Heterogeneous and dynamic priority fused V2G-VPP resource aggregation method

Abstract

The invention relates to a V2G-VPP resource aggregation method fusing isomerism and dynamic priority, and the method comprises the following steps: carrying out the processing of communication data of various types of equipment involved in V2G-VPP through protocol non-sensitive access, and achieving the automatic analysis and conversion of a cross-protocol equipment instruction; extracting trip chain features and social attributes based on the analyzed and converted data, constructing a user elasticity quantitative model, and calculating an elasticity coefficient; and constructing a dynamic coupling priority function of power grid demand and resource state dual drive by combining the power grid frequency modulation demand, the predicted movement probability and the elastic coefficient, dynamically evaluating the resource priority, performing descending sorting and screening based on the resource priority of each electric vehicle, generating a resource priority scheduling sequence, and performing optimization. Compared with the prior art, the method has the advantages that the protocol conversion efficiency can be improved, the high-value resource identification accuracy is improved, and the V2G schedulable capacity evaluation error is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle-to-grid interactive virtual power plant technology, and in particular to a V2G-VPP resource aggregation method that integrates heterogeneity and dynamic priority. Background Technology

[0002] Vehicle-to-Grid (V2G-VPP) virtual power plants utilize intelligent aggregation systems to transform dispersed electric vehicles (EVs) into dispatchable energy storage units, constructing virtual power plants that dynamically interact with the power grid. Their core value lies in leveraging the mobile energy storage characteristics of EVs to charge during off-peak hours and feed back power during peak hours, achieving peak shaving and valley filling. Simultaneously, they rapidly respond to frequency regulation commands, replacing traditional generator sets and enhancing grid resilience. Users gain electricity price differences and subsidies by participating in charge / discharge regulation, while the grid significantly improves its capacity to absorb renewable energy, simultaneously promoting the coordinated low-carbon transformation of transportation and energy systems.

[0003] Traditional V2G-VPP models face a modeling dilemma when integrating EVs and their collaborative resources (PV, energy storage, adjustable loads) due to the coupling of multi-source heterogeneity and behavioral randomness. Existing technologies rely on static probabilistic models to predict resource behavior, but their theoretical assumptions fundamentally conflict with the dynamic evolution of V2G resources: the spatiotemporal randomness of EV user behavior (such as travel chains and temperature control needs), differences in social attributes, and environmental disturbances lead to non-steady-state migration characteristics of resource states. Such models cannot characterize the evolutionary patterns of EV and multi-source collaborative behavior, causing systematic biases in aggregable capacity assessment. This weakens the reliability of VPP's assessment of V2G schedulable capacity and severely restricts the efficiency of vehicle-to-grid resource aggregation.

[0004] At present, the VPP scheduling architecture's ability to aggregate V2G collaborative multi-source resources is limited by the following technical bottlenecks.

[0005] (1) The priority evaluation mechanism for EV clusters is rigid: it mainly adopts fixed rules (such as equipment type or contract level) to preset the resource call sequence, and lacks the dynamic response capability to transient fluctuations in grid demand and real-time changes in EV mobility status. High-priority V2G resources often fail due to physical constraints (such as vehicle mobility), while low-priority available resources (such as idle air conditioning load) are often not activated due to rigid strategies, resulting in a structural mismatch between V2G resource accessibility and scheduling strategy; (2) Deep Heterogeneity of V2G and Multi-Source Protocols: The communication protocols of EV collaborative units such as photovoltaic, energy storage, V2G devices (such as charging piles), and adjustable loads (air conditioning) are deeply heterogeneous at the syntax structure (message format) and semantic logic (data meaning) layers. For example, the control commands of air conditioning load (such as start-stop adjustment) and V2G charging and discharging commands lack unified semantic mapping rules at the protocol stack layer, resulting in data fusion requiring time-consuming protocol conversion and semantic alignment. This heterogeneity makes the response latency of EV clusters far exceed the second-level scheduling requirements, directly hindering the real-time collaboration of vehicle-to-grid interactive resources.

[0006] On the other hand, when the existing V2G-VPP economic dispatch scheme expands across regions, the V2G aggregation efficiency decreases in a gradient manner because it does not couple the elasticity differences of EV users and regional characteristics, further amplifying the bottleneck of V2G resource aggregation. (1) Lack of EV incentive efficiency dimension: The current unified subsidy strategy has the problem of confusion of the elastic response level of EV users. Low elastic EV users (such as rigid commuters) have a fixed behavior pattern, and the marginal effect of their response threshold is close to zero. Excessive subsidies lead to waste of resources; High elastic EV users (such as ride-hailing drivers) have dynamic response capabilities, and a single incentive cannot match their non-linear behavior characteristics, resulting in the loss of high-quality V2G resources; (2) V2G cross-domain coordination contradictions: regional power grid topology differences (such as urban distribution networks and rural microgrids) and climate-driven EV charging load characteristics differences (such as differences in air conditioning usage time in the north and south) create deep conflicts with the existing model parameter solidification mechanism, which leads to the need for complex model reconstruction when promoting across regions, seriously weakening the potential for large-scale deployment of V2G-VPP.

[0007] Chinese patent CN120355157A discloses a method and system for load forecasting and dynamic response of a virtual power plant integrating vehicle-grid interaction. The method includes: acquiring and processing historical charging data to generate a user dataset; extracting and analyzing the user dataset to generate charging behavior prediction results; collecting traffic flow data in real time and matching it to generate traffic impact coefficients; constructing a regional charging demand prediction model to generate a regional charging demand distribution; acquiring and processing external environmental event data to generate sudden load correction values; integrating multi-level factors to generate a comprehensive load prediction value; adjusting charging pile power allocation and charging priority; and outputting scheduling instructions to complete closed-loop operation. This solves the load forecasting deviation problem caused by the randomness of user behavior, the dynamic coupling of traffic, and the unpredictability of sudden events, achieving accurate modeling and collaborative optimization of multi-level dynamic factors and improving the scheduling efficiency of the virtual power plant for vehicle-grid interaction resources. However, this solution still has the following unresolved technical defects: (1) Lack of multi-source protocol adaptive fusion capability: The problem of deep heterogeneity of communication protocols among EV collaborative units such as photovoltaic, energy storage, and air conditioning has not been solved. Multi-source data fusion requires manual protocol conversion, which is time-consuming and far exceeds the second-level scheduling requirement. This defect leads to the inability of vehicle-to-grid interaction resources to coordinate in real time, and the protocol conversion of different types of power supplies and equipment takes too long, which seriously affects the performance and operating efficiency of the V2G-VPP system.

[0008] (2) Lack of dynamic priority matching mechanism: Fixed rules are used to preset priorities, and no dynamic coupling function between grid demand and resource status is established. During peak frequency regulation, high-priority fast charging piles may be unable to provide service because EVs are in motion, while low-priority air conditioning loads are not activated because control commands and charging / discharging commands lack semantic mapping. Such fixed rules may cause some high-priority resources to fail or some low-priority resources to be idle.

[0009] (3) Failure to consider user elasticity differences: The user elasticity levels are not differentiated. Low-elasticity users (such as rigid commuters) are not sensitive to subsidies, and excessive subsidies can easily lead to resource waste; high-elasticity users (such as ride-hailing drivers) have dynamically changing response capabilities, and a single incentive mechanism can easily lead to the loss of high-quality resources. The essence of the above defects lies in the lack of adaptive fusion capability of multi-source protocols for core V2G scenarios (compatible with EV collaborative units such as photovoltaics, energy storage, and air conditioning), as well as the dynamic matching mechanism between V2G resource value and grid demand, which leads to the aggregation system falling into a triple dilemma of "inflated V2G capacity - delayed response - user churn".

[0010] Therefore, a V2G-VPP collaborative aggregation method is needed to resolve the fundamental contradiction between V2G resource heterogeneity compatibility and real-time optimized scheduling. Summary of the Invention

[0011] The purpose of this invention is to address the fundamental contradiction between heterogeneous V2G resource compatibility and real-time optimized scheduling by providing a V2G-VPP resource aggregation method that integrates heterogeneity and dynamic priority. Through protocol-seamless access, elastic hierarchical evaluation, and dynamic priority generation, this method solves multiple problems caused by heterogeneous multi-source protocols and rigid scheduling mechanisms, such as inflated schedulable capacity of electric vehicles, delayed response, and user churn. It significantly improves the utilization rate of vehicle-to-grid interaction resources, reduces system response latency, and provides reliable technical support for the large-scale participation of electric vehicles in grid collaborative regulation.

[0012] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a V2G-VPP resource aggregation method integrating heterogeneity and dynamic prioritization is provided, the method comprising the following steps: Through protocol-independent access, communication data of various types of devices involved in V2G-VPP is processed to achieve automatic parsing and conversion of cross-protocol device commands; Based on the parsed and transformed data, we extract travel chain features and social attributes, construct a user elasticity quantification model, and calculate the elasticity coefficient. By combining grid frequency regulation demand, predicted mobility probability, and resilience coefficient, a dynamic coupled priority function driven by both grid demand and resource status is constructed. Resource priorities are dynamically evaluated, and based on the resource priorities of each electric vehicle, they are sorted and filtered in descending order to generate a resource priority scheduling sequence and optimize it, thereby realizing V2G-VPP resource aggregation.

[0013] As a preferred technical solution, the protocol-seamless access uses a protocol feature decoupling unit, a semantic standardization engine, and a dynamic protocol adapter to parse and transform communication data, wherein... The protocol feature decoupler receives the original device communication data stream, builds a protocol template library, establishes a hierarchical decoupled standardized description framework, decouples the features of key description dimensions of the protocol, and generates standardized device features that are independent of the protocol. The semantic standardization engine constructs a semantic mapping library by mapping standardized device features to standardized coding rules, which is used to generate protocol parameter mappings. When the dynamic protocol adapter sends standardized control commands at the application layer, it calls the semantic mapping library in the semantic standardization engine to map standardized device feature descriptions to standardized coding rules, generate protocol parameter mappings, and realize standardized processing and unified semantic expression of device data.

[0014] As a preferred technical solution, the key description dimensions include: transmission channel characteristics, data encapsulation format, instruction structure characteristics, and field semantic mapping relationships.

[0015] As a preferred technical solution, the dynamic protocol adapter achieves dynamic adaptation of communication between devices through the following steps, ensuring seamless interoperability between different protocols: Receive standardized control commands sent by the application layer and trigger the dynamic protocol adapter to start; Parse standardized control commands and extract key parameters, which include at least the device type and command type; Based on the key parameters, the semantic standardization engine is invoked to search for the corresponding protocol parameters in the semantic mapping library; The extracted protocol parameters are checked for value range, unit, and threshold to ensure the validity and consistency of the data; Standardized control commands are mapped to protocol parameters to generate dynamic message structures that conform to the target protocol, i.e., protocol parameter mapping. Based on the dynamic message structure, a recognizable device dynamic message is constructed through binary stream encoding, XML document encapsulation, and checksum generation. Send device dynamic messages to the physical interface, and transmit them to the target device through physical layer communication; After the target device returns a response message, the response is verified.

[0016] As a preferred technical solution, the user elasticity quantification model is expressed as follows: , in, Indicates the characteristics of the travel chain. Indicates social attributes, Indicates other related attributes, , , For the corresponding weights, and oh 1+ oh 2+ oh 3 = 1; It is the elasticity coefficient.

[0017] As a preferred technical solution, the travel chain feature represents the characteristics of user travel behavior and is determined based on schedulable time periods and commuting distance: , in, T free Indicates the user's free and schedulable time. T total It represents the total time of a day. D commute This indicates the user's average daily commute distance. D max This represents a reference value for the maximum commuting distance. The aforementioned social attributes are determined based on occupation type and electricity price sensitivity: , in, S job This indicates the user's career type rating. S max This indicates the highest career type score. P sensitivity This indicates the user's electricity price sensitivity score. P max This indicates the highest electricity price sensitivity score; The other relevant attributes refer to other attributes that may affect user resilience, determined based on vehicle type and charging facility usage frequency: , in, Vtype Indicates the vehicle type rating. V max This indicates the highest rating for the vehicle type. F charge This indicates a score reflecting the frequency of use of charging facilities. F max This indicates the score for the highest frequency of use of charging facilities.

[0018] As a preferred technical solution, the dynamic coupling priority function is expressed as follows: , in, Indicates the first i The priority of calling electric vehicles; Δ f This represents the power grid frequency deviation, which is proportional to the frequency regulation demand, Δ. f A value greater than 0.5 indicates an emergency frequency adjustment; P m,i Represents the first GPS trajectory prediction i The probability of a vehicle moving. P m,i A value greater than 0.6 indicates that the device is about to move. Indicates the power grid demand weighting coefficient; Indicates the first i The elasticity coefficient of an electric vehicle.

[0019] As a preferred technical solution, the step of sorting and filtering the resources based on the resource priorities of each electric vehicle in descending order, generating a resource priority scheduling sequence, and then optimizing it, specifically involves: Electric vehicles are sorted in descending order of resource priority, a priority failure threshold is preset, and electric vehicles with resource priority higher than the priority failure threshold are selected to form a resource priority scheduling sequence. Update resource priorities according to the following rules to optimize the resource priority scheduling sequence: The power grid demand weighting coefficient is dynamically adjusted based on the power grid frequency deviation. When the power grid frequency deviation exceeds the preset threshold, the power grid demand weighting coefficient is increased to the preset value, and the resource priority is updated. When the probability of a vehicle moving exceeds a preset threshold, the resource priority of the corresponding electric vehicle is reduced according to preset rules to ensure the flexibility of the scheduling strategy.

[0020] As a preferred technical solution, the method further includes: Resource elasticity levels are classified according to elasticity coefficients, including high, medium, and low levels. Power resources are allocated sequentially based on the optimized resource priority scheduling sequence. , in,P i Indicates the first i The allocation power of each resource; α i Indicates the first i The elasticity coefficient of a resource; This represents the sum of the elasticity coefficients of all high-elasticity resources; P total Indicates total dispatchable power; k This indicates an adjustment coefficient that is dynamically adjusted based on the urgency of the demand.

[0021] As a preferred technical solution, the method further includes: The system monitors the power output of each resource in real time and dynamically adjusts power allocation based on grid frequency deviation and resource status. When the grid frequency returns to normal or the resource status changes, the system adjusts the power distribution by adjusting the coefficients. k Adjust power distribution to ensure stable system operation; When the sum of the dispatchable power of high-elasticity-level electric vehicles exceeds the total dispatchable power, the sum of the dispatchable power of high-elasticity-level electric vehicles is reduced to the total dispatchable power through redundancy design. At the same time, the portion of the dispatchable power of high-elasticity-level electric vehicles that exceeds the total dispatchable power is transferred and allocated to other electric vehicle users as a certain proportion of emergency power resources to deal with emergencies and ensure rapid response capability in emergency scenarios. The other electric vehicle users are determined by comprehensively considering dynamic resource priority, elasticity coefficient and mobility probability.

[0022] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0023] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0024] Compared with the prior art, the present invention has the following beneficial effects: (1) Multi-source protocol adaptive fusion: This invention achieves automatic parsing and conversion of cross-protocol device commands through protocol-insensitive access technology. Specifically, this invention automatically identifies the device protocol type, parses the command semantics, and converts it into a unified format, eliminating the time consumption of protocol conversion. Taking photovoltaic-energy storage system collaboration as an example, after the conversion efficiency is improved, the response latency is compressed from hours to seconds, solving the obstacle of real-time collaboration, improving resource aggregation efficiency, and enabling the V2G cluster response to meet the grid's second-level scheduling requirements.

[0025] (2) Dynamic Priority Assessment Mechanism: This invention constructs a user elasticity quantification model, combining grid frequency regulation demand, predicted mobility probability, and elasticity coefficient to generate a dual-drive dynamic coupling priority function. This function matches grid transient fluctuations and EV mobility status in real time, avoiding resource mismatch caused by fixed rules. During peak frequency regulation periods, the system prioritizes scheduling available and responsive resources, while activating low-priority but available air conditioning loads to participate in frequency regulation, thereby reducing the error in schedulable capacity assessment and resolving the structural contradiction between high-priority failures and low-priority idleness.

[0026] (3) User Elasticity Differentiated Incentives: This invention extracts user travel chain characteristics and social attributes, quantifies user elasticity coefficients, and designs differentiated incentive strategies for high / low elasticity users. Based on elasticity coefficient resource allocation, appropriate subsidies are used for low elasticity users to avoid waste, while dynamic incentives are used for high elasticity users to match their nonlinear behavioral characteristics. This strategy can optimize V2G incentive efficiency, avoid resource waste and loss of high-quality resources caused by uniform subsidies, and improve resource aggregation efficiency. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the V2G-VPP system framework of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 This is a schematic diagram illustrating the process of seamless access according to the protocol of the present invention; Figure 4 This is a schematic diagram of the protocol feature decoupling process of the present invention; Figure 5 This is a schematic diagram illustrating the data interaction between the semantic standardization engine and the dynamic protocol adapter of the present invention; Figure 6 This is a schematic diagram illustrating the processing procedure of the dynamic protocol adapter of the present invention. Detailed Implementation

[0028] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0030] Example 1 This embodiment provides a V2G-VPP resource aggregation method that integrates heterogeneity and dynamic prioritization, implemented through three collaborative components: protocol-seamless access, elasticity assessment, and dynamic priority generation. Protocol-seamless access utilizes a three-level processing mechanism to automatically parse and convert cross-protocol device commands, supporting plug-and-play functionality and dynamic expansion with new devices. Elasticity assessment integrates tripartite characteristics and social attributes to construct a user elasticity quantification model, accurately distinguishing between high, medium, and low elasticity resources and improving the accuracy of high-value resource identification. Dynamic priority generation employs a dynamically coupled priority function driven by both grid demand and resource status, dynamically assessing resource priorities and generating resource priority scheduling sequences. The power allocation scheme optimizes resource scheduling and power allocation based on the priority assessment results, ensuring grid frequency regulation effectiveness and user satisfaction. Typical case tests validate the efficiency and universality of this solution under scenarios of resource diversity and grid fluctuations.

[0031] The system framework of V2G-VPP is as follows: Figure 1As shown, the V2G-VPP system aggregates heterogeneous resources from multiple sources through protocol-independent technology: photovoltaics, energy storage, adjustable loads, and EV charging piles are connected to the VPP resource module via different protocols, integrating real-time status data; the central control center evaluates the schedulable sequence of EVs based on a dynamic priority algorithm, comprehensively considering grid demand, electricity price fluctuations, and vehicle response elasticity to optimize the resource allocation order. During peak hours, highly elastic EVs are prioritized for reverse power supply, while during off-peak hours, energy storage charging or load power adjustment is guided. Through multi-protocol compatibility and dynamic priority optimization, resource utilization and grid response speed are significantly improved, ensuring the efficient and stable operation of the V2G-VPP system.

[0032] based on Figure 1 The system architecture shown is as follows: Figure 2 As shown, the V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority provided in this embodiment includes the following steps: S1 processes communication data from various types of devices involved in V2G-VPP through protocol-seamless access, enabling automatic parsing and conversion of cross-protocol device commands; S2, based on the parsed and transformed data, extracts travel chain features and social attributes, constructs a user elasticity quantification model, and calculates the elasticity coefficient; S3 combines grid frequency regulation demand, predicted mobility probability, and resilience coefficient to construct a dynamic coupled priority function driven by both grid demand and resource status. It dynamically evaluates resource priorities, sorts and filters them in descending order based on the resource priorities of each electric vehicle, generates a resource priority scheduling sequence, and optimizes it to achieve V2G-VPP resource aggregation.

[0033] This step demonstrates the following three core innovations of the present invention: (1) Seamless Adaptive Access Mechanism for Multi-Source Protocols: To address the response latency issues caused by heterogeneous protocols such as photovoltaic, energy storage, vehicle-to-grid (V2G), and air conditioning, this invention pioneers a bidirectional protocol syntax-semantics parsing engine. By constructing a standardized information tuple model, heterogeneous instructions such as ISO 15118 (V2G), Modbus (photovoltaics), and BACnet (air conditioning) are dynamically mapped to unified control semantics, achieving "plug-and-play devices and automatic protocol adaptation." This breaks through the latency bottleneck of traditional line-by-line conversion, improving protocol conversion efficiency by 16 times (measured latency ≤ 50ms), and fully meeting the second-level control requirements of the power grid.

[0034] (2) EV Elasticity Classification Model Based on Travel Chain and Social Attributes: To address resource misallocation caused by differences in user response elasticity, an elasticity quantification method integrating spatiotemporal behavior and social characteristics is proposed. Based on vehicle GPS trajectory mining, travel chain characteristics such as commuting distance and available time slots are identified. Dynamic weighting coefficients are constructed by combining social attributes such as occupation type and electricity price sensitivity, enabling accurate classification of EV users from "rigid commuters" to "highly elastic ride-hailing users." This model can significantly improve the accuracy of identifying high-value resources and avoid user churn and capacity bubbles caused by inefficient subsidies.

[0035] (3) Dynamic Priority Decision-Making, Resource Allocation, and Power Allocation Model Driven by Power Grid and Resources: Overcoming the structural defects of static priority rules, a real-time coupling function between power grid frequency regulation demand margin and EV mobility probability is designed. Power grid frequency deviation is used as the demand driver, vehicle mobility probability as the state constraint, and user elasticity coefficient as the value adjustment term to dynamically generate a resource allocation priority sequence. When the vehicle mobility probability exceeds a threshold, unavailable resources are automatically downgraded, achieving millisecond-level synchronization between the scheduling strategy and power grid fluctuations and resource status, significantly reducing the error in V2G schedulable capacity assessment.

[0036] The vehicle-to-grid interaction scenario involves the differences in multi-source protocols of photovoltaic, energy storage, EV charging piles, air conditioning and other equipment, including: (1) Syntactic heterogeneity: differences in message format; (2) Semantic conflict: logical contradictions in similar instructions; (3) Real-time bottleneck: traditional protocol conversion requires manual configuration of each line, which cannot meet the grid's second-level response requirements.

[0037] In one preferred implementation, such as Figure 3 As shown, the protocol-seamless access in this embodiment uses a protocol feature decoupling unit, a semantic standardization engine, and a dynamic protocol adapter to parse and transform communication data, solving the protocol compatibility problem of multi-source heterogeneous energy devices, establishing a unified device communication interface layer, and simultaneously realizing the following functions: (1) Automatic parsing and conversion of cross-protocol device commands (2) Decoupling of upper-layer applications and lower-layer device protocols (3) Plug and play multi-source devices (4) Dynamic expansion capability of new equipment protocols Specifically, the protocol feature decoupler receives the original device communication data stream, builds a protocol template library, establishes a standardized description framework for layered decoupling, decouples the features of key description dimensions of the protocol, and generates standardized device features that are independent of the protocol. The semantic standardization engine constructs a semantic mapping library by mapping standardized device features to standardized coding rules, which is used to generate protocol parameter mappings; When the dynamic protocol adapter sends standardized control commands at the application layer, it calls the semantic mapping library in the semantic standardization engine to map standardized device feature descriptions to standardized coding rules, generate protocol parameter mappings, and realize standardized processing and unified semantic expression of device data.

[0038] To address resource mismatch caused by differences in user responsiveness, a method for quantifying elasticity coefficients that integrates spatiotemporal behavior and social characteristics is proposed. Based on vehicle GPS trajectory mining, features such as commuting distance and available scheduling time periods are extracted. Dynamic weighting coefficients are constructed by combining these with social attributes such as occupation type and electricity price sensitivity. This enables precise resource classification of EV users from "rigid commuters" to "highly flexible ride-hailing users," building an "EV Elasticity Classification Assessment Model Based on Travel Chain and Social Attributes." This model can significantly improve the accuracy of identifying high-value resources and avoid user churn and capacity bubbles caused by inefficient subsidies.

[0039] First, a user elasticity quantification model is constructed, primarily based on travel chain characteristics and social attributes, while also considering other relevant attributes, as follows: , in, Indicates the characteristics of the travel chain. Indicates social attributes, Indicates other related attributes, , , The corresponding weights were obtained through training with 100,000 user data points, and oh 1+ oh 2+ oh 3 = 1; This is the elasticity coefficient. For example, the length of time a ride-hailing driver can be dispatched ( X 1. High), electricity price sensitive ( X 3 (high), where α ≥ 0.7; commuter bus scheduling time slots are fixed (X1 low), where α ≤0.3.

[0040] 1. Characteristics of the travel chain The characteristics representing user travel behavior are determined based on schedulable time periods and commuting distances. These characteristics reflect the user's flexibility in time and space: , in, T free This represents the user's free and schedulable time (in hours). T total It represents the total time of a day (24 hours). D commute This indicates the user's average daily commute distance (in kilometers). Dmax This indicates a reference value for the maximum commuting distance (such as the average commuting distance in a city). T free / T totale This represents the proportion of time that a user can schedule; a higher value indicates greater time flexibility for the user. D commute / D max This represents the ratio of a user's commuting distance to the city's average commuting distance; a higher value indicates lower spatial flexibility for the user.

[0041] 2. Social attributes The table represents the user's social attribute characteristics, determined based on occupation type and electricity price sensitivity. These characteristics reflect the user's responsiveness to price changes and the impact of their social role: , in, S job This indicates the user's career type rating (based on career flexibility and job nature, on a scale of 1-5). S max This indicates the highest career type score (default is 5 points). P sensitivity This represents the user's electricity price sensitivity rating (1-5 points). P max This indicates the highest electricity price sensitivity score (default is 5 points). S job / S max This indicates the relative flexibility of a user's occupation type; a higher value indicates that the user's occupation allows for more flexibility. P sensitivity / P max This indicates how sensitive users are to changes in electricity prices; a higher value means users are more concerned about changes in electricity prices.

[0042] 3. Other related attributes Other attributes that may affect user resilience are identified based on vehicle type and charging facility usage frequency. These characteristics reflect the user's technological capabilities and infrastructure dependence: , in, V type The rating indicates the vehicle type (based on vehicle flexibility and suitability, 1-5 points). V max This indicates the highest vehicle type rating (default is 5 points). Fcharge The rating indicates the frequency of use of charging facilities (1-5 points). F max This indicates the highest frequency of use of charging facilities (default is 5 points). V type / V max This indicates the relative flexibility of the user's vehicle types; a higher value indicates that the vehicle is more suitable for flexible dispatch. F charge / F max This indicates how much users rely on charging facilities; a higher value means that users are more likely to find charging facilities.

[0043] To overcome the structural defects of static priority rules, a real-time coupling function between grid frequency regulation demand margin and EV mobility probability is designed. Using grid frequency deviation as the demand driver, vehicle mobility probability as the state constraint, and user resilience coefficient as the value adjustment term, a resource allocation priority sequence is dynamically generated, constructing a "grid-resource dual-driven dynamic priority decision-making, resource allocation, and power distribution model." When the vehicle mobility probability exceeds a threshold, unavailable resources are automatically downgraded, achieving millisecond-level synchronization between scheduling strategies and grid fluctuations and resource status, significantly reducing V2G schedulable capacity assessment errors.

[0044] Specifically, the dynamic coupling priority function driven by both power grid demand and resource status is expressed as: , in, Indicates the first i The priority of calling electric vehicles; Δ f This represents the power grid frequency deviation, which is proportional to the frequency regulation demand, Δ. f A value greater than 0.5 indicates an emergency frequency adjustment; P m,i Represents the first GPS trajectory prediction i The probability of a vehicle moving. P m,i A value greater than 0.6 indicates that the device is about to move. This represents the power grid demand weighting coefficient (default 0.6). Indicates the first i The elasticity coefficient of an electric vehicle.

[0045] After determining the resource priorities, they are called according to the priority of each EV. The EVs are sorted in descending order, a reasonable priority failure threshold is set, and EVs with priorities higher than the priority failure threshold are selected to form a priority scheduling sequence, as shown in the following formula: Priority scheduling sequence = { EVi | >P th},according to Sort in descending order. in, P th =0.4 is the priority failure threshold, used to automatically remove unreachable resources.

[0046] Subsequently, the priority scheduling sequence is optimized. The optimization objective is to ensure that the resource allocation sequence can adapt to changes in grid demand and resource status in real time, thereby maximizing scheduling efficiency and resource utilization.

[0047] The optimization steps are as follows: 1) Real-time resource status assessment ① Motion probability assessment: Predict the motion probability of each vehicle based on GPS trajectory. P m,i .

[0048] ② Failure detection: Set failure threshold P th =0.4, when P m,i > P th If the resource is deemed unreachable, it will be automatically removed.

[0049] ③ Health status check: Regularly check the vehicle's battery status, charging facility status, etc., to ensure resource availability.

[0050] 2) Priority dynamically adjusted ① Demand-driven adjustment: based on grid frequency deviation Δ f The power grid demand weighting coefficient is dynamically adjusted. Specifically, Δ f When the frequency is >1Hz, the demand weighting coefficient is automatically increased to 0.8.

[0051] ② State constraint adjustment: When the probability of vehicle movement P m,i When the value is greater than 0.6, its priority should be appropriately reduced to ensure the flexibility of the scheduling strategy.

[0052] ③ Elasticity coefficient adjustment: Based on the user's elasticity coefficient α Adjust the value adjustment items of resources. Prioritize the scheduling of highly elastic resources and reserve less elastic resources.

[0053] 3) Scheduling queue generation: ① Sorting rules: A priority-sorted scheduling queue is generated based on a comprehensive score of demand weight coefficient, movement probability, and elasticity coefficient.

[0054] ② Real-time updates: The scheduling queue is updated once per second to ensure millisecond-level synchronization with power grid fluctuations and resource status.

[0055] ③ Redundancy Design: A certain proportion of redundant resources is reserved for critical tasks to ensure system reliability under extreme conditions. Specifically, this involves: constructing a cross-device redundant resource pool based on protocol-insensitive access technology to achieve semantic mapping and data fusion of heterogeneous units such as photovoltaics, energy storage, and air conditioning; calculating elasticity coefficients using a user elasticity quantification model to reserve a higher redundancy ratio for resources corresponding to highly elastic users to cope with behavioral fluctuations; and using a dynamic priority evaluation mechanism, based on the dual driving functions of grid frequency regulation demand and resource status, incorporating low-priority available redundant resources when updating the scheduling queue in real time to generate a scheduling sequence containing redundant resources. This ensures the system maintains reliable response under extreme conditions such as grid fluctuations or equipment failures, improving the fault tolerance and scheduling efficiency of V2G-VPP resource aggregation.

[0056] Example 2 This embodiment, based on embodiment 1, provides a detailed explanation of the implementation methods of the three core components of protocol-integrated access.

[0057] I. Protocol Feature Decoupling (1) Technical principle: Protocol features are abstracted through protocol metadata description.

[0058] (2) Basic implementation method: By constructing a protocol template library, a standardized description framework with layered decoupling is established to realize the unified mapping of protocol features from the physical layer to the application layer.

[0059] (3) Key descriptive dimensions include: 1) Transmission channel characteristics (TCP / UDP / serial port) 2) Data encapsulation format (binary / XML / JSON) 3) Instruction structure characteristics (frame header / address field / data field / checksum) 4) Field semantic mapping relationship (4) The main workflow is as follows Figure 4 As shown, it mainly includes: 1) Data acquisition stage: Receiving the original device communication data stream; 2) Feature parsing stage: Three core dimensions of the decoupling protocol; 3) Template construction phase: Generate structured metadata and establish a protocol feature index based on the protocol template library; 4) Abstraction output stage: Generate a device parameter model that is independent of the protocol, obtain a standardized device feature description, and pass it to the upper layer system.

[0060] (4) Typical protocol description example Taking the ISO 15118 protocol and the BACnet protocol as examples, both protocols use metadata modeling to transform heterogeneous protocols into programmable semantic descriptions, supporting plug-and-play and automated scheduling of the energy Internet of Things.

[0061] 1) ISO 15118 protocol The ISO 15118 protocol uses an XML tree structure to describe the communication and interaction between electric vehicles (EVs) and charging stations (EVSEs). It achieves machine-readable charging and discharging commands through structured XML tags, is compatible with protocols such as OCPP, and solves interoperability issues between charging stations from different manufacturers. Its core data model includes: ① Charging mode selection (Requested Energy Mode): Defines the charging method (AC / DC), power level, and V2G bidirectional control indicator; ② Energy demand (EAmount): Precisely describes the charging / discharging energy requested by the vehicle (unit: Wh), and supports dynamic adjustment; ③ Maximum current (EVMaxCurrent): Constrains the upper limit of charging current to ensure battery safety.

[0062] 2) BACnet Protocol The BACnet protocol uses an "object-attribute" model to abstractly describe building equipment. A typical structure includes... ① Object type: For example, analog objects (Analog Input) map to temperature sensor data, and binary objects (Binary Output) map to lighting switch status; ② Attribute values: such as temperature setpoint (Present_Value), device start / stop status (Reliability), etc., which support dynamic reading and writing.

[0063] ③ Technical effect: By standardizing object identifiers (such as Device ID=1001) and attribute codes, the differences in underlying hardware are masked, enabling unified management and control of cross-brand equipment such as HVAC and lighting.

[0064] II. Semantic Standardization Engine (1) Work process The semantic parsing process is a core component of the standardization engine. Through the collaborative work of multiple modules, it achieves standardized processing and unified semantic expression of device data, enabling automatic translation between "device function" and "protocol instructions," without requiring upper-layer applications to be aware of underlying protocol differences. The semantic parsing process is as follows: Figure 5 As shown, it mainly includes: (1) Application layer: Send standardized commands (such as "query device power") to trigger the semantic engine to start the parsing process; (2) Semantic standardization engine: Maps the parameters output by the protocol feature decoupler to the standardized coding rules to generate a data structure in a unified format; (3) Semantic mapping library (protocol parameter mapping): Based on the device type, match the corresponding semantic tags and standardized encoding rules to build a semantic mapping library (protocol parameter mapping); (4) Protocol Adapter (Level 3): The Level 3 protocol adapter calls the semantic mapping library of this level, and extracts key parameters by parsing the original protocol data to achieve device and instruction type and protocol adaptation; (5) Parameter feedback: Returns the converted protocol parameter mapping for use by upper-layer application systems.

[0065] This process ensures data consistency and readability across different devices, improving system compatibility and scalability. By dynamically loading semantic templates, the process is compatible with both new and old devices, ensuring data readability and system scalability.

[0066] (2) Semantic mapping library (protocol parameter mapping) The semantic mapping library structure achieves intelligent conversion between standardized instructions and heterogeneous protocols through a layered and decoupled design. Its core consists of a device type index layer, an instruction semantic parsing layer, and a protocol parameter mapping layer.

[0067] Among them, the device type index layer serves as the entry point for the semantic engine. It identifies heterogeneous devices (such as EVs, photovoltaics, and energy storage) in V2G-VPP through protocol-seamless access technology. It establishes a device type-protocol index relationship based on message header identifiers and data field structures, coordinates dynamic priority evaluation to allocate initial weights, and supports subsequent resource scheduling and classification.

[0068] The instruction semantic parsing layer combines a user elastic quantization model to parse the instruction intent, extract behavioral features and social attributes, and uses a semantic mapping library to map heterogeneous protocol instructions into unified semantic actions, thus eliminating protocol semantic differences.

[0069] The protocol parameter mapping layer calls the device protocol parameter template according to the dynamic priority sequence, maps standardized semantic actions into specific instructions, and realizes automatic parameter encapsulation and distribution through protocol-insensitive access technology, optimizes parameter values ​​to reduce response latency, and ensures real-time collaboration between cross-protocol devices.

[0070] The semantic mapping library establishes a unified mapping relationship through structured parameter templates (such as Modbus register addresses, BACnet object attributes, etc.), supports dynamic parameter constraint verification and multi-protocol adaptation, ensures accurate conversion of control commands to device messages, and improves the compatibility and execution efficiency of cross-protocol communication. In one embodiment, the semantic mapping library is shown in Table 1.

[0071] Table 1. Semantic Mapping Library for V2G-VPP (Protocol Parameter Mapping)

[0072] III. Dynamic Protocol Adapter (1) Work process like Figure 6 As shown, the dynamic protocol adapter achieves dynamic adaptation of communication between devices through the following steps, ensuring seamless integration between different protocols: 1) Receive standardized control commands sent by the application layer and trigger the dynamic protocol adapter to start; 2) Parse standardized control commands and extract key parameters, which include at least the device type and command type; 3) Based on the aforementioned key parameters, the semantic standardization engine is invoked to search for the corresponding protocol parameters in the semantic mapping library; 4) Perform value range, unit, and threshold checks on the extracted protocol parameters to ensure data validity and consistency; 5) Map standardized control commands to protocol parameters to generate dynamic message structures that conform to the target protocol, i.e., protocol parameter mapping; 6) Based on the dynamic message structure, construct a recognizable device dynamic message through binary stream encoding, XML document encapsulation, and checksum generation; 7) Send device dynamic messages to the physical interface, and transmit them to the target device through physical layer communication; 8) After the target device returns a response message, verify the response.

[0073] The entire process achieves efficient conversion and interaction between different protocols through dynamic adaptation, improving the system's flexibility and compatibility.

[0074] (2) Typical examples Taking "EV sequence control, photovoltaic power control, air conditioning temperature control, and energy storage system scheduling" as examples, a dynamic protocol adaptation process covering "command reception → protocol conversion → parameter mapping → dynamic generation" is established.

[0075] EV Sequence Control: For charging start commands, a JSON request (Remote Start Transaction) containing a transaction ID is generated via the OCPP protocol, achieving standardized control of the electric vehicle charging process. A typical example of this control process is as follows: Standardized instructions (power set to 1500W) → Modbus protocol conversion → Key parameter mapping (register address 0x4001) → Binary encoding (0x05DC) → Dynamic message construction Photovoltaic power control: Standardized power setting commands are converted via the Modbus protocol, mapped to a specified register address (0x4001), and binary encoded (0x05DC) to ultimately generate an executable dynamic message. A typical example of this control process is as follows: Standardized command (set temperature 26℃) → BACnet protocol conversion → Object attribute mapping (AnalogValue object, Present_Value attribute) → Write operation (priority 8).

[0076] Air conditioning temperature control: Based on standardized temperature control commands, these are converted into object attribute write operations (Analog Value.Present_Value) using the BACnet protocol, and dynamic adjustment of device parameters is achieved through priority 8. A typical example of this control process is as follows: Standardized instructions (charging power limit 20kW) → ISO 15118 protocol conversion → XML structure encapsulation (EAmount node, EVMaxCurrent node) → Dynamic message generation Energy storage system scheduling: Charging power limiting commands are adapted to the ISO 15118 protocol, and dynamic messages conforming to electric vehicle communication standards are generated through XML structured encapsulation (EAmount / EVMaxCurrent nodes). A typical example of this control process is as follows: Standardized instruction (charging sequence start) → OCPP protocol conversion → Transaction ID mapping (TransactionId) → JSON structure encapsulation (RemoteStartTransaction request) Example 3 This embodiment, based on Embodiment 1, provides a method based on the elastic coefficient. α This method categorizes resources by size. Specifically, EV resources are divided into three levels to optimize resource allocation and improve system efficiency.

[0077] Table 2 V2G-VPP EV Resource Elasticity Classification Strategy

[0078] Example 4 This embodiment provides a power allocation method based on embodiment 3.

[0079] (1) Optimization objective While ensuring the efficient execution of dispatching strategies, power resources should be allocated rationally to maximize the grid frequency regulation effect and user satisfaction.

[0080] (2) Distribution principle 1) Principle of prioritizing demand According to the power grid frequency deviation Δ f The severity of the situation will determine priority for emergency frequency regulation needs. When Δ f When the frequency exceeds 0.5Hz, highly resilient resources are immediately activated for a rapid response to ensure that the grid frequency quickly returns to normal.

[0081] 2) Principle of resource balance While meeting demand, power resources should be allocated as evenly as possible to avoid overuse of any single resource. For resources that are under high load for extended periods, their power allocation should be appropriately reduced to extend their lifespan.

[0082] 3) User Incentive Principles Based on user elasticity coefficient α Additional incentives will be provided to users with high electricity demand to encourage more users to participate in frequency regulation services. For users with low electricity demand, a reasonable compensation mechanism will be provided to ensure that their basic electricity needs are not affected.

[0083] (3) Real-time power allocation Power resources are allocated one by one according to the resource priority scheduling sequence to ensure that the power output of each resource meets the current demand.

[0084] The power distribution formula is: , in, P i Indicates the first i The allocation power of each resource; α i Indicates the first i The elasticity coefficient of a resource; This represents the sum of the elasticity coefficients of all resources; P total Indicates total dispatchable power; k This indicates an adjustment coefficient that is dynamically adjusted based on the urgency of the demand.

[0085] (4) Power adjustment mechanism As a preferred technical solution, the method further includes: The system monitors the power output of each resource in real time and dynamically adjusts power allocation based on grid frequency deviation and resource status. When the grid frequency returns to normal or the resource status changes, the system adjusts the power distribution by adjusting the coefficients. k Adjust power distribution to ensure stable system operation.

[0086] Furthermore, based on the resource elasticity levels defined in Example 3, when the sum of the dispatchable power of electric vehicles at high elasticity levels ∑ P 高弹性 Exceeding the total schedulable power P totalAt that time, through redundant design, the sum of the dispatchable power of electric vehicles with high elasticity level ∑ P 高弹性 Reduced to total dispatchable power P total At the same time, the sum of the dispatchable power of electric vehicles with high elasticity level ∑ P 高弹性 Exceeding total dispatchable power P total Partial ∑ P 高弹性超出 =∑ P 高弹性 －P total The power resources are transferred and allocated to other EV users with higher dynamic priority, higher elasticity coefficient and lower mobility probability as a certain proportion of emergency power resources to deal with emergencies and ensure rapid response capability in emergency scenarios.

[0087] Example 5 This embodiment provides a typical test case of the resource aggregation method based on embodiments 1 to 4 to illustrate the effectiveness of the method.

[0088] 1. Case Background Ten EV users in a certain city were selected to participate in the V2G-VPP frequency regulation service. High-value resources were selected through an elastic hierarchical model, and a priority sequence was generated based on a dynamic coupling function. Finally, the rationality of the V2G-VPP power allocation scheme and its adaptability to large-scale scheduling operation were verified.

[0089] 2. Calculation of elasticity coefficient ( α ) User parameter settings are based on user behavior characteristics and technical solution requirements, covering dimensions such as occupation type and charging habits, to ensure the diversity of new EV attributes and verify the universality of this patented technical solution in diverse scenarios and the effectiveness of resource scheduling strategies, as shown in Table 3.

[0090] Table 3 User parameter settings for typical test cases

[0091] Taking EV1~EV10 as examples, among which, T total =24 hours; the average maximum commuting distance for the above 10 EV users is set to 24 hours. D max =35km, demonstrating the calculation process of the elastic coefficient.

[0092] (1) Characteristics of the travel chain X 1) Based on the formula and parameter settings, the formula for calculating X1 is as follows.

[0093] X 1= T free / 24+ D commute / 35 The calculation results for X1 are shown in Table 4.

[0094] Table 4 Calculation results of X1

[0095] (2) Social attributes X 2) Based on the formula and parameter settings, X 2. The calculation formula is as follows.

[0096] X 2 =S job / 5 +P sensitivity / 5 The calculation results for X2 are shown in Table 5.

[0097] Table 5 Calculation results of X2

[0098] (3) Other attributes ( X 3) Based on the formula and parameter settings, X 3. The calculation formula is as follows.

[0099] X 3= V type / 5+ F charge / 5 The calculation results for X3 are shown in Table 6.

[0100] Table 6 Calculation results of X3

[0101] (4) Elasticity coefficient ( α ) Typical weighting coefficients can be set as follows: oh 1 = 0.4 oh 2 = 0.3, oh 3 = 0.3. Elastic modulus. α The calculation formula is as follows: α =0.4 X 1+0.3 X 2 + 0.3 X 3 Elasticity coefficient αThe calculation results are shown in Table 7.

[0102] Table 7 Elasticity Coefficient α Calculation results

[0103] Resource elasticity grading Based on the elasticity coefficient calculation results of each EV user and the resource elasticity classification strategy, EV1 to EV10 are divided into different resource elasticity levels, as shown in Table 8.

[0104] Table 8 V2G-VPP EV Resource Classification Strategy

[0105] 3. Dynamic priority generation ( P i ) (1) Parameter setting Let the power grid frequency deviation Δ during a certain period be... f =1.2Hz indicates that the power grid is in urgent frequency regulation demand, and the demand weighting coefficient is set to l =0.8.

[0106] Based on the movement patterns of EVs, the movement probability of each EV user is calculated. P m,i Set to: P m,1 =0.4, P m,2 =0.5, P m,3 =0.3, P m,4 =0.4, P m,5 =0.2, P m,6 =0.7, P m,7 =0.4, P m,8 =0.5, P m,9 =0.3, P m,10 =0.4.

[0107] (2) Dynamic priority Dynamic priority of each EV user P i The calculation formula is as follows: P i =0.8×1.2×(1− P m,i )+(1−0.8)×α i The calculation results are shown in Table 9.

[0108] Table 9 Dynamic Priority P i Calculation results

[0109] (3) EV user priority ranking Based on the calculation results in Table 9, EV users are ranked from highest to lowest priority as follows.

[0110] EV5>EV3>EV9> EV7> EV1> EV4= EV10>EV8>EV2>EV6.

[0111] (4) Priority scheduling sequence generation Based on the above dynamic priority calculation results, the lowest EV user priority is EV6. P 6 > P th =0.4, indicating that all EVs meet the minimum resilience response standard. Therefore, all EV1 to EV10 vehicles can be included in the V2G-VPP scheduling sequence, arranged in descending order of EV user priority, resulting in the following priority scheduling sequence: Priority scheduling sequence = {EV5,EV3,EV9,EV7,EV1,EV4,EV10,EV8,EV2,EV6}; The V2G-VPP scheduling center can determine the scheduling order of EV users based on priority scheduling sequence and sorting.

[0112] 4. Power Allocation Verification (1) Parameter setting Assume the total dispatchable power of V2G-VPP P total =200kW, the adjustment factor for emergency demand scenarios is set to... k =1.2, then the formula for calculating the allocated power for each EV user is as follows.

[0113] P i =1.2× α i / ∑α 高弹性 ×200 ∑ α 高弹性 The total elasticity coefficient of EV high-elasticity resources is calculated based on the results in Tables 7 and 8. ∑ α高弹性 = α 1+ α 3+ α 4+ α 5+ α 7+ α 8+ α 9+ α 10 =10.82 The calculation results of the power allocation for each EV user are shown in Table 10.

[0114] Table 10 Power Allocation P i Calculation results

[0115] Among them, the total power of highly elastic resources is ∑ P 高弹性 = P 1+ P 3+ P 4+ P 5+ P 7+ P 8+ P 9+ P 10 =206.14kW ∑ P 高弹性 Exceeding total dispatchable power P total The portion of the 200kW power is: ∑ P 高弹性超出 =206.14-200=6.14kW The total dispatchable power of highly elastic resources can be automatically reduced to 200kW through redundancy design (adjusted proportionally during actual allocation). Among them, EV5, due to its highest dynamic priority, high elasticity coefficient, and lowest mobility probability, can obtain the largest excess power allocation. Other excess power can be allocated to users such as EV3 and EV9 to meet priority calling under emergency frequency regulation needs.

[0116] 5. Case Test Summary By selecting 10 EV users in a certain city to participate in a large-scale test of the V2G-VPP frequency modulation service (10 EVs), the effectiveness and advancement of the following key technologies were verified: (1) The elastic grading model is based on a quantitative method that integrates travel chain characteristics and social attributes. It can effectively screen V2G-VPP high elastic resources (a total of 8 high elastic EV resources, accounting for 80%), ensure the callable response capability of V2G-VPP high elastic resources, and avoid inefficient subsidies.

[0117] (2) The dynamic priority algorithm couples the different EV user attribute weights with the grid demand to achieve millisecond-level response in emergency frequency regulation scenarios, generate priority resource call sequences, and meet the real-time scheduling requirements of the power grid.

[0118] (3) The power allocation mechanism ensures the fairness and stability of large-scale V2G-VPP resource scheduling through proportional coefficient adjustment, redundancy design and power optimization allocation, and meets the priority call response requirements in emergency scenarios.

[0119] (4) The case design covers resource diversity (occupation, vehicle type gradient) and power grid fluctuation scenarios (normal / emergency frequency regulation), which verifies the efficiency and universality of the technical solution in large-scale V2G-VPP application and provides core algorithm support for the commercialization of V2G-VPP.

[0120] Example 6 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0121] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0123] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0124] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0126] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority, characterized in that, The method includes the following steps: By enabling seamless access via protocols, multi-source heterogeneous communication data from various types of devices involved in V2G-VPP is processed, achieving automatic parsing and conversion of cross-protocol device commands. Based on the parsed and transformed data, we extract travel chain features and social attributes, construct a user elasticity quantification model, and calculate the elasticity coefficient. By combining grid frequency regulation demand, predicted mobility probability, and resilience coefficient, a dynamic coupled priority function driven by both grid demand and resource status is constructed. Resource priorities are dynamically evaluated, and based on the resource priorities of each electric vehicle, they are sorted and filtered in descending order to generate a resource priority scheduling sequence and optimize it, thereby realizing V2G-VPP resource aggregation.

2. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 1, characterized in that, The protocol-based seamless access utilizes a protocol feature decoupling engine, a semantic standardization engine, and a dynamic protocol adapter to parse and transform communication data. The protocol feature decoupler receives the original device communication data stream, builds a protocol template library, establishes a hierarchical decoupled standardized description framework, decouples the features of key description dimensions of the protocol, and generates standardized device features that are independent of the protocol. The semantic standardization engine constructs a semantic mapping library by mapping standardized device features to standardized coding rules, which is used to generate protocol parameter mappings. When the dynamic protocol adapter sends standardized control commands at the application layer, it calls the semantic mapping library in the semantic standardization engine to map standardized device feature descriptions to standardized coding rules, generate protocol parameter mappings, and realize standardized processing and unified semantic expression of device data.

3. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 2, characterized in that, The key descriptive dimensions include: transmission channel characteristics, data encapsulation format, instruction structure characteristics, and field semantic mapping relationships.

4. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 2, characterized in that, The dynamic protocol adapter achieves dynamic adaptation of communication between devices through the following steps, ensuring seamless integration between different protocols: Receive standardized control commands sent by the application layer and trigger the dynamic protocol adapter to start; Parse standardized control commands and extract key parameters, which include at least the device type and command type; Based on the key parameters, the semantic standardization engine is invoked to search for the corresponding protocol parameters in the semantic mapping library; The extracted protocol parameters are checked for value range, unit, and threshold to ensure the validity and consistency of the data; Standardized control commands are mapped to protocol parameters to generate dynamic message structures that conform to the target protocol, i.e., protocol parameter mapping. Based on the dynamic message structure, a recognizable device dynamic message is constructed through binary stream encoding, XML document encapsulation, and checksum generation. Send device dynamic messages to the physical interface, and transmit them to the target device through physical layer communication; After the target device returns a response message, the response is verified.

5. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 1, characterized in that, The user elasticity quantification model is expressed as follows: ， in, Indicates the characteristics of the travel chain. Indicates social attributes, Indicates other related attributes, , , For the corresponding weights, and ω 1+ ω 2+ ω 3 = 1; It is the elasticity coefficient.

6. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 5, characterized in that, The travel chain feature represents the characteristics of user travel behavior, determined based on schedulable time periods and commuting distance: ， in, T free Indicates the user's free and schedulable time. T total Indicates the total time of a day. D commute This indicates the user's average daily commute distance. D max This represents a reference value for the maximum commuting distance. The aforementioned social attributes are determined based on occupation type and electricity price sensitivity: ， in, S job This indicates the user's career type rating. S max This indicates the highest career type score. P sensitivity This indicates the user's electricity price sensitivity score. P max This indicates the highest electricity price sensitivity score; The other relevant attributes refer to other attributes that affect user resilience, determined based on vehicle type and charging facility usage frequency: ， in, V type Indicates the vehicle type rating. V max This indicates the highest rating for the vehicle type. F charge This indicates a score reflecting the frequency of use of charging facilities. F max This indicates the score for the highest frequency of use of charging facilities.

7. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 1, characterized in that, The dynamic coupling priority function is expressed as follows: ， in, Indicates the first i The priority of calling electric vehicles; Δ f This represents the power grid frequency deviation, which is proportional to the frequency regulation demand, Δ. f A value greater than 0.5 indicates an emergency frequency adjustment; P m,i Represents the first GPS trajectory prediction i The probability of a vehicle moving. P m,i A value greater than 0.6 indicates that the device is about to move. Indicates the power grid demand weighting coefficient; Indicates the first i The elasticity coefficient of an electric vehicle.

8. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 1, characterized in that, The process involves sorting and filtering each electric vehicle in descending order based on its resource priority, generating a resource priority scheduling sequence, and then optimizing it. Specifically: Electric vehicles are sorted in descending order of resource priority, a priority failure threshold is preset, and electric vehicles with resource priority higher than the priority failure threshold are selected to form a resource priority scheduling sequence. Update resource priorities according to the following rules to optimize the resource priority scheduling sequence: The power grid demand weighting coefficient is dynamically adjusted based on the power grid frequency deviation. When the power grid frequency deviation exceeds the preset threshold, the power grid demand weighting coefficient is increased to the preset value, and the resource priority is updated. When the probability of a vehicle moving exceeds a preset threshold, the resource priority of the corresponding electric vehicle is reduced according to preset rules to ensure the flexibility of the scheduling strategy.

9. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 1, characterized in that, The method further includes: Resource elasticity levels are classified according to elasticity coefficients, including high, medium, and low levels. Power resources are allocated sequentially based on the optimized resource priority scheduling sequence. ， in, P i Indicates the first i The allocation power of each resource; α i Indicates the first i The elasticity coefficient of a resource; This represents the sum of the elasticity coefficients of all high-elasticity resources; P total Indicates the total dispatchable power; k This indicates an adjustment coefficient that is dynamically adjusted based on the urgency of the demand.

10. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority according to claim 9, characterized in that, The method further includes: The system monitors the power output of each resource in real time and dynamically adjusts power allocation based on grid frequency deviation and resource status. When the grid frequency returns to normal or the resource status changes, the system adjusts the power distribution by adjusting the coefficients. k Adjust power distribution to ensure stable system operation; When the sum of the dispatchable power of high-elasticity-level electric vehicles exceeds the total dispatchable power, the sum of the dispatchable power of high-elasticity-level electric vehicles is reduced to the total dispatchable power through redundancy design. At the same time, the portion of the dispatchable power of high-elasticity-level electric vehicles that exceeds the total dispatchable power is transferred and allocated to other electric vehicle users as a certain proportion of emergency power resources to deal with emergencies and ensure rapid response capability in emergency scenarios. The other electric vehicle users are determined by comprehensively considering dynamic resource priority, elasticity coefficient and mobility probability.

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