Interface flexible regulation system and method based on vehicle-network interaction and multi-platform cooperation

Abstract

This invention discloses a flexible control system based on vehicle-to-grid interaction and multi-platform collaboration, comprising a multi-platform communication interface module, an adjustable load aggregation and evaluation module, an interactive energy control module, a distributed execution unit, and a dynamic feedback and adaptive optimization module. The device achieves secure transmission of control requirements and operational information through multi-platform data interconnection, generates energy control strategies based on aggregated evaluation results, and performs hierarchical flexible control through the distributed execution unit. The scenario adaptive submodule can achieve differentiated adjustment based on load type and regional characteristics, while the dynamic feedback module performs closed-loop correction of the control results, realizing rolling strategy optimization. This device significantly improves the response accuracy of vehicle-to-grid energy interaction and the system's adaptive capability.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for power systems, specifically to a flexible control system based on vehicle-grid interaction and multi-platform collaboration, belonging to the application technology of smart grids, virtual power plants and vehicle-grid integrated control. Background Technology

[0002] Existing electric vehicle charging and grid coordination control technologies typically employ a hierarchical architecture. A power dispatching platform is responsible for overall scheduling, station-level or regional controllers perform local management, and terminal equipment executes specific charging and discharging control. Each layer exchanges operational data, commands, and status information through communication interfaces to achieve basic energy regulation functions.

[0003] In terms of multi-platform data interaction, data synchronization between the power system and the vehicle-to-everything (V2X) platform is typically achieved through interface gateways or middleware. The main transmitted content includes power demand, equipment status, and control commands. Communication methods are mostly periodic polling or event-triggered, with security mechanisms primarily based on encryption and authentication. Due to differences in platform protocols and data formats, system compatibility and real-time performance are often limited.

[0004] In the load aggregation and assessment phase, existing methods mostly rely on historical operating data and static parameters for capacity calculation, failing to adequately consider the dynamic adjustability of the load and regional differences, resulting in limited accuracy of assessment results in complex scenarios. Energy regulation strategies generally rely on the regulation instructions from the upper-level platform, generating power allocation schemes through rule bases or model calculations, and then the execution end issues control signals according to the set proportions.

[0005] In execution-level control, station-level or area controllers typically distribute power commands uniformly, and all terminals execute according to the same logic. To ensure operational stability, the system often employs fixed thresholds or simple closed-loop control strategies, making it difficult to differentiate adjustments for different types of equipment or regional characteristics. Feedback mechanisms are mostly based on periodic data acquisition and statistical analysis, and optimizing the control effect often requires manual adjustment or post-event correction.

[0006] Overall, existing vehicle-to-everything (V2X) and multi-platform collaborative control systems can achieve basic load management and energy balance, but there is still room for improvement in real-time performance, differentiated control, adaptive optimization, and multi-source platform collaborative efficiency. Summary of the Invention

[0007] To address the aforementioned problems in the prior art, this invention proposes a flexible interface control system based on vehicle-to-grid interaction and multi-platform collaboration. The system includes: A multi-platform communication interface module is used to establish secure data communication connections between power control platforms, vehicle networking platforms, and energy management systems. The adjustable load aggregation and evaluation module is used to aggregate and model the adjustable load resources of multiple charging stations, energy storage devices and charging and discharging equipment, and generate an adjustable capacity database. The adjustable capacity database is used to comprehensively evaluate the adjustable capacity based on historical operating data, real-time power grid load and equipment operating status, and generate aggregated control capacity evaluation results. The interactive energy regulation module is used to generate corresponding energy regulation strategies, determine charging and discharging control modes, and form power allocation schemes based on the regulation requirements and aggregated regulation capability assessment results issued by the upper-level platform. A distributed execution unit is used to receive the energy regulation strategy, decompose the power allocation scheme into execution end parameters, and dynamically adjust the execution end parameters according to the terminal operating status. The distributed execution unit further includes a scenario adaptive submodule, which is used to dynamically select differentiated control strategies based on load type and regional power grid characteristics. The scene adaptive submodule includes: The load classification and identification unit is used to identify the operating characteristics of terminal equipment to determine the load type. The strategy matching unit is used to invoke a pre-stored control algorithm based on the load type. A local scheduling unit is used to independently execute the control algorithm within the area controller and adjust the execution parameters in real time; The dynamic feedback and adaptive optimization module is used to collect load response data after the control is executed, compare the expected control target with the actual response result, generate correction factors and feed them back to the adjustable load aggregation and evaluation module to realize the rolling optimization of the control strategy.

[0008] The multi-platform communication interface module includes: A communication adapter unit is used to access the communication protocols of the power control platform, vehicle network platform, and energy management system. The data encapsulation and parsing unit is used for unified formatting and bidirectional parsing conversion of multi-source data. The encryption authentication unit is used for encrypting and encapsulating transmitted data and verifying its signature. The synchronization management unit is used to achieve time synchronization between control requirements and status data.

[0009] The adjustable load aggregation and evaluation module includes: The resource access unit is used to receive operating parameters and basic attribute information from multiple charging stations, energy storage devices and charging and discharging equipment, and to uniformly identify the accessed resources. The capacity modeling unit is used to establish an adjustable capacity database and form an adjustable capacity model based on resource type, equipment characteristic parameters and operating constraints. The availability analysis unit is used to determine the availability and adjustability of resources based on equipment operating status and energy constraints. The capacity assessment unit is used to perform aggregate calculations on the modeled and analyzed resources, and output aggregate adjustable capacity and response characteristics to provide quantitative assessment results for upper-level energy regulation.

[0010] The interactive energy regulation module includes: The regulation demand receiving unit is used to receive regulation instructions and aggregated evaluation results from the superior platform; The strategy generation unit is used to generate energy regulation strategies in conjunction with the power grid operating status. A control mode determination unit is used to determine the system operating mode based on the energy regulation strategy. The power allocation unit is used to generate a power allocation scheme based on the operating mode and resource parameters, and to send the allocation scheme to the execution layer to achieve system power coordination and balance.

[0011] The distributed execution unit includes: The strategy receiving unit is used to receive the power allocation scheme issued by the upper-level energy control module and to perform integrity and timing verification on the instruction packets to ensure the order and real-time performance of the control commands. The parameter decomposition unit is used to convert the power allocation scheme into executable parameters. Based on the hierarchical structure of station level, pile level and energy storage unit, the global power command is refined into the corresponding target power value, and the correction is performed in combination with the capacity limit and operation constraint of each terminal device. The execution monitoring unit is used to monitor the terminal's execution status and power output. It compares the collected real-time voltage, current, power factor, and temperature information with the target power. When the deviation exceeds the threshold, it triggers local correction or abnormal reporting. The scenario adaptive submodule includes a load classification and identification unit, a strategy matching unit, and a local scheduling unit. The scenario adaptive submodule is used to dynamically adjust the control algorithm and execution strategy according to the load characteristics in order to achieve differentiated regulation of multiple types of loads.

[0012] The strategy matching unit is pre-set with multiple control algorithm templates, and uses a dynamic adjustment gain function to correct the output amplitude of the control signal during the algorithm call process, so as to achieve adaptive adjustment for different load types.

[0013] The expression for the dynamically adjustable gain function is:

[0014] in, For the terminal Dynamically adjustable gain; Three weighting coefficients are used to balance the control proportions of power deviation, thermal constraint, and voltage stability at different operating stages; The slope adjustment factor of the Sigmoid function; Power deviation balance constant; For terminal temperature, The upper limit of the safe temperature allowed by the equipment; Node voltage reference value; : a small constant.

[0015] The dynamic feedback and adaptive optimization module includes: The data acquisition unit is used to acquire load response data after the control and regulation are implemented. The deviation analysis unit is used to compare and analyze the actual response data with the expected control target in order to determine the execution deviation of each node; The correction factor generation unit is used to generate correction factors based on the deviation analysis results to reflect the degree of control deviation of each node. The parameter update unit is used to feed back the correction factor to the adjustable load aggregation and evaluation module to update the adjustability evaluation parameters, thereby realizing adaptive optimization and rolling correction of the control strategy.

[0016] Based on the same inventive concept, this application also provides a flexible interface control method based on vehicle-to-everything (V2X) interaction and multi-platform collaboration, including: Establish a secure data communication connection between the power control platform, the vehicle network platform, and the energy management system; The adjustable load resources of multiple charging stations, energy storage devices and charging and discharging equipment are aggregated and modeled by acquiring historical operating data, real-time grid load and equipment operating status through the data communication connection, and an adjustable capacity database is generated. Based on historical operating data, real-time power grid load and equipment operating status, the adjustable capacity database is used to comprehensively evaluate the adjustable capacity and generate aggregated control capacity evaluation results. Based on the control requirements issued by the upper-level platform and the evaluation results of the aggregated control capability, a corresponding energy control strategy is generated, the charging and discharging control mode is determined, and a power allocation scheme is formed. Based on the energy regulation strategy, the power allocation scheme is decomposed into execution parameters, and the execution parameters are dynamically adjusted according to the terminal operating status.

[0017] Based on the same inventive concept, this application also provides a readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements a flexible interface control method based on vehicle-to-everything (V2X) interaction and multi-platform collaboration.

[0018] Beneficial effects: This invention introduces a multi-platform collaborative and hierarchical flexible control mechanism into the vehicle-to-grid (V2G) interaction system, achieving data connectivity and dynamic response among the power control platform, V2G platform, and energy management system. Based on adjustable load aggregation and real-time evaluation results, the device can generate adaptive energy control strategies and accurately allocate power, improving the balance and security of the power grid operation. Distributed execution units, combined with scenario-adaptive submodules, achieve differentiated control, while dynamic feedback and optimization modules form a closed-loop correction, enabling the system to continuously learn and continuously optimize, thereby significantly improving the control accuracy, response speed, and energy utilization efficiency during V2G interaction. Attached Figure Description

[0019] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to unduly limit the invention. In the drawings: Figure 1 This diagram illustrates the overall system structure of the flexible control system based on vehicle-to-everything (V2X) interaction and multi-platform collaboration of the present invention.

[0020] Figure 2 The illustration shows the functional composition of the scene adaptation submodule within the distributed execution unit. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0022] like Figure 1 As shown, this embodiment provides a flexible control system based on vehicle-to-grid interaction and multi-platform collaboration, comprising a multi-platform communication interface module 101, an adjustable load aggregation and evaluation module 102, an interactive energy control module 103, a distributed execution unit 104, and a dynamic feedback and adaptive optimization module 105. These modules function in tandem to form a closed-loop control system encompassing data interaction, resource aggregation, energy control, hierarchical execution, and feedback optimization, achieving dynamic balance and efficient collaboration between charging infrastructure and the power grid.

[0023] The multi-platform communication interface module 101 is used to establish a secure data communication connection between the power control platform, the vehicle network platform, and the energy management system. This module supports heterogeneous protocol access and employs an encrypted verification mechanism to achieve bidirectional data transmission, ensuring real-time synchronization and secure reliability of control requirements, operational data, and status information. Through this module, the power supply side can promptly obtain vehicle charging needs and adjustable load information, while the vehicle network side can obtain grid control signals, realizing data connectivity between vehicles, charging piles, and the grid.

[0024] The adjustable load aggregation and evaluation module 102 is used to aggregate and model the adjustable load resources of multiple charging stations, energy storage devices, and charging and discharging equipment. This module establishes an adjustable capacity database, uniformly identifies, registers, and analyzes the availability of connected resources. Based on historical operating data, real-time grid load, and equipment operating status, the module comprehensively evaluates adjustable capabilities and generates aggregated control capability evaluation results, providing a quantitative basis for subsequent energy regulation strategies.

[0025] The interactive energy regulation module 103 generates energy regulation strategies based on regulation requirements and aggregated evaluation results from the upper-level platform. This module determines the optimal charging and discharging control mode through a load optimization algorithm, including various operating states such as V2G, V1G, or ordered charging. Based on the adjustable capacity, response speed, and geographical location of each resource node, the module generates a power allocation scheme and distributes it to the execution layer via the communication interface module, ensuring the smoothness and safety of the overall system power curve.

[0026] like Figure 2 As shown, the distributed execution unit 104 receives energy regulation strategies and decomposes power allocation schemes into execution parameters. This unit is deployed at the station-level or regional control layer, making real-time adjustments based on the terminal's operating status to achieve hierarchical flexible control. To enhance the system's adaptability and local optimization capabilities, a scenario-adaptive submodule 104a is included within the distributed execution unit. This submodule includes a load classification and identification unit 104a1, a strategy matching unit 104a2, and a local scheduling unit 104a3. The load classification and identification unit 104a1 determines the load type by identifying the terminal device's operating characteristics (including charging rate, power level, and response time); the strategy matching unit 104a2 calls the corresponding control algorithm based on the load type; the local scheduling unit 104a3 independently executes the algorithm within the regional controller and adjusts the execution parameters based on real-time voltage, current, and temperature status information, thereby achieving differentiated and precise regulation in an environment where multiple types of devices operate in combination.

[0027] The dynamic feedback and adaptive optimization module 105 collects load response data after control execution and compares the expected control target with the actual response result. This module generates correction factors by analyzing deviation data and feeds them back to the adjustable load aggregation and evaluation module 102 to update resource adjustability evaluation parameters, achieving rolling optimization of the control strategy. Through this closed-loop feedback mechanism, the system can continuously correct strategy parameters in different operating cycles, improving control accuracy and energy utilization.

[0028] The device in this embodiment can realize bidirectional interaction between the power grid and the vehicle network in a multi-platform collaborative environment. Through differentiated execution control and dynamic feedback optimization, it can achieve system-level flexible regulation and continuous adaptive evolution. It is suitable for various application environments such as ultra-fast charging scenarios, virtual power plant aggregation regulation and regional distribution network load balancing.

[0029] The multi-platform communication interface module 101 undertakes the core functions of data interconnection and information exchange in the system. Its structure includes a communication adaptation unit, a data encapsulation and parsing unit, an encryption and authentication unit, and a synchronization management unit. The communication adaptation unit is used to access different communication protocols of the power control platform, vehicle network platform, and energy management system, supporting multiple protocol standards such as IEC 61850, MQTT, RESTful API, and WebSocket. During the module initialization phase, this unit automatically identifies the access terminal type and matches the corresponding communication template according to the port attributes and protocol identifiers, realizing rapid adaptation and data channel establishment between heterogeneous systems.

[0030] The data encapsulation and parsing unit is used to achieve format unification and semantic mapping of multi-source data during transmission. This unit encapsulates uplink data according to a unified structure, including four parts: a message header, a timestamp, a checksum, and a data body. The message header carries the source identifier and priority information, while the data body contains control requirements, operating parameters, and status signals. In the downlink direction, this unit performs parsing and conversion according to the target platform's communication protocol specifications, ensuring data structure consistency and timing integrity across systems.

[0031] The encryption authentication unit employs a hybrid symmetric and asymmetric encryption mechanism to encrypt and encapsulate transmitted data and verify its signature. This unit generates digital signatures using the Chinese national cryptographic algorithms SM2, SM4, and SHA256 hash functions, and performs bidirectional verification of the certificates of both communicating parties, thereby ensuring the confidentiality and integrity of data during cross-platform transmission. To prevent unauthorized access and data tampering, the module incorporates a whitelist mechanism and a dynamic token update mechanism, performing periodic security checks on the communication link.

[0032] The synchronization management unit ensures real-time synchronization between control requirements and status data. This unit features a time-series synchronization thread pool and a cache queue, supporting millisecond-level message latency control. During multi-platform data interaction, the module sorts and synchronizes data streams from different platforms based on timestamp alignment principles and priority queue algorithms, thereby avoiding execution errors caused by timing inconsistencies in control commands. During system operation, when communication is interrupted or network latency exceeds a set threshold, the module automatically activates cache retransmission and difference compensation mechanisms to ensure the continuity and reliability of data interaction.

[0033] Through the above technical configuration, the multi-platform communication interface module 101 realizes high-security, high-compatibility and high-real-time data interoperability between the power control platform, the vehicle network platform and the energy management system, providing a stable data support environment for subsequent load aggregation, energy control and feedback optimization.

[0034] The adjustable load aggregation and assessment module 102 includes a resource access unit, a capacity modeling unit, an availability analysis unit, and a capacity assessment unit. The resource access unit receives operating parameters and basic attribute information from multiple charging stations, energy storage devices, and charging / discharging equipment, and uniformly identifies each accessed resource. The identification information includes the device number, rated power, geographical location, communication address, and the grid node number of its region. By establishing a resource access list, the system can achieve unified management and dynamic tracking of different types of load resources.

[0035] The capacity modeling unit is used to establish an adjustable capacity database based on parameters provided by the resource access unit. This unit employs a hierarchical modeling approach, with resource type as the first layer, equipment characteristic parameters as the second layer, and operating constraints as the third layer, forming a hierarchical adjustable capacity model. Model parameters include rated capacity, charge / discharge rate limits, charging priority, response time, and adjustable duration. During system initialization, this unit automatically calculates the theoretical adjustable capacity of each resource and adjusts the model parameters based on real-time monitoring data to reflect the available adjustment potential for the current period.

[0036] The availability analysis unit is used to determine the adjustability status of different devices. This unit comprehensively assesses resource availability based on device operating mode, current SOC level, operating conditions, and maintenance status. To improve analysis accuracy, this unit employs a time-sliding window algorithm to smooth operating data from recent sampling periods, thereby eliminating the impact of instantaneous fluctuations. For energy storage devices and charging / discharging equipment, this unit also considers their energy balance constraints and lifetime loss factors, dynamically adjusting their upper limit of adjustable capacity.

[0037] The capacity assessment unit performs aggregated assessments of all access resources after capacity modeling and availability analysis are completed. This unit weights and summarizes the adjustable capacity of individual devices by region, type, and response level to form an aggregated adjustable capacity index. During the assessment process, the system identifies the effective adjustable capacity under constraints based on real-time grid load data, node voltage distribution, and power flow calculation results. The output of the capacity assessment unit includes the aggregated adjustable power limit, response speed level, duration, and regional distribution characteristics, providing quantifiable strategy inputs for upper-level energy regulation modules.

[0038] During the system's operating cycle, the adjustable load aggregation and evaluation module 102 periodically updates the adjustable capacity database and corrects the evaluation results in real time based on the power grid's operating status. Through these steps, the module achieves unified aggregation modeling and dynamic evaluation of multi-source load resources, providing high-precision adjustable capacity data support for the flexible control system.

[0039] The interactive energy regulation module 103 includes a regulation demand receiving unit, a strategy generation unit, a control mode determination unit, and a power allocation unit. The regulation demand receiving unit obtains real-time regulation commands and aggregated evaluation results from the upper-level platform, including target power curves, regulation periods, load constraints, and safety margin parameters. After receiving data packets through the communication interface module, this unit performs integrity verification and timestamp comparison to ensure the validity and timing consistency of the commands. The parsed data is then transferred to the strategy generation unit, providing input for subsequent energy regulation calculations.

[0040] The strategy generation unit generates an overall energy regulation strategy after acquiring the aggregated evaluation results and combining them with the current power grid operating status. This unit incorporates a multi-objective optimization algorithm, using system power balance, peak-valley reduction, and equipment lifetime constraints as comprehensive objective functions. It employs a hierarchical solution approach to dynamically optimize the regulation strategy. First, the overall power regulation direction and target magnitude are determined at the global level. Second, load regulation ratios are allocated at the regional level. Finally, specific power setpoints are determined at the equipment level. The optimization process comprehensively considers response delay, power fluctuation rate, and line capacity constraints, ensuring that the resulting strategy maximizes energy utilization efficiency while guaranteeing system safety.

[0041] The control mode determination unit determines the current system operating mode based on the output of the strategy generation unit. This unit supports three typical operating modes: V2G (vehicle-to-grid), V1G (vehicle-to-grid), and orderly charging, and can automatically switch modes according to the grid load status. Mode switching is based on power demand direction and resource availability: when the grid is under peak load, V2G mode is prioritized, with vehicles providing discharge support; when the load is low or renewable energy output is excessive, the system enters V1G mode, performing time-sharing charging to absorb surplus energy; in scenarios with multiple vehicles connected simultaneously, the system enters orderly charging mode, allocating startup timing and power level differences through scheduling algorithms to reduce instantaneous load impacts.

[0042] The power allocation unit generates a power allocation scheme based on the determined operating mode and resource parameters. This unit performs weighted calculations based on the adjustable capacity, response speed, and geographical location of each resource node to form a hierarchical power allocation table. The allocation process employs a linear programming algorithm based on node priority to distribute the global power adjustment to each regional node, and fine-tunes it within the same region according to equipment performance and real-time status. To ensure the continuity and smoothness of the allocation results, the power allocation unit uses a sliding window filtering mechanism to constrain the power change rate, thereby suppressing short-term fluctuations and spike responses. The final generated power allocation scheme is sent to the execution layer in the form of structured instructions through a multi-platform communication interface module. The instructions include the node number, target power value, allowable deviation range, and execution timestamp.

[0043] Through the above design, the interactive energy regulation module 103 can realize real-time strategy generation and precise power allocation under multi-source regulation requirements and dynamic operating environment, ensuring the balance of energy flow between the power grid and vehicle network and the stability of system operation.

[0044] The distributed execution unit 104 is installed at the station-level or region-level control layer. Its structure includes a policy receiving unit, a parameter decomposition unit, an execution monitoring unit, and a scene adaptive submodule 104a. The policy receiving unit receives the power allocation scheme issued by the interactive energy regulation module and performs integrity and timing verification on the command packets. This unit adopts an asynchronous communication mechanism, ensuring the order and real-time performance of regulation commands through timestamp comparison and sequence number verification. When delays, packet loss, or duplicate commands are detected, the system automatically invokes the cache recovery mechanism and redundancy verification process to maintain the stability and consistency of data transmission.

[0045] The parameter decomposition unit transforms the power allocation scheme into executable parameters. Based on the hierarchical structure of station-level, pile-level, and energy storage unit levels, this unit refines the global power command into target power values ​​for different levels and performs constraint corrections based on the maximum capacity, current limits, and temperature rise boundaries of each terminal device. During the decomposition process, the system comprehensively considers equipment response speed and health status, assigning dynamic priority weights to load nodes to achieve a balance between safety and efficiency.

[0046] The monitoring unit is used to monitor the terminal's execution status and power output. This unit collects the terminal's voltage, current, power factor, and temperature information in real time through a high-speed data acquisition interface and compares the sampled results with the target power. If the deviation exceeds a threshold, the system automatically triggers a local correction or anomaly reporting mechanism, and simultaneously records the monitoring results to the feedback database for subsequent adaptive optimization processes.

[0047] like Figure 2As shown, the scene adaptive submodule 104a includes a load classification and identification unit 104a1, a strategy matching unit 104a2, and a local scheduling unit 104a3. The load classification and identification unit 104a1 identifies the load type by analyzing the operating characteristics of the terminal device, constructs a model using multi-dimensional feature vectors, takes charging rate, power level, and response time as key feature inputs, determines its category through a fuzzy clustering algorithm, and updates the identification results in real time and inputs them to the strategy matching unit.

[0048] The strategy matching unit 104a2 is pre-loaded with various control algorithm templates, including constant power control, time-sharing scheduling, current limiting protection, and adaptive compensation algorithms. To improve the flexibility and differentiation accuracy of the control strategy, this unit uses a power regulation gain function when calling the algorithm. This function automatically corrects the control output amplitude based on the dynamic response characteristics of different types of loads, thereby maintaining the stability and coordination of overall power when multiple types of equipment are running in parallel. The dynamic regulation gain expression is as follows:

[0049] in, For the terminal Dynamically adjustable gain; Three weighting coefficients are used to balance the control proportions of power deviation, thermal constraint, and voltage stability at different operating stages; they can be self-adjusted through system calibration or online learning. The slope adjustment coefficient of the Sigmoid function, which determines the response speed of the control system to power deviation; Power deviation balance constant, used to define the allowable steady-state error range of the system; The thermal safety threshold of the equipment determines the non-linear rise range of the temperature term; Node voltage reference value, usually the nominal operating voltage; : A small constant to prevent the denominator from being zero and to ensure computational stability.

[0050] First item This is the power deviation response term. Wherein, Indicates terminal The real-time power deviation (the difference between the target power and the actual power). This is the balance constant, used to define the allowable static error band of the system; This is the sensitivity coefficient, used to adjust the steepness of the response curve. This term uses a sigmoid function to ensure the control gain remains linearly amplified at small deviations and gradually saturates at large deviations, thus avoiding over-adjustment and oscillations. This is achieved through weighting coefficients. Control its contribution ratio to the overall gain. When near At this time, the output is a smooth and continuous gain correction value, which is suitable for steady-state deviation compensation of fast charging devices.

[0051] Second item This is a thermal dynamic suppression term. Among them, For terminal temperature, This represents the upper limit of the equipment's permissible safe temperature. This value reflects the equipment's power regulation capability across different temperature ranges using a sine function: when the temperature is low, When the function value is small, the system's regulation intensity is low; when the temperature approaches the safe upper limit, the function tends to 1, and the system automatically limits power increase or performs derating operation, thereby achieving self-protection under thermal constraints. Adjusting the weight of this item in the overall control allows for differentiated settings for devices with different heat dissipation capabilities or environmental conditions.

[0052] Third item This is a voltage stability compensation term. Among them, For node voltage, The reference voltage set for the system. To prevent the use of tiny constants with a denominator of zero, this term uses a logarithmic mapping to nonlinearly amplify the node voltage deviation. When the voltage deviation is small, the gain adjustment is limited; when the voltage deviation approaches the safety boundary, the gain value increases rapidly to enhance local control and maintain voltage stability. Adjust its impact on the overall gain so that the system can quickly suppress overshoot in areas with large voltage fluctuations.

[0053] The above expression works through the combined effect of three structural terms: the first term controls the deviation amplification rate in the form of a sigmoid function, achieving smooth adjustment of the response speed; the second term uses a temperature sine term to achieve thermal dynamic limitation; and the third term uses a logarithmic term to perform nonlinear compensation for voltage offset, thereby maintaining the system's self-balance and dynamic adaptability under multivariate disturbances. This expression is the core formula in the distributed execution unit used to describe the dynamic response characteristics of the terminal and the self-adjustment law of the control gain. By coupling and mapping three interrelated physical variables—power deviation, electrical temperature, and voltage offset—it achieves a differentiated, continuous, and adaptive power regulation gain mechanism. This enables the system to automatically adjust the control signal strength according to the real-time operating conditions of each node in a multi-device heterogeneous environment, thereby improving the smoothness and stability of the overall regulation.

[0054] This dynamically adjustable gain function employs an adaptive nonlinear response mechanism with three-variable coupling. It overcomes the limitations of existing technologies that rely on "single proportional correction of power error" or "independent temperature limiting," enabling the system to achieve coordinated and balanced regulation of power, temperature, and voltage under complex dynamic operating environments. This formula provides a mathematically continuous, realizable control law with verifiable boundaries for distributed execution units, significantly improving the flexibility and stability of the device under parallel control conditions of multi-source loads.

[0055] The local scheduling unit 104a3 is deployed within the area controller and is used to independently execute the matched control algorithm. This unit is based on... The system dynamically adjusts the amplitude of the output control signal and performs millisecond-level power regulation in conjunction with real-time current, voltage, and temperature data. Within the local control cycle, the system continuously iterates power commands to maintain the smoothness of the terminal device's output curve. When an abnormal operating condition is detected, the local scheduling unit immediately adjusts the gain and reports to the upper-level module, achieving a unified approach to protection and regulation.

[0056] Through the above design, the distributed execution unit 104 adds a dynamic adjustment gain function to the original power allocation and execution monitoring framework. This innovative formula achieves adaptive amplification and suppression of load response characteristics, enabling the control algorithm to flexibly adjust its execution intensity under different operating conditions. It couples power error, thermal constraints, and voltage deviation in calculations, significantly improving local coordination and system flexibility control performance in mixed load environments.

[0057] The dynamic feedback and adaptive optimization module 105 includes a data acquisition unit, a deviation analysis unit, a correction factor generation unit, and a parameter update unit. The data acquisition unit obtains load response data after the control operation, including the actual power output, response delay, energy exchange, and terminal equipment operating status information of each node. This unit maintains a real-time connection with the distributed execution unit via a high-speed communication interface and records the load response curve at a set sampling period. To ensure data accuracy and timing consistency, the acquisition unit performs timestamp synchronization and outlier removal operations after receiving data, thereby obtaining a reliable load response dataset.

[0058] The deviation analysis unit is used to compare and analyze the collected actual response data with the expected control target. This unit incorporates a multi-dimensional error calculation model, calculating indicators such as power deviation, time deviation, and response rate deviation by comparing the target power curve with the actual output curve. To improve the accuracy of the analysis, this unit employs a sliding time window and a weighted average algorithm to smooth short-term fluctuations and avoid interference from sudden noise. The analysis results are used to quantify the execution deviation of each node and are classified into three categories based on the deviation type: overshoot, undershoot, or delayed execution, providing data support for the subsequent generation of correction factors.

[0059] The correction factor generation unit determines the corresponding correction parameters based on the deviation analysis results. This unit employs a proportional-integral adaptive algorithm to map power deviation and response delay into control weight adjustment coefficients, generating a correction factor matrix. The correction factors include power correction coefficients, time response coefficients, and dynamic weight coefficients, reflecting the degree of control deviation of each node in the current cycle. The generated correction factors are applied to the aggregation evaluation and strategy optimization processes in subsequent cycles to achieve dynamic calibration of the adjustability parameters.

[0060] The parameter update unit feeds back the correction factors to the adjustable load aggregation and assessment module 102 to update the resource adjustability assessment parameters. This unit transmits correction data via a bidirectional communication channel, updating information including the adjustable capacity of a single device, response level, and availability weight. The parameter update employs a gradual replacement strategy, progressively correcting the resource model over multiple operating cycles to maintain the stability and continuity of the assessment results. After the update is complete, the system recalculates the aggregation and control capabilities, ensuring the new assessment results better reflect the current operating status.

[0061] Through the above design, the dynamic feedback and adaptive optimization module 105 forms a closed-loop feedback system from data acquisition and deviation analysis to parameter updates. The system automatically corrects the strategy parameters after each operating cycle, enabling the energy control strategy to continuously self-optimize, thereby improving overall control accuracy and energy utilization, and enhancing the system's adaptability to changes in the operating environment.

[0062] Preferably, this embodiment also provides a flexible control method based on vehicle-to-grid interaction and multi-platform collaboration. This method establishes a secure data communication connection between the power control platform, the vehicle-to-grid platform, and the energy management system to acquire historical operating data of multiple charging stations, energy storage devices, and charging / discharging equipment, as well as real-time grid load and equipment operating status. It then aggregates and models the adjustable load resources of multiple charging stations, energy storage devices, and charging / discharging equipment to generate an adjustable capacity database. Based on historical operating data, real-time grid load, and equipment operating status, the adjustable capacity database is used to comprehensively evaluate the adjustability and generate an aggregated control capability evaluation result. Based on the control requirements issued by the upper-level platform and the aggregated control capability evaluation result, a corresponding energy control strategy is generated, the charging / discharging control mode is determined, and a power allocation scheme is formed. Based on this energy control strategy, the power allocation scheme is decomposed into execution parameters, and the execution parameters are dynamically adjusted according to the terminal operating status.

[0063] The above description is only a preferred embodiment of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of this patent application are included in the scope of this patent application.

Claims

1. A flexible control system based on vehicle-to-everything (V2X) interaction and multi-platform collaboration, characterized in that: The device includes: A multi-platform communication interface module is used to establish secure data communication connections between power control platforms, vehicle networking platforms, and energy management systems. The adjustable load aggregation and evaluation module is used to aggregate and model the adjustable load resources of multiple charging stations, energy storage devices and charging and discharging equipment, and generate an adjustable capacity database. The adjustable capacity database is used to comprehensively evaluate the adjustable capacity based on historical operating data, real-time power grid load and equipment operating status, and generate aggregated control capacity evaluation results. The interactive energy regulation module is used to generate corresponding energy regulation strategies, determine charging and discharging control modes, and form power allocation schemes based on the regulation requirements and aggregated regulation capability assessment results issued by the upper-level platform. A distributed execution unit is used to receive the energy regulation strategy, decompose the power allocation scheme into execution end parameters, and dynamically adjust the execution end parameters according to the terminal operating status. The distributed execution unit further includes a scenario adaptive submodule, which is used to dynamically select differentiated control strategies based on load type and regional power grid characteristics. The scene adaptive submodule includes: The load classification and identification unit is used to identify the operating characteristics of terminal equipment to determine the load type. The strategy matching unit is used to invoke a pre-stored control algorithm based on the load type. A local scheduling unit is used to independently execute the control algorithm within the area controller and adjust the execution parameters in real time; The dynamic feedback and adaptive optimization module is used to collect load response data after the control is executed, compare the expected control target with the actual response result, generate correction factors and feed them back to the adjustable load aggregation and evaluation module to realize the rolling optimization of the control strategy.

2. The interface flexible control system based on vehicle-to-grid interaction and multi-platform collaboration as described in claim 1, characterized in that: The multi-platform communication interface module includes: A communication adapter unit is used to access the communication protocols of the power control platform, vehicle network platform, and energy management system. The data encapsulation and parsing unit is used for unified formatting and bidirectional parsing conversion of multi-source data. The encryption authentication unit is used for encrypting and encapsulating transmitted data and verifying its signature. The synchronization management unit is used to achieve time synchronization between control requirements and status data.

3. The interface flexible control system based on vehicle-to-everything (V2X) interaction and multi-platform collaboration as described in claim 1, characterized in that: The adjustable load aggregation and evaluation module includes: The resource access unit is used to receive operating parameters and basic attribute information from multiple charging stations, energy storage devices and charging and discharging equipment, and to uniformly identify the accessed resources. The capacity modeling unit is used to establish an adjustable capacity database and form an adjustable capacity model based on resource type, equipment characteristic parameters and operating constraints. The availability analysis unit is used to determine the availability and adjustability of resources based on equipment operating status and energy constraints. The capacity assessment unit is used to perform aggregate calculations on the modeled and analyzed resources, and output aggregate adjustable capacity and response characteristics to provide quantitative assessment results for upper-level energy regulation.

4. The interface flexible control system based on vehicle-to-everything (V2X) interaction and multi-platform collaboration as described in claim 1, characterized in that: The interactive energy regulation module includes: The control demand receiving unit is used to receive control instructions and aggregated control capability assessment results from the superior platform. The strategy generation unit is used to generate energy regulation strategies in conjunction with the power grid operating status. A control mode determination unit is used to determine the system operating mode based on the energy regulation strategy. The power allocation unit is used to generate a power allocation scheme based on the operating mode and resource parameters, and to send the allocation scheme to the execution layer to achieve system power coordination and balance.

5. The interface flexible control system based on vehicle-to-grid interaction and multi-platform collaboration as described in claim 1, characterized in that: The distributed execution unit includes: The strategy receiving unit is used to receive the power allocation scheme issued by the upper-level energy control module and to perform integrity and timing verification on the instruction packets to ensure the order and real-time performance of the control commands. The parameter decomposition unit is used to convert the power allocation scheme into executable parameters. Based on the hierarchical structure of station level, pile level and energy storage unit, the global power command is refined into the corresponding target power value, and the correction is performed in combination with the capacity limit and operation constraint of each terminal device. The execution monitoring unit is used to monitor the terminal's execution status and power output. It compares the collected real-time voltage, current, power factor, and temperature information with the target power. When the deviation exceeds the threshold, it triggers local correction or abnormal reporting. The scenario adaptive submodule includes a load classification and identification unit, a strategy matching unit, and a local scheduling unit. The scenario adaptive submodule is used to dynamically adjust the control algorithm and execution strategy according to the load characteristics in order to achieve differentiated regulation of multiple types of loads.

6. The interface flexible control system based on vehicle-to-everything (V2X) interaction and multi-platform collaboration as described in claim 5, characterized in that: The strategy matching unit pre-sets multiple control algorithm templates and uses a dynamic adjustment gain function to correct the output amplitude of the control signal during the algorithm call process, so as to achieve adaptive adjustment for different load types.

7. The interface flexible control system based on vehicle-to-grid interaction and multi-platform collaboration as described in claim 6, characterized in that: The expression for the dynamically adjustable gain function is: in, For the terminal Dynamically adjustable gain; Three weighting coefficients are used to balance the control proportions of power deviation, thermal constraint, and voltage stability at different operating stages; The slope adjustment factor of the Sigmoid function; Power deviation balance constant; For terminal temperature, The upper limit of the safe temperature allowed by the equipment; Node voltage reference value; : a small constant.

8. The interface flexible control system based on vehicle-to-grid interaction and multi-platform collaboration as described in claim 1, characterized in that: The dynamic feedback and adaptive optimization module includes: The data acquisition unit is used to acquire load response data after the control and regulation are implemented. The deviation analysis unit is used to compare and analyze the actual response data with the expected control target in order to determine the execution deviation of each node; The correction factor generation unit is used to generate correction factors based on the deviation analysis results to reflect the degree of control deviation of each node. The parameter update unit is used to feed back the correction factor to the adjustable load aggregation and evaluation module to update the adjustability evaluation parameters, thereby realizing adaptive optimization and rolling correction of the control strategy.

9. A flexible interface control method based on vehicle-to-everything (V2X) interaction and multi-platform collaboration, characterized in that, include: Establish a secure data communication connection between the power control platform, the vehicle network platform, and the energy management system; The adjustable load resources of multiple charging stations, energy storage devices and charging and discharging equipment are aggregated and modeled by acquiring historical operating data, real-time grid load and equipment operating status through the data communication connection, and an adjustable capacity database is generated. Based on historical operating data, real-time power grid load and equipment operating status, the adjustable capacity database is used to comprehensively evaluate the adjustable capacity and generate aggregated control capacity evaluation results. Based on the control requirements issued by the upper-level platform and the evaluation results of the aggregated control capability, a corresponding energy control strategy is generated, the charging and discharging control mode is determined, and a power allocation scheme is formed. Based on the energy regulation strategy, the power allocation scheme is decomposed into execution parameters, and the execution parameters are dynamically adjusted according to the terminal operating status.

10. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the interface flexible control method based on vehicle-to-network interaction and multi-platform collaboration as described in claim 9.

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