Fault processing method and system for source network load storage and charging collaborative operation

Abstract

The embodiment of the application provides a kind of source network load storage and charging collaborative operation-oriented fault processing method and system, belong to power system operation control technical field.The method comprises: obtaining the running state data and fault event data of source network load storage and charging system under unified time reference, and determine the fault propagation arrival time corresponding to each subsystem;Corresponding to each subsystem, the fault propagation phase window is constructed, and the target subsystem in the propagation sensitive area is determined;Based on the operation deviation data of target subsystem, the action freezing band criterion is constructed, and the consistency debt value is calculated for the candidate control action of each target subsystem, to sort the candidate control action based on consistency debt value, generate fault coordination action sequence;Based on fault coordination action sequence, the control action of corresponding subsystem is executed.The scheme of the present application realizes the timing coordination and conflict suppression of multi-subsystem control action, improves the overall stability and consistency in the process of fault.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and specifically to a fault handling method and system for coordinated operation of power generation, grid, load, energy storage and charging. Background Technology

[0002] In the current construction of new power systems, distributed power sources, energy storage devices, adjustable loads, and electric vehicle charging facilities are gradually being connected to the grid, forming a complex system structure with multiple elements coordinating operation of power sources, grid, load, storage, and charging. While improving energy utilization efficiency and regulation capabilities, this type of system also significantly increases coupling relationships and dynamic uncertainties during operation. Especially under fault scenarios, different subsystems exhibit significant differences in their response paths, response speeds, and control methods to disturbances. For example, the power source side mainly uses voltage or power control, the energy storage side has rapid bidirectional regulation capabilities, the load side exhibits varying degrees of mitigation or transferability characteristics, and the charging side is also subject to constraints such as discrete power regulation. These differences result in significant temporal inconsistencies in the propagation of fault effects within the system.

[0003] Existing technologies for handling such faults typically focus on protecting a single subsystem or making scheduling decisions based on global optimization. For example, they might implement load shedding, energy storage support, or power generation limiting by setting fixed priorities. While these methods are structurally straightforward, they are prone to new problems in multi-subsystem coupled scenarios. For instance, different subsystems may trigger control actions at different times, leading to overlapping or even conflicting adjustments that amplify voltage fluctuations, frequency shifts, or power oscillations. Furthermore, existing decision-making mechanisms are often based on the current state, lacking assessment of the short-term evolution after the control action is executed. This can easily result in locally effective but globally mismatched actions—meaning that a single action may be reasonable, but introduces new disturbances into the overall timing sequence.

[0004] Therefore, in the coordinated operation environment of power generation, grid, load, storage and charging, how to comprehensively consider the differences in response timing of each subsystem and the impact of control actions on the future state of the system after a fault occurs, and construct a fault handling method that can avoid the superposition of multiple actions and suppress secondary disturbances, has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a fault handling method and system for coordinated operation of power generation, grid, load, storage and charging, so as to at least solve the problem that inconsistent response timing of each subsystem during fault propagation leads to superimposed conflict of control actions and causes secondary disturbances.

[0006] To achieve the above objectives, the first aspect of the present invention provides a fault handling method for the coordinated operation of power generation, grid, load, storage, and charging systems. The method includes: acquiring operational status data and fault event data of the power generation, grid, load, storage, and charging system under a unified time reference; determining the fault propagation arrival time of each subsystem based on the operational status data and the fault event data; constructing a fault propagation phase window for each subsystem based on the fault propagation arrival time of each subsystem; determining target subsystems located in the propagation sensitive zone based on the fault propagation phase window; constructing an action freeze zone criterion based on the operational deviation data of the target subsystems; calculating a consistency debt value for candidate control actions of each target subsystem; sorting the candidate control actions based on the consistency debt value to generate a fault coordination action sequence; and executing control actions of the corresponding subsystems based on the fault coordination action sequence.

[0007] Optionally, acquiring the operating status data and fault event data of the power generation, grid-load, energy storage, and charging system under a unified time reference includes: collecting voltage, frequency, and power data of each subsystem within a preset sampling period in the power generation, grid-load, energy storage, and charging system to form the original operating data sequence corresponding to each subsystem; performing time alignment processing on the original operating data sequence based on the unified time reference to obtain the operating status data corresponding to each subsystem; determining the fault triggering time based on the voltage change characteristics, frequency offset characteristics, and power change characteristics in the operating status data, and extracting the operating status data within a preset time window before and after the fault occurrence as fault analysis data to construct fault event data corresponding to the fault triggering time.

[0008] Optionally, determining the fault propagation arrival time of each subsystem based on the operating status data and the fault event data includes: determining the fault triggering time based on the fault event data, and using the fault triggering time as a time reference point, extracting the state change sequence of each subsystem after the fault triggering time from the operating status data; calculating the time response characteristics of voltage change, frequency change, and power change relative to the fault triggering time for each subsystem's state change sequence, and determining the initial response time of each subsystem based on the time response characteristics; obtaining the sampling delay parameter, communication delay parameter, and execution delay parameter of each subsystem, and performing superposition calculation based on the initial response time, the sampling delay parameter, the communication delay parameter, and the execution delay parameter to obtain the fault propagation arrival time of each subsystem.

[0009] Optionally, a fault propagation phase window is constructed based on the fault propagation arrival time of each subsystem, and a target subsystem within the propagation sensitive zone is determined based on the fault propagation phase window. This includes: for each subsystem, using the corresponding fault propagation arrival time as a time reference, and combining the response duration parameter and power change sensitivity parameter of each subsystem in the operating status data, a fault propagation phase window is constructed to characterize the propagation process of the fault impact in the time dimension; based on the fault propagation phase window, the phase position of each subsystem at the current time is calculated to obtain the propagation phase parameter of each subsystem; the propagation phase parameter is matched with a preset phase interval to determine the subsystem falling into the preset propagation sensitive interval as the target subsystem.

[0010] Optionally, based on the fault propagation phase window, the phase position of each subsystem at the current moment is calculated to obtain the propagation phase parameters of each subsystem. This includes: obtaining the current moment and calculating the time difference between the current moment and the fault propagation arrival time of each subsystem to obtain the relative propagation time of each subsystem; determining the response duration parameter of each subsystem based on the operating status data, and normalizing the relative propagation time with the response duration parameter to obtain the normalized propagation time of each subsystem; and mapping the normalized propagation time to a preset phase interval to obtain the propagation phase parameters of each subsystem.

[0011] Optionally, an action freeze zone criterion is constructed based on the operational deviation data of the target subsystem, and a consistency debt value is calculated for the candidate control actions of each target subsystem. The candidate control actions are then sorted based on the consistency debt value to generate a fault coordination action sequence. This includes: extracting the voltage deviation, frequency deviation, and power deviation of each target subsystem based on the operational deviation data, and constructing an action freeze zone criterion to characterize the degree of coupling between multiple subsystem actions in the current state by combining the power change correlation between each subsystem; filtering the candidate control actions of each target subsystem based on the action freeze zone criterion to determine a set of candidate control actions that meet the action release conditions; calculating the consistency debt value of each candidate control action in the candidate control action set based on the degree of influence on voltage constraints, frequency constraints, and power balance constraints within a preset time range after executing the corresponding candidate control action; and sorting the consistency debt values ​​of each candidate control action to generate a fault coordination action sequence arranged by execution priority.

[0012] Optionally, the candidate control actions of each target subsystem are screened based on the action freeze zone criterion to determine a set of candidate control actions that meet the action release conditions. This includes: acquiring the candidate control actions corresponding to each target subsystem, and extracting the power change amplitude and power change rate corresponding to each candidate control action to form candidate action feature parameters; matching the candidate action feature parameters with a preset freeze zone threshold based on the action freeze zone criterion to determine the freeze state identifier of each candidate control action; and screening each candidate control action according to the freeze state identifier, removing candidate control actions in the freeze state, and retaining candidate control actions in the release state to form a set of candidate control actions that meet the action release conditions.

[0013] Optionally, for each candidate control action in the candidate control action set, based on the degree of influence on voltage constraints, frequency constraints, and power balance constraints after executing the corresponding candidate control action within a preset time range, the consistency debt value of each candidate control action is calculated, including: for each candidate control action, starting from the execution time of the corresponding candidate control action, constructing a predicted operating state sequence for each subsystem within the preset time range, and obtaining voltage change, frequency change, and power deviation based on the predicted operating state sequence; determining the voltage constraint deviation of the corresponding candidate control action based on the degree of deviation between the voltage change and the preset voltage constraint range; determining the frequency constraint deviation of the corresponding candidate control action based on the degree of deviation between the frequency change and the preset frequency constraint range; determining the power balance constraint deviation of the corresponding candidate control action based on the degree of deviation between the power deviation and the power balance constraint; accumulating the voltage constraint deviation, the frequency constraint deviation, and the power balance constraint deviation within the preset time range to obtain a comprehensive constraint deviation index for each candidate control action, and determining the consistency debt value of each candidate control action based on the comprehensive constraint deviation index.

[0014] Optionally, executing control actions for the corresponding subsystem based on the fault coordination action sequence includes: sequentially selecting each candidate control action according to the execution order in the fault coordination action sequence, and determining the execution subsystem and execution time of the corresponding candidate control action; for each candidate control action, issuing a control command to the corresponding execution subsystem at the corresponding execution time to drive the execution subsystem to perform control operations according to the power adjustment mode corresponding to the candidate control action; continuously acquiring the operating status data of the corresponding execution subsystem during the execution of each candidate control action, and updating the operating status of the corresponding subsystem based on the operating status data; and outputting the processing result of the corresponding fault handling process based on the updated operating status data.

[0015] A second aspect of the present invention provides a fault handling system for the coordinated operation of power generation, grid, load, storage, and charging systems. The system includes: a data acquisition unit, configured to acquire operational status data and fault event data of the power generation, grid, load, storage, and charging systems under a unified time reference, and determine the fault propagation arrival time of each corresponding subsystem based on the operational status data and the fault event data; a subsystem filtering unit, configured to construct a fault propagation phase window for each subsystem based on the fault propagation arrival time of each subsystem, and determine target subsystems located in the propagation sensitive zone based on the fault propagation phase window; an action sequence generation unit, configured to construct an action freeze zone criterion based on the operational deviation data of the target subsystems, calculate a consistency debt value for candidate control actions of each target subsystem, sort the candidate control actions based on the consistency debt value, and generate a fault coordination action sequence; and a control execution unit, configured to execute control actions of the corresponding subsystems based on the fault coordination action sequence.

[0016] Through the above technical solution, this invention analyzes the operating status of the power grid-load-storage-charging system under a unified time reference to determine the arrival time of fault propagation in each subsystem. Furthermore, it constructs a fault propagation phase window to achieve a detailed characterization of the fault's impact propagation process over time. Based on this, it identifies target subsystems in the propagation-sensitive zone, avoiding blindly triggering control actions during critical fault stages. Simultaneously, by constructing an action freeze-line criterion, it constrains control actions prone to coupling conflicts and introduces a consistency debt value to quantitatively evaluate the system constraint impact after the execution of candidate control actions, thereby achieving orderly sequencing and coordinated execution of control actions. This effectively reduces the superimposed conflicts of control actions across multiple subsystems in time, lowers the risk of secondary disturbances such as voltage fluctuations, frequency shifts, and power oscillations, and improves the stability and consistency of the fault handling process.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the steps of a fault handling method for source-grid-load-storage-charging coordinated operation provided by one embodiment of the present invention; Figure 2 This is a detailed flowchart of step S20 of a fault handling method for source-grid-load-storage-charging coordinated operation provided in one embodiment of the present invention. Figure 3This is a schematic diagram of the fault propagation phase window and propagation sensitive area division provided by one embodiment of the present invention; Figure 4 This is a detailed flowchart of step S30 of a fault handling method for source-grid-load-storage-charging coordinated operation provided in one embodiment of the present invention. Figure 5 This is a system structure diagram of a fault handling system for source-grid-load-storage-charging coordinated operation provided by one embodiment of the present invention; Figure 6 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] like Figure 1 As shown, embodiments of the present invention provide a fault handling method for coordinated operation of source-grid-load-storage-charging systems, the method comprising: Step S10: Obtain the operating status data and fault event data of the source-grid-load-storage-charging system under a unified time reference, and determine the fault propagation arrival time of each subsystem based on the operating status data and the fault event data.

[0021] Specifically, acquiring operational status data and fault event data of the power generation, grid-load, energy storage, and charging system under a unified time reference includes: collecting voltage, frequency, and power data of each subsystem within a preset sampling period to form the original operational data sequence for each subsystem; performing time alignment processing on the original operational data sequence based on the unified time reference to obtain operational status data for each subsystem; determining the fault triggering time based on voltage mutation characteristics, frequency offset characteristics, and power change characteristics in the operational status data, and extracting operational status data within a preset time window before and after the fault occurrence as fault analysis data to construct fault event data corresponding to the fault triggering time.

[0022] Furthermore, determining the fault propagation arrival time of each subsystem based on the operating status data and the fault event data includes: determining the fault trigger time based on the fault event data, and using the fault trigger time as a time reference point, extracting the state change sequence of each subsystem after the fault trigger time from the operating status data; calculating the time response characteristics of voltage change, frequency change, and power change relative to the fault trigger time for each subsystem's state change sequence, and determining the initial response time of each subsystem based on the time response characteristics; obtaining the sampling delay parameter, communication delay parameter, and execution delay parameter of each subsystem, and superimposing them based on the initial response time, the sampling delay parameter, the communication delay parameter, and the execution delay parameter to obtain the fault propagation arrival time of each subsystem.

[0023] In this embodiment of the invention, each subsystem may include a power supply side subsystem, a grid side subsystem, a load side subsystem, an energy storage side subsystem, and a charging side subsystem. The power supply side subsystem is used to provide power output, the grid side subsystem is used to realize power transmission and distribution, the load side subsystem is used to characterize power demand, the energy storage side subsystem is used to perform energy storage and release, and the charging side subsystem is used to realize the charging load access of electric equipment. During operation, each subsystem forms a coupling relationship through power interaction, and together constitutes a source-grid-load-storage-charging collaborative operation system.

[0024] Voltage, frequency, and power data for each subsystem are acquired through their respective measurement devices, such as the internal measurement module of the power supply-side inverter, the power control unit of the energy storage system, and the monitoring terminal on the load side. These data, in their raw state, form discrete time series, with sampling periods set to preset values ​​based on equipment capabilities. To eliminate time misalignment caused by different sampling links, all raw operating data sequences are mapped to a unified time base, and alignment is achieved through time interpolation or time resampling. The aligned data have a one-to-one correspondence on the same time scale, forming the operating status data used for subsequent analysis.

[0025] Based on this, the fault trigger moment is determined by identifying local changes in the operating status data. This approach does not rely on thresholds for a single physical quantity, but rather integrates three types of features: voltage spikes, frequency shifts, and power changes. For example, when the bus voltage drops significantly within a short period, accompanied by a frequency shift and a sudden change in the power exchange direction, the system can be identified as entering an abnormal state. Around this trigger moment, data within a preset time window is extracted forward and backward to construct fault event data. The length of this time window can be set according to the system's inertial characteristics, covering both the stable state before the fault occurs and the initial propagation process of the fault.

[0026] After obtaining fault event data, the focus shifts to the response process of each subsystem to the fault. Due to differences in physical structure and control logic, the response times of different subsystems to the same disturbance are not consistent. To characterize this difference, starting from the fault triggering moment, the state change sequence of each subsystem is extracted, and the trends of voltage, frequency, and power over time are analyzed. By calculating the time response characteristics of these change curves, the time node at which each subsystem first shows a significant response can be determined, i.e., the initial response moment. This moment reflects the system's perception time of the fault under ideal zero-delay conditions.

[0027] However, in actual operation, data acquisition, communication transmission, and execution control all introduce delays. To more accurately describe the true arrival time of fault effects in each subsystem, these delay factors need to be taken into account. Sampling delay reflects the time required for a sensor to acquire data and complete an update; communication delay corresponds to the time overhead of data transmission between different devices; and execution delay reflects the time difference between the issuance of a control command and its actual effect on the device. By superimposing the initial response time with the above delay parameters, the fault propagation arrival time of each subsystem can be obtained. This time not only includes the physical disturbance propagation process but also comprehensively considers the impact of the control system links, making it closer to engineering reality.

[0028] The significance of the above process lies in mapping the response behaviors originally dispersed across various subsystems onto the same time axis, making the sequence of responses between different subsystems clearly discernible. This prevents the misjudgment of responses that should be staggered as synchronous behaviors during subsequent phase division or control action coordination, thus providing a reliable basis for subsequent control decisions.

[0029] In one specific embodiment, the system has a total of The first subsystem, The voltage, frequency, and power of each subsystem in time The measured values ​​at each location are expressed as follows: , and After processing with a unified time base, a discrete time series is obtained. ,in To standardize the time scale.

[0030] Fault trigger time It can be determined through a joint criterion of multiple features, for example: in, , , These are preset weight parameters used to adjust the contribution of different physical quantities to the trigger determination.

[0031] In determining Afterwards, regarding the first Each subsystem constructs its state change response function using a normalized combinatorial form: in, , , For normalized weights. This is achieved by setting a response threshold. Determine the initial response time : Taking further consideration of delay factors, the first Fault propagation arrival time of each subsystem Represented as: in, This is the sampling delay parameter. For communication delay parameters, The execution delay parameters are specified. These parameters can be obtained through equipment calibration or historical data statistics.

[0032] This calculation process yields the set of fault propagation arrival times for each subsystem. This set is used in subsequent steps to construct the fault propagation phase window, thereby achieving a unified characterization of the response timing of multiple subsystems.

[0033] It should be noted that the above calculation process is only one implementation method, used to illustrate how to combine response characteristics with delay parameters. In specific implementations, the composition of the response function or the threshold setting can be adjusted according to the system scale and data acquisition conditions. However, as long as a unified characterization of the response time and its delay impact of each subsystem can be achieved, it falls within the protection scope of this invention.

[0034] Step S20: Construct a fault propagation phase window for each subsystem based on the fault propagation arrival time of each subsystem, and determine the target subsystem in the propagation sensitive area based on the fault propagation phase window.

[0035] Specifically, after obtaining the fault propagation arrival time of each subsystem, it is used as a time base to segment the evolution process of the fault impact. The specific approach involves constructing corresponding fault propagation phase windows by combining the response persistence characteristics and power change sensitivity of each subsystem as reflected in the operational status data, making the response stages of different subsystems comparable on the same time axis. By mapping the current moment into the phase window of each subsystem, the phase position reflecting its propagation stage can be obtained, thus distinguishing different stages such as no response, responding, and the response tending to stabilize. Based on this, a propagation sensitivity interval is preset, and the phase positions of each subsystem are matched to screen out target subsystems in the critical propagation stage. This process allows subsequent control decisions to focus on subsystems with a significant impact on system stability, avoiding unnecessary control interventions in non-critical stages. Specifically, such as... Figure 2 Step S20 includes the following steps: Step S201: For each subsystem, using the corresponding fault propagation arrival time as the time reference, and combining the response duration parameter and power change sensitivity parameter of each subsystem in the operation status data, construct a fault propagation phase window to characterize the propagation process of fault impact in the time dimension.

[0036] Specifically, taking the fault propagation arrival time of each subsystem as the starting point, the response process over a subsequent period is normalized. A response duration parameter is introduced to characterize the time elapsed from response initiation to reaching a relatively stable state. This parameter can be obtained through historical operational data statistics or extracted from the current fault event by observing points where power changes tend to level off. Simultaneously, a power change sensitivity parameter is introduced to reflect the subsystem's response strength to disturbances per unit time. Subsystems with larger power change sensitivity parameters typically exhibit steeper response processes and more significant impacts on the overall system coupling.

[0037] Based on the two parameters mentioned above, a fault propagation phase window is constructed under a unified time reference. This phase window is not a simple time interval, but rather maps the response processes of each subsystem with different lengths and intensities to a unified phase space by scaling the time. In this way, at the same moment, different subsystems can be characterized by their response stage through phase values, such as the initial response stage, the rapidly changing stage, or the stage approaching stability. This representation avoids the bias caused by directly comparing absolute times, making the subsequent identification of critical subsystems more consistent.

[0038] In one specific embodiment, the first The fault propagation arrival time of each subsystem is The current analysis time is Then its relative propagation time can be expressed as: The response duration parameter is denoted as The power change sensitive parameter is denoted as Based on these two parameters, the relative propagation time is normalized, and a sensitivity modulation factor is introduced to obtain the propagation phase parameter. : in, , where is the modulation coefficient, used to balance the influence between response duration and power variation sensitivity. This expression allows for a unified mapping of the response processes of different subsystems to a dimensionless phase space. When When it is in a smaller range, it indicates that the subsystem has just entered the response phase; when When the response is close to or exceeds the preset upper limit, it indicates that the response process is nearing completion.

[0039] Based on the above phase parameters, a fault propagation phase window can be further constructed, that is, a phase window is defined for each subsystem. This is a set of phase intervals for variables, used to divide different propagation stages. In subsequent steps, by matching the current phase parameters with preset intervals, subsystems located in propagation-sensitive regions can be identified.

[0040] It should be noted that the methods for obtaining the response duration parameter and the power change sensitivity parameter can be adjusted according to the specific system configuration, and the normalized expression is not limited to the above function form. As long as a unified characterization of the response process of different subsystems can be achieved, it falls within the protection scope of this invention.

[0041] Step S202: Based on the fault propagation phase window, calculate the phase position of each subsystem at the current time to obtain the propagation phase parameters of each subsystem.

[0042] Specifically, the current time is obtained, and the time difference between the current time and the fault propagation arrival time of each corresponding subsystem is calculated to obtain the relative propagation time of each corresponding subsystem; the response duration parameter of each corresponding subsystem is determined based on the operating status data, and the relative propagation time is normalized using the response duration parameter to obtain the normalized propagation time of each corresponding subsystem; the normalized propagation time is mapped to a preset phase interval to obtain the propagation phase parameter of each corresponding subsystem.

[0043] In this embodiment of the invention, the response starting points of each subsystem differ after a fault occurs, and comparing only absolute times would mask this difference. For example, an energy storage system might begin responding relatively late after a fault occurs, while the power supply inverter experiences power changes within a very short time. If the response process is described by directly subtracting the fault trigger time from the current time, the two systems would be mistakenly considered to be at similar stages. By introducing the fault propagation arrival time as a reference point, the response processes of each subsystem can be realigned, unifying their response starting points to zero, thus obtaining the relative propagation time. This relative propagation time reflects the evolution time experienced by the subsystem since it sensed the fault.

[0044] After obtaining the relative propagation time, the timescale differences in the response processes of different subsystems must also be considered. For example, the power regulation process of an energy storage system may last for hundreds of milliseconds, while the response on the load side may be completed in a shorter time. Directly using the relative propagation time for comparison still fails to reflect the respective stages. Therefore, a response duration parameter is introduced to normalize the relative propagation time, mapping response processes of different lengths to a uniform scale. The normalized time value can be understood as the proportion of the response process completed, thus achieving comparability.

[0045] Furthermore, to facilitate stage division within the control logic, the normalized propagation time is mapped to a preset phase interval. This phase interval can be divided into several continuous segments based on system characteristics, such as the initial response segment, the rapidly changing segment, and the convergence segment. Through this mapping relationship, the continuously changing time quantity can be transformed into phase parameters with clear physical meaning. At this point, the propagation phase parameters of each subsystem at the current moment not only contain its response start-up information but also reflect its stage position in the entire response process.

[0046] In one specific embodiment, the current time is , No. The fault propagation arrival time of each subsystem is Then its relative propagation time Represented as: in, This indicates the length of time that the subsystem has experienced since receiving the effects of the fault. When When this occurs, it indicates that the subsystem has not yet entered the response phase.

[0047] Further set the first The response duration parameter of each subsystem is: This parameter can be obtained by analyzing the time points in the operating status data where power changes tend to stabilize. Based on this parameter, the relative propagation time is normalized to obtain the normalized propagation time. : in, For a dimensionless quantity, when When the time is reached, it indicates that the response process of the subsystem is nearing completion.

[0048] To facilitate phase division in the control strategy, the normalized propagation time is mapped to a preset phase interval. Let the preset phase interval function be... Then the propagation phase parameter It can be represented as: in, It can be a piecewise mapping function or a continuous mapping function. For example, when At that time, you can directly order ,when When this happens, a compression function can be used to map it to the upper limit range to avoid the phase value from growing indefinitely.

[0049] Through the above calculations, the set of propagation phase parameters corresponding to each subsystem at the current moment can be obtained. This set provides a foundation for subsequent identification of propagation-sensitive regions, making it possible to compare different subsystems within a unified phase space.

[0050] It should be noted that the above normalization method and mapping function form can be adjusted according to the actual needs of the system. For example, a nonlinear function can be introduced to amplify the fast response stage or compress the slowly changing stage. As long as the response process of each subsystem can be uniformly mapped to a comparable phase space, it falls within the protection scope of this invention.

[0051] Step S203: Match the propagation phase parameters with a preset phase interval to determine the subsystem that falls within the preset propagation sensitive interval as the target subsystem.

[0052] Specifically, phase parameters are continuously changing quantities, and their numerical values ​​alone are insufficient to directly influence control decisions. Therefore, it is necessary to divide the system into stages using preset phase intervals. The setting of phase intervals does not depend on the characteristics of a single device, but rather on the overall dynamic response characteristics of the system, making it possible to compare different subsystems under a unified standard.

[0053] In practical applications, the phase interval is divided into several continuous segments, some of which correspond to the stages where fault effects propagate rapidly and control actions are prone to coupling effects—these are called propagation-sensitive intervals. These intervals typically cover critical periods in the response process of each subsystem, such as the rapid power adjustment phase or the stage before the control loop converges. Within this phase, simultaneous adjustment actions by multiple subsystems can easily trigger superposition effects; therefore, it is necessary to identify the subsystems within this interval separately.

[0054] By matching the propagation phase parameters of each subsystem with a preset phase interval, it can be determined whether it falls within the propagation sensitive range. When the propagation phase parameter of a subsystem is within this range, it is marked as the target subsystem. This marking result serves as the input for subsequent action freeze zone construction and consistency debt value calculation, enabling the control strategy to focus on subsystems that have a more significant impact on system stability.

[0055] The method of dividing the phase interval can be adjusted according to the different operating characteristics of the system. For example, a fixed interval division can be used, or the sensitive interval can be dynamically corrected by combining historical data. As long as the key response stage can be effectively identified, it falls within the protection scope of this invention.

[0056] In one specific implementation, such as Figure 3 This paper explains the propagation process of a fault in a power generation, grid, load, energy storage, and charging system. The fault trigger time is marked at the beginning of the time axis. The subsystems do not respond synchronously; instead, their response start points appear at different time points, specifically manifested by significant differences in the positions of the black dots on the curves of each subsystem. The power source side responds first, and its response curve enters an upward phase within a short time. The charging side responds subsequently, with its response start point lagging behind that of the power source side. The responses of the energy storage and load sides are further delayed, exhibiting a step-by-step propagation characteristic.

[0057] Following the response start point of each subsystem, a corresponding fault propagation phase window is constructed on the time axis based on its response process, as shown by the dashed box in the figure. The phase window covers the time interval from the response start point to the response stabilizing, representing the complete response evolution process of the subsystem. Within the phase window, a propagation sensitive region is further defined. This region is located in the stage where the response curve changes significantly and has a substantial impact on changes in the system's operating state.

[0058] As can be clearly seen from the diagram, the phase windows and propagation sensitive zones of different subsystems are staggered along the time axis, meaning that the times when each subsystem enters the sensitive phase do not overlap. For example, the propagation sensitive zone on the source side is located earlier, while the propagation sensitive zone on the load side is clearly located later. This staggered relationship allows for selective adjustment of the target subsystem based on its propagation phase during control decision-making. This avoids multiple subsystems simultaneously being in the sensitive phase and performing large-scale control actions within the same time period, thus providing a basis for coordinating subsequent actions.

[0059] Step S30: Construct an action freeze zone criterion based on the operation deviation data of the target subsystem, calculate the consistency debt value for the candidate control actions of each target subsystem, sort the candidate control actions based on the consistency debt value, and generate a fault coordination action sequence.

[0060] Specifically, operational deviation data reflects the degree of deviation of voltage, frequency, and power from the normal operating range, and also implies the mutual influence between various subsystems. Based on this deviation information, an action freeze zone criterion is constructed to identify control actions that are not suitable for immediate execution in the current state, thereby avoiding the superimposed effects caused by the simultaneous adjustment of multiple subsystems at critical stages.

[0061] After screening candidate control actions, a consistency debt value is introduced to evaluate each action. This debt value quantifies the impact of the action on system operating constraints within a preset time range, reflecting its comprehensive effect on voltage stability, frequency stability, and power balance. By ranking the consistency debt values ​​of each candidate control action, a fault coordination action sequence with execution priority can be obtained. This sequence, while maintaining the regulation capability of each subsystem, constrains their execution order, making the control process more orderly and reducing the risk of secondary disturbances caused by the superposition of multiple actions. Specifically, such as... Figure 4 Step S30 includes the following steps: Step S301: Based on the operational deviation data, extract the voltage deviation, frequency deviation, and power deviation of each target subsystem, and combine the power change correlation between each subsystem to construct an action freeze zone criterion to characterize the degree of action coupling of multiple subsystems in the current state.

[0062] Specifically, voltage deviation, frequency deviation, and power deviation of each target subsystem are extracted from the operational status data. Voltage deviation reflects the offset of node voltage from its rated value, frequency deviation reflects the changing trend of the system's power balance, and power deviation directly describes the magnitude of change in subsystem power output or consumption. These three types of deviations together constitute a basic description of the current operational status. From the perspective of a single subsystem, these deviations can be used to determine whether adjustment is needed. However, in multi-subsystem collaborative scenarios, it is more important to pay attention to the mutual influence between the actions of different subsystems.

[0063] To address this, a power change correlation is introduced to describe the impact of a power change in one subsystem on other subsystems. This correlation can be obtained through historical data statistics or estimated during the current fault process by analyzing the correlation of power changes. For example, when an energy storage system discharges rapidly, it may alleviate the power deficit on the load side, thus affecting the regulation demand on the power supply side; conversely, if multiple subsystems simultaneously make significant adjustments, it may create a cumulative effect on the bus side. By combining these correlations with the deviations of each subsystem, a criterion reflecting the current degree of coupling between actions can be constructed.

[0064] The purpose of the action freeze criterion is to identify control actions that are not suitable for immediate execution under the current coupling state. When the system is in a highly coupled state, too many control actions triggered simultaneously can amplify disturbances, so it is necessary to postpone some actions. This criterion does not directly provide control commands, but rather provides a basis for subsequent candidate action selection.

[0065] In one specific embodiment, the first The voltage deviation, frequency deviation, and power deviation of each target subsystem are respectively , and Introducing a power change correlation coefficient matrix. ,in Indicates the first The power change of the first subsystem affects the first The influence weights of each subsystem can be defined based on this. Coupling strength index of each subsystem : in, The number of target subsystems, Indicates the first The absolute value of the power deviation of each subsystem.

[0066] Furthermore, by integrating voltage deviation, frequency deviation, and coupling strength, an action freeze band criterion function is constructed. : in, , , This is a weighting parameter used to adjust the degree of influence of different deviations on the criterion. When When the preset threshold is exceeded, the subsystem can be considered to be in a highly coupled and sensitive state, and its related control actions should be included in the freeze zone.

[0067] The above expression provides a feasible calculation method. In practical applications, the weight parameters or correlation coefficients can be adjusted according to the system scale and operating characteristics, and other types of deviations can also be introduced into the calculation. As long as it can effectively represent the degree of coupling of the actions of multiple subsystems, it falls within the protection scope of this invention.

[0068] Step S302: Based on the action freeze criterion, the candidate control actions of each target subsystem are screened to determine the set of candidate control actions that meet the action release conditions.

[0069] Specifically, candidate control actions corresponding to each target subsystem are obtained, and the power change amplitude and power change rate corresponding to each candidate control action are extracted to form candidate action feature parameters; based on the action freeze zone criterion, the candidate action feature parameters are matched with a preset freeze zone threshold to determine the freeze state identifier of each candidate control action; each candidate control action is screened according to the freeze state identifier, candidate control actions in the freeze state are removed, and candidate control actions in the release state are retained to form a set of candidate control actions that meet the action release conditions.

[0070] In this embodiment of the invention, during actual operation, each target subsystem typically has multiple selectable adjustment methods, such as power boosting, power reduction, or maintaining the current state. These adjustment methods exist in the control system as candidate control actions. If these candidate actions are not constrained and screened, multiple actions can easily be triggered simultaneously when the coupling strength is high, resulting in over-adjustment in a localized area.

[0071] Therefore, in step S302, the action freeze criterion is directly applied to the candidate control actions. Each candidate control action can be described by the power change characteristics it may cause, including both the magnitude and rate of change. The magnitude of the power change reflects the degree of influence of the action on the system power balance, while the rate of change reflects the intensity of its impact on the dynamic process. In cases of high coupling, large-amplitude or rapidly changing control actions are more likely to superimpose on the actions of other subsystems, creating adverse effects.

[0072] Based on this consideration, the feature parameters of candidate control actions are matched with the action freeze zone criterion. The matching process, combined with the current coupling strength, evaluates the potential additional perturbations that each candidate action may bring. Through this process, a freeze state identifier can be assigned to each candidate control action to distinguish whether it is suitable for execution at the current stage. Candidate actions in the freeze state are temporarily suspended at the current moment, while candidate actions in the release state are retained for subsequent sorting stages.

[0073] This screening mechanism allows the control strategy to no longer rely solely on the deviation of a subsystem, but rather to comprehensively consider the overall coupling state of the system. In stages with high coupling, some actions are intentionally delayed to avoid concentrated adjustments across multiple subsystems. As the system state changes, these frozen actions can still be reassessed and released in later stages, and therefore are not permanently removed.

[0074] In one specific embodiment, the first The first target subsystem The power change amplitude of each candidate control action is The rate of change of power is The two are combined to form a candidate action feature vector. : Based on the action freeze criterion function obtained in the previous step Construct freeze determination indicators for corresponding candidate control actions. : in, and This is a weighting coefficient used to adjust the proportion of influence of the power change magnitude and change rate in the determination.

[0075] Let the preset freezing zone threshold be... Then the frozen state flag of the candidate control action It can be defined as: in, This indicates that the candidate control action is in a frozen state. This indicates that the device is in a released state.

[0076] Based on the above criteria, a set of actions that meet the release conditions can be selected from all candidate control actions: This set is the set of candidate control actions used in subsequent consistent debt value calculations.

[0077] It should be noted that the above combination of freezing criteria and action features is only one possible implementation. In practical applications, the weight parameters can be adjusted according to the dynamic characteristics of different systems, or other feature quantities reflecting the impact of actions, such as response duration, can be introduced. As long as it can effectively screen the executability of candidate control actions in the current coupling state, it falls within the protection scope of this invention.

[0078] Step S303: For each candidate control action in the candidate control action set, calculate the consistency debt value of each candidate control action based on the degree of influence on voltage constraints, frequency constraints and power balance constraints within a preset time range after executing the corresponding candidate control action.

[0079] Specifically, for each candidate control action, starting from the execution time of the corresponding candidate control action, a predicted operating state sequence for each subsystem is constructed within the preset time range. Based on the predicted operating state sequence, voltage change, frequency change, and power deviation are obtained. The voltage constraint deviation of the corresponding candidate control action is determined based on the degree of deviation between the voltage change and the preset voltage constraint range. The frequency constraint deviation of the corresponding candidate control action is determined based on the degree of deviation between the frequency change and the preset frequency constraint range. The power balance constraint deviation of the corresponding candidate control action is determined based on the degree of deviation between the power deviation and the power balance constraint. The voltage constraint deviation, frequency constraint deviation, and power balance constraint deviation are accumulated within the preset time range to obtain a comprehensive constraint deviation index for each candidate control action. Based on the comprehensive constraint deviation index, the consistency debt value for each candidate control action is determined.

[0080] In this embodiment of the invention, for each candidate control action, a corresponding predicted operating state sequence is constructed starting from its expected execution time. This prediction process is based on current operating state data and the system's dynamic response characteristics to extrapolate the voltage, frequency, and power change trajectories of each subsystem within a preset time range. This prediction does not require high-precision global modeling; rather, it focuses on reflecting the changing trends after the introduction of the control action and its impact on constraint boundaries.

[0081] Based on the predicted operating state sequence, voltage changes are extracted. Frequency change and power deviation These quantities are compared with the system's operational constraints to obtain the corresponding degree of deviation. Taking voltage as an example, let the voltage constraint range be... Then the voltage constraint deviation can be expressed as: in, Indicates at time The deviation of the voltage from the constraint range; when the voltage is within the allowable range, this value is zero.

[0082] Frequency constraint deviation and power balance deviation can be constructed in a similar manner. Let the frequency constraint range be... ,but: The power balance deviation reflects the degree of supply-demand imbalance in the system, expressed as: in, This is the reference power for the corresponding subsystem in equilibrium.

[0083] The changes in the three deviations over time collectively describe the impact of the candidate control action on system constraints within a preset time range. To transform these into comparable quantities, the changes in each deviation over time are analyzed. Cumulative calculations are performed within the scope, where This represents the execution time of the candidate control action. The preset time window length. The comprehensive constraint deviation index can be expressed as: in, Indicates the first The comprehensive constraint deviation index corresponding to each candidate control action. , , These are weighting parameters used to adjust the impact of voltage, frequency, and power deviations on the overall assessment.

[0084] This comprehensive index essentially characterizes the cumulative constraint deviation caused by the system executing the candidate control action within a preset time range. Based on this, As the basis for calculating the value of consistent liabilities, we can obtain ,in, Indicates the first The consistency debt value of each candidate control action. The larger the debt value, the higher the degree of overdraft on system constraints in the short term, and its priority should be reduced accordingly.

[0085] The above method allows for the unified quantification of the impact of different candidate control actions on the future state of the system into a single, standardized index. Compared to methods that rely solely on current deviations, this approach reflects the temporal continuity of control actions, making subsequent ranking more consistent with the overall system operation. For source-grid-load-storage-charge systems of different scales or structures, the aforementioned prediction model, weight parameters, and time window length can all be adjusted according to actual conditions. Any system capable of quantifying the future impact of candidate control actions falls within the scope of this invention.

[0086] Step S304: Sort the candidate control actions based on their consistency debt values ​​to generate a sequence of fault coordination actions arranged by execution priority.

[0087] Specifically, all candidate control actions are arranged in ascending order of consistency debt value. Control actions with lower debt values ​​have a lower overdraft on the system's future operational constraints and are more suitable for priority execution; control actions with higher debt values ​​are reserved for later stages as adjustment measures. In this way, the timing of actions can be reorganized without changing the control capabilities of each subsystem, making the system adjustment process more orderly. For example, when the power regulation of the energy storage system makes a significant contribution to frequency recovery and has a low debt value, it can be executed first; while some large-scale load shedding can quickly reduce power deviation, but its debt value is high, so whether to execute it can be decided in a later stage depending on the system status.

[0088] It should be noted that the sorting method is not limited to simple ascending order. In practical applications, factors such as action type and equipment constraints can be combined to group and sort or execute in segments. As long as the overall principle still constrains the execution order based on the consistency debt value, it falls within the protection scope of this invention. The fault coordination action sequence formed through this step provides a basis for the orderly issuance of subsequent control commands.

[0089] Step S40: Execute the control action of the corresponding subsystem based on the fault coordination action sequence.

[0090] Specifically, according to the execution order in the fault coordination action sequence, each candidate control action is selected sequentially, and the corresponding execution subsystem and execution time of the candidate control action are determined. For each candidate control action, a control command is issued to the corresponding execution subsystem at the corresponding execution time to drive the execution subsystem to perform control operations according to the power adjustment mode corresponding to the candidate control action. During the execution of each candidate control action, the operating status data of the corresponding execution subsystem is continuously acquired, and the operating status of the corresponding subsystem is updated based on the operating status data. Based on the updated operating status data, the processing result of the corresponding fault handling process is output.

[0091] In this embodiment of the invention, the fault coordination action sequence essentially provides the execution priority and relative order of each candidate control action, but its real function is to guide the issuance and execution of control commands, enabling multiple subsystems to coordinate and adjust according to a predetermined sequence under the same fault event. Since each subsystem in the source-grid-load-storage-charge system has different control interfaces and response mechanisms, simply providing a set of actions is insufficient to complete the control; it is necessary to bind the action sequence with specific execution objects and execution times.

[0092] In practical implementation, candidate control actions are selected one by one according to the sequence of fault coordination actions, and the corresponding execution subsystem is identified. For example, for an energy storage system, candidate control actions may correspond to increasing or decreasing discharge power; for the load side, they may correspond to load reduction or restoration; and for the power supply side, they may involve adjusting output power. Simultaneously, the execution time of each action needs to be determined based on the current operating status and system adjustment rhythm, ensuring reasonable time intervals between adjacent actions and avoiding concentrated issuance of control commands.

[0093] During execution, the dispatch control unit sends control commands to the corresponding subsystems. These commands may include parameters such as target power value, adjustment direction, and adjustment rate. Upon receiving the control commands, each subsystem executes them according to its own control strategy, for example, adjusting output through a power control loop. Due to response lag and execution errors in actual equipment, it is necessary to continuously collect the operating status data of the corresponding subsystems during the control process to track their execution effects. This process enables the control system to monitor the real-time status changes of each subsystem, providing a reference for subsequent actions.

[0094] As each candidate control action is executed sequentially, the overall system operating state gradually evolves. By summarizing and analyzing the updated operating state data, the processing result of this fault handling process can be obtained. This result not only reflects the final operating state of each subsystem but can also be used to evaluate the execution effect of the action sequence, such as whether voltage and frequency have recovered to within allowable ranges and whether power balance has been improved. In practical applications, this processing result can also serve as a basis for subsequent strategy optimization or parameter adjustment.

[0095] It should be noted that the specific format of the control instructions, the method of determining the execution time, and the frequency of status updates can all be adjusted according to the actual system configuration. As long as orderly control execution based on action sequences can be achieved, they all fall within the protection scope of this invention.

[0096] like Figure 5As shown, this invention provides a fault handling system for the coordinated operation of power generation, grid, load, storage, and charging systems. The system includes: a data acquisition unit, used to acquire operating status data and fault event data of the power generation, grid, load, storage, and charging systems under a unified time reference, and determine the fault propagation arrival time of each subsystem based on the operating status data and the fault event data; a subsystem filtering unit, used to construct a fault propagation phase window for each subsystem based on the fault propagation arrival time of each subsystem, and determine the target subsystem in the propagation sensitive zone based on the fault propagation phase window; an action sequence generation unit, used to construct an action freeze zone criterion based on the operating deviation data of the target subsystem, calculate a consistency debt value for the candidate control actions of each target subsystem, sort the candidate control actions based on the consistency debt value, and generate a fault coordination action sequence; and a control execution unit, used to execute the control actions of the corresponding subsystem based on the fault coordination action sequence.

[0097] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The network interface A02 is used for communication with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements a fault handling method for source-grid-load-storage-charging coordinated operation.

[0098] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0099] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0100] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A fault handling method for coordinated operation of power generation, grid, load, storage, and charging systems, characterized in that: The method includes: The system acquires operational status data and fault event data of the power generation, grid, load, storage, and charging system under a unified time reference, and determines the fault propagation arrival time of each subsystem based on the operational status data and the fault event data. Based on the arrival time of fault propagation in each subsystem, a fault propagation phase window is constructed for each subsystem, and the target subsystem in the propagation sensitive area is determined based on the fault propagation phase window. Based on the operational deviation data of the target subsystem, an action freeze zone criterion is constructed, and a consistency debt value is calculated for the candidate control actions of each target subsystem. The candidate control actions are then sorted based on the consistency debt value to generate a fault coordination action sequence. The corresponding subsystem's control actions are executed based on the fault coordination action sequence.

2. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 1, characterized in that, Acquire operational status data and fault event data of the power generation, grid, load, storage, and charging system under a unified time base, including: Voltage, frequency and power data of each subsystem in the source-grid-load-storage-charging system are collected within a preset sampling period to form the original operating data sequence of each subsystem. Based on a unified time reference, time alignment processing is performed on the original running data sequence to obtain the running status data of each subsystem. The fault triggering time is determined based on the voltage change characteristics, frequency offset characteristics, and power change characteristics in the operating status data, and the operating status data within a preset time window before and after the fault occurs is extracted as fault analysis data to construct fault event data corresponding to the fault triggering time.

3. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 2, characterized in that, Determining the fault propagation arrival time for each subsystem based on the operational status data and the fault event data includes: Based on the fault event data, the fault trigger time is determined, and the fault trigger time is used as a time reference point to extract the state change sequence of each subsystem after the fault trigger time from the operating status data. For the state change sequence of each subsystem, the time response characteristics of voltage change, frequency change and power change relative to the fault trigger time are calculated respectively, and the initial response time of each subsystem is determined based on the time response characteristics. The sampling delay parameters, communication delay parameters, and execution delay parameters of each subsystem are obtained, and the fault propagation arrival time of each subsystem is obtained by superimposing the initial response time, the sampling delay parameters, the communication delay parameters, and the execution delay parameters.

4. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 1, characterized in that, Based on the fault propagation arrival time of each subsystem, a fault propagation phase window is constructed for each subsystem, and the target subsystem in the propagation sensitive area is determined based on the fault propagation phase window, including: For each subsystem, using the corresponding fault propagation arrival time as the time reference, and combining the response duration parameter and power change sensitivity parameter of each subsystem in the operation status data, a fault propagation phase window is constructed to characterize the propagation process of fault impact in the time dimension. Based on the fault propagation phase window, the phase position of each subsystem at the current moment is calculated to obtain the propagation phase parameters of each subsystem. The propagation phase parameters are matched with a preset phase interval to determine the subsystems that fall within the preset propagation sensitive interval as target subsystems.

5. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 4, characterized in that, Based on the fault propagation phase window, the phase position of each subsystem at the current moment is calculated to obtain the propagation phase parameters of each subsystem, including: Obtain the current time and calculate the time difference between the current time and the fault propagation arrival time of each corresponding subsystem to obtain the relative propagation time of each corresponding subsystem; Based on the operational status data, the response duration parameters of each subsystem are determined, and the relative propagation time is normalized using the response duration parameters to obtain the normalized propagation time of each subsystem. The normalized propagation time is mapped to a preset phase interval to obtain the propagation phase parameters of each subsystem.

6. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 1, characterized in that, Based on the operational deviation data of the target subsystem, an action freeze zone criterion is constructed, and a consistency debt value is calculated for the candidate control actions of each target subsystem. The candidate control actions are then sorted based on the consistency debt value to generate a fault coordination action sequence, including: Based on the operational deviation data, the voltage deviation, frequency deviation, and power deviation of each target subsystem are extracted. Combined with the power change correlation between each subsystem, an action freeze zone criterion is constructed to characterize the degree of coupling of actions of multiple subsystems in the current state. Based on the action freeze criteria, candidate control actions of each target subsystem are screened to determine a set of candidate control actions that meet the action release conditions. For each candidate control action in the candidate control action set, the consistency debt value of each candidate control action is calculated based on the degree of influence on voltage constraints, frequency constraints and power balance constraints within a preset time range after the execution of the corresponding candidate control action. The consistency debt values ​​of each candidate control action are sorted to generate a sequence of fault coordination actions arranged by execution priority.

7. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 6, characterized in that, Based on the aforementioned action freeze criterion, candidate control actions for each target subsystem are screened to determine a set of candidate control actions that meet the action release conditions, including: Obtain candidate control actions corresponding to each target subsystem, and extract the power change amplitude and power change rate corresponding to each candidate control action to form candidate action feature parameters; Based on the action freeze zone criterion, the candidate action feature parameters are matched with the preset freeze zone threshold to determine the freeze state identifier of each candidate control action. Based on the frozen state identifier, each candidate control action is filtered, and the candidate control actions in the frozen state are eliminated, while the candidate control actions in the released state are retained, forming a set of candidate control actions that meet the release conditions.

8. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 7, characterized in that, For each candidate control action in the candidate control action set, based on the degree of impact on voltage constraints, frequency constraints, and power balance constraints within a preset time range after executing the corresponding candidate control action, the consistency debt value of each candidate control action is calculated, including: For each candidate control action, starting from the execution time of the corresponding candidate control action, a predicted operating state sequence for each subsystem is constructed within the preset time range, and the voltage change, frequency change and power deviation are obtained based on the predicted operating state sequence. Based on the degree of deviation between the voltage change and the preset voltage constraint range, the voltage constraint deviation of the corresponding candidate control action is determined; Based on the degree of deviation between the frequency change and the preset frequency constraint range, the frequency constraint deviation of the corresponding candidate control action is determined. Based on the degree of deviation between the power deviation and the power balance constraint, the power balance constraint deviation of the corresponding candidate control action is determined. The voltage constraint deviation, frequency constraint deviation, and power balance constraint deviation are cumulatively calculated within the preset time range to obtain a comprehensive constraint deviation index for each candidate control action, and the consistency debt value for each candidate control action is determined based on the comprehensive constraint deviation index.

9. The fault handling method for source-grid-load-storage-charging coordinated operation according to claim 1, characterized in that, Based on the fault coordination action sequence, control actions of the corresponding subsystems are executed, including: According to the execution order in the fault coordination action sequence, each candidate control action is selected in turn, and the execution subsystem and execution time of the corresponding candidate control action are determined. For each candidate control action, a control command is sent to the corresponding execution subsystem at the corresponding execution time to drive the execution subsystem to perform the control operation according to the power adjustment method corresponding to the candidate control action; During the execution of each candidate control action, the operating status data of the corresponding execution subsystem is continuously acquired, and the operating status of the corresponding subsystem is updated based on the operating status data. Based on the updated operational status data, the processing results of the corresponding fault handling process are output.

10. A fault handling system for coordinated operation of power generation, grid, load, storage, and charging systems, characterized in that: The system includes: The data acquisition unit is used to acquire the operating status data and fault event data of the source-grid-load-storage-charging system under a unified time reference, and to determine the fault propagation arrival time of each corresponding subsystem based on the operating status data and the fault event data. The subsystem filtering unit is used to construct a fault propagation phase window for each subsystem based on the fault propagation arrival time of each subsystem, and to determine the target subsystem in the propagation sensitive area based on the fault propagation phase window. The action sequence generation unit is used to construct an action freeze zone criterion based on the operation deviation data of the target subsystem, and calculate the consistency debt value for the candidate control actions of each target subsystem, so as to sort the candidate control actions based on the consistency debt value and generate a fault coordination action sequence. The control execution unit is used to execute control actions of the corresponding subsystem based on the fault coordination action sequence.

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