Bidirectional charging pile energy switching control method and system supporting vehicle network interaction

Abstract

The invention relates to the technical field of electric pile energy switching, and discloses a bidirectional charging pile energy switching control method and system supporting vehicle-network interaction, and the method comprises the steps: collecting the load state of a power grid, and constructing a load evaluation model in which a historical load is combined with real-time data; selecting a common charging mode or a V2G mode according to an evaluation result; switching the charging module according to the charging mode requirement; constructing a load attention model according to power grid dispatching and load distribution; optimal configuration is applied to charging control, and peak clipping and valley filling are carried out in cooperation with a power system; the system comprises a load acquisition module, a state judgment and mode driving module, a module interface switching control module, a local load modeling module, a multi-channel power optimization module, a strategy execution control module and a storage module. According to the method, a power grid load identification mechanism constructed by historical load statistics and a time period standard deviation model is introduced, and a power boundary threshold value is dynamically set through formula modeling, so that whether a load is in a tension state or not can be judged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile energy switching, in particular to a bidirectional charging pile energy switching control method and system supporting vehicle-to-grid interaction. BACKGROUND

[0002] In recent years, with the rapid increase in the penetration rate of new energy vehicles, the system role of electric vehicles as "movable energy storage units" has gradually been clarified, and the promotion of vehicle-to-grid (V2G) has become an important part of the energy internet. Especially under the background of the increasing maturity of peak-valley electricity price mechanism and the increasing pressure on power grid regulation year by year, building flexible load regulation capability based on electric vehicles has become one of the important trends of future distribution side energy management.

[0003] The existing charging pile energy switching control technology, most V2G systems use centralized strategy framework for management, usually through setting fixed load threshold or time period (such as evening peak period) to trigger discharge response. At the same time, in the design of charging pile structure, the mainstream method is still "single function module configuration", that is, the system has been determined to be charging type or bidirectional type in the design stage, and real-time switching or module replacement is difficult to realize in operation.

[0004] However, the existing charging pile energy switching control technology often appears that the regulation strategy takes effect when the load anomaly has passed or has not reached the dispatch boundary. Secondly, it is difficult to fully consider the micro characteristics of local voltage deviation and power fluctuation in node regulation, which easily leads to excessive load in some areas and idle in other nodes, forming dispatch imbalance. Therefore, the present application provides a bidirectional charging pile energy switching control method and system supporting vehicle-to-grid interaction to solve the problems existing in the prior art. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a bidirectional charging pile energy switching control method and system supporting vehicle-to-grid interaction, which solves the problems of existing charging pile energy switching control technology, such as charging and discharging mode response lag, node power regulation rigidity, lack of intelligent identification and hot plug protection for module replacement, and lack of dynamic feedback mechanism in control process.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction, comprising the following steps:

[0007] S1, collecting the power grid load state of the area where the charging device is located, and constructing a power load evaluation model based on historical load curve and real-time data, the power load evaluation model being used to identify whether the current is in a load ample state or a load stressed state;

[0008] S2, based on the result of the power load evaluation model recognition, the driving control system selects to enter the ordinary charging mode or the V2G reverse power mode, and configures the corresponding charging strategy parameters;

[0009] S3, after the charging mode is switched, according to the requirement of the charging mode, the control system controls the modular interface to switch the charging module correspondingly;

[0010] S4, after the module switching is completed, based on the current power grid scheduling state and the geographical and load distribution of each charging node, a local load attention model is constructed, and a multi-channel power regulation algorithm is used to optimize and configure the charging behavior of each node;

[0011] S5, the optimized configuration result is applied to the actual charging behavior control, which maximizes the cooperation with the peak load shifting or power supply buffer task of the power system.

[0012] Preferably, in step S1, the power load evaluation model for identifying the current load surplus state or load stressed state comprises the following steps:

[0013] Record the real-time load power value and power fluctuation rate data of the regional public transformer area every 15 minutes as the sampling period;

[0014] Based on the historical load curve of the past three months, a typical daily load model is constructed, and an interval distribution fitting algorithm is used to calculate the percentile position of the current load value in the model;

[0015] The threshold function is used as the boundary to calculate the power grid load state.

[0016] Preferably, in step S2, the configuration of the corresponding charging strategy parameters comprises the following steps:

[0017] After receiving the current load state result, call the internal strategy control unit to switch the system main mode flag bit;

[0018] If it is judged as a load stressed state, activate the V2G discharge protocol, and select the vehicle terminal with V2G capability according to the built vehicle list;

[0019] Set the charging strategy parameters, including current limit, voltage maintenance time and power scheduling period, and write them into the cache control area of each charging node.

[0020] Preferably, in step S3, the control system controls the modular interface to switch the charging module correspondingly, which comprises the following steps:

[0021] Get the current module plug-in state, identify the type of the module that has been accessed through the embedded RFID chip;

[0022] Send a hot plug authorization instruction to the charging controller, and perform a module disconnection operation when the voltage drop is confirmed to be less than a set value.

[0023] After completing the module insertion and removal, close the relay and reinitialize the protocol communication to ensure that the communication port of the switching module matches the control interface.

[0024] Preferably, in step S4, the optimization configuration of the charging behavior of each node using a multi-channel power adjustment algorithm includes the following steps:

[0025] All charging pile nodes are clustered according to their geographical location in the area to form a local area index table;

[0026] Calculate the load weight value for each node and construct a load attention distribution map based on the difference between the current voltage offset and the mean of neighboring nodes;

[0027] The multi-channel scheduling algorithm is invoked to calculate the optimal allocation strategy so that the power adjustment coefficient of each node satisfies the local power stability condition.

[0028] Preferably, in step S5, applying the optimized configuration result to the actual charging behavior control includes the following steps:

[0029] The target charging power and current limit value of each node in the optimization results are simultaneously sent to the power regulation controller inside each pile.

[0030] The output power is adjusted in real time using the PWM control signal.

[0031] A power fluctuation monitoring callback mechanism is set up so that when the power deviation of any node exceeds the set threshold for three consecutive cycles, a scheduling rollback operation is triggered and the local allocation strategy is recalculated.

[0032] Preferably, the threshold judgment boundary value for calculating the power grid load state adopts a threshold function formula, which is: ;

[0033] In the formula, Indicates the current time Boundary values ​​for judging the power grid load status. For the present moment in the past The average load power during the corresponding time period of the day This represents the standard deviation for that period. This is a coefficient adjusted according to the season or time period.

[0034] Preferably, the load weight value of each node is calculated using a voltage offset normalized weighting formula, which is as follows:

[0035] ;

[0036] In the formula, For the first a load weight value of the node, a voltage offset of the current node , a set of adjacent nodes defined in the topology graph for the node, a stability correction factor.

[0037] Preferably, the calculation of the local allocation strategy adopts a form of mean square deviation function, and the mean square deviation function is: ;

[0038] In the formula, represents a current power regulation balance degree of the system, is a real-time power output value of the node, is an average power output value of all participating nodes, is a total number of nodes currently participating in control.

[0039] A bidirectional charging pile energy switching control system supporting vehicle-to-grid interaction is also provided, comprising:

[0040] a load collection module, configured to periodically collect real-time data and historical load curves of a power grid in an area where a charging device is located, and construct a power load evaluation model based on the data;

[0041] a state judgment and mode driving module, configured to judge a load state of a current power grid according to an output result of the load evaluation model, and drive the system to select an ordinary charging mode or a V2G reverse power mode according to the load state;

[0042] a module interface switching control module, configured to control the modular interface to switch to a corresponding ordinary charging module or V2G module after the state judgment and mode driving module generates a charging mode result;

[0043] a local load modeling module, configured to construct a local load attention model based on a current power grid dispatching state, geographical positions of nodes and load distribution conditions after the module switching is completed;

[0044] a multi-channel power optimization module, configured to call a multi-channel power regulation algorithm to allocate target powers of all charging nodes after the local load attention model is constructed;

[0045] a strategy execution control module, configured to issue target powers of the nodes and related control strategies to corresponding charging pile controllers after the power optimization configuration is completed, and drive power output dynamic adjustment through a PWM signal regulation mode;

[0046] The storage module is used to save historical load data, model parameters, vehicle file information and strategy parameter tables, and supports historical data callback and strategy update operations.

[0047] This invention provides a bidirectional charging pile energy switching control method and system that supports vehicle-to-grid interaction. It has the following beneficial effects:

[0048] 1. This invention introduces a power grid load identification mechanism based on historical load statistics and a time-period standard deviation model. By dynamically setting power boundary thresholds through formula modeling, it can more accurately determine whether the system is under load. This approach breaks away from the rigid logic of traditional mode switching based on fixed power thresholds or time periods, significantly improving the slow response and strategy failure issues of existing systems in peak-shifting identification scenarios.

[0049] 2. This invention utilizes a voltage offset normalized weighted algorithm to achieve real-time calculation of the control weight for each node. The node's response no longer depends solely on the physical topology but also considers the sensitivity to local voltage disturbances. This control method is more flexible and engineering-adaptable than the original method of allocating power based on the node's static power level, effectively solving the bottleneck of the original system's one-size-fits-all power control and severe blind spots.

[0050] 3. This invention establishes a closed-loop mechanism for module identification and interface switching. The controller automatically unloads and replaces modules based on the selected mode, while simultaneously performing hot-swap determination based on electrical status to ensure a stable process without power loss. Compared to traditional charging piles that rely on manual judgment and module replacement, this design makes the switching process more intelligent and significantly reduces the safety hazards caused by human intervention.

[0051] 4. This invention constructs a dynamic power balance assessment model based on the mean square error function. The system monitors the control deviation in real time and triggers power backoff and redistribution logic, no longer relying on a fixed-period adjustment strategy. This deviation-driven feedback mechanism solves the problems of delayed control response and balance failure in the old scheme, and is especially suitable for multi-node distributed scenarios, resulting in more refined system adjustment and more stable operation. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method steps of the present invention;

[0053] Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see the appendix Figure 1 The present invention provides a bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction, comprising the following steps:

[0056] S1. Collect the power grid load status of the area where the charging device is located, and construct a power load assessment model based on historical load curves and real-time data. The power load assessment model is used to identify whether the current load is in a state of ample or tight load.

[0057] S2. Based on the results identified by the power load assessment model, the drive control system selects to enter the normal charging mode or the V2G reverse power supply mode and configures the corresponding charging strategy parameters.

[0058] S3. After the charging mode is switched, the control system controls the modular interface to switch the charging module accordingly based on the charging mode requirements.

[0059] S4. After completing the module switching, based on the current power grid dispatch status and the geographical and load distribution of each charging node, a local load attention model is constructed, and the charging behavior of each node is optimized and configured through a multi-channel power regulation algorithm.

[0060] S5. Apply the optimized configuration results to the actual charging behavior control to maximize the cooperation with the power system's peak shaving and valley filling or power supply buffering tasks.

[0061] For step S1, in this embodiment, the power grid operation status of the area where the charging device is located is first continuously monitored. The collected data includes, but is not limited to: real-time load power value per unit time (e.g., with a sampling period of 15 minutes), and the corresponding electricity consumption fluctuation rate index for that time period. Generally, this data can be obtained through smart meters connected to the public transformer area, electricity monitoring platforms, or edge power routers. The collected data will be used as input to feed into the power grid load assessment model to identify whether the current area is in a state of ample load or a state of tight load.

[0062] In one possible implementation, the power grid load assessment model is built based on historical continuous data. Specifically, load curve data from the past three months can be selected, power change trends formed on different dates and time periods can be categorized, and a standard distribution reference curve can be constructed using a typical daily load modeling method.

[0063] As an alternative, this model combines interval distribution fitting and percentile interpolation in determining specific load conditions. Based on this, by calculating the percentile of the real-time load power at the current moment within the aforementioned model distribution, a preliminary judgment can be made as to whether it falls within the normal operating range, the warning boundary range, or the high-voltage load range.

[0064] Furthermore, in some embodiments, the quantification boundary judgment criterion for the load state can adopt the following threshold function form:

[0065] ;

[0066] In the formula, Indicates the current time Boundary values ​​for judging the power grid load status. This indicates that the current time period is in the past. Average load power value within the day This represents the standard deviation for that period. This is an adjustable adjustment coefficient that can be dynamically assigned based on seasonal attributes, electricity pricing strategies, city energy efficiency levels, or user-defined strategies. For example, during peak summer air conditioning electricity consumption... It can be set to 1.5 to improve load sensitivity, while in the spring and autumn flat seasons, A value of 0.8 can be used to reduce false positives.

[0067] This interval-sensitive threshold assessment method can dynamically adapt to changes in grid load status, improving the robustness of the energy dispatch response mechanism. It exhibits good generalization and implementation flexibility, especially in typical high-energy-density scenarios such as urban residential communities, commercial parks, or office building clusters.

[0068] In another implementation, the system can configure different μ and σ sampling windows for different time periods (such as peak, trough, and flat periods). For example, during morning and evening peak hours, data windows from the same time period within the past 7 days can be selected for statistical analysis, while during early morning or weekend periods, a 30-day data window can be selected to enhance robustness. By introducing this "time period adaptability" mechanism, the model achieves a better balance between stability and response sensitivity.

[0069] It is worth noting that the model outputs Boundary values ​​are not directly used as the final judgment indicator, but are compared with the current real-time load power value. When the current power value is greater than... When the load is low, the system is considered to be in a "load stress" state and the drive system enters the V2G energy feedback mode; otherwise, it maintains or switches to the normal charging mode.

[0070] In some extended applications, the model can also be embedded into regional EMS systems to predict regional load trends, provide users with charging suggestions several hours in advance, improve user experience, and reduce relay impact caused by frequent switching.

[0071] For step S2, in this embodiment, the current grid load status is collected, the grid load assessment model is calculated, and the need to switch to bidirectional charging mode (V2G mode) is determined based on the assessment results. The core objective of this process is to identify the current grid load status and select an appropriate charging strategy based on that status.

[0072] The power grid load assessment model first compares historical load curves with real-time data to calculate the current load status of the power grid. Based on historical power grid load data, this model dynamically assesses the power grid load status by fusing standard deviation model data with time-period data. In this way, the power grid load assessment model can reflect load changes in real time, thus providing a precise basis for decision-making and control.

[0073] For energy switching of charging piles, this invention proposes a switching judgment criterion based on the current load status. By analyzing the current load status of the power grid, the system calculates parameters related to that status. Specifically, the calculation formula is as follows:

[0074] ;

[0075] In the formula, This represents the average power of the grid load, in kW. For the first Real-time power output of each charging node, in kW; This refers to the number of charging pile nodes participating in the evaluation.

[0076] The grid load assessment results are used to calculate the average power value using this formula and compared with a set threshold. If the current power demand of the grid is high, and it is during the peak load period, the system will determine to enter V2G mode, switch to bidirectional charging, and feed battery energy back to the grid, thereby alleviating grid load pressure.

[0077] Under normal circumstances, when the grid load is below a certain threshold, the system will select charging mode to maximize the charging of electric vehicles. At the same time, as an option, if the grid load remains high and is expected to continue, the system will switch to V2G mode to provide feedback power to the grid when the grid load is too high, thus avoiding grid anomalies caused by overload.

[0078] Furthermore, this invention employs an optimized control strategy to address real-time load fluctuations in complex power grid environments. By establishing a functional relationship between load and power, the system can quickly react and adjust the charging pile's operating mode upon receiving feedback on the power grid load status. Specifically, in some embodiments, the control system dynamically adjusts the charging power output of the charging pile based on the current power grid load conditions, combined with the amplitude and frequency of power fluctuations.

[0079] For step S3, in this embodiment, the system identifies the type of the charging pile module to determine whether a module switching operation is required. Specifically, after assessing the grid load and making a charging mode switching decision, the system needs to determine whether to switch to a module that is more compatible with the grid load based on the current module's operating status and performance requirements, in order to achieve a more efficient charging or discharging process.

[0080] The system identifies the type of currently connected module using a built-in RFID chip and determines whether the module meets the switching criteria based on the identification result. This process relies on the adaptability between the charging pile and the power grid load; that is, when the power grid load changes, if the existing module cannot meet the new load demand, the system will optimize energy transmission efficiency by switching modules.

[0081] Specifically, after module identification, the system triggers a module switching operation based on the grid load status assessment. At this point, the module switching considers both the current grid load's power demand and the module's power output capability. If the grid load is high and the existing module cannot provide sufficient charging power, the system will switch to a module with higher power. Conversely, when the grid load is low and the existing module's output power is high, the system may choose to switch to a module with lower power to avoid placing excessive burden on the grid.

[0082] Under this mechanism, the present invention introduces an intelligent decision-making mechanism for module switching. Specifically, the system first obtains the current module's status information through the built-in RFID identification chip, and determines whether the module is suitable for the current load conditions based on preset power and voltage standards. When the voltage or power exceeds the current module's adaptability range, the system automatically replaces the module to ensure a stable and efficient charging process.

[0083] In one possible implementation, if the grid load is high and a fast response is required, the system will activate the hot-swap function of the modules to achieve a smooth transition between modules and avoid affecting the charging effect due to voltage or power fluctuations during the transition.

[0084] For step S4, in this embodiment, by fusing node topology relationships and load offset characteristics, a multi-channel power regulation algorithm is used to perform fine-grained power allocation to each charging node within the region, ensuring the local stability and network adaptability of the regulation behavior. This not only supports dynamic optimization of energy allocation but also forms the foundational algorithm for matching the local power structure in the entire charge and discharge control strategy.

[0085] The system first performs clustering processing on all charging pile terminal nodes in the area based on the geographical coordinates of their respective substations.

[0086] Specifically, based on a pre-set GIS index table, the system can group all charging pile terminals under the same transformer area or transformer node into the same cluster area, establishing a local area index structure. This index structure facilitates local load constraint modeling during subsequent control processes and supports synchronous response mechanisms between adjacent nodes.

[0087] In some embodiments, for each transformer substation cluster region, the system further calculates the load weight parameters of each node. The weight calculation is based on the voltage offset characteristics of the node's location and the voltage coordination of neighboring nodes within the region. To this end, the present invention proposes the following normalized voltage offset weighting function:

[0088] ;

[0089] In the formula, Indicates the first Normalized load scheduling weights for each node; For the current node The voltage offset, that is, the difference between its voltage value and the steady-state reference voltage; For the first The set of neighboring nodes defined by a node in the topology graph; This is a stability correction factor used to prevent numerical perturbations with a denominator of zero; its typical value is 0.01-0.05.

[0090] This model effectively identifies nodes at the high-voltage disturbance boundary by introducing a voltage difference evaluation mechanism, and assigns them higher response weights for subsequent power configuration prioritization.

[0091] Under normal circumstances, the system will construct the power adjustment target set for the current scheduling period based on the weight of the nodes.

[0092] In one possible implementation, by combining the geographical clustering relationships of regional nodes, the system constructs the following power allocation optimization model:

[0093] ;

[0094] In the formula, For nodes The target is the desired charge / discharge power value. This represents the maximum total power resource that is currently available for scheduling in this local area; For nodes The weighted values ​​are derived from the normalized weighted function mentioned above.

[0095] This objective function enables hierarchical control of scheduling priorities when regional power resources are limited, avoiding the concentration of resources on inefficient nodes and improving overall network stability.

[0096] As an option, the system will also provide feedback corrections based on the power configuration results. This involves evaluating the deviation between the predicted power demand of each node and the current power configuration. If the deviation exceeds a preset threshold, the local values ​​will be dynamically adjusted or a node reduction strategy will be implemented to ensure the stability and convergence of the system scheduling process.

[0097] It is worth noting that this adjustment algorithm has good scalability and can adaptively adjust according to the capabilities of the devices connected to the system, the number of nodes, and the completeness of geographic information, adapting to various deployment models, including centralized and distributed deployments.

[0098] For step S5, in this embodiment, the optimized strategy is transformed into actual power commands, and the charging and discharging behavior of the vehicle is dynamically controlled through the power electronic interface, thereby effectively matching the regional power grid load status and mitigating local voltage fluctuations. Especially in vehicle-to-grid interaction scenarios, nodes are widely distributed and vehicle access status changes frequently. Therefore, it is necessary to establish a feedback-based, continuously adjustable power execution and callback mechanism to ensure that the system maintains a balanced power distribution during execution.

[0099] In this embodiment, after receiving the power configuration instructions for each node, the control system first sets the target charging power limit and current output limit for each charging node according to the control interface protocol of each charging node, and then sends them to the corresponding charging controller simultaneously.

[0100] Specifically, the system issues unified command frames through the main control platform. These frames include the target power value, current limiting parameters, control frequency, and node identifier. Upon receiving the command, each charging pile controller invokes its local power regulation module and adjusts the output power in real time via PWM control signals. The PWM duty cycle is linearly mapped to the target power, and the system dynamically adjusts the duty cycle through an internal control loop to ensure synchronization between power execution and voltage change response.

[0101] In general, to improve the dynamic adaptability of power execution, this invention introduces a power fluctuation monitoring and feedback mechanism. During controller operation, the system continuously collects the real-time power output of each node and calculates its variation within a set period, serving as an important basis for judging whether power execution deviates from the optimization target.

[0102] In one possible implementation, the judgment period is set to every 3 consecutive scheduling cycles. If the actual power offset of a node exceeds the set threshold during this period, the fallback mechanism is triggered, forcing the controller to perform a power reallocation action and sending its current state back to the main control system to recalculate the node power weight.

[0103] In addition, to measure the overall balance of power distribution, the following mean square error evaluation function is introduced in this embodiment:

[0104] ;

[0105] In the formula, This indicates the current power regulation balance of the system; For the first Real-time power output value of each node; This represents the average power output value across all participating nodes. This represents the total number of nodes currently participating in control.

[0106] The smaller the value of this function, the more balanced the power distribution at the nodes, which can be used to dynamically evaluate the overall control effect. When the function value shows an upward trend over several consecutive periods, the system will automatically adjust the weight coefficients of some key nodes and re-initiate the allocation of optimization instructions to maintain the convergence and stability of the power control system.

[0107] As an alternative, in scenarios where some vehicles lack precise power response capabilities, the system can also set a tolerance error threshold range, triggering feedback adjustment only when the actual power deviates from the target by more than this error range, thereby improving system fault tolerance and avoiding frequent scheduling.

[0108] The bidirectional charging pile energy switching control system supporting vehicle-to-grid interaction described below can be referred to in correspondence with the bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction described above.

[0109] Please see the appendix Figure 2 The present invention also provides a bidirectional charging pile energy switching control system that supports vehicle-to-grid interaction, comprising:

[0110] The load acquisition module is used to periodically acquire real-time power grid load data and historical load curves in the area where the charging device is located, and to build a power load assessment model based on the data.

[0111] The status judgment and mode driving module is used to determine the current load status of the power grid based on the output results of the load assessment model, and drive the system to select to enter the normal charging mode or the V2G reverse power transmission mode accordingly.

[0112] The module interface switching control module is used to control the modular interface to switch to the corresponding ordinary charging module or V2G module after the state judgment and mode driving module generates the charging mode result.

[0113] The local load modeling module is used to construct a local load attention model based on the current power grid dispatch status, the geographical location of each node, and the load distribution after the module switch is completed.

[0114] The multi-channel power optimization module is used to call the multi-channel power adjustment algorithm to allocate the target power of all charging nodes after the local load attention model is built.

[0115] The strategy execution control module is used to send the target power and related control strategies of each node to the corresponding charging pile controller after the power optimization configuration is completed, and drive the power output to be dynamically adjusted through PWM signal adjustment.

[0116] The storage module is used to save historical load data, model parameters, vehicle file information and strategy parameter tables, and supports historical data callback and strategy update operations.

[0117] The system in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so they will not be described again here.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction, characterized in that, Includes the following steps: S1. Collect the power grid load status of the area where the charging device is located, and construct a power load assessment model based on historical load curves and real-time data. The power load assessment model is used to identify whether the current load is in a state of ample or tight load. S2. Based on the results identified by the power load assessment model, the drive control system selects to enter the normal charging mode or the V2G reverse power supply mode and configures the corresponding charging strategy parameters. S3. After the charging mode is switched, the control system controls the modular interface to switch the charging module accordingly based on the charging mode requirements. S4. After completing the module switching, based on the current power grid dispatch status and the geographical and load distribution of each charging node, a local load attention model is constructed, and the charging behavior of each node is optimized and configured through a multi-channel power regulation algorithm. S5. Apply the optimized configuration results to the actual charging behavior control to maximize the cooperation with the power system's peak shaving and valley filling or power supply buffering tasks.

2. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 1, characterized in that, In step S1, the power load assessment model is used to identify whether the current load is in a state of ample or tight load, including the following steps: Record the real-time load power value and power consumption fluctuation rate data of the public transformer substation area every 15 minutes; A typical daily load model is constructed based on the historical load curves of the past three months, and the percentile position of the current load value in the model is calculated by using an interval distribution fitting algorithm. The power grid load status is calculated using a threshold function as the boundary.

3. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 1, characterized in that, In step S2, configuring the corresponding charging strategy parameters includes the following steps: After receiving the current load status result, the internal strategy control unit is invoked to switch the system main mode flag. If the load is determined to be under strain, the V2G discharge protocol is activated, and vehicle terminals with V2G capability are selected from the list of registered vehicles. Configure charging strategy parameters, including current limits, voltage sustaining time, and power scheduling cycle, and write them to the cache control area of ​​each charging node.

4. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 1, characterized in that, In step S3, the control system switches the charging module accordingly via the modular interface, including the following steps: Obtain the current module insertion / removal status and identify the type of connected module through the embedded RFID chip; Send a hot-plug authorization command to the charging controller, and perform a module disconnection operation when it is confirmed that the voltage drop is less than the set value; After completing the module insertion and removal, close the relay and reinitialize the protocol communication to ensure that the communication port of the switching module matches the control interface.

5. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 1, characterized in that, In step S4, the optimization configuration of the charging behavior of each node through the multi-channel power adjustment algorithm includes the following steps: All charging pile nodes are clustered according to their geographical location in the area to form a local area index table; Calculate the load weight value for each node and construct a load attention distribution map based on the difference between the current voltage offset and the mean of neighboring nodes; The multi-channel scheduling algorithm is invoked to calculate the optimal allocation strategy so that the power adjustment coefficient of each node satisfies the local power stability condition.

6. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 1, characterized in that, In step S5, applying the optimized configuration results to the actual charging behavior control includes the following steps: The target charging power and current limit value of each node in the optimization results are simultaneously sent to the power regulation controller inside each pile. The output power is adjusted in real time using the PWM control signal. A power fluctuation monitoring callback mechanism is set up so that when the power deviation of any node exceeds the set threshold for three consecutive cycles, a scheduling rollback operation is triggered and the local allocation strategy is recalculated.

7. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 2, characterized in that, The threshold value for calculating the power grid load status is determined using a threshold function formula, which is as follows: ； In the formula, Indicates the current time Boundary values ​​for judging the power grid load status. For the present moment in the past The average load power during the corresponding time period of the day This represents the standard deviation for that period. This is a coefficient adjusted according to the season or time period.

8. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 5, characterized in that, The load weight value for each node is calculated using a voltage offset normalized weighting formula, which is as follows: ； In the formula, For the first The load weight value of each node. For the current node voltage offset, For the first The set of neighboring nodes defined by a node in the topology graph. This is a stability correction factor.

9. The bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction according to claim 6, characterized in that, The local allocation strategy is calculated using a mean squared error function, which is: ； In the formula, This indicates the current power regulation balance of the system. For the first Real-time power output value of each node, The average power output value of all participating nodes. This represents the total number of nodes currently participating in control.

10. A bidirectional charging pile energy switching control system supporting vehicle-to-grid interaction, applied to the bidirectional charging pile energy switching control method supporting vehicle-to-grid interaction as described in any one of claims 1-9, characterized in that, include: The load acquisition module is used to periodically acquire real-time power grid load data and historical load curves in the area where the charging device is located, and to build a power load assessment model based on the data. The status judgment and mode driving module is used to determine the current load status of the power grid based on the output results of the load assessment model, and drive the system to select to enter the normal charging mode or the V2G reverse power transmission mode accordingly. The module interface switching control module is used to control the modular interface to switch to the corresponding ordinary charging module or V2G module after the state judgment and mode driving module generates the charging mode result. The local load modeling module is used to construct a local load attention model based on the current power grid dispatch status, the geographical location of each node, and the load distribution after the module switch is completed. The multi-channel power optimization module is used to call the multi-channel power adjustment algorithm to allocate the target power of all charging nodes after the local load attention model is built. The strategy execution control module is used to send the target power and related control strategies of each node to the corresponding charging pile controller after the power optimization configuration is completed, and drive the power output to be dynamically adjusted through PWM signal adjustment. The storage module is used to save historical load data, model parameters, vehicle file information and strategy parameter tables, and supports historical data callback and strategy update operations.

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