segment, it is considered a continuous segment. Each path in the path set must be temporally unique and non-repeating. After all paths are processed, an aggregation operation is performed to append all paths to a unified data structure, ultimately generating a complete set of power coupling mutation time series locations.

[0030] Please see Figure 4 The steps to obtain S3 are as follows: S301: Based on each set of coordinate points in the power coupling abrupt change time series location set, extract the path range data interval between two points, call the original power sampling information, retrieve all output measurement point positions in the corresponding path segment between each pair of coordinates, and construct the time domain sequence range according to the time span to obtain the path segment output position set; Each path sequence in the set is read item by item, and the coordinates of two adjacent points are extracted as the endpoints of the path segment. The span of the path segment is determined by the time difference between the two points. Then, the original sampling information of the microgrid is called to perform a search on the span range of each path segment, using the timestamp of the sampling point. As a basis, determine whether the sampling point is within the closed interval formed by the path endpoints. For example, the path segment is... Then, a logical judgment is performed on each sampling point during the period. Those that meet the criteria are added to the corresponding output location set. The retrieved output points are sorted in ascending order of time and written into the output location set of the path segment. This action is repeated for each mutation path.

[0031] S302: Based on the power output location set of the path segment, extract the trend vector parameters corresponding to the path direction for the power vector state of all power output points in each path segment, match the angle direction between the power vector of each power output point and the path trend vector, determine whether the vector directions are consistent, and output the power output set of the path segment trend difference. For each path segment, read the power status of all its output points one by one and record it as follows: The direction vector of the path segment is extracted by the coordinate difference between the start and end points. Then, for each output point, a trend vector is extracted based on the power difference between it and the previous reference point within the path segment. The process performs direction matching by calculating the cosine angle or sign relationship between two vectors to determine if their directions are consistent. For example, if the direction vector is positive (power increasing), but the power at a certain sampling point decreases, then the directions are determined to be inconsistent. The determination operation is based solely on power. The direction sign of the axis is used as the primary matching criterion. All those marked as having directional differences are written into the path segment trend difference output set.

[0032] S303: Call the output set of the trend difference of the path segment, perform unified response processing operation on the coordinate values ​​of the output points with inconsistent directions, adjust the power state of the output points according to the reference parameters of the path trend vector, integrate the adjusted coordinates with other output information in the path segment, and output the output adjustment result set of the scheduling path segment. Extract the power value from the points with different outputs, and call the average power value of the points with the same direction within the path segment. For reference. If the difference points are in the opposite direction, forming local depressions or peaks, adjust them using smoothing to align them with the reference mean. (Setting) To align the power curve with the global trend, all discrepancies are adjusted and written into a new coordinate set. The adjusted points are then merged with the original consistent points by time index to ensure the continuity of the power curve. After merging, a new output sequence for the complete path segment is generated and written into the power adjustment result set for the scheduling path segment.

[0033] Please see Figure 5 The steps to obtain S4 are as follows: S401: Call all path segment position relationship data in the power output adjustment result set of the scheduling path segment, extract the set of path segments that are continuously connected to the location of the switching area in the time domain based on the time boundary information of the energy switching transition zone, and construct a time series according to the coordinate arrangement order between the path segments to obtain the continuous path segment sequence set of the transition zone. The energy switching transition zone is defined as the edge period between the start / stop of diesel generators and the charging / discharging of energy storage (e.g., the photovoltaic grid connection period from 6:00 to 7:00 AM). The transition zone boundary time is called. The adjacency determination of path segments and boundaries is performed. If the time distance between the endpoint of a path segment and the boundary is less than 2 sampling periods, it is determined to be a transition zone adjacency path. The path segments that meet the conditions are chained together in chronological order to generate several continuous and uninterrupted path sequences. Each sequence is composed of multiple path segments connected end to end.

[0034] S402: Based on the sequence set of continuous path segments in the transition zone, extract the trend vector of each path segment in the order of the path segments, construct trend vector pairs according to the path adjacency relationship, identify trend changes based on whether the direction of the angle change between each set of trend vectors is repeated, and obtain the path trend change segment index set. Perform a two-term subtraction on the path chain to obtain the direction vector. Generate vector pairs for two adjacent segments. Perform angle change recognition: If If a symbol flips (e.g., from rising to falling) and this feature appears in two consecutive places, it is identified as a trend repetition and jump. The index of this position is recorded as the abrupt change segment index. For example, if the direction sequence is rising, falling, rising, falling, and there is a repetition and flip, the index is recorded, and finally, these are summarized to form a path trend abrupt change segment index set.

[0035] S403: Call the location range of the corresponding path segment in the path trend change segment index set, extract the power data point at the center of each path segment, integrate the coordinates of the middle of all path segments in chronological order, and use this type of coordinate point as the basis to represent the location of the regulation anomaly in the transition area, and output the time sequence of the regulation anomaly area in the transition scheduling zone. Calculate the midpoint of execution time based on the coordinates of the corresponding path segment retrieved from the index: The center time point of each mutation segment path is used as a representative point and stored in the central coordinate set. The above steps are repeated for all mutation segments, sorted in ascending chronological order. After sorting, the central coordinate points are... The sequence is output line by line, describing the location of the regulation anomaly caused by the mismatch of output characteristics during the energy switching sensitive period.

[0036] Please see Figure 6 The steps to obtain S5 are as follows: S501: Call the transition scheduling zone adjustment abnormal area time period sequence, combine the time period number information recorded in the multi-source power output fluctuation characteristic sequence, match the number position corresponding to each group of abnormal coordinate points, and extract all numbers into a set in time order to obtain the abnormal number position set; Read each abnormal time point item one by one and match it with the time range of the scheduling unit. For example, abnormal points If a point falls within the range of cell number 7, that number is extracted. For points falling at cell boundaries, adjacency checks are performed, and the nearest neighbor number is selected. After matching is complete, all number values ​​are aggregated, sorted in ascending order by time, and the set of abnormal number locations is output.

[0037] S502: Based on the set of abnormal numbers, group the number values ​​in the set according to the recurrence, extract the number of times each group of numbers recurs in the set, and form a pair of data frames with the number and its corresponding recurrence to obtain the number recurrence frequency data frame. Perform a count on all numbers in the set. For example, if number 7 appears 4 times in the list, it is counted as a repetition count of 4. This operation is repeated for all numbers in the set, constructing a data frame: the first column is the unit number, and the second column is the repetition frequency. For example: row 1: 7,4, row 2: 15,2. This frequency reflects the fluctuation sensitivity of the scheduling unit during multiple trend adjustments.

[0038] S503: Call the number repetition frequency data frame, compare the number of times each number appears with the frequency filtering benchmark value, retain all number positions that meet the benchmark, perform a set extraction operation on the scheduling unit information corresponding to these position numbers, and output the multi-source collaborative scheduling verification and control status results; Set the frequency filtering benchmark value to 2. Filter the data frames based on the number of repetitions. If the number is not specified, retain that number. Extract and merge the scheduling unit information (original power, adjusted power, deviation value, etc.) corresponding to numbers 7 and 15 from the sequence. Finally, output this set as the microgrid multi-source coordinated scheduling verification and control status result, which is used to instruct the dispatcher to implement key power reservation or load-side response compensation for these high-frequency abnormal areas.

[0039] 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 collaborative optimization scheduling method for a hybrid microgrid of wind, solar, diesel, and energy storage for renewable energy, characterized in that, Includes the following steps: S1: Obtain time series data of power output and load of each unit of wind, solar, diesel and storage, divide the scheduling area into four time periods, compare the multi-source power output status in each area, organize the comparison results by time period number, and output the power fluctuation characteristic sequence of multi-source power output. S2: Read the power fluctuation characteristic sequence of the multi-source output, extract the time edge of the trend change between adjacent numbers, connect the power coordinates of two points with different trends, and splice them in the order of start and end to obtain the set of power coupling change time sequence positions. S3: Using the aforementioned set of power coupling mutation timing locations, extract the coordinates of the start and end points of the scheduling path, read the output points within the path in the connection order, compare the adjustment status of each point with the path trend to see if they are consistent, perform unified adjustment of the response direction for inconsistent points, and output the adjusted points as a set of output adjustment results for the scheduling path segment. S4: Call the output adjustment result set of the scheduling path segment, locate the path segment close to the energy switching transition position, make a continuous judgment on the adjustment direction, extract the middle time period of the path with inconsistent direction, and generate the time period sequence of the adjustment abnormal area in the transition scheduling zone.

2. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that: The power fluctuation characteristic sequence of the multi-source output includes time period identification number, local power difference, and fluctuation change type. The power coupling mutation time sequence location set includes trend change point coordinates, mutation path segment index, and adjacent time period number pairs. The output adjustment result set of the scheduling path segment includes unified adjustment output point coordinates, path segment response mark, and power deviation correction value. The time period sequence of the adjustment abnormal area in the transition scheduling zone includes the abnormal area center time point, repeated trend mutation location, and time domain connection relationship.

3. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that, The steps for obtaining S1 are as follows: S101: Obtain the power values ​​of the wind, solar, diesel and storage units and loads at each sampling location, and according to the sampling order of the time axis, form a scheduling unit by grouping every four adjacent time periods, call the power value parameters in each group of units, extract the power change information according to the time arrangement direction, and form a power state vector set of the scheduling unit. S102: Based on the data content of each group in the power state vector set of the scheduling unit, the power values ​​between adjacent time periods in each group of data are judged by difference. The power offset reference threshold is used to screen out the output combinations that change more than the reference standard, and they are classified into two response categories respectively. The power state classification information of the scheduling unit is output. S103: Call the power status classification information of the scheduling unit, assign numbers according to different response categories, and map the number information into the time position set of each scheduling unit. Combine the power value content contained in each group of units, and output the multi-source output fluctuation characteristic sequence through the correspondence between the number and the value.

4. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 3, characterized in that: The specific method for performing difference judgment on the power values ​​between adjacent time periods within each data set is as follows: the power values ​​of the four adjacent time periods contained in each scheduling unit within the power state vector set of the scheduling unit are compared in pairs according to time order, and the power value of the last time period is compared with the power value of the first time period to form four sets of difference records. The four sets of difference records are compared one by one with the power offset reference threshold. The output combination corresponding to the difference that is greater than the power offset reference threshold is classified as a violent response, and the output combination corresponding to the difference that is less than or equal to the power offset reference threshold is classified as a linear response. When there is at least one output combination that falls under the category of severe response within the same scheduling unit, the power state classification information of the scheduling unit is recorded as having fluctuations, and the number of output combinations with severe response and the number of output combinations with linear response are recorded. The specific method of assigning numbers according to different response categories is as follows: severe responses are assigned number 1, linear responses are assigned number 0, and number 1 or number 0, along with the time location set of the corresponding scheduling unit and the corresponding power value, are written into the multi-source output fluctuation characteristic sequence.

5. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that, The steps for obtaining S2 are as follows: S201: Obtain the adjacent time period numbers in the multi-source power output fluctuation characteristic sequence, extract the number value item by item for the adjacent number sequence, call the power response information between adjacent numbers in the sequence, take the number pair formed by adjacent numbers as the unit, perform the difference judgment operation on the power state within the number pair, and judge whether the adjustment direction corresponding to the number pair remains continuous based on the power change benchmark value, so as to obtain the adjacent time period trend change identifier set. S202: Based on the adjacent time period trend change identifier set, for the numbered pairs with trend change identifiers, call the power data frames of the two edge points of the corresponding time period, and perform coordinate filtering operation on the time and power parameters of the two points in the data frame. Based on the positioning coordinates of the two points at the edge of the time period, extract the coordinate sequence unit to obtain the edge coordinate sequence set. S203: Call the edge coordinate sequence set, and perform time-domain position linking operation on the coordinate sequence units in sequence according to the start and end order of the coordinates in the sequence. The connection path of the coordinate sequence forms a continuous path set, and all path sets are aggregated into a power coupling mutation time-series position set to obtain the power coupling mutation time-series position set.

6. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that, The steps for obtaining S3 are as follows: S301: Based on each set of coordinate points in the power coupling abrupt change time sequence location set, extract the path range data interval between the two points, call the original power sampling information, retrieve all output measurement point positions in the corresponding path segment between each pair of coordinates, and construct the time domain sequence range according to the time span to obtain the path segment output position set; S302: Based on the set of output positions of the path segment, for the power vector state of all output points in each path segment, extract the trend vector parameters corresponding to the path direction, match the angle direction between the power vector of each output point and the path trend vector, determine whether the vector directions are consistent, and output the set of outputs with trend differences in the path segment. S303: Call the path segment trend difference output set, perform unified response processing on the coordinate values ​​of output points with inconsistent directions, adjust the power state of output points according to the reference parameters of the path trend vector, integrate the adjusted coordinates with other output information in the path segment, and output the scheduling path segment output adjustment result set.

7. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that, The steps for obtaining S4 are as follows: S401: Call all path segment position relationship data in the power output adjustment result set of the scheduling path segment, extract the set of path segments that are continuously connected to the location of the switching area in the time domain according to the time boundary information of the energy switching transition zone, and construct a time series according to the coordinate arrangement order between the path segments to obtain the continuous path segment sequence set of the transition zone. S402: Based on the continuous path segment sequence set of the transition zone, extract the trend vector of each path segment in the order of the path segments, construct trend vector pairs according to the path adjacency relationship, identify trend changes based on whether the direction of the angle change between each set of trend vectors repeats, and obtain the path trend change segment index set. S403: Call the location range of the corresponding path segment in the path trend change segment index set, extract the power data point at the center of each path segment, integrate the coordinates of the middle of all path segments in chronological order, and use these coordinate points as the basis to represent the abnormal location of the regulation in the transition area, and output the time sequence of the abnormal regulation area in the transition scheduling zone.

8. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 7, characterized in that: The step of extracting a set of path segments that are continuously connected to the location of the switching area within the time domain based on the time boundary information of the energy switching transition zone is specifically as follows: arranging all path segment position relationship data in the power output adjustment result set of the scheduling path segment in chronological order, reading the start time and end time of each path segment one by one, and comparing the start time or end time with the time boundary information of the energy switching transition zone, retaining path segments whose interval with the time boundary information of the energy switching transition zone does not exceed two sampling periods; The retained path segments are connected sequentially according to the connection relationship between their termination time and the start time of adjacent path segments, and written into the transition zone continuous path segment sequence set in coordinate order. The method of identifying trend changes based on whether the angle change direction between each set of trend vectors repeats is as follows: compare the trend vector pairs formed by adjacent path segments in the continuous path segment sequence set of the transition zone one by one, record the power change direction corresponding to each set of trend vectors, retain the position where the power change direction is reversed and repeats in two consecutive sets of trend vector pairs, and write the retained position into the path trend change segment index set according to the path segment arrangement order. The extraction of power data points at the center of each path segment specifically involves: based on the location range of the corresponding path segment in the path trend change segment index set, reading the start time, end time, and corresponding central power data of each path segment, and outputting the time sequence of the adjustment abnormal area in the transition scheduling zone in ascending order of time.

9. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 1, characterized in that, The method further includes: S5: Combining the time sequence of the abnormal adjustment area in the transitional scheduling zone with the sequence of variable output fluctuation characteristics, the positions with repeated numbers are statistically analyzed, the numbers with high frequency are extracted, and the output is the microgrid multi-source collaborative scheduling verification and control status result; The results of the microgrid multi-source collaborative scheduling verification and control status include frequency statistics, key scheduling area numbers, and system operation stability levels.

10. The method for coordinated optimization scheduling of wind-solar-diesel-storage hybrid microgrids for renewable energy as described in claim 9, characterized in that, The steps for obtaining S5 are as follows: S501: Call the time period sequence of the transition scheduling zone adjustment abnormal area, combine it with the time period number information recorded in the multi-source power output fluctuation characteristic sequence, match the number position corresponding to each group of abnormal coordinate points, and extract all numbers into a set in time order to obtain the abnormal number position set; S502: Based on the set of abnormal number locations, group the number values ​​in the set according to the recurrence, extract the number of times each group of numbers recurs in the set, and form a paired data frame with the number and its corresponding recurrence count to obtain the number recurrence frequency data frame. S503: Call the number repetition frequency data frame, compare the number of times each number appears with the frequency filtering benchmark value, retain all number positions that meet the benchmark, perform a set extraction operation on the scheduling unit information corresponding to these position numbers, and output the multi-source collaborative scheduling verification and control status results.

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