Multi-microgrid cooperative active support control method

Abstract

The invention discloses a multi-microgrid cooperative active support control method, and relates to the technical field of energy cooperative control, and the method monitors the operation state information in the continuous operation period of a power distribution system, and switches to a cooperative active support mode when the operation state continuously deviates from a preset allowed operation interval. A power withdrawal analysis time window is determined by constructing an operation quantity time sequence and combining stable interval judgment and change direction consistency analysis. Constructing a power retracement variable quantity delta P in a retracement stage, and forming a retracement consistency index Cret through difference integral analysis to perform retracement state evaluation; and constructing a retracement stability index Rsta in a consistent structure state to carry out rhythm stability evaluation. And when the withdrawal rhythm of the micro-grid is stable, constructing a collaborative support constraint quantity Uad, mapping participation depth limitation information into collaborative active support control parameters, and enabling a multi-micro-grid collaborative active support process to form a closed-loop control structure based on withdrawal behavior analysis.

Description

Technical Field

[0001] This invention relates to the field of energy collaborative control technology, specifically a collaborative active support control method for multiple microgrids. Background Technology

[0002] With the continuous change in the proportion of distributed power sources connected to power distribution systems, the traditional operating structure centered on centralized power sources has gradually evolved into a distributed operating model based on microgrids as the basic unit. Microgrids, by integrating distributed power sources, energy storage devices, and load resources within a local area, give power distribution systems new characteristics in terms of operational flexibility and local regulation capabilities. When multiple microgrids are connected to the same power distribution system in parallel and participate in system support under abnormal operating conditions or disturbances, the independent regulation of a single microgrid is no longer sufficient to meet overall operational needs, thus forming a collaborative regulation scenario involving multiple microgrids. In this context, multi-microgrid collaborative active support control has gradually become an important component of power distribution system operation control. Its core lies in guiding multiple microgrids to form coordinated behavior in time and direction of regulation when the system deviates from its normal operating state, in order to maintain the overall controllability of system operation.

[0003] Existing multi-microgrid collaborative active support control primarily focuses on the support phase itself, paying insufficient attention to the power withdrawal process after support ends. It typically employs a fixed withdrawal strategy or allows each microgrid to exit independently. While these approaches possess clear control logic during the support triggering and power output phases, they lack a unified assessment and constraint of the collective behavior of multiple microgrids during the withdrawal phase, making it difficult to characterize the differences in withdrawal rhythm, direction of change, and temporal evolution paths among different microgrids. Due to the lack of quantitative analysis on withdrawal consistency, smoothness, and path morphology, existing methods often struggle to structurally assess the collaborative operating state during the withdrawal phase and fail to feed back the withdrawal behavior results to subsequent collaborative support control parameter configuration. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-microgrid collaborative active support control method, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-microgrid collaborative active support control method, comprising the following steps: S1. Monitor the operating status information of the power distribution system in real time during the continuous operation cycle of the power distribution system, and generate a collaborative active support triggering flag when the operating status information deviates, issue a collaborative support scheduling command to the microgrid and switch to the collaborative active support mode. S2. During the execution of the collaborative active support mode, construct the time series of the operation volume, combine the stability interval determination and the consistency analysis of the change direction to judge the collaborative operation status of multiple microgrids, and confirm the power withdrawal analysis time window [ts,te] when the stability convergence condition is met. S3. Collect the output power of the microgrid within the power pullback analysis time window [ts,te], perform differential operation to construct the power pullback change ΔP, and construct the pullback consistency index Cret based on the power pullback change ΔP to evaluate the pullback status. S4. When the pullback state is evaluated as a consistent structure state, construct the pullback smoothness index Sret and fit it with the pullback consistency index Cret to construct the pullback stability index Rsta for rhythm stability evaluation. S5. When the microgrid pullback rhythm is stable, construct the pullback path deviation index Dpa and fit it with the pullback stability index Rsta to construct the collaborative support constraint Uad, and map the participation depth limit information into collaborative active support control parameters.

[0006] Preferably, S1 includes S11; S11. Real-time monitoring of the operating status information of the power distribution system during the continuous operation cycle of the power distribution system, and comparison and judgment based on the operating status information and the preset allowable operating range of the power distribution system under the standard state. When it is determined that the operating status information of the power distribution system continuously deviates from the allowable operating range during the continuous monitoring cycle, a collaborative active support triggering identifier is generated. The operating status includes system voltage level, system frequency status, and power balance characteristics; The collaborative active support triggering identifier is used to indicate that the power distribution system enters the multi-microgrid collaborative active support control stage, marking the current power distribution system switching from the normal operation monitoring state to the multi-microgrid collaborative active support control state.

[0007] Preferably, S1 further includes S12 and S13; S12. After the collaborative active support trigger identifier is generated, the current operating status of each microgrid participating in the collaboration is collected, and the power regulation range, power regulation direction and grid connection point operation information of each microgrid that can be used for support are obtained to form collaborative support scheduling constraints. Based on the scheduling constraints, and according to the existing multi-microgrid collaborative support control strategy, collaborative support scheduling instructions corresponding to each microgrid are constructed. The effective order and effective time of the collaborative support scheduling instructions are configured according to the phase angle response of each microgrid's grid connection point. Then, the collaborative support scheduling instructions are sent to the participating microgrids in a synchronous manner through a centralized instruction distribution mechanism. S13. After receiving the collaborative support dispatch instruction, each microgrid parses the collaborative support dispatch instruction and switches its local operation control mode from the normal operation mode to the collaborative active support mode based on the parsing result. In the collaborative active support mode, the corresponding power output regulation operation is executed based on the existing active and reactive power regulation control logic, so that the microgrid forms a collaborative support state. During the execution of collaborative active support, the power change process and phase angle change information at each microgrid grid connection point are recorded synchronously, and the power change process and phase angle change information are recorded to provide a basis for the next configuration.

[0008] Preferably, S2 includes S21 and S22; S21. During the execution of the collaborative active support mode, the operating volume of multiple microgrids during collaborative operation is continuously collected to form an operating volume time series. The operating volume time series is judged point by point within a fixed analysis time window. If there is a sampling point that exceeds the stable interval, it is determined that the current operating state is not stable. When the operating volume enters the stable interval, the changing trend of adjacent sampling points is analyzed. If the changing trend shows a reverse change characteristic of shifting from the boundary of the stable interval to the outside, it is determined that the stable state is not sustainable, and the collaborative support state is maintained unchanged. The operational quantities include the frequency offset, voltage offset, active power balance deviation, and reactive power balance deviation of the microgrid parallel system. S22. Based on the stable range of the operating volume, the operating volume is processed by time difference within a continuous time window to construct the direction vector of the operating volume change, and the direction angle change information of the change direction vector is extracted in adjacent time windows. When the direction angle change value is within the preset angle fluctuation range and no reverse change of direction angle occurs within the continuous time window, it is determined that the change direction of the multi-microgrid cooperative operation state is consistent and enters the stable convergence stage. When the coordinated operation of multiple microgrids meets the stable convergence condition, a coordinated support end flag is generated and the corresponding time is marked as the start time ts of the power withdrawal phase, which is calibrated as the start time of the withdrawal analysis. Combined with the length of time that the operation state remains in the stable range, the end time te of the withdrawal phase is determined, and the power withdrawal analysis time window [ts, te] is constructed.

[0009] Preferably, S3 includes S31 and S32; S31. After the collaborative active support ends and the power withdrawal phase begins, the actual output power of each microgrid during the withdrawal phase is continuously collected using the withdrawal start time ts as the time reference point. The power is stored in the historical operation buffer at a uniform time granularity to form the power time series dataset of the withdrawal phase. Then, differential operation is performed on the output power at adjacent sampling times to construct the power withdrawal change ΔP. S32. During the pullback phase, within the time interval [ts, te], perform time series analysis on the power change ΔP of each microgrid, extract the direction of change during the power pullback process, and map the direction of change into a time direction angle sequence. Based on the power pullback change ΔP, the power change of different microgrids in the same time period is aligned step by step. Then, the difference integral is calculated to obtain the difference accumulation characteristics in the power pullback. The difference accumulation characteristics are then summarized and processed to construct the pullback consistency index Cret, which is used to represent the degree of consistency of the cooperative behavior of multiple microgrids in the pullback phase, as follows. ; Where N represents the number of microgrids participating in the consensus analysis, T represents the time span of the rollback analysis, and ΔP i (t) and ΔP j (t) represents the power change of the i-th microgrid and the j-th microgrid at time t relative to the previous sampling time, respectively, and dt represents the time integral variable.

[0010] Preferably, S3 further includes S33; S33. Extract several event samples of support completion and pullback completion, calculate the pullback consistency index Cret for each sample and sort them in ascending order. Extract the 90th percentile as the preset pullback consistency alarm threshold Tc according to the percentile method, and then compare it with the real-time acquired pullback consistency index Cret. Generate a pullback status assessment based on the comparison results, as follows. When the pullback consistency index Cret ≤ the pullback consistency alarm threshold Tc, it indicates that the pullback behavior presents a consistent structure state. At this time, the pullback rhythm constraint activation command is issued and the pullback stability analysis is triggered. When the pullback consistency index Cret > the pullback consistency alarm threshold Tc, it indicates that the pullback consistency has crossed the stable sample boundary and the difference in the group's pullback behavior is abnormal. At this time, a consistency deviation flag is issued and an alignment correction operation is performed to resample each power time series dataset to a unified time axis index set according to a unified time dimension.

[0011] Preferably, S4 includes S41; S41. When the pullback state assessment indicates that the pullback behavior presents a consistent structural state, the acceleration of power change during the pullback process is analyzed, the second-order time change of power change is calculated, and then the second-order time change is integrated over time and normalized over intervals to calculate the pullback smoothness index Sret, which is used to measure the continuity of the power change rhythm during the pullback process, as follows. ; Among them, Sret i τ represents the drawdown smoothness exponent of the i-th microgrid.i Let dt represent the duration of the current pullback phase of the i-th microgrid, and let dt represent the time calculus variable.

[0012] Preferably, S4 further includes S42; S42 is used to perform pullback consistency analysis and rhythm stability assessment on each microgrid, specifically including S421 and S422; S421. After uniformly calibrating the time intervals corresponding to the pullback consistency index Cret and the pullback smoothness index Sret, the structure fusion operation is performed with the pullback consistency index Cret as the overall pullback structure constraint term and the pullback smoothness index Sret of each microgrid as the individual pullback rhythm constraint term to construct the pullback stability index Rsta, which represents the overall stable state of the pullback behavior of multiple microgrids after the end of this collaborative active support, as follows; ; Where N represents the number of microgrids.

[0013] Preferably, in step S422, the pullback consistency index Cret is statistically processed after the historical collaborative active support ends and the microgrid pullback rhythm is stable. The mean value is extracted and preset as the pullback stability threshold Ts. It is then compared with the obtained pullback stability index Rsta. Based on the comparison results, a rhythm stability assessment is generated, as follows. When the pullback stability index Rsta ≤ the pullback stability threshold Ts, it indicates that the microgrid pullback rhythm is stable, and this pullback is marked as meeting the standard pullback quality. When the pullback stability index Rsta > the pullback stability threshold Ts, it indicates that there is a local instability in the microgrid pullback rhythm. At this time, the current pullback batch is marked as having rhythm quality risk and transmitted to the management personnel for manual intervention.

[0014] Preferably, S5 includes S51, S52, S53 and S54; S51. When the microgrid pullback rhythm is stable, retrieve the actual power sequence of the i-th microgrid within the power pullback analysis time window [ts, te]. By continuously describing the power change relationship between adjacent sampling times, map the discrete time sampling points into a continuous trajectory of the power evolution relationship over time, forming the pullback time unfolding trajectory Gj. Perform geometric analysis on the evolution characteristics of the pullback time unfolding trajectory Gj in the time dimension to obtain the path length Cd of the pullback process unfolding shape on the time axis. Simultaneously obtain the corresponding pullback duration Sj. Construct the pullback path deviation index Dpa by the ratio of the pullback path length Cd to the pullback duration Sj to quantify the time complexity of the pullback path. S52. Fit the retracement stability index Rsta and the retracement path deviation index Dpa to construct a collaborative support constraint Uad, which is used to represent the overall retracement behavior state after the end of this round of collaborative support. Specifically: Among them, Dpa i This represents the deviation index of the pullback path for the i-th microgrid; S53. Based on the collaborative support constraint quantity Uad in multiple historical collaborative support cycles, perform statistical distribution analysis to construct a constraint reference interval set, and map the collaborative support constraint quantity Uad corresponding to the current collaborative support cycle to the constraint reference interval set to obtain the interval position of the current collaborative support cycle. Match the corresponding participation depth restriction information for each microgrid according to the interval position. The set of constraint reference intervals includes stable intervals, transition intervals, and restricted intervals; S54. Perform parameterized analysis on the participation depth limitation information to obtain the allowable power regulation boundary range of each microgrid in the next round of collaborative active support control. Based on the correspondence between the participation depth limitation information and the collaborative active support control parameters, perform boundary constraint mapping on the collaborative active support control parameters, match the available value range of the collaborative active support control parameters with the current collaborative support constraint quantity Uad, and then send the collaborative active support control parameters after constraint mapping and the collaborative active support scheduling command to each microgrid control unit as input conditions for the next round of collaborative active support control.

[0015] This invention provides a collaborative active support control method for multiple microgrids. It has the following beneficial effects: (1) This method starts with monitoring the operating status of the power distribution system during its continuous operation cycle. By continuously comparing and judging the voltage level, frequency status, and power balance characteristics of the power distribution system, it objectively identifies the triggering conditions for collaborative active support. After triggering, it switches the control state from normal operation to multi-microgrid collaborative active support mode. On this basis, by centrally collecting and uniformly configuring the operating status, power regulation range, regulation direction, and phase angle response relationship of each participating microgrid, it generates and synchronously issues collaborative support dispatching instructions in an orderly manner, so that the multi-microgrid forms consistent support behavior in terms of time and direction. Subsequently, during the execution of collaborative active support, it introduces the construction of operating quantity time series, determination of stable range, and consistency analysis of change direction. It not only judges whether the operating quantity falls into the stable range, but also further examines the continuity and trend consistency of the stable state in the time dimension, completes the determination of the end time of collaborative support, and marks the power withdrawal analysis time window, laying a unified time foundation for subsequent withdrawal behavior analysis.

[0016] (2) In the power withdrawal phase, this method continuously collects and differentially calculates the output power of each microgrid within the withdrawal analysis time window to construct the power withdrawal change ΔP, and describes the directional relationship of the power change trend during the withdrawal process in the form of a time direction angle. On this basis, by aligning and integrating the power change ΔP of different microgrids at the same time dimension, a withdrawal consistency index Cret is formed, so that the degree of difference in the withdrawal behavior of the group can be uniformly quantified. Compared with the existing technology that judges based only on the withdrawal rate of a single microgrid or the power deviation at a single point, this method introduces a cross-microgrid and cross-time overall consistency assessment mechanism. At the same time, under the premise that the withdrawal consistency structure is established, the second-order time change of power change is further analyzed to construct the withdrawal smoothness index Sret, and the withdrawal smoothness index Sret and the withdrawal consistency index Cret are structurally integrated to form the withdrawal stability index Rsta, so that the assessment of the withdrawal phase covers both the group consistency and the individual rhythm continuity, and characterizes the rhythm stability state during the withdrawal process.

[0017] (3) After confirming that the microgrid pullback rhythm is in a stable state, this method continuously describes the time unfolding trajectory of the pullback process. By analyzing the geometric shape of the pullback path on the time axis, a pullback path deviation index Dpa is constructed to quantify the time complexity of the pullback path. The pullback path deviation index Dpa is then fitted with the pullback stability index Rsta to form the collaborative support constraint Uad. By analyzing the statistical distribution of the collaborative support constraint Uad in historical collaborative support cycles, a set of constraint reference intervals is constructed. The collaborative support constraint Uad of the current cycle is mapped to the corresponding set of constraint reference intervals, completing the matching and parameterization analysis of the participation depth restriction information of each microgrid. As a result, the collaborative active support control parameters are no longer fixed, but are mapped to the boundary constraints based on the overall collaborative state reflected by the previous pullback behavior, and used as the input conditions for the next round of collaborative active support control, so that the multi-microgrid collaborative active support control process forms a closed-loop operation structure based on pullback behavior constraints. Through the above steps, this method completes the full-process management of multi-microgrid collaborative active support from triggering, execution, termination to feedback, enabling collaborative control to have cross-cycle evolution characteristics and forming a controllable and traceable collaborative operation mechanism at the power distribution system level. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the steps of a multi-microgrid collaborative active support control method according to the present invention; Figure 2 This is a block diagram illustrating the principle and logic of a multi-microgrid collaborative active support control method according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 This invention provides a collaborative active support control method for multiple microgrids. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps: S1. Monitor the operating status information of the power distribution system in real time during the continuous operation cycle of the power distribution system, generate a collaborative active support triggering flag when the operating status information deviates, issue a collaborative support scheduling command to the microgrid and switch to the collaborative active support mode. S2. During the execution of the collaborative active support mode, construct the time series of the operation volume, combine the stability interval judgment and the consistency analysis of the change direction to judge the collaborative operation status of multiple microgrids, and confirm the power withdrawal analysis time window [ts,te] when the stability convergence condition is met. S3. Collect the output power of the microgrid within the power pullback analysis time window [ts,te], perform differential operation to construct the power pullback change ΔP, and construct the pullback consistency index Cret based on the power pullback change ΔP to evaluate the pullback status. S4. When the pullback state is evaluated as a consistent structure state, construct the pullback smoothness index Sret and fit it with the pullback consistency index Cret to construct the pullback stability index Rsta for rhythm stability evaluation. S5. When the microgrid pullback rhythm is stable, construct the pullback path deviation index Dpa and fit it with the pullback stability index Rsta to construct the collaborative support constraint Uad, and map the participation depth limit information into collaborative active support control parameters.

[0021] In this embodiment, during the continuous operation of the power distribution system, a collaborative active support triggering and mode switching mechanism based on deviations in operating status is established. This enables the collaborative control of multiple microgrids to automatically enter the collaborative active support phase based on the continuous evolution of the power distribution system's operating status. During the execution of collaborative support, by constructing an operational quantity time series and introducing stability interval determination and change direction consistency analysis, the collaborative operating status of multiple microgrids is continuously tracked and judged. This not only focuses on whether the operational quantity has entered a stable interval but also simultaneously examines the consistency of its change trend over time, providing a clear basis for confirming the timing of the collaborative support termination and defining a unified analysis time window for the subsequent power withdrawal phase. During the power withdrawal phase, the analysis focus shifts from a single power value to the power change process itself. By performing differential operations on the output power to construct the power withdrawal change ΔP and constructing the withdrawal consistency index Cret, the overall behavior of the multiple microgrids during the withdrawal phase is characterized. Under the condition of a consistent structure in the pullback state assessment, an analysis of the second-order time characteristics of power changes is introduced to construct a pullback smoothness index Sret, which is then fused with the pullback consistency index Cret to form a pullback stability index Rsta. This allows the assessment of the pullback phase to simultaneously cover the degree of group consistency and the continuity of individual rhythms. Compared to techniques that rely solely on pullback magnitude or a single threshold, this method provides a more detailed description of the structural and rhythmic characteristics of pullback behavior, making the operational state of the pullback phase more interpretable. Based on the confirmation that the microgrid pullback rhythm is in a stable state, a pullback path deviation index Dpa is introduced to quantify the temporal unfolding of the pullback process and fit it with the pullback stability index Rsta to construct a collaborative support constraint quantity Uad, used to characterize the overall pullback behavior state after the collaborative support ends. Based on the collaborative support constraint quantity Uad, the participation depth limitation information is mapped to collaborative active support control parameters, ensuring that subsequent collaborative support control has parameter boundaries that match historical pullback behavior. As a result, the collaborative active support control of multiple microgrids has shifted from a single scheduling to a cross-cycle correlation operation mode, enabling the control strategy to gradually form an adjustment boundary that adapts to the actual evolution characteristics of the system during continuous operation, and constructing a collaborative operation structure with constraint feedback characteristics.

[0022] Example 2 Please refer to Figure 2 Specifically: S1 includes S11; S11. Real-time monitoring of the operating status information of the power distribution system during the continuous operation cycle of the power distribution system, and comparison and judgment based on the operating status information and the preset allowable operating range of the power distribution system under the standard state. When it is determined that the operating status information of the power distribution system continuously deviates from the allowable operating range during the continuous monitoring cycle, a collaborative active support triggering identifier is generated. The operating status includes system voltage level, system frequency status, and power balance characteristics; The collaborative active support triggering identifier is used to indicate that the power distribution system enters the multi-microgrid collaborative active support control stage, marking the current power distribution system switching from the normal operation monitoring state to the multi-microgrid collaborative active support control state.

[0023] S1 also includes S12 and S13; S12. After the collaborative active support trigger identifier is generated, the current operating status of each microgrid participating in the collaboration is collected, and the power regulation range, power regulation direction and grid connection point operation information of each microgrid that can be used for support are obtained to form collaborative support scheduling constraints. Based on the scheduling constraints, and according to the existing multi-microgrid collaborative support control strategy, collaborative support scheduling instructions corresponding to each microgrid are constructed. The effective order and effective time of the collaborative support scheduling instructions are configured according to the phase angle response of each microgrid's grid connection point. Then, the collaborative support scheduling instructions are sent to the participating microgrids in a synchronous manner through a centralized instruction distribution mechanism. S13. After receiving the collaborative support dispatch instruction, each microgrid parses the collaborative support dispatch instruction and switches its local operation control mode from the normal operation mode to the collaborative active support mode based on the parsing result. In the collaborative active support mode, the corresponding power output regulation operation is executed based on the existing active and reactive power regulation control logic to enable the microgrid to form a collaborative support state. During the execution of collaborative active support, the power change process and phase angle change information at each microgrid grid connection point are recorded synchronously. The power change process and phase angle change information are recorded to provide a basis for the next configuration until the subsequent collaborative active support end judgment conditions are met. The phase angle change information indicates the correspondence between the power adjustment direction and the system phase angle offset trend.

[0024] In this embodiment, S11 monitors the voltage level, frequency status, and power balance characteristics of the power distribution system in real time and continuously compares them with the preset allowable operating range to form a collaborative active support triggering mechanism based on continuous deviation. This allows the power distribution system's operating state to switch orderly from the conventional monitoring stage to the multi-microgrid collaborative active support control stage. S12, after the trigger identifier is generated, centrally collects the current operating status of each participating microgrid to clarify the power adjustment range, adjustment direction, and grid connection point operating information available for support by each microgrid. It then constructs collaborative support scheduling constraints and, based on this, combines existing... A multi-microgrid collaborative support control strategy generates collaborative support dispatch instructions that match the operating characteristics of each microgrid. Simultaneously, based on the phase angle response at the grid connection point, the effective sequence and timing of the dispatch instructions are configured to ensure coordination in both time and direction during the collaborative support process. In step S13, after each microgrid receives and parses the dispatch instructions, it switches its local control mode to a collaborative active support mode. It then executes corresponding power output regulation based on its existing active and reactive power regulation control logic, continuously recording the power change process and phase angle change information at the grid connection point during the support process, using this as a reference for subsequent dispatch configuration. Through this implementation method, a complete process is completed from collaborative support triggering, dispatch instruction generation and issuance, to multi-microgrid collaborative execution and operational information feedback, establishing collaborative support behavior based on real-time operating status and grid connection response characteristics. Compared to techniques that rely solely on fixed thresholds or single microgrid states for dispatch, this implementation method provides clear constraints on the participation sequence, regulation direction, and regulation range of multiple microgrids during the support phase, and provides a basis for continuous correction and configuration during the dispatch process.

[0025] Example 3 Please refer to Figure 2 Specifically: S2 includes S21 and S22; S21. During the execution of the collaborative active support mode, the operating volume of multiple microgrids during collaborative operation is continuously collected to form an operating volume time series. Within a fixed analysis time window, the operating volume time series is judged point by point to determine whether each sampling point continuously falls within the preset stable range. If there is a sampling point that exceeds the stable range, it is determined that the current operating state is not stable. When the operating volume enters the stable range, the changing trend of adjacent sampling points is analyzed. If the changing trend shows a reverse change characteristic of shifting from the boundary of the stable range to the outside, it is determined that the stable state is not sustainable, and the collaborative support state remains unchanged. The operational quantities include the frequency offset, voltage offset, active power balance deviation, and reactive power balance deviation of the microgrid parallel system. S22. Based on the stable range of the operating volume, the operating volume is processed by time difference within a continuous time window to construct the direction vector of the operating volume change, which is used to describe the change trend of the operating state in the time dimension. The direction angle change information of the change direction vector is extracted in adjacent time windows to describe the consistency of the change trend of the operating state in time. When the direction angle change value is within the preset angle fluctuation range and no reverse change of direction angle occurs within the continuous time window, it is determined that the change direction of the multi-microgrid collaborative operation state is consistent and enters the stable convergence stage. When the coordinated operation of multiple microgrids meets the stable convergence condition, a coordinated support end flag is generated and the corresponding time is marked as the start time ts of the power withdrawal phase, which is calibrated as the start time of the withdrawal analysis. Combined with the length of time that the operation state remains in the stable range, the end time te of the withdrawal phase is determined, and the power withdrawal analysis time window [ts, te] is constructed.

[0026] In this embodiment, in S21, frequency offset, voltage offset, active power balance deviation, and reactive power balance deviation are continuously collected, and each point is judged within a fixed analysis time window to determine whether it continuously falls within the preset stable range, thereby completing the basic identification of whether the operating state has entered the stable range. At the same time, combined with the trend analysis of adjacent sampling points, when the operating quantity enters the stable range but shows a reverse change characteristic of shifting outward from the boundary of the stable range, the misjudgment caused by a brief drop or occasional fluctuation is clearly excluded, and the collaborative support state remains unchanged. In S22, under the premise that the operating quantity is continuously in the stable range, time difference processing is introduced to construct the direction vector of the operating quantity change, and by extracting the direction angle change information within adjacent time windows, the consistency of the operating state change trend in the time dimension is judged. Only when the change direction remains consistent within the continuous time window and no reverse change occurs is it confirmed that the multi-microgrid collaborative operation state has entered the stable convergence stage, and a collaborative support end flag is generated accordingly, and the power withdrawal analysis time window [ts,te] is accurately calibrated. Through the above implementation methods, this solution achieves the objective identification of the end time of collaborative support and the unified definition of the time range of pullback analysis, so that the consistency and rhythm analysis of subsequent power pullback are based on a reliable time basis, avoiding premature pullback judgment caused by instantaneous fluctuations or local stabilization.

[0027] Example 4 Please refer to Figure 2 Specifically: S3 includes S31 and S32; S31. After the collaborative active support ends and the power withdrawal phase begins, the actual output power of each microgrid during the withdrawal phase is continuously collected using the withdrawal start time ts as the time reference point. The power is stored in the historical operation buffer at a uniform time granularity to form a power time series dataset for the withdrawal phase. Then, differential operation is performed on the output power at adjacent sampling times to construct the power withdrawal change ΔP, which is used to describe the instantaneous change characteristics of power during the withdrawal process. S32. During the pullback phase, within the time interval [ts, te], perform time series analysis on the power change ΔP of each microgrid, extract the direction of change during the power pullback process, and map the direction of change into a time direction angle sequence. The directional angle is used to represent the directional relationship of the power withdrawal trend in the time dimension, including the continuous withdrawal direction, the reverse adjustment direction that occurs during the withdrawal process, and the directional description corresponding to the withdrawal stagnation state; it does not involve spatial geometric meaning and is used to uniformly describe the directional characteristics of the power change trend of different microgrids in the withdrawal stage. Based on the power pullback change ΔP, the power change of different microgrids in the same time period is aligned step by step. Then, the difference integral is calculated to obtain the difference accumulation characteristics in the power pullback. The difference accumulation characteristics are then summarized and processed to construct the pullback consistency index Cret, which is used to represent the degree of consistency of the cooperative behavior of multiple microgrids in the pullback phase, as follows. ; Where N represents the number of microgrids participating in the consensus analysis, T represents the time span of the rollback analysis, and ΔP i (t) and ΔP j (t) represents the power change of the i-th microgrid and the j-th microgrid at time t relative to the previous sampling time, respectively, and dt represents the time integral variable.

[0028] S3 also includes S33; S33. Extract several event samples of support completion and pullback completion, calculate the pullback consistency index Cret for each sample and sort them in ascending order. Extract the 90th percentile as the preset pullback consistency alarm threshold Tc according to the percentile method, and then compare it with the real-time acquired pullback consistency index Cret. Generate a pullback status assessment based on the comparison results, as follows. When the pullback consistency index Cret ≤ the pullback consistency alarm threshold Tc, it indicates that the pullback behavior presents a consistent structure state. At this time, the pullback rhythm constraint activation command is issued and the pullback stability analysis is triggered. When the pullback consistency index Cret > the pullback consistency alarm threshold Tc, it indicates that the pullback consistency has crossed the stable sample boundary and the difference in the group's pullback behavior is abnormal. At this time, a consistency deviation flag is issued and an alignment correction operation is performed to resample each power time series dataset to a unified time axis index set according to a unified time dimension.

[0029] In this embodiment, after the collaborative active support ends in S31, the actual output power of each microgrid during the power withdrawal phase is continuously collected using the withdrawal start time ts as a unified time reference. Through unified time granularity caching and alignment processing, a power time-series dataset for the withdrawal phase is constructed, eliminating the impact of differences in sampling rhythms among different microgrids on subsequent analysis from a data organization perspective. By performing differential operations on the power at adjacent sampling times, the power withdrawal change ΔP is obtained, transforming the power withdrawal process from discrete power values ​​into a time-series signal that can describe instantaneous changes. In S32, within the withdrawal analysis time window [ts, te], time-series analysis is performed on the power withdrawal change ΔP, uniformly mapping the power change trend to a directional angle sequence with only time-direction significance. This achieves a unified description of the withdrawal trend direction relationship among different microgrids without introducing spatial geometric assumptions. By aligning the power changes of each microgrid within the same time period and integrating and summarizing the differences in changes between microgrids, a withdrawal consistency index Cret is constructed, enabling the group collaborative behavior during the withdrawal phase to be expressed in a single quantitative indicator. S33 incorporates historical data and pullback event samples to analyze the statistical distribution of the pullback consistency index Cret, and determines the pullback consistency alarm threshold Tc using the percentile method, providing a clear historical reference for pullback status assessment. When the pullback consistency index Cret is less than or equal to the pullback consistency alarm threshold Tc, the pullback behavior is automatically determined to exhibit a consistent structure, and the subsequent pullback stability analysis process begins. When the pullback consistency index Cret is greater than the pullback consistency alarm threshold Tc, a consistency deviation flag is triggered, and a time axis alignment correction operation is performed, bringing the power data back to a comparable state. Through the above implementation method, this step completes a systematic characterization of the power pullback stage's change process, trend direction, and group consistency, transforming the pullback status from empirical judgment to a quantitative assessment result based on time evolution characteristics. In multi-microgrid collaborative scenarios, this provides a clear and reusable basis for subsequent rhythm stability analysis and control parameter constraints.

[0030] Example 5 Please refer to Figure 2 Specifically: S4 includes S41; S41. When the pullback state assessment indicates that the pullback behavior presents a consistent structural state, the acceleration of power change during the pullback process is analyzed, the second-order time change of power change is calculated, and then the second-order time change is integrated over time and normalized over intervals to calculate the pullback smoothness index Sret, which is used to measure the continuity of the power change rhythm during the pullback process, as follows. ; Among them, Sret i τ represents the drawdown smoothness exponent of the i-th microgrid. i Let dt represent the duration of the current pullback phase of the i-th microgrid, and let dt represent the time calculus variable.

[0031] S4 also includes S42; S42 is used to perform pullback consistency analysis and rhythm stability assessment on each microgrid, specifically including S421 and S422; S421. After uniformly calibrating the time intervals corresponding to the pullback consistency index Cret and the pullback smoothness index Sret, the structure fusion operation is performed with the pullback consistency index Cret as the overall pullback structure constraint term and the pullback smoothness index Sret of each microgrid as the individual pullback rhythm constraint term to construct the pullback stability index Rsta, which represents the overall stable state of the pullback behavior of multiple microgrids after the end of this collaborative active support, as follows; ; Where N represents the number of microgrids.

[0032] S422. After the historical collaborative active support ends and the microgrid pullback rhythm is stable, the pullback consistency index Cret is statistically processed, the mean is extracted and preset as the pullback stability threshold Ts, and compared with the obtained pullback stability index Rsta. Based on the comparison results, a rhythm stability assessment is generated, as follows. When the pullback stability index Rsta ≤ the pullback stability threshold Ts, it indicates that the microgrid pullback rhythm is stable, and this pullback is marked as meeting the standard pullback quality. When the pullback stability index Rsta > the pullback stability threshold Ts, it indicates that there is a local instability in the microgrid pullback rhythm. At this time, the current pullback batch is marked as having rhythm quality risk and transmitted to the management personnel for manual intervention.

[0033] In this embodiment, when the power withdrawal state is determined to be a consistent structure state during the power withdrawal phase, S41 analyzes the time acceleration of power change during the withdrawal process of each microgrid. By calculating the second-order time change of power change and performing time integration and interval normalization within the withdrawal duration, the corresponding withdrawal smoothness index Sret is obtained, which is used to characterize the continuity of power change rhythm in the time dimension of each microgrid during the withdrawal phase. S421 performs a unified analysis of the group behavior and individual rhythm during the withdrawal phase. By calibrating the time interval of the withdrawal consistency index Cret and the withdrawal smoothness index Sret, the withdrawal phase is analyzed. The consistency index Cret serves as the overall pullback structure constraint, while the pullback smoothness index Sret of each microgrid serves as the individual rhythm constraint for structural fusion calculation, constructing the pullback stability index Rsta. This allows the overall stability of the pullback phase to be expressed by a single quantitative indicator. In S422, the pullback consistency index corresponding to the end of historical collaborative active support and the pullback rhythm being in a stable state is statistically processed to form the pullback stability threshold Ts, which is compared with the real-time obtained pullback stability index Rsta to determine the rhythm status of the current pullback batch, providing a clear basis for whether the pullback process is within an acceptable rhythm range. Through the above implementation method, the pullback phase after the end of collaborative active support for multiple microgrids is no longer judged solely based on whether the power has recovered to the target value, but is simultaneously incorporated into a comprehensive analysis of the pullback structure consistency and rhythm continuity. This provides a quantifiable and distinguishable judgment result for the quality of pullback behavior and a clear decision-making basis for whether manual intervention is needed.

[0034] Example 6 Please refer to Figure 2 Specifically: S5 includes S51, S52, S53 and S54; S51. When the microgrid pullback rhythm is stable, retrieve the actual power sequence of the i-th microgrid within the power pullback analysis time window [ts, te]. By continuously describing the power change relationship between adjacent sampling times, map the discrete time sampling points into a continuous trajectory of the power evolution relationship over time, forming the pullback time unfolding trajectory Gj. Perform geometric analysis on the evolution characteristics of the pullback time unfolding trajectory Gj in the time dimension to obtain the path length Cd of the pullback process unfolding shape on the time axis. Simultaneously obtain the corresponding pullback duration Sj. Construct the pullback path deviation index Dpa by the ratio of the pullback path length Cd to the pullback duration Sj to quantify the time complexity of the pullback path. S52. Fit the retracement stability index Rsta and the retracement path deviation index Dpa to construct a collaborative support constraint Uad, which is used to represent the overall retracement behavior state after the end of this round of collaborative support. Specifically: Among them, Dpai This represents the deviation index of the pullback path for the i-th microgrid; S53. Based on the collaborative support constraint quantity Uad in multiple historical collaborative support cycles, perform statistical distribution analysis to construct a constraint reference interval set, and map the collaborative support constraint quantity Uad corresponding to the current collaborative support cycle to the constraint reference interval set to obtain the interval position of the current collaborative support cycle. Match the corresponding participation depth restriction information for each microgrid according to the interval position. The set of constraint reference intervals includes stable intervals, transition intervals, and restricted intervals; The participation depth limit range is given in the form of a power regulation range, which is used to limit the maximum regulation range or variable range that the microgrid is allowed to perform during the collaborative active support process. S54. Perform parameterized analysis on the participation depth limit information to obtain the allowable power regulation boundary range of each microgrid in the next round of collaborative active support control. Based on the correspondence between the participation depth limit information and the collaborative active support control parameters, perform boundary constraint mapping on the collaborative active support control parameters, match the available value range of the collaborative active support control parameters with the current collaborative support constraint quantity Uad, and then send the collaborative active support control parameters after constraint mapping and the collaborative active support scheduling command to each microgrid control unit as the input conditions for the next round of collaborative active support control, so that the multi-microgrid collaborative active support control process forms a closed-loop operation structure based on the withdrawal behavior constraint. The collaborative active support control parameters are the control parameters originally set by multiple microgrids during the execution of collaborative active support control, which are used to limit the execution boundary of the microgrid power regulation behavior.

[0035] In this embodiment, when it is determined that the microgrid pullback rhythm is in a stable state, in step S51, the actual power sequence of each microgrid is retrieved within the power pullback analysis time window [ts, te]. By continuously describing the power change relationship between adjacent sampling times, the discrete sampling points are mapped to the continuous pullback time expansion trajectory Gj of power evolution over time. Based on the geometric characteristics of this trajectory in the time dimension, the pullback path length Cd and the corresponding pullback duration Sj are calculated. The ratio of the two is used to construct the pullback path deviation index Dpa, which is used to characterize the complexity of the power evolution path on the time axis during the pullback process. In step S52, the pullback stability index Rsta, which characterizes the pullback rhythm state of the group, is fitted with the pullback path deviation index Dpa of each microgrid to form a collaborative support constraint Uad, so that the individual pullback path characteristics and the overall pullback rhythm state are uniformly expressed under the same quantitative framework. S53 constructs a set of constraint reference intervals based on the statistical distribution of the collaborative support constraint quantity Uad in multiple historical collaborative support cycles, and maps the current cycle's collaborative support constraint quantity Uad to the corresponding interval position. Based on the interval position, it matches the participation depth limitation information for each microgrid, limiting its adjustment boundary in the subsequent collaborative active support process in the form of a power regulation amplitude range. S54 performs parameterized analysis on the participation depth limitation information and performs boundary constraint mapping according to its correspondence with the collaborative active support control parameters, ensuring that the parameter value range adopted in the next round of collaborative active support control remains consistent with the previous withdrawal behavior state. Through the above implementation method, collaborative active support control no longer relies solely on the operational results of a single support phase, but incorporates the temporal evolution characteristics of the withdrawal phase into the control parameter constraint basis. This enables multiple microgrids to form a closed-loop regulation structure based on withdrawal behavior feedback in continuous operating cycles, allowing the collaborative support participation level to be adaptively constrained according to the actual evolution state of the distribution system, forming an orderly and traceable collaborative operation process.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended technical solutions and their equivalents.

Claims

1. A method for coordinated active support control of multiple microgrids, characterized in that: Includes the following steps: S1. Monitor the operating status information of the power distribution system in real time during the continuous operation cycle of the power distribution system, and generate a collaborative active support triggering flag when the operating status information deviates, issue a collaborative support scheduling command to the microgrid and switch to the collaborative active support mode. S2. During the execution of the collaborative active support mode, construct the time series of the operation volume, combine the stability interval determination and the consistency analysis of the change direction to judge the collaborative operation status of multiple microgrids, and confirm the power withdrawal analysis time window [ts,te] when the stability convergence condition is met. S3. Collect the output power of the microgrid within the power pullback analysis time window [ts,te], perform differential operation to construct the power pullback change ΔP, and construct the pullback consistency index Cret based on the power pullback change ΔP to evaluate the pullback status. S4. When the pullback state is evaluated as a consistent structure state, construct the pullback smoothness index Sret and fit it with the pullback consistency index Cret to construct the pullback stability index Rsta for rhythm stability evaluation. S5. When the microgrid pullback rhythm is stable, construct the pullback path deviation index Dpa and fit it with the pullback stability index Rsta to construct the collaborative support constraint Uad, and map the participation depth limit information into collaborative active support control parameters.

2. The multi-microgrid collaborative active support control method according to claim 1, characterized in that: S1 includes S11; S11. Real-time monitoring of the operating status information of the power distribution system during the continuous operation cycle of the power distribution system, and comparison and judgment based on the operating status information and the preset allowable operating range of the power distribution system under the standard state. When it is determined that the operating status information of the power distribution system continuously deviates from the allowable operating range during the continuous monitoring cycle, a collaborative active support triggering identifier is generated. The operating status includes system voltage level, system frequency status, and power balance characteristics; The collaborative active support triggering identifier is used to indicate that the power distribution system enters the multi-microgrid collaborative active support control stage, marking the current power distribution system switching from the normal operation monitoring state to the multi-microgrid collaborative active support control state.

3. The multi-microgrid collaborative active support control method according to claim 2, characterized in that: S1 also includes S12 and S13; S12. After the collaborative active support trigger identifier is generated, the current operating status of each microgrid participating in the collaboration is collected, and the power regulation range, power regulation direction and grid connection point operation information of each microgrid that can be used for support are obtained to form collaborative support scheduling constraints. Based on the scheduling constraints, and according to the existing multi-microgrid collaborative support control strategy, collaborative support scheduling instructions corresponding to each microgrid are constructed. The effective order and effective time of the collaborative support scheduling instructions are configured according to the phase angle response of each microgrid's grid connection point. Then, the collaborative support scheduling instructions are sent to the participating microgrids in a synchronous manner through a centralized instruction distribution mechanism. S13. After receiving the collaborative support dispatch instruction, each microgrid parses the collaborative support dispatch instruction and switches its local operation control mode from the normal operation mode to the collaborative active support mode based on the parsing result. In the collaborative active support mode, the corresponding power output regulation operation is executed based on the existing active and reactive power regulation control logic, so that the microgrid forms a collaborative support state. During the execution of collaborative active support, the power change process and phase angle change information at each microgrid grid connection point are recorded synchronously, and the power change process and phase angle change information are recorded to provide a basis for the next configuration.

4. The multi-microgrid collaborative active support control method according to claim 3, characterized in that: S2 includes S21 and S22; S21. During the execution of the collaborative active support mode, the operating volume of multiple microgrids during collaborative operation is continuously collected to form an operating volume time series. The operating volume time series is judged point by point within a fixed analysis time window. If there is a sampling point that exceeds the stable interval, it is determined that the current operating state is not stable. When the operating volume enters the stable interval, the changing trend of adjacent sampling points is analyzed. If the changing trend shows a reverse change characteristic of shifting from the boundary of the stable interval to the outside, it is determined that the stable state is not sustainable, and the collaborative support state is maintained unchanged. The operational quantities include the frequency offset, voltage offset, active power balance deviation, and reactive power balance deviation of the microgrid parallel system. S22. Based on the stable range of the operating volume, the operating volume is processed by time difference within a continuous time window to construct the direction vector of the operating volume change, and the direction angle change information of the change direction vector is extracted in adjacent time windows. When the direction angle change value is within the preset angle fluctuation range and no reverse change of direction angle occurs within the continuous time window, it is determined that the change direction of the multi-microgrid cooperative operation state is consistent and enters the stable convergence stage. When the coordinated operation of multiple microgrids meets the stable convergence condition, a coordinated support end flag is generated and the corresponding time is marked as the start time ts of the power withdrawal phase, which is calibrated as the start time of the withdrawal analysis. Combined with the length of time that the operation state remains in the stable range, the end time te of the withdrawal phase is determined, and the power withdrawal analysis time window [ts, te] is constructed.

5. The multi-microgrid collaborative active support control method according to claim 4, characterized in that: S3 includes S31 and S32; S31. After the collaborative active support ends and the power withdrawal phase begins, the actual output power of each microgrid during the withdrawal phase is continuously collected using the withdrawal start time ts as the time reference point. The power is stored in the historical operation buffer at a uniform time granularity to form the power time series dataset of the withdrawal phase. Then, differential operation is performed on the output power at adjacent sampling times to construct the power withdrawal change ΔP. S32. During the pullback phase, within the time interval [ts, te], perform time series analysis on the power change ΔP of each microgrid, extract the direction of change during the power pullback process, and map the direction of change into a time direction angle sequence. Based on the power pullback change ΔP, the power change of different microgrids in the same time period is aligned step by step. Then, the difference integral is calculated to obtain the difference accumulation characteristics in the power pullback. The difference accumulation characteristics are then summarized and processed to construct the pullback consistency index Cret, which is used to represent the degree of consistency of the cooperative behavior of multiple microgrids in the pullback phase, as follows. ； Where N represents the number of microgrids participating in the consensus analysis, T represents the time span of the rollback analysis, and ΔP i (t) and ΔP j (t) represents the power change of the i-th microgrid and the j-th microgrid at time t relative to the previous sampling time, respectively, and dt represents the time integral variable.

6. The multi-microgrid collaborative active support control method according to claim 5, characterized in that: S3 also includes S33; S33. Extract several event samples of support completion and pullback completion, calculate the pullback consistency index Cret for each sample and sort them in ascending order. Extract the 90th percentile as the preset pullback consistency alarm threshold Tc according to the percentile method, and then compare it with the real-time acquired pullback consistency index Cret. Generate a pullback status assessment based on the comparison results, as follows. When the pullback consistency index Cret ≤ the pullback consistency alarm threshold Tc, it indicates that the pullback behavior presents a consistent structure state. At this time, the pullback rhythm constraint activation command is issued and the pullback stability analysis is triggered. When the pullback consistency index Cret > the pullback consistency alarm threshold Tc, it indicates that the pullback consistency has crossed the stable sample boundary and the difference in the group's pullback behavior is abnormal. At this time, a consistency deviation flag is issued and an alignment correction operation is performed to resample each power time series dataset to a unified time axis index set according to a unified time dimension.

7. The multi-microgrid collaborative active support control method according to claim 6, characterized in that: S4 includes S41; S41. When the pullback state assessment indicates that the pullback behavior presents a consistent structural state, the acceleration of power change during the pullback process is analyzed, the second-order time change of power change is calculated, and then the second-order time change is integrated over time and normalized over intervals to calculate the pullback smoothness index Sret, which is used to measure the continuity of the power change rhythm during the pullback process, as follows. ； Among them, Sret i τ represents the drawdown smoothness exponent of the i-th microgrid. i Let dt represent the duration of the current pullback phase of the i-th microgrid, and let dt represent the time calculus variable.

8. The multi-microgrid collaborative active support control method according to claim 7, characterized in that: S4 also includes S42; S42 is used to perform pullback consistency analysis and rhythm stability assessment on each microgrid, specifically including S421 and S422; S421. After uniformly calibrating the time intervals corresponding to the pullback consistency index Cret and the pullback smoothness index Sret, the structure fusion operation is performed with the pullback consistency index Cret as the overall pullback structure constraint term and the pullback smoothness index Sret of each microgrid as the individual pullback rhythm constraint term to construct the pullback stability index Rsta, which represents the overall stable state of the pullback behavior of multiple microgrids after the end of this collaborative active support, as follows; ； Where N represents the number of microgrids.

9. The multi-microgrid collaborative active support control method according to claim 8, characterized in that: S422. After the historical collaborative active support ends and the microgrid pullback rhythm is stable, the pullback consistency index Cret is statistically processed, the mean is extracted and preset as the pullback stability threshold Ts, and compared with the obtained pullback stability index Rsta. Based on the comparison results, a rhythm stability assessment is generated, as follows. When the pullback stability index Rsta ≤ the pullback stability threshold Ts, it indicates that the microgrid pullback rhythm is stable, and this pullback is marked as meeting the standard pullback quality. When the pullback stability index Rsta > the pullback stability threshold Ts, it indicates that there is a local instability in the microgrid pullback rhythm. At this time, the current pullback batch is marked as having rhythm quality risk and transmitted to the management personnel for manual intervention.

10. The multi-microgrid collaborative active support control method according to claim 9, characterized in that: S5 includes S51, S52, S53 and S54; S51. When the microgrid pullback rhythm is stable, retrieve the actual power sequence of the i-th microgrid within the power pullback analysis time window [ts, te]. By continuously describing the power change relationship between adjacent sampling times, map the discrete time sampling points into a continuous trajectory of the power evolution relationship over time, forming the pullback time unfolding trajectory Gj. Perform geometric analysis on the evolution characteristics of the pullback time unfolding trajectory Gj in the time dimension to obtain the path length Cd of the pullback process unfolding shape on the time axis. Simultaneously obtain the corresponding pullback duration Sj. Construct the pullback path deviation index Dpa by the ratio of the pullback path length Cd to the pullback duration Sj to quantify the time complexity of the pullback path. S52. Fit the retracement stability index Rsta and the retracement path deviation index Dpa to construct a collaborative support constraint Uad, which is used to represent the overall retracement behavior state after the end of this round of collaborative support. Specifically: Among them, Dpa i This represents the deviation index of the pullback path for the i-th microgrid; S53. Based on the collaborative support constraint quantity Uad in multiple historical collaborative support cycles, perform statistical distribution analysis to construct a constraint reference interval set, and map the collaborative support constraint quantity Uad corresponding to the current collaborative support cycle to the constraint reference interval set to obtain the interval position of the current collaborative support cycle. Match the corresponding participation depth restriction information for each microgrid according to the interval position. The set of constraint reference intervals includes stable intervals, transition intervals, and restricted intervals; S54. Perform parameterized analysis on the participation depth limitation information to obtain the allowable power regulation boundary range of each microgrid in the next round of collaborative active support control. Based on the correspondence between the participation depth limitation information and the collaborative active support control parameters, perform boundary constraint mapping on the collaborative active support control parameters, match the available value range of the collaborative active support control parameters with the current collaborative support constraint quantity Uad, and then send the collaborative active support control parameters after constraint mapping and the collaborative active support scheduling command to each microgrid control unit as input conditions for the next round of collaborative active support control.

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