A load centralized control method and system based on a standard model of an electric vehicle

Abstract

A load control method and system based on an electric vehicle standard model, characterized in that the method comprises the following steps: step 1, obtaining the device model parameters of the electric vehicle ontology, charging pile and charging station from the general information model specified in the IEC61970 standard, and constructing a device model; step 2, based on the device model, collecting parameter information from electric vehicle ontologies, charging piles and charging station devices of different models to generate a device model cluster; step 3, comparing each device model in the device model cluster with the standard device model defined in the IEC61970 standard, and realizing the association, derivation and mapping of the models to update the device model cluster; step 4, based on the updated device model cluster, simulating the electric vehicle charging load, and obtaining the optimal load control scheme according to the simulation results.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically, to a load control method, system, device, and medium based on a standard electric vehicle model. Background Technology

[0002] With the rapid development of 5G communication and IoT technologies and the deepening integration of physical and information technologies, the technological level of terminal devices such as electric vehicles with adjustable loads has been greatly improved. The smart grid's accurate perception and interconnection of end loads has become a reality, and the operation and management of electric vehicles is shifting towards a wide-area, big data, and multi-coupling model.

[0003] Currently, there are various electric vehicle service operators in society, which are based on different communication platforms and database management platforms. The load control platform has a large amount of data information exchange with these heterogeneous electric vehicle operators, and faces a series of problems such as poor data compatibility and inconsistent interface standards.

[0004] To ensure accurate interaction between power company application systems, each system needs a consistent understanding of the exchanged information. Furthermore, to prevent phased integration projects from becoming large-scale "information silos," information across the power company should utilize a unified semantic data model, namely the Common Information Modeling (CIM).

[0005] The International Electrotechnical Commission (IEC) defines two series of standards, IEC 61968 and IEC 61970. IEC 61970, developed by Working Group 13 of Technical Committee 57, describes the Energy Management System Application Programming Interface (EMS-API); while IEC 61968, developed by Working Group 14 of Technical Committee 57, describes the Interface Reference Model (IRM) for distribution management systems. Together, these two series of standards define a common information model for power systems—CIM.

[0006] The Common Information Model (CIM) for power grid enterprises is primarily composed of the basic CIM portion defined in IEC 61970-301 and the extended portion of the distribution network information model defined in IEC 61968-11. The basic CIM portion of IEC 61968-301 defines the standardized interface semantics for the Energy Management System (EMS) API, while the IEC 61970-11 portion defines the standardized semantics for the distribution management system interface. The CIM describes the main object classes, class attributes, and relationships between classes in the business domains of the power enterprise's EMS (Energy Management System) and DMS (Distribution Management System). It provides a comprehensive logical view of EMS and DMS information and is an abstract model representing all major objects of the power enterprise.

[0007] However, as a general-purpose model, the CIM model is not well compatible with the general information models in the standards. For the specific service of electric vehicle charging, there is currently no universal model in the power system that can accurately, error-free, and universally define various types of electric vehicles, charging piles, and charging stations, so that the electric vehicle charging service can have a compliant CIM model.

[0008] In addition, with the gradual development of electric vehicle charging technology, the electric vehicles, charging piles, charging stations and other related products currently in use are not entirely limited to the relevant parameters mentioned in the general information model in the application standard when participating in the centralized control business of electric vehicle charging.

[0009] To address the aforementioned issues, there is an urgent need for a load control method and system based on the standard model of electric vehicles. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides a load control method, system, device, and medium based on a standard electric vehicle model. It constructs a cluster of equipment models by referencing the general information model specified in the IEC 61970 standard, and associates, derives, and maps standard equipment models to update the equipment model cluster, thereby enabling it to participate in load control.

[0011] The present invention adopts the following technical solution.

[0012] The first aspect of this invention relates to a load control method based on a standard electric vehicle model. The method includes the following steps: Step 1, obtaining equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC 61970 standard, and constructing equipment models; Step 2, collecting parameter information from different models of electric vehicle bodies, charging piles, and charging station equipment based on the equipment models to generate an equipment model cluster; Step 3, comparing each equipment model in the equipment model cluster with the standard equipment model defined in the IEC 61970 standard, and realizing the association, derivation, and mapping of the models to update the equipment model cluster; Step 4, performing electric vehicle charging load simulation based on the updated equipment model cluster, and obtaining the optimal load control scheme based on the simulation results.

[0013] Preferably, the equipment model of the electric vehicle body is implemented by describing the functional devices of each electrical subsystem in the electric vehicle body as classes; the equipment model of the electric vehicle body includes at least the following classes: power battery pack, converter, capacitor pack, inverter, permanent magnet synchronous motor and battery pack management system.

[0014] Preferably, the parameter information of the electric vehicle body is obtained based on the status parameters of various devices; the device model parameters of the electric vehicle body include at least the maximum charging voltage, maximum charging current, maximum allowable temperature, current state of charge, and current operating voltage.

[0015] Preferably, the equipment model of the electric vehicle charging pile is implemented by describing the electrical equipment, charging control method, charging capacity and compatible electric vehicle types of the electric vehicle charging pile through classes; the equipment model of the electric vehicle charging pile includes at least the following classes: charging pile controller, electricity meter, charging pile charging curve, electric vehicle, power battery pack and battery management system.

[0016] Preferably, the parameter information of the electric vehicle charging pile is obtained based on monitoring the status parameters of various devices; the equipment model parameters of the electric vehicle charging pile include at least the charging pile type, charging mode, operating temperature, operating humidity, rated output voltage, rated input voltage, rated output power, internal resistance, and rated operating temperature.

[0017] Preferably, the equipment model for electric vehicle charging piles includes power system resource limits, energy storage units, converters, off-board chargers, electric vehicle charging piles, regular interval plans, and electric vehicle charging plans.

[0018] Preferably, the equipment model parameters of the electric vehicle charging station include at least the inter-class relationship between the charging piles and the electric vehicle itself.

[0019] Preferably, the scheduling requirements of electric vehicle charging stations are obtained, and the inter-class relationship between electric vehicles and electric vehicle charging piles is calculated based on the requirements; and, an equipment model of electric vehicle charging stations is constructed based on the inter-class relationship, and the equipment model is compared with the standard equipment model defined in the IEC 61970 standard to realize the association, derivation and mapping of the model.

[0020] A second aspect of this invention relates to a load control system based on a standard electric vehicle model. The system includes a construction module, a clustering module, an update module, and a control module. The construction module is used to obtain equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC 61970 standard, and construct equipment models. The clustering module is used to collect parameter information from different models of electric vehicles, charging piles, and charging station equipment based on the equipment models to generate an equipment model cluster. The update module is used to compare each equipment model in the equipment model cluster with the standard equipment model defined in the IEC 61970 standard, and to perform model association, derivation, and mapping to update the equipment model cluster. The control module is used to perform electric vehicle charging load simulation based on the updated equipment model cluster, and to obtain the optimal load control scheme based on the simulation results.

[0021] A third aspect of the present invention relates to a load control device based on a standard electric vehicle model, the device comprising a processor and a storage medium; wherein the storage medium is used to store instructions; and the processor is used to perform operations according to the instructions to execute the steps of the method in the first aspect of the present invention.

[0022] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method of the first aspect of the present invention.

[0023] The beneficial effects of this invention are that, compared with the prior art, the load control method, system, device, and medium based on the standard model of electric vehicles in this invention can construct a cluster of equipment models by referring to the general information model specified in the IEC 61970 standard, and associate, derive, and map the standard equipment models to update the equipment model cluster, thereby participating in load control. This invention is effective, reliable, and has a clear approach. Based on the IEC 61970 standard, it proposes an extension method for the information model, analyzes the composition and characteristics of electric vehicles, and addresses the information mismatch problem in the economic scheduling of electric vehicle clusters by the load control platform. It extends the information models of the electric vehicle itself, electric vehicle charging piles, and charging stations to meet the information interaction needs of the load control platform in a heterogeneous environment. Attached Figure Description

[0024] Figure 1This is a schematic diagram illustrating the steps of a load centralized control method based on a standard electric vehicle model according to the present invention.

[0025] Figure 2 This is a schematic diagram of the electric vehicle body in the load centralized control method based on the standard model of electric vehicles of the present invention;

[0026] Figure 3 This is a schematic diagram of an electric vehicle charging pile in a load centralized control method based on an electric vehicle standard model according to the present invention.

[0027] Figure 4 This is a schematic diagram of an electric vehicle charging station in a load centralized control method based on an electric vehicle standard model according to the present invention.

[0028] Figure 5 This is a schematic diagram illustrating model association, derivation, and mapping in a load control method based on a standard electric vehicle model according to the present invention. Detailed Implementation

[0029] 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 invention are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments not described in this invention obtained by those skilled in the art based on the embodiments described in this invention without creative effort should fall within the protection scope of this invention.

[0030] Figure 1 This is a schematic diagram illustrating the steps of a load central control method based on a standard electric vehicle model according to the present invention. Figure 1 As shown, the first aspect of the present invention relates to a load control method based on a standard model of electric vehicles, the method comprising steps 1 to 4.

[0031] Step 1: Obtain the equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC 61970 standard, and construct the equipment model.

[0032] It is understood that the purpose of the method of the present invention is to construct a device model that conforms to the energy interaction interface specification specified in the IEC61970 standard, and that the device model can be integrated into the CIM model to achieve unified and universal centralized control with other services.

[0033] Therefore, this invention first needs to extract an existing general information model from the IEC 61970 standard, and use this model as the basis for the model actually constructed in this invention, and then update the model based on this model. In order to achieve standard-based model construction, this invention needs to extract the basic parameters mentioned in the model provided by the standard, and map the specific values ​​of the relevant parameters in the actual application product to the basic parameters.

[0034] Step 2: Based on the equipment model, collect parameter information from different models of electric vehicles, charging piles, and charging station equipment to generate an equipment model cluster.

[0035] After acquiring the aforementioned basic parameters, this invention can collect relevant information about these parameters from the actual device. In the same steps, information about other important device parameters, which are not the aforementioned basic parameters, can also be extracted as extended information. This extended information can be used in subsequent processes to generate new classes, as well as class associations, derivations, and mappings.

[0036] This invention can investigate electric vehicles from different manufacturers, divide the electrical subsystems of electric vehicles, consider the commonality of electrical parameters of different electric vehicles, and add attributes and descriptions such as maximum charging voltage, maximum charging current, maximum allowable temperature, state of charge, and current operating voltage to electric vehicles to obtain parameter information of the electric vehicle itself.

[0037] In addition, this invention can study the relationship between charging piles and electric vehicles, consider different types of charging piles and charging modes, and add attributes and descriptions such as rated output voltage, rated input voltage, rated output power, and current output voltage to the charging piles, thereby obtaining parameter information of electric vehicle charging piles.

[0038] In addition, the present invention can also collect basic parameter information related to various electrical devices in electric vehicle charging stations, as well as charging piles, other charging equipment, electric vehicle bodies, charging and discharging methods, types, and control types of charging stations in a similar manner.

[0039] Based on the above information, clustering can be used to construct a single device model for the same information, while for multiple different device models, clustering can be achieved through association and other methods.

[0040] Step 3: Compare each device model in the device model cluster with the standard device model defined in the IEC 61970 standard, and perform model association, derivation and mapping to update the device model cluster.

[0041] In this invention, in order to update and upgrade the models in the device model cluster so that they can fully meet the needs of centralized control services, the model generated by the method of this invention needs to be continuously optimized.

[0042] Specifically, since the Common Information Model (CIM) is scalable, when extending the model, we should try to keep it consistent with the existing model to maximize the reuse and compatibility of the existing CIM model and ensure that the model we build has better versatility.

[0043] First, an abstract analysis is performed on the electric vehicle body equipment, charging piles, and charging stations to be expanded, extracting the attributes and attribute values ​​of the electric vehicle abstract class. Then, a difference analysis is conducted between the extracted electric vehicle abstract class and the power system common information model defined in the IEC 61970 series standards. If the extracted class corresponds to an existing class in the standard CIM, no new class needs to be extended; only a mapping relationship needs to be established with the existing class. If the extracted class does not correspond to an existing class in the standard CIM, a new class needs to be derived from the existing class, its package determined based on its function, and its corresponding attributes extended through analysis. If the extended new class is related to an existing class, a corresponding mapping relationship is established based on the actual situation.

[0044] There are three main static relationships between CIM classes: inheritance, association, and aggregation. Inheritance represents the relationship between a more general class and a more specific class; aggregation represents the relationship between a part class and a whole class; association represents a connection between classes that can specify roles, describing the logical relationship between objects in the system.

[0045] Figure 5 This diagram illustrates model association, derivation, and mapping in a load control method based on a standard electric vehicle model according to the present invention. Figure 5 As shown, through the above method, the equipment models and equipment model parameters of the electric vehicle body, charging pile and charging station extracted by the present invention include at least the following contents.

[0046] Preferably, the equipment model of the electric vehicle body is implemented by describing the functional devices of each electrical subsystem in the electric vehicle body as classes; the equipment model of the electric vehicle body includes at least the following classes: power battery pack, converter, capacitor pack, inverter, permanent magnet synchronous motor and battery pack management system.

[0047] Figure 2 This is a schematic diagram of the electric vehicle body in the load control method based on a standard electric vehicle model of the present invention. Figure 2As shown, it can be understood that in the method of this invention, the electric vehicle body can be divided into electrical and mechanical parts. Furthermore, according to the function of each electrical subsystem of the electric vehicle, it can be divided into components such as a battery unit, a DC-DC boost converter, a DC-DC converter, a supercapacitor bank, a PWM inverter, and a permanent magnet synchronous machine. The battery unit class is extended in the core package to describe the electric vehicle battery cells, and a battery management system (BMS) class is also extended within this class. This system describes the battery's attributes by monitoring state parameters such as voltage, current, temperature, and state of charge.

[0048] Preferably, the equipment model of the electric vehicle charging pile is implemented by describing the electrical equipment, charging control method, charging capacity and compatible electric vehicle types of the electric vehicle charging pile through classes; the equipment model of the electric vehicle charging pile includes at least the following classes: charging pile controller, electricity meter, charging pile charging curve, electric vehicle, power battery pack and battery management system.

[0049] Figure 3 This is a schematic diagram of an electric vehicle charging pile in a load centralized control method based on an electric vehicle standard model according to the present invention. In one embodiment of the present invention, the charging pile consists of a pile body, an energy meter, and a controller, etc. The input end of the charging pile is directly connected to the AC power grid, and the output end is equipped with a charging plug for charging electric vehicles. In addition, a new charging pile class (Charging Pile) inherits from the equipment container class (Equipment Container), and the energy meter class (Watt Hour Meter) and the charging pile controller class (Charging Pile Controller) are extended in the charging pile class.

[0050] The charging pile controller and the battery pack management system interact in real time, therefore it is necessary to establish an association between the charging pile controller class and the battery pack management system class. The charging pile class is extended with a charging pile curve class to implement the function of detecting the charging status of electric vehicles. Charging piles can be divided into AC charging piles and DC charging piles. The charging pile type class is extended with a wire package class with attributes of AC charging pile (AC Charging Pile) and DC charging pile (DC Charging Pile).

[0051] AC charging piles provide AC power to the on-board chargers of electric vehicles, with lower output power, corresponding to the "slow charging" mode. DC charging piles, on the other hand, provide DC power to the power batteries of electric vehicles, with a wider range of adjustable voltage and current, corresponding to the "fast charging" mode. For these two charging modes, a charging mode class is added to the charging cable package, with attributes of "Quick Charging" and "Slow Charging".

[0052] Preferably, the equipment model for electric vehicle charging piles includes power system resource limits, energy storage units, converters, off-board chargers, electric vehicle charging piles, regular interval plans, and electric vehicle charging plans.

[0053] Figure 4 This is a schematic diagram of an electric vehicle charging station model in a load control method based on an electric vehicle standard model according to the present invention. In this way, the main equipment of the electric vehicle charging station in the present invention includes charging piles and off-board chargers. Simultaneously, the charging station should be equipped with energy storage units and converters to ensure that the charging of electric vehicles is not affected. A new charging station class (EV Charging Station) inherits from the power system resource class, and extends the charging pile class, off-board charger class, and energy storage unit class within the charging station class. To efficiently utilize the charging equipment and save charging time, the charging station class extends the electric vehicle charging schedule class (EV Station Charging Schedule) to describe this functionality, which inherits from the regular interval schedule class.

[0054] Preferably, the equipment model parameters of the electric vehicle charging station include at least the inter-class relationship between the charging piles and the electric vehicle itself.

[0055] In addition, the present invention can reasonably obtain the scheduling requirements of electric vehicle charging stations, and calculate the inter-class relationship between electric vehicles and electric vehicle charging piles based on the requirements; and construct an equipment model of electric vehicle charging stations based on the inter-class relationship, and compare the equipment model with the standard equipment model defined in the IEC61970 standard to realize the association, derivation and mapping of the model.

[0056] Specifically, the scheduling requirements of electric vehicle charging stations can be achieved in various ways. In one embodiment of this invention, the requirements can be obtained from the scheduling requirements of the load control platform. In the prior art, the content of scheduling requirements is constantly changing and being upgraded to meet the needs of the control platform. With the continuous expansion of the power system, the collection of information on electric vehicle users, the diversification and real-time upgrading of power grid resource allocation methods, the scheduling requirements can contain more and more information, and can also be obtained by more detailed modeling of electric vehicle charging stations and more accurate calculation of model parameters. Therefore, this invention will study all possible inter-class relationships between the vehicle and the charging pile, and update the model as the demand model and control algorithm are upgraded. The model update here mainly refers to the update and expansion of classes and attributes, such as the expanded electric vehicle charging plan class (EV Station ChargingSchedule) mentioned above.

[0057] Step 4: Perform electric vehicle charging load simulation based on the updated equipment model cluster, and obtain the optimal load control scheme based on the simulation results.

[0058] It is understandable that the model obtained through the scheduling requirements in this invention will ultimately be applied to the demand control algorithm. By retrieving and making reasonable use of the various parameters in the model through the algorithm, the actual load control scheme can be obtained through the scheduling requirements.

[0059] The model construction process in this invention is universal and scalable. Therefore, the constructed model can be used in a variety of different load control algorithms and also has strong scalability and adaptability. It can always provide good data model support under the premise that the control algorithm is constantly updated and iterated.

[0060] A second aspect of this invention relates to a load control system based on a standard electric vehicle model. The system includes a construction module, a clustering module, an update module, and a control module. The construction module is used to obtain equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC 61970 standard, and construct equipment models. The clustering module is used to collect parameter information from different models of electric vehicles, charging piles, and charging station equipment based on the equipment models to generate an equipment model cluster. The update module is used to compare each equipment model in the equipment model cluster with the standard equipment model defined in the IEC 61970 standard, and to perform model association, derivation, and mapping to update the equipment model cluster. The control module is used to perform electric vehicle charging load simulation based on the updated equipment model cluster, and to obtain the optimal load control scheme based on the simulation results.

[0061] A third aspect of the present invention relates to a load control device based on a standard electric vehicle model, the device comprising a processor and a storage medium; wherein the storage medium is used to store instructions; and the processor is used to perform operations according to the instructions to execute the steps of the method according to the first aspect of the present invention.

[0062] It is understood that, in order to implement the various functions in the methods provided in the embodiments of this application, the load control device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0063] This application embodiment can divide the load control device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0064] The device includes at least one processor, a bus system, and at least one communication interface. The processor may be a central processing unit (CPU), or it may be replaced by a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or other hardware, or the FPGA or other hardware may work together with the CPU as a processor.

[0065] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via a bus. The memory can also be integrated with the processor.

[0066] The hard drive can be a mechanical hard drive or a solid-state drive (SSD), etc. The interface card can be a host bus adapter (HBA), a redundant array of independent disks (RID), an expander card, or a network interface controller (NIC), etc., and this embodiment of the invention is not limited to any particular type. The interface card in the hard drive module communicates with the hard drive. The storage node communicates with the interface card of the hard drive module to access the hard drive in the hard drive module.

[0067] The hard drive interface can be Serial Attached Small Computer System Interface (SAS), Serial Advanced Technology Attachment (SATA), or Peripheral Component Interconnect Express (PCIe), etc.

[0068] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0069] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method of the first aspect of the present invention.

[0070] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0071] The beneficial effects of this invention are that, compared with the prior art, the load control method, system, device, and medium based on the standard model of electric vehicles in this invention can construct a cluster of equipment models by referring to the general information model specified in the IEC 61970 standard, and associate, derive, and map the standard equipment models to update the equipment model cluster, thereby participating in load control. This invention is effective, reliable, and has a clear approach. Based on the IEC 61970 standard, it proposes an extension method for the information model, analyzes the composition and characteristics of electric vehicles, and addresses the information mismatch problem in the economic scheduling of electric vehicle clusters by the load control platform. It extends the information models of the electric vehicle itself, electric vehicle charging piles, and charging stations to meet the information interaction needs of the load control platform in a heterogeneous environment.

[0072] 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 load central control method based on a standard electric vehicle model, characterized in that, The method includes the following steps: Step 1: Obtain the equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC 61970 standard, and construct the equipment model; Step 2: Based on the device model, collect parameter information from different models of electric vehicles, charging piles, and charging station equipment to generate a device model cluster; Step 3: Compare each device model in the device model cluster with the standard device model defined in the IEC 61970 standard, and realize the association, derivation and mapping of the models to update the device model cluster; Step 4: Perform electric vehicle charging load simulation based on the updated equipment model cluster, and obtain the optimal load control scheme based on the simulation results.

2. The load control method based on a standard electric vehicle model as described in claim 1, characterized in that: The device model of the electric vehicle body is implemented by classifying the functional devices of each electrical subsystem in the electric vehicle body; The equipment model of the electric vehicle body includes power battery packs, converters, capacitor banks, inverters, permanent magnet synchronous motors, and battery management systems.

3. The load central control method based on the standard model of electric vehicles as described in claim 2, characterized in that: The parameter information of the electric vehicle body is obtained based on the status parameters of various monitoring devices; The device model parameters of the electric vehicle body include maximum charging voltage, maximum charging current, maximum allowable temperature, current state of charge, and current operating voltage.

4. The load control method based on the standard model of electric vehicles as described in claim 1, characterized in that: The equipment model of the electric vehicle charging pile is implemented by describing the electrical equipment, charging control method, charging capacity and compatible electric vehicle types of the electric vehicle charging pile through classes. The equipment model of the electric vehicle charging pile includes charging pile controller, electricity meter, charging pile charging curve, electric vehicle, power battery pack and battery management system.

5. The load control method based on the standard model of electric vehicles according to claim 4, characterized in that: The parameter information of the electric vehicle charging pile is obtained based on monitoring the status parameters of various types of equipment; The equipment model parameters of the electric vehicle charging pile include charging pile type, charging mode, operating temperature, operating humidity, rated output voltage, rated input voltage, rated output power, internal resistance, and rated operating temperature.

6. The load central control method based on the standard model of electric vehicles as described in claim 1, characterized in that: The equipment model of the electric vehicle charging station includes the upper limit of power system resources, energy storage unit, converter, off-board charger, electric vehicle charging pile, regular interval plan and electric vehicle charging plan.

7. The load control method based on the standard model of electric vehicles according to claim 6, characterized in that: The equipment model parameters of the electric vehicle charging station include the inter-class relationship between the charging piles and the electric vehicle itself.

8. The load central control method based on the standard model of electric vehicles as described in claim 7, characterized in that: Obtain the scheduling requirements of electric vehicle charging stations, and calculate the inter-class relationship between electric vehicles and electric vehicle charging piles based on the requirements; Furthermore, an electric vehicle charging station equipment model is constructed based on the aforementioned inter-class relationships, and this equipment model is compared with the standard equipment model defined in the IEC 61970 standard to achieve model association, derivation, and mapping.

9. A load control system based on a standard electric vehicle model, characterized in that: The system includes a construction module, a cluster module, an update module, and a centralized control module; wherein, The construction module is used to obtain the equipment model parameters of the electric vehicle body, charging pile, and charging station from the general information model specified in the IEC61970 standard, and to construct the equipment model. The cluster module is used to collect parameter information from different models of electric vehicles, charging piles, and charging station equipment based on the device model, so as to generate a device model cluster. The update module is used to compare each device model in the device model cluster with the standard device model defined in the IEC 61970 standard, and to perform model association, derivation and mapping in order to update the device model cluster. The centralized control module is used to perform electric vehicle charging load simulation based on the updated equipment model cluster, and obtain the optimal load centralized control scheme based on the simulation results.

10. A load control device based on a standard electric vehicle model, characterized in that: The device includes a processor and a storage medium; wherein... The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.

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