A power distribution and utilization safety intelligent management and control method and system for vehicle-network interaction

Abstract

A kind of face-to-face vehicle network interaction's matching power safety intelligent management and control method and system.In the vehicle network interaction system, the multi-source heterogeneous data of EV charging process is collected and the actual influence of various data on charging station is evaluated;According to the value of influence index, the corresponding interval of safety level is set, and different control measures are implemented according to the safety level of charging station node;Among them, for the charging station node with good safety level, a two-dimensional classification mapping mechanism of charging demand and SOC, discharge power and discharge time is established, and different power and price strategies are preset respectively;For the charging station node with poor safety level, the influence matrix of the node is constructed by using the influence index of all charging piles;By analyzing the influence weight of fault items in the matrix, the fault source is located, and the differentiated fault disposal process is executed.The application can adjust the operation mode of charging pile in real time, optimize power distribution, reduce the risk of power grid failure and ensure the stable operation of power grid.

Description

Technical Field

[0001] This invention relates to the field of power distribution network safety management, and more specifically, to a method and system for intelligent control of power distribution safety oriented towards vehicle-grid interaction. Background Technology

[0002] With the increasing popularity of electric vehicles, vehicle-to-grid (V2G) interaction systems are gradually taking shape and becoming more mature. The charging and discharging process of electric vehicles affects the safety of the vehicles themselves and the stability of the power distribution network. Especially in the context of large-scale charging, the power demand of charging piles, concentrated charging periods, and charging behavior have a profound impact on the fluctuation of power distribution network load. How to effectively monitor, evaluate, and manage the interaction between electric vehicles and the power distribution network in V2G systems has become an important research topic in the fields of power system and smart grid technology.

[0003] Patent application CN105024432A utilizes processes such as load forecasting, vehicle information collection, power optimization, and user-driven decision-making to achieve peak shaving and valley filling for electric vehicle clusters, smoothing load curves in large-scale clusters and balancing power supply and demand with user economic benefits. Patent application CN120474043A uses a benefit-cost trade-off algorithm to dynamically classify users and formulate differentiated incentive strategies. While ensuring charging demand and grid security, it improves user response rates, reduces hidden costs, and maximizes the aggregator's profits in the energy-frequency regulation market. Patent application CN118195239A, based on the mobile energy storage characteristics of electric vehicles, improves renewable energy absorption, reduces grid peak-valley differences and fluctuations, enhances system stability, alleviates traffic congestion, and achieves coordinated operation of the power-transportation network and a balance of interests among multiple parties.

[0004] However, there are still shortcomings in real-time data sharing and intelligent analysis between electric vehicle charging facilities and power distribution network monitoring systems. Real-time transmission and in-depth analysis of key information such as charging load and charging pile status have not yet been fully realized. This results in limited intelligence in power grid load scheduling and optimization, failing to fully assess the real-time impact of electric vehicle charging behavior on power grid stability, and leaving room for further improvement in data sharing and intelligent analysis capabilities. Summary of the Invention

[0005] The main objective of this invention is to provide an intelligent management and control method for power distribution safety oriented towards vehicle-grid interaction. Through the collection of multi-source heterogeneous data, the application of adaptive weighting algorithms, and the research of multi-dimensional matrix management strategies, this method improves the safety, stability, and intelligent management level of the power grid system. This method can not only intelligently assess the impact of electric vehicle charging behavior on the power grid, but also pinpoint the specific fault type of a charging pile at an electric vehicle charging station node, adjust the charging pile's operation mode in real time, optimize power distribution, reduce the risk of power grid faults, and ensure the stable operation of the power grid.

[0006] The first aspect of this application discloses a method for intelligent control of power distribution safety for vehicle-to-grid interaction, which adopts the following technical solution: Under the vehicle-to-grid (V2G) interactive system, multi-source heterogeneous data of the EV charging process are collected; based on the pre-processed multi-source heterogeneous data, the influence index of charging station nodes and charging piles is constructed to assess the actual influence of various data on the charging station. Based on the value of the influence index, a corresponding range of safety levels is set, and the safety performance of charging station nodes is assessed; different control measures are implemented according to the safety level of the charging station nodes. Among them, the control measures for charging station nodes with a good safety level are to establish a two-dimensional hierarchical mapping mechanism between charging demand and SOC, and between discharge power and discharge duration, and to preset differentiated power and electricity price strategies respectively. The control measures for charging station nodes with poor safety levels are as follows: construct an influence matrix for the node based on the influence index of all charging piles within the node; locate the source of the fault by analyzing the influence weight of the fault items in the matrix, and implement differentiated fault handling procedures.

[0007] Furthermore, the calculation process of the influence index includes: For the The influence index of each of the aforementioned charging station nodes This is the sum of the influence indices of all charging piles in this node; For nodes The first in Each charging station, its influence index This is the weighted sum of the influence of all types of data on the charging station on that charging station.

[0008] Furthermore, the calculation process for the impact of various data on charging piles includes: For nodes The first in The first charging pile For data of this type, the difference between the actual value and the reference value is calculated, and the absolute value of the difference is then compared with the reference value. Compare the ratio to 0; the larger of the two is the first value. Class data pairs nodes The first in The impact of a single charging station.

[0009] Furthermore, the weighting of various data points on the influence of their respective charging piles is calculated as follows: For nodes The first in Data types of charging piles Its charging pile The influence weight is a fraction, with the numerator being the first... Class data for charging piles The influence of charging piles is the denominator. All types of data for charging piles The sum of their influence.

[0010] Furthermore, for charging station nodes with a safety level of "good," the specific control measures include: Determine the charging demand model for EV users and define the charging and discharging power constraints for electric vehicles; Establish a two-dimensional classification rule based on charging time and battery state of charge (SOC), dividing charging demand and SOC into multiple levels; exhaustively combine charging demand levels and SOC levels, and preset differentiated charging power percentages for each pair of combinations. A two-dimensional classification rule based on discharge power and discharge duration is established, dividing discharge power and discharge duration into multiple levels respectively; exhaustive combinations of discharge power level and discharge duration level are performed, and a differentiated discharge price coefficient is preset for each pair of combinations.

[0011] Furthermore, the process of locating the fault source includes: Charging piles with an influence index greater than or equal to a preset threshold in the influence matrix are marked as fault items; For each fault item, construct a weight matrix by representing the influence weights of all its data types; compare the influence weight of each data type with the mean of the weight matrix; and include the influence weights that are greater than or equal to the mean in the priority processing set. The data type corresponding to the maximum value in the priority processing set is identified as the source of the fault and treated as a safety response event.

[0012] Furthermore, the differentiated fault handling process includes: After physically isolating the fault items, the fault type is determined based on the fault source. The fault type includes internal faults of the charging pile and EV charging faults. For internal faults in the charging pile, guide the user to change charging locations; remove the faulty charging pile from the list of available devices and update the next charging pile impact calculation.

[0013] The second aspect of this application discloses a power distribution safety intelligent management and control system for vehicle-to-grid interaction, implementing the intelligent management and control method described in the first aspect of this application. The system includes: Charging station node evaluation module; used to collect multi-source heterogeneous data of EV charging process under vehicle-to-grid interaction system; based on the pre-processed multi-source heterogeneous data, construct the influence index of charging station nodes and charging piles, and evaluate the actual influence of various data on charging stations; Differential control module; used to set corresponding ranges for security levels based on the value of the influence index, to conduct safety performance assessment of charging station nodes; and to implement different control measures according to the security level of the charging station nodes. The load distribution and control module is used to manage and control charging station nodes with a good safety level; it establishes a two-dimensional hierarchical mapping mechanism between charging demand and SOC, and discharge power and discharge duration, and presets differentiated power and electricity price strategies respectively. The fault location and handling module is used to manage and control charging station nodes with poor safety levels. It constructs an influence matrix for the node based on the influence index of all charging piles within the node. By analyzing the influence weight of fault items in the matrix, it locates the fault source and executes differentiated fault handling procedures.

[0014] The beneficial effects of this invention are that, compared with the prior art, 1. By collecting and processing multi-source heterogeneous data in real time, an impact index for electric vehicle charging station nodes and charging piles was constructed. Through this comprehensive evaluation system, the vehicle-to-grid (V2G) system can accurately assess the actual impact of charging station nodes on the power distribution network in real time. Based on the impact index, the V2G system classifies the safety level of charging stations and takes corresponding control measures according to different safety levels. This intelligent assessment and control not only optimizes the load distribution of charging piles but also improves the robustness of the V2G system by precisely matching the charging power and duration of electric vehicles and adjusting the output power of charging piles in real time. This avoids problems such as excessive grid load or voltage fluctuations, achieving the grid load management goal of "peak shaving and valley filling."

[0015] 2. When a safety incident occurs at a charging station node, the fault item is marked using an influence matrix, and differentiated processing is performed based on the influence weight and priority of the fault item. Furthermore, different response strategies are adopted for different types of faults. For charging pile faults, the faulty pile is isolated from other charging piles, and users are guided to move their vehicles to other available charging piles. For electric vehicle charging faults, the "isolate vehicle, restore pile" approach ensures that the charging pile returns to normal operation. In addition, the safety of the power grid and electric vehicle users is also considered during the fault response process, effectively preventing the fault from spreading and protecting the privacy information of electric vehicle users, ensuring the safety and user experience during the electric vehicle charging process. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of the intelligent power distribution safety management method in this embodiment is shown. Figure 2 A schematic diagram of the process for security level control is shown in an embodiment of this disclosure; Figure 3 A flowchart illustrating an example of a well-classified security control method in an embodiment of this disclosure is shown. Figure 4 A schematic diagram of the system units of the intelligent power distribution safety management method in the embodiments of this disclosure is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0018] As an embodiment of this application, a specific implementation method for intelligent control and management of power distribution safety in vehicle-to-grid interaction is disclosed, referring to... Figure 1 , Figure 1 A flowchart illustrating the steps of an intelligent power distribution safety management method for vehicle-to-grid interaction according to an embodiment of this disclosure is shown.

[0019] S1: In one embodiment of this example, multi-source heterogeneous data of electric vehicle charging stations in the vehicle-to-grid interaction system are collected and preprocessed.

[0020] In vehicle-to-grid (V2G) interactive systems, the charging process of electric vehicles (EVs) and their interaction with the power grid generate a large amount of multi-source heterogeneous data. This multi-source heterogeneous data is collected, including: EV data: Charging process data, including charging current, voltage, SOC (state of charge), charging time, etc. Distribution network data: Power data such as current, voltage, and load of the distribution network; Internet data: Internet data related to vehicle-to-everything (V2X) interaction, such as weather information, vehicle operating status, and charging station usage.

[0021] In one optional implementation, the data collection frequency is dynamically adjusted based on the real-time load of the power grid, the usage status of charging stations, and environmental factors (such as weather). For example, when the power grid load is high or the weather is unstable, the collection cycle is shortened to ensure timely detection of potential safety hazards. When the power grid load is low or the weather is stable, the collection cycle is extended to reduce resource consumption and improve system efficiency.

[0022] In a further implementation, the aforementioned collected multi-source heterogeneous data undergoes preprocessing, including data cleaning, noise reduction, and standardization. The data cleaning step includes outlier identification and removal, noise elimination, etc., to ensure the accuracy and reliability of the input data. Data from different sources is converted into a unified format and normalized according to specific needs, providing accurate data support for subsequent intelligent evaluation and decision-making.

[0023] S2: In one embodiment of this example, the safety status of power supply for electric vehicle charging stations is intelligently assessed.

[0024] In a further implementation, standardized multi-source heterogeneous data from charging stations (including electric vehicle charging data, power distribution network data, vehicle status, environmental data, etc.) are compared with thresholds set by national standards or relevant regulations. The actual impact of various data types on the charging stations is assessed.

[0025] An influence index is constructed for charging station nodes and charging piles within those nodes to assess the actual impact of various data on charging stations. The influence index is expressed as: ;

[0026] In this formula, Indicates the first The influence index of an electric vehicle charging station node is such that the smaller the value, the more stable the charging station node is and the smaller its impact on the power distribution network. Indicates the first The first electric vehicle charging station node The influence index of a charging pile; Indicates the first Class data for charging station nodes The first in The impact of a charging station is calculated as follows: ,in, and Charging station nodes The first in The first charging pile Actual and reference values ​​for class data; Indicates the first The total number of charging piles in each electric vehicle charging station node; For the first Class data for charging station nodes The first in The influence weight of each charging pile is calculated as follows: The influence weight determines the importance of this type of data in node security assessment; the greater the influence, the greater the influence weight coefficient. Indicates charging station node The first in The total number of multi-source heterogeneous data categories collected from each charging pile.

[0027] Furthermore, based on the influence index value and the corresponding value range of the set safety level, the safety performance of the charging station nodes is assessed; specifically: ; in, Indicates charging station node The security level is indicated by Excellent, Good, and Poor, which represent excellent, good, and poor security levels, respectively.

[0028] S3: In one embodiment of this example, refer to Figure 2 Different control measures are implemented based on the safety level of the electric vehicle charging station nodes output by S2. Specifically: 3.1: Furthermore, when the safety level of the charging station node... When the result is excellent, it meets national standards and no control measures are required. 3.2: Further, when the safety level of the charging station node... When the condition is good, load distribution is optimized by adjusting the output power of the charging piles and guiding electric vehicles to discharge reasonably. The influence index is then assessed. If the node's security level becomes excellent, the control process ends; otherwise, it returns to the security level assessment process. In a further specific implementation, step 3.2 is implemented as follows: 3.2.1: Define the relevant constraints of this embodiment: (1) Determine the charging demand model for electric vehicle (EV) users to ensure a minimum emergency power reserve is maintained during charging to meet sudden travel needs. Therefore, the charging demand model considers a dynamic SOC lower limit constraint: ; ; ; ; in, for Time of the first The state of charge of the EV battery; They are respectively Time of the first The charging power and discharging power of an EV; These represent the charging and discharging efficiency of the EV, respectively; E represents the rated capacity of a single EV battery. For the first Minimum expected SOC for EVs off-grid; They are respectively Time of the first Upper and lower limits of EV battery SOC; For the first The lower limit of the state-of-the-art (SOC) of EV batteries; Don't be the first The timing of EV grid connection and disconnection; The minimum growth rate of SOC; This is for adjusting the time interval.

[0029] (2) Charging and discharging power constraints for electric vehicles: ; in, and They represent the first Rated charging and discharging power of the EV; T represents grid connection time.

[0030] 3.2.2: Establish a two-dimensional grading rule based on charging duration and battery state of charge (SOC), dividing charging demand and SOC into multiple levels. Exhaustively combine charging demand levels with SOC levels, and preset differentiated charging power percentages for each combination. While meeting basic user needs, proactively reduce and smooth charging load to avoid overload or voltage fluctuations on the distribution network.

[0031] For example, batteries are classified into Class A (0~20%), Class B (20%~60%), and Class C (60%~100%) according to the state of charge (SOC) of the electric vehicle; and into Class I (≤3 hours), Class II (3~8 hours), and Class III (>8 hours) according to the charging time of the electric vehicle.

[0032] Adjust the charging station's output power based on the State of Charge (SOC) and charging duration: Category AⅠ: The output power of the charging pile is the rated power (100%). Class AⅡ: The charging pile output power is 70% of the rated power; Category AⅢ: The output power of the charging pile is 40% of the rated power; Category BⅠ: The charging pile's output power is 70% of its rated power; Class BⅡ: The charging pile's output power is 50% of its rated power; Class BⅢ: The output power of the charging pile is 30% of the rated power; Category CⅠ: The charging pile's output power is 40% of its rated power; Class CⅡ: The output power of the charging pile is 30% of the rated power; Class CⅢ: The output power of the charging pile is 20% of the rated power.

[0033] After setting the output power of the charging pile according to the state of charge of the electric vehicle and the charging time, charging of the electric vehicle begins. At this time, it is necessary to ensure that the power adjustment during the charging process meets the operating requirements of the power distribution network to ensure the safe and stable operation of the power distribution network and avoid problems such as excessive grid load or voltage fluctuations caused by electric vehicle charging.

[0034] 3.2.3: Following the same technical approach as 3.2.2, a two-dimensional grading rule based on discharge power and discharge duration is established, dividing discharge power and discharge duration into multiple levels. An exhaustive combination of discharge power and discharge duration levels is performed, and a differentiated discharge price coefficient is preset for each combination. Adhering to the principle that the greater the discharge power and the longer the duration, the higher the discharge price subsidy multiple, economic levers are used to guide user discharge behavior and incentivize users to provide high-quality (high-power, long-duration) vehicle-to-grid (V2G) services.

[0035] For example, see Figure 3 Based on the discharge power of electric vehicles, the discharge power is divided into Class A (80%~100% of rated discharge power), Class B (40%~80% of rated discharge power) and Class C (less than 40% of rated discharge power); based on the discharge duration of electric vehicles, they are divided into Class 1 (≥4 hours), Class 2 (2~4 hours) and Class 3 (<2 hours).

[0036] Electricity pricing for electric vehicles is allocated based on discharge power and discharge duration: Category A1: The electricity price for electric vehicles during discharge is 300% of the electricity price for charging; Category A2: The electricity price for electric vehicles during discharge is 280% of the electricity price for charging; Category A3: The electricity price for electric vehicles during discharge is 260% of the electricity price during charging; Category B1: The electricity price for electric vehicles during discharge is 240% of the electricity price for charging; Category B2: The electricity price for electric vehicles during discharge is 220% of the electricity price for charging; Category B3: The electricity price for electric vehicles during discharge is 200% of the electricity price for charging; Category C1: The electricity price for electric vehicles during discharge is 180% of the electricity price for charging; Category C2: The electricity price for electric vehicles during discharge is 160% of the electricity price for charging. Category C3: The electricity price for electric vehicles during discharge is 140% of the electricity price for charging.

[0037] After setting the discharge price based on the discharge power and duration of the electric vehicles, the discharge behavior of electric vehicles will be guided. At this time, it is necessary to ensure that the setting of the discharge price during the discharge process complies with market rules and the requirements of the power distribution network, so as to ensure the compliance and rationality of the electric vehicle discharge behavior, and to solve the safety issues of electric vehicle charging station nodes to the greatest extent while ensuring the needs of electric vehicle users.

[0038] 3.3: Furthermore, when the safety level of the charging station node... When the result is poor, the fault type is determined through the safety incident response mechanism. Then, corresponding measures are taken to isolate the charging fault, followed by an assessment of the impact index. If the node's security level becomes excellent, the control process ends; otherwise, it returns to the security level assessment process.

[0039] In a further specific implementation, step 3.3 is implemented as follows: 3.3.1: Determine the fault type through the safety incident response mechanism, and first construct the charging station node influence matrix. , used to represent charging station nodes The specific influence index of each charging station in the matrix. The formula is as follows: ; In the formula, For the aforementioned first The first electric vehicle charging station node The influence index of each charging station is determined through an influence matrix. It can directly quantify and display the specific impact value of each charging station.

[0040] Furthermore, to protect the security of power distribution and consumption data of the power distribution network during vehicle-to-grid interaction and to prevent the leakage of EV users' personal privacy information during data transmission or cloud reporting, and to confirm the specific fault types of charging piles, an influence matrix is ​​used. The influence of charging piles Charging piles with values ​​greater than a preset threshold (e.g., 0.3) are marked as faults and recorded as fault items. , Only for The fault markers are not the same.

[0041] Furthermore, regarding the fault items Without considering the specific data information and influence of its multi-source heterogeneous data. Instead of making judgments, it uses the influence weights of multi-source heterogeneous data. This analysis avoids the dissemination and interaction of specific information, while allowing for targeted measures based on different fault types. This effectively ensures the information security of the power distribution network and the privacy of electric vehicles and users. The resulting matrix is ​​as follows: ; Based on the influence weight of multi-source heterogeneous data The calculation formula shows the influence weight of multi-source heterogeneous data for a given charging pile. The sum is 1, that is Then, the influence weight of each multi-source heterogeneous data in the fault item is compared with... By comparison, influence weights that are greater than the average are classified as priority items.

[0042] Furthermore, the influence weights of multi-source heterogeneous data in the priority processing items are sorted in order of magnitude, and the multi-source heterogeneous data type corresponding to the multi-source heterogeneous data weight with the largest value is taken as the security response event, such as voltage security response event, current security response event, electric vehicle battery security response event, etc.

[0043] 3.3.2: First, based on the safety event response type and fault location determined in step 3.3.1, the system immediately triggers the fuse protection mechanism, sending a trip command to the corresponding smart circuit breaker to quickly disconnect all electrical connection circuits of the faulty charging pile. This aims to physically cut off energy transmission, ensuring that the faulty item is isolated in a timely and thorough manner, preventing the electrical fault from spreading through the power distribution network, thus not affecting the operation of other normally functioning electric vehicle charging piles, effectively curbing the spread of potential risks, reducing safety hazards and chain accidents, and ensuring overall that the faulty item will not affect the normal operation of the charging station node.

[0044] Furthermore, after completing physical isolation, the system classifies and identifies fault items based on fault characteristic data, mainly into two categories: internal faults of the charging pile and charging faults of the electric vehicle (itself).

[0045] (1) Internal faults of charging piles: refers to abnormal states caused by internal component failures or program malfunctions of charging piles, such as power module damage, communication controller crash, insulation detection failure, etc. These are problems caused by the charging pile itself and are unrelated to the connected vehicles.

[0046] (2) Electric vehicle charging failure: refers to abnormalities caused by internal power battery failure or charging circuit problems in electric vehicles, such as battery management system (BMS) alarm, on-board charger failure, battery overheating or short circuit, etc. In this case, the charging object is faulty, and the charging pile itself is in good condition.

[0047] For the two different fault types mentioned above, the system executes the following differentiated control strategies: Internal fault management strategy for charging piles: (1) First, after the circuit is disconnected, the system first initiates the user service guarantee process, generates a “transfer charging” instruction for the car that is charging at the charging station, and pushes it to the user through the mobile APP or on-site screen, guiding the user to move the vehicle to other idle and normal charging stations in the station to continue charging, ensuring that the charging needs of charging car users are not interrupted for a long time. (2) Then, a maintenance work order is generated for the specific fault type diagnosed, and the maintenance personnel are notified to remotely reset or repair the fault in the pile, so as to ensure that the fault can be dealt with in a timely manner. (3) Finally, before the fault is repaired, the system automatically updates the algorithm model and excludes the charging pile from the list of currently available devices, so as not to include it in the subsequent calculation of the impact of the charging pile, in order to avoid abnormal data interfering with the accuracy of the assessment of the safety level of the entire charging station node.

[0048] Electric vehicle charging fault management strategy: (1) First, after disconnecting the circuit, considering the risk of thermal runaway caused by battery failure, the system first generates a "move vehicle" instruction for the car being charged by the station and pushes it to the user or maintenance personnel with high priority. It is recommended to move the car to an open and safe place or fire isolation area to avoid secondary safety accidents such as fire. (2) Then, after confirming that the faulty vehicle has been disconnected and moved away, the system performs a self-test on the charging pile. After confirming that it is undamaged, it connects all the circuits of the isolated charging pile and restores the normal use of the charging pile so that it can provide services for other healthy electric vehicles. (3) Finally, the system re-integrates the charging pile into the monitoring network and re-includes all the multi-source heterogeneous data of the charging pile into the scope of subsequent calculation of the charging pile's influence, ensuring that the service capabilities and perception data of the charging station remain intact.

[0049] As an optional implementation method in this embodiment, to ensure that the system can achieve real-time monitoring and decision-making, this invention uses 5G communication technology for data transmission. 5G technology features high speed and low latency, ensuring timely data upload and processing. Simultaneously, some computing tasks are offloaded to field devices through edge computing technology, reducing data transmission latency and further improving the system's response speed and processing efficiency.

[0050] The collected and processed data is uploaded to a cloud platform or remote monitoring center to achieve full-process monitoring and safety management of the power distribution network. The monitoring center can view the safety status of the power grid in real time and make timely dispatching and management decisions based on the assessment results.

[0051] As an embodiment of this application, Figure 4 The diagram illustrates a system unit schematic of a power distribution safety intelligent management and control method for vehicle-to-grid interaction according to an embodiment of this disclosure. Through the collaborative work of multiple units, intelligent safety assessment and management of the power distribution network are achieved, including the following core units: (1) Data Analysis Unit: The data analysis unit is the foundation of the entire system, responsible for collecting and processing multi-source heterogeneous data from different data sources. It includes two sub-units: Data Acquisition Subunit: Responsible for real-time acquisition of interaction data between the power distribution network and electric vehicles through smart sensors, on-board equipment, and other devices, ensuring data integrity and timeliness. The acquired data includes parameters such as charging current, voltage, SOC, and charging time of electric vehicles; current, voltage, and load data of the power distribution network; and other environmental data related to power grid operation.

[0052] The data processing subunit is responsible for preprocessing the collected data, including data cleaning, noise reduction, and outlier removal, to ensure the accuracy and reliability of subsequent analysis. The processed data will be standardized to meet the input requirements of the security assessment unit.

[0053] (2) Data Assessment Unit: Based on data analysis, this unit conducts power grid security assessment, including real-time data analysis and security level evaluation. It comprises the following two sub-units: Safety Fault Detection Subunit: Based on data such as electric vehicle charging load and power distribution network operation status, it detects power grid safety risks in real time, such as power overload and voltage fluctuations, and promptly identifies potential faults.

[0054] Assessment Report Generation Subunit: Based on the output results of safety fault detection, automatically generate a power grid safety assessment report. The report includes the current power grid safety level, potential fault risks, and recommended response strategies. The assessment report helps provide timely decision-making basis for power grid managers.

[0055] (3) Safety management and control unit, which performs real-time optimization and intelligent adjustment of the distribution network based on the safety assessment results, includes four sub-units: Charging pile power management subunit: When the power grid safety assessment result is "good", the output power of the charging pile is dynamically adjusted by comprehensively considering the real-time charging demand of electric vehicles and the load margin of the distribution network. By smoothing the charging curve, the charging pile effectively avoids excessive grid load while ensuring the user's charging experience, achieving a balance between charging efficiency and grid load optimization.

[0056] Dynamic electricity pricing management subunit: When the grid security assessment result is "good", the electricity price lever is used to guide user behavior, and economic incentives are used to guide electric vehicles to participate in vehicle-to-grid (V2G) interaction in an orderly manner. Higher discharge prices are set for electric vehicles with higher discharge power and longer discharge time, thereby achieving the dual objectives of peak shaving and valley filling for the grid and reducing charging costs for users.

[0057] Safety Incident Response Subunit: Responsible for accurate fault identification and prioritization. First, fault items are located using the influence matrix of charging station nodes; then, an abnormal multi-source heterogeneous data is filtered using an influence weighting algorithm, identifying indicators with weight values ​​greater than the average as priority items; finally, based on the magnitude of the influence weight, the corresponding safety incident response strategy and level are automatically matched and determined.

[0058] Charging fault isolation subunit: Responsible for the physical execution and closed-loop handling of faults. Upon receiving a response command, it immediately disconnects the faulty circuit to implement electrical isolation.

[0059] (1) For internal faults in charging piles: The system issues a transfer charging instruction to guide affected vehicles to replace the charging pile; at the same time, targeted repairs are carried out according to the fault type. During the repair, the charging pile is marked as "unavailable", and its data is temporarily removed from the calculation range of the influence index.

[0060] (2) For electric vehicle malfunctions: The system guides the vehicle to physical safety isolation (e.g., moving it to a safe area) to prevent secondary accidents; after confirming safety, the charging pile circuit is reconnected to restore its service capability. The multi-source heterogeneous data after the charging pile resumes operation will be re-sensed by the system and included in the influence index calculation sequence to ensure the maximization of the utilization rate of the station equipment.

[0061] (4) Data transmission unit, responsible for efficiently transmitting the processed data and security assessment results, this unit includes: 5G Data Transmission Subunit: Utilizes 5G communication technology to achieve high-speed, low-latency data transmission, ensuring real-time data transmission from the acquisition unit and evaluation unit to the cloud platform or remote monitoring center, and guaranteeing that the vehicle-to-grid system can respond quickly to power grid safety incidents.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for intelligent control and management of power distribution safety oriented towards vehicle-to-grid interaction, characterized in that, include: Under the vehicle-to-grid interactive system, multi-source heterogeneous data of the EV charging process are collected; Based on the preprocessed multi-source heterogeneous data, an influence index for charging station nodes and charging piles is constructed to assess the actual influence of various types of data on charging stations. Based on the value of the influence index, a corresponding range of safety levels is set, and the safety performance of charging station nodes is assessed; different control measures are implemented according to the safety level of the charging station nodes. Among them, the control measures for charging station nodes with a good safety level are to establish a two-dimensional hierarchical mapping mechanism between charging demand and SOC, and between discharge power and discharge duration, and to preset differentiated power and electricity price strategies respectively. The control measures for charging station nodes with poor safety levels are to construct an influence matrix for the node based on the influence index of all charging piles within the node. By analyzing the influence weight of fault items in the matrix, the source of the fault can be located, and a differentiated fault handling process can be implemented.

2. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 1, characterized in that, The calculation process for the influence index includes: For the The influence index of each of the aforementioned charging station nodes This is the sum of the influence indices of all charging piles in this node; For nodes The first in Each charging station, its influence index This is the weighted sum of the influence of all types of data on the charging station on that charging station.

3. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 2, characterized in that, The calculation process for the impact of various data on charging piles includes: For nodes The first in The first charging pile For data of this type, the difference between the actual value and the reference value is calculated, and the absolute value of the difference is then compared with the reference value. Compare the ratio to 0; the larger of the two is the first value. Class data pairs nodes The first in The impact of a single charging station.

4. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 2, characterized in that, The weighting of various data points on the influence of their respective charging piles is calculated as follows: For nodes The first in Data types of charging piles Its charging pile The influence weight is a fraction, with the numerator being the first... Class data for charging piles The influence of charging piles is the denominator. All types of data for charging piles The sum of their influence.

5. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 1, characterized in that, For charging station nodes with a safety level of "good", the specific control measures include: Determine the charging demand model for EV users and define the charging and discharging power constraints for electric vehicles; Establish a two-dimensional classification rule based on charging time and battery state of charge (SOC), dividing charging demand and SOC into multiple levels; exhaustively combine charging demand levels and SOC levels, and preset differentiated charging power percentages for each pair of combinations. A two-dimensional classification rule based on discharge power and discharge duration is established, dividing discharge power and discharge duration into multiple levels respectively; exhaustively combining discharge power level and discharge duration level, and pre-setting a differentiated discharge price coefficient for each pair of combinations.

6. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 1, characterized in that, The process of locating the fault source includes: Charging piles with an influence index greater than or equal to a preset threshold in the influence matrix are marked as fault items; For each fault item, construct a weight matrix by representing the influence weights of all its data types; compare the influence weight of each data type with the mean of the weight matrix; and include the influence weights that are greater than or equal to the mean in the priority processing set. The data type corresponding to the maximum value in the priority processing set is identified as the source of the fault and treated as a safety response event.

7. The intelligent power distribution safety management method for vehicle-to-grid interaction according to claim 1, characterized in that, The differentiated fault handling process includes: After physically isolating the fault items, the fault type is determined based on the fault source. The fault type includes internal faults of the charging pile and EV charging faults. For internal faults in the charging pile, guide the user to change charging locations; remove the faulty charging pile from the list of available devices and update the next charging pile impact calculation.

8. A power distribution safety intelligent control system for vehicle-to-grid interaction, executing the intelligent control method as described in any one of claims 1-7, characterized in that, The system includes: Charging station node evaluation module; used to collect multi-source heterogeneous data of EV charging process under vehicle-to-grid interaction system; based on the preprocessed multi-source heterogeneous data, construct the influence index of charging station nodes and charging piles, and evaluate the actual influence of various data on charging stations; Differential control module; used to set corresponding ranges for security levels based on the value of the influence index, to conduct safety performance assessment of charging station nodes; and to implement different control measures according to the security level of the charging station nodes. The load distribution and control module is used to manage and control charging station nodes with a good safety level; it establishes a two-dimensional hierarchical mapping mechanism between charging demand and SOC, and discharge power and discharge duration, and presets differentiated power and electricity price strategies respectively. The fault location and handling module is used to manage and control charging station nodes with poor safety levels. It constructs an influence matrix for the node based on the influence index of all charging piles within the node. By analyzing the influence weight of fault items in the matrix, it locates the fault source and executes differentiated fault handling procedures.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the intelligent control method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent control method according to any one of claims 1-7.

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