V2g-vpp resource aggregation method fusing isomerism and dynamic priority

Abstract

The application relates to a V2G-VPP resource aggregation method fusing isomerism and dynamic priority, which comprises the following steps: through protocol non-inductive access, communication data of various types of devices involved in V2G-VPP are processed, automatic analysis and conversion of cross-protocol device instructions are realized; based on the analyzed and converted data, line chain characteristics and social attributes are extracted, a user elastic quantization model is constructed, and an elastic coefficient is calculated; a dynamic coupling priority function driven by power grid demand and resource state is constructed by combining power grid frequency modulation demand, predicted moving probability and the elastic coefficient, resource priority is dynamically evaluated, each electric vehicle is sorted and screened in descending order based on the resource priority, a resource priority scheduling sequence is generated and optimized. Compared with the prior art, the application has the advantages of being capable of improving protocol conversion efficiency, improving high-value resource identification accuracy, greatly reducing V2G schedulable capacity evaluation error and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle-to-grid (V2G) virtual power plant (VPP), and particularly relates to a V2G-VPP resource aggregation method fusing heterogeneous and dynamic priority. BACKGROUND

[0002] Vehicle-to-grid (V2G) virtual power plant (VPP) is a virtual power plant dynamically interacting with the power grid by converting dispersed electric vehicles (EVs) into a dispatchable energy storage unit through an intelligent aggregation system. The core value lies in utilizing the mobile energy storage characteristics of EVs to charge in the valley period and feedback in the peak period, realizing peak clipping and valley filling; at the same time, responding quickly to frequency modulation instructions to replace traditional generator sets and improve the resilience of the power grid. Users obtain electricity price difference and subsidy income by participating in charging and discharging regulation, while the power grid side greatly improves the new energy consumption capacity and promotes the coordinated low-carbon transformation of the transportation and energy systems.

[0003] When integrating EVs and their collaborative resources (photovoltaic, energy storage, and adjustable load), the traditional V2G-VPP faces the modeling dilemma of coupling of multi-source heterogeneity and behavior randomness. The existing technology relies on static probability models to predict resource behavior, which fundamentally conflicts with the evolution nature of V2G dynamic resources: the spatiotemporal randomness, social attribute difference, and environmental disturbance factors of EV user behavior (such as travel chain and temperature control demand) cause the resource state to present non-steady-state migration characteristics. Such models cannot represent the evolution law of EV and multi-source collaborative behavior, causing systematic bias in the evaluation of aggregable capacity, which further weakens the reliability of VPP in evaluating the dispatchable capacity of V2G, and seriously restricts the aggregation efficiency of vehicle-to-grid interactive resources.

[0004] At present, the aggregation ability of VPP's dispatching architecture for V2G collaborative multi-source resources is limited by the following technical bottlenecks.

[0005] (1) The priority evaluation mechanism for EV clusters is rigid: mainly using fixed rules (such as device type or contract level) to preset the resource calling sequence, lacking dynamic response ability to the transient fluctuations of power grid demand and the real-time changes of EV mobile state. 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) often fail to be activated due to the rigidity of the strategy, causing structural mismatch between V2G resource accessibility and dispatching strategy;

[0006] (2) V2G and multi-source protocol deep heterogeneity: The communication protocols of EV collaborative units such as photovoltaic, energy storage, V2G equipment (such as charging piles), and adjustable loads (air conditioners) have deep heterogeneity in syntax structure (message format) and semantic logic (data meaning) layers. For example, the control instructions of air conditioning loads (such as start-stop adjustment) and V2G charging and discharging instructions lack unified semantic mapping rules at the protocol stack layer, resulting in time-consuming protocol conversion and semantic alignment during data fusion. This heterogeneity makes the EV cluster response time delay far exceed the second-level scheduling requirement, directly hindering the real-time collaboration of vehicle-to-grid interactive resources.

[0007] On the other hand, the existing V2G-VPP economic scheduling scheme presents a gradient decline in V2G aggregation efficiency when expanded across regions, further amplifying the V2G resource aggregation bottleneck due to the uncoupling of EV user elasticity differences and regional characteristics:

[0008] (1) EV incentive efficiency dimension missing: The current uniform subsidy strategy has a problem of confusing the response levels of EV users. Low-elasticity EV users (such as rigid commuters) have a marginal effect close to zero on their response threshold due to their fixed behavior patterns, and excessive subsidies result in resource waste. The response ability of high-elasticity EV users (such as ride-hailing drivers) changes dynamically, and a single incentive cannot match their nonlinear behavior characteristics, resulting in the loss of high-quality V2G resources.

[0009] (2) V2G cross-domain collaboration contradiction: Differences in regional power grid topology (such as urban distribution networks and rural microgrids) and climate-driven EV charging load characteristics (such as differences in air conditioner usage time between north and south) create deep conflicts with the existing model parameter solidification mechanism, resulting in complex model reconstruction when expanding across regions, which severely weakens the potential for large-scale deployment of V2G-VPP.

[0010] Chinese Patent CN120355157A discloses a virtual power plant load prediction and dynamic response method and system that integrates vehicle-to-grid interaction. The method includes: obtaining and processing charging history data to generate a user data set; extracting and analyzing the user data set to generate a charging behavior prediction result; real-time collection of traffic flow data and matching to generate a traffic influence coefficient; constructing a regional charging demand prediction model to generate a regional charging demand distribution; obtaining external environmental event data and processing to generate a sudden load correction value; fusing 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, thereby solving the load prediction deviation problem caused by user behavior randomness, traffic dynamic coupling, and unpredictability of sudden events, achieving precise modeling and collaborative optimization of multi-level dynamic factors, and improving the scheduling efficiency of virtual power plants for vehicle-to-grid interactive resources. However, this scheme still has the following unresolved technical defects:

[0011] (1) Lack of multi-source protocol adaptive fusion capability: The communication protocol deep heterogeneity problem between EV collaborative units such as photovoltaic, energy storage, air conditioner, etc. is not solved, and manual protocol conversion is required for multi-source data fusion, which takes a long time and far exceeds the requirement of second-level scheduling. This defect leads to the fact that the vehicle-to-grid interaction resources cannot be coordinated in real time, and the protocol conversion of different types of power sources and devices takes too long, which seriously affects the performance and operation efficiency of the V2G-VPP system.

[0012] (2) No dynamic priority matching mechanism: Fixed rules are used to preset priority, and no dynamic coupling function of power grid demand and resource state is established. During frequency modulation peak, high-priority fast charging piles may not be able to serve due to EV driving, and low-priority air conditioning loads are not activated due to the lack of semantic mapping of control instructions and charging and discharging instructions. Such fixed rules may lead to the failure of some high-priority resources, and also may cause some low-priority resources to be idle.

[0013] (3) User flexibility difference is not considered: User flexibility levels are not distinguished. Low-elasticity users (such as rigid commuters) are less sensitive to subsidies, and excessive subsidies may lead to resource waste; high-elasticity users (such as online taxi drivers) have dynamic response capabilities, and a single incentive mechanism may lead to the loss of high-quality resources. The essence of the above defects lies in the fact that the existing technology lacks multi-source protocol adaptive fusion capability (compatible with photovoltaic, energy storage, air conditioner, etc. EV collaborative units) for V2G core scenarios, and a dynamic matching mechanism for V2G resource value and power grid demand, leading to the three dilemmas of "V2G capacity virtual high-response lag-user loss" for the aggregation system.

[0014] Therefore, there is currently a need for a V2G-VPP collaborative aggregation method to solve the fundamental contradiction between V2G resource heterogeneity compatibility and real-time optimization scheduling. SUMMARY

[0015] The purpose of the present application is to provide a V2G-VPP resource aggregation method that fuses heterogeneity and dynamic priority to solve the fundamental contradiction between V2G resource heterogeneity compatibility and real-time optimization scheduling. Through protocol non-inductive access-elasticity grading evaluation-priority dynamic generation, the multiple problems of electric vehicle dispatchable capacity virtual high-response lag and user loss caused by multi-source protocol heterogeneity and rigid scheduling mechanism are solved, the utilization rate of vehicle-to-grid interaction resources is significantly improved, the system response time is compressed, and reliable technical support is provided for large-scale electric vehicles participating in power grid collaborative regulation.

[0016] The purpose of the present application can be achieved by the following technical solutions:

[0017] According to a first aspect of the present application, a V2G-VPP resource aggregation method that fuses heterogeneity and dynamic priority is provided, which comprises the following steps:

[0018] The communication data of various types of devices involved in the V2G-VPP is processed through the protocol non-sensing access, automatic analysis and conversion of cross-protocol device instructions are realized.

[0019] Based on the parsed and converted data, the line characteristics and social attributes are extracted, the user elastic quantization model is constructed, and the elastic coefficient is calculated.

[0020] The dynamic coupling priority function driven by the power grid demand and resource state is constructed by combining the power grid frequency modulation demand, the predicted moving probability and the elastic coefficient, the resource priority is dynamically evaluated, the resource priority scheduling sequence is generated and optimized based on the descending order sorting and screening of the resource priority of each electric vehicle, and the V2G-VPP resource aggregation is realized.

[0021] As a preferred technical solution, the protocol non-sensing access analyzes and converts the communication data through a protocol feature decoupler, a semantic standardization engine and a dynamic protocol adapter, wherein,

[0022] The protocol feature decoupler receives the original device communication data stream, constructs a protocol template library, establishes a hierarchical decoupled standardized description framework, decouples the features of the protocol key description dimension, and generates standardized device features independent of the protocol;

[0023] The semantic standardization engine maps the standardized device features and the standardized coding rules to construct a semantic mapping library for generating protocol parameter mapping;

[0024] The dynamic protocol adapter maps the standardized device feature description and the standardized coding rule to generate protocol parameter mapping when sending the standardized control instruction in the application layer, realizes the standardized processing and unified semantic expression of device data.

[0025] As a preferred technical solution, the key description dimension includes transmission channel features, data encapsulation format, instruction structure features and field semantic mapping relationship.

[0026] As a preferred technical solution, the dynamic protocol adapter realizes the dynamic adaptation of inter-device communication through the following steps to ensure seamless connection between different protocols:

[0027] Receive the standardized control instruction sent by the application layer to trigger the dynamic protocol adapter to start;

[0028] Parse the standardized control instruction to extract key parameters, the key parameters at least including device type and instruction type;

[0029] Based on the key parameters, the semantic standardization engine is called to find the corresponding protocol parameters in the semantic mapping library;

[0030] Value range, unit and threshold check is performed on the extracted protocol parameters to ensure the validity and consistency of the data;

[0031] The standardized control instruction is mapped with the protocol parameters to generate a dynamic message structure conforming to the target protocol, i.e., protocol parameter mapping;

[0032] Based on the dynamic message structure, a recognizable device dynamic message is constructed through binary stream encoding, XML document encapsulation and check code generation;

[0033] The device dynamic message is sent to a physical interface and transmitted to the target device through physical layer communication;

[0034] After the target device returns a response message, response verification is performed.

[0035] As a preferred technical solution, the user elasticity quantization model is expressed as:

[0036] ,

[0037] wherein, represents the travel chain feature, represents the social attribute, represents other related attributes, , , is the corresponding weight, and ω 1+ ω 2+ ω 3=1; is the elasticity coefficient.

[0038] As a preferred technical solution, the travel chain feature represents the characteristics of the user's travel behavior, which is determined based on the schedulable period and the commuting distance:

[0039] ,

[0040] wherein, T free represents the user's free schedulable time, T total represents the total time of a day, D commute represents the user's daily commuting distance, D max represents the maximum commuting distance reference value;

[0041] The social attribute is determined based on the occupation type and the electricity price sensitivity:

[0042] ,

[0043] wherein, S jobThis 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;

[0044] The other relevant attributes refer to other attributes that may affect user resilience, determined based on vehicle type and charging facility usage frequency:

[0045] ,

[0046] 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.

[0047] As a preferred technical solution, the dynamic coupling priority function is expressed as follows:

[0048] ,

[0049] 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.

[0050] 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:

[0051] 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.

[0052] The resource priority is updated according to the following rules to optimize the resource priority scheduling sequence:

[0053] The grid demand weight coefficient is dynamically adjusted according to the grid frequency deviation, and when the grid frequency deviation exceeds a preset threshold, the grid demand weight coefficient is increased to a preset value to update the resource priority;

[0054] When the vehicle movement probability exceeds a preset threshold, the resource priority of the corresponding electric vehicle is reduced according to a preset rule to ensure the flexibility of the scheduling strategy.

[0055] As a preferred technical solution, the method further comprises:

[0056] The resource elasticity level is divided according to the elasticity coefficient, including three levels of high, medium and low;

[0057] The power resource is allocated in sequence according to the optimized resource priority scheduling sequence:

[0058] ,

[0059] wherein, P i represents the allocated power of the i-th resource; i i represents the elasticity coefficient of the i-th resource; α total represents the total schedulable power; i represents the sum of the elasticity coefficients of all high elasticity level resources; P total represents the total schedulable power; k represents the adjustment coefficient dynamically adjusted according to the demand urgency.

[0060] As a preferred technical solution, the method further comprises:

[0061] The power output of each resource is monitored in real time, and the power allocation is dynamically adjusted according to the grid frequency deviation and the resource state, and when the grid frequency returns to normal or the resource state changes, the adjustment coefficient k adjusts the power allocation to ensure stable operation of the system;

[0062] When the sum of the schedulable power of the high elasticity level electric vehicles exceeds the total schedulable power, the sum of the schedulable power of the high elasticity level electric vehicles is reduced to the total schedulable power through redundancy design, and the part of the sum of the schedulable power of the high elasticity level electric vehicles exceeding the total schedulable power is transferred and allocated to other electric vehicle users as a certain proportion of emergency power resource for coping with emergency situations to ensure the rapid response capability in emergency scenarios, wherein the other electric vehicle users are determined by comprehensively considering the dynamic resource priority, the elasticity coefficient and the movement probability.

[0063] According to a second aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method when executing the program.

[0064] According to a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the method.

[0065] Compared with the prior art, the present application has the following beneficial effects:

[0066] (1) Multi-source protocol adaptive fusion: The present application realizes automatic analysis and conversion of cross-protocol device instructions through protocol non-inductive access technology. Specifically, the present application automatically identifies device protocol types, analyzes instruction semantics and converts them into a unified format, eliminating protocol conversion time consumption. Taking photovoltaic- energy storage system coordination as an example, after conversion efficiency is improved, response time is compressed from hours to seconds, solving real-time coordination obstacles, improving resource aggregation efficiency, and making V2G cluster response meet the needs of grid second-level scheduling.

[0067] (2) Dynamic priority evaluation mechanism: The present application constructs a user elasticity quantization model, combines grid frequency modulation demand, predicted mobile probability and elasticity coefficient to generate a double-driven dynamic coupling priority function. This function real-time matches grid transient fluctuation and EV mobile state, avoiding resource mismatch caused by fixed rules. At the frequency modulation peak, the system preferentially schedules resources that are available and have strong response ability, while activating air conditioning loads with low priority but available to participate in frequency modulation, realizing the reduction of schedulable capacity evaluation error, and solving the structural contradiction between high priority failure and low priority idling.

[0068] (3) User elasticity differentiated incentive: The present application extracts user trip chain characteristics and social attributes, quantizes user elasticity coefficient, and designs high / low elasticity user differentiated incentive strategy. Based on the elasticity coefficient resource allocation, moderate subsidies are used for low elasticity users to avoid waste, and dynamic incentives are used for high elasticity users to match their nonlinear behavior 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. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 is a system framework schematic diagram of the V2G-VPP of the present application;

[0070] Figure 2 is a method flowchart of the present application;

[0071] Figure 3 is a processing process schematic diagram of the protocol non-inductive access of the present application;

[0072] Figure 4Process diagram for decoupling protocol features of the present invention from the process;

[0073] Figure 5 Process diagram for data interaction between the semantic standardization engine and the dynamic protocol adapter of the present invention;

[0074] Figure 6 Process diagram for the processing of the dynamic protocol adapter of the present invention. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the protection scope of the present application.

[0076] Unless otherwise defined, technical terms or scientific terms used in the present application should be understood as the common meanings thereof by those of ordinary skill in the art to which the present application pertains. The terms "one", "a", "an", "the", and similar terms in the present application do not denote a singular number or quantity but include a plural number or quantity, unless otherwise defined. The terms "comprise", "comprising", "include", "including", "have", "having", and any variations thereof in the present application are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a list of steps or modules (units) is not limited to the listed steps or units, but can further include other steps or units not listed or can further include other steps or units inherent to such a process, method, system, product, or device. The terms "connect", "connected", "coupling", and similar terms in the present application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "multiple" in the present application refers to two or more. The term "and / or" describes the association relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. The character " / " generally means that the associated objects before and after are in an "or" relationship. The terms "first", "second", "third", and the like in the present application are only to distinguish similar objects, and do not represent a specific order for the objects.

[0077] Embodiment 1

[0078] The embodiment provides a V2G-VPP resource aggregation method fusing heterogeneous and dynamic priorities, which is realized by three parts of protocol non-inductive access, elastic evaluation and dynamic priority generation. The protocol non-inductive access realizes automatic analysis and conversion of cross-protocol device instructions through a three-level processing mechanism, supports plug-and-play and dynamic expansion of new devices. The elastic evaluation fuses travel chain characteristics and social attributes, constructs a user elastic quantification model, accurately distinguishes high, medium and low elastic resources, and improves the identification accuracy of high-value resources. The dynamic priority generation designs a dynamic coupling priority function driven by power grid demand and resource state, dynamically evaluates the priority of resources, and generates a resource priority scheduling sequence. The power allocation scheme optimizes resource scheduling and power allocation according to the priority evaluation results, ensures the frequency modulation effect of the power grid and the user satisfaction. The typical case test verifies the efficiency and universality of the scheme under the resource diversity and power grid fluctuation scenario.

[0079] The system framework of V2G-VPP is shown in Figure 1 . The V2G-VPP system realizes multi-source heterogeneous resource aggregation through protocol non-inductive technology: photovoltaic, energy storage, adjustable load and EV charging piles access the VPP resource module through different protocols, and integrate real-time state data; the centralized control center evaluates the EV scheduling sequence based on the dynamic priority algorithm, considers the power grid demand, price fluctuation and vehicle response elasticity, and optimizes the resource calling sequence. High-elastic EV reverse power supply is preferentially scheduled during peak period, and energy storage charging or load power consumption is guided during valley period. Through multi-protocol compatibility and dynamic priority optimization, the resource utilization rate and the power grid response speed are significantly improved, and the efficient and stable operation of the V2G-VPP system is ensured.

[0080] Based on the system architecture shown in Figure 1 , as shown in Figure 2 , the V2G-VPP resource aggregation method fusing heterogeneous and dynamic priorities provided by the embodiment comprises the following steps:

[0081] S1, the communication data of various types of devices involved in the V2G-VPP is processed through protocol non-inductive access, realizing automatic analysis and conversion of cross-protocol device instructions;

[0082] S2, based on the analyzed and converted data, the travel chain characteristics and social attributes are extracted, a user elastic quantification model is constructed, and an elastic coefficient is calculated;

[0083] S3, the dynamic coupling priority function driven by power grid demand and resource state is constructed by combining the power grid frequency modulation demand, the predicted moving probability and the elastic coefficient, the priority of resources is dynamically evaluated, the resource priority scheduling sequence is generated and optimized based on the descending order sorting and screening of the priorities of each electric vehicle, and the V2G-VPP resource aggregation is realized.

[0084] This step represents the following three core innovations of the present application:

[0085] (1) Multi-source protocol non-inductive adaptive access mechanism: In view of the response time delay problem caused by heterogeneous protocols of photovoltaic, energy storage, vehicle-to-grid interaction equipment and air conditioners, the application first creates a protocol syntax-semantic bidirectional analysis engine. By constructing a standardized information tuple model, heterogeneous instructions such as ISO 15118 (V2G), Modbus (photovoltaic), BACnet (air conditioner) are dynamically mapped to unified control semantics, realizing "plug and play" and automatic protocol adaptation. Breakthrough the time delay bottleneck of traditional conversion, the protocol conversion efficiency is improved by 16 times (actual delay ≤50ms), which completely meets the demand of grid second-level regulation.

[0086] (2) EV elastic classification model based on travel chain and social attributes: In order to solve the resource mismatch caused by the difference in user response elasticity, an elastic quantification method combining time and space behavior and social characteristics is proposed. Based on the vehicle GPS trajectory, the travel chain characteristics such as commuting distance and schedulable time period are mined, and the dynamic weight coefficient is constructed combined with the social attributes such as occupation type and electricity price sensitivity, realizing the accurate classification of EV users from "rigid commuters" to "highly elastic online taxi". This model can significantly improve the accuracy of identifying high-value resources and avoid user loss and capacity bubbles caused by inefficient subsidies.

[0087] (3) Grid-resource dual-driven dynamic priority decision, resource calling and power allocation model: Breakthrough the structural defects of static priority rules, design the real-time coupling function of grid frequency regulation demand margin and EV moving probability. Taking the grid frequency deviation as the demand driving item, the vehicle moving probability as the state constraint item, and the user elasticity coefficient as the value adjustment item, the resource calling priority sequence is dynamically generated. When the vehicle moving probability exceeds the threshold, the unreachable resources are automatically de-weighted, realizing the millisecond-level synchronization of scheduling strategy, grid fluctuation and resource state, and greatly reducing the V2G schedulable capacity evaluation error.

[0088] The vehicle-to-grid interaction scene involves the multi-source protocol differences of photovoltaic, energy storage, EV charging piles, air conditioners and other devices, including: (1) syntax heterogeneity: message format difference; (2) semantic conflict: logical contradiction of similar instructions; (3) real-time bottleneck: traditional protocol conversion needs manual configuration, which cannot meet the second-level response demand of the grid.

[0089] In a preferred embodiment, as shown in Figure 3 The protocol non-inductive access of the present embodiment analyzes and converts communication data through the protocol feature decoupler, semantic standardization engine and dynamic protocol adapter, solves the protocol compatibility problem of multi-source heterogeneous energy equipment, establishes a unified device communication interface layer, and synchronously realizes the following functions:

[0090] (1) Automatic analysis and conversion of cross-protocol device instructions

[0091] (2) Decoupling of upper-layer applications and lower-layer device protocols

[0092] (3) Plug and play multi-source devices

[0093] (4) Dynamic expansion capability of new equipment protocols

[0094] 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.

[0095] 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;

[0096] 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.

[0097] 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.

[0098] 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:

[0099] ,

[0100] 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 ω 1+ ω 2+ ω 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), with a ≥ 0.7; fixed dispatchable period for commuter vehicles (X1 low), with a ≤ 0.3. α

[0101] 1. Travel chain features

[0102] Features representing user travel behavior, determined based on dispatchable period and commuting distance, reflecting user flexibility in time and space:

[0103] ,

[0104] where, T free represents the user's free dispatchable time (hours), T total represents the total time of a day (24 hours), D commute represents the user's average daily commuting distance (kilometers), D max represents the maximum commuting distance reference value (such as the average commuting distance in the city); T free / T totale represents the proportion of user dispatchable time, the higher the value, the greater the user's time flexibility; D commute / D max represents the proportion of user commuting distance relative to the average commuting distance in the city, the higher the value, the lower the user's spatial flexibility.

[0105] 2. Social attributes

[0106] Table represents user social attribute features, determined based on occupation type and price sensitivity, reflecting the degree of user response to price changes and the influence of social roles:

[0107] ,

[0108] where, S job represents the user's occupation type score (based on occupation flexibility and job nature, 1-5 points), S max represents the highest occupation type score (default 5 points), P sensitivity represents the user's price sensitivity score (1-5 points), P max represents the highest price sensitivity score (default 5 points); S ​job / S max represents the relative flexibility of the user's occupation type, with higher values indicating that the user's occupation allows more flexibility; P sensitivity / P max represents the user's sensitivity to price changes, with higher values indicating that the user is more concerned about price changes.

[0109] 3. Other related attributes

[0110] represents other attributes that may affect user flexibility, determined based on vehicle type and charging facility usage frequency, reflecting the user's technical conditions and infrastructure dependency:

[0111] ,

[0112] wherein, V type represents the vehicle type score (based on vehicle flexibility and suitability, 1-5 points), V max represents the highest vehicle type score (default 5 points), F charge represents the charging facility usage frequency score (1-5 points), F max represents the highest charging facility usage frequency score (default 5 points); V type / V max represents the relative flexibility of the user's vehicle type, with higher values indicating that the vehicle is more suitable for flexible scheduling; F charge / F max represents the user's dependence on charging facilities, with higher values indicating that the user is more likely to find charging facilities.

[0113] To overcome the structural defects of static priority rules, a real-time coupling function of grid frequency regulation demand margin and EV movement probability is designed. Taking grid frequency deviation as the demand driving item, vehicle movement probability as the state constraint item, and user flexibility coefficient as the value adjustment item, the resource calling priority sequence is dynamically generated, and the "grid-resource dual-driven dynamic priority decision, resource calling and power allocation model" is constructed. When the vehicle movement probability exceeds the threshold, the inaccessible resources are automatically devalued, realizing the millisecond-level synchronization of scheduling strategy and grid fluctuation, resource state, and significantly reducing the evaluation error of V2G schedulable capacity.

[0114] Specifically, the dynamic coupling priority function driven by grid demand-resource state is represented as:

[0115] ,

[0116] 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.

[0117] 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:

[0118] Priority scheduling sequence = { EVi | >Ψ th},according to Sort in descending order.

[0119] in, Ψ th =0.4 is the priority failure threshold, used to automatically remove unreachable resources.

[0120] 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.

[0121] The optimization steps are as follows:

[0122] 1) Real-time resource status assessment

[0123] ① Motion probability assessment: Predict the motion probability of each vehicle based on GPS trajectory. P m,i .

[0124] ② Failure detection: Set failure threshold Ψ th =0.4, when P m,i > Ψ thWhen the resource is determined to be an unreachable resource, it is automatically removed.

[0125] ③ Health status check: Regularly check the battery status of the vehicle, the status of the charging facility, etc. to ensure the availability of the resource.

[0126] 2) Dynamic priority adjustment

[0127] ① Demand-driven adjustment: dynamically adjust the demand weight coefficient according to the grid frequency deviation Δ f . Specifically, when Δ f > 1 Hz, the demand weight coefficient is automatically increased to 0.8.

[0128] ② State constraint adjustment: when the vehicle movement probability P m,i > 0.6, its priority is appropriately reduced to ensure the flexibility of the scheduling strategy.

[0129] ③ Elasticity coefficient adjustment: adjust the value adjustment term of the resource according to the user elasticity coefficient α . High elasticity resources are preferentially scheduled, and low elasticity resources are used as backup.

[0130] 3) Generation of scheduling queue:

[0131] ① Sorting rule: generate a priority-ordered scheduling queue according to the comprehensive score of demand weight coefficient, movement probability and elasticity coefficient.

[0132] ② Real-time update: the scheduling queue is updated every second to ensure millisecond-level synchronization with grid fluctuations and resource status.

[0133] ③ Redundancy design: reserve a certain proportion of redundant resources for critical tasks to ensure the reliability of the system in extreme cases. The specific method is: based on the protocol non-inductive access technology to build a cross-device redundant resource pool, realize the semantic mapping and data fusion of heterogeneous units such as photovoltaic, energy storage and air conditioner; combined with the user elasticity quantization model to calculate the elasticity coefficient, higher redundancy proportion is reserved for the resources corresponding to high elasticity users to cope with behavior fluctuations; through the dynamic priority evaluation mechanism, according to the grid frequency regulation demand and resource state double-driven function, real-time update of the scheduling queue, low priority available redundant resources are included, and a scheduling sequence containing redundant resources is generated, to ensure that the system can still maintain reliable response in extreme cases such as grid fluctuations or device failures, and improve the fault tolerance ability and scheduling efficiency of V2G-VPP resource aggregation.

[0134] Embodiment 2

[0135] This embodiment provides specific implementation method of the three core components of protocol non-inductive access based on embodiment 1.

[0136] I. Protocol feature decoupler

[0137] (1) Technical principle: Protocol feature abstraction is achieved through protocol metadata description.

[0138] (2) Basic implementation: By constructing a protocol template library, a standardized description framework is established to achieve uniform mapping of protocol features from the physical layer to the application layer.

[0139] (3) Key description dimensions include:

[0140] 1) Transmission channel characteristics (TCP / UDP / serial port)

[0141] 2) Data encapsulation format (binary / XML / JSON)

[0142] 3) Instruction structure characteristics (frame header / address field / data field / verification)

[0143] 4) Field semantic mapping relationship

[0144] (4) The main workflow is shown in Figure 4 , which mainly includes:

[0145] 1) Data collection stage: Receive raw device communication data stream;

[0146] 2) Feature analysis stage: Decouple the three core dimension features of the protocol;

[0147] 3) Template construction stage: Generate structured metadata based on the protocol template library and establish protocol feature index;

[0148] 4) Abstract output stage: Generate protocol-independent device parameter model to get standardized device feature description and pass it to the upper layer system.

[0149] (4) Typical protocol description example

[0150] Taking ISO 15118 protocol and BACnet protocol as an example, both protocols are modeled through metadata to convert heterogeneous protocols into programmable semantic descriptions, supporting plug-and-play and automatic scheduling of energy Internet of Things.

[0151] 1) ISO 15118 protocol

[0152] ISO 15118 protocol uses XML tree structure to describe the communication interaction between electric vehicles (EV) and charging piles (EVSE), and realizes machine-readable charging and discharging instructions through structured XML tags, compatible with OCPP and other protocols, solving the interoperability problem of charging piles from different manufacturers. Its core data model includes:

[0153] ① Requested Energy Mode: define the charging mode (AC / DC), power level and V2G bidirectional control flag;

[0154] ② EAmount: accurately describe the requested charging / discharging energy (unit: Wh) of the vehicle, support dynamic adjustment;

[0155] ③ EVMaxCurrent: constrain the upper limit of charging current, ensure the safety of the battery.

[0156] 2) BACnet protocol

[0157] BACnet protocol uses the "object-attribute" model to abstractly describe building equipment, and the typical structure includes

[0158] ① Object type: such as analog input (Analog Input) mapping temperature sensor data, binary object (Binary Output) mapping lighting switch status;

[0159] ② Attribute value: such as temperature set value (Present_Value), device start-stop state (Reliability), etc., support dynamic reading and writing.

[0160] ③ Technical effect: through standardized object identification (such as Device ID=1001) and attribute coding, shield the difference of underlying hardware, realize the unified control of HVAC, lighting and other cross-brand equipment.

[0161] II. Semantic standardization engine

[0162] (1) Workflow

[0163] The semantic analysis process is the core link of the standardization engine, which realizes the standardization processing and unified semantic expression of device data through the cooperative work of multiple modules, realizes the automatic translation of "device function-protocol instruction", and the upper layer application does not need to perceive the difference of underlying protocol. The semantic analysis process is shown in Figure 5 , which mainly includes:

[0164] (1) Application layer: send standardized instructions (such as "query device power"), trigger the semantic engine to start the analysis process;

[0165] (2) Semantic standardization engine: map the parameters output by the protocol feature decoupler with the standardization coding rules to generate a unified format data structure;

[0166] (3) Semantic mapping library (protocol parameter mapping): according to the device type, match the corresponding semantic label and standardization coding rule, and build the semantic mapping library (protocol parameter mapping);

[0167] (4) Protocol adapter (third level): The third level protocol adapter calls the semantic mapping library of the current level, extracts key parameters by parsing the original protocol data, and realizes the adaptation of devices and instruction types to protocols;

[0168] (5) Parameter feedback: Return the converted protocol parameter mapping for the upper layer application system to call.

[0169] This process ensures the consistency and readability of data between different devices, and improves the compatibility and scalability of the system. This process supports new and old devices through dynamic loading of semantic templates, ensuring data readability and system scalability.

[0170] (2) Semantic mapping library (protocol parameter mapping)

[0171] The semantic mapping library structure is designed by layering and decoupling to realize intelligent conversion between standardized instructions and heterogeneous protocols. Its core includes a device type index layer, an instruction semantic analysis layer, and a protocol parameter mapping layer.

[0172] Among them, the device type index layer serves as the entrance to the semantic engine, and identifies heterogeneous devices (such as EV, photovoltaic, and energy storage) in V2G-VPP through protocol non-sensing access technology. Based on the message header identification and data field structure, the device type-protocol index relationship is established, and the initial weight is allocated through dynamic priority evaluation to support subsequent resource scheduling classification.

[0173] The instruction semantic analysis layer combines the user flexible quantization model to analyze the instruction intent, extract the behavior characteristics and social attributes, and map the heterogeneous protocol instructions to unified semantic actions through the semantic mapping library, eliminating protocol semantic differences.

[0174] The protocol parameter mapping layer calls the device protocol parameter template according to the dynamic priority sequence, maps the standardized semantic actions to specific instructions, and realizes parameter automatic packaging and delivery through protocol non-sensing access technology, optimizes parameter values to reduce response time delay, and ensures real-time collaboration of cross-protocol devices.

[0175] The semantic mapping library establishes a unified mapping relationship through structured parameter templates (such as Modbus register address, BACnet object attribute, etc.), supports dynamic parameter constraint verification and multi-protocol adaptation, ensures accurate conversion of control instructions 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.

[0176] Table 1 Semantic mapping library (protocol parameter mapping) of V2G-VPP

[0177]

[0178] Three, Dynamic Protocol Adapter

[0179] (1) Workflow

[0180] As shown in the following figure, the dynamic protocol adapter realizes the dynamic adaptation of inter-device communication through the following steps, ensuring seamless connection between different protocols: Figure 6

[0181] 1) Receive the standardized control instruction sent by the application layer, trigger the dynamic protocol adapter to start;

[0182] 2) Analyze the standardized control instruction and extract key parameters, including at least device type and instruction type;

[0183] 3) Based on the key parameters, call the semantic standardization engine to find the corresponding protocol parameters in the semantic mapping library;

[0184] 4) Check the value range, unit and threshold of the extracted protocol parameters to ensure the validity and consistency of the data;

[0185] 5) Map the standardized control instruction with the protocol parameters to generate a dynamic message structure that conforms to the target protocol, i.e. protocol parameter mapping;

[0186] 6) Based on the dynamic message structure, construct a recognizable device dynamic message through binary stream encoding, XML document encapsulation and checksum generation;

[0187] 7) Send the device dynamic message to the physical interface and transmit it to the target device through physical layer communication;

[0188] 8) After the target device returns the response message, perform response verification.

[0189] The entire process realizes efficient conversion and interaction between different protocols through dynamic adaptation, improving the flexibility and compatibility of the system.

[0190] (2) Typical examples

[0191] Taking "EV sequence control, photovoltaic power control, air conditioning temperature control, energy storage system scheduling" as examples, a protocol dynamic adaptation process covering "instruction reception → protocol conversion → parameter mapping → dynamic generation" is established.

[0192] EV sequence control: For the charging start instruction, through OCPP protocol conversion, a JSON request (Remote Start Transaction) containing Transaction Id is generated, realizing the standardized control of electric vehicle charging process. The typical example of this control process is as follows:

[0193] ​Standardized instruction (set power 1500W) → Modbus protocol conversion → key parameter mapping (register address 0x4001) → binary encoding (0x05DC) → dynamic message construction

[0194] Photovoltaic power control: Standardized power setting instructions are converted through Modbus protocol, mapped to the specified register address (0x4001) and binary encoded (0x05DC), and finally executable dynamic messages are generated. A typical example of this control process is as follows:

[0195] Standardized instruction (set temperature 26℃) → BACnet protocol conversion → object attribute mapping (AnalogValue object, Present_Value attribute) → write operation (priority 8).

[0196] Air conditioning temperature control: Based on the standardized temperature control instruction, the object attribute write operation (Analog Value.Present_Value) of the BACnet protocol is converted, and the device parameter dynamic adjustment is realized through priority 8. A typical example of this control process is as follows:

[0197] Standardized instruction (charge power limit 20kW) → ISO 15118 protocol conversion → XML structure packaging (EAmount node, EVMaxCurrent node) → dynamic message generation

[0198] Energy storage system scheduling: The charge power limit instruction is adapted to the ISO 15118 protocol, and the dynamic message conforming to the electric vehicle communication standard is generated through the XML structured packaging (EAmount / EVMaxCurrent node). A typical example of this control process is as follows:

[0199] Standardized instruction (charge sequence start) → OCPP protocol conversion → transaction ID mapping (TransactionId) → JSON structure packaging (RemoteStartTransaction request)

[0200] Embodiment 3

[0201] This embodiment is based on embodiment 1 and provides a method of resource grading according to the size of the elasticity coefficient α . Specifically, the EV resources are divided into three levels to optimize resource allocation and improve system efficiency.

[0202] Table 2 EV resource elasticity grading strategy of V2G-VPP

[0203]

[0204] Embodiment 4

[0205] This embodiment provides a power allocation method based on embodiment 3.

[0206] (1) Optimization objective

[0207] While ensuring the efficient execution of dispatching strategies, power resources should be allocated rationally to maximize the grid frequency regulation effect and user satisfaction.

[0208] (2) Distribution principle

[0209] 1) Principle of prioritizing demand

[0210] 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.

[0211] 2) Principle of resource balance

[0212] 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.

[0213] 3) User Incentive Principles

[0214] 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.

[0215] (3) Real-time power allocation

[0216] 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.

[0217] The power distribution formula is:

[0218] ,

[0219] 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 the total dispatchable power; k This indicates an adjustment coefficient that is dynamically adjusted based on the urgency of the demand.

[0220] (4) Power adjustment mechanism

[0221] As a preferred technical solution, the method further comprises:

[0222] Real-time monitoring of the power output of each resource, dynamic adjustment of power distribution according to the grid frequency deviation and resource state, and adjustment of the adjustment coefficient when the grid frequency returns to normal or the resource state changes k Adjust the power distribution to ensure stable operation of the system.

[0223] In addition, based on the resource elasticity level divided in Example 3, when the sum of the dispatchable power of the high-elasticity-level electric vehicles ∑ P 高弹性 exceeds the total dispatchable power P total , the sum of the dispatchable power of the high-elasticity-level electric vehicles ∑ P 高弹性 is reduced to the total dispatchable power P total , and the sum of the dispatchable power of the high-elasticity-level electric vehicles ∑ P 高弹性 exceeds the total dispatchable power P total The part ∑ P 高弹性超出 =∑ P 高弹性 －P total is transferred to other EV users with higher dynamic priority, higher elasticity coefficient, and lower moving probability, and is reserved as a certain proportion of emergency power resources to cope with emergencies and ensure rapid response capability in emergency scenarios.

[0224] Example 5

[0225] This embodiment provides a typical test case of a resource aggregation method based on Examples 1-4 to illustrate the effectiveness of the method.

[0226] 1. Case background

[0227] Select 10 EV users in a city to participate in V2G-VPP frequency modulation services, select high-value resources through an elasticity grading model, generate a priority sequence based on a dynamic coupling function, and finally verify the rationality of the V2G-VPP power distribution scheme and the adaptability of large-scale scheduling operation.

[0228] 2. Elasticity coefficient calculation α )

[0229] User parameter setting is based on user behavior characteristics and technical solution demand, covering professional type, charging habit and other dimensions, to ensure the diversity of new EV attributes, verify the universality of the patent technical solution in diversified scenarios and the effectiveness of resource scheduling strategy, as shown in Table 3.

[0230] Table 3 User parameter setting of typical test cases

[0231]

[0232] Take EV1-EV10 as an example, wherein, T total =24 hours; the average maximum commuting distance of the above 10 EV users is set to D max =35km, showing the elastic coefficient calculation process.

[0233] (1) Travel chain characteristics (X1) X 1)

[0234] According to the formula and parameter setting, the calculation formula of X1 is as follows.

[0235] X 1= T free / 24+ D commute / 35

[0236] The calculation result of X1 is shown in Table 4.

[0237] Table 4 Calculation result of X1

[0238]

[0239] (2) Social attributes (X2) X 2)

[0240] According to the formula and parameter setting, X 2 calculation formula is as follows.

[0241] X 2 =S job / 5 +P sensitivity / 5

[0242] The calculation result of X2 is shown in Table 5.

[0243] Table 5 Calculation result of X2

[0244]

[0245] (3) Other attributes (X3) X 3)

[0246] According to the formula and parameter setting, X 3The calculation formula is as follows.

[0247] X 3= V type / 5+ F charge / 5

[0248] The calculation results of X3 are shown in Table 6.

[0249] Table 6 Calculation results of X3

[0250]

[0251] (4) Elasticity coefficient (K) α )

[0252] The typical weight coefficient can be set as: ω 1=0.4, ω 2=0.3, ω 3=0.3. The calculation formula of the elasticity coefficient (K) is as follows: α

[0253] α =0.4 X 1+0.3 X 2+0.3 X 3

[0254] The calculation results of the elasticity coefficient (K) are shown in Table 7. α Table 7 Calculation results of the elasticity coefficient (K)

[0255] α

[0256]

[0257] Resource elasticity classification

[0258] According to the calculation results of the elasticity coefficient of each EV user and the resource elasticity classification strategy, EV1-EV10 are divided into different resource elasticity levels, as shown in Table 8.

[0259] Table 8 EV resource classification strategy of V2G-VPP

[0260]

[0261] 3, Dynamic priority generation Ψ i )

[0262] (1) Parameter setting

[0263] Suppose the power grid frequency deviation Δf of a certain period is 0.05 Hz, the power grid frequency deviation Δf of the previous period is 0.03 Hz, and the power grid frequency deviation Δf of the next period is 0.02 Hz. f ​​​=1.2Hz indicates that the power grid is in urgent frequency regulation demand, and the demand weighting coefficient is set to λ =0.8.

[0264] 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.

[0265] (2) Dynamic priority

[0266] Dynamic priority of each EV user Ψ i The calculation formula is as follows:

[0267] Ψ i =0.8×1.2×(1− P m,i )+(1−0.8)× α i

[0268] The calculation results are shown in Table 9.

[0269] Table 9 Dynamic Priority Ψ i Calculation results

[0270]

[0271] (3) EV user priority ranking

[0272] Based on the calculation results in Table 9, EV users are ranked from highest to lowest priority as follows.

[0273] EV5>EV3>EV9> EV7> EV1> EV4= EV10>EV8>EV2>EV6.

[0274] (4) Priority scheduling sequence generation

[0275] According to the calculation results of the above dynamic priority, the priority of the lowest EV user priority EV6 is Ψ 6 Ψ th =0.4, indicating that all EVs meet the minimum elastic response standard. Therefore, all EV1~EV10 vehicles can be included in the V2G-VPP scheduling sequence, arranged in order from high to low according to the priority of each EV user, and the priority scheduling sequence is as follows:

[0276] Priority scheduling sequence={EV5, EV3, EV9, EV7, EV1, EV4, EV10, EV8, EV2, EV6};

[0277] The V2G-VPP scheduling center can develop a scheduling order for EV users according to the priority scheduling sequence and order.

[0278] 4. Power allocation verification

[0279] (1) Parameter setting

[0280] Let the total schedulable power of V2G-VPP be P total =200kW, and the adjustment coefficient under the emergency demand scenario be set to k =1.2, then the calculation formula of the allocated power of each EV user is as follows.

[0281] P i =1.2× α i / ∑α 高弹性 ×200

[0282] ∑ α 高弹性 The total elasticity coefficient of EV high elasticity resources is represented by

[0283] ∑ α 高弹性 = α 1+ α 3+ α 4+ α 5+ α 7+ α 8+ α 9+ α 10 =10.82

[0284] The calculation results of the allocated power of each EV user are shown in Table 10. ​

[0285] Table 10 Power allocation P i The calculation result

[0286]

[0287] wherein the total power of the high-elasticity resource is

[0288] ∑ P 高弹性 = P 1+ P 3+ P 4+ P 5+ P 7+ P 8+ P 9+ P 10 = 206.14 kW

[0289] ∑ P 高弹性 The part of power exceeding the total schedulable power P total = 200 kW is:

[0290] ∑ P 高弹性超出 = 206.14-200 = 6.14 kW

[0291] The total schedulable power of the high-elasticity resource can be automatically reduced to 200 kW by redundancy design (proportionally adjusted in actual allocation). Among them, EV5 can obtain the maximum excess power allocation due to the highest dynamic priority, higher elasticity coefficient and lowest moving probability, and other excess power can be allocated to EV3 and EV9 and other users to meet the priority call under the emergency frequency modulation demand.

[0292] 5. Case test summary

[0293] Through the selection of 10 EV users in a city participating in V2G-VPP frequency modulation service (10 EVs) for large-scale testing, the following key technologies are verified to be effective and advanced:

[0294] (1) The elasticity grading model based on the quantitative method of the integration of travel chain characteristics and social attributes can effectively screen the V2G-VPP high-elasticity resource (a total of 8 high-elasticity EV resources, accounting for 80%), guarantee the callable response ability of the V2G-VPP high-elasticity resource, and avoid inefficient subsidies.

[0295] (2) The dynamic priority algorithm realizes millisecond-level response in the emergency frequency modulation scene by coupling different EV user attribute weights and grid demand, generates a priority resource call sequence, and meets the real-time scheduling demand of the grid.

[0296] (3) The power allocation mechanism adjusts through a proportional coefficient, redundant design and power optimization allocation to ensure the fairness and stability of large-scale V2G-VPP resource scheduling, and to meet the priority call response demand in the emergency scene.

[0297] (4) The case design covers resource diversity (occupation, vehicle type gradient) and power grid fluctuation scene (normal / urgent frequency modulation), verifies the efficiency and universality of the technical scheme in the application of large-scale V2G-VPP, and provides core algorithm support for the commercialization of V2G-VPP.

[0298] Embodiment 6

[0299] The electronic device of the present application includes a central processing unit (CPU) that can perform various appropriate actions and processes in accordance with computer program instructions stored in a read-only memory (ROM) or computer program instructions loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0300] A plurality of components in the device are connected to the I / O interface, including: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, a speaker, etc.; a storage unit such as a magnetic disk, an optical disk, etc.; and a communication unit such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0301] The processing unit performs the various methods and processes described above, such as methods S1-S3. For example, in some embodiments, methods S1-S3 can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1-S3 described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform methods S1-S3 by any other appropriate means (e.g., by means of firmware).

[0302] The functions described above in the specification can be implemented, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0303] 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.

[0304] 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.

[0305] 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. 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; The elastic coefficient; 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 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 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; 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. This represents the power grid demand weighting coefficient; Indicates the first i The elasticity coefficient of an electric vehicle.

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 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.

6. 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.

7. The V2G-VPP resource aggregation method integrating heterogeneity and dynamic priority as described in claim 6, 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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