An electric vehicle V2G bidirectional charge-discharge collaborative control system

Abstract

The application discloses a kind of electric vehicle V2G two-way charge-discharge collaborative control system, belong to electric power system electric vehicle charge-discharge operation control technical field.Adopt edge perception layer, regional collaborative controller, cloud collaborative management platform three-level layered architecture;Regional collaborative controller is built-in multi-agent collaborative scheduling module, heterogeneity SOC queue equalization management module and variable step power dynamic compensation module, generate power distribution benchmark value by distributed collaborative computing, combined with SOC priority queue is weighted adjustment, and based on distribution network frequency deviation differential setting band precision compensation coefficient feedforward compensation channel, adaptive adjustment sampling step generates final power output target value and executes.This application gives consideration to global optimization and local real-time response, improves the scheduling efficiency, SOC uniformity and power grid response accuracy under the multi-car V2G scene.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging and discharging operation control technology in power systems, specifically to a V2G bidirectional charging and discharging cooperative control system for electric vehicles. Background Technology

[0002] With the rapid growth of electric vehicle ownership and the large-scale grid connection of renewable energy generation, the power system is facing increasingly severe supply and demand balance pressure. Vehicle-to-Grid (V2G) technology, as a key technology that combines the energy storage characteristics of electric vehicles with the operational needs of the power grid, allows electric vehicles to feed energy back to the grid during peak load periods and absorb energy from the grid to charge during off-peak periods, thus playing a role in peak shaving, frequency regulation, and backup support. However, existing V2G bidirectional charging and discharging systems still have the following shortcomings in implementation:

[0003] First, existing V2G systems mostly adopt a centralized control architecture, and there is a lack of effective coordination and interaction mechanisms between charging piles. When multiple electric vehicles are connected to the grid for charging and discharging at the same time, the charging and discharging behaviors of each vehicle are independent of each other. There is a lack of joint response capability to local capacity constraints of the distribution network, which can easily lead to problems such as power competition between charging piles, line overload or transformer over-limit, affecting the safety and stability of grid operation.

[0004] Secondly, existing technologies lack effective means to balance the state of charge (SOC) of multiple electric vehicles participating in V2G services. Different vehicles have different battery rated capacity, initial SOC, access duration and expected disconnection time. If they are uniformly scheduled without differentiation, some vehicles may be over-discharged, affecting users' travel needs, while other vehicles may not make full use of their energy storage capacity. Overall scheduling efficiency is low and users are not very enthusiastic about participating.

[0005] Furthermore, existing V2G control systems lack the ability to dynamically coordinate and optimize charging and discharging power when responding to grid frequency regulation and peak shaving commands. They mostly adopt fixed charging and discharging power strategies, which cannot adaptively adjust according to the real-time status of the grid, nor can they take into account the differentiated constraints of different vehicles, resulting in low energy transmission efficiency and insufficient power response accuracy. Summary of the Invention

[0006] The purpose of this invention is to provide a V2G bidirectional charging and discharging collaborative control system for electric vehicles. By constructing a three-level architecture consisting of an edge perception layer, a regional collaborative controller, and a cloud-based collaborative management platform, this invention addresses the problems in existing V2G management and control, such as lag in scheduling response, insufficient adaptation to vehicle heterogeneity, lack of SOC balance management, difficulty in balancing power control accuracy and response speed, and weak multi-level collaborative capabilities. This enables large-scale orderly bidirectional charging and discharging of electric vehicles and supports efficient vehicle-network collaborative interaction.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A V2G bidirectional charging and discharging collaborative control system for electric vehicles includes an edge sensing layer, a regional collaborative controller, and a cloud-based collaborative management platform;

[0009] The edge sensing layer includes several bidirectional charging and discharging charging piles, which are used to connect to the power distribution network and connect to electric vehicles to realize the physical execution of charging and discharging and the sensing of vehicle operating status, and receive power control commands to execute corresponding power output.

[0010] The regional collaborative controller is communicatively connected to the edge sensing layer and establishes a data interface with the power distribution network automation dispatching platform; the cloud-based collaborative management platform is communicatively connected to the regional collaborative controller and is used to realize the aggregated and optimized dispatching of cross-regional charging and discharging resources.

[0011] The regional collaborative controller has a built-in multi-agent collaborative scheduling module, a heterogeneous SOC queue balancing management module, and a variable step size power dynamic compensation module.

[0012] The multi-agent collaborative scheduling module is used to model each electric vehicle and its corresponding bidirectional charging and discharging pile as an independent intelligent agent, receive real-time scheduling command signals from the power distribution network, determine the power allocation factor and power allocation benchmark value corresponding to each intelligent agent in the region through distributed collaborative computing, and output them; the heterogeneous SOC queue balancing management module is used to dynamically construct a charging and discharging priority queue according to the SOC of the electric vehicle, and output the power weight signal of the corresponding electric vehicle according to the priority order.

[0013] The variable step size power dynamic compensation module is used to receive the power allocation reference value, the power weight signal, and the real-time dispatch command signal and frequency monitoring signal from the distribution network.

[0014] The variable step size power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of the distribution network dispatch command, obtains the power weight signal based on the sampling step size, and performs weighted adjustment on the power allocation benchmark value. The variable step size power dynamic compensation module is equipped with a feedforward compensation channel, which is used to calculate the feedforward compensation amount based on the differential value of the deviation of the distribution network frequency signal. The variable step size power dynamic compensation module is also used to configure an accuracy compensation coefficient, and use the accuracy compensation coefficient as a weighting factor to superimpose the feedforward compensation amount onto the weighted adjusted power allocation benchmark value to generate the final power output target value, and send it to the corresponding bidirectional charging pile in the edge sensing layer.

[0015] Furthermore, the bidirectional charging and discharging pile integrates a metering unit and a communication unit; the metering unit is used to collect the state of charge (SOC), dispatchable capacity, and off-grid time heterogeneous operating parameters of each electric vehicle in real time; the communication unit is used to report the collected operating parameters to the regional collaborative controller and receive the power output target value issued by the regional collaborative controller.

[0016] Furthermore, the bidirectional charging and discharging charging pile is equipped with a bidirectional AC-DC converter and a bidirectional DC-DC converter, which are connected by a DC bus. The bidirectional DC-DC converter adopts a bidirectional conversion circuit based on a dual active bridge topology and uses an extended phase shift control method. The power magnitude and transmission direction are adjusted by adjusting the phase shift ratio phi between the primary and secondary bridge arms and the internal phase shift ratio between the two primary bridge arms.

[0017] Furthermore, the final power output target value generated by the variable step size power dynamic compensation module is simultaneously fed back as a state update quantity to the multi-agent cooperative scheduling module, serving as the basis for the initial vehicle state in the next round of distributed cooperative scheduling iteration.

[0018] Furthermore, the regional collaborative controller also includes an adjustable capacity aggregation calculation unit, which calculates vehicle capacity using the following formula. Increased adjustment capacity and reduce adjustment capacity :

[0019]

[0020]

[0021] in, Minimum value function For the first Each battery's rated capacity; The current moment; For the first The state of charge of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first Maximum charging power of each battery; Discharge depth protection value Charging saturation protection value The regional collaborative controller will manage all online vehicles. Summing up yields the total upward adjustment capacity for this region. ,Will Summing yields the total downward adjustment capacity. It periodically reports to the cloud-based collaborative management platform and dynamically responds to the real-time adjustable capacity query of the distribution network automation dispatch platform.

[0022] Furthermore, the multi-agent collaborative scheduling module updates the power allocation factor of each agent through distributed consensus iterative updates. The convergence criterion for the iteration is that the change in the power allocation factor between two adjacent iterations is less than a preset threshold. The state information of each agent includes... ;

[0023] For the first The state of charge of each battery; For the first The network access time of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first The maximum charging power of each battery;

[0024] When the regional collaborative controller receives the total power command issued by the distribution network automation dispatching platform A positive value indicates that the discharge power needs to be scheduled from V2G resources, while a negative value indicates that the charging power needs to be absorbed. After that, the multi-agent collaborative scheduling module starts the distributed consistency iteration process.

[0025] Furthermore, the heterogeneous SOC queue balancing management module presets a discharge depth protection value for each vehicle. and charging saturation protection value When any vehicle Below Automatically exit the discharge queue when Higher than It will automatically exit the charging queue.

[0026] Furthermore, the heterogeneous SOC queue balancing management module allocates power weights in the following manner: In discharge mode, electric vehicles are sorted from high to low SOC, with higher SOC electric vehicles having a greater discharge power weight; in charging mode, electric vehicles are sorted from low to high SOC, with lower SOC electric vehicles having a greater charging power weight.

[0027] The sampling step size includes a first control step size and a second control step size, and the configuration accuracy compensation coefficient includes a first accuracy compensation coefficient and a second accuracy compensation coefficient. The variable step size power dynamic compensation module has a first rate of change threshold and a second rate of change threshold, and the first rate of change threshold is greater than the second rate of change threshold. When the rate of change of the scheduling command is greater than or equal to the first rate of change threshold, it operates with the first control step size and the first accuracy compensation coefficient. When the rate of change of the scheduling command is less than or equal to the second rate of change threshold, it operates with the second control step size and the second accuracy compensation coefficient. When the rate of change of the scheduling command is between the second rate of change threshold and the first rate of change threshold, it operates with an intermediate control step size between the first and second control steps and an intermediate accuracy compensation coefficient between the first and second accuracy compensation coefficients. The first control step size is smaller than the second control step size, the first accuracy compensation coefficient is greater than the second accuracy compensation coefficient, and the rate of change of the scheduling command is the rate of change of the total power command of the distribution network over time, which is obtained by the variable step size power dynamic compensation module through differential calculation of continuously received scheduling commands.

[0028] Furthermore, the output end of the variable step size power dynamic compensation module is provided with an output shaping unit that is linked to the accuracy compensation coefficient. The output shaping unit is embedded with limiting logic based on the SOC change rate. When the variable step size power dynamic compensation module is running with the first accuracy compensation coefficient, if the SOC change rate of any vehicle exceeds a preset threshold, the limiting logic automatically reduces the power allocation weight of the vehicle or performs derating on the power command.

[0029] Furthermore, the cloud-based collaborative management platform incorporates a dual-layer optimization scheduling engine. The upper-layer optimization engine uses regional collaborative controllers as basic scheduling units and formulates scheduling power plans for each region based on power market signals. The lower-layer optimization engine runs the multi-agent collaborative scheduling module within each regional collaborative controller. The communication cycle of the upper-layer optimization engine is longer than the iteration update cycle of the lower-layer optimization engine, forming a hierarchical optimization architecture with slow and fast dual time scales.

[0030] Furthermore, the system also has an emergency response mode, which is automatically triggered by the regional collaborative controller or the cloud-based collaborative management platform based on the degree of deviation of the power grid frequency. In the emergency response mode, the heterogeneous SOC queue balancing management module suspends power weight allocation and instead puts the electric vehicles into full-power charging and discharging in sequence according to the SOC of the electric vehicles. The variable step power dynamic compensation module fixes the power adjustment cycle to a preset minimum value.

[0031] The scheduling command signal of this invention is the total power command issued by the scheduling platform.

[0032] The proposed electric vehicle V2G bidirectional charging and discharging collaborative control system addresses the problems in existing V2G scheduling systems, such as high centralized computing power pressure, insufficient vehicle heterogeneity adaptation, difficulty in balancing power regulation response speed and control accuracy, and limited grid frequency support. Through a three-level architecture design—edge sensing layer, regional collaborative controller, and cloud collaborative management platform—it integrates three core mechanisms: multi-agent collaborative scheduling, heterogeneous SOC queue balancing management, and variable step-size power dynamic compensation. This achieves efficient collaborative regulation of V2G charging and discharging resources, with specific beneficial technical effects as follows:

[0033] 1. Construct a three-tiered hierarchical control architecture that balances local real-time response with wide-area global optimization capabilities. The edge sensing layer uses bidirectional charging piles to locally perceive vehicle operating status and execute charging / discharging operations, effectively reducing end-to-end communication latency and improving power command response speed. The regional collaborative controller connects to the distribution network automation scheduling platform, undertaking local charging / discharging collaborative calculations and command issuance. This eliminates the need for complete reliance on cloud computing power, reducing the cloud computing load and preventing single-point failures from causing global scheduling failures, significantly improving system reliability and robustness. The cloud-based collaborative management platform enables aggregated and optimized scheduling of cross-regional charging / discharging resources, adapting to the hierarchical scheduling management system of the distribution network and supporting the coordinated control of large-scale V2G resources. This architecture ensures real-time local control while possessing wide-area resource coordination capabilities, adapting to the full-stage application needs of V2G services from small-scale pilots to large-scale deployments.

[0034] 2. Employing multi-agent distributed collaborative scheduling enhances the system's dynamic adaptability and operational stability. The multi-agent collaborative scheduling module models each electric vehicle and its corresponding bidirectional charging pile as an independent agent. Distributed collaborative computation determines the power allocation factor and baseline value for each agent within the region. Compared to traditional centralized scheduling, the distributed collaborative mechanism eliminates the need for a single central node to complete all calculations, significantly reducing the computational burden on the regional collaborative controller. Furthermore, the dynamic access, exit, or partial failure of a single agent does not interfere with the overall scheduling operation, perfectly adapting to real-world application scenarios where electric vehicles can be connected and disconnected as needed, and the number of participants changes dynamically in real time. This significantly improves the system's scheduling flexibility and operational stability. The distributed solution mechanism also accelerates the convergence speed of power allocation schemes, meeting the timeliness requirements of real-time power distribution network scheduling.

[0035] 3. Achieve balanced management of heterogeneous SOC queues, balancing grid dispatching needs with user vehicle usage rights. The heterogeneous SOC queue balancing management module dynamically constructs charging and discharging priority queues based on the State of Charge (SOC) of electric vehicles (EVs), and outputs power weight signals for the corresponding EVs according to their priorities, fully considering the differences in SOC and the heterogeneity of charging and discharging capabilities among different EVs. This mechanism avoids the problems of some vehicles over-discharging and resulting in insufficient power for travel, and some vehicles overcharging and aggravating battery wear, which are common in traditional average power allocation modes. Under the premise of strictly responding to the overall power demand of the distribution network, it achieves balanced management of EV SOC within the region, ensuring users' travel power needs, extending the lifespan of power batteries, maintaining the continuity of adjustable charging and discharging capacity in the region, and improving user acceptance and long-term operational feasibility of V2G interaction.

[0036] 4. Adaptive variable step size weighted adjustment, balancing power response speed and control accuracy. The variable-step power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of distribution network dispatch commands. It acquires power weight signals based on the sampling step size and adjusts the power allocation benchmark value accordingly. When distribution network dispatch commands fluctuate significantly or operating conditions change rapidly, the sampling step size is automatically increased to improve the tracking response speed of power regulation and quickly match the grid dispatch requirements. When the system is in steady-state operation and dispatch commands change gradually, the sampling step size is automatically decreased to refine the granularity of power regulation and improve the control accuracy of power allocation. This mechanism effectively overcomes the inherent contradiction in traditional fixed-step adjustment—that "fast response speed leads to poor steady-state accuracy, and high accuracy leads to lag"—and can adaptively match various operating conditions such as normal distribution network operation, load fluctuations, and disturbances, achieving optimal regulation results across all scenarios.

[0037] 5. A feedforward correction mechanism with precision compensation coefficient enhances the grid frequency support performance. The variable step-size power dynamic compensation module sets up a feedforward compensation channel, calculates the feedforward compensation amount based on the differential value of the distribution network frequency deviation, and configures a precision compensation coefficient. Using the precision compensation coefficient as a weighting factor, the feedforward compensation amount is superimposed onto the weighted adjusted power allocation benchmark value to generate the final power output target value for execution. Compared to the traditional pure feedback regulation mode, feedforward correction based on the frequency deviation differential can capture frequency change trends, achieve proactive regulation, significantly improve the response speed of the V2G system in participating in grid frequency support, and accelerate the frequency fluctuation smoothing rate. The precision compensation coefficient can flexibly adjust the superposition ratio of the feedforward compensation amount according to the grid operating conditions. When the grid frequency fluctuates significantly, the weight is increased to enhance the response capability; when the grid is in steady-state operation, the weight is reduced to avoid regulation overshoot and power oscillation, achieving a balance between frequency regulation response speed and control stability, effectively enhancing the practical value of V2G in participating in grid ancillary services. Attached Figure Description

[0038] Figure 1This is a diagram showing the overall architecture of the electric vehicle V2G bidirectional charging and discharging coordinated control system of the present invention.

[0039] Figure 2 This is a structural diagram of the internal functional modules of the regional collaborative controller of the present invention;

[0040] Figure 3 This is a flowchart illustrating the workflow of the multi-agent collaborative scheduling module of the present invention.

[0041] Figure 4 This is a schematic diagram of the scheduling queue arrangement of the heterogeneous SOC queue balancing management module of the present invention;

[0042] Figure 5 This is a control logic block diagram of the variable step size power dynamic compensation module of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] This invention provides a V2G bidirectional charging and discharging collaborative control system for electric vehicles, including an edge sensing layer, a regional collaborative controller, and a cloud-based collaborative management platform;

[0045] The edge sensing layer includes several bidirectional charging and discharging charging piles, which are used to connect to the power distribution network and connect to electric vehicles to realize the physical execution of charging and discharging and the sensing of vehicle operating status, and receive power control commands to execute corresponding power output.

[0046] The regional collaborative controller is communicatively connected to the edge sensing layer and establishes a data interface with the power distribution network automation dispatching platform; the cloud-based collaborative management platform is communicatively connected to the regional collaborative controller and is used to realize the aggregated and optimized dispatching of cross-regional charging and discharging resources.

[0047] The regional collaborative controller has a built-in multi-agent collaborative scheduling module, a heterogeneous SOC queue balancing management module, and a variable step size power dynamic compensation module.

[0048] The multi-agent collaborative scheduling module is used to model each electric vehicle and its corresponding bidirectional charging and discharging pile as an independent agent, and to determine and output the power allocation factor and power allocation benchmark value of each agent in the region through distributed collaborative computing; the heterogeneous SOC queue balancing management module is used to dynamically construct a charging and discharging priority queue according to the SOC of the electric vehicle, and output the power weight signal of the corresponding electric vehicle according to the priority order.

[0049] The variable step size power dynamic compensation module is used to receive the power allocation reference value, the power weight signal, and the real-time dispatch command signal and frequency monitoring signal from the distribution network.

[0050] The variable step size power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of the distribution network dispatch command, obtains the power weight signal based on the sampling step size, and performs weighted adjustment on the power allocation benchmark value. The variable step size power dynamic compensation module is equipped with a feedforward compensation channel, which is used to calculate the feedforward compensation amount based on the differential value of the deviation of the distribution network frequency signal. The variable step size power dynamic compensation module is also used to configure an accuracy compensation coefficient, and use the accuracy compensation coefficient as a weighting factor to superimpose the feedforward compensation amount onto the weighted adjusted power allocation benchmark value to generate the final power output target value, and send it to the corresponding bidirectional charging pile in the edge sensing layer.

[0051] This solution enables integrated collaborative control of vehicles, charging piles, networks, and the cloud, improving scheduling efficiency, SOC balance, and grid response accuracy in multi-vehicle V2G scenarios.

[0052] Furthermore, in one embodiment of the present invention, in order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific embodiments. Figure 1 As shown in the figure, this is the overall architecture diagram of the electric vehicle V2G bidirectional charging and discharging cooperative control system. The main body of the electric vehicle V2G bidirectional charging and discharging cooperative control system includes a regional cooperative controller, which is communicatively connected to the edge sensing layer and establishes a data interface with the power distribution network automation dispatching platform.

[0053] The edge sensing layer, serving as the physical execution and data acquisition front-end of the system of this invention, consists of several bidirectional charging and discharging charging piles.

[0054] The bidirectional charging and discharging charging pile is equipped with a bidirectional AC-DC converter and a bidirectional DC-DC converter, which are connected by a DC bus. The bidirectional DC-DC converter adopts a bidirectional conversion circuit based on a dual active bridge topology. It uses a full-bridge circuit on the primary and secondary sides in conjunction with a high-frequency transformer to achieve electrical isolation and bidirectional power flow. In charging mode, it operates in rectification mode, and in V2G discharging mode, it operates in inverter mode.

[0055] The bidirectional DC-DC converter adopts an extended phase-shift control method. By independently adjusting the phase shift ratio phi between the primary and secondary bridge arms (ranging from -1 to 1) and the inward phase shift ratio between the two primary bridge arms, it achieves precise control over the direction and magnitude of power transmission. When phi > 0, power flows from the vehicle battery to the DC bus for discharging; when phi < 0, power flows from the DC bus to the battery for charging. The high-precision metering unit built into the edge sensing layer collects the voltage, current, power direction, and power data of each charging pile in real time. It also interacts with the vehicle BMS through a communication unit conforming to the ISO15118 protocol to exchange battery status information, including SOC, allowable charge and discharge power, and temperature. At the same time, it reports the above sensing data to the regional collaborative controller through the Modbus TCP protocol and receives the power output target value issued by the regional collaborative controller for closed-loop execution.

[0056] Furthermore, the extended phase-shift EPS control capability of the bidirectional converter enables the charging pile to continuously adjust the power and direction with millisecond-level precision, providing hardware execution guarantee for the upper-level multi-agent collaborative scheduling module and variable step size compensation module, overcoming the shortcomings of traditional charging piles that can only switch power in steps or have large switching delays.

[0057] Specifically, the bidirectional charging and discharging charging pile integrates a high-precision energy metering unit and a communication unit. The metering unit is used to collect the state of charge (SOC), dispatchable capacity, and off-grid time heterogeneous operating parameters of each electric vehicle in real time. The communication unit communicates with the battery management system (BMS) to report the collected operating parameters to the regional collaborative controller and receive the power output target value issued by the regional collaborative controller.

[0058] These heterogeneous operating parameter data are the prerequisite for the heterogeneous SOC queue balancing management module to dynamically construct charging and discharging priority queues. Without the high-frequency, high-precision status feedback provided by the edge perception layer, the scheduling decisions of the upper-layer modules would be unsustainable. The bidirectional closed loop between the edge perception layer and the regional collaborative controller makes real-time feedback control of perception-decision-execution-re-perception possible. The convergence of multi-agent distributed consensus iteration and the effect of variable step-size feedforward compensation in this invention both depend on the edge perception layer quickly and accurately executing the power control commands of the regional collaborative controller and returning the true power value. Thus, the edge perception layer is the key support that distinguishes this invention from the existing centralized command issuance-open-loop execution scheme. It forms a tight closed loop of perception-coordination-execution-compensation with the three modules in the regional collaborative controller, together constituting a complete technical solution.

[0059] The regional collaborative controller, serving as the edge computing core of the system of this invention, communicates with the edge sensing layer and interfaces with the power distribution network automation dispatching platform. Specifically, the regional collaborative controller also includes an adjustable capacity aggregation computing unit, which calculates the capacity for each currently connected electric vehicle. Calculate the vehicle using the following formula Increased adjustment capacity and reduce adjustment capacity :

[0060]

[0061]

[0062] in, Minimum value function For the first Each battery's rated capacity; The current moment; For the first The state of charge of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first Maximum charging power of each battery; Discharge depth protection value Charging saturation protection value ; , Unit: kW; , Unit: kW; , , Units range from 0% to 100%; Unit: kWh; The unit is h (hour). The regional collaborative controller will manage all online vehicles. Summing up yields the total upward adjustment capacity for this region. ,Will Summing yields the total downward adjustment capacity. It periodically reports to the cloud-based collaborative management platform and dynamically responds to the real-time adjustable capacity query of the distribution network automation dispatch platform.

[0063] The core significance of this formula lies in taking the minimum of the power ceiling and the energy / power conversion value for the remaining time. This ensures that the calculated schedulable capacity neither exceeds the instantaneous power capability of the converter and battery, nor exceeds the physical feasible boundary of the vehicle based on remaining time and energy margin before it goes offline. The regional cooperative controller will manage all online vehicles... Summing up yields the total upward adjustment capacity for this region. ,Will Summing yields the total downward adjustment capacity. It periodically reports to the cloud-based collaborative management platform and dynamically responds to the real-time adjustable capacity query of the distribution network automation dispatch platform.

[0064] Furthermore, the adjustable capacity aggregation calculation unit serves as a mathematical bridge connecting vehicle heterogeneity constraints with grid dispatch requirements. Existing V2G systems, when reporting dispatchable capacity, either simply sum the rated charging and discharging power of each vehicle, ignoring remaining time and electric vehicle SOC constraints, leading to unexecutable dispatch plans; or they completely disregard capacity aggregation, forcing the dispatch center to issue commands blindly. This invention addresses this by introducing an adjustable capacity aggregation calculation unit... , , , , The aggregation formula for multiple parameters simultaneously incorporates the time constraint of when each electric vehicle will leave the grid and the energy constraint of the available power range into the calculation, ensuring that the reported adjustable capacity is strictly feasible and significantly improving the reliability and execution success rate of the scheduling plan. Secondly, this formula provides a preliminary decision-making basis for the heterogeneous SOC queue balancing management module. When the regional coordinated controller receives the distribution network peak-shaving scheduling instruction, it can directly use the aggregated parameters... or It determines whether local V2G resources are sufficient to respond to commands. If not, it rejects the command or negotiates with the cloud to adjust the planned value, thus avoiding ineffective iterations in the power allocation process of the multi-agent collaborative scheduling module.

[0065] The system of the present invention also has an emergency response mode, which is automatically triggered by the regional collaborative controller or the cloud collaborative management platform according to the degree of deviation of the power grid frequency. In the emergency response mode, the heterogeneous SOC queue balancing management module suspends the power weight allocation and instead puts the electric vehicles into full power charging and discharging in sequence according to the SOC of the electric vehicles. The variable step size power dynamic compensation module fixes the power adjustment cycle to a preset minimum value.

[0066] The adjustable capacity aggregation calculation unit and the emergency response mode are logically linked: Under normal operation, the regional collaborative controller uses the above formula to calculate vehicle... Increased adjustment capacity and reduce adjustment capacity Make a conservative capacity estimate; in emergency mode, The constraint is temporarily suspended, assuming the vehicle does not actively leave the network during the emergency period; the formula degenerates into: Except for vehicles protected by the lower limit of SOC, this design releases greater instantaneous adjustment capabilities. This allows the same regional cooperative controller to adaptively switch capacity assessment strategies between prioritizing economy and prioritizing safety, a capability not found in existing centralized V2G control systems. In summary, the adjustable capacity aggregation calculation unit is a key technical feature that enables the regional cooperative controller to play a crucial role in bridging the gap between upper and lower levels. Together with the multi-agent cooperative scheduling module and the heterogeneous SOC queue balancing management module, it forms a complete control chain from capacity assessment to power allocation and dynamic compensation.

[0067] The regional collaborative controller incorporates a multi-agent collaborative scheduling module. This module iteratively updates the power allocation factors of each agent through distributed consensus. The convergence criterion for this iteration is that the change in the power allocation factor between two consecutive iterations is less than a preset threshold. The state information of each agent includes... ;

[0068] For the first The state of charge of each battery; For the first The network access time of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first The maximum charging power of each battery;

[0069] When the regional collaborative controller receives the total power command issued by the distribution network automation dispatching platform A positive value indicates that the discharge power needs to be scheduled from V2G resources, while a negative value indicates that the charging power needs to be absorbed. After that, the multi-agent collaborative scheduling module starts the distributed consistency iteration process.

[0070] When the total power command is issued by the distribution network automation dispatching platform When the value is 0, it indicates that there is currently no need for charging and discharging regulation in this area. At this time, the multi-agent collaborative scheduling module can suspend the consistency iteration process and directly instruct each charging pile to perform zero power output; or, while maintaining the current queue state, it can only perform SOC monitoring and respond quickly upon receiving a non-zero instruction. This zero instruction processing logic enables the system to maintain low-power standby in the idle state, avoiding unnecessary communication and computational overhead.

[0071] Distributed consensus iterative update of power allocation factors for each agent refers to: each agent maintaining its own power allocation factor, repeatedly exchanging information with other agents and updating this factor according to rules, until all agents' factors converge to a consensus, thereby calculating how much power each device should generate / use. Specifically, this includes:

[0072] First, each intelligent agent Initialize power allocation factor =1 / N, where N is the total number of currently online agents. Subsequently, each agent exchanges its current power allocation factor value with neighboring agents through the regional cooperative controller and updates it according to the following consensus iterative formula;

[0073] The consistency iteration formula is:

[0074] in, Representation and intelligent agent A set of intelligent agents that are in close communication proximity is usually defined by physical distance or communication topology; For the (k+1)th power allocation factor, This is the convergence coefficient, typically taken as 0.5-1.0;

[0075] This represents the number of iteration steps.

[0076] The state information of each agent includes ;

[0077] For the first The state of charge of each battery; For the first The network access time of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first The maximum charging power of each battery.

[0078] After iterative convergence, the total power command issued by the distribution network is combined with the total power command. (Positive values ​​represent total discharge demand, negative values ​​represent total charging demand), calculate the first... Power allocation benchmark value for each agent :

[0079]

[0080] At this point, the sum of the baseline values ​​of all vehicles equals the total commanded power, satisfying the global power constraint.

[0081] Furthermore, the multi-agent collaborative scheduling module in this invention overcomes the limitations of the centralized scheduling architecture of traditional V2G systems. In the prior art, the allocation of charging and discharging power among multiple vehicles is usually calculated uniformly by a central scheduler based on a global optimization model. On the one hand, the computational complexity increases exponentially with the number of vehicles; on the other hand, once the central node fails, the entire system is paralyzed. This invention employs a distributed multi-agent consensus algorithm, distributing power allocation decision-making power among various agents. Only a small amount of information exchange between adjacent agents is required for the entire system to reach consensus, offering the following unique advantages: First, the algorithm complexity is linearly related to the vehicle scale; as the number of connected vehicles expands within a region, the number of iteration steps only increases logarithmically, solving the scalability problem of large-scale V2G scheduling. Second, it naturally adapts to the weakly centralized structure of the power distribution network; each charging pile can autonomously negotiate an allocation scheme that satisfies the total power command without relying on a central scheduler. A single point of failure does not affect the continued operation of other agents, significantly improving system robustness. Third, the multi-agent collaborative scheduling module and the heterogeneous SOC queue balancing management module complement each other rather than simply superimpose: the heterogeneous SOC queue balancing management module provides priority guidance on who should receive more power, while the multi-agent collaborative scheduling module provides a specific mechanism for achieving power allocation through distributed negotiation. The former is a qualitative or semi-quantitative weight decision, while the latter is a convergent and quantifiable numerical calculation process. The combination of the two gives the system both physical fairness and mathematical convergence and real-time performance. Furthermore, the correction-re-iteration mechanism introduced in the multi-agent cooperative scheduling module ensures the hard satisfaction of safety constraints, avoiding the shortcomings of traditional consensus algorithms in handling individual upper / lower bound limitations. These technical effects are not simply a matter of applying multi-agent theory, but rather an engineering improvement designed to address the specific needs of V2G scenarios such as vehicle heterogeneity, dynamic access / exit, and safety boundaries. This constitutes a key innovation of this invention that distinguishes it from existing V2G control schemes.

[0082] like Figure 2 and Figure 4 As shown, the heterogeneous SOC queue balancing management module runs in the regional collaborative controller and shares vehicle status data with the multi-agent collaborative scheduling module. It is used to dynamically construct charging and discharging priority queues based on the electric vehicle's state of charge (SOC) and output the corresponding electric vehicle's power weight signal according to the priority order.

[0083] Specifically, the heterogeneous SOC queue balancing management module presets the discharge depth protection value for each vehicle. and charging saturation protection value When any vehicle Below Automatically exit the discharge queue when Higher than Automatically exit the charging queue; The heterogeneous SOC queue balancing management module constructs a charging and discharging priority queue and allocates power weights in the following way: In discharge mode, it is sorted by SOC from high to low, and the higher the SOC, the greater the discharge power weight; In charging mode, it is sorted by SOC from low to high, and the lower the SOC, the greater the charging power weight.

[0084] Furthermore, this invention transforms the multi-vehicle SOC balancing problem into a dynamic priority queue scheduling problem. Its technical contributions are reflected in the following three aspects: First, it achieves a fair reverse allocation mechanism where vehicles with high SOC discharge more and vehicles with low SOC charge more. This mechanism naturally aligns with the control objective of maintaining a consistent SOC across all vehicles, achieving an approximate Nash equilibrium effect without complex optimization, resulting in extremely low computational overhead and suitability for large-scale real-time scheduling. Second, the heterogeneous SOC queue balancing management module and the multi-agent collaborative scheduling module form a weight-oriented + distributed negotiation hierarchical decision-making architecture: the heterogeneous SOC queue balancing management module outputs priority-based power weight suggestions; qualitatively, the multi-agent collaborative scheduling module then transforms these suggestions into an allocation scheme that is precisely executable and satisfies the total power constraint of the entire network through consensus iteration; quantitatively, the collaboration between the two avoids the limitations of single-module decision-making. If only the multi-agent collaborative scheduling module is used, the allocation result may ignore SOC balance; if only the heterogeneous SOC queue balancing management module is used, the precise following of the total power command cannot be guaranteed. Third, the heterogeneous SOC queue balancing management module has built-in... and The protection mechanism and dynamic queue exit / reentry logic directly encode users' travel security needs into hard constraints for system operation, rather than relying on soft constraints from upper-level scheduling, greatly improving the system's acceptability to users. Experimental data shows that after adopting the heterogeneous SOC queue balancing management module, during a continuous 12-hour V2G scheduling process, the standard deviation of each vehicle's SOC decreased significantly, and no vehicle's SOC fell below the protection value, greatly alleviating users' travel anxiety.

[0085] like Figure 3 and Figure 5 As shown, the variable step size power dynamic compensation module receives the power allocation benchmark value output by the multi-agent collaborative scheduling module, as well as the real-time scheduling command signal and frequency monitoring signal from the distribution network. The variable step size power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of the distribution network scheduling command. The variable step size power dynamic compensation module is equipped with a feedforward compensation channel and configured with an accuracy compensation coefficient. After generating the final power output target value, the variable step size power dynamic compensation module sends it to the corresponding bidirectional charging and discharging charging pile in the edge perception layer.

[0086] Specifically, the sampling step size includes a first control step size and a second control step size, and the configuration accuracy compensation coefficient includes a first accuracy compensation coefficient and a second accuracy compensation coefficient. The variable step size power dynamic compensation module has a first rate of change threshold and a second rate of change threshold, and the first rate of change threshold is greater than the second rate of change threshold. When the rate of change of the scheduling command is greater than or equal to the first rate of change threshold, it operates with the first control step size and the first accuracy compensation coefficient. When the rate of change of the scheduling command is less than or equal to the second rate of change threshold, it operates with the second control step size and the second accuracy compensation coefficient. When the rate of change of the scheduling command is between the second rate of change threshold and the first rate of change threshold, it operates with an intermediate control step size between the first and second control steps and an intermediate accuracy compensation coefficient between the first and second accuracy compensation coefficients. The first control step size is less than the second control step size, and the first accuracy compensation coefficient is greater than the second accuracy compensation coefficient. The rate of change of the scheduling command is the rate of change of the total power command of the distribution network over time, which is obtained by the variable step size power dynamic compensation module through differential calculation of continuously received scheduling commands.

[0087] Furthermore, the variable step-size power dynamic compensation module resolves the contradiction between response speed and control accuracy in existing V2G systems. First, current V2G power control technologies often employ PID regulation with a fixed sampling step size. If the step size is too small, the communication and computational burden is heavy; if the step size is too large, it cannot respond to rapid grid events. This invention uses the rate of change of scheduling commands as the adaptive switching basis for the sampling step size and accuracy compensation coefficient, enabling the system to maintain low overhead in steady state and automatically enter a high-precision, high-response mode in dynamic states, achieving on-demand adjustment. This adaptive mechanism complements the multi-agent collaborative scheduling module: the multi-agent collaborative scheduling module is responsible for the macro-level decision of how much to allocate, while the variable step-size power dynamic compensation module is responsible for the micro-level execution of how to accurately follow the trend; both are indispensable. Second, a feedforward compensation channel based on the differential of the grid frequency signal deviation is introduced. Compared to traditional pure feedback regulation, the feedforward can output compensation power as soon as the frequency deviation trend appears, significantly shortening the response delay. Third, the variable step size power dynamic compensation module and the heterogeneous SOC queue balancing module form an indirect synergy: In rapid adjustment scenarios, using a smaller step size and a larger compensation coefficient may increase the instantaneous power fluctuation of some vehicles whose SOC is close to the boundary; to this end, the output end of the variable step size power dynamic compensation module is equipped with an output shaping unit linked to the precision compensation coefficient. The output shaping unit is embedded with limiting logic based on the SOC change rate. When the variable step size power dynamic compensation module is running with the first precision compensation coefficient, if the SOC change rate of any vehicle exceeds a preset threshold, the limiting logic automatically reduces the power allocation weight of that vehicle or performs derating processing on the power command.

[0088] In summary, the variable step size power dynamic compensation module is not a simple parameter adjustment of conventional PID, but an innovative control architecture proposed to meet the dual requirements of diverse grid commands and battery safety constraints in V2G scenarios, which significantly improves the dynamic performance and adaptability of the system.

[0089] The cloud-based collaborative management platform is deployed in the data centers of power companies or aggregators. It connects to multiple regional collaborative controllers via wide-area communication networks, such as dedicated power networks, 5G, or MQTT protocols, for cross-regional aggregated and optimized scheduling. Specifically, the cloud-based collaborative management platform incorporates a two-layer optimization scheduling engine. The upper-layer optimization engine uses each regional collaborative controller as the basic scheduling unit, formulating scheduling power plans for each region based on power market signals. The lower-layer optimization engine runs the multi-agent collaborative scheduling module within each regional collaborative controller. The communication cycle of the upper-layer optimization engine is longer than the iteration update cycle of the lower-layer optimization engine, forming a slow-fast dual-timescale layered optimization architecture.

[0090] Furthermore, the design of the cloud-based collaborative management platform addresses two core contradictions encountered when large-scale V2G resources participate in grid interaction: first, the mismatch between the timescale of electricity market clearing (minutes to hours) and the grid's dynamic response requirements (seconds to milliseconds); and second, the difficulty in coordinating global economic optimization with local security constraints. This invention employs a layered architecture with both slow and fast timescales, clearly separating long-term economic scheduling (upper-layer optimization engine) from short-term distributed collaborative control (lower-layer optimization engine). The upper-layer optimization engine does not need to perceive the fine-grained status of each vehicle, only needing to know the aggregated capacity envelope of each region, significantly reducing the communication bandwidth requirements and optimization computational complexity between the cloud platform and the regional controller. The lower-layer optimization engine operates in a local closed loop, capable of independently responding to real-time fluctuations in the distribution network. Even when communication with the cloud platform is interrupted, it can still maintain local V2G scheduling functionality relying on the regional collaborative controller, greatly improving the system's robustness. Furthermore, this hierarchical optimization architecture with slow and fast dual time scales forms a complete collaborative chain with the aforementioned modules: the upper-layer optimization engine provides the economically optimal scheduling target for the regional layer, the lower-layer optimization engine utilizes the multi-agent collaborative scheduling module and the SOC queue balancing module to achieve fine-grained power allocation within the region, and the variable step-size compensation module is responsible for error-free tracking of power commands. This seamless decomposition and collaborative control capability from market signals to charging pile power is something that existing centralized V2G scheduling systems do not possess.

[0091] The emergency response mode of this invention enables the system to not only be applicable to economic dispatch and routine peak shaving in the power market, but also to provide rapid support for sudden frequency events in the power grid. This fills the gap in existing V2G control systems, which lack a unified architecture between normal economic dispatch and emergency frequency support. It achieves adaptive switching of control objectives, transforming from SOC balancing and economic power allocation under normal conditions to rapid maximum power switching under emergency conditions. This meets the differentiated needs of the power system for resources affected by disturbances at different time scales. Most existing V2G systems are designed only for peak shaving and lack emergency frequency response capabilities or require additional upper-level computer intervention. In emergency mode, only the weight allocation of the heterogeneous SOC queue balancing module is suspended, but its core sorting logic based on SOC is retained, ensuring priority discharge for high-SOC vehicles and priority charging for low-SOC vehicles, still implicitly implying SOC balancing. The variable step size module has a fixed minimum step size but retains feedforward compensation, ensuring a balance between response speed and stability. This inherited emergency mode design allows the system to quickly and smoothly return to normal control after an emergency event, avoiding power jumps or oscillations caused by control mode switching. Emergency mode is triggered locally by the regional collaborative controller, without relying on cloud decision-making, and the latency can be controlled within 100 milliseconds, which meets the strict requirements of the power grid's primary frequency regulation for response time.

[0092] Application example:

[0093] The electric vehicle V2G bidirectional charging and discharging coordinated control system of the present invention operates according to the following timing sequence within a complete control cycle:

[0094] Step 1: State Awareness and Capacity Calculation

[0095] Each bidirectional charging and discharging pile in the edge perception layer collects real-time data on voltage, current, power direction, and battery level of each connected electric vehicle through its high-precision metering unit. It also interacts with the vehicle's BMS (Battery Management System) via a communication unit conforming to the ISO15118 protocol to obtain battery status information, including… The permissible charging and discharging power, temperature, and other sensing data are reported by each charging pile to the regional collaborative controller via the Modbus TCP protocol. After receiving the data, the regional collaborative controller uses the adjustable capacity aggregation calculation unit to calculate the upward adjustment capacity for each online vehicle according to the following formula. and reduce adjustment capacity :

[0096]

[0097]

[0098] The calculation results define the upper limit of power adjustment for each vehicle, serving as a hard constraint for subsequent multi-agent cooperative scheduling.

[0099] Step 2: Multi-agent cooperative scheduling and queue balancing

[0100] When the regional collaborative controller receives the total power command issued by the distribution network automation dispatching platform Positive values ​​indicate that discharge power needs to be scheduled, while negative values ​​indicate that charging power needs to be absorbed. Then, the multi-agent collaborative scheduling module is activated. This module models each electric vehicle and its corresponding charging station as an independent agent, and the state information of each agent includes... Each agent updates the power allocation factor through consistent iteration. After the iteration converges, the results are combined... The power allocation baseline value for each vehicle corresponding to each agent is calculated; in parallel with the above iterative process, the heterogeneous SOC queue balancing management module calculates the power allocation baseline value for each vehicle based on the power allocation baseline value for each agent. A dynamic charging / discharging priority queue is constructed, and the power weight signal corresponding to each electric vehicle is output according to the priority order: in discharge mode, the vehicles are sorted from high to low State of Charge (SOC), with higher SOC vehicles having greater discharge power weight; in charging mode, the vehicles are sorted from low to high SOC, with lower SOC vehicles having greater charging power weight. This power weight signal is used to adjust the power allocation benchmark value output by the multi-agent collaborative scheduling module, so that the allocation result takes into account both grid command tracking and SOC balance.

[0101] Step 3: Dynamic Compensation and Command Output

[0102] The variable step size power dynamic compensation module is used to receive the power allocation reference value, the power weight signal, and the real-time dispatch command signal and frequency monitoring signal from the distribution network;

[0103] The variable step size power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of the distribution network dispatching command and configures the accuracy compensation coefficient; the variable step size power dynamic compensation module is equipped with a feedforward compensation channel, which is used to calculate the feedforward compensation amount based on the differential value of the deviation of the distribution network frequency signal.

[0104] The sampling step size includes a first control step size and a second control step size, and the configured accuracy compensation coefficient includes a first accuracy compensation coefficient and a second accuracy compensation coefficient. The variable step size power dynamic compensation module has a first rate of change threshold and a second rate of change threshold, and the first rate of change threshold is greater than the second rate of change threshold. When the rate of change of the scheduling command is greater than or equal to the first rate of change threshold, it operates with the first control step size and the first accuracy compensation coefficient. When the rate of change of the scheduling command is less than or equal to the second rate of change threshold, it operates with the second control step size and the second accuracy compensation coefficient. When the rate of change of the scheduling command is between the second rate of change threshold and the first rate of change threshold, it operates with an intermediate control step size between the first and second control steps and an intermediate accuracy compensation coefficient between the first and second accuracy compensation coefficients. The first control step size is smaller than the second control step size, and the first accuracy compensation coefficient is greater than the second accuracy compensation coefficient.

[0105] The variable step size power dynamic compensation module acquires the power weight signal based on the sampling step size and adjusts the power allocation benchmark value by weighting; then, using the configured accuracy compensation coefficient as the weighting factor, the feedforward compensation amount is superimposed on the weighted power allocation benchmark value to generate the final power output target value, and sent to the corresponding bidirectional charging and discharging charging pile in the edge perception layer.

[0106] After the target power output value is checked by the limiting logic based on the SOC change rate in the output shaping unit, if the SOC decrease rate of a vehicle exceeds 2% / minute, the derated value will be automatically reduced. The value will be sent to the corresponding bidirectional charging pile in the edge perception layer through the communication interface of the regional collaborative controller. After receiving the target power output value, the charging pile will use the extended phase shift control method of the bidirectional DC-DC converter to adjust the phase shift ratio to phi. When phi>0, the charging will discharge, and when phi<0, the charging will charge, thus accurately executing the power output and realizing closed-loop control.

[0107] Step 4: Global Coordination and Emergency Response

[0108] The cloud-based collaborative management platform has a built-in dual-layer optimization scheduling engine. The upper-layer optimization engine uses regional collaborative controllers as basic scheduling units and formulates scheduling power plans for each region based on power market signals. The lower-layer optimization engine runs the multi-agent collaborative scheduling module within each regional collaborative controller. The communication cycle of the upper-layer optimization engine is longer than the iteration update cycle of the lower-layer optimization engine, forming a slow-fast dual-timescale hierarchical optimization architecture.

[0109] The cloud-based collaborative management platform executes the upper-level optimization engine on a slow time scale, formulating dispatch power plans for each region based on electricity market signals. The regional collaborative controller executes the lower-level optimization engine on a fast time scale, running the multi-agent collaborative scheduling module within each regional collaborative controller. This constitutes a hierarchical optimization architecture with both slow and fast time scales. When the grid frequency deviation exceeds a preset threshold, the system automatically triggers an emergency response mode: the heterogeneous SOC queue balancing management module suspends power weight allocation and instead sequentially engages full-power charging and discharging based on the electric vehicle SOC; the variable-step power dynamic compensation module fixes the power adjustment cycle to a preset minimum value, prioritizing grid safety.

[0110] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0111] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An electric vehicle V2G bidirectional charge and discharge collaborative control system, characterized in that: This includes an edge awareness layer, a regional collaborative controller, and a cloud-based collaborative management platform; The edge sensing layer includes several bidirectional charging and discharging charging piles, which are used to connect to the power distribution network and connect to electric vehicles to realize the physical execution of charging and discharging and the sensing of vehicle operating status, and receive power control commands to execute corresponding power output. The regional collaborative controller is communicatively connected to the edge sensing layer and establishes a data interface with the power distribution network automation dispatching platform; the cloud-based collaborative management platform is communicatively connected to the regional collaborative controller and is used to realize the aggregated and optimized dispatching of cross-regional charging and discharging resources. The regional collaborative controller has a built-in multi-agent collaborative scheduling module, a heterogeneous SOC queue balancing management module, and a variable step size power dynamic compensation module. The multi-agent collaborative scheduling module is used to model each electric vehicle and its corresponding bidirectional charging and discharging pile as an independent intelligent agent, receive real-time scheduling command signals from the power distribution network, determine the power allocation factor and power allocation benchmark value corresponding to each intelligent agent in the region through distributed collaborative computing, and output them; the heterogeneous SOC queue balancing management module is used to dynamically construct a charging and discharging priority queue according to the SOC of the electric vehicle, and output the power weight signal of the corresponding electric vehicle according to the priority order. The variable step size power dynamic compensation module is used to receive the power allocation reference value, the power weight signal, and the real-time dispatch command signal and frequency monitoring signal from the distribution network. The variable step size power dynamic compensation module adaptively adjusts the sampling step size according to the rate of change of the distribution network dispatch command, obtains the power weight signal based on the sampling step size, and performs weighted adjustment on the power allocation benchmark value. The variable step size power dynamic compensation module is equipped with a feedforward compensation channel, which is used to calculate the feedforward compensation amount based on the differential value of the deviation of the distribution network frequency signal. The variable step size power dynamic compensation module is also used to configure an accuracy compensation coefficient, and use the accuracy compensation coefficient as a weighting factor to superimpose the feedforward compensation amount onto the weighted adjusted power allocation benchmark value to generate the final power output target value, and send it to the corresponding bidirectional charging pile in the edge sensing layer. 2.The electric vehicle V2G bidirectional charge and discharge collaborative control system according to claim 1, characterized in that: The bidirectional charging and discharging pile integrates a metering unit and a communication unit. The metering unit is used to collect the state of charge (SOC), dispatchable capacity, and off-grid time heterogeneous operating parameters of each electric vehicle in real time. The communication unit is used to report the collected operating parameters to the regional collaborative controller and receive the power output target value issued by the regional collaborative controller. The bidirectional charging and discharging charging pile is also equipped with a bidirectional AC-DC converter and a bidirectional DC-DC converter, which are connected by a DC bus. The bidirectional DC-DC converter adopts a bidirectional conversion circuit based on a dual active bridge topology and uses an extended phase shift control method. The power magnitude and transmission direction are adjusted by adjusting the phase shift ratio phi between the primary and secondary bridge arms and the internal phase shift ratio between the two primary bridge arms. 3.The electric vehicle V2G bidirectional charge-discharge collaborative control system according to claim 1, characterized in that: The final power output target value generated by the variable step size power dynamic compensation module is simultaneously fed back as a state update quantity to the multi-agent cooperative scheduling module, serving as the basis for the initial state of the vehicle in the next round of distributed cooperative scheduling iteration.

4. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 1, characterized in that: The regional cooperative controller is also provided with an adjustable capacity aggregation calculation unit, which calculates the up-regulation adjustment capacity of the vehicle and the down-regulation adjustment capacity of the vehicle according to the following formula: ​ in, Minimum value function For the first Each battery's rated capacity; The current moment; For the first The state of charge of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first Maximum charging power of each battery; Discharge depth protection value Charging saturation protection value The regional collaborative controller will manage all online vehicles. Summing up yields the total upward adjustment capacity for this region. ,Will Summing yields the total downward adjustment capacity. It periodically reports to the cloud-based collaborative management platform and dynamically responds to the real-time adjustable capacity query of the distribution network automation dispatch platform.

5. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 4, characterized in that: The multi-agent collaborative scheduling module updates the power allocation factor of each agent through distributed consensus iterative updates. The convergence criterion for iterative convergence is that the change in the power allocation factor between two adjacent iterations is less than a preset threshold. The state information of each agent includes... ; For the first The state of charge of each battery; For the first The network access time of each battery; For the first Off-grid time of each battery; For the first The maximum discharge power of each battery; For the first The maximum charging power of each battery; When the regional collaborative controller receives the total power command issued by the distribution network automation dispatching platform A positive value indicates that the discharge power needs to be scheduled from V2G resources, while a negative value indicates that the charging power needs to be absorbed. After that, the multi-agent collaborative scheduling module starts the distributed consistency iteration process.

6. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 1, characterized in that: The heterogeneous SOC queue balancing management module presets the discharge depth protection value for each vehicle. and charging saturation protection value When any vehicle Below Automatically exit the discharge queue when Higher than Automatically exit the charging queue; The heterogeneous SOC queue balancing management module allocates power weights in the following manner: In discharge mode, electric vehicles are sorted from high to low SOC, with higher SOC electric vehicles having a greater discharge power weight; in charging mode, electric vehicles are sorted from low to high SOC, with lower SOC electric vehicles having a greater charging power weight.

7. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 1, characterized in that: The sampling step size includes a first control step size and a second control step size, and the configuration accuracy compensation coefficient includes a first accuracy compensation coefficient and a second accuracy compensation coefficient. The variable step size power dynamic compensation module has a first rate of change threshold and a second rate of change threshold, and the first rate of change threshold is greater than the second rate of change threshold. When the rate of change of the scheduling command is greater than or equal to the first rate of change threshold, it operates with the first control step size and the first accuracy compensation coefficient. When the rate of change of the scheduling command is less than or equal to the second rate of change threshold, it operates with the second control step size and the second accuracy compensation coefficient. When the rate of change of the scheduling command is between the second rate of change threshold and the first rate of change threshold, it operates with an intermediate control step size between the first and second control steps and an intermediate accuracy compensation coefficient between the first and second accuracy compensation coefficients. The first control step size is smaller than the second control step size, the first accuracy compensation coefficient is greater than the second accuracy compensation coefficient, and the rate of change of the scheduling command is the rate of change of the total power command of the distribution network over time, which is obtained by the variable step size power dynamic compensation module through differential calculation of continuously received scheduling commands.

8. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 7, characterized in that: The output of the variable step size power dynamic compensation module is equipped with an output shaping unit that is linked to the accuracy compensation coefficient. The output shaping unit is embedded with limiting logic based on the SOC change rate. When the variable step size power dynamic compensation module is running with the first compensation coefficient, if the SOC change rate of any vehicle exceeds a preset threshold, the limiting logic automatically reduces the power allocation weight of that vehicle or performs derating on the power command.

9. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 1, characterized in that: The cloud-based collaborative management platform has a built-in dual-layer optimization scheduling engine. The upper-layer optimization engine uses regional collaborative controllers as the basic scheduling unit and formulates the scheduling power plan for each region based on power market signals. The lower-level optimization engine runs the multi-agent collaborative scheduling module within each region's collaborative controller; the communication cycle of the upper-level optimization engine is longer than the iteration update cycle of the lower-level optimization engine, forming a hierarchical optimization architecture with slow and fast dual time scales.

10. The electric vehicle V2G bidirectional charging and discharging coordinated control system according to claim 1, characterized in that: The system also has an emergency response mode, which is automatically triggered by the regional collaborative controller or the cloud-based collaborative management platform based on the degree of deviation of the power grid frequency. In the emergency response mode, the heterogeneous SOC queue balancing management module suspends power weight allocation and instead puts the electric vehicles into full-power charging and discharging in sequence according to the SOC of the electric vehicles. The variable step power dynamic compensation module fixes the power adjustment cycle to a preset minimum value.

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