V2G bidirectional energy aggregation scheduling method and system for overcharge station

Abstract

The invention discloses a V2G bidirectional energy aggregation scheduling method and system for an over-charge station, and relates to the technical field of V2G energy scheduling, and the method comprises the steps: enabling a communication bus to be communicated with an energy scheduling host, and determining a key energy scheduling characteristic parameter set; determining a preset scheduling threshold conforming to a new energy industry specification; configuring a correction coefficient of the preset scheduling threshold, and setting an energy transmission dynamic constraint mechanism; and performing synchronous verification on the energy aggregation scheduling parameter corresponding to the energy scheduling host, and outputting an energy aggregation scheduling strategy of the target overcharge station. The technical problems that in the prior art, due to the fact that V2G two-way energy scheduling of the overcharge station lacks a dynamic constraint mechanism, scheduling accuracy, safety and operation reliability are insufficient are solved, and the technical effects that accurate controllability and safe operation of V2G two-way energy aggregation scheduling of the overcharge station are achieved, and the efficiency and reliability of energy scheduling are improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of V2G energy scheduling technology, specifically to a V2G bidirectional energy aggregation scheduling method and system for supercharging stations. Background Technology

[0002] As a core infrastructure for electric vehicle energy replenishment, supercharging stations face an increasingly urgent need for V2G bidirectional energy interaction with the power grid. Current energy dispatching methods for supercharging stations mostly employ fixed threshold control strategies, formulating dispatching plans solely based on supercharging station rated parameters or single grid operation indicators. This fails to fully consider the dynamic changes in station-level configuration factors, battery characteristics, and grid operation factors. Furthermore, it lacks real-time perception and threshold correction mechanisms for battery SOC status, grid load status, and energy transmission loss status. This leads to problems such as power matching imbalance and delayed dispatching response during energy dispatching. Not only is it difficult to guarantee the safety and stability of bidirectional energy transmission between supercharging stations and the grid, but it also results in low energy transmission efficiency and insufficient resource utilization, failing to meet the new energy industry's demand for precise and efficient V2G bidirectional energy aggregation and dispatching of supercharging stations.

[0003] The lack of dynamic constraint mechanism in the existing V2G bidirectional energy dispatching of supercharging stations leads to technical problems such as insufficient dispatching accuracy, safety and operational reliability. Summary of the Invention

[0004] This application provides a V2G bidirectional energy aggregation and scheduling method and system for supercharging stations, which addresses the technical problem that the lack of dynamic constraint mechanism in the V2G bidirectional energy scheduling of supercharging stations in the prior art leads to insufficient scheduling accuracy, security and operational reliability.

[0005] In view of the above problems, this application provides a V2G bidirectional energy aggregation and scheduling method and system for supercharging stations.

[0006] The first aspect of this application provides a V2G bidirectional energy aggregation and scheduling method for supercharging stations, the method comprising: The energy dispatch host is connected via a communication bus, and the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station are associated to determine the set of key energy dispatch characteristic parameters. Based on the rated dispatch parameters of the target supercharging station and the bidirectional energy transmission standard, a preset dispatch threshold conforming to the new energy industry standard is determined. Through battery SOC status, grid load status, and energy transmission loss status, a correction coefficient for the preset dispatch threshold is configured, and a dynamic energy transmission constraint mechanism is set in conjunction with the set of key energy dispatch characteristic parameters. Based on the dynamic energy transmission constraint mechanism and the grid safety intervention mechanism, the energy aggregation dispatch parameters corresponding to the energy dispatch host are synchronously verified, and the energy aggregation dispatch strategy of the target supercharging station is output.

[0007] A second aspect of this application provides a V2G bidirectional energy aggregation and scheduling system for supercharging stations, the system comprising: The parameter set determination module is used to connect to the energy dispatch host via a communication bus, and associate the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station to determine the key energy dispatch characteristic parameter set. The preset dispatch threshold determination module is used to determine the preset dispatch threshold that conforms to the new energy industry standards based on the rated dispatch parameters of the target supercharging station and the bidirectional energy transmission standard. The constraint mechanism setting module is used to configure the correction coefficient of the preset dispatch threshold through the battery SOC state, grid load state, and energy transmission loss state, and set the energy transmission dynamic constraint mechanism in combination with the key energy dispatch characteristic parameter set. The dispatch strategy output module is used to synchronously verify the energy aggregation dispatch parameters corresponding to the energy dispatch host based on the energy transmission dynamic constraint mechanism and the grid safety intervention mechanism, and output the energy aggregation dispatch strategy of the target supercharging station.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The energy dispatch host is connected via a communication bus to determine a set of key energy dispatch characteristic parameters. Based on the rated dispatch parameters of the target supercharging station and bidirectional energy transmission standards, a preset dispatch threshold conforming to new energy industry specifications is determined. A correction coefficient for the preset dispatch threshold is configured, and a dynamic energy transmission constraint mechanism is set in conjunction with the set of key energy dispatch characteristic parameters. Based on the dynamic energy transmission constraint mechanism and the power grid safety intervention mechanism, the energy aggregation dispatch parameters corresponding to the energy dispatch host are synchronously verified, and the energy aggregation dispatch strategy for the target supercharging station is output. This achieves the technical effect of realizing precise, controllable, and safe operation of V2G bidirectional energy aggregation dispatch for supercharging stations, improving the efficiency and reliability of energy dispatch. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic diagram of a V2G bidirectional energy aggregation and scheduling method for supercharging stations provided in this application embodiment; Figure 2 This is a schematic diagram of a V2G bidirectional energy aggregation and scheduling system for supercharging stations, provided as an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: Parameter set determination module 10, preset scheduling threshold determination module 20, constraint mechanism setting module 30, and scheduling strategy output module 40. Detailed Implementation

[0012] This application provides a V2G bidirectional energy aggregation and scheduling method and system for supercharging stations, which addresses the technical problem that the lack of dynamic constraint mechanism in the V2G bidirectional energy scheduling of supercharging stations in the prior art leads to insufficient scheduling accuracy, security and operational reliability.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] Example 1, as Figure 1 As shown, this application provides a V2G bidirectional energy aggregation scheduling method for supercharging stations, the method comprising: Step S100: Use the communication bus to connect to the energy dispatch host, associate the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station, and determine the set of key energy dispatch characteristic parameters.

[0015] Specifically, a highly reliable communication link is established using CAN bus or industrial Ethernet to achieve bidirectional data interaction and command transmission with the energy dispatch host. Through bus communication protocols, such as Modbus TCP, the three core data factors of the target supercharging station are accurately analyzed. The station-level configuration factors cover hardware and layout parameters such as the number of supercharging piles deployed, the rated charging and discharging power of a single pile, the capacity and type of the energy storage system in the station, the power distribution line topology, and the voltage level configuration. The battery characteristic factors include the type of electric vehicle power battery connected, such as ternary lithium and lithium iron phosphate, the rated voltage and capacity, the charge and discharge efficiency curve, the SOC safe operating range, and the maximum charge and discharge rate. The grid operation factors include grid-side operation parameters such as the voltage level of the grid connection point, real-time transmission capacity, current load value, load peak and valley period distribution, frequency stability range, and interactive power limits given by the grid dispatching department. After deduplication, outlier removal, and normalization preprocessing of the three types of basic parameters collected, the core parameters that have a significant impact on energy scheduling efficiency, security, and compliance are selected by using a random forest feature screening algorithm or a mutual information entropy feature importance assessment method. Finally, these parameters are integrated to form a structured and reusable set of key energy scheduling feature parameters.

[0016] Step S200: Determine the preset scheduling threshold that conforms to the new energy industry standards based on the rated scheduling parameters and bidirectional energy transmission standards of the target supercharging station.

[0017] Specifically, a comprehensive review of the rated scheduling parameters for the target supercharging station was conducted, covering core hardware and operational rated indicators such as the rated charging and discharging power of a single supercharging pile, the upper limit of the total aggregated power within the station, the rated voltage / current for charging and discharging of energy storage units, the allowable voltage fluctuation range of the distribution bus, and the response time for switching charging and discharging modes. Simultaneously, strict adherence to relevant standards for bidirectional energy transmission in the new energy industry was maintained, such as the technical requirements for V2G interaction in the GB / T 38775 series electric vehicle bidirectional charging interface standards, the IEEE 1547 distributed power source grid access technical specifications, and the GB / T 20234 electric vehicle conductive charging connection device standard. Combined with grid safety operation procedures, industry energy efficiency assessment standards, and regional grid dispatch management regulations, a parameter threshold calibration algorithm was used to integrate rated parameter boundary constraints and standard compliance range analysis to determine preset scheduling thresholds covering multiple dimensions, including upper and lower limits of charging and discharging power, voltage fluctuation thresholds, current change rate limits, lower limits of energy transmission efficiency, and frequency stability ranges. This ensures that all thresholds match the hardware operation capabilities of the supercharging station and fully comply with new energy industry standards and grid safety requirements.

[0018] Step S300: Configure the correction coefficient of the preset scheduling threshold based on the battery SOC status, grid load status, and energy transmission loss status, and set up a dynamic constraint mechanism for energy transmission in conjunction with the key energy scheduling feature parameter set.

[0019] Specifically, through the Battery Management System (BMS), grid dispatch terminal, and energy metering equipment of the supercharging station, three types of core operating status data are collected in real time: battery SOC status (including the current SOC value, SOC change rate, and SOC safety threshold boundary of the connected vehicle's power battery); grid load status (including real-time load power at the grid connection point, load growth rate, load forecast for the next 1-3 hours, and load peak and valley period identifiers); and energy transmission loss status (including distribution line transmission loss, charge / discharge conversion equipment loss, loss coefficients at different power levels, and temperature-related loss compensation values). A correction coefficient calculation model is constructed based on a multiple linear regression algorithm. After normalizing the three types of status parameters, they are used as model input variables. The model parameters are trained and optimized using historical operating data, and the dynamic correction coefficients of the preset dispatch threshold under different scenarios are output. For example, the voltage fluctuation threshold correction coefficient during peak grid load periods is set at 0.92, the charging and discharging power threshold correction coefficient is set at 0.78 when the battery SOC is below 25%, and the efficiency threshold correction coefficient is set at 1.05 under low-loss operation scenarios. Subsequently, based on the key energy dispatch characteristic parameter set as the basic framework, the dynamic correction thresholds of power, voltage, current, efficiency and other dimensions are integrated to construct a dynamic constraint mechanism for energy transmission from multiple dimensions. Specifically, this includes power constraints, which match the battery charging and discharging capacity with the grid's carrying capacity limit in real time; voltage and current constraints, which adapt to the battery's tolerance range under different SOC states and the grid's stability requirements; loss constraints, which control the loss throughout the energy transmission process to be below the preset threshold; and response time constraints, which meet the real-time requirements of charging and discharging mode switching, ensuring that the constraint mechanism can dynamically adapt to the real-time operating status changes of supercharging stations, batteries and grids.

[0020] Step S400: Based on the energy transmission dynamic constraint mechanism and the power grid security intervention mechanism, the energy aggregation scheduling parameters corresponding to the energy scheduling host are synchronously verified, and the energy aggregation scheduling strategy of the target supercharging station is output.

[0021] Specifically, based on the constructed dynamic energy transmission constraint mechanism as the core constraint foundation, and integrating the power grid safety intervention mechanism, a multi-dimensional collaborative verification model is constructed, encompassing overvoltage and overcurrent real-time protection logic, power grid frequency / voltage anomaly regulation strategy, fault emergency disconnection triggering mechanism, and regional power grid dispatch command response rules. This model covers initial energy aggregation and dispatch parameters generated by the energy dispatch host, including the charging and discharging power allocation values ​​of each supercharging pile, the charging and discharging start / stop status and power setpoint of energy storage units, the interactive power plan value with the power grid, and the charging and discharging mode switching sequence. The verification dimensions include whether the parameters meet the dynamically corrected power / voltage / current thresholds and whether they comply with energy transmission requirements. The algorithm considers factors such as transmission loss constraints and response time requirements, whether it adapts to the real-time carrying capacity and safe operation standards of the power grid, and whether it matches the station-level configuration and battery characteristic parameters of the supercharging station. If the verification results show that all parameters meet the constraints and safety requirements, the energy aggregation scheduling strategy is directly output. If there are parameters that do not meet the standards, a gradient descent iterative optimization algorithm is started based on the verification deviation value. The scheduling parameters are adjusted with compliance as the priority, safety as the guarantee, and energy efficiency as the goals. During the iteration process, the parameter adaptability is continuously verified synchronously until all parameters meet the constraints and safety requirements. Finally, the target supercharging station energy aggregation scheduling strategy that takes into account compliance, safety, and real-time adaptability is output.

[0022] In one possible implementation, step S300 further includes: Step S310: Use battery degradation status, interface contact status, and line heating fatigue status as dynamic correction factors for the preset scheduling threshold.

[0023] Step S320: Map the dynamic correction factor to a risk level to generate an equipment safety status score.

[0024] Step S330: When the equipment safety status score is lower than the safety threshold, freeze the preset scheduling threshold adjustment based on the correction coefficient, and inject the dynamic correction factor into the power grid safety intervention mechanism as a trigger condition for cutting off the V2G channel.

[0025] Step S340: Otherwise, within the allowable range of the equipment's safety status, the dynamic correction factor is used as a weight constraint to apply a safety limit to the correction coefficients configured based on the battery SOC status, grid load status, and energy transmission loss status, thereby generating a comprehensive adjustment factor.

[0026] Step S350: Update the preset scheduling threshold based on the comprehensive adjustment factor.

[0027] Specifically, in the dynamic correction system for preset scheduling thresholds, new dynamic correction factors related to equipment safety are added, explicitly including battery degradation status, interface contact status, and line thermal fatigue status in the correction dimensions. Battery degradation status is quantified by collecting parameters such as the cumulative number of battery cycles, the ratio of actual capacity to rated capacity degradation, the increase in internal resistance, and the trend of charge / discharge efficiency degradation. Interface contact status is comprehensively reflected by indicators such as contact resistance detection data of V2G connection interfaces, cumulative insertion / removal count statistics, signal transmission bit error rate, and interface temperature changes. Line thermal fatigue status is accurately captured by data such as real-time operating temperature of the power distribution line, temperature rise rate, cumulative heating duration, insulation aging detection results, and line impedance change rate. All three types of status parameters are standardized and transformed into quantifiable dynamic correction factors, providing core constraints at the equipment level for subsequent safety-oriented correction of scheduling thresholds.

[0028] The weight allocation of three types of dynamic correction factors—battery degradation state, interface contact state, and line thermal fatigue state—was determined using the analytic hierarchy process (AHP). For example, the weight of battery degradation state was 0.4, the weight of interface contact state was 0.3, and the weight of line thermal fatigue state was 0.3. Based on the priority calibration of equipment safety impact and referring to the safety industry standards for new energy V2G equipment and the rated parameters of the equipment at the factory, risk classification rules were set for each type of correction factor. Core parameters such as battery capacity degradation rate, interface contact resistance, and real-time line temperature were divided into four levels: safe, low risk, medium risk, and high risk. Each level corresponds to a clear quantitative range and risk score. For example, a battery capacity degradation rate ≤10% is a safe level with a score of 8-10; 10%-20% is a low risk level with a score of 6-7; 20%-30% is a medium risk level with a score of 3-5; and >30% is a high risk level with a score of 0-2. After matching the risk levels and quantifying the scores of the three types of dynamic correction factors, a weighted summation calculation is performed according to the preset weights to generate an equipment safety status score of 0 to 10, which intuitively quantifies the current operational safety level of the equipment.

[0029] A safety threshold for the equipment safety status score is pre-set based on V2G equipment safety operation specifications, industry safety standards, and actual operating limit parameters of the equipment. For example, a score of 6 can be dynamically calibrated based on equipment type and application scenario. After obtaining the equipment safety status score, it is compared with the safety threshold in real time. If the score is detected to be lower than the safety threshold, it indicates that battery degradation, interface contact, or line overheating fatigue has reached a high risk level. Further adjustment of the preset scheduling threshold may cause equipment failure or safety accidents. At this time, the safety protection mechanism is immediately triggered. On the one hand, the adjustment operation of the preset scheduling threshold based on the correction coefficient configured based on battery SOC status, grid load status, and energy transmission loss status is frozen to prevent the threshold from shifting further into the unsafe range. On the other hand, the dynamic correction factors corresponding to battery degradation status, interface contact status, and line overheating fatigue status, including specific quantitative parameters and risk level information, are fully injected into the grid safety intervention mechanism. This is clearly defined as a specific trigger condition for cutting off the V2G bidirectional energy transmission channel. When the risk parameters corresponding to the subsequent dynamic correction factors continue to deteriorate or meet the preset trigger logic, the grid safety intervention mechanism will automatically start the V2G channel cutting-off process to quickly terminate energy interaction and avoid safety risks such as equipment damage and circuit failure.

[0030] If the equipment safety status score is higher than or equal to the preset safety threshold, it indicates that the battery, interface, and circuit are in a safe operating range. At this point, within the equipment's safety allowable range, the three dynamic correction factors—battery degradation status, interface contact status, and circuit thermal fatigue status—are converted into corresponding weight constraint coefficients. Based on the real-time quantization parameters and safety level of each correction factor, a normalization algorithm maps them to weight values ​​in the range of 0 to 1. For example, a 15% battery degradation rate corresponds to a weight of 0.3, normal interface contact resistance corresponds to a weight of 0.1, and circuit temperature within a safe range corresponds to a weight of 0.1. Using these weight constraint coefficients as constraints, the previous data based on… The initial correction coefficients configured for battery SOC status, grid load status, and energy transmission loss status are subject to safety limiting processing. Extreme values ​​of correction coefficients that exceed the equipment's safe carrying capacity are eliminated. For example, when the weight constraint coefficient corresponding to battery degradation is high, the upper limit of the initial correction coefficient related to charging and discharging power is lowered. The initial correction coefficients after safety limiting are combined with the weight constraint coefficients of the dynamic correction factor through a weighted fusion algorithm to generate a comprehensive adjustment factor that takes into account both the adaptability of the operating status and the safety of the equipment. This ensures that subsequent scheduling threshold updates meet the real-time operating requirements of the supercharging station, battery, and grid without exceeding the equipment's own safety boundaries.

[0031] The core update is based on the generated comprehensive adjustment factor, which integrates the safety-limited correction coefficients corresponding to battery SOC state, grid load state, and energy transmission loss state, as well as the weight constraints of battery degradation state, interface contact state, and line heating fatigue state transformation, taking into account both operational adaptability and equipment safety. For the multi-dimensional indicators covered by the preset scheduling thresholds, such as the upper and lower limits of charging and discharging power, voltage fluctuation range, current change rate, and lower limit of energy transmission efficiency, a factor-weighted correction algorithm is used to multiply the comprehensive adjustment factor with the original preset scheduling thresholds for each dimension, generating updated scheduling thresholds for each dimension. During the update process, the new thresholds are simultaneously verified to ensure compliance with the bidirectional energy transmission standards of the new energy industry, the rated operating parameters of the supercharging station, and grid safety specifications, ensuring that the updated preset scheduling thresholds can dynamically adapt to the real-time operating status changes of the supercharging station, battery, and grid, while strictly adhering to the equipment's safe operating boundaries.

[0032] In one possible implementation, step S330 further includes: Step S331: Extract energy transmission degradation features, and combine the loss increment value of the energy transmission degradation features with the voltage fluctuation level under the current grid load to determine the potential dispatch failure acceleration factor.

[0033] Step S332: Determine the potential scheduling failure propagation path based on the similarity between the triggering conditions of historical fault modes in the energy transmission degradation characteristics and the current scheduling parameters.

[0034] Step S333: Based on the potential scheduling failure acceleration factor and the potential scheduling failure propagation path, set the power grid security intervention mechanism.

[0035] Specifically, a multi-source sensor collaborative acquisition architecture is adopted. Through Rogowski coil current sensors, high-precision voltage transformers, and fiber optic temperature sensors deployed on the supercharging station's distribution lines, the efficiency monitoring module built into the charge / discharge conversion equipment, and the grid-side load monitoring terminal, energy transmission degradation characteristic parameters are collected and extracted in real time. These parameters include active / reactive power loss data of the distribution lines, charge / discharge conversion efficiency values, current harmonic distortion rate (THD), voltage sag / surge amplitude and duration, interface contact resistance changes, and energy storage unit charge / discharge response delay time, among other core indicators. Based on a pre-set healthy operating condition benchmark parameter library, benchmark loss and efficiency data from the equipment's factory installation and stable operation period are stored. The absolute value of the real-time loss increment for each degradation characteristic is calculated using the difference method, i.e., real-time line loss - healthy benchmark loss. Simultaneously, the ratio method is used to obtain the percentage of loss increment, i.e., absolute value of loss increment / healthy benchmark loss × 100%, forming a multi-dimensional set of loss increment values. Simultaneously, the current grid status is obtained through the grid dispatch SCADA system. The model uses load level and real-time load power, combined with the effective voltage value, peak voltage fluctuation, and voltage fluctuation frequency within 3 minutes collected by voltage monitoring equipment, to quantify the voltage fluctuation level under the current grid load using the root mean square error (RMSE). A coupled calculation model based on an improved BP neural network is constructed. This model uses the set of loss increment values ​​and the quantified voltage fluctuation level as input layer neurons, with a total of 8-12 input nodes matching the degradation feature dimensions. The hidden layer has two layers, each with 16-24 neurons, using the ReLU activation function. The output layer is a single neuron, corresponding to a potential scheduling failure acceleration factor with a value range of 0-5. The model is trained using historical fault data, including scheduling failure evolution data under different combinations of loss increments and voltage fluctuations. The weight parameters are optimized using the cross-entropy loss function, ultimately outputting a potential scheduling failure acceleration factor that accurately quantifies the rate of deterioration of scheduling failure risk. A larger factor value indicates a stronger coupling effect between degradation and voltage fluctuation, and a faster rate of scheduling failure risk propagation.

[0036] A pre-built database of historical energy transmission degradation fault modes is constructed. This database, based on past operational data of the supercharging station V2G system, categorizes and stores the complete triggering conditions for different types of faults, such as line overheating faults, poor interface contact faults, and scheduling imbalance faults caused by battery degradation. This includes threshold values ​​for energy transmission degradation characteristic parameters at the time of the fault, such as the percentage of loss increment and the upper limit of harmonic distortion rate, as well as corresponding scheduling parameter combinations, such as charging and discharging power setpoints, voltage fluctuation thresholds, energy interaction timing, and fault evolution data. Through data standardization, the triggering conditions of historical fault modes are mapped to the same data dimension along with current scheduling parameters, including real-time charging and discharging power allocation, dynamically corrected scheduling thresholds, and grid interaction power plans. An improved cosine similarity algorithm is used, with core parameters in energy transmission degradation features, such as loss increment, voltage fluctuation level, and response delay time, as feature vectors. The similarity between historical fault triggering conditions and current scheduling parameters is calculated. Simultaneously, a decision tree model is used to filter out highly correlated historical fault patterns with similarity values ​​exceeding a preset threshold. Based on the fault propagation links of these highly correlated historical fault patterns, such as from increased interface contact resistance to localized heating, a surge in energy transmission loss, scheduling power imbalance, and abnormal energy interaction across the entire network, combined with the real-time evolution trend of current energy transmission degradation features, a fault propagation topology is constructed using graph theory analysis. This identifies which device nodes and parameter-related paths the fault is most likely to spread along, ultimately determining potential scheduling failure propagation paths.

[0037] Based on the quantitative values ​​of potential scheduling failure acceleration factors, multiple risk levels are defined, such as 0-1 for low risk, 1-3 for medium risk, and 3-5 for high risk. Corresponding intervention response priorities and intervention intensity thresholds are assigned to each risk level: low risk corresponds to routine intervention, medium risk to enhanced intervention, and high risk to emergency intervention. Simultaneously, considering the topological structure of the potential scheduling failure propagation path, key nodes in fault propagation are identified, such as interface modules, distribution lines, energy storage units, grid connection points, parameter correlation links, and diffusion sequences. Based on the risk level and propagation path analysis results, specific rules for targeted power grid safety intervention mechanisms are established. At low-risk levels, flexible intervention strategies such as power fine-tuning and voltage compensation are set based on non-critical nodes in the transmission path. At medium-risk levels, preventive intervention measures such as load diversion, current limiting, and voltage limiting are initiated for critical nodes in the transmission path. At high-risk levels, a rapid disconnection logic for the entire transmission path is preset, and the triggering sequence and execution process of V2G channel disconnection are clearly defined. At the same time, the rate of change of potential scheduling failure acceleration factors is incorporated into the intervention logic. When the factor value rises sharply in a short period of time, the intervention level is automatically upgraded to ensure that the power grid safety intervention mechanism can accurately match the risk level and fault propagation characteristics, and achieve targeted blocking and hierarchical prevention and control of scheduling failure risks.

[0038] In one possible implementation, step S100 further includes: The station-level configuration factors include the number of charging piles, power module capacity, and energy storage unit configuration data collected by the station-level monitoring nodes. The station-level monitoring nodes are deployed in the power distribution center and energy storage area of ​​the supercharging station.

[0039] Specifically, the station-level configuration factors include core configuration data collected by station-level monitoring nodes of the supercharging station. These monitoring nodes are centrally deployed in the supercharging station's power distribution center and energy storage area, adhering to the principles of accurate data collection and comprehensive coverage. The monitoring nodes in the power distribution center are responsible for collecting core parameters in real time, such as the actual number of charging piles deployed within the station, the rated capacity of the power modules corresponding to each charging pile, and feedback data on the operating status of the power modules. The monitoring nodes in the energy storage area focus on collecting key data such as the configuration type of energy storage units, such as lithium-ion battery energy storage and vanadium redox flow storage, the rated capacity of a single energy storage unit, the total installed capacity of energy storage units, the series and parallel connection method of energy storage units, and the communication parameters of the energy storage management system. The data collected by the two types of monitoring nodes are standardized and integrated to jointly constitute the core data support for the station-level configuration factors.

[0040] In one possible implementation, step S100 further includes: The battery characteristic factors include data on battery capacity decay rate, charge / discharge rate limit, and temperature adaptation range collected by battery-level monitoring nodes, which are deployed at the charging interface and the battery management system interface.

[0041] Specifically, the battery characteristic factors include core performance data collected by battery-level monitoring nodes. These monitoring nodes are precisely deployed at the charging interface of the supercharging station and the BMS interface, following the principles of close-range acquisition and high-precision feedback. The monitoring node at the charging interface collects the ratio of the battery's current actual capacity to its rated capacity, i.e., the battery capacity decay rate, and the maximum allowable charge / discharge rate limit under different SOC ranges, including dynamic performance parameters such as the rate threshold when switching between fast and slow charging modes, through real-time communication with the power battery. The monitoring node at the BMS interface extracts the temperature adaptation range verified by long-term battery operation by parsing the standard communication protocol data output by the battery management system, including key information such as the minimum allowable operating temperature, the maximum allowable operating temperature and the optimal operating temperature range, and the performance decay correction coefficient under extreme temperatures. The data collected by the two types of nodes, after consistency verification and standardization processing, together constitute the core data system of battery characteristic factors.

[0042] In one possible implementation, step S100 further includes: The power grid operation factors include power grid voltage fluctuations, frequency stability, and line transmission capacity data collected by power grid-level monitoring nodes, which are deployed at the connection interface between the supercharging station and the power grid, as well as at key nodes of the lines.

[0043] Specifically, the power grid operation factors include core power grid operation data collected by power grid-level monitoring nodes. These monitoring nodes are precisely positioned at the connection interface between the supercharging station and the power grid, as well as key nodes on the lines, adhering to the principle of full coverage of key nodes and comprehensive data collection. At the connection interface, monitoring nodes use high-precision power sensors to collect real-time power grid voltage fluctuation data, including voltage RMS deviation, voltage sag and droop amplitude and duration, harmonic distortion rate, and frequency stability parameters, including real-time frequency values, frequency fluctuation range, and fluctuation frequency. Monitoring nodes at key line nodes utilize load monitoring devices and transmission capacity detection modules to collect data such as real-time transmission power, remaining transmission capacity, and load rate trends, while simultaneously capturing auxiliary parameters such as line voltage drop and current imbalance. All data collected by the monitoring nodes undergoes unified protocol parsing, outlier removal, and time synchronization calibration to form a complete power grid operation factor data system.

[0044] In one possible implementation, step S100 further includes: The scheduling control excitation signal is sent to the station-level monitoring node, battery-level monitoring node, and power grid-level monitoring node through the communication bus.

[0045] When the target supercharging station performs bidirectional energy transfer, the station-level monitoring node, battery-level monitoring node, and grid-level monitoring node receive the scheduling control excitation signal and use a hierarchical collaborative mechanism based on energy loss distribution to perform the allocation and management of energy scheduling monitoring tasks.

[0046] Specifically, through the pre-set industrial Ethernet communication architecture of the supercharging station, such as Profinet, EtherNet / IP, or CAN bus, the scheduling control excitation signal generated by the energy scheduling control center includes core control parameters such as monitoring cycle instructions, data acquisition priority configuration, parameter sampling accuracy requirements, task trigger thresholds, and collaborative interaction protocols. This signal is precisely transmitted to station-level monitoring nodes, battery-level monitoring nodes, and grid-level monitoring nodes via the differential transmission link of the communication bus. During the transmission process, a CRC check and timestamp synchronization mechanism is used to ensure that the excitation signal is transmitted without packet loss or distortion across multiple nodes, and that each monitoring node receives the control instructions synchronously. Simultaneously, the communication bus supports a bidirectional feedback channel, with each monitoring node immediately returning an acknowledgment response after receiving the signal. If an abnormal signal is detected, a retransmission mechanism is triggered, ensuring that the scheduling control excitation signal can be efficiently and reliably transmitted to the three types of monitoring nodes.

[0047] During the V2G bidirectional energy transmission process at the target supercharging station, the station-level monitoring node, battery-level monitoring node, and grid-level monitoring node receive scheduling and control excitation signals sent through the communication bus in real time, including core instructions such as monitoring frequency, acquisition accuracy, and priority configuration. Then, a hierarchical collaborative mechanism based on energy loss distribution is initiated to orderly execute the allocation and management of energy scheduling and monitoring tasks. The mechanism first uses the energy transmission path loss model pre-stored at each monitoring node, combined with real-time collected initial operating data, to accurately analyze the distribution characteristics of energy loss within the station, clarifying the proportion and key generating nodes of different loss types such as distribution line transmission loss, charge / discharge conversion loss, interface contact loss, and grid interaction loss. Then, based on the loss distribution results, it divides the monitoring tasks into three layers: station-level monitoring nodes, leveraging the deployment advantages of the distribution center and energy storage area, undertake the station-level global loss monitoring task, responsible for coordinating cross-equipment collaborative monitoring matters such as the operating status of charging pile clusters, energy conversion efficiency of energy storage units, and total loss statistics within the station; battery-level monitoring nodes, utilizing the close-range acquisition characteristics of the charging interface and BMS interface, undertake the task of monitoring local losses on the battery side, focusing on battery charge / discharge loss, capacity decay-related loss, battery temperature-related loss, and other performance-related monitoring content; grid-level monitoring nodes, based on the deployment location of grid connection interfaces and key line nodes, are responsible for grid-side interaction loss monitoring tasks, focusing on monitoring grid-side parameters such as losses caused by grid voltage fluctuations, line transmission capacity-related losses, and grid frequency stability-related losses. Simultaneously, a real-time collaborative interaction channel is established between the three-layer nodes. When an abnormal loss is detected at a certain level, cross-level task linkage adjustment is automatically triggered. For example, when the battery-side loss changes abruptly, station-level load diversion monitoring and grid-side current limiting monitoring are activated simultaneously. The dynamic load balancing algorithm optimizes the allocation of monitoring resources for each node to ensure that the monitoring tasks are accurately matched with the energy loss distribution characteristics, thereby achieving efficient collaboration and comprehensive coverage of energy dispatch monitoring.

[0048] In one possible implementation, step S300 further includes: Step S360: Connect to the historical energy scheduling failure event database and extract matching historical events by combining the energy scheduling feature matrix.

[0049] Step S370: Determine the duration of potential scheduling performance degradation based on the cumulative periodic distribution of energy transmission load of the matched historical events and the current energy transmission growth rate.

[0050] Step S380: Determine the scope of potential scheduling risks based on the matched historical events.

[0051] Step S390: Set the dynamic constraint mechanism for energy transmission based on the duration of potential scheduling performance degradation and the scope of impact of potential scheduling risks.

[0052] Specifically, a historical energy dispatch failure event database is pre-built and connected. This database system compiles various dispatch failure events that have occurred in the V2G bidirectional energy transmission process of supercharging stations, and fully stores the energy transmission load data, equipment operating status parameters, including status indicators such as battery degradation, interface contact, and line heating, as well as grid operating conditions such as voltage fluctuations, frequency stability, and line transmission capacity, failure triggering timing, fault evolution path, and final impact results for each event. Simultaneously, a multi-dimensional energy dispatch feature matrix is ​​constructed, covering key features such as energy transmission power, load growth rate, equipment safety status score, grid voltage fluctuation amplitude, line transmission loss increment, and charge / discharge conversion efficiency. Real-time operating data under the current energy dispatch scenario is standardized according to the corresponding dimensions and then filled into the feature matrix to form the current dispatch feature vector. An improved cosine similarity algorithm is used to calculate the similarity between the current dispatch feature vector and the feature vectors of each event in the historical energy dispatch failure event database. A similarity threshold, such as 0.75, is set, and events with similarity higher than this threshold are selected as matching historical events.

[0053] For the extracted matching historical events, the data parsing module extracts the cumulative periodic distribution data of the energy transmission load, including the complete time span from the initial state of load accumulation to triggering scheduling failure, the proportion of cumulative duration corresponding to different load levels, such as low load, medium load, and high load, the frequency and timing of peak occurrences during the load accumulation process, and the periodic characteristics of the load decay stage. At the same time, based on the real-time monitoring data of the current supercharging station's V2G bidirectional energy transmission, the sliding window algorithm is used to calculate the energy transmission growth rate per unit time, such as the power growth amplitude per minute / hour and the trend of load growth rate changes, and the growth rate is smoothed to eliminate instantaneous fluctuation interference. A time series prediction model integrating historical periodic distribution and real-time growth rate is constructed, such as an improved LSTM model. The load accumulation periodic distribution data matching historical events is used as the model training sample, and the current energy transmission growth rate is used as the real-time input parameter. The model learns the correlation between historical load accumulation and performance degradation, and dynamically extrapolates the load accumulation process in combination with the current growth rate. Finally, the estimated time for the potential scheduling performance to gradually decay from the normal state to the critical threshold under the current scheduling scenario is output, that is, the duration of potential scheduling performance degradation, which provides a quantitative basis for the timeliness configuration of subsequent dynamic constraint mechanisms.

[0054] Structured data parsing tools were used to extract core fault impact data from historical events, including a list of affected charging pile numbers, the number of fault groups in energy storage units, coordinates of fault sections in grid access lines, functional area divisions of energy transmission interruptions, node association graphs of fault propagation paths, types of affected equipment, and energy transmission interruption power thresholds during the fault duration. Using a supercharging station topology modeling tool, a scaled-down digital topology model was constructed based on current supercharging station equipment deployment CAD drawings, energy storage unit series-parallel topology relationships, and grid access line GIS map data. Real-time data access interfaces were used to import current energy transmission load distribution data for each device and grid operating status parameters. A graph-based fault propagation simulation algorithm was then applied to match historical events. The fault triggering node, propagation rate, and influence weight of associated nodes are input into a digital topology model to simulate the propagation path and impact boundary of the fault in the current scenario. A device correlation weight matrix is ​​set, with core control devices assigned high weights and ordinary execution devices assigned low weights. The importance level of energy transmission links is classified, with trunk links assigned as Level 1 and branch links assigned as Levels 2 to 3. A risk impact range quantification algorithm is used, combining device correlation weights and energy transmission link importance levels, to calculate the number of devices that may be affected by potential scheduling risks, the total length of energy transmission links, the functional area coverage, and the number of associated nodes on the grid side. A potential scheduling risk impact range report is generated, including a specific device list, link segments, regional coordinates, and impact level classification.

[0055] Based on the quantitative criteria of the duration of potential scheduling performance degradation and the scope of potential scheduling risk impact, a hierarchical and categorized dynamic constraint mechanism configuration system for energy transmission is constructed. First, the duration of potential scheduling performance degradation is divided into three intervals: short-term, medium-term, and long-term. The scope of potential scheduling risk impact is divided into three levels: small-scale, medium-scale, and large-scale, based on the number of affected devices, link length, and area. Differentiated constraint strategies are formulated for different time-scope combinations: If the degradation duration is short-term and the risk impact scope is small-scale, a flexible constraint strategy is adopted, appropriately lowering the upper limit of charging and discharging power and fine-tuning the allowable voltage fluctuation threshold, while maintaining the regular scheduling parameter adjustment cycle and reserving a certain amount of scheduling flexibility. If the degradation duration is medium-term or the risk impact scope is medium-scale, an enhanced constraint mechanism is activated, significantly reducing the charging and discharging power constraint threshold, shortening the dynamic adjustment cycle of scheduling parameters, increasing the frequency of status monitoring of key equipment, and simultaneously setting the load growth rate. To prevent rapid load accumulation from exacerbating performance degradation, an emergency constraint strategy is implemented if the degradation duration is long-term or the risk impact range is wide. This strategy strictly limits the total load limit for energy transmission, delineates risk isolation zones, and implements current limiting or temporary disconnection of energy transmission channels within these zones. A risk diffusion blocking threshold is set, and when monitoring data shows that the risk is approaching the boundary of the impact range, intervention measures such as load diversion, energy storage unit group scheduling, and grid connection point power regulation are automatically triggered. At the same time, a dynamic adjustment logic for the constraint mechanism is established. Based on the real-time collected performance degradation rate and risk diffusion progress, the constraint parameters are continuously optimized to ensure that the dynamic constraint mechanism for energy transmission can effectively delay the performance degradation process of scheduling and accurately control the risk diffusion range, thus ensuring the stability and security of V2G bidirectional energy transmission at supercharging stations.

[0056] In one possible implementation, step S360 further includes: Step S361: Determine the power transmission limit of the grid interface, the charging and discharging response speed of the battery, and the regulation capacity parameters of the energy storage unit through the set of key energy dispatch characteristic parameters.

[0057] Step S362: Based on the power transmission limit of the grid interface, the charging and discharging response speed of the battery, and the regulation capacity parameters of the energy storage unit, perform feature correlation analysis to construct the energy dispatch feature matrix.

[0058] Specifically, relying on the key energy dispatch characteristic parameter set, an iterative approximation algorithm is used to extract parameters such as grid-side voltage fluctuation amplitude, frequency stability, and line transmission capacity. Combined with the rated load threshold of the grid interface hardware and safe operation specifications, the grid stability under different power input and output conditions is repeatedly simulated to accurately determine the maximum input power threshold and maximum feedback power threshold that the grid interface allows for bidirectional energy transmission to supercharging stations. A timestamp difference calculation method is used to extract characteristic parameters such as battery capacity decay rate, charge / discharge rate limit, and temperature adaptation range. Based on the real-time command response data output by the battery management system, the actual power output from the battery receiving charge / discharge commands is statistically analyzed. By fitting the response speed curves with test data from different SOC ranges during the time interval between output or input, the charging and discharging response speed of the battery under all operating conditions is finally determined. Configuration parameters such as rated capacity, charge and discharge cycle count, and energy conversion efficiency of the energy storage unit are extracted through a dynamic calculation model of capacity margin. Combined with real-time energy load demand data and loss compensation coefficients within the station, the energy regulation capability of the energy storage unit during the switching process of charging and discharging states is simulated to determine the maximum energy value that the energy storage unit can release instantly and the maximum energy value that it can absorb quickly, thus forming a complete parameter system for grid interface power transmission limit, battery charging and discharging response speed, and energy storage unit regulation capacity.

[0059] Based on three established core parameters—grid interface power transmission limit, battery charge / discharge response speed, and energy storage unit regulation capacity—a multi-dimensional feature correlation analysis process was initiated. The Pearson correlation coefficient algorithm was used to calculate the linear correlation between parameters, while a grey relational analysis model was combined to uncover the nonlinear mapping relationships between parameters. This clarified key correlation features such as the constraint boundary of the grid interface power transmission limit on the energy storage unit regulation capacity, the positive correlation between battery charge / discharge response speed and energy interaction efficiency, and the inverse influence mechanism of the energy storage unit regulation capacity on the battery charge / discharge response speed. Subsequently, the three core parameters were used as the first-level row dimension of the matrix, and the derived features of each core parameter were used as the second-level row dimension. Among these, the grid interface power transmission... The derived features of the limits include the maximum input power threshold, the maximum feedback power threshold, and the power regulation fluctuation rate. The derived features of the battery charging and discharging response speed include the response time in different SOC ranges, the average command delay, and the response speed decay coefficient. The derived features of the energy storage unit's regulation capacity include the maximum release energy value, the maximum absorbable energy value, and the capacity regulation response time. Taking the full-condition operation scenario of V2G bidirectional energy transmission of supercharging stations as the matrix column dimension, the data normalization processing method is used to map the values ​​of each parameter and derived feature to the same dimension range. The matrix elements are filled according to the correspondence between parameter-scenario-correlation degree, and finally, an energy scheduling feature matrix with complete dimensions, clear feature correlation, and standardized data is constructed.

[0060] Example 2, based on the same inventive concept as the V2G bidirectional energy aggregation and scheduling method for supercharging stations in the foregoing examples, such as... Figure 2 As shown, this application provides a V2G bidirectional energy aggregation and scheduling system for supercharging stations. The system and method embodiments in this application are based on the same inventive concept. The system includes: The parameter set determination module 10 is used to connect to the energy dispatch host via the communication bus, associate the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station, and determine the key energy dispatch characteristic parameter set.

[0061] The preset scheduling threshold determination module 20 is used to determine a preset scheduling threshold that conforms to the new energy industry standards based on the rated scheduling parameters of the target supercharging station and the bidirectional energy transmission standard.

[0062] The constraint mechanism setting module 30 is used to configure the correction coefficient of the preset scheduling threshold based on the battery SOC status, grid load status, and energy transmission loss status, and to set the energy transmission dynamic constraint mechanism in combination with the key energy scheduling feature parameter set.

[0063] The scheduling strategy output module 40 is used to synchronously verify the energy aggregation scheduling parameters corresponding to the energy scheduling host based on the energy transmission dynamic constraint mechanism and the power grid safety intervention mechanism, and output the energy aggregation scheduling strategy of the target supercharging station.

[0064] Furthermore, the system is also used to implement the following functions: Battery degradation status, interface contact status, and line thermal fatigue status are used as dynamic correction factors for the preset scheduling threshold. Risk levels are mapped to these dynamic correction factors to generate an equipment safety status score. When the equipment safety status score is lower than the safety threshold, the preset scheduling threshold adjustment based on the correction coefficient is frozen, and the dynamic correction factor is injected into the power grid safety intervention mechanism as a trigger condition for cutting off the V2G channel. Otherwise, within the allowable range of equipment safety status, the dynamic correction factor is used as a weight constraint to safely limit the correction coefficients configured based on battery SOC status, power grid load status, and energy transmission loss status, generating a comprehensive adjustment factor. The preset scheduling threshold is updated based on the comprehensive adjustment factor.

[0065] Furthermore, the system is also used to implement the following functions: Extract energy transmission degradation features, and determine potential scheduling failure acceleration factors by combining the loss increment value of the energy transmission degradation features with the voltage fluctuation level under the current grid load; determine potential scheduling failure propagation paths by the similarity between the triggering conditions of historical fault modes in the energy transmission degradation features and the current scheduling parameters; and set up the grid security intervention mechanism based on the potential scheduling failure acceleration factors and the potential scheduling failure propagation paths.

[0066] Furthermore, the system is also used to implement the following functions: The station-level configuration factors include the number of charging piles, power module capacity, and energy storage unit configuration data collected by the station-level monitoring nodes. The station-level monitoring nodes are deployed in the power distribution center and energy storage area of ​​the supercharging station.

[0067] Furthermore, the system is also used to implement the following functions: The battery characteristic factors include data on battery capacity decay rate, charge / discharge rate limit, and temperature adaptation range collected by battery-level monitoring nodes, which are deployed at the charging interface and the battery management system interface.

[0068] Furthermore, the system is also used to implement the following functions: The power grid operation factors include power grid voltage fluctuations, frequency stability, and line transmission capacity data collected by power grid-level monitoring nodes, which are deployed at the connection interface between the supercharging station and the power grid, as well as at key nodes of the lines.

[0069] Furthermore, the system is also used to implement the following functions: Through the communication bus, the scheduling control incentive signal is sent to the station-level monitoring node, battery-level monitoring node, and grid-level monitoring node. When the target supercharging station performs bidirectional energy transmission, the station-level monitoring node, battery-level monitoring node, and grid-level monitoring node receive the scheduling control incentive signal and use a hierarchical collaborative mechanism based on energy loss distribution to perform the allocation and management of energy scheduling monitoring tasks.

[0070] Furthermore, the system is also used to implement the following functions: Connect to the historical energy dispatch failure event database and extract matching historical events by combining the energy dispatch feature matrix; determine the potential dispatch performance degradation duration based on the cumulative periodic distribution of energy transmission load and the current energy transmission growth rate of the matching historical events; determine the potential dispatch risk impact range based on the matching historical events; and set the energy transmission dynamic constraint mechanism based on the potential dispatch performance degradation duration and the potential dispatch risk impact range.

[0071] Furthermore, the system is also used to implement the following functions: By using the set of key energy dispatch feature parameters, the power transmission limit of the grid interface, the charge and discharge response speed of the battery, and the regulation capacity parameters of the energy storage unit are determined. Based on the power transmission limit of the grid interface, the charge and discharge response speed of the battery, and the regulation capacity parameters of the energy storage unit, feature correlation analysis is performed to construct the energy dispatch feature matrix.

[0072] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0073] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0074] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A V2G bidirectional energy aggregation and scheduling method for supercharging stations, characterized in that, The method includes: The energy dispatch host is connected via a communication bus, and the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station are associated to determine the set of key energy dispatch characteristic parameters. Based on the rated scheduling parameters and bidirectional energy transmission standards of the target supercharging station, a preset scheduling threshold that conforms to the new energy industry standards is determined. By configuring the correction coefficient of the preset scheduling threshold based on the battery SOC status, grid load status, and energy transmission loss status, and combining the key energy scheduling feature parameter set, a dynamic energy transmission constraint mechanism is set. Based on the aforementioned energy transmission dynamic constraint mechanism and power grid security intervention mechanism, the energy aggregation scheduling parameters corresponding to the energy scheduling host are synchronously verified, and the energy aggregation scheduling strategy of the target supercharging station is output.

2. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 1, characterized in that, The method includes: Battery degradation status, interface contact status, and line heating fatigue status are used as dynamic correction factors for the preset scheduling threshold. The dynamic correction factor is mapped to a risk level to generate an equipment safety status score; When the equipment safety status score is lower than the safety threshold, the preset scheduling threshold adjustment based on the correction coefficient is frozen, and the dynamic correction factor is injected into the power grid safety intervention mechanism as a trigger condition for cutting off the V2G channel. Otherwise, within the allowable range of equipment safety status, the dynamic correction factor is used as a weight constraint to safely limit the correction coefficients configured based on battery SOC status, grid load status, and energy transmission loss status, thereby generating a comprehensive adjustment factor. The preset scheduling threshold is updated based on the comprehensive adjustment factor.

3. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 2, characterized in that, The method further includes injecting the dynamic correction factor into the power grid security intervention mechanism: Extract energy transmission degradation features, and combine the loss increment value of the energy transmission degradation features with the voltage fluctuation level under the current grid load to determine the potential dispatch failure acceleration factor. Based on the similarity between the triggering conditions of historical failure modes in the energy transmission degradation characteristics and the current scheduling parameters, potential scheduling failure propagation paths are determined; Based on the potential scheduling failure acceleration factor and the potential scheduling failure propagation path, the power grid security intervention mechanism is set.

4. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 1, characterized in that, The method involves associating the target supercharging station's station-level configuration factors, battery characteristic factors, and grid operation factors with the following components: The station-level configuration factors include the number of charging piles, power module capacity, and energy storage unit configuration data collected by the station-level monitoring nodes. The station-level monitoring nodes are deployed in the power distribution center and energy storage area of ​​the supercharging station.

5. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 4, characterized in that, The method further includes: The battery characteristic factors include data on battery capacity decay rate, charge / discharge rate limit, and temperature adaptation range collected by battery-level monitoring nodes, which are deployed at the charging interface and the battery management system interface.

6. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 5, characterized in that, The method further includes: The power grid operation factors include power grid voltage fluctuations, frequency stability, and line transmission capacity data collected by power grid-level monitoring nodes, which are deployed at the connection interface between the supercharging station and the power grid, as well as at key nodes of the lines.

7. The V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 6, characterized in that, The method includes: The scheduling control excitation signal is sent to the station-level monitoring node, battery-level monitoring node, and power grid-level monitoring node through the communication bus. When the target supercharging station performs bidirectional energy transfer, the station-level monitoring node, battery-level monitoring node, and grid-level monitoring node receive the scheduling control excitation signal and use a hierarchical collaborative mechanism based on energy loss distribution to perform the allocation and management of energy scheduling monitoring tasks.

8. A V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 1, characterized in that, Based on the aforementioned set of key energy scheduling characteristic parameters, a dynamic constraint mechanism for energy transfer is established. The method includes: Connect to the historical energy scheduling failure event database and extract matching historical events by combining the energy scheduling feature matrix; Based on the cumulative periodic distribution of energy transmission load matching historical events and the current energy transmission growth rate, the duration of potential scheduling performance degradation is determined; Based on the matched historical events, determine the scope of impact of potential scheduling risks; The energy transmission dynamic constraint mechanism is set based on the duration of potential scheduling performance degradation and the scope of impact of potential scheduling risks.

9. A V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in claim 8, characterized in that, The method further includes extracting matching historical events by combining energy scheduling feature matrix. The power transmission limit of the grid interface, the charging and discharging response speed of the battery, and the regulation capacity parameters of the energy storage unit are determined by the set of key energy dispatch characteristic parameters. Based on the power transmission limit of the grid interface, the charging and discharging response speed of the battery, and the regulation capacity parameters of the energy storage unit, feature correlation analysis is performed to construct the energy dispatch feature matrix.

10. A V2G bidirectional energy aggregation and scheduling system for supercharging stations, characterized in that, The system is used to implement the V2G bidirectional energy aggregation and scheduling method for supercharging stations as described in any one of claims 1-9, and the system comprises: The parameter set determination module is used to connect to the energy dispatch host via the communication bus, associate the station-level configuration factors, battery characteristic factors, and grid operation factors of the target supercharging station, and determine the key energy dispatch characteristic parameter set. The preset scheduling threshold determination module is used to determine a preset scheduling threshold that conforms to the new energy industry standards based on the rated scheduling parameters of the target supercharging station and the bidirectional energy transmission standard. The constraint mechanism setting module is used to configure the correction coefficient of the preset scheduling threshold based on the battery SOC status, grid load status, and energy transmission loss status, and to set the dynamic constraint mechanism for energy transmission in combination with the key energy scheduling feature parameter set. The scheduling strategy output module is used to synchronously verify the energy aggregation scheduling parameters corresponding to the energy scheduling host based on the energy transmission dynamic constraint mechanism and the power grid security intervention mechanism, and output the energy aggregation scheduling strategy of the target supercharging station.

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