Electric vehicle charging and discharging operation regulation platform

Abstract

An electric vehicle charging and discharging operation regulation platform, comprising: a data collection cluster, which is configured to receive and parse collected data sent by an external system, wherein the external system comprises multiple terminals, an Internet-of-Vehicles platform and a power grid dispatching platform; a data processing cluster, which is configured to perform processing analysis and periodic sampling storage on real-time remote signaling / telemetering data; a data storage cluster, which has a distributed memory database constructed therein, and is configured to implement the large-capacity storage of historical data and high-concurrency access to the historical data; and a micro-service cluster, which is configured to implement, on the basis of data stored in the data storage cluster, a service application micro-service and a platform micro-service by means of a micro-service architecture.

Description

Electric vehicle charging and discharging operation control platform

[0001] This application claims priority to Chinese Patent Application No. 202410566115.0, filed with the Chinese Patent Office on May 9, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of electric vehicle control technology, and for example to an electric vehicle charging and discharging operation control platform. Background Technology

[0003] A new round of technological revolution and industrial transformation is booming, and the automotive industry is rapidly integrating with technologies in energy, transportation, information and communication, promoting the optimization of energy consumption structure, improving the intelligence level of transportation systems and urban operations, and helping to steadily achieve the goal of carbon neutrality. With the advancement of technology, electric vehicles are rapidly becoming more and more widespread.

[0004] The widespread application of electric vehicles has also led to a rapid increase in the amount of data. Simultaneously, with the development of the ubiquitous power internet of things, a massive number of devices will be connected to the distribution network system to realize a digital society. The challenge this presents is that conventional data processing systems are far from sufficient. Therefore, research is needed on an efficient and comprehensive distribution network electric vehicle operation and control platform. Summary of the Invention

[0005] This application provides an electric vehicle charging and discharging operation control platform that enables access to a massive number of diverse terminals and heterogeneous networks, as well as the effective processing of massive amounts of data.

[0006] An electric vehicle charging and discharging operation control platform, comprising:

[0007] The data acquisition cluster is configured to receive and parse the acquired data sent by external systems, including multiple terminals, a vehicle networking platform, and a power grid dispatching platform.

[0008] The data processing cluster is configured to process and analyze real-time remote signaling / telemetry data and perform periodic sampling and storage.

[0009] The data storage cluster includes a distributed in-memory database, configured to enable large-capacity storage and high-concurrency access to historical data.

[0010] The microservice cluster is configured to implement business application microservices and platform microservices using a microservice architecture based on the data stored in the data storage cluster.

[0011] Optionally, the multi-terminal includes V2G charging and discharging stations, centralized charging stations, fast charging piles, and smart distribution transformer terminals.

[0012] Optionally, the external system interacts with the control platform via IoT plug-and-play technology. The control platform includes an IoT plug-and-play module, which comprises:

[0013] The resource model library is set up to store the resource model definitions for IoT devices.

[0014] The plug-and-play service unit is configured to enable synchronization of IoT device models between the control platform and the IoT management platform.

[0015] The data parsing service unit is configured to convert IoT business data into information that the control platform can recognize.

[0016] The IoT management platform is designed to connect to IoT devices and provide a unified API interface to enable interaction between device models and business data.

[0017] Optionally, the multi-terminal transmits the collected data to the data acquisition cluster based on the MQTT protocol, and the vehicle networking platform transmits the collected data to the data acquisition cluster via an HTTP interface.

[0018] Optionally, the data collected by the vehicle network platform includes charging pile operation data, charging pile adjustability margin data, and user charging order data.

[0019] Optionally, the data processing cluster includes a stream computing engine and a distributed task scheduling framework, wherein,

[0020] The stream computing engine is configured to process and analyze real-time data, including topology calculation, telemetry over-limit, telemetry tripping, tripping accident synthesis, SCADA operation stream processing, and real-time data push interface stream processing.

[0021] The distributed task scheduling framework periodically samples real-time data by scheduling distributed sampling tasks and submits the data to the data storage cluster.

[0022] Optionally, the data storage cluster may also include a relational database, a real-time database, a time-series historical database, an ES alarm database, and a high-speed cache.

[0023] Optionally, the business application microservices include distribution network status awareness, risk monitoring and assessment, distribution network overload reduction, voltage regulation, charging guidance, and flexible resource supply and demand matching.

[0024] Optionally, the platform microservices include alarm services, communication protocol processing, model management, topology identification and analysis, and database management.

[0025] Optionally, it also includes:

[0026] The system management and operations cluster is configured for cluster management, container scheduling and orchestration, system operations and monitoring, and continuous system integration and deployment. Attached Figure Description

[0027] Figure 1 is a schematic diagram of the control platform of this application;

[0028] Figure 2 is a flowchart of the plug-and-play process for the edge device in this application;

[0029] Figure 3 is a flowchart of the plug-and-play process for the terminal device in this application;

[0030] Figure 4 is a flowchart of the data parsing process in this application;

[0031] Figure 5 is a schematic diagram of the distributed task scheduling framework used in the embodiments of this application;

[0032] Figure 6 is a schematic diagram of the data flow of the control platform of this application;

[0033] Figure 7 is a schematic diagram of the microservice cluster function in an embodiment of this application;

[0034] Figure 8 is a logic diagram of the voltage regulation strategy in an embodiment of this application;

[0035] Figure 9 is a logic diagram of the overload reduction strategy in an embodiment of this application;

[0036] Figure 10 is a logic diagram of the flexible resource supply and demand matching strategy in the embodiments of this application;

[0037] Figure 11 is a schematic diagram of the cloud-edge-device collaborative architecture of this application;

[0038] Figure 12 is a schematic diagram of a deployment architecture in an embodiment of this application. Detailed Implementation

[0039] The present application will now be described in conjunction with the accompanying drawings and specific embodiments. This embodiment is based on the technical solution of the present application and provides detailed implementation methods and specific operating procedures.

[0040] Example 1

[0041] Referring to Figure 1, this embodiment provides an electric vehicle charging and discharging operation control platform, including a data acquisition cluster, a data processing cluster, a data storage cluster, and a microservice cluster. The data acquisition cluster is configured to receive and parse acquired data sent by external systems, including multi-terminals, a vehicle-to-everything (V2X) platform, and a power grid dispatching platform. The data processing cluster is configured to process and analyze real-time remote signaling / telemetry data and perform periodic sampling and storage. The data storage cluster includes a distributed in-memory database, configured to achieve large-capacity storage and high-concurrency access to historical data. The microservice cluster is configured to implement business application microservices and platform microservices based on the data stored in the data storage cluster using a microservice architecture.

[0042] The aforementioned electric vehicle charging and discharging control platform provides generalized IoT services, with open access devices, supporting heterogeneous network access and management of massive numbers of terminals / devices across multiple types, protocols, and levels. "Multi-type" refers to the generalized IoT service's ability to access multiple types of devices, with scalable device types, supporting access for medium and low-voltage devices, enhancing the main station's access capabilities, and enabling massive IoT access and management. "Multi-protocol" means that the generalized IoT service not only provides access for MQTT IoT protocol terminals but also has access capabilities for IEC standard and other protocol terminals / devices, and supports protocol scalability. "Multi-level" means that the generalized IoT service not only enables rapid access for terminal devices directly connected to the main station but also supports rapid IoT access for non-directly connected devices.

[0043] In this embodiment, the external system interacts with the control platform through IoT Plug-and-Play technology. The control platform includes an IoT Plug-and-Play module, which comprises: a resource model library for storing resource model definitions of IoT devices; a plug-and-play service unit for synchronizing IoT device models between the control platform and the IoT management platform; a data parsing service unit for converting IoT business data into information recognizable by the control platform; and an IoT management platform for connecting to IoT devices and providing a unified API interface for interaction between device models and business data. The resource model library includes a device ledger and device models. The device model is a JSON file that defines device types and functions from three dimensions: attributes, services, and events.

[0044] In this embodiment, the plug-and-play process can be divided into two parts: edge device and end device access. The initial access steps for the edge device are shown in Figure 2, including:

[0045] Step 1: The plug-and-play service imports the object model file of the edge device and calls the API interface of the IoT management platform to complete the creation of the edge device type in the business system and the IoT management platform. The same type of edge device only needs to be created once, corresponding to ① and ② in Figure 2.

[0046] Step 2: Submit the edge device ledger information to the Plug and Play service by scanning QR codes with a handheld terminal. The Plug and Play service synchronously calls the API interface of the IoT management platform to complete the creation of the edge device ledger in the business system and the IoT management platform, corresponding to ②, ③ and ④ in Figure 2.

[0047] Step 3: After the device is installed and powered on on-site, send registration information to the IoT management platform, including device identification, manufacturer, device model, etc., corresponding to ⑤ in Figure 2.

[0048] Step 4: The IoT management platform reviews the submitted registration information, compares the device identifier, manufacturer, device model and other information to see if they are consistent with the device ledger information. If the review is successful, it responds and completes the automatic registration of the device, corresponding to step ⑥ in Figure 2.

[0049] The initial connection steps for the terminal device are shown in Figure 3, including:

[0050] Step 1: Import the device model file of the plug-and-play service, call the API interface of the IoT management platform, and complete the creation of the device type of the business system and the IoT management platform. The same type of device only needs to be created once, corresponding to ① and ② in Figure 3.

[0051] Step 2: The IoT management platform downloads the terminal device's APP application from the app store and installs it on the edge device, corresponding to ③ and ④ in Figure 3.

[0052] Step 3: After the terminal device is installed and powered on in the field, it automatically registers with the edge device through the discovery and registration mechanism, corresponding to ⑤ in Figure 3.

[0053] Step 4: After receiving the automatic registration information from the end device, the edge device sends the end device registration message to the IoT management platform, which includes information such as device identifier, manufacturer, device model, and the edge device to which it belongs, corresponding to ⑥ in Figure 3.

[0054] Step 5: After receiving the terminal device registration message, the IoT management platform reviews the registration information, compares the manufacturer, device model, and whether the corresponding edge device exists. After the review is passed, it creates a terminal device ledger and pushes the terminal device addition information to the Plug and Play service. At the same time, it sends a successful registration message to the edge device, corresponding to ⑦ and ⑧ in Figure 3.

[0055] Step 6: After receiving the device addition information pushed by the IoT management platform, the Plug and Play service creates a device ledger in the business system, corresponding to ⑨ in Figure 3.

[0056] Step 7: After receiving the successful registration response, the edge device notifies the end device to report the collected data, corresponding to ⑩ in Figure 3.

[0057] Step 8: After receiving the collected data from the end device, the edge device will report the collected data to the IoT management platform via the MQTT protocol. The platform will verify the data according to the definition of the object model. After the verification is passed, the collected data will be pushed to the control platform, corresponding to Figure 3.

[0058] Step 9: The data parsing service parses and processes the business data, making it available for business applications and enabling the use of data collected by end devices, corresponding to Figure 3. and

[0059] In this embodiment, the data parsing service unit receives data from the topics and interfaces provided by the IoT management platform and forwards it to different business topics for processing according to the type of received data. In practical applications, the types of upstream business data are diverse, including not only telemetry and telemetry business data, but also online / offline status messages, command response messages, alarm event messages, file upload messages, and some customized business data. To improve the performance of data parsing, it is necessary to transform and classify various types of messages. In addition, due to the large scale and rapid growth of IoT messages, traditional data parsing frameworks cannot cope with them, and streaming computing frameworks such as Spark and Flink need to be introduced for processing. According to the different message types, specific Spark transformation operators are used to process each batch of data in batches, realizing the parsing of JSON format business messages into measurement and business objects. In this embodiment, the data parsing flowchart is shown in Figure 4, including the following steps:

[0060] Step 1: Model download. Obtain the static model information and object model mapping information of the device from the commercial library and the object model configuration file, respectively, and download them to Redis. When the static model and object model change, they are dynamically updated through messages.

[0061] Step 2: Message subscription. Subscribe to messages by category from the rules engine and start different Spark operators for parsing.

[0062] Step 3: Data parsing. Different Spark operators retrieve the required model data from Redis, parse the subscribed data in batches according to business rules, and send it to the corresponding topic for further processing.

[0063] The aforementioned electric vehicle charging and discharging control platform can interact with multiple business modules such as converged terminals, vehicle networks, and power distribution automation master stations through wireless networks or dedicated networks to achieve heterogeneous networks.

[0064] The aforementioned electric vehicle charging and discharging operation control platform adopts advanced technologies such as cloud computing, big data, Internet of Things, and artificial intelligence to collect and access various operation, status, and environmental data of medium and low voltage distribution networks, achieve comprehensive perception of the distribution network, comprehensively improve the lean and intelligent management level of the distribution network, and realize the deep integration of big data and business applications.

[0065] In the aforementioned electric vehicle charging and discharging operation control platform, the data processing cluster includes a stream computing engine and a distributed task scheduling framework. The stream computing engine is configured to process and analyze real-time data, including topology calculation, telemetry limit exceedance, telemetry jump, tripping accident synthesis, SCADA operation stream processing, and real-time data push interface stream processing. The distributed task scheduling framework periodically samples real-time data by scheduling distributed sampling tasks and submits it to the data storage cluster. In this embodiment, the distributed task scheduling management mainly consists of a task scheduling center, a task communication module, and task executors, supporting event-driven scheduling and clock-driven scheduling. The framework diagram is shown in Figure 5.

[0066] In Figure 5, the task scheduling center is responsible for defining, managing, and scheduling tasks. It does not undertake any business operations itself, is not limited by any task type, and is completely decoupled from business tasks. The task scheduling center provides rich task scheduling strategies and has functions such as task management, task association, and disaster recovery. Different types of tasks can be flexibly customized through a visual interface. The scheduling center centrally and dynamically manages task information, selects appropriate resources for executing tasks in the submitted task queue, and provides real-time monitoring and display of task execution status, including the task scheduling process, task execution process, and task alarms. Furthermore, the task scheduling center supports multi-node deployment to ensure its high availability, and database locking ensures that only one scheduling center node triggers task scheduling at a time.

[0067] The task communication module is a distributed service framework that primarily provides stable and high-performance RPC remote service invocation functionality. The task scheduling center communicates with task executors through the task communication module, including registration and discovery, task assignment, task monitoring, and execution result reporting.

[0068] The task executor is responsible for receiving task data sent by the scheduling center, executing the task business logic, and returning the task execution result to the scheduling center.

[0069] To accommodate the access of diverse, heterogeneous, and massive amounts of distribution network data, the operation and control platform in this embodiment constructs a data storage cluster based on a distributed in-memory database, distributed cache, time-series historical database, relational database, and distributed file system. This cluster meets the requirements for large-capacity storage and high-concurrency access to historical data such as real-time measurements, models and parameters, sampling, and alarms. A relational distributed in-memory database is constructed using cluster management, read-write separation, and data synchronization technologies to store grid and equipment model attributes and parameter data with low change frequency, enabling high-concurrency access to relational real-time data and providing a standard SQL access interface. A distributed high-speed cache stores frequently changing real-time measurement data of the distribution network's operation, monitoring, and events, enabling fast access. Simultaneously, the platform records the changes in the status of intelligent distribution equipment and alarm information over time, achieving high-speed storage of massive amounts of historical data in the time-series historical database through distributed parallel processing.

[0070] In the aforementioned electric vehicle charging and discharging operation control platform, the data storage cluster stores relational data, real-time data, historical data, and file data. High-speed storage and management of real-time data achieves dynamic and static separation of real-time model data and real-time measurement data through distributed caching and a distributed real-time library cluster, supporting high-capacity, high-concurrency access. Massive historical data storage and management, based on a time-series database, enables high-speed acquisition and storage of massive historical data, meeting the performance requirements of minute-level sampling at tens of millions of measurement points. The data storage cluster constructs a relational distributed in-memory database through cluster management, read-write separation, and data synchronization technologies. This database stores low-frequency changing power grid and equipment model attributes and parameter data, enabling high-concurrency access to relational real-time data and providing a standard SQL access interface. A distributed high-speed cache stores frequently changing real-time measurement data of the distribution network's operation, monitoring, and events, enabling fast access. Simultaneously, it records and transmits the status changes and alarm information of intelligent power distribution equipment over time. Through distributed parallel processing, it achieves high-speed storage of massive historical data in a time-series historical database. Big data analysis can be performed on the real-time and historical data in the data storage cluster, and the relevant analysis results can be written back to the data storage cluster.

[0071] As shown in Figure 6, data collected by the intelligent distribution transformer terminal and charging piles is sent to the IoT access platform via the IoT management platform based on the MQTT protocol. The IoT access platform parses the data and forwards it to the big data analysis and processing platform. Through a stream computing engine, it performs functions such as real-time remote signaling / telemetry data processing, topology calculation, telemetry limit exceeding, telemetry tripping, tripping accident synthesis, SCADA operation stream processing, and real-time data push interface stream processing, and writes the data to a distributed cache. The distributed task scheduling framework of the big data analysis and processing platform schedules distributed sampling tasks to periodically sample data from the real-time data storage and submit it to the time-series historical database. The control platform provides microservices based on data storage, including a real-time database, relational database, cache, historical sampling query, permissions, and model modification, supporting application microservices and front-end display.

[0072] As shown in Figure 7, in the above-mentioned electric vehicle charging and discharging control platform, the microservice cluster adopts a microservice architecture to implement business application microservices and platform microservices. The business application microservices include distribution network status awareness, risk monitoring and assessment, distribution network overload reduction, voltage regulation, charging guidance, and flexible resource supply and demand matching, etc. The platform microservices include alarm services, communication protocol processing, model management, topology identification and analysis, and database management, etc., and can also implement data microservices.

[0073] (1) Distribution network status perception

[0074] The distribution network status perception is divided into two main parts: transformer substation status perception and charging pile status perception. Transformer substation status perception can display basic electrical quantities, load, load forecast curves, and load rate curves based on the selected transformer substation. Charging pile status perception can query based on the selected transformer substation, status, and charging pile to display the operating information of charging piles and charging vehicles.

[0075] The charging stations include V2G charging stations and orderly charging stations. V2G charging stations, as devices that directly access measurement points from the converged terminal to collect and transmit data, can not only display conventional voltage, current, and power, but also connect to the State of Charge (SOC) to display the charging and discharging curves of the vehicle. Orderly charging stations obtain real-time charging and discharging status and data through the vehicle network interface. The combination of the two fully covers all charging and discharging equipment under the substation area.

[0076] (2) Risk monitoring and assessment

[0077] In scenarios where a large number of electric vehicles are randomly connected to the power distribution network, the risk monitoring and assessment module can monitor the operating status of the distribution areas in real time. When a distribution area experiences voltage exceeding limits or overload, the electric vehicle charging and discharging operation control platform triggers a risk alarm, generates an alarm record, and performs risk statistical analysis to assess the risk situation of each distribution area. The risk monitoring and assessment module monitors the voltage and load rate of all distribution areas in real time, and generates a risk alarm record when a voltage exceeding limits or overload is detected.

[0078] (3) Voltage regulation

[0079] Voltage regulation monitors and judges real-time voltage data of distribution areas. For distribution areas with substandard voltage, it provides reasonable regulation target values ​​and issues them to the distribution areas. The distribution areas then perform edge computing processing and adjust the controllable loads under the distribution areas to achieve the target voltage of the distribution areas within a reasonable operating range. The specific regulation strategy is shown in Figure 8. In the regulation strategy, the control platform obtains the real-time voltage of the distribution areas from the fusion terminal and judges whether the real-time voltage is lower than the lower voltage limit. If so, it obtains the real-time total adjustable margin P of the distribution areas from the vehicle network. low The margin P of the converged terminal can be reduced. r Based on the real-time total adjustable margin P of the distribution area low Generate target adjustment value P tj Otherwise, no action will be taken; determine whether the area contains a V2G charging station; if not, the control platform will P tj Send the data to the vehicle network, which then controls the charging station to make adjustments. If the situation is as described, then determine P. tj Is it less than P? r If so, the control platform will P tj Send the information to the converged terminal, which then controls the charging pile to adjust accordingly; otherwise, P... r Send to the converged terminal, and send P tj -P r Send it to the vehicle network for adjustment.

[0080] (4) Heavy load reduction

[0081] Heavy load reduction involves real-time monitoring and assessment of transformer area load rates. For heavily loaded transformer areas, reasonable adjustment target values ​​are assigned and distributed to the areas. The areas then perform edge computing processing to adjust the controllable load under their load, thereby reducing the heavy load. In actual V2G-based heavy load reduction verification, the transformer area load rate decreased from 82% to 77%.

[0082] Taking a 70% load as an example, if the load exceeds 70%, the target is to reduce the load rate of the transformer area to 60%. The required load reduction for the transformer area is calculated and compared with the area's adjustable margin. The smaller value is selected, and an adjustment command is issued. The strategy logic is shown in Figure 9.

[0083] (5) Charging guidance

[0084] The charging guidance function monitors the load and power flow of various devices in the power grid in real time. It can count the number of heavy overloads in the distribution area within a week and set over-limit thresholds. Distribution areas that exceed the thresholds will be notified to adjust the charging and discharging prices of the vehicle network. With the optimization goal of reducing the time spent charging electric vehicles and improving the service balance of charging stations, it guides the load of multiple charging station terminals to improve the power quality of the distribution area by guiding users' consumption habits in the long term.

[0085] When a power distribution area has a load regulation requirement, it sends a power distribution area regulation request to the vehicle-to-everything (V2X) platform. Based on the relevant information, the V2X platform increases the charging station service fee according to the heavy load of the power distribution area and reminds users of the electricity price adjustment through user-end activities to attract users to charge and discharge in adjacent power distribution areas. Finally, the price before and after the adjustment and the adjustment time are sent back to the regulation platform for subsequent data analysis and statistics.

[0086] (6) Flexible resource supply and demand matching

[0087] Flexible resource supply and demand matching leverages the load adjustability of charging piles and the advantages of V2G (vehicle-to-grid) charging piles to achieve interaction between electric vehicles and the power grid. Specifically, when the peak load of a transformer area is high, peak shaving is achieved, and when the valley load of a transformer area is low, valley filling is achieved, thereby reducing the peak-valley difference and achieving flexible resource supply and demand matching.

[0088] Based on the predicted load / load rate curve of the distribution area, the difference between the maximum and minimum load rates is calculated. If the difference is greater than 40%, and the load rate of the distribution area with the highest load rate is higher than 70%, then a target load rate of 60% is set, and the power reduction value is calculated. This value is then compared with the predicted reduction margin of the distribution area, and the smaller value is selected to generate an adjustment strategy. The strategy logic is shown in Figure 10. Utilizing the adjustable charging and discharging power capability (supply) of charging piles, interaction between electric vehicles and the power grid is achieved during peak and valley periods (demand) of the distribution area load. Specifically, when the peak load of the distribution area is high, peak shaving is achieved by reducing the charging power of charging piles and conducting V2G discharge; when the valley load of the distribution area is low, valley filling is achieved by guiding charging piles to charge, thereby reducing the peak-valley difference and achieving flexible resource supply and demand matching.

[0089] The aforementioned electric vehicle charging and discharging operation control platform adopts cluster management technology. Through load balancing and fault redundancy processing, it achieves the allocation and management of the server cluster with one master and one backup, realizing the distributed task allocation of real-time data processing cluster, data storage cluster, and microservice cluster.

[0090] The electric vehicle charging and discharging operation control platform of this embodiment can be constructed into a cloud-edge-device collaborative system architecture with the vehicle network platform, charging piles and the integrated terminal connected to the charging piles, as shown in Figure 11.

[0091] (1) System Architecture

[0092] Functionally, the architecture is mainly divided into three layers, each equipped with a corresponding control unit as the carrier for policy implementation:

[0093] The cloud layer serves as the overall control layer, primarily composed of the electric vehicle charging and discharging operation control platform of this embodiment. Through cooperation with other layers, it collects operational information from the entire distribution network, optimizes and controls the overall output of each area within the distribution network, and distributes the control strategies to each edge controller. As the first-layer control unit of the active distribution network and its central management unit, the control platform establishes an overall distribution network control model with the goal of global optimization to ensure the safe operation of the entire system. This model optimizes the controllable units on the feeders, interconnecting switches, and the output of each area.

[0094] The edge layer is a distributed coordination layer, mainly composed of edge controllers in each region, including vehicle-to-everything (V2X) platforms and converged terminals. Combining the resource distribution within the region with the control commands issued by the global control layer, a distributed coordination model for the distribution network is established to ensure its own safe and economical operation. Through frequent and limited information exchanges with the control platform, the optimal energy distribution of each controllable distributed generation unit and flexible load unit within the region is determined.

[0095] The local control layer mainly consists of controllable units such as distributed photovoltaics, energy storage, and controllable loads. As the third-level control unit of the active distribution network, it uploads local operating information and output information of each unit, and controls each controllable unit according to the control commands issued by the upper layer. Depending on the control method of the interface converter, the control methods at this layer mainly include power control, constant voltage and constant frequency control, droop control, and virtual synchronous machine control. Based on constant voltage / constant frequency control (U / F control), a micro-power source is selected as the master power source to ensure the system voltage stability and support the system frequency. Other micro-power sources act as slave power sources, operating under constant power control, supplying power to the system according to resource availability in the microgrid system, without being responsible for regulating the system voltage and frequency. Based on droop control, peer-to-peer control simulates the reactive power-voltage characteristics and active power-frequency characteristics in a traditional power grid, automatically coordinating voltage and frequency control using local active and reactive power information.

[0096] (2) Security Architecture Design

[0097] Physical isolation is achieved between the control platform and the production control area, and logical isolation is achieved between the control platform and other business systems in the management information area. The control platform should deploy secure access services to provide security protection for edge devices and end devices accessing the control platform and interacting with business. Security isolation devices, secure access gateways, and hardware firewalls should be deployed at the access boundaries between the control platform and edge devices and end devices.

[0098] The edge device supports a power distribution IoT security chip, enabling trusted startup, key storage, and cryptographic algorithm support for the edge device. It also ensures the integrity, confidentiality, and availability of data interactions between the edge device and the control platform, terminal devices, and other edge devices, and supports the confidentiality and integrity protection of locally stored data.

[0099] The terminal device supports a power distribution IoT security chip, enabling key storage and cryptographic algorithm support for the terminal device. It also protects the integrity, confidentiality, and availability of data exchanged between the terminal device and the control platform and edge devices, and supports the confidentiality and integrity protection of locally stored data.

[0100] (3) Data acquisition interface design

[0101] Data from the converged terminal: Charging pile operation data and transformer area information are accessed via the MQTT protocol. The converged terminal device establishes communication with the charging and discharging facilities through an IoT controller, collects data from the entire transformer area, and sends adjustment and control commands to the charging and discharging facilities. The converged terminal also communicates with the distribution cloud master station via remote communication (4G) to transmit data. Simultaneously, the charging and discharging facilities synchronously upload their operation data to the vehicle network platform via remote communication (4G). Based on the data from the vehicle network platform and the equipment operation information within the transformer area, the distribution master station formulates global collaborative control requirements (i.e., long-term charging pile load allocation strategies) and sends them to the transformer area's converged terminal. This serves as the core input for the transformer area's converged terminal to implement its local control strategy. Simultaneously, the master station receives the actual charging operation data reported by the converged terminal, performs data display and closed-loop data analysis, and continuously optimizes the orderly charging and discharging strategy.

[0102] Data from the vehicle-to-everything (V2X) platform includes charging pile operation data, charging pile adjustability margin data, and user charging order data, which are transmitted from the V2X platform to the control platform via an HTTP interface. The electric vehicle charging and discharging operation control platform needs to interface with the V2X platform for strategy interaction and control, including the following interfaces: an operation data interaction interface, where charging data collected by the V2X platform is forwarded to the electric vehicle charging and discharging operation control platform; a strategy forwarding interface, where the electric vehicle charging and discharging operation control platform analyzes and forwards the optimized control objectives to the V2X platform, which then calculates and generates charging and discharging guidance and scheduling tasks; and charging and discharging user information, where the electric vehicle charging and discharging operation control platform obtains charging and discharging user information and future charging and discharging plans from the V2X platform, and based on this, performs various load forecasts, including ultra-short-term and short-term forecasts.

[0103] Data from the power grid dispatching platform: Medium and high voltage data are transmitted to the control platform via E-files through a dedicated SFTP network by power grid dispatching platforms such as the Qingpu D5000 system.

[0104] (4) Design of control command issuance methods in different scenarios

[0105] In various application scenarios, the strategies generated by the control platform, if adjusting areas without integrated terminals, are directly sent to the vehicle network platform for execution by the vehicle network; if adjusting areas with integrated terminals, the following options are selected based on the final distribution of charging pile areas:

[0106] When adjusting the strategies generated by the control platform for areas that do not include converged terminals and V2G charging piles, the strategies are directly sent to the vehicle network platform for decomposition and execution by the vehicle network.

[0107] When adjusting a distribution area that only contains V2G charging piles, the strategy generated by the control platform directly sends the instruction to the converged terminal, which then decomposes and executes it.

[0108] In the strategy generated by the control platform, when adjusting a substation that includes both V2G charging piles and ordinary charging piles, the instruction is sent to the vehicle network platform, which decomposes the instruction. The adjustment of ordinary charging piles is directly issued by the vehicle network, while the adjustment of V2G charging piles is returned to the control platform by the vehicle network, which then issues the instruction to the converged terminal for execution.

[0109] In this embodiment, the electric vehicle charging and discharging operation control platform is deployed using a hybrid approach of physical machines and virtual machines. This ensures that computing, storage, and network hardware resources are allocated and utilized efficiently on demand, eliminating resource imbalances and enabling dynamic expansion of hardware resources. The deployment architecture of the control platform in this embodiment is shown in Figure 12. Most modules can run directly on virtual machines (VMs), while some high-performance computing and storage nodes (such as stream computing nodes and relational databases) can be deployed on physical machines or bare metal servers (PMs).

[0110] In this embodiment, the electric vehicle charging and discharging operation control platform is deployed in the management information area. Its hardware includes the platform's management node server, network management node server, computing node server, array storage device, storage fiber optic switch, BMC access switch, firewall, management switch, service switch and core switch.

[0111] Example 2

[0112] The control platform provided in this embodiment also includes a system management and operation cluster, configured for cluster management, container scheduling and orchestration, system operation and monitoring, and continuous system integration and deployment. Other aspects are the same as in Embodiment 1.

[0113] The main features of the above-mentioned electric vehicle charging and discharging operation control platform are as follows:

[0114] (1) Real-time data high-speed access and streaming processing. Through distributed high-speed caching technology in a cluster environment, fast storage and access of real-time measurement data are realized; through real-time library cluster management technology, massive real-time model data storage and high-concurrency access are realized; through streaming data processing technology, the horizontal expansion of real-time data processing capabilities is realized.

[0115] (2) Massive historical data storage and management. Through massive historical data storage and management and distributed acquisition technology, we can achieve large-capacity storage and high-concurrency access of historical sampling data, meeting the storage requirements of tens of millions of measurement points and minute-level sampling.

[0116] (3) Microservice-based business applications. Based on the Spring framework, microservice applications are rapidly developed, elastically scaled, and containerized for deployment, enabling unified configuration and call chain tracing analysis. It possesses full lifecycle management, service quality control, flexible on-demand orchestration, and rapid fault diagnosis capabilities, improving the operational quality of business applications.

[0117] All platform functions are developed as components, based on microservice architecture technology, to abstract basic common business functions and support the common needs of business applications.

[0118] (4) Container orchestration and container cluster management. Through container cluster management and distributed deployment technology, container isolation is used to achieve load balancing and elastic scaling of services; microservice applications are deployed and started through containers and resources are isolated, thereby improving system resource utilization.

[0119] The control platform manages the cluster through containers, enabling containerized deployment of platform microservices, supporting elastic scaling, fault recovery, and load balancing.

[0120] (5) Integrated Operation and Maintenance. The cloud platform is based on DevOps development and operation and maintenance technology. By providing standardized and normalized software design, development, testing, deployment, operation and maintenance processes, as well as application monitoring, it realizes continuous integration, automated deployment and automated operation and maintenance of microservice applications, ensuring the safe and reliable operation of the system.

[0121] Compared with related technologies, this application has the following advantages:

[0122] 1. This application establishes a real-time information management architecture to enable access to a massive number of diverse terminals and heterogeneous networks, as well as the effective processing of massive amounts of data.

[0123] 2. This application achieves rapid storage and access to real-time measurement data through distributed high-speed caching technology in a cluster environment; achieves massive real-time model data storage and high-concurrency access through real-time library cluster management technology; and achieves horizontal expansion of real-time data processing capabilities through streaming data processing technology.

[0124] 3. This application has a data storage cluster, which realizes large-capacity storage and high-concurrency access of historical sampling data through massive historical data storage management and distributed acquisition technology, meeting the storage needs of tens of millions of measurement points and minute-level sampling.

[0125] 4. The control platform of this application has plug-and-play IoT access functionality, which is applicable to scenarios that require access to a large number of diverse devices, including converged terminals with data aggregation, edge computing and uplink communication functions.

[0126] 5. This application is based on the Spring framework for rapid development, elastic scaling and containerized deployment of microservice applications. It enables unified configuration of microservices and traceability analysis of microservice application call chains. It has the ability to manage the entire lifecycle, control service quality, flexibly orchestrate on demand and quickly diagnose faults, thereby improving the operational quality of business.

[0127] 6. This application utilizes container cluster management and distributed deployment technologies to isolate microservice applications using containers, achieving load balancing and elastic scaling of services; it enables microservice applications to be deployed and started via containers with resource isolation, improving system resource utilization. Simultaneously, based on DevOps development and operations technologies, it provides standardized and regulated software design, development, testing, deployment, and operation processes, as well as application monitoring, to achieve continuous integration, automated deployment, and automated operation and maintenance of microservice applications, ensuring the secure and reliable operation of the system.

Claims

1. An electric vehicle charging and discharging operation control platform, comprising: The data acquisition cluster is configured to receive and parse the acquired data sent by external systems, including multiple terminals, a vehicle networking platform, and a power grid dispatching platform. The data processing cluster is configured to process and analyze real-time remote signaling / telemetry data and perform periodic sampling and storage. The data storage cluster includes a distributed in-memory database, configured to enable large-capacity storage and high-concurrency access to historical data. The microservice cluster is configured to implement business application microservices and platform microservices using a microservice architecture based on the data stored in the data storage cluster.

2. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The multi-terminals include V2G charging and discharging stations, centralized charging stations, fast charging piles, and intelligent distribution transformer terminals.

3. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The external system interacts with the control platform via IoT plug-and-play technology. The control platform includes an IoT plug-and-play module, which comprises: The resource model library is set up to store the resource model definitions for IoT devices. The plug-and-play service unit is configured to enable synchronization of IoT device models between the control platform and the IoT management platform. The data parsing service unit is configured to convert IoT business data into information that the control platform can recognize. The IoT management platform is designed to connect to IoT devices and provide a unified API interface to enable interaction between device models and business data.

4. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The multi-terminal transmits the collected data to the data acquisition cluster based on the MQTT protocol, and the vehicle networking platform transmits the collected data to the data acquisition cluster via the HTTP interface.

5. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The data collected by the vehicle-to-everything (V2X) platform includes charging pile operation data, charging pile adjustability margin data, and user charging order data.

6. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The data processing cluster includes a stream computing engine and a distributed task scheduling framework, wherein... The stream computing engine is configured to process and analyze real-time data, including topology calculation, telemetry over-limit, telemetry tripping, tripping accident synthesis, SCADA operation stream processing, and real-time data push interface stream processing. The distributed task scheduling framework periodically samples real-time data by scheduling distributed sampling tasks and submits the data to the data storage cluster.

7. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The data storage cluster also includes a relational database, a real-time database, a time-series historical database, an Elasticsearch alarm database, and a high-speed cache.

8. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The business application microservices include distribution network status awareness, risk monitoring and assessment, distribution network overload reduction, voltage regulation, charging guidance, and flexible resource supply and demand matching.

9. The electric vehicle charging and discharging operation control platform according to claim 1, wherein, The platform's microservices include alarm services, communication protocol processing, model management, topology identification and analysis, and database management.

10. The electric vehicle charging and discharging operation control platform according to claim 1 further includes: The system management and operations cluster is configured for cluster management, container scheduling and orchestration, system operations and monitoring, and continuous system integration and deployment.

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