Simulation platform, simulation method and simulation system of distributed power system

Abstract

The invention provides a simulation platform, a simulation method and a simulation system of a distributed power system. The simulation platform comprises a cloud computing platform which comprises a virtual machine manager, a resource scheduler and a security manager, the virtual machine manager is used for managing a virtual machine, the resource scheduler is used for allocating computing resources, and the security manager is used for maintaining the security of a computing process; the distributed simulation engine comprises a task scheduler, a simulation controller and a data synchronizer, the task scheduler is used for distribution of simulation tasks, the simulation controller is used for controlling starting or stopping of the simulation tasks, and the data synchronizer is used for keeping data synchronization among the sub-task nodes. Through the method, the problem of low efficiency of a power system simulation method in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of simulation of power systems, and in particular to a simulation platform, a simulation method and a simulation system for distributed power systems. Background Art

[0002] As the complexity and scale of power systems continue to increase, traditional simulation technologies have encountered many challenges in dealing with large-scale distributed power systems. These challenges include limited computing resources, low simulation efficiency, and the complexity of data interaction. At the same time, the introduction of new technologies such as renewable energy and smart grids in modern power systems has also put forward new requirements for simulation technology.

[0003] Distributed power system simulation is an important branch of power system simulation, involving multiple aspects such as the topology, component models, and control strategies of the power system. Traditional distributed power system simulation mainly relies on local computing resources, such as high-performance computers and dedicated simulation hardware. These methods work well when dealing with small to medium-sized systems, but may encounter bottlenecks in large-scale systems and complex scenarios. For example: Computing resource limitations: Traditional methods based on high-performance computer clusters require expensive hardware and complex configurations, which limits their application in large-scale and flexible scenarios. Efficiency issues: Although existing cloud computing-based solutions provide elastic computing resources, they usually lack specific optimizations for power system simulation and may be inefficient in data synchronization, real-time interaction, etc. Insufficient scalability and flexibility: Existing solutions may encounter bottlenecks when dealing with complex simulation scenarios and large-scale systems, especially when the simulation scale and parameters need to be adjusted quickly. Real-time interaction and data synchronization issues: Existing cloud computing solutions may have deficiencies in real-time interaction and data synchronization, which may affect the consistency and accuracy of the simulation process.

[0004] Therefore, a new power system simulation method is needed to solve at least one of the above problems. Summary of the invention

[0005] The main purpose of the present application is to provide a simulation platform, a simulation method and a simulation system for a distributed power system, so as to at least solve the problem of low efficiency of the power system simulation method in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a simulation platform for a distributed power system is provided, including: a cloud computing platform, including a virtual machine manager, a resource scheduler and a security manager, the virtual machine manager is used to manage virtual machines, the resource scheduler is used to allocate computing resources, and the security manager is used to maintain the security of the computing process; a distributed simulation engine, including a task scheduler, a simulation controller and a data synchronizer, the task scheduler is used to allocate simulation tasks, the simulation controller is used to control the start or stop of the simulation tasks, and the data synchronizer is used to maintain data synchronization between each subtask node.

[0007] Optionally, the simulation platform of the distributed power system also includes: a power system model library, including component models and scenario templates, for storing simulation models of each of the power systems, the component models at least including mathematical models of power components, and the scenario templates at least including fault scenario templates.

[0008] Optionally, the simulation platform of the distributed power system also includes: a user interface, including a configuration interface, a monitoring interface and an analysis tool, the configuration interface is used to configure the parameters of the simulation task, the monitoring interface is used to monitor the real-time status of the simulation task, and the analysis tool is used to analyze the simulation results.

[0009] According to another aspect of the present application, a simulation method for a distributed power system is provided, comprising: obtaining simulation requirements, configuring simulation resources according to the simulation requirements, wherein the simulation requirements include at least storage capacity requirements, the simulation resources include at least storage capacity resources, and the simulation resources correspond to the simulation requirements one-to-one; obtaining a simulation task, dividing the simulation task into a plurality of subtasks, and dividing the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system; establishing a communication connection between a plurality of the subtask nodes, and synchronously executing the subtasks on the subtask nodes to obtain sub-simulation results, and summarizing the sub-simulation results to obtain a simulation result of the power system.

[0010] Optionally, simulation requirements are obtained and simulation resources are configured according to the simulation requirements, including: obtaining computing requirements and determining a processor according to the computing requirements, wherein the computing requirements represent computing performance requirements; obtaining storage capacity requirements and determining a storage device according to the storage capacity requirements; and obtaining network requirements and determining a network bandwidth according to the network requirements.

[0011] Optionally, the simulation task is divided into a plurality of subtasks, including any one of the following: dividing the simulation task into a plurality of subtasks according to the topological structure of the power system; dividing the simulation task into a plurality of subtasks according to the equipment type of the power system; dividing the simulation task into a plurality of subtasks according to target data corresponding to the simulation task, wherein the target data represents data for data interaction during the execution of the simulation task; dividing the simulation task into a plurality of subtasks according to the simulation step.

[0012] Optionally, the sub-simulation results are summarized to obtain the simulation result of the power system, including: obtaining a data transmission format and a data transmission protocol, and transmitting the sub-simulation results according to the data transmission format through the data transmission protocol; and summarizing the sub-simulation results of the same time step to obtain the simulation result.

[0013] Optionally, a communication connection is established between the plurality of subtask nodes, including: loading the power system model at each of the subtask nodes respectively, and initializing the node voltage and node current of each of the subtask nodes; performing simulation calculations for each step at each of the subtask nodes, and synchronizing the data of the subtask nodes after the simulation calculations for each step are completed.

[0014] Optionally, after obtaining the simulation results of the power system, the method also includes: visually displaying the simulation results; performing data analysis on the simulation results, generating a simulation report and outputting the simulation report, wherein the simulation report at least includes data generated during the simulation process, simulation results and data analysis results.

[0015] According to another aspect of the present application, a simulation system of a distributed power system is provided, comprising: a simulation platform of any one of the distributed power systems described; a controller, comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a simulation method for executing any one of the distributed power systems described.

[0016] Using the technical solution of the present application, the cloud computing platform includes a virtual machine manager, a resource scheduler and a security manager. The virtual machine manager is used to manage virtual machines, the resource scheduler is used to allocate computing resources, and the security manager is used to maintain the security of the computing process; the distributed simulation engine includes a task scheduler, a simulation controller and a data synchronizer. The task scheduler is used to allocate simulation tasks, the simulation controller is used to control the start or stop of simulation tasks, and the data synchronizer is used to maintain data synchronization between subtask nodes. Compared with the prior art, the power system simulation method has problems such as low efficiency. By establishing a cloud computing platform and a distributed simulation engine, the simulation of the distributed power system is carried out, and cloud computing resources are used to reduce the cost of hardware and configuration, making the distributed power system simulation more efficient. Therefore, the simulation efficiency of the power system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of a simulation platform for a distributed power system provided by an embodiment of the present application is shown;

[0019] Figure 2 A hardware structure block diagram of a mobile terminal for executing a simulation method for a distributed power system provided in an embodiment of the present application is shown;

[0020] Figure 3 A schematic diagram of a flow chart of a simulation method for a distributed power system provided in an embodiment of the present application is shown;

[0021] Figure 4 A data synchronization flow chart provided by an embodiment of the present application is shown;

[0022] Figure 5 A schematic flow chart of a specific distributed power system simulation method provided in an embodiment of the present application is shown.

[0023] The above drawings include the following reference numerals:

[0024] 10. Cloud computing platform; 20. Distributed simulation engine; 102. Processor; 104. Memory; 106. Transmission equipment; 108. Input and output equipment. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background technology, the power system simulation method in the prior art is inefficient. In order to solve the problem of low efficiency of the power system simulation method, the embodiments of the present application provide a simulation platform, simulation method and simulation system for a distributed power system.

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] The embodiments of the present application also provide a simulation platform for a distributed power system. The device is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0031] The following is an introduction to the simulation platform of the distributed power system provided in the embodiments of the present application.

[0032] Figure 1 Schematic diagram of a simulation platform for a distributed power system according to an embodiment of the present application. Figure 1 As shown, the device comprises:

[0033] The cloud computing platform 10 includes a virtual machine manager, a resource scheduler, and a security manager. The virtual machine manager is used to manage virtual machines, the resource scheduler is used to allocate computing resources, and the security manager is used to maintain the security of the computing process.

[0034] Specifically, the cloud computing platform is the foundation of the entire system, providing elastic and scalable computing resources, including the virtual machine manager: responsible for the creation, destruction and monitoring of virtual machines. Resource scheduler: dynamically allocates computing, storage and network resources according to the needs of simulation tasks. Security manager: ensures the security of simulation data and computing processes.

[0035] The distributed simulation engine 20 includes a task scheduler, a simulation controller and a data synchronizer. The task scheduler is used to allocate simulation tasks, the simulation controller is used to control the start or stop of the simulation tasks, and the data synchronizer is used to maintain data synchronization between various subtask nodes.

[0036] Specifically, the distributed simulation engine is the core of the system, responsible for the allocation and coordination of simulation tasks. Task scheduler: allocates simulation tasks to appropriate virtual machines for execution. Simulation controller: controls the start, pause and stop of the simulation process. Data synchronizer: responsible for data synchronization and consistency between simulation nodes.

[0037] As an optional solution, the simulation platform of the distributed power system also includes: a power system model library, including component models and scenario templates, for storing simulation models of each of the power systems, the component models at least including mathematical models of power components, and the scenario templates at least including fault scenario templates.

[0038] In the specific implementation process, the power system model library stores models of various power system components. Component models: mathematical models of transformers, generators, lines, etc. Scenario templates: predefined simulation scenarios, such as fault analysis, optimal scheduling, etc.

[0039] In an optional scheme, the simulation platform of the distributed power system also includes: a user interface, including a configuration interface, a monitoring interface and an analysis tool, the configuration interface is used to configure the parameters of the simulation task, the monitoring interface is used to monitor the real-time status of the simulation task, and the analysis tool is used to analyze the simulation results.

[0040] Specifically, the user interface provides a friendly operation interface. Configuration interface: allows users to configure the parameters of the simulation task. Monitoring interface: displays the real-time status and results of the simulation process. Analysis tools: provide data analysis and visualization tools.

[0041] In this embodiment, the cloud computing platform includes a virtual machine manager, a resource scheduler and a security manager. The virtual machine manager is used to manage virtual machines, the resource scheduler is used to allocate computing resources, and the security manager is used to maintain the security of the computing process; the distributed simulation engine includes a task scheduler, a simulation controller and a data synchronizer. The task scheduler is used to allocate simulation tasks, the simulation controller is used to control the start or stop of simulation tasks, and the data synchronizer is used to maintain data synchronization between subtask nodes. Compared with the prior art, the power system simulation method has problems such as low efficiency. By establishing a cloud computing platform and a distributed simulation engine, the simulation of the distributed power system is carried out, and cloud computing resources are used to reduce the cost of hardware and configuration, so that the simulation of the distributed power system is more efficient. Therefore, the simulation efficiency of the power system can be improved.

[0042] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 2 FIG. 1 is a hardware structure block diagram of a mobile terminal of a distributed power system simulation method according to an embodiment of the present invention. Figure 2 As shown, the mobile terminal may include one or more ( Figure 2 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 2 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 2 More or fewer components as shown, or with Figure 2 Different configurations are shown.

[0043] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the simulation method of the distributed power system in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0044] In this embodiment, a simulation method for a distributed power system running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] Figure 3 is a flow chart of a simulation method for a distributed power system according to an embodiment of the present application. Figure 3 As shown, the method comprises the following steps:

[0046] Step S201, obtaining simulation requirements, and configuring simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one;

[0047] Specifically, in the first step of task allocation, the system needs to conduct a comprehensive demand analysis of the simulation task to be executed. This includes the analysis of computing requirements, storage requirements, and network requirements. Computing requirements: Computing requirements are usually determined by the complexity of the simulation task, the required computing accuracy, and the time requirement.

[0048] Step S202, obtaining a simulation task, dividing the simulation task into a plurality of subtasks, and dividing the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system;

[0049] Specifically, task allocation is the process of reasonably allocating simulation tasks to various nodes of the cloud computing platform. Analyze task requirements: First analyze the computing, storage and network requirements of the simulation task. Select appropriate resources: Select appropriate virtual machines and computing resources according to the requirements. In distributed power system simulation, the main goal of task allocation is to reasonably allocate tasks to various nodes of the cloud computing platform according to the specific requirements of the simulation task, so as to make full use of computing resources and ensure the efficiency and accuracy of the simulation process. Parallel computing is to perform simulation calculations simultaneously through multiple computing nodes to improve efficiency. Divide computing tasks: Divide the simulation task into multiple subtasks, each of which is responsible for the simulation calculation of a part of the power system. Distribute subtasks: Distribute subtasks to different computing nodes for execution. Collect results: Collect the calculation results of each computing node and integrate them into a complete simulation result. In distributed power system simulation, parallel computing is to improve the overall computing efficiency by dividing the entire simulation task into multiple subtasks and assigning these subtasks to different computing nodes for simultaneous execution. The rationality of task division directly affects the efficiency and accuracy of the simulation.

[0050] Step S203, establishing a communication connection between the plurality of subtask nodes, and synchronously executing the subtasks on the subtask nodes to obtain sub-simulation results, and summarizing the sub-simulation results to obtain a simulation result of the power system.

[0051] Specifically, real-time interaction is the key to ensuring the collaborative work between simulation nodes. Establish a communication connection: Establish a real-time communication connection between simulation nodes. Synchronize data: Synchronize data between nodes through communication connections to ensure the consistency of the simulation process. The detailed explanation is as follows: First, the present invention adopts PTP (Precision Time Protocol) clock synchronization technology to achieve nanosecond time synchronization accuracy; secondly, each simulation node loads the power system model respectively and initializes the voltage and current of each node. Then, each simulation node starts to perform simulation calculations. During the simulation calculation process, each simulation node needs to synchronize data through a real-time interaction mechanism.

[0052] Through this embodiment, simulation requirements are obtained, and simulation resources are configured according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, and the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one; a simulation task is obtained, the simulation task is divided into multiple subtasks, and the simulation resources are divided according to the subtasks to obtain subtask nodes corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system; a communication connection is established between the multiple subtask nodes, and the subtasks are synchronously executed on the subtask nodes to obtain sub-simulation results, and the sub-simulation results are summarized to obtain the simulation results of the power system. Compared with the prior art, the power system simulation method has problems such as low efficiency. By establishing a cloud computing platform and a distributed simulation engine, the simulation of a distributed power system is performed, and cloud computing resources are used to reduce the cost of hardware and configuration, so that the simulation of a distributed power system is more efficient. Therefore, the simulation efficiency of the power system can be improved.

[0053] In the specific implementation process, the above step S201 obtains the simulation requirements, and configures the simulation resources according to the simulation requirements, which can be implemented by the following steps: Step S2011: obtain the computing requirements, and determine the processor according to the computing requirements, wherein the computing requirements represent the computing performance requirements; Step S2012: obtain the storage capacity requirements, and determine the storage device according to the storage capacity requirements; obtain the network requirements, and determine the network bandwidth according to the network requirements. The method configures the simulation resources by the above steps, and can simulate the operation of a real power system.

[0054] Specifically, for example, for a large-scale power system simulation task involving multiple generators, transformers and complex network topology, a high-performance CPU (Central Processing Unit, CPU for short) or GPU (Graphics Processing Unit, GPU for short) may be required to handle a large number of parallel calculations. Example: Assume that the simulation task involves a power network containing 500 nodes, including 50 generators and 100 transformers, and the simulation time requirement is real-time (i.e., second-level response). The computing requirements of this task can be roughly estimated to require a high-performance CPU with at least 64 cores, as well as GPU resources that support parallel computing. Storage requirements: The storage requirements are determined by the amount of data generated by the simulation task and the required data retention time. For example, when performing large-scale simulations, the amount of data generated by each node may reach hundreds of GB, so sufficient storage capacity is required to save this data. In addition, if the task requires long-term preservation of historical data, high-capacity storage devices also need to be considered. Example: For the above-mentioned 500-node power network simulation task, each simulation cycle may generate about 500MB of data. If the simulation task needs to run for 24 hours and all intermediate results need to be saved, the total storage requirement can reach 12TB. Network requirements: Network requirements mainly involve the communication frequency and data transmission rate between simulation nodes. For example, in real-time simulation, network latency and bandwidth are crucial because each simulation node needs to exchange data frequently. Example: In a 500-node simulation, assume that each node needs to exchange 10MB of data per second, which means that the total network bandwidth requirement is 5GB / s. In addition, to ensure real-time performance, network latency must be controlled at the millisecond level. Select appropriate resources: According to the task requirements analyzed above, the system will select appropriate virtual machines and computing resources. The selection criteria include CPU / GPU specifications, storage capacity, and network bandwidth. Selection of virtual machines: The system can select virtual machine instances that are suitable for task requirements on the cloud platform. Computation-optimized (such as C6g instances) and memory-optimized (such as R6g instances), users can choose the appropriate instance type according to task requirements. Example: For simulation tasks with high computing requirements, you can choose the compute-optimized instance C6g.16xlarge, which has 64 CPUs and 128GB of memory and supports high-speed parallel computing. For tasks that require GPU acceleration, you can choose NVIDIA A100 GPU (NVIDIA is a graphics processor technology), which is suitable for deep learning and large-scale parallel computing tasks. Selection of storage resources: According to storage requirements, the system can choose high-performance storage options in cloud storage services. Selection of network resources: According to network requirements, select the appropriate network configuration. For example, in scenarios where high bandwidth is required, up to 25Gbps of network bandwidth can be provided, which is suitable for high-throughput simulation tasks.Example: In order to meet the network bandwidth requirement of 5GB / s, you can configure the Elastic Network Adapter of AWS (Amazon Web Services, AWS for short) and select instances with high network bandwidth, such as C6gn instances, to ensure low latency and high throughput data transmission between simulation nodes. The specific implementation example is as follows: Assume that we have a large-scale power system simulation task with 500 nodes. The system allocates the following resources based on the analysis results: Computing resources: Select multiple C6g.16xlarge instances, equipped with 64 vCPUs, which are suitable for large-scale parallel computing tasks. In addition, select P4d instances to use GPU for computing acceleration. Storage resources: Configure high-performance EBS io2 storage volumes with a capacity of 12TB for each instance to ensure sufficient storage space to save the large amount of data generated during the simulation process. Network resources: Enable enhanced networking and select instances with high network bandwidth, such as C6gn, to meet high bandwidth requirements and ensure that data exchange between various simulation nodes can be carried out with low latency and high efficiency. Through reasonable task demand analysis and resource allocation, it is ensured that the simulation task can be executed efficiently and accurately on the cloud computing platform to meet the requirements of real-time and large-scale processing. Start simulation node: Start the simulation node on the selected resource and load the required power system model. Monitor task execution: Monitor the execution status of simulation tasks in real time and dynamically adjust resource allocation as needed.

[0055] In some optional implementations, the simulation task is divided into multiple subtasks, including any one of the following: dividing the simulation task according to the topological structure of the power system to obtain multiple subtasks; dividing the simulation task according to the equipment type of the power system to obtain multiple subtasks; dividing the simulation task according to the target data corresponding to the simulation task to obtain multiple subtasks, wherein the target data represents data for data interaction during the execution of the simulation task; dividing the simulation task according to the simulation step length to obtain multiple subtasks. The method divides the tasks through the above steps, so that the divided tasks can be calculated and executed in parallel.

[0056] Specifically, 1. Topological structure of power system: power system usually consists of multiple subnets, regions and nodes, which can be used as the basis for dividing subtasks. Specific division basis includes: Regional division: subtasks are divided according to geographical or logical regions of power system. For example, a large power grid is divided into different regions (such as North District, South District, etc.), and the simulation of each region is a separate subtask. Example: For a power system simulation covering the whole country, it can be divided according to provinces or regions, for example, the North China Power Grid and the South China Power Grid are divided into two separate subtasks. Subnet division: If the power system contains multiple relatively independent subnets, these subnets can be divided into subtasks separately. The simulation nodes in each subnet are tightly coupled and have less interaction with each other, which is suitable for independent simulation. Example: In a city's power system, there may be multiple independently operated industrial park power grids, commercial area power grids and residential area power grids, each of which can be simulated as a separate subtask. 2. Component or equipment type: division according to different types of components or equipment in the power system. For example, generators, transformers, transmission lines, loads, etc. each have different dynamic behaviors and computing requirements. Dividing tasks based on component types can improve the efficiency of parallel computing. Generator simulation: Divide the simulation tasks of all generators into one subtask, because there is usually a tight coupling of frequency and power between generators. Example: In a power grid, there are multiple types of generators (such as coal, gas and nuclear power), and the dynamic simulation of these generators can be used as an independent subtask. Load simulation: The dynamic behavior of the load affects the stability of the power grid, and the load simulation of different areas can be divided into subtasks. Example: In a city power grid simulation, the loads of residential areas, commercial areas and industrial areas can be used as different subtasks. 3. Computational load balancing: In order to avoid overloading some computing nodes and reducing the overall simulation efficiency, the computational complexity of each subtask needs to be considered when dividing tasks, and the computational load of each computing node should be balanced as much as possible. Including workload balancing: evenly divide according to the computing resources required by each subtask (such as CPU time, memory usage, etc.). It is necessary to evaluate the simulation complexity of each part of the power system and split the part with larger workload into smaller subtasks. Example: If the dynamic response of the generators in a subnet is very complex and the load changes frequently, it may be necessary to split the subnet into smaller parts, such as further dividing by time step or geographical area. 4. Data dependency and communication requirements: In order to reduce the communication overhead and data synchronization delay between computing nodes, parts with strong data dependency should be divided into one subtask to reduce cross-node data interaction. Low data dependency division: Assign components that need to exchange data frequently to the same computing node to reduce cross-node communication.Example: Substations in a region and their connected transmission lines, loads, and distributed generation usually have frequent data interactions, so these components can be divided into a subtask and processed on the same computing node. 5. Time synchronization and simulation step size: In real-time simulation, the simulation step size and time synchronization requirements of different subtasks may be different. These factors should be considered when dividing to ensure the synchronization between the computing nodes. Synchronization requirements: According to the requirements of time synchronization, components with high synchronization requirements are divided into a subtask to avoid frequent cross-node time synchronization operations. Example: In high-frequency dynamic simulation (such as relay protection action simulation), all simulation tasks related to protection devices should be divided into the same subtask to ensure the accuracy of time synchronization. The specific implementation example is summarized as follows: Assuming that we want to simulate a large-scale power system containing multiple regions and component types, the task division may be as follows: Regional division: The power system is divided into three subtasks in the north, south, and central according to geographical regions, and the power dynamic behavior of each region is simulated respectively. Component type division: In the northern region, the simulation task of all generators is divided into one subtask, and the simulation task of transmission lines and substations is divided into another subtask. Computational load balancing: Based on the complexity of each subtask, the simulation of large generators in the central region is further split to ensure load balancing of each computing node. Data dependency: Substations with frequent data interactions in the southern region are divided into the same subtask together with their loads to reduce data transmission across nodes. Based on the above division basis, simulation tasks can be reasonably allocated to multiple computing nodes, maximizing parallel computing efficiency and ensuring the accuracy and consistency of simulation results.

[0057] In some optional embodiments, the sub-simulation results are summarized to obtain the simulation result of the power system, including: obtaining a data transmission format and a data transmission protocol, and transmitting the sub-simulation results according to the data transmission format through the data transmission protocol; summarizing the sub-simulation results of the same time step to obtain the simulation result.

[0058] Specifically, in the simulation of distributed power systems based on cloud computing, collecting results is a key step to aggregate and integrate partial simulation data generated by each computing node into complete simulation results. This process needs to ensure the integrity, consistency and accuracy of the data while maximizing the integration efficiency. The following is a detailed implementation process: 1. Result collection mechanism: First, an efficient result collection mechanism needs to be designed to ensure that the simulation results of each computing node can be transmitted to the centralized storage or aggregation node in a timely and accurate manner. Data transmission protocol: Select a suitable data transmission protocol (such as HTTP / HTTPS, gRPC, message queue MQTT, etc.) to transmit the simulation results. The choice of protocol should be based on transmission efficiency, reliability and security requirements. Example: Use gRPC protocol for efficient point-to-point communication to ensure low latency and high throughput data transmission. Data format standardization: Define a unified result data format (such as JSON, XML, binary format, etc.) to ensure that the results generated by different nodes can be seamlessly integrated. Example: Use JSON format to store the simulation results of each subtask, including key data such as timestamp, node status, power flow, etc. 2. Data aggregation and storage: Aggregate the simulation results of each computing node into a centralized storage system for subsequent integration and analysis. Centralized storage system: Use distributed storage solutions (such as Amazon S3, Google Cloud Storage, HDFS, etc.) to store the simulation results of each node to ensure high availability and scalability. Example: After each subtask is completed, upload the result file to the specified storage bucket of Amazon S3 and store it by region or component type. Database management: For simulation results that need to be frequently accessed and queried, you can use distributed databases (such as Apache Cassandra, MongoDB, PostgreSQL, etc.) to manage them. Example: Use MongoDB to store real-time simulation data to support fast query and data analysis. 3. Data integration and synchronization: Integrate the simulation results distributed on different nodes into a complete simulation output to ensure data consistency and time synchronization. Time synchronization: Ensure that the simulation time step and timestamp of all subtasks are consistent so that the data of each part can be correctly integrated. Example: Use a unified time synchronization service (such as NTP or the time synchronization function provided by cloud services) to ensure that the system time of all nodes is consistent to avoid data misalignment caused by time deviation. Data merging strategy: Design a data merging strategy to merge the results of each subtask according to the time step or spatial region according to the requirements of the simulation task. Example: For each time step, first extract the data of the corresponding time from each subtask, and then integrate these data into the state of the overall power grid according to the topology of the power grid. 4. Data verification and consistency check: During the integration process, the collected data needs to be verified to ensure the integrity and consistency of the data.Integrity check: Check whether all expected subtask results have been collected and ensure that there is no missing data. Example: Before integration, verify whether the results of all 500 node simulation tasks have been received. If there is any missing, trigger the retransmission mechanism. Consistency check: Ensure that the integrated data is logically consistent by comparing the data dependencies between subtasks. Example: Check whether the boundary node data of adjacent areas matches to ensure that the power flow and voltage level are seamlessly connected at the boundary of the area division. Error detection and correction: Identify and correct errors that may occur during data transmission or generation. Example: Use checksums or hash values ​​to verify data integrity. If data corruption is found, re-request the data.

[0059] In some optional implementations, the above step S203 establishes a communication connection between the multiple subtask nodes, which can be achieved by the following steps: loading the power system model at each subtask node, and initializing the node voltage and node current of each subtask node; performing simulation calculations for each step length at each subtask node, and after the simulation calculations for each step length are completed, synchronizing the data of the subtask node. The method establishes a communication connection through the above steps, so that the synchronous simulation of each node can be achieved.

[0060] Specifically, the following is a specific embodiment of communication connection and synchronous simulation. Step 1: Data collection: 1. Subtask completion: After each computing node completes its assigned simulation subtask, it packages the result data. 2. Data upload: Upload the result to a centralized storage system (such as Amazon S3) through a predetermined data transmission protocol (such as gRPC). 3. Notify the aggregation system: After the subtask is completed, send a notification to the aggregation system (such as through the message queue RabbitMQ) to indicate that the new data has been uploaded. Step 2: Data storage and preprocessing: 1. Data storage: After receiving the notification, the aggregation system obtains the newly uploaded result data from the storage system. 2. Preprocessing: Perform preprocessing operations such as format conversion and timestamp correction on the data to ensure data format and time synchronization. 3. Data indexing: Store the preprocessed data in the database for fast retrieval and query. Step 3: Data integration: 1. Time step alignment: Align the data of each subtask according to a unified time step. 2. Spatial region merging: According to the topological structure of the power grid, spatially merge the data of different regions or subnets. 3. Status update: Update the status information of the entire power grid, such as voltage level, power flow, etc., to ensure that the integrated data reflects the true status of the entire system. Step 4: Consistency and integrity verification 1. Data integrity check: Confirm that the data of all subtasks have been collected and there are no omissions. 2. Logical consistency check: Ensure that the merged data is logically self-consistent, such as power conservation, load matching, etc. 3. Error handling: For inconsistencies or errors found, trigger the corresponding correction mechanism, such as re-requesting data or performing data interpolation. Step 5: Generate complete simulation results: 1. Result generation: Generate a complete simulation result file or report from the integrated data. 2. Data archiving: Archive the complete results to a long-term storage system for subsequent query and analysis. 3. Result distribution: Distribute the complete simulation results to the user interface or analysis tool according to user needs. During the simulation calculation process, each simulation node needs to synchronize data through a real-time interaction mechanism. The data synchronization process is as follows: Figure 4 As shown in the figure, a single step starts, the branch historical current solution and control loop solution are performed to obtain the historical current and control quantity, the network matrix solution is performed to obtain the node voltage, the observation quantity calculation is performed, and the single step ends. A single simulation step is divided into three stages: branch historical current solution and control loop solution, network matrix solution, and observation quantity calculation. Before and after the end of each stage, each simulation node exchanges the relevant data in the figure. Again, when all simulation calculations are completed, each simulation node summarizes the calculation results to the central node. Finally, the central node integrates the summarized calculation results and generates the final simulation results.

[0061] In some optional implementations, after obtaining the simulation results of the power system, the method further includes the following steps: visually displaying the simulation results; performing data analysis on the simulation results, generating a simulation report and outputting the simulation report, wherein the simulation report at least includes data generated during the simulation process, simulation results and data analysis results. The method performs visual display through the above steps, so that the simulation results can be displayed to the user.

[0062] In the specific implementation process, data visualization is to present the simulation process and results to the user in an intuitive way. Real-time display: Real-time display of key data in the simulation process through charts, curves, etc. Analysis tools: Provide data analysis tools to support users to conduct in-depth analysis of simulation results. Report generation: Support the generation of detailed simulation reports, including various statistical data and analysis conclusions.

[0063] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the simulation method of the distributed power system of the present application will be described in detail below in combination with specific embodiments.

[0064] This embodiment relates to a specific simulation method for a distributed power system, such as Figure 5 As shown, the following steps are included:

[0065] Step S1: Task allocation: analyzing task requirements, selecting appropriate resources, starting simulation nodes, and monitoring task execution;

[0066] Step S2: divide the computing tasks, distribute subtasks, and collect results;

[0067] Step S3: Establishing communication connection, synchronizing data, and coordinating calculations;

[0068] Step S4: Real-time display, analysis tools, and report generation.

[0069] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the simulation method of the distributed power system.

[0070] Specifically, the simulation method of the distributed power system includes:

[0071] Step S201, obtaining simulation requirements, and configuring simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one;

[0072] Specifically, in the first step of task allocation, the system needs to conduct a comprehensive demand analysis of the simulation task to be executed. This includes the analysis of computing requirements, storage requirements, and network requirements. Computing requirements: Computing requirements are usually determined by the complexity of the simulation task, the required computing accuracy, and the time requirement.

[0073] Step S202, obtaining a simulation task, dividing the simulation task into a plurality of subtasks, and dividing the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system;

[0074] Specifically, task allocation is the process of reasonably allocating simulation tasks to various nodes of the cloud computing platform. Analyze task requirements: First analyze the computing, storage and network requirements of the simulation task. Select appropriate resources: Select appropriate virtual machines and computing resources according to the requirements. In distributed power system simulation, the main goal of task allocation is to reasonably allocate tasks to various nodes of the cloud computing platform according to the specific requirements of the simulation task, so as to make full use of computing resources and ensure the efficiency and accuracy of the simulation process. Parallel computing is to perform simulation calculations simultaneously through multiple computing nodes to improve efficiency. Divide computing tasks: Divide the simulation task into multiple subtasks, each of which is responsible for the simulation calculation of a part of the power system. Distribute subtasks: Distribute subtasks to different computing nodes for execution. Collect results: Collect the calculation results of each computing node and integrate them into a complete simulation result. In distributed power system simulation, parallel computing is to improve the overall computing efficiency by dividing the entire simulation task into multiple subtasks and assigning these subtasks to different computing nodes for simultaneous execution. The rationality of task division directly affects the efficiency and accuracy of the simulation.

[0075] Step S203, establishing a communication connection between the plurality of subtask nodes, and synchronously executing the subtasks on the subtask nodes to obtain sub-simulation results, and summarizing the sub-simulation results to obtain a simulation result of the power system.

[0076] Specifically, real-time interaction is the key to ensuring the collaborative work between simulation nodes. Establish a communication connection: Establish a real-time communication connection between simulation nodes. Synchronize data: Synchronize data between nodes through communication connections to ensure the consistency of the simulation process. The detailed explanation is as follows: First, the present invention adopts PTP (Precision Time Protocol) clock synchronization technology to achieve nanosecond time synchronization accuracy; secondly, each simulation node loads the power system model respectively and initializes the voltage and current of each node. Then, each simulation node starts to perform simulation calculations. During the simulation calculation process, each simulation node needs to synchronize data through a real-time interaction mechanism.

[0077] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, at least the following steps are implemented:

[0078] Step S201, obtaining simulation requirements, and configuring simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one;

[0079] Specifically, in the first step of task allocation, the system needs to conduct a comprehensive demand analysis of the simulation task to be executed. This includes the analysis of computing requirements, storage requirements, and network requirements. Computing requirements: Computing requirements are usually determined by the complexity of the simulation task, the required computing accuracy, and the time requirement.

[0080] Step S202, obtaining a simulation task, dividing the simulation task into a plurality of subtasks, and dividing the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system;

[0081] Specifically, task allocation is the process of reasonably allocating simulation tasks to various nodes of the cloud computing platform. Analyze task requirements: First analyze the computing, storage and network requirements of the simulation task. Select appropriate resources: Select appropriate virtual machines and computing resources according to the requirements. In distributed power system simulation, the main goal of task allocation is to reasonably allocate tasks to various nodes of the cloud computing platform according to the specific requirements of the simulation task, so as to make full use of computing resources and ensure the efficiency and accuracy of the simulation process. Parallel computing is to perform simulation calculations simultaneously through multiple computing nodes to improve efficiency. Divide computing tasks: Divide the simulation task into multiple subtasks, each of which is responsible for the simulation calculation of a part of the power system. Distribute subtasks: Distribute subtasks to different computing nodes for execution. Collect results: Collect the calculation results of each computing node and integrate them into a complete simulation result. In distributed power system simulation, parallel computing is to improve the overall computing efficiency by dividing the entire simulation task into multiple subtasks and assigning these subtasks to different computing nodes for simultaneous execution. The rationality of task division directly affects the efficiency and accuracy of the simulation.

[0082] Step S203, establishing a communication connection between the plurality of subtask nodes, and synchronously executing the subtasks on the subtask nodes to obtain sub-simulation results, and summarizing the sub-simulation results to obtain a simulation result of the power system.

[0083] Specifically, real-time interaction is the key to ensuring the collaborative work between simulation nodes. Establish a communication connection: Establish a real-time communication connection between simulation nodes. Synchronize data: Synchronize data between nodes through communication connections to ensure the consistency of the simulation process. The detailed explanation is as follows: First, the present invention adopts PTP (Precision Time Protocol) clock synchronization technology to achieve nanosecond time synchronization accuracy; secondly, each simulation node loads the power system model respectively and initializes the voltage and current of each node. Then, each simulation node starts to perform simulation calculations. During the simulation calculation process, each simulation node needs to synchronize data through a real-time interaction mechanism.

[0084] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0085] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method described in each embodiment of the present application:

[0086] Step S201, obtaining simulation requirements, and configuring simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one;

[0087] Specifically, in the first step of task allocation, the system needs to conduct a comprehensive demand analysis of the simulation task to be executed. This includes the analysis of computing requirements, storage requirements, and network requirements. Computing requirements: Computing requirements are usually determined by the complexity of the simulation task, the required computing accuracy, and the time requirement.

[0088] Step S202, obtaining a simulation task, dividing the simulation task into a plurality of subtasks, and dividing the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system;

[0089] Specifically, task allocation is the process of reasonably allocating simulation tasks to various nodes of the cloud computing platform. Analyze task requirements: First analyze the computing, storage and network requirements of the simulation task. Select appropriate resources: Select appropriate virtual machines and computing resources according to the requirements. In distributed power system simulation, the main goal of task allocation is to reasonably allocate tasks to various nodes of the cloud computing platform according to the specific requirements of the simulation task, so as to make full use of computing resources and ensure the efficiency and accuracy of the simulation process. Parallel computing is to perform simulation calculations simultaneously through multiple computing nodes to improve efficiency. Divide computing tasks: Divide the simulation task into multiple subtasks, each of which is responsible for the simulation calculation of a part of the power system. Distribute subtasks: Distribute subtasks to different computing nodes for execution. Collect results: Collect the calculation results of each computing node and integrate them into a complete simulation result. In distributed power system simulation, parallel computing is to improve the overall computing efficiency by dividing the entire simulation task into multiple subtasks and assigning these subtasks to different computing nodes for simultaneous execution. The rationality of task division directly affects the efficiency and accuracy of the simulation.

[0090] Step S203, establishing a communication connection between the plurality of subtask nodes, and synchronously executing the subtasks on the subtask nodes to obtain sub-simulation results, and summarizing the sub-simulation results to obtain a simulation result of the power system.

[0091] Specifically, real-time interaction is the key to ensuring the collaborative work between simulation nodes. Establish a communication connection: Establish a real-time communication connection between simulation nodes. Synchronize data: Synchronize data between nodes through communication connections to ensure the consistency of the simulation process. The detailed explanation is as follows: First, the present invention adopts PTP (Precision Time Protocol) clock synchronization technology to achieve nanosecond time synchronization accuracy; secondly, each simulation node loads the power system model respectively and initializes the voltage and current of each node. Then, each simulation node starts to perform simulation calculations. During the simulation calculation process, each simulation node needs to synchronize data through a real-time interaction mechanism.

[0092] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0093] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0099] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0101] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0102] 1) The simulation platform of the distributed power system of the present application, the cloud computing platform, includes a virtual machine manager, a resource scheduler and a security manager. The virtual machine manager is used for the management of virtual machines, the resource scheduler is used for the allocation of computing resources, and the security manager is used for maintaining the security of the computing process; the distributed simulation engine includes a task scheduler, a simulation controller and a data synchronizer. The task scheduler is used for the allocation of simulation tasks, the simulation controller is used for controlling the start or stop of simulation tasks, and the data synchronizer is used for maintaining data synchronization between each subtask node. Compared with the prior art, the power system simulation method has problems such as low efficiency. By establishing a cloud computing platform and a distributed simulation engine, the simulation of the distributed power system is carried out, and cloud computing resources are utilized to reduce the cost of hardware and configuration, making the simulation of the distributed power system more efficient. Therefore, the simulation efficiency of the power system can be improved.

[0103] 2) The simulation method of the distributed power system of the present application obtains simulation requirements, configures simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one; obtains a simulation task, divides the simulation task into multiple subtasks, and divides the simulation resources according to the subtasks to obtain subtask nodes corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system; establishes a communication connection between the multiple subtask nodes, and synchronously executes the subtasks on the subtask nodes to obtain sub-simulation results, summarizes the sub-simulation results, and obtains the simulation results of the power system. Compared with the prior art, the power system simulation method has problems such as low efficiency. By establishing a cloud computing platform and a distributed simulation engine, the simulation of the distributed power system is carried out, and cloud computing resources are used to reduce the cost of hardware and configuration, so that the simulation of the distributed power system is more efficient. Therefore, the simulation efficiency of the power system can be improved.

[0104] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A simulation platform for distributed power systems, characterized in that: include: A cloud computing platform, comprising a virtual machine manager, a resource scheduler and a security manager, wherein the virtual machine manager is used to manage virtual machines, the resource scheduler is used to allocate computing resources, and the security manager is used to maintain the security of the computing process; The distributed simulation engine includes a task scheduler, a simulation controller and a data synchronizer. The task scheduler is used to allocate simulation tasks, the simulation controller is used to control the start or stop of the simulation tasks, and the data synchronizer is used to maintain data synchronization between various subtask nodes.

2. The distributed power system simulation platform according to claim 1, characterized in that: The simulation platform of the distributed power system also includes: The power system model library includes component models and scenario templates, and is used to store simulation models of each of the power systems. The component models at least include mathematical models of power components, and the scenario templates at least include fault scenario templates.

3. The distributed power system simulation platform according to claim 1, characterized in that: The simulation platform of the distributed power system also includes: The user interface includes a configuration interface, a monitoring interface and an analysis tool, wherein the configuration interface is used to configure the parameters of the simulation task, the monitoring interface is used to monitor the real-time status of the simulation task, and the analysis tool is used to analyze the simulation results.

4. A simulation method for a distributed power system, characterized in that: A simulation platform for a distributed power system according to any one of claims 1 to 3, wherein a simulation method for a distributed power system comprises: Acquire simulation requirements, and configure simulation resources according to the simulation requirements, wherein the simulation requirements at least include storage capacity requirements, the simulation resources at least include storage capacity resources, and the simulation resources correspond to the simulation requirements one by one; Acquire a simulation task, divide the simulation task into a plurality of subtasks, and divide the simulation resources according to the subtasks to obtain a subtask node corresponding to each subtask, wherein the simulation task represents a task for simulating the operation process of the power system; A communication connection is established between the plurality of subtask nodes, and the subtasks are synchronously executed on the subtask nodes to obtain sub-simulation results, which are then summarized to obtain simulation results of the power system.

5. The simulation method of a distributed power system according to claim 4, characterized in that: Acquiring simulation requirements and configuring simulation resources according to the simulation requirements includes: Obtaining a computing requirement, and determining a processor according to the computing requirement, wherein the computing requirement indicates a computing performance requirement; Acquire the storage capacity requirement, and determine a storage device according to the storage capacity requirement; A network requirement is obtained, and a network bandwidth is determined according to the network requirement.

6. The simulation method of a distributed power system according to claim 4, characterized in that: The simulation task is divided into multiple subtasks, including any one of the following: Dividing the simulation task into a plurality of subtasks according to the topological structure of the power system; Dividing the simulation task into a plurality of subtasks according to the equipment type of the power system; Dividing the simulation task into a plurality of subtasks according to target data corresponding to the simulation task, wherein the target data represents data for data interaction during the execution of the simulation task; The simulation task is divided according to the simulation step length to obtain a plurality of subtasks.

7. The simulation method of a distributed power system according to claim 4, characterized in that: The sub-simulation results are summarized to obtain the simulation results of the power system, including: Acquire a data transmission format and a data transmission protocol, and transmit the sub-simulation result according to the data transmission format and through the data transmission protocol; The sub-simulation results of the same time step are aggregated to obtain the simulation result.

8. The simulation method of a distributed power system according to claim 4, characterized in that: Establishing a communication connection between the plurality of subtask nodes includes: Loading the power system model at each of the subtask nodes respectively, and initializing the node voltage and node current of each of the subtask nodes; Simulation calculation of each step length is performed at each subtask node, and after the simulation calculation of each step length is completed, data synchronization is performed on the data of the subtask node.

9. The simulation method of a distributed power system according to claim 4, characterized in that: After obtaining the simulation result of the power system, the method further includes: Visually displaying the simulation results; Perform data analysis on the simulation results, generate a simulation report and output the simulation report, wherein the simulation report at least includes data generated during the simulation process, simulation results and data analysis results.

10. A simulation system for a distributed power system, characterized in that: include: A simulation platform for a distributed power system as claimed in any one of claims 1 to 3; A controller comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing a simulation method of a distributed power system as described in any one of claims 4 to 9.

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