Virtual power plant panoramic monitoring and decision method and system based on digital twinning

Abstract

This invention belongs to the field of smart grid operation and control technology, providing a method and system for panoramic monitoring and decision-making of virtual power plants based on digital twins. The method includes: collecting electrical topology, operating status, and initial transient physical characteristics of charging pile plugs in a virtual power plant in a highway service area to construct a digital twin model; dynamically updating the damping ground state of the distribution network and charging pile coupling system; calibrating the access impact weight by combining feeder electrical distance and contact interface degradation degree; generating an idle interface risk distribution map; calibrating port impedance in response to plug-in trigger events; reverse-deriving the double-constraint self-consistent equivalent negative resistance interval and quantifying the probability of oscillation instability; and dynamically outputting a stable control strategy for rolling convergence of the negative resistance interval in multi-vehicle connection scenarios. This invention achieves transient port impedance calibration, self-consistent interval reverse derivation, and multi-vehicle rolling evaluation, solving the problem of inaccurate damping derivation caused by unknown charging power in highway service area scenarios, while balancing evaluation accuracy and computing efficiency.

Description

Technical Field

[0001] This invention belongs to the field of smart grid operation and control technology, specifically relating to a method and system for panoramic monitoring and decision-making of virtual power plants based on digital twins. Background Technology

[0002] The highway service area is located at the end of a long feeder in the distribution network, resulting in a large equivalent impedance of the grid. The DC fast charging piles within the station are constant power loads with negative resistance characteristics at their ports. After coupling with the inductive impedance of the grid, the equivalent damping of the service area distribution network and the pile group coupling system is jointly determined by the positive damping of the line and the negative damping of the pile group. When the amplitude of the equivalent negative resistance exceeds the positive resistance of the line, the damping ratio changes from positive to negative, and the coupling system will experience oscillation and instability.

[0003] Digital twins can predict damping states through mechanism deduction, making them a core means of proactive control. However, their deduction requires real-time charging power from the charging pile as the core input. In highway service area scenarios, charging power exhibits dual physical uncertainties: the power of already connected vehicles dynamically changes with battery SOC, resulting in inherent measurement lag; the power of vehicles waiting to be connected cannot be pre-obtained by sensors before establishing an electrical connection with the charging gun. Simultaneously, the contact interface of the charging gun gradually deteriorates with repeated insertions and removals, exhibiting discrete and time-varying contact resistance, further altering port impedance characteristics and amplifying damping calculation errors caused by parameter deviations.

[0004] Because the damping ratio of the service area distribution network and pile group coupling system is highly sensitive to power and impedance deviations, even a small error can cause the damping ratio calculation sign to reverse, resulting in a stability judgment that is completely opposite to the actual state. This fundamental contradiction is determined by the physical nature of constant power loads, the random access attributes of traffic flow, and the dynamic degradation of contact impedance. It cannot be solved by increasing computing power or model accuracy, and it easily leads to the failure of digital twin pre-control commands or even exacerbates the risk of oscillation, becoming the core bottleneck restricting the implementation of digital twins in highway service area scenarios.

[0005] Therefore, there is an urgent need for a digital twin monitoring and decision-making solution adapted to the scenario of highway service areas. Through port transient calibration, self-consistent negative resistance range extrapolation and rolling risk assessment, it can achieve precise control of damping instability risk under the constraints of unknown charging power and dynamic degradation of contact impedance, and support the safe and stable operation of virtual power plants. Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a virtual power plant panoramic monitoring and decision-making method based on digital twins.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a virtual power plant panoramic monitoring and decision-making method based on digital twins, comprising the following steps: S1. Collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in in the virtual power plant of the highway service area to construct a digital twin model of the highway service area. S2. By using the digital twin model of the high-speed service area, the damping ground state of the service area distribution network and pile group coupling system is dynamically updated by combining electrical topology data and real-time operating status. Based on the feeder electrical distance and contact resistance degradation prediction, the access impact weight is assigned to each idle electric vehicle charging interface, and an idle interface risk distribution map is generated. S3. Respond to the electric vehicle plug-in trigger event, perform hierarchical deduction based on the risk distribution map of idle interface, correct the charging pile port impedance parameters in the digital twin model of the highway service area using the transient physical characteristic data of the charging pile, take the damped ground state as the benchmark, and derive the self-consistent equivalent negative resistance interval based on the electrical topology and operating boundary constraints, quantify the damping ratio distribution and instability probability in the self-consistent equivalent negative resistance interval, and output the damped oscillation instability risk assessment results. S4. For multi-vehicle access scenarios, the self-consistent equivalent negative resistance range is narrowed by successively measuring the power, the damping oscillation instability risk results are updated, and the stable control strategy of the virtual power plant in the highway service area is output.

[0008] According to the above technical solution, the steps of collecting electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in points in the virtual power plant of the highway service area, and constructing a digital twin model of the highway service area, include: S110. Obtain the electrical topology data of the virtual power plant in the highway service area, including the rated parameters of the distribution transformer, the topology connection relationship of the feeder network, the coordinates of the charging pile access point, and the equivalent impedance parameters of the line.

[0009] S120: Real-time acquisition of operating status data of virtual power plants in highway service areas, including distribution transformer low-voltage bus voltage, real-time current and active and reactive power of each feeder, real-time charging power of charging piles, and grid connection point operating margin parameters.

[0010] S130. Retrieve historical operational statistics of highway service areas, including historical traffic flow data, historical charging order data, and charging pile contact resistance degradation curves.

[0011] S140. During the initial time window after the electric vehicle plugs in the charging gun, collect transient physical characteristic data of the charging pile port, including the initial voltage of the port, the pre-charging circuit current, and the transient impedance characteristic of the contact circuit.

[0012] According to the above technical solution, the steps of dynamically updating the damping ground state of the service area distribution network and pile group coupling system by using a digital twin model of the high-speed service area, combining electrical topology data and real-time operating status, and allocating access impact weights to each idle electric vehicle charging interface based on feeder electrical distance and contact resistance degradation prediction, and generating an idle interface risk distribution map include: S210. Based on electrical topology data, combined with the rated parameters of the distribution transformer and the equivalent impedance parameters of the line, calculate the equivalent positive damping base value of the distribution network in the high-speed service area; based on the feeder network topology connection relationship and the coordinates of the charging pile access point, calculate the feeder electrical distance between each charging interface and the distribution transformer, and assign electrical distance weights to each idle charging interface.

[0013] S220: Using the real-time charging power of the charging piles, the real-time current of each feeder, and the active and reactive power as inputs, combined with the distribution network topology impedance parameters and the negative damping characteristics of the constant power load, the current actual damping value of the coupling system between the distribution network and the charging pile group is calculated; combined with historical traffic flow data and historical charging order data, the number of charging piles that can be connected in the short term is estimated; based on the electrical distance weight distribution of the idle charging interface, the future short-term damping margin range is calculated; the two together constitute the system's dynamic damping ground state.

[0014] S230. Based on historical charging order data, the cumulative number of plugging and unplugging operations for each idle interface is counted. Combined with the contact resistance degradation curve of the same batch of charging piles, the current contact resistance range of each idle charging interface is estimated. The electrical distance weight is used as the basic risk weight. Combined with the risk gain correction factor corresponding to the degree of degradation of the charging pile gun contact interface, the access impact weight of each idle charging pile interface is calibrated.

[0015] S240. Based on the access impact weight of each idle charging interface, establish a mapping relationship between the access impact weight and the damping simulation start-up urgency and sampling density level, and construct an idle interface risk distribution map containing hierarchical scheduling attributes.

[0016] According to the above technical solution, the steps of responding to the electric vehicle charging port trigger event, performing hierarchical deduction based on the idle interface risk distribution map, correcting the charging pile port impedance parameters in the digital twin model of the highway service area using transient physical characteristic data of the charging pile, using the damped ground state as a reference, deriving the self-consistent equivalent negative resistance interval based on electrical topology and operating boundary constraints, quantifying the damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval, and outputting the damped oscillation instability risk assessment result include: S310. In response to the electric vehicle plug-in trigger event, retrieve the access impact weight of the corresponding interface in the idle interface risk distribution map, match and determine the urgency and sampling density level of the damping simulation and start the simulation; extract the transient physical characteristic data of the charging pile port in the initial time window of the plug-in, calibrate the port impedance parameters before the main charging load is fully applied, converge and correct the value boundary of the estimated contact resistance, and determine the initial value range of the port equivalent negative resistance.

[0017] S320. Based on the initial range of equivalent negative resistance of the port, taking the system dynamic damping ground state as the calculation benchmark, combining the distribution network electrical topology constraints and operating boundary constraints, and taking the damping stability critical condition as the convergence criterion, the range of equivalent negative resistance values ​​that satisfy the self-consistency of the system electrical operation is derived in reverse, and the self-consistent equivalent negative resistance range is obtained by convergence. The system stability tolerance of charging load access is quantified by the negative resistance safety boundary.

[0018] S330. According to the sampling density level determined by matching the risk distribution map of the idle interface, perform probabilistic sampling calculation within the self-consistent equivalent negative resistance interval, statistically obtain the probability distribution law of the system damping ratio and the probability of oscillation instability, and output the damping oscillation instability risk assessment result.

[0019] According to the above technical solution, the steps for outputting a stable control strategy for a virtual power plant in a highway service area, based on the multi-vehicle access scenario, include: (1) Successively narrowing the self-consistent equivalent negative resistance range through measured power, updating the damped oscillation instability risk results, and then outputting the strategy. S410. In the scenario of multiple vehicles connecting to the highway service area, as each charging interface is connected in sequence and enters the charging steady state, the real-time charging power is collected one by one to converge the equivalent negative resistance of the corresponding port from an interval quantity to a steady-state definite value. Based on the port equivalent negative resistance parameters determined one by one, the current actual damping value of the system is updated continuously. At the same time, the safety boundary of the self-consistent equivalent negative resistance interval of the remaining idle interfaces is narrowed in the direction of decreasing absolute value of equivalent negative resistance. The system damping ratio probability distribution and oscillation instability probability are recalculated in an asymptotic convergence manner, and the damping oscillation instability risk assessment results are updated in real time.

[0020] S420. Combining the real-time updated damped oscillation instability risk assessment results with the service area operation boundary constraints, a virtual power plant stability control strategy for the highway service area is generated.

[0021] Secondly, this application provides a virtual power plant panoramic monitoring and decision-making system based on digital twins, including: The data acquisition and modeling module is used to collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in, in order to build a digital twin model of the highway service area. The ground state classification module is used to dynamically update the damping ground state of the service area distribution network and pile group coupling system by combining the digital twin model of the high-speed service area with electrical topology data and real-time operating status. Based on the feeder electrical distance and contact resistance degradation prediction, it assigns access impact weights to each idle electric vehicle charging interface and generates an idle interface risk distribution map. The negative resistance deduction module is used to respond to electric vehicle plug-in trigger events. It performs hierarchical deduction based on the risk distribution map of idle interfaces. In the digital twin model of the highway service area, it corrects the charging pile port impedance parameters with the transient physical characteristic data of the charging pile. Based on the damping ground state, it derives the self-consistent equivalent negative resistance interval in reverse based on the electrical topology and operating boundary constraints. It quantifies the damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval and outputs the damping oscillation instability risk assessment results. The rolling control module is used to narrow the self-consistent equivalent negative resistance range by successively measuring the power in multi-vehicle access scenarios, update the damped oscillation instability risk results, and output the stable control strategy of the virtual power plant in the highway service area.

[0022] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a virtual power plant panoramic monitoring and decision-making method based on digital twin by calling the computer program stored in the memory.

[0023] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a virtual power plant panoramic monitoring and decision-making method based on digital twins.

[0024] Compared with the prior art, this application has the following advantages and beneficial effects: This application achieves transient calibration of the negative resistance characteristics of charging pile ports and reverse derivation of the system-level self-consistent equivalent negative resistance interval, overcoming the problems of misjudgment and omission of instability risk caused by the reliance on fixed thresholds and the unknown negative resistance characteristics before charging load access in traditional distribution network damping assessments. It constructs a hierarchical deduction and scheduling mechanism based on access impact weights, improving the computational efficiency of damping assessment in peak multi-vehicle concurrent scenarios at highway service areas and the response time in high-risk scenarios. Through interval rolling convergence and dynamic risk iteration in multi-vehicle connection scenarios, it ensures the accuracy of damping oscillation instability risk assessment while guaranteeing the real-time adaptability of virtual power plant stability control strategies in highway service areas. These three elements together constitute a complete closed loop for damping stability assessment and safety management, from pre-classification and prediction to accurate in-process hierarchical deduction and dynamic rolling control of multiple vehicles. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall process of the virtual power plant panoramic monitoring and decision-making method based on digital twin provided in the embodiments of this application; Figure 2 This is a schematic diagram of the process for generating a risk distribution map of idle interfaces provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the output damped oscillation instability risk assessment results provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the virtual power plant panoramic monitoring and decision-making system based on digital twin provided in the embodiments of this application. Detailed Implementation

[0026] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0027] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the virtual power plant panoramic monitoring and decision-making method based on digital twin provided in the embodiments of this application, which specifically includes the following steps: S1. Collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in in the virtual power plant of the highway service area to construct a digital twin model of the highway service area.

[0028] In this embodiment, step S1 includes the following specific contents: S110. Obtain the electrical topology data of the virtual power plant in the highway service area, including the rated parameters of the distribution transformer, the topology connection relationship of the feeder network, the coordinates of the charging pile access point, and the equivalent impedance parameters of the line.

[0029] Obtain electrical topology data of the virtual power plant in the highway service area. All data are uniformly numbered according to the three-level nodes of distribution transformer, feeder section and charging pile access point to form a structured topology parameter set, which is imported into the digital twin model of the highway service area as the physical benchmark for distribution network damping calculation and operation constraint determination.

[0030] The rated parameters of the distribution transformer are taken from the equipment factory test report and distribution design documents, and are archived according to the unique equipment number of each distribution transformer, including rated capacity, rated voltage on the low voltage side, short-circuit impedance percentage, and winding DC resistance value. Based on the short-circuit impedance percentage, rated capacity, and low-voltage side rated voltage indicated on the transformer nameplate, the low-voltage side reference impedance value is first calculated by dividing the square of the low-voltage side rated voltage by the rated capacity. Then, the short-circuit impedance percentage is multiplied by the low-voltage side reference impedance value to obtain the transformer equivalent reactance referred to the low-voltage side, which constitutes the reactance component of the positive damping effect on the transformer side. The DC resistance value of the high-voltage side winding is uniformly reduced to the low-voltage side according to the square relationship of the transformer turns ratio. It is then summed with the original value of the DC resistance of the low-voltage side winding to obtain the transformer equivalent resistance reduced to the low-voltage side, which constitutes the resistance component of the positive damping effect on the transformer side.

[0031] Based on the rated capacity of the transformer and the rated voltage of the low-voltage side, the rated operating boundary benchmark value of the low-voltage side of the distribution network is calibrated, including two core indicators: the rated voltage of the low-voltage side and the rated capacity of the distribution transformer. All the converted equivalent resistance and equivalent reactance parameters of the transformer, along with the original impedance parameters of the feeders that are also converted to the low-voltage side, are stored in the structured topology parameter set.

[0032] The feeder network topology connection relationship is generated based on the low-voltage power distribution primary as-built wiring diagram. It records the start and end node numbers, series and parallel connection logic and circuit numbers of each feeder segment, and fully covers all electrical links from the power distribution bus to each charging pile access point. Each feeder segment is labeled with the corresponding start and end node number and circuit number, and stored as a structured topology parameter set according to the electrical connection level.

[0033] The coordinates of the charging pile access point are based on the low-voltage busbar of the distribution transformer. The axial electrical path length of each charging pile access point is measured one by one along the actual laying path of each feeder, and the unique number of the corresponding charging pile equipment and the feeder circuit number are bound together.

[0034] The equivalent impedance parameters of the line are calculated based on the cable type, conductor material, and actual laying length of each feeder segment. First, refer to the electrical parameter standards for power cables to find the DC resistance and reactance per unit length of the corresponding cable type. Then, multiply by the actual laying length of the feeder segment to calculate the equivalent resistance and equivalent reactance of each feeder segment. All feeder impedances are nominal values ​​on the low-voltage side and are on the same benchmark as the parameters converted on the transformer side, so they can be directly connected in series and superimposed.

[0035] The calculation results correspond to the start and end node numbers of the feeder segment and the topology connection relationship, and are stored in the structured topology parameter set.

[0036] S120: Real-time acquisition of operating status data of virtual power plants in highway service areas, including distribution transformer low-voltage bus voltage, real-time current and active and reactive power of each feeder, real-time charging power of charging piles, and grid connection point operating margin parameters.

[0037] The system collects operational status data of highway service areas synchronously through on-site sensing devices and industrial internet platforms. All data is stamped with a unified timestamp and bound to the corresponding three-level node numbering system of distribution transformers, feeder sections, and charging pile access points defined by S110. All data is stored in the real-time operation database of the digital twin model of the highway service area.

[0038] The voltage of the low-voltage busbar of the distribution transformer is continuously acquired by a three-phase voltage transmitter installed on the low-voltage side busbar of the distribution transformer, which outputs the effective value timing data of the three-phase voltage and binds it to the corresponding transformer equipment number.

[0039] The real-time current and active and reactive power of each feeder are collected by the multi-functional power meter at the outgoing circuit of each feeder, and the time-series data of three-phase real-time current, active power and reactive power are output and bound to the corresponding feeder circuit number.

[0040] The real-time charging power of each charging pile is synchronously read from the local controller of each charging pile through the industrial internet platform. The real-time charging power timing data of the DC output side of each charging pile is collected and bound to the corresponding charging pile device number.

[0041] The grid connection point operation margin parameter uses the low-voltage busbar of the distribution transformer as the statistical benchmark to quantify the safety margin of the current distribution network operation state from the rated boundary. Using the currently collected low-voltage busbar voltage of the distribution transformer and the total feeder load power obtained by summing the active power of each feeder as input, and combining the rated operation boundary benchmark value of the distribution network stored in S110, two sub-item margins are calculated respectively: The voltage margin is based on the rated voltage on the low-voltage side and the allowable deviation range of three-phase low-voltage power supply as specified in GB / T12325 "Power Quality Power Supply Voltage Deviation". In this embodiment, the standard limit of ±7% of the rated voltage is adopted. The ratio of the absolute value of the deviation between the current bus voltage and the rated voltage on the low-voltage side to the allowable deviation limit is calculated. The remaining ratio is the voltage margin. The higher the voltage margin value, the further the voltage operating level is from the limit boundary. The load margin is based on the rated capacity of the distribution transformer. The ratio of the current total load power of the feeder to the rated capacity is calculated, and the remaining load capacity ratio is the load margin. The higher the load margin value, the more sufficient the remaining load-carrying capacity of the distribution transformer.

[0042] The smaller value between voltage margin and load margin is taken as the grid connection point operation margin parameter. The overall safety margin is determined by the indicator closest to the operating boundary, avoiding the possibility of a single indicator exceeding the limit. Finally, the comprehensive margin indicator is bound to the unique equipment number of the corresponding distribution transformer and stored in the operation parameter library of the digital twin model of the highway service area, serving as the basis for subsequent operation boundary constraint verification and stability control decisions.

[0043] S130. Retrieve historical operational statistics of highway service areas, including historical traffic flow data, historical charging order data, and charging pile contact resistance degradation curves.

[0044] The system retrieves all historical statistical data from the service area operation management system, charging pile operation and maintenance platform, and equipment testing database. It adopts the unified numbering system of three-level nodes (distribution transformer, feeder section, and charging pile access point) and stores it in the historical parameter database of the digital twin model of the highway service area, providing historical reference for risk prediction and damping simulation before charging.

[0045] Historical traffic flow data is taken from the historical statistical ledgers of the vehicle recognition system at the service area entrance and exit, with a minimum statistical period of 15 minutes, and archived in chronological order according to two scenarios: national statutory working days and statutory holidays. The core statistics include the number of electric vehicles entering the station and the proportion of different vehicle types in each time period, the charging power conversion factor for each vehicle type, and the traffic flow increase factor during holidays. The number of electric vehicles entering the station is directly generated by the license plate recognition equipment. The charging power conversion factor for each vehicle type is the average steady-state charging power of all historical charging orders for that model (the statistical average of the power value after the charging process completes its power ramp-up and enters a stable operating phase). The traffic flow increase factor during holidays is the statistical average ratio of the number of vehicles entering the station during the same period on historical holidays to the number entering the station during the same period on weekdays. The three types of data are used together to estimate the scale of short-term new charging load, supporting the prediction of the margin boundary of the system's dynamic damping ground state.

[0046] Historical charging order data is taken from the historical order records of the charging pile operation management system. Each order is bound to the corresponding charging pile equipment number and includes the charging start and end time, steady-state charging power, and the port equivalent negative resistance characteristic value confirmed by the measured power convergence under the corresponding operating conditions. This characteristic value is the equivalent negative resistance value referred to the low voltage side under the steady-state charging condition of the vehicle. For detailed calculation and convergence verification logic, please refer to the S410 rolling update steps.

[0047] The cumulative number of plugging and unplugging operations for each interface is counted according to the charging pile equipment number. Simultaneously, the steady-state charging power and port equivalent negative resistance characteristic value of each order are used as associated data pairs and archived into the historical dataset of the corresponding charging pile one by one to form a power-negative resistance historical mapping library for a single pile. When a vehicle is connected in the future, historical mapping data with similar power levels can be matched to narrow the initial negative resistance estimation range and reduce the number of simulation convergence steps.

[0048] The charging pile contact resistance degradation dataset includes the factory degradation test curves of charging piles in the same batch and the measured correction nodes of each pre-charge calibration. It is archived by charging pile batch number and associated with all charging pile equipment numbers under the corresponding batch. It represents the correspondence between the number of insertions and removals and the range of contact resistance values, serving as a unified benchmark for predicting contact resistance before plugging in the charging gun.

[0049] The factory degradation test curve is taken from the factory type test report of the same batch of charging piles. It is a batch-wide curve for the nominal value of contact resistance under the corresponding number of insertions and removals, serving as a safety margin for the entire life cycle. The estimated value of the contact resistance of a single pile shall not be less than the nominal value of the factory curve under the same number of insertions and removals, ensuring the conservatism of the estimated results.

[0050] The measured correction nodes for each pre-charge calibration are derived from the actual contact resistance values ​​measured during each pre-charge plugging stage, and are written back and stored along with the cumulative number of plugging and unplugging cycles for the corresponding charging pile. Based on the dynamic fitting of the personalized degradation trajectory of a single pile using multiple measured nodes, the range of estimated contact resistance is narrowed on the basis of the factory safety margin, thereby improving the accuracy of the calculation of the access impact weight.

[0051] S140. During the initial time window after the electric vehicle plugs in the charging gun, collect transient physical characteristic data of the charging pile port, including the initial voltage of the port, the pre-charging circuit current, and the transient impedance characteristic of the contact circuit.

[0052] Using the electric vehicle plug-in handshake confirmation signal as the data acquisition trigger event, the electrical characteristic data of the port is collected synchronously within a preset initial time window. All data is labeled with a unified trigger timestamp, bound to the corresponding charging pile equipment number, and stored in the digital twin model of the highway service area.

[0053] The initial time window begins when the charging pile and the vehicle battery management system complete the plug-in handshake and the pre-charging circuit is officially connected, and ends when the main power circuit contactor closes and the charging power starts to climb. For example, in this embodiment, the window duration is set to 200ms. The setting is based on the following: the preset initial time window corresponds to the pre-charging steady-state stage of the DC fast charging process. At this time, the main charging load has not yet been applied, the port contact interface is not affected by the thermal effect of large current, and the collected impedance characteristics can truly reflect the inherent electrical state of the contact interface; the window duration matches the typical timing range of the national standard DC fast charging pre-charging stage, and the port parameter acquisition can be completed before the service area distribution network and the charging pile coupling system are subjected to load impact.

[0054] A unified steady-state sampling rule is adopted within the initial time window: transient sampling points during the pre-charge voltage ramp-up establishment phase are removed, and only the effective sampling interval after the pre-charge voltage enters a stable state is retained; in this embodiment, the voltage ramp-up time of mainstream national standard DC fast charging piles is about 50ms, corresponding to the removal of transient sampling points in the first 50ms.

[0055] The initial voltage at the port is collected by a high-precision voltage sensor on the DC output side of the charging pile. The arithmetic average of the voltage during the steady-state period within the initial time window is taken and bound to the corresponding charging pile device number.

[0056] The pre-charge circuit current is collected by the current sensor of the pre-charge branch, and the current value of the steady-state section within the initial time window is taken and bound to the corresponding charging pile equipment number.

[0057] The transient impedance characteristic of the contact circuit is calculated based on the voltage and current values ​​synchronously sampled in the steady-state section: at the same sampling time, the total impedance of the pre-charge circuit is obtained by dividing the port voltage value by the pre-charge circuit current value; the inherent impedance of the pre-calibrated pre-charge branch is subtracted to obtain the transient impedance value of the contact circuit at the corresponding time, and the arithmetic mean of all effective sampling points in the steady-state section is taken as the final transient impedance characteristic value of the contact circuit.

[0058] The inherent impedance of the pre-charge branch is retrieved from the factory calibration parameters of the charging pile. It includes the nominal resistance value of the fixed current limiting resistor of the pre-charge circuit, as well as the inherent impedance of the output cable between the output terminal of the charging pile and the sampling point. Each charging pile has a unique calibration value, which is archived with the equipment number.

[0059] S2. By using a digital twin model of the high-speed service area, and combining electrical topology data with real-time operating status, the damping ground state of the service area distribution network and pile group coupling system is dynamically updated. Based on the feeder electrical distance and contact resistance degradation prediction, access impact weights are assigned to each idle electric vehicle charging interface, and an idle interface risk distribution map is generated.

[0060] In this embodiment, as Figure 2 As shown, step S2 includes the following specific steps: S210. Based on electrical topology data, combined with the rated parameters of the distribution transformer and the equivalent impedance parameters of the line, calculate the equivalent positive damping base value of the distribution network in the high-speed service area; based on the feeder network topology connection relationship and the coordinates of the charging pile access point, calculate the feeder electrical distance between each charging interface and the distribution transformer, and assign electrical distance weights to each idle charging interface.

[0061] Extract the DC resistance value of the windings, the equivalent reactance of the transformer, and the equivalent resistance and equivalent reactance of each feeder segment from the structured topology parameters of the centralized distribution transformer. Using the low-voltage busbar of the distribution transformer as the reference node, impedance parameters are accumulated segment by segment along the feeder network topology connection relationship. The equivalent resistance of the entire path corresponding to the access point of each charging pile is calculated for each charging pile interface. At the same time, the overall equivalent resistance and equivalent reactance of the distribution network side are summarized. The equivalent resistance constitutes the line equivalent positive damping base value of the distribution network in the high-speed service area, and the corresponding reference node number is stored in the structured topology parameter set. The equivalent reactance is archived as a system topology parameter and used for oscillation natural frequency analysis, but is not included in the positive damping numerical calculation.

[0062] The axial path lengths of each interface marked with the coordinates of the charging pile access point are extracted from the structured topology parameter set. Combined with the loop affiliation relationship of the feeder network topology connection relationship, the total feeder electrical distance from each charging pile access point to the low-voltage bus of the distribution transformer is obtained.

[0063] For all currently idle charging ports, the largest feeder electrical distance is used as the benchmark value, and the ratio of the feeder electrical distance of each idle port to this benchmark value is the corresponding electrical distance weight. The electrical distance weighting is based on the following: the longer the electrical distance of the feeder, the greater the total path impedance from the charging interface to the distribution bus, the more significant the weakening effect on the positive damping of the system when the same charging power load is connected, and the higher the contribution of the connection risk. The weight value is positively correlated with the degree of influence of the interface on the system damping.

[0064] The electrical distance weights of all idle charging interfaces are bound to the corresponding charging pile device numbers and stored in a structured topology parameter set.

[0065] S220: Using the real-time charging power of the charging piles, the real-time current of each feeder, and the active and reactive power as inputs, combined with the distribution network topology impedance parameters and the negative damping characteristics of the constant power load, the current actual damping value of the coupling system between the distribution network and the charging pile group is calculated; combined with historical traffic flow data and historical charging order data, the number of charging piles that can be connected in the short term is estimated; based on the electrical distance weight distribution of the idle charging interface, the future short-term damping margin range is calculated; the two together constitute the system's dynamic damping ground state.

[0066] The equivalent positive damping base value of the line is used as the inherent damping reference of the system; For each electric vehicle charging pile in operation, the corresponding load current is obtained by dividing the real-time charging power of the electric vehicle charging pile by the rated output voltage of the low-voltage side of the distribution transformer. Since the voltage fluctuation of the low-voltage distribution network is small, the approximate calculation based on the rated voltage is reasonable in engineering. The equivalent resistance of the entire path is accumulated along the topology path at the charging pile access point. The path voltage drop value is obtained by multiplying the load current by the equivalent resistance of the entire path. The actual working voltage of the charging pile port is obtained by subtracting the path voltage drop value from the rated output voltage of the low-voltage side of the distribution transformer. Then, the absolute value of the equivalent negative resistance of the port under the corresponding operating condition is calculated according to the equivalent resistance rule of constant power load, which is the damping attenuation amount, and is used as the damping reduction amount of the corresponding charging pile access point.

[0067] After summing up the damping reduction of all charging piles in the grid, the actual current damping value of the distribution network and the charging pile group coupling system is obtained by subtracting it from the equivalent positive damping base value of the line.

[0068] The system matches the current date to a weekday / holiday type from a historical parameter database and retrieves historical traffic flow statistics for the corresponding time period. Using the average number of electric vehicles entering the station during the historical statistical period as a base, the estimated total number of newly added vehicles within the short-term prediction window is calculated by adjusting the duration. This is then combined with the vehicle type ratio and charging power conversion factor for each vehicle type to calculate the total scale of new charging load within the prediction window. For example, this embodiment uses a 5-minute short-term prediction window to match the short-term fluctuation scale of traffic flow in the service area, balancing prediction accuracy with the real-time update of damping status, corresponding to a 15-minute historical statistical granularity.

[0069] Based on the current actual damping value, retrieve the full-path equivalent resistance parameters of all idle charging interfaces, and calculate the total scale of new charging loads connected to the charging pile interface with the largest full-path equivalent resistance. Under this condition, the path voltage drop is the largest, the corresponding damping reduction is the largest, and the total system damping is the lowest, which is the most unfavorable condition. The corresponding lower limit value of damping is obtained. Based on the calculation that all the newly added charging loads are connected to the interface with the smallest equivalent resistance of the entire path, the path voltage drop is the smallest, the corresponding damping reduction is the smallest, the total system damping is the highest, which is the most favorable operating condition, and the corresponding upper limit value of damping is obtained. The calculation rules for the damping reduction of new loads are completely consistent with those for charging piles already in operation. The upper and lower limit ranges are the future short-term damping margin ranges.

[0070] The current actual damping value and the future short-term damping margin range together constitute the system's dynamic damping ground state, which is synchronously updated to the damping reference parameter library of the digital twin model.

[0071] S230. Based on historical charging order data, the cumulative number of plugging and unplugging operations for each idle interface is counted. Combined with the contact resistance degradation curve of the same batch of charging piles, the current contact resistance range of each idle charging interface is estimated. The electrical distance weight is used as the basic risk weight. Combined with the risk gain correction factor corresponding to the degree of degradation of the charging pile gun contact interface, the access impact weight of each idle charging pile interface is calibrated.

[0072] For each available car charging station interface, the cumulative number of plug-in / unplug cycles corresponding to the charging station's device number is extracted from historical charging order data. This data is then substituted into the contact resistance degradation curve for the same batch of charging stations to calculate the current estimated nominal contact resistance value of the car charging station interface. The quantitative relationship of the contact resistance degradation curve is as follows: ; in: For cumulative insertion and removal The nominal value of the contact resistance after this step; The initial contact resistance benchmark value of a brand new charging pile in its factory condition is taken from the factory test report of the same batch of charging piles. This is the degradation rate coefficient, reflecting the basic rate of degradation of the contact resistance. The degradation acceleration index reflects the degree of acceleration of the degradation process. Both are fitting parameters of the contact resistance degradation power function model. They are fitted and calibrated based on the standard insertion and removal life test data in the factory degradation test report of the same batch of charging piles, and are batch-wide parameters. The cumulative number of times the charging pile interface has been plugged in and unplugged is taken from the historical charging order statistics.

[0073] For each idle charging interface, the estimated nominal value of the contact resistance of the idle charging interface is divided by the maximum nominal value of the contact resistance of the charging piles in the same batch (taken from the upper limit of the degradation curve of the entire life cycle of the charging piles in the same batch, which is a common parameter of the batch) to obtain the corresponding damping influence coefficient. The value range is 0~1. The higher the damping influence coefficient, the stronger the attenuation effect of the degradation of the charging pile gun head contact on the system damping.

[0074] Using electrical distance weight as the basic weight for charging pile interface access risk, and combining it with the risk gain correction factor corresponding to the degree of degradation of the charging pile gun contact interface, the access impact weight of the corresponding idle charging interface is obtained by multiplicative correction of the basic weight. The calculation formula is as follows: ; in: For the first The weighting of access to an idle charging station interface; The electrical distance weight of the idle charging pile interface serves as the core benchmark for risk assessment. The damping influence coefficient of the idle charging pile interface, with a value range of 0 to 1, represents the degree of risk gain caused by contact degradation.

[0075] For example, this embodiment adopts a unity-gain correction rule: when the contact resistance of the charging pile deteriorates to the maximum nominal value of the contact resistance throughout the entire life cycle of the same batch (i.e., the bottom-line boundary state of the most severe contact deterioration), the corresponding damping influence coefficient is... Taking the maximum value of 1, after substituting it into the weighting formula, the influence weight is twice the basic electrical distance weight, that is, contact degradation can increase the basic risk weight by up to 100%. The setting is based on the fact that the inherent impedance of the line is the dominant factor determining the damping influence level of a single interface, so the electrical distance weight is used as the core benchmark. Contact interface degradation mainly increases the prediction deviation of port parameters, and positive gain correction is made on the basic weight to reflect the hierarchical logic of basic risk and degradation amplification, which is in line with the impedance composition law of distribution network in high-speed service area.

[0076] The scenario adjustment rules are as follows: when the distribution network feeder coverage is wide, the length of each interface line varies greatly, and the inherent impedance accounts for a higher proportion of the total impedance, the gain amplitude of contact degradation correction can be reduced to further highlight the dominant role of electrical distance; when the overall distribution network line is relatively short and the electrical distance of each interface varies little, the gain amplitude of contact degradation correction can be increased to improve the differentiation of contact degradation risk in hierarchical dispatch.

[0077] The impact weight of accessing all idle charging interfaces is bound to the corresponding charging pile device number and stored in a structured topology parameter set.

[0078] S240. Based on the access impact weight of each idle charging interface, establish a mapping relationship between the access impact weight and the damping simulation start-up urgency and sampling density level, and construct an idle interface risk distribution map containing hierarchical scheduling attributes.

[0079] The influence weights of each idle charging interface output by S230 are retrieved, and the coordinates of the charging pile access points in the S110 structured topology parameter set are matched. The correspondence between the weights and scheduling attributes is established by using a continuous adaptive proportional mapping method, and an idle interface risk distribution map with dynamic scheduling attributes is generated. All data follows the unique numbering system of charging pile equipment.

[0080] Extract the access impact weights of all currently idle charging ports, and determine the maximum and minimum values ​​of the current weight set; For each idle charging interface, the normalized relative influence coefficient is calculated by subtracting the minimum weight of all idle interfaces from the corresponding access influence weight as the numerator, and the difference between the maximum and minimum weights (i.e., the weight range) as the denominator. The value range is 0 to 1.

[0081] The higher the relative influence coefficient, the greater the influence of the idle charging interface on the system damping among all currently idle interfaces; if the access influence weight of all currently idle interfaces is the same, the relative influence coefficient is uniformly taken as 0.5.

[0082] The urgency of initiation is derived from the reverse mapping damping based on the relative influence coefficient. The urgency is reflected by the calculation period; the shorter the period, the higher the urgency. The calculation relationship is as follows: ; in: For the first The damping calculation cycle corresponding to each idle charging interface; To preset the longest simulation period, corresponding to the lowest urgency, this embodiment uses 10 seconds; To preset the shortest simulation period, corresponding to the highest urgency, this embodiment takes 1 second; For the first The relative influence coefficient of each idle charging port.

[0083] The period values ​​are set based on the following criteria: the shortest period of 10s matches the typical response time scale of low-frequency oscillations in distribution networks, which can ensure sufficient real-time verification when the risk of instability increases; the longest period matches low-risk scenarios with sufficient system damping, which can reduce the computing power overhead of redundant simulations. The values ​​of both cover the normal operating conditions of distribution networks in high-speed service areas.

[0084] The higher the relative impact coefficient of a charging pile interface, the shorter the simulation update cycle, which can promptly capture damping fluctuations at high-risk nodes.

[0085] Based on the positive mapping of relative influence coefficients to sampling density levels, sampling density is reflected by the proportion of effective sampling points; the higher the proportion, the higher the corresponding sampling density level. The calculation relationship is as follows: ; in: For the first The sampling density ratio corresponding to each idle charging port; This represents the highest sampling density, corresponding to full sampling; in this example, 100% is used. The minimum sampling density corresponds to a downsampling ratio, which is set to 20% in this embodiment. For the first The relative influence coefficient of each idle charging port.

[0086] The higher the relative influence coefficient of the charging pile interface, the higher the proportion of effective sampling data participating in the damping simulation, and the more guaranteed the calculation accuracy.

[0087] This continuous adaptive proportional mapping method does not require a preset fixed grading threshold. It dynamically and adaptively adjusts the interface weight distribution based entirely on the current operating conditions, and can adapt to service area scenarios with different distribution network scales and load levels. By linking scheduling accuracy and frequency through weight ratio, it can reduce the computational redundancy of low-risk nodes while ensuring the calculation accuracy of high-risk nodes.

[0088] The risk distribution map of idle interfaces uses the low-voltage busbar of the distribution transformer as the reference coordinate system. It binds the spatial location, access impact weight, corresponding simulation period and sampling density attribute of each idle charging interface one by one, and stores them in the structured topology parameter set after arranging them according to the charging pile equipment number.

[0089] S3. Respond to the electric vehicle plug-in trigger event, perform hierarchical deduction based on the idle interface risk distribution map, correct the charging pile port impedance parameters in the digital twin model of the highway service area using the transient physical characteristic data of the charging pile, take the damped ground state as the benchmark, and derive the self-consistent equivalent negative resistance interval based on the electrical topology and operating boundary constraints, quantify the damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval, and output the damped oscillation instability risk assessment result.

[0090] In this embodiment, as Figure 3 As shown, step S3 includes the following specific steps: S310. In response to the electric vehicle plug-in trigger event, retrieve the access impact weight of the corresponding interface in the idle interface risk distribution map, match and determine the urgency and sampling density level of the damping simulation and start the simulation; extract the transient physical characteristic data of the charging pile port in the initial time window of the plug-in, calibrate the port impedance parameters before the main charging load is fully applied, converge and correct the value boundary of the estimated contact resistance, and determine the initial value range of the port equivalent negative resistance.

[0091] Real-time monitoring of the plug-in handshake confirmation signal of the charging pile port. After the plug-in handshake confirmation signal is triggered, the corresponding charging pile device number is immediately locked. After verifying that the interface is currently idle, the access impact weight, damping simulation period and sampling density ratio parameters corresponding to the charging pile interface are retrieved from the idle interface risk distribution map generated by S240. The simulation period corresponds to the urgency of initiating the damping simulation for that interface; a shorter period indicates higher urgency. The sampling density ratio corresponds to the sampling density level of the idle charging pile interface; a higher ratio indicates higher calculation accuracy. After configuring the operating parameters for this damping simulation, the simulation calculation process is officially started.

[0092] The port impedance calibration is performed entirely within the initial time window after the charging gun is inserted. The initial time window begins when the charging pile and the vehicle battery management system complete the handshake after the charging gun is inserted and the pre-charge circuit is officially connected, and ends when the main power circuit contactor closes and the charging power starts to climb. In this embodiment, the default initial time window duration is set to 200ms. The transient impedance characteristic value of the corresponding charging pile port contact circuit collected by S140 is retrieved as the measured reference value and compared and calibrated with the estimated contact resistance range obtained in S230 based on the cumulative number of insertions and removals and the contact resistance degradation curve: the measured reference value is used as the center, and the preset measurement system error tolerance is superimposed to form the measured confidence interval. The measurement system error tolerance is the inherent measurement uncertainty of the acquisition hardware, which is jointly determined by the sensor accuracy and signal conditioning deviation. For example, in this embodiment, ±2% of the measured reference value is taken. The setting is based on the conventional accuracy level of the high-precision voltage and current sampling module of the charging pile to ensure that the measured range has sufficient confidence.

[0093] By eliminating the redundant portions at the upper and lower boundaries of the original estimated range that fall outside the measured confidence interval, the range of contact resistance values ​​after calibration is obtained. Compared to the original estimated contact resistance range, the calibrated interval boundary converges to the measured value, the value range is narrowed, the boundary amplification effect caused by the statistical deviation of the degradation curve is eliminated, and the input accuracy of subsequent damping deduction is improved.

[0094] After the contact resistance calibration is completed, the initial range of the equivalent negative resistance of the port is determined: According to the calculation rules of the equivalent negative resistance of the port for constant power load, the charging power remains constant in constant power charging mode. When the port voltage fluctuates slightly, the load current shows an inverse trend. When the voltage rises, the current decreases and when the voltage falls, the current increases, which is equivalent to a negative resistance characteristic. This negative resistance characteristic will offset the inherent positive damping of the system and is the core factor affecting the stability of low-frequency oscillations in the distribution network. The absolute value of the equivalent negative resistance of the port corresponds to the damping reduction under the current operating conditions.

[0095] The quantitative relationship between the port equivalent negative resistance, port operating voltage, and steady-state charging power satisfies the calculation formula for the equivalent negative resistance of a constant power load. In the initial estimation stage, the rated output voltage of the low-voltage side of the distribution transformer is used as the benchmark. ; The equivalent negative resistance of the port is denoted as , and the nominal value referred to the low-voltage side is denoted as , which characterizes the negative damping effect exhibited by the constant power charging load. The rated output voltage of the low-voltage side of the distribution transformer is taken from the inherent parameters of the equipment in the structured topology parameter set; The steady-state charging power of the charging pile is taken from real-time collected charging pile operation data.

[0096] The numerical relationship can be clearly defined from the formula for calculating the equivalent negative resistance of a constant power load: charging power The larger the value, the smaller the absolute value of the equivalent negative resistance; charging power The smaller the value, the larger the absolute value of the equivalent negative resistance. This is considered in conjunction with the rated charging power range of the charging station. The range of equivalent negative resistance values ​​at the port of the electric vehicle charging load itself can be calculated (excluding contact impedance and feeder line impedance). .

[0097] The calibrated contact resistance range As a series positive resistance component, it is superimposed on the equivalent negative resistance range of the charging load body, ultimately yielding the initial range of the equivalent negative resistance of the port as seen from the distribution network side: ; The initial range of equivalent negative resistance values ​​for the port serves as the input boundary for subsequent damped stability derivation, and its accuracy directly determines the accuracy of subsequent self-consistent negative resistance range derivation and risk assessment.

[0098] S320. Based on the initial range of equivalent negative resistance of the port, taking the system dynamic damping ground state as the calculation benchmark, combining the distribution network electrical topology constraints and operating boundary constraints, and taking the damping stability critical condition as the convergence criterion, the range of equivalent negative resistance values ​​that satisfy the self-consistency of the system electrical operation is derived in reverse, and the self-consistent equivalent negative resistance range is obtained by convergence. The system stability tolerance of charging load access is quantified by the negative resistance safety boundary.

[0099] The actual current damping value in the system dynamic damping ground state output by S220 is retrieved as the system damping benchmark for this single-pile connection simulation. This value already includes the inherent positive resistance component of the feeder path and distribution transformer, as well as the damping reduction effect of the currently connected charging piles, corresponding to the actual operating damping level of the system before connection.

[0100] After the charging pile is connected, the equivalent negative resistance of the port corresponding to the charging pile is connected in series with the feeder path impedance. Negative damping is injected into the system through the feeder circuit to offset part of the inherent positive damping. The longer the electrical distance of the charging pile, the greater the equivalent resistance of the whole path. Under the same charging power, the path voltage drop is greater and the port operating voltage is lower. The absolute value of the equivalent negative resistance of the constant power load increases accordingly, and the weakening effect on the total damping of the system is stronger.

[0101] In the digital twin model of the highway service area, the total system damping after connecting a single charging pile is obtained by subtracting the damping reduction amount corresponding to the charging pile from the current actual damping value before connection. The damping reduction amount is the absolute value of the equivalent negative resistance of the port under the corresponding operating condition. The corresponding value has fully reflected the influence of electrical distance on damping through the equivalent resistance of the whole path. The calculation rule is consistent with the calculation logic of the current actual damping value of the system in S220.

[0102] The derivation process is subject to two types of constraints to ensure that the results conform to the actual electrical operating logic. One type is the electrical topology constraint: the equivalent resistance of the feeder along the entire path and the cable impedance parameters corresponding to the charging interface are inherently determined by the distribution network topology, and the topology parameters output by the corresponding S210 remain unchanged; the equivalent negative resistance of the port is connected in series to the end of the corresponding feeder, and its reduction in the total system damping is fully reflected by the voltage drop effect of the equivalent resistance along the entire path, and cannot be taken independently of the topology connection relationship.

[0103] Another type is the operational boundary constraint. After connecting the corresponding charging load, this embodiment assumes that the voltage deviation of the low-voltage bus of the power distribution should be controlled within ±7% of the rated voltage, and the limit standard is consistent with S120; the current of the feeder branch should not exceed the rated current carrying capacity of the cable, and the charging power should not exceed the rated power range of the charging pile, so as to ensure that the derived negative resistance values ​​correspond to the actual and feasible operating conditions.

[0104] The system's total damping is taken as equal to the preset damping stability critical value as the damping stability critical condition, and this is used as the convergence criterion: if the system's total damping is higher than the preset damping stability critical value, the system is stable; if the system's total damping is lower than the preset damping stability critical value, there is a risk of low-frequency oscillation.

[0105] Substituting the critical damping stability condition into the total damping calculation rule of S220, the critical damping reduction amount that satisfies the damping stability requirement is derived in reverse, which corresponds to the critical absolute value of the equivalent negative resistance of the port under the current topology: ; in, The actual damping value of the system before connection is taken from the system dynamic damping ground state output by S220; The preset damping stability critical value is a nominal value referred to the low-voltage side, corresponding to the positive damping threshold of the service area distribution network and pile group coupling system at the oscillation instability boundary point; for example, in this embodiment, the equivalent resistance value corresponding to the system damping ratio of 5% is taken, and the setting basis is the general criterion for low-frequency oscillation stability analysis of power system. When the damping ratio is not less than 5%, the service area distribution network and pile group coupling system has sufficient oscillation suppression capability and can effectively resist the continuous oscillation caused by load disturbance; This is the critical damping reduction, which is the critical absolute value of the equivalent negative resistance at the port. When the absolute value of the equivalent negative resistance at the port exceeds this critical absolute value, the total damping of the system will fall below the stability critical value and enter the instability risk zone.

[0106] Within the initial range of the equivalent negative resistance of the port output by S310, two rounds of constraint screening are performed to complete the interval convergence.

[0107] The first round of screening is based on the critical damping condition. The range of negative resistance values ​​where the absolute value of the equivalent negative resistance at the port exceeds the critical damping reduction amount is eliminated, and the range of values ​​that meet the damping stability requirements is retained. The second round of screening is based on the operational boundary constraints. Within the remaining value range, the corresponding operating conditions are verified point by point: the equivalent negative resistance value is back-calculated using the constant power negative resistance formula, and then the bus voltage and feeder current are calculated by combining the equivalent resistance of the entire path. Value segments that exceed the operating limits are eliminated, and the final continuous value range is the self-consistent equivalent negative resistance range.

[0108] If there are no valid values ​​that satisfy the double constraints in the initial interval, the self-consistent equivalent negative resistance interval is empty. After the corresponding interface is connected, the system will inevitably become unstable, and the instability probability is taken as 100%.

[0109] All equivalent negative resistance values ​​within the self-consistent equivalent negative resistance range simultaneously satisfy the inherent parameters of the distribution network topology, electrical operation limits, and damping stability conditions, without any logical contradictions, and possess complete electrical operation self-consistency.

[0110] The most negative boundary in the self-consistent equivalent negative resistance interval is the negative resistance safety boundary. The absolute value of this negative resistance safety boundary directly represents the stability tolerance of the digital twin model of the highway service area to the charging pile interface access: the larger the absolute value of the boundary negative resistance, the stronger the negative damping impact that can be withstood, the higher the maximum charging power allowed to be connected to the charging pile interface, and the more sufficient the stability margin; conversely, it means that the charging pile interface is more likely to reach the damping stability limit after connection, and the stability tolerance is lower.

[0111] Finally, the self-consistent equivalent negative resistance interval and the negative resistance safety boundary value are bound to the corresponding charging pile equipment number, which serves as the calculation domain for subsequent probability sampling risk assessment.

[0112] S330. According to the sampling density level determined by matching the risk distribution map of the idle interface, perform probabilistic sampling calculation within the self-consistent equivalent negative resistance interval, statistically obtain the probability distribution law of the system damping ratio and the probability of oscillation instability, and output the damping oscillation instability risk assessment result.

[0113] The sampling density ratio of the charging pile interface is retrieved from the risk distribution map of the S240 idle interface, and the total number of effective samples in this simulation is calculated: Total number of effective samples = Preset basic sample base × Sampling density ratio; For example, in this embodiment, the basic sample base is 1000 groups. The sampling density ratio of the interface with the higher the influence weight is, the larger the effective sample size is, the higher the evaluation accuracy is, and the computing power resources are dynamically adapted to the interface risk level.

[0114] Within the self-consistent equivalent negative resistance interval obtained by S320 convergence, a corresponding number of equivalent negative resistance sample points are generated using an equidistant stratified sampling algorithm: each sample point corresponds to a specific equivalent negative resistance value and charging power condition; the self-consistent equivalent negative resistance interval is divided into several sub-intervals according to the resistance value, and an equal number of samples are drawn in each sub-interval to ensure that the samples are uniformly covered in the continuous value range, eliminate the local clustering bias of pure random sampling, and approximate the damping risk distribution law of the entire interval with the sample statistical results.

[0115] The damping ratio of the service area distribution network coupled with the pile group system is linearly proportional to the total equivalent positive damping resistance. For each group of equivalent negative resistance samples, the system damping ratio under the corresponding operating condition is calculated using the following formula: ; in: For the first The system damping ratio corresponding to the group of samples; The critical damping ratio is 5% in this embodiment, which corresponds one-to-one with the critical damping stability value of S320. The actual damping value of the system before connection is taken from the system dynamic damping ground state output by S220; For the first The absolute value of the equivalent negative resistance sample group, that is, the damping reduction amount under the corresponding working condition; This is the preset damping stability critical value, which is consistent with the value of S320.

[0116] The system damping ratio directly reflects the ability of the service area distribution network and pile group coupling system to suppress oscillations. The higher the damping ratio, the stronger the system's ability to resist disturbances and the more stable its operation; the lower the damping ratio, the easier it is for the system to induce low-frequency oscillations.

[0117] After completing the calculations for all samples, the damping ratio distribution curves of all samples were fitted using the Gaussian kernel density estimation algorithm. The bandwidth of the kernel function was determined using the Silverman rule: taking the standard deviation of the damping ratio samples and the total sample size as inputs, and then... The optimal bandwidth is calculated, where The standard deviation of the damping ratio sample set. This represents the total number of valid samples in this simulation.

[0118] The continuous probability distribution law of the system damping ratio is obtained by fitting; the critical damping ratio is used as the instability judgment threshold, and the proportion of the number of valid samples with damping ratios lower than the instability judgment threshold to the total number of samples in this simulation is the probability of system oscillation instability after the charging interface is connected.

[0119] The final output is a damped oscillation instability risk assessment result, including the instability probability value and the 95% confidence interval of the damping ratio. The 95% confidence interval of the damping ratio is derived from the sample distribution statistics and represents the reliable fluctuation range of the assessment result. The narrower the interval, the higher the assessment accuracy. The results are simultaneously updated to the corresponding interface attributes in the idle interface risk distribution map, providing a quantitative basis for subsequent charging scheduling.

[0120] S4. For multi-vehicle access scenarios, the self-consistent equivalent negative resistance range is narrowed by successively measuring the power, the damping oscillation instability risk results are updated, and the stable control strategy of the virtual power plant in the highway service area is output.

[0121] In this embodiment, step S4 includes the following specific steps: S410. In the scenario of multiple vehicles connecting to the highway service area, as each charging interface is connected in sequence and enters the charging steady state, the real-time charging power is collected one by one to converge the equivalent negative resistance of the corresponding port from an interval quantity to a steady-state definite value. Based on the port equivalent negative resistance parameters determined one by one, the current actual damping value of the system is updated in a rolling manner. At the same time, the safety boundary of the self-consistent equivalent negative resistance interval of the remaining idle interfaces is pushed to narrow in the direction of decreasing absolute value of equivalent negative resistance. The system damping ratio probability distribution and oscillation instability probability are recalculated in a gradual convergence manner, and the damping oscillation instability risk assessment results are updated in real time. The charging load of electric vehicles in highway service areas exhibits typical time-series characteristics of vehicle-by-vehicle arrival, continuous connection, and steady-state continuity. The initial risk assessment based on the rated power range estimation is inherently conservative. Therefore, a rolling iteration mechanism is designed for the multi-vehicle continuous connection scenario in highway service areas. The mechanism takes the entry of a single charging pile into steady-state charging as the trigger node, gradually eliminates power uncertainty as the connection status changes, and progressively improves the assessment accuracy.

[0122] After each vehicle is plugged in and enters the steady-state charging phase, the real-time steady-state charging power and port operating voltage of the charging pile interface are collected. Based on the constant power load equivalent negative resistance calculation rules, the steady-state equivalent negative resistance of the charging pile interface is determined, which replaces the negative resistance range corresponding to the power range in the initial assessment, eliminating the boundary redundancy caused by power prediction.

[0123] The steady-state equivalent negative resistance value is synchronously written back to the historical charging order dataset of the corresponding charging pile, and the single-pile power-negative resistance feature library is continuously updated to gradually improve the initial interval prediction accuracy of subsequent access.

[0124] Each cumulative access After a charging pile reaches steady-state operation, the current actual value of the system's damping is updated continuously using the following formula: ; in: For access The actual current damping value of the system after the steady-state charging pile is installed; The actual current damping value of the system before the multi-vehicle connection is taken from the system dynamic damping ground state output by S220, which already includes the damping reduction effect of existing charging piles on the grid. For the first The absolute value of the equivalent negative resistance of a charging pile in steady-state operation is the damping reduction amount under the corresponding operating condition. By accumulating the damping reduction amounts of all connected steady-state charging piles, the real-time damping level of the system can be accurately updated. The negative damping contribution of the connected ports is updated from the initial interval converted value to the steady-state determined value, so that the system damping benchmark gradually becomes more accurate with the actual connection state.

[0125] To determine the cumulative number of steady-state charging piles connected in the multi-vehicle connection scenario at this highway service area, a steady-state event-driven counting method is adopted, without the need for preset fixed values: relying on the existing charging pile power acquisition channel, when the fluctuation range of a single pile power for three consecutive sampling cycles (1s by default in this embodiment) does not exceed ±2% of the rated power, it is determined to enter steady-state charging, the value is automatically incremented by 1, and a system damping benchmark rolling refresh is triggered synchronously.

[0126] The absence of a fixed value is to adapt to the fluctuating traffic flow characteristics of the service area. When the traffic flow is dense, the refresh rate is automatically accelerated, and when the traffic flow is smooth, the frequency is automatically reduced. This balances the real-time performance of the evaluation with the efficiency of computing power. For the remaining unconnected idle charging interfaces, the updated actual damping value of the system is used as the calculation benchmark, and the self-consistent equivalent negative resistance range derivation and probabilistic risk assessment process are repeatedly executed.

[0127] As the operational status of the connected interfaces gradually becomes clear, the uncertainty of the system damping benchmark continues to decrease, the critical damping reduction that the remaining interfaces can withstand decreases one by one, and the safety boundary of the self-consistent equivalent negative resistance range gradually narrows in the direction of decreasing absolute value of equivalent negative resistance. The corresponding oscillation instability probability results are updated synchronously, avoiding the conservative bias of estimation based on the initial full power range, and the evaluation accuracy gradually improves with the progress of connection.

[0128] Finally, the damped oscillation instability risk assessment results are updated in real time, and the corresponding attributes of the idle interface risk distribution map are updated simultaneously, providing real-time quantitative basis for dynamic charging scheduling.

[0129] S420. Combining the real-time updated damped oscillation instability risk assessment results with the service area operation boundary constraints, a virtual power plant stability control strategy for the highway service area is generated.

[0130] Retrieve the real-time updated full-interface damping instability risk assessment results and idle interface risk distribution map, and combine them with the S120 distribution network operation boundary constraints to generate control strategies based on two risk levels: When the current actual damping value of the system is higher than the damping stability critical value and the margin is sufficient, and the bus voltage deviation and feeder current are both within the rated operating limit range, charging is opened normally according to the vehicle arrival sequence to ensure the charging service efficiency of the service area; for example, in this embodiment, a damping remaining margin greater than 15% is determined to be sufficient, where the damping remaining margin is the ratio of the portion of the current actual damping value that exceeds the damping stability critical value to the damping stability critical value.

[0131] When the system damping margin is no more than 15%, or the bus voltage deviation reaches 90% of the ±7% operating limit, or the feeder current reaches 90% of the cable's rated current carrying capacity, stability control is initiated: prioritize guiding subsequent vehicles to connect to charging interfaces with lower instability probability, and simultaneously implement charging power upper limit restrictions on high-risk interfaces; if the system continues to approach the stability boundary after control, then temporarily suspend unnecessary charging loads according to the interface instability probability from high to low, to ensure that the distribution network operation does not exceed the stability boundary and electrical limits.

[0132] For example, in this embodiment, 90% of the operating limit is taken as the early warning trigger ratio, which is a conventional conservative margin for power system operation early warning. This can trigger intervention in advance to prevent parameters from directly exceeding the limit.

[0133] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a virtual power plant panoramic monitoring and decision-making system based on digital twins provided in this application embodiment. This embodiment illustrates the composition architecture and functional division of the system's four core modules, including: The data acquisition and modeling module is used to collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in, in order to build a digital twin model of the highway service area. The ground state classification module is used to dynamically update the damping ground state of the service area distribution network and pile group coupling system by combining the digital twin model of the high-speed service area with electrical topology data and real-time operating status. Based on the feeder electrical distance and contact resistance degradation prediction, it assigns access impact weights to each idle electric vehicle charging interface and generates an idle interface risk distribution map. The negative resistance deduction module is used to respond to electric vehicle plug-in trigger events. It performs hierarchical deduction based on the risk distribution map of idle interfaces. In the digital twin model of the highway service area, it corrects the charging pile port impedance parameters with the transient physical characteristic data of the charging pile. Based on the damping ground state, it derives the self-consistent equivalent negative resistance interval in reverse based on the electrical topology and operating boundary constraints. It quantifies the damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval and outputs the damping oscillation instability risk assessment results. The rolling control module is used to narrow the self-consistent equivalent negative resistance range by successively measuring the power in multi-vehicle access scenarios, update the damped oscillation instability risk results, and output the stable control strategy of the virtual power plant in the highway service area.

[0134] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a virtual power plant panoramic monitoring and decision-making method based on digital twins, which can be loaded and executed by the processor, as provided in the above embodiments.

[0135] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the digital twin-based virtual power plant panoramic monitoring and decision-making method provided in the above embodiments. The data storage area may store data involved in the digital twin-based virtual power plant panoramic monitoring and decision-making method provided in the above embodiments.

[0136] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit the specific implementation.

[0137] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0138] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a virtual power plant panoramic monitoring and decision-making method based on digital twins.

[0139] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, a random access memory, a read-only memory, an erasable programmable read-only memory, a podium random access memory, a portable compressed disk read-only memory, a digital multifunction disk, a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0140] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0141] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A virtual power plant panoramic monitoring and decision-making method based on digital twins, characterized in that, Includes the following steps: S1. Collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in in the virtual power plant of the highway service area to construct a digital twin model of the highway service area. S2. By using the digital twin model of the high-speed service area, the damping ground state of the service area distribution network and pile group coupling system is dynamically updated by combining electrical topology data and real-time operating status. Based on the feeder electrical distance and contact resistance degradation prediction, the access impact weight is assigned to each idle electric vehicle charging interface, and an idle interface risk distribution map is generated. S3. Respond to the electric vehicle plug-in trigger event, perform hierarchical deduction based on the risk distribution map of idle interface, correct the charging pile port impedance parameters in the digital twin model of the highway service area using the transient physical characteristic data of the charging pile, take the damped ground state as the benchmark, and derive the self-consistent equivalent negative resistance interval based on the electrical topology and operating boundary constraints, quantify the damping ratio distribution and instability probability in the self-consistent equivalent negative resistance interval, and output the damped oscillation instability risk assessment results. S4. For multi-vehicle access scenarios, the self-consistent equivalent negative resistance range is narrowed by successively measuring the power, the damping oscillation instability risk results are updated, and the stable control strategy of the virtual power plant in the highway service area is output.

2. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 1, characterized in that, The process involves collecting electrical topology data, operational status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in at a virtual power plant in a highway service area. A digital twin model of the highway service area is then constructed, including the following steps: Obtain electrical topology data of the virtual power plant in the highway service area, including the rated parameters of the distribution transformer, the topology connection relationship of the feeder network, the coordinates of the charging pile access point, and the equivalent impedance parameters of the line; Real-time collection of operational status data of the virtual power plant in the highway service area, including the voltage of the low-voltage bus of the distribution transformer, the real-time current and active and reactive power of each feeder, the real-time charging power of the charging pile, and the operating margin parameters of the grid connection point; Retrieve historical operational statistics for highway service areas, including historical traffic flow data, historical charging order data, and charging pile contact resistance degradation curves; Within the initial time window after the electric vehicle plugs in, transient physical characteristic data of the charging pile port are collected, including the initial voltage of the port, the pre-charging circuit current, and the transient impedance characteristics of the contact circuit.

3. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 2, characterized in that, By using a digital twin model of a high-speed service area, and combining electrical topology data with real-time operating status, the damping ground state of the service area's distribution network and pile group coupling system is dynamically updated, including the following steps: Based on electrical topology data, combined with the rated parameters of the distribution transformer and the equivalent impedance parameters of the line, the equivalent positive damping base value of the distribution network in the high-speed service area is calculated; based on the feeder network topology connection relationship and the coordinates of the charging pile access point, the feeder electrical distance between each charging interface and the distribution transformer is calculated, and an electrical distance weight is assigned to each idle charging interface. Using the real-time charging power of the charging piles, the real-time current of each feeder, and the active and reactive power as inputs, combined with the distribution network topology impedance parameters and the negative damping characteristics of the constant power load, the current actual damping value of the coupling system between the distribution network and the charging pile group is calculated; combined with historical traffic flow data and historical charging order data, the number of charging piles that can be connected in the short term is estimated, and the future short-term damping margin range is calculated based on the electrical distance weight distribution of the idle charging interface. The two together constitute the dynamic damping ground state of the system.

4. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 3, characterized in that, Based on the predicted degradation of feeder electrical distance and contact resistance, an access impact weight is assigned to each idle electric vehicle charging interface, generating an idle interface risk distribution map, including the following steps: Based on historical charging order data, the cumulative number of plugging and unplugging operations for each idle interface is counted. Combined with the contact resistance degradation curve of the same batch of charging piles, the current contact resistance range of each idle charging interface is estimated. The electrical distance weight is used as the basic risk weight. Combined with the risk gain correction factor corresponding to the degree of degradation of the charging pile gun contact interface, the access impact weight of each idle charging pile interface is calibrated. Based on the access impact weight of each idle charging interface, a mapping relationship is established between the access impact weight and the damping simulation start-up urgency and sampling density level, and an idle interface risk distribution map containing hierarchical scheduling attributes is constructed.

5. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 4, characterized in that, In response to the electric vehicle charging port trigger event, a tiered simulation is performed based on the idle interface risk distribution map. The charging pile port impedance parameters are then corrected using transient physical characteristic data of the charging pile in the digital twin model of the highway service area, including the following steps: In response to the electric vehicle plug-in trigger event, the access impact weight of the corresponding interface in the idle interface risk distribution map is retrieved, the damping simulation urgency and sampling density level are matched and determined, and the simulation is started; the transient physical characteristic data of the charging pile port in the initial time window of the plug-in are extracted, the port impedance parameters are calibrated before the main charging load is fully applied, the boundary of the estimated contact resistance is converged and corrected, and the initial value range of the port equivalent negative resistance is determined.

6. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 5, characterized in that, Based on the damped ground state, the self-consistent equivalent negative resistance interval is derived in reverse based on the electrical topology and operating boundary constraints. The damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval are quantified, and the damped oscillation instability risk assessment results are output, including the following steps: Based on the initial range of equivalent negative resistance of the port, taking the system dynamic damping ground state as the calculation benchmark, combining the distribution network electrical topology constraints and operation boundary constraints, and taking the damping stability critical condition as the convergence criterion, the range of equivalent negative resistance values ​​that satisfy the self-consistency of the system electrical operation is derived in reverse, and the self-consistent equivalent negative resistance range is obtained by convergence. The system stability tolerance of charging load access is quantified by the negative resistance safety boundary. Based on the sampling density level determined by matching the risk distribution map of the idle interface, probabilistic sampling calculation is performed within the self-consistent equivalent negative resistance interval to statistically obtain the probability distribution law of the system damping ratio and the probability of oscillation instability, and output the damping oscillation instability risk assessment result.

7. The virtual power plant panoramic monitoring and decision-making method based on digital twins according to claim 6, characterized in that, For multi-vehicle access scenarios, the self-consistent equivalent negative resistance range is narrowed by successively measuring the power, the damped oscillation instability risk results are updated, and a stable control strategy for the virtual power plant in the highway service area is output, including the following steps: In the scenario of multiple vehicles connecting to the highway service area, as each charging interface is connected in sequence and enters a steady charging state, the real-time charging power is collected one by one to converge the equivalent negative resistance of the corresponding port from an interval quantity to a steady-state definite value. Based on the port equivalent negative resistance parameters determined one by one, the current actual damping value of the system is continuously refreshed, and the safety boundary of the self-consistent equivalent negative resistance interval of the remaining idle interfaces is simultaneously pushed to narrow in the direction of decreasing absolute value of equivalent negative resistance. The system damping ratio probability distribution and oscillation instability probability are recalculated in an asymptotic convergence manner, and the damping oscillation instability risk assessment results are updated in real time. By combining the real-time updated damped oscillation instability risk assessment results with the service area operation boundary constraints, a virtual power plant stability control strategy for highway service areas is generated.

8. A virtual power plant panoramic monitoring and decision-making system based on digital twins, implemented based on the virtual power plant panoramic monitoring and decision-making method based on digital twins as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition and modeling module is used to collect electrical topology data, operating status data, and transient physical characteristic data of charging piles within the initial window of electric vehicle plug-in, in order to build a digital twin model of the highway service area. The ground state classification module is used to dynamically update the damping ground state of the service area distribution network and pile group coupling system by combining the digital twin model of the high-speed service area with electrical topology data and real-time operating status. Based on the feeder electrical distance and contact resistance degradation prediction, it assigns access impact weights to each idle electric vehicle charging interface and generates an idle interface risk distribution map. The negative resistance deduction module is used to respond to electric vehicle plug-in trigger events. It performs hierarchical deduction based on the risk distribution map of idle interfaces. In the digital twin model of the highway service area, it corrects the charging pile port impedance parameters with the transient physical characteristic data of the charging pile. Based on the damping ground state, it derives the self-consistent equivalent negative resistance interval in reverse based on the electrical topology and operating boundary constraints. It quantifies the damping ratio distribution and instability probability within the self-consistent equivalent negative resistance interval and outputs the damping oscillation instability risk assessment results. The rolling control module is used to narrow the self-consistent equivalent negative resistance range by successively measuring the power in multi-vehicle access scenarios, update the damped oscillation instability risk results, and output the stable control strategy of the virtual power plant in the highway service area.

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