Cloud simulation experiment system based on alternating current and direct current hybrid power grid
By combining the power grid model module, dynamic simulation module, cloud computing module and data interaction module in the cloud simulation experimental system, dynamic update model parameters are solved, and the problem of insufficient accuracy and real-time simulation of AC and DC hybrid power grids in the existing technology is solved, and efficient and accurate simulation results are achieved.
Patent Information
- Application Number
- CN202411691516.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-02-28
AI Technical Summary
The simulation platforms in the prior art cannot fully meet the real-time simulation accuracy and real-time requirements of AC and DC hybrid power grids under different operating conditions.
It provides a cloud simulation experimental system based on AC and DC hybrid power grid, including power grid model module, dynamic simulation module, cloud computing module, data interaction module and control center. Through improved neural network models and real-time data interaction, model parameters are dynamically updated to ensure the accuracy and real-timeness of simulation results.
It realizes efficient and accurate simulation and verification of AC and DC hybrid grid under different operating conditions, meets the needs of real-time simulation and improves simulation accuracy and real-timeness.
Smart Images
Figure CN119538467A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of power system simulation, and in particular to a cloud-based simulation experiment system based on an AC / DC hybrid power grid. Background Art
[0002] Microgrid, also known as microgrid, is a miniaturized power grid system that can operate independently of the main power grid or in parallel with the main power grid. Microgrids are usually composed of distributed power sources (such as solar photovoltaic panels, wind turbines, fuel cells, micro turbines, etc.), energy storage devices (such as batteries, supercapacitors), loads (electrical equipment) and control systems. As a flexible, efficient and environmentally friendly energy system, microgrids are gradually becoming an important part of the future energy system.
[0003] DC Microgrid and AC Microgrid are the two main forms of microgrid technology. Relatively speaking, the research on DC Microgrid is relatively late, but no matter from which field, DC Microgrid is more efficient and reliable than AC Microgrid. At present, my country still mainly uses AC Microgrid, so hybrid microgrid with both AC and DC microgrid characteristics has become the current mainstream direction. Simulating the model of AC / DC hybrid power grid can solve the problem of relying on past experience to conduct on-site inspections at the project site, which is affected by subjective factors and has great safety hazards. The model of AC / DC hybrid power grid is very complex and contains a large number of electrical components and control systems, which makes the simulation platform unable to fully meet the needs of real-time simulation.
[0004] Therefore, there is an urgent need for a cloud-based simulation system based on an AC / DC hybrid power grid to meet the requirements of accuracy and real-time performance of real-time simulation. Summary of the invention
[0005] In view of this, it is necessary to provide a cloud-based simulation experiment system based on an AC / DC hybrid power grid to solve the problem that the accuracy and real-time performance of the simulation platform in the prior art cannot fully meet the needs of real-time simulation.
[0006] A cloud simulation experiment system based on AC / DC hybrid power grid, comprising:
[0007] The power grid model module is used to simulate the operation and maintenance status of the power grid under different operating conditions;
[0008] A dynamic simulation module, used to verify the power grid model module based on preset parameters, and adjust the power grid model module according to the verification result;
[0009] A cloud computing module, used for calculating simulation data information based on the operation and maintenance data output by the power grid model module through an improved neural network model;
[0010] A data interaction module, used to obtain the output results of the dynamic simulation module and the cloud computing module, and output them to a user interface;
[0011] The control center is used to control the power grid model module, dynamic simulation module, cloud computing module, and data interaction module, and to schedule the operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module.
[0012] Preferably, the control center schedules the operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module, including:
[0013] The control center includes a real-time scheduling unit, a voltage droop control unit, a current droop control unit, a frequency droop control unit and a ripple droop control unit;
[0014] The real-time scheduling unit uses a response ratio priority scheduling algorithm to schedule the operation and maintenance tasks of the voltage droop control unit, the current droop control unit, the frequency droop control unit, and the ripple droop control unit; wherein,
[0015] The voltage droop control unit is used to control the voltage level of the power grid by adjusting the generator output voltage; the current droop control unit is used to control the current level of the power grid by adjusting the generator output current; the frequency droop control unit is used to control the frequency level of the power grid by adjusting the generator output frequency; the ripple droop control unit is used to control the voltage ripple in the power grid.
[0016] Preferably, the data interaction module is also used to obtain actual real-time data of the power grid and send the actual real-time data to the power grid model module.
[0017] Preferably, the control center receives the actual real-time data from the data interaction module, and generates a control signal according to the real-time data to adjust the operating parameters of the power grid model module.
[0018] Preferably, it also includes a simulation analysis module for evaluating the operation and maintenance effect of the power grid based on the simulation data information output by the cloud computing module.
[0019] Preferably, the simulation analysis module is also used to send the result of evaluating the operation and maintenance effect of the power grid to the data interaction module; the data interaction module outputs the result of evaluating the operation and maintenance effect of the power grid to the user interface.
[0020] Preferably, the simulation analysis module includes an ANSYS analysis unit, and the operation and maintenance effect evaluation is performed through the ANSYS analysis unit.
[0021] Preferably, the results of the operation and maintenance effect evaluation performed by the ANSYS analysis unit include providing analysis result charts and curves to the user interface.
[0022] Preferably, the cloud computing module includes an encoding unit, a balance calculation unit, a parameter factor generation unit, a convolution calculation unit and a model parameter adaptive adjustment unit;
[0023] The encoding unit is used to encode the operation and maintenance data output by the power grid model module;
[0024] The parameter factor generating unit is used to generate parameter factors according to the encoded operation and maintenance data;
[0025] The convolution calculation unit is used to calculate the simulation data information according to the encoded operation and maintenance data and parameter factors;
[0026] The model parameter adaptive adjustment unit is used to adjust the operating parameters of the power grid model module.
[0027] Preferably, it also includes a distributed storage module for receiving and storing simulation result data from the cloud computing module, and receiving and storing actual real-time data of the power grid from the data interaction module.
[0028] Compared with the prior art, the beneficial effect of the present application is that the present application provides a cloud-based simulation experiment system that can efficiently and accurately simulate and verify the operation and maintenance status of AC / DC hybrid power grids under different operating conditions. The real-time data of the actual power grid is obtained in real time through the data interaction module, and it is passed to the power grid model module to ensure that the model can be updated in time to reflect the latest status of the actual power grid, and the power grid model module dynamically updates the model parameters according to the real-time data to ensure that the model can accurately reflect the operating status of the actual power grid, ensuring the accuracy and real-time nature of the simulation, and meeting the needs of real-time simulation. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 It is a structural diagram of a cloud-based simulation experiment system 100 based on an AC / DC hybrid power grid provided in an embodiment of the present application.
[0030] Figure 2 It is a structural diagram of the control center provided in the embodiment of the present application.
[0031] Figure 3 It is a workflow diagram of the real-time scheduling unit response ratio priority scheduling algorithm provided in an embodiment of the present application.
[0032] Figure 4 It is a schematic diagram of the hardware working process of updating job priority provided in an embodiment of the present application. DETAILED DESCRIPTION
[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0034] See also Figure 1 , Figure 1 1 is a structural diagram of a cloud-based simulation experiment system 100 based on an AC / DC hybrid power grid provided in an embodiment of the present application. The cloud-based simulation experiment system 100 based on an AC / DC hybrid power grid includes:
[0035] The power grid model module 101 is used to simulate the operation and maintenance status of the power grid under different operating conditions. The operation and maintenance status includes at least load change, equipment overload, voltage fluctuation, line tripping, equipment damage, insulation breakdown or power outage. The power grid model module 101 can input the topological structure data of the power grid (such as the location and connection relationship of substations, transmission lines, distribution lines, transformers, switches and other equipment), equipment parameters (such as the rated capacity and impedance of the transformer, the resistance and reactance of the line, the rated power and speed regulation characteristics of the generator, etc.), load data (such as the size, type, distribution, etc. of the load), renewable energy data (such as solar photovoltaic, wind energy, etc.), and energy storage system data (such as battery energy storage devices, supercapacitors, etc.).
[0036] The dynamic simulation module 102 is used to verify the power grid model module based on preset parameters and adjust the power grid model module according to the verification results; the power consumption, logical functions and performance indicators of the AC / DC hybrid power grid are tested and verified by setting target parameters, circuit models and power grid architecture.
[0037] The topological structure and equipment parameters of the power grid model, the load model and renewable energy model, the energy storage system model and simulation parameters (such as voltage, current, frequency, power, etc.) can be used as input data, and the dynamic simulation module 102 outputs simulation result data (such as voltage fluctuations, current changes, power loss, etc.).
[0038] The cloud computing module 103 is used to calculate the simulation data information based on the operation and maintenance data output by the power grid model module through the improved neural network model; by setting the AC / DC hybrid power grid operation parameters, the simulation data information is calculated through the improved neural network model, and the simulation result data information is compared with the set comparison data. The simulation data calculation is performed using the improved neural network model to improve the accuracy and efficiency of the calculation.
[0039] Furthermore, distributed computing technology can be used to process simulation data in real time to ensure the real-time nature of the simulation.
[0040] The data interaction module 104 is used to obtain the output results of the dynamic simulation module and the cloud computing module and output them to the user interface. Furthermore, the data interaction module is also used to obtain the actual real-time data of the power grid and send the actual real-time data to the power grid model module.
[0041] The control center 105 is used to control the power grid model module, dynamic simulation module, cloud computing module, and data interaction module, and to schedule operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module.
[0042] Furthermore, the control center schedules the operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module.
[0043] like Figure 2 The structural schematic diagram of the control center shown in the figure, the control center includes a real-time scheduling unit, a voltage droop control unit, a current droop control unit, a frequency droop control unit and a ripple droop control unit;
[0044] The real-time scheduling unit uses a response ratio priority scheduling algorithm to schedule the operation and maintenance tasks of the voltage droop control unit, the current droop control unit, the frequency droop control unit, and the ripple droop control unit; wherein,
[0045] The voltage droop control unit is used to control the voltage level of the power grid by adjusting the generator output voltage; the current droop control unit is used to control the current level of the power grid by adjusting the generator output current; the frequency droop control unit is used to control the frequency level of the power grid by adjusting the generator output frequency; the ripple droop control unit is used to control the voltage ripple in the power grid.
[0046] Furthermore, the control center receives the actual real-time data from the data interaction module, generates a control signal according to the real-time data, and adjusts the operating parameters of the power grid model module.
[0047] Specifically, the real-time scheduling unit adopts a response ratio priority scheduling algorithm to schedule the task sequence according to the ratio of the AC / DC hybrid power grid operation and maintenance time to the waiting time.
[0048] like Figure 3 The working process of the real-time scheduling unit response ratio priority scheduling algorithm shown in FIG. 1 includes:
[0049] S301, calculating the initial priority;
[0050] The control priority is set according to the operation and maintenance status of the AC / DC hybrid power grid and the response time, adjustment range and adjustment accuracy of the voltage droop control unit, the current droop control unit, the frequency droop control unit and the ripple droop control unit;
[0051] The initial priority is calculated based on the initial task waiting time (W) and the task required service time (Y).
[0052] The initial priority output function is:
[0053]
[0054] In formula (1), P is the initial priority, ɑ is the environmental impact weight factor, β is the weight factor affecting the operation and maintenance status of the power grid itself; W is the characteristic value of the environmental impact factor of the AC / DC hybrid power grid operation and maintenance, Y represents the characteristic value of the influence factor of the operation and maintenance status of the AC / DC hybrid power grid itself; M, N, H, G are the activation functions of the voltage droop control unit, the current droop control unit, the frequency droop control unit, and the ripple droop control unit;
[0055] S302, analyzing the fluctuation parameters of the operation and maintenance state of the AC / DC hybrid power grid, starting the voltage droop control unit, the current droop control unit, the frequency droop control unit or the ripple droop control unit in a working state, and adjusting the activation priority; adjusting the task priority based on the remaining task urgency and task waiting time of the simulation task;
[0056] The priority adjustment output function is:
[0057]
[0058] In formula (2), K represents the priority adjustment output function, P represents the initial priority output function, e represents the function stability influencing factor affecting the operation of each droop control unit, and W h represents the attenuation rate weight matrix of different droop control units, h represents the attenuation rate weight matrix attribute; b h Represents the bias vector of each droop control unit; t c is the task service demand time, c represents the demand time identifier; E represents the response capability of each droop control unit, D is the task deadline, θ and γ are the function stability and the average weight factor of each droop control unit working stability;
[0059] S303, scheduling a decision task of a voltage droop control unit, a current droop control unit, a frequency droop control unit or a ripple droop control unit according to the response ratio;
[0060] Based on the response ratio priority scheduling algorithm, the priority order of the voltage droop control unit, current droop control unit, frequency droop control unit or ripple droop control unit is obtained. The control unit with the highest response ratio is activated first to cope with the current power grid conditions. The simulation tasks are arranged in order of priority and the tasks are executed in order of priority. The task adjustment formula is:
[0061]
[0062] In formula (3), Q is the task adjustment order, n is the number of jobs, S i is the service demand time of job i;
[0063] S304, updating the job priority;
[0064] The job priority order is calculated again based on the adjusted job order. The priority order formula is:
[0065]
[0066] In formula (4), U is the emergency priority, I is the job importance, P is the initial priority, μ and δ are weight factors; ci represents the characteristic value of the running status of all jobs, xi represents the information set of job i, and xn represents the continuously updated data information set.
[0067] In specific applications, such as Figure 4 The schematic diagram of the hardware working process of updating the job priority shown in the figure is used to realize the power grid task scheduling based on the response ratio priority scheduling algorithm. Each module is interconnected through a communication network and works together to optimize the operation efficiency and response capability of the power grid. For example, the data acquisition and processing unit uses sensors, data acquisition cards and microprocessors / DSPs, the response ratio calculation module uses a response ratio calculation engine, a priority sorter, etc., the task scheduling module uses a task queue manager, a task execution controller; the job priority update module uses a priority update engine, a weight factor adjuster; the information processing and storage unit uses memory / solid-state storage, a database management system, and a human-machine interface (HMI) such as a display screen and an input device.
[0068] S305, updating the task progress in real time;
[0069] Update task attribute data in real time, recalculate the priorities of remaining simulation tasks and update the priority order when a new task starts to execute.
[0070] As a further technical solution of the present invention, multiple virtual machines can also be used to execute distributed work. The multiple virtual machine system includes a data sharding module and a replica replication module. The disk space on each independent computer is abstracted as a virtual machine node through the segmentation module. The data sharding module divides the data into multiple parts and each part is stored in a different node. The replica replication module stores each data copy on a different node, and each node communicates and cooperates with each other through a network connection.
[0071] Furthermore, it also includes a simulation analysis module 106, which is used to evaluate the operation and maintenance effect of the power grid according to the simulation data information output by the cloud computing module. Furthermore, the simulation analysis module is also used to send the result of the evaluation of the operation and maintenance effect of the power grid to the data interaction module; the data interaction module outputs the result of the evaluation of the operation and maintenance effect of the power grid to the user interface.
[0072] Furthermore, the simulation analysis module includes an ANSYS analysis unit, and the operation and maintenance effect evaluation is performed through the ANSYS analysis unit. The results of the operation and maintenance effect evaluation performed through the ANSYS analysis unit include providing analysis result charts and curves to the user interface. As a further technical solution of the present invention, the evaluation principle of the ANSYS analysis unit is that the finite element analysis discretizes the simulation model of the AC / DC hybrid power grid, divides the simulation model of the entire AC / DC hybrid power grid into multiple small geometric units, and then obtains an approximate solution for each small unit; defines constraints and loading conditions for the AC / DC hybrid power grid simulation model, and the constraints are used to limit the displacement or rotation of small geometric nodes; the loading conditions include voltage, overload, and load to simulate various loading conditions that occur in the AC / DC hybrid power grid in actual work, and finally the finite element analysis derives the analysis results by solving a group of linear equations.
[0073] Furthermore, the cloud computing module includes an encoding unit, a balance computing unit, a parameter factor generating unit, a convolution computing unit and a model parameter adaptive adjusting unit;
[0074] The encoding unit is used to encode the operation and maintenance data output by the power grid model module; the parameter factor generation unit is used to generate parameter factors based on the encoded operation and maintenance data; the convolution calculation unit is used to obtain simulation data information based on the encoded operation and maintenance data and the parameter factors; the model parameter adaptive adjustment unit is used to adjust the operating parameters of the power grid model module.
[0075] In a specific embodiment, the neural network model of the cloud computing module receives data from the AC / DC hybrid power grid simulation model, and uses distributed computing technology to calculate the data. The data is sent to the data encoding module for data encoding and the parameter factor generation module automatically generates parameter factors based on the data. The encoded data and the parameter factors are sent together to the convolution calculation module for calculation and output results. The model parameter adaptive adjustment module automatically generates simulation system parameters based on the AC / DC hybrid power grid simulation model and system status, calculates gain feedback based on the system parameters, and efficiently calculates the simulation data required in the experimental process, thereby improving the accuracy.
[0076] Preferably, it also includes a distributed storage module 107, which is used to receive and store simulation result data from the cloud computing module, and receive and store actual real-time data of the power grid from the data interaction module.
[0077] In summary, this application constructs a cloud-based simulation experiment system that can efficiently and accurately simulate and verify the operation and maintenance status of AC / DC hybrid power grids under different operating conditions. Since the operating status of the actual power grid is dynamically changing, it is difficult for traditional simulation systems to process and reflect these changes in real time, resulting in delayed and inaccurate simulation results. This application obtains real-time data of the actual power grid in real time through the data interaction module, and passes it to the power grid model module to ensure that the model can be updated in time to reflect the latest status of the actual power grid, and the power grid model module dynamically updates the model parameters according to the real-time data to ensure that the model can accurately reflect the operating status of the actual power grid, thereby improving the accuracy and real-time performance of the simulation results.
Claims
1. A cloud-based simulation experiment system based on an AC / DC hybrid power grid, characterized in that: include: The power grid model module is used to simulate the operation and maintenance status of the power grid under different operating conditions; A dynamic simulation module, used to verify the power grid model module based on preset parameters, and adjust the power grid model module according to the verification result; A cloud computing module, used for calculating simulation data information based on the operation and maintenance data output by the power grid model module through an improved neural network model; A data interaction module, used to obtain the output results of the dynamic simulation module and the cloud computing module, and output them to a user interface; The control center is used to control the power grid model module, dynamic simulation module, cloud computing module, and data interaction module, and to schedule the operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module.
2. The system according to claim 1, characterized in that The control center schedules the operation and maintenance tasks of the power grid model module according to the simulation results of the power grid model module, including: The control center includes a real-time scheduling unit, a voltage droop control unit, a current droop control unit, a frequency droop control unit and a ripple droop control unit; The real-time scheduling unit uses a response ratio priority scheduling algorithm to schedule the operation and maintenance tasks of the voltage droop control unit, the current droop control unit, the frequency droop control unit, and the ripple droop control unit; wherein, The voltage droop control unit is used to control the voltage level of the power grid by adjusting the generator output voltage; the current droop control unit is used to control the current level of the power grid by adjusting the generator output current; the frequency droop control unit is used to control the frequency level of the power grid by adjusting the generator output frequency; the ripple droop control unit is used to control the voltage ripple in the power grid.
3. The system according to claim 1, characterized in that The data interaction module is also used to obtain actual real-time data of the power grid and send the actual real-time data to the power grid model module.
4. The system according to claim 3, characterized in that The control center receives the actual real-time data from the data interaction module, generates a control signal according to the real-time data, and adjusts the operating parameters of the power grid model module.
5. The system according to claim 1, characterized in that It also includes a simulation analysis module for evaluating the operation and maintenance effect of the power grid based on the simulation data information output by the cloud computing module.
6. The system according to claim 5, characterized in that The simulation analysis module is also used to send the evaluation result of the operation and maintenance effect of the power grid to the data interaction module; the data interaction module outputs the evaluation result of the operation and maintenance effect of the power grid to the user interface.
7. The system according to claim 6, characterized in that The simulation analysis module includes an ANSYS analysis unit, through which operation and maintenance effect evaluation is performed.
8. The system according to claim 7, characterized in that The results of the operation and maintenance effect evaluation performed by the ANSYS analysis unit include providing analysis result charts and curves to the user interface.
9. The system according to claim 1, characterized in that The cloud computing module includes an encoding unit, a balance calculation unit, a parameter factor generation unit, a convolution calculation unit and a model parameter adaptive adjustment unit; The encoding unit is used to encode the operation and maintenance data output by the power grid model module; The parameter factor generating unit is used to generate parameter factors according to the encoded operation and maintenance data; The convolution calculation unit is used to calculate the simulation data information according to the encoded operation and maintenance data and parameter factors; The model parameter adaptive adjustment unit is used to adjust the operating parameters of the power grid model module.
10. The system according to claim 1, characterized in that It also includes a distributed storage module, which is used to receive and store simulation result data from the cloud computing module, and to receive and store actual real-time data of the power grid from the data interaction module.