CN119538467A - Cloud simulation experiment system based on AC/DC hybrid power grid - Google Patents
Cloud simulation experiment system based on AC/DC hybrid power grid Download PDFInfo
- Publication number
- CN119538467A CN119538467A CN202411691516.5A CN202411691516A CN119538467A CN 119538467 A CN119538467 A CN 119538467A CN 202411691516 A CN202411691516 A CN 202411691516A CN 119538467 A CN119538467 A CN 119538467A
- Authority
- CN
- China
- Prior art keywords
- power grid
- module
- simulation
- data
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
本申请涉及电力系统仿真技术领域，尤其涉及一种基于交直流混合电网的云端仿真实验系统。包括：电网模型模块，用于模拟电网在不同运行条件下的运维状态；动态仿真模块，用于基于预设参数对电网模型模块进行验证，以及根据验证结果对电网模型模块进行调整；云端计算模块，用于通过改进型神经网络模型计算仿真数据信息；数据交互模块，用于获取动态仿真模块以及云端计算模块的输出结果，并输出至用户界面；控制中心，用于对上述模块进行控制，以及对电网模型模块的运维任务进行调度。本申请通过实时获取实际电网的实时数据，并将其传递给电网模型模块，确保模型能够及时更新，反映实际电网的最新状态，确保了仿真的准确性和实时性。
The present application relates to the technical field of power system simulation, and in particular to a cloud-based simulation experimental system based on an AC/DC hybrid power grid. It includes: a power grid model module, which is used to simulate the operation and maintenance status of the power grid under different operating conditions; a dynamic simulation module, which 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; a cloud computing module, which is used to calculate simulation data information through an improved neural network model; a data interaction module, which is used to obtain the output results of the dynamic simulation module and the cloud computing module, and output them to the user interface; a control center, which is used to control the above modules and schedule the operation and maintenance tasks of the power grid model module. The present application obtains real-time data of the actual power grid in real time 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, thereby ensuring the accuracy and real-time performance of the simulation.
Description
Technical Field
The application relates to the technical field of power system simulation, in particular to a cloud simulation experiment system based on an alternating current-direct current hybrid power grid.
Background
Micro grid (Microgrid), also known as a micro grid, is a miniaturized grid system that can operate independently of or in parallel with a main grid. Microgrids typically consist of distributed power sources (e.g., solar photovoltaic panels, wind turbines, fuel cells, micro turbines, etc.), energy storage devices (e.g., batteries, supercapacitors), loads (powered devices), and control systems. As a flexible, efficient, environment-friendly energy system, micro-grids are becoming an important component of future energy systems.
DC micro-grid (DC micro-grid) and AC micro-grid (AC micro-grid) are two main forms of micro-grid technology, and DC micro-grid is relatively late in research, but DC micro-grid has higher efficiency and reliability than AC micro-grid in any field, so that AC micro-grid is still the main source of AC micro-grid in China, and thus a hybrid micro-grid with both AC and DC micro-grid characteristics has become the main stream of current. The simulation is carried out on the model of the AC/DC hybrid power grid, so that the problems of large influence of subjective factors and potential safety hazards can be solved by means of the past experience to the field investigation of the engineering site. The model of the ac-dc hybrid power grid is very complex and contains a large number of electrical elements and control systems, which results in that the simulation platform cannot fully meet the requirements of real-time simulation.
Therefore, a cloud simulation system based on an ac/dc hybrid power grid is needed to meet the requirements of accuracy, instantaneity and the like of real-time simulation.
Disclosure of Invention
In view of the above, it is necessary to provide a cloud simulation experiment system based on an ac/dc hybrid power grid, so as to solve the problem that the accuracy and instantaneity of the simulation platform in the prior art cannot fully meet the requirement of real-time simulation.
A cloud simulation experiment system based on an AC/DC hybrid power grid comprises:
The power grid model module is used for simulating the operation and maintenance states of the power grid under different operation conditions;
the dynamic simulation module is used for verifying the power grid model module based on preset parameters and adjusting the power grid model module according to a verification result;
the cloud computing module is used for computing simulation data information based on the operation and maintenance data output by the power grid model module through the improved neural network model;
the data interaction module is used for acquiring output results of the dynamic simulation module and the cloud computing module and outputting the output results to a user interface;
The control center is used for controlling the power grid model module, the dynamic simulation module, the cloud computing module and the data interaction module and scheduling operation and maintenance tasks of the power grid model module according to the simulation result of the power grid model module.
Preferably, the control center schedules the operation and maintenance tasks of the power grid model module according to the simulation result of the power grid model module, including:
The control center comprises 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 dispatching unit adopts a response ratio priority dispatching algorithm to dispatch 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 sag control unit is used for controlling the voltage level of the power grid by adjusting the output voltage of the generator, the current sag control unit is used for controlling the current level of the power grid by adjusting the output current of the generator, the frequency sag control unit is used for controlling the frequency level of the power grid by adjusting the output frequency of the generator, and the ripple sag control unit is used for controlling voltage ripples in the power grid.
Preferably, the data interaction module is further configured to obtain actual real-time data of the power grid, and send the actual real-time data to the power grid model module.
Preferably, 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 operation parameters of the power grid model module.
Preferably, the system further comprises a simulation analysis module, wherein the simulation analysis module is used for evaluating the operation and maintenance effect of the power grid according to the simulation data information output by the cloud computing module.
Preferably, the simulation analysis module is further configured to send a result of the evaluation of the operation and maintenance effect of the power grid to the data interaction module, and the data interaction module outputs the result of the evaluation of the operation and maintenance effect of the power grid to the user interface.
Preferably, the simulation analysis module comprises an ANSYS analysis unit, and the operation and maintenance effect is evaluated through the ANSYS analysis unit.
Preferably, the result of the operation and maintenance effect evaluation by the ANSYS analysis unit includes providing an analysis result chart and a curve to a user interface.
Preferably, the cloud computing module comprises an encoding unit, a balance computing unit, a parameter factor generating unit, a convolution computing unit and a model parameter self-adaptive adjusting unit;
The encoding unit is used for encoding the operation and maintenance data output by the power grid model module;
the parameter factor generating unit is used for generating parameter factors according to the encoded operation data;
The convolution calculation unit is used for calculating simulation data information according to the encoded operation and maintenance data and parameter factors;
the model parameter self-adaptive adjusting unit is used for adjusting the operation parameters of the power grid model module.
Preferably, the system further comprises a distributed storage module, wherein the distributed storage module is used 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.
Compared with the prior art, the cloud simulation experiment system has the beneficial effects that the cloud simulation experiment system can efficiently and accurately simulate and verify the operation and maintenance states of the AC/DC hybrid power grid under different operation conditions. The real-time data of the actual power grid is obtained in real time through the data interaction module and is transmitted to the power grid model module, so that the model can be updated in time to reflect the latest state of the actual power grid, the power grid model module dynamically updates model parameters according to the real-time data, the model can accurately reflect the running state of the actual power grid, the simulation accuracy and the real-time performance are ensured, and the real-time simulation requirement is met.
Drawings
Fig. 1 is a schematic structural diagram of a cloud simulation experiment system 100 based on an ac/dc hybrid power grid according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a control center according to an embodiment of the present application.
Fig. 3 is a schematic workflow diagram of a real-time scheduling unit response ratio priority scheduling algorithm according to an embodiment of the present application.
FIG. 4 is a schematic diagram of a process of updating job priority hardware according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a cloud simulation experiment system 100 based on an ac/dc hybrid power grid according to an embodiment of the present application, where the cloud simulation experiment system 100 based on the ac/dc hybrid power grid includes:
The power grid model module 101 is used for simulating the operation and maintenance states of the power grid under different operation conditions. The operation and maintenance state at least comprises load change, equipment overload, voltage fluctuation, line tripping, equipment damage, insulation breakdown or power failure. The power grid model module 101 may input topology data (such as positions and connection relations of devices such as a transformer substation, a power transmission line, a distribution line, a transformer, a switch, etc.), device parameters (such as rated capacity and impedance of the transformer, resistance and reactance of the line, rated power and speed regulation characteristics of a generator, etc.), load data (such as size, type and distribution of load), renewable energy data (such as solar photovoltaic and wind energy, etc.), and energy storage system data (such as a battery energy storage device and a supercapacitor, etc.).
The dynamic simulation module 102 is used for verifying the power grid model module based on preset parameters, adjusting the power grid model module according to the verification result, and testing and verifying the power consumption, logic function and performance index of the AC/DC hybrid power grid by setting target parameters, a circuit model and a power grid framework.
The topology and device parameters of the grid model, the load model and the renewable energy model, the energy storage system model, simulation parameters (such as voltage, current, frequency, power, etc.), etc. may be used as input data, and the dynamic simulation module 102 outputs simulation result data (such as voltage fluctuation, current variation, power loss, etc.).
The cloud computing module 103 is used for computing simulation data information based on the operation data output by the power grid model module through the improved neural network model, computing the simulation data information through the improved neural network model by setting the operation parameters of the AC/DC hybrid power grid, and comparing the simulation result data information with the setting comparison data. And the improved neural network model is used for carrying out simulation data calculation, so that the accuracy and the efficiency of calculation are improved.
Furthermore, simulation data can be processed in real time through a distributed computing technology, so that the real-time performance of simulation is ensured.
The data interaction module 104 is configured to obtain output results of the dynamic simulation module and the cloud computing module, and output the output results to a user interface. Further, the data interaction module is further configured to obtain actual real-time data of the power grid, and send the actual real-time data to the power grid model module.
The control center 105 is configured to control the power grid model module, the dynamic simulation module, the cloud computing module, and the data interaction module, and schedule operation and maintenance tasks of the power grid model module according to a simulation result of the power grid model module.
Further, the control center schedules operation and maintenance tasks of the power grid model module according to the simulation result of the power grid model module.
As shown in the structural schematic diagram of the control center in fig. 2, 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 dispatching unit adopts a response ratio priority dispatching algorithm to dispatch 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 sag control unit is used for controlling the voltage level of the power grid by adjusting the output voltage of the generator, the current sag control unit is used for controlling the current level of the power grid by adjusting the output current of the generator, the frequency sag control unit is used for controlling the frequency level of the power grid by adjusting the output frequency of the generator, and the ripple sag control unit is used for controlling voltage ripples in the power grid.
Further, 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 operation parameters of the power grid model module.
Specifically, the real-time scheduling unit adopts a response ratio priority scheduling algorithm to perform scheduling processing of task sequences according to the operation and maintenance time and the waiting time proportion of the AC/DC hybrid power grid.
As shown in the workflow diagram of the real-time scheduling unit response to the priority scheduling algorithm in fig. 3, the working procedure of the real-time scheduling unit response to the priority scheduling algorithm includes:
s301, calculating initial priority;
Setting control priority according to the operation and maintenance state of the AC/DC hybrid power grid, and response time, adjustment range and adjustment precision of the voltage droop control unit, the current droop control unit, the frequency droop control unit and the ripple droop control unit;
The waiting time (W) of the initial task and the task demand service time (Y) calculate an initial priority,
The initial priority output function is:
In the formula (1), P is an initial priority, alpha is an environmental influence weight factor, beta is an operation and maintenance state influence weight factor of the power grid, and Y is an operation and maintenance state influence factor characteristic value of the AC/DC hybrid power grid, and M, N, H, G is an activation function of a voltage droop control unit, a current droop control unit, a frequency droop control unit and a ripple droop control unit;
S302, analyzing fluctuation parameters of the operation and maintenance states of the AC/DC hybrid power grid, and starting a voltage droop control unit, a current droop control unit, a frequency droop control unit or a ripple droop control unit to be in a working state to adjust an activation priority;
the priority adjustment output function is:
In the formula (2), K represents a priority adjustment output function, P represents an initial priority output function, E represents a function stability influence factor influencing the operation of each sagging control unit, W h represents an attenuation rate weight matrix of the operation of different sagging control units, h represents an attenuation rate weight matrix attribute, b h represents a bias vector of the operation of each sagging control unit, t c represents task service demand time, c represents demand time identification, E represents the response capability of the operation of each sagging control unit, D represents task cut-off time, and θ and γ represent function stability and operation stability average weight factors of each sagging control unit;
s303, deciding a task according to a response ratio scheduling voltage droop control unit, a current droop control unit, a frequency droop control unit or a ripple droop control unit;
The control unit with highest response ratio is activated first to cope with the current power grid condition, simulation tasks are arranged in sequence according to the priority order, tasks are executed in sequence according to the priority order, and a task adjustment formula is as follows:
In the formula (3), Q is a task adjustment sequence, n is the number of jobs, and S i is the service demand time of the job i;
S304, updating the job priority;
re-calculating the job priority order based on the adjusted job order, the priority order formula being
In the formula (4), U is urgent priority, I is job importance, P is initial priority, mu and delta are weight factors, ci represents characteristic values of running states of all jobs, xi represents an information set of the jobs I, and xn represents a continuously updated data information set.
In a specific application, as shown in the working process schematic diagram of the update job priority hardware, the update job priority is used for realizing power grid task scheduling based on a response ratio priority scheduling algorithm. Each module is interconnected by a communication network and works together to optimize the operating efficiency and response capability of the power grid. For example, the data acquisition and processing unit uses sensors, data acquisition cards, microprocessors/DSPs and the like, the response ratio calculation module uses a response ratio calculation engine, a priority sequencer and the like, the task scheduling module uses a task queue manager, a task execution controller, the job priority updating module uses a priority updating engine and a weight factor adjuster, the information processing and storage unit uses a memory/solid state storage and a database management system, and the human-machine interface (HMI) uses a display screen, an input device and the like.
S305, updating task processes in real time;
and updating task attribute data in real time, and when the new task starts to be executed, recalculating the priorities of the rest simulation tasks and updating the priority sequence.
The invention further provides a distributed work executing method of the virtual machines, wherein the virtual machine system comprises a data slicing module and a copy copying module, the disk space on each independent computer is abstracted into a virtual machine node through the slicing module, the data slicing module divides the data into a plurality of parts, each part is respectively stored in different nodes, the copy copying module stores each data copy on different nodes, and each node is mutually communicated and cooperated through network connection.
Further, the system further comprises a simulation analysis module 106, which is configured to evaluate the operation and maintenance effects of the power grid according to the simulation data information output by the cloud computing module. Further, the simulation analysis module is further configured to send a result of the evaluation of the operation and maintenance effect of the power grid to the data interaction module, and the data interaction module outputs the result of the evaluation of the operation and maintenance effect of the power grid to the user interface.
Further, the simulation analysis module comprises an ANSYS analysis unit, and operation and maintenance effect evaluation is carried out through the ANSYS analysis unit. The results of the operation and maintenance effect evaluation by the ANSYS analysis unit include providing an analysis result chart and curve to a user interface. According to the evaluation principle of the ANSYS analysis unit, the simulation model of the AC/DC hybrid power grid is discretized, the simulation model of the whole AC/DC hybrid power grid is divided into a plurality of small geometric units, an approximate solution is obtained for each small unit, constraint conditions and loading conditions are defined for the simulation model of the AC/DC hybrid power grid, the constraint conditions are used for limiting displacement or rotation of the small geometric nodes, the loading conditions comprise voltage, overload and load to simulate various loading conditions of the AC/DC hybrid power grid in actual work, and finally the finite element analysis is used for deriving an analysis result by solving a linear equation set.
Further, the cloud computing module comprises a coding unit, a balance computing unit, a parameter factor generating unit, a convolution computing unit and a model parameter self-adaptive adjusting unit;
The system comprises a power grid model module, a coding unit, a parameter factor generating unit, a convolution calculating unit and a model parameter self-adaptive adjusting unit, wherein the power grid model module is used for generating operation data, the coding unit is used for coding operation data output by the power grid model module, the parameter factor generating unit is used for generating parameter factors according to the coded operation data, the convolution calculating unit is used for calculating simulation data information according to the coded operation data and the parameter factors, and the model parameter self-adaptive adjusting unit is used for adjusting operation parameters of the power grid model module.
In a specific embodiment, the neural network model of the cloud computing module receives data from the ac/dc hybrid power grid simulation model, calculates the data by adopting a distributed computing technology, sends the data to the data encoding module for data encoding, automatically generates a parameter factor according to the data by the parameter factor generating module, and sends the encoded data and the parameter factor to the convolution computing module for calculation and outputting a result. The model parameter self-adaptive adjusting module can automatically generate simulation system parameters according to the alternating current-direct current hybrid power grid simulation model and the system state, gain feedback is calculated according to the system parameters, simulation data required in the experimental process are calculated efficiently, and accuracy is improved.
Preferably, the system further comprises a distributed storage module 107, configured 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.
In conclusion, the cloud simulation experiment system capable of efficiently and accurately simulating and verifying the operation and maintenance states of the AC/DC hybrid power grid under different operation conditions is constructed. Because the operating state of the actual power grid is dynamically changed, the conventional simulation system is difficult to process and reflect the changes in real time, and the simulation result is lagged and inaccurate. According to the application, the real-time data of the actual power grid is obtained in real time through the data interaction module and is transmitted to the power grid model module, so that the model can be updated in time to reflect the latest state of the actual power grid, and the power grid model module dynamically updates the model parameters according to the real-time data, so that the model can accurately reflect the running state of the actual power grid, and the accuracy and the instantaneity of a simulation result are improved.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411691516.5A CN119538467A (en) | 2024-11-25 | 2024-11-25 | Cloud simulation experiment system based on AC/DC hybrid power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411691516.5A CN119538467A (en) | 2024-11-25 | 2024-11-25 | Cloud simulation experiment system based on AC/DC hybrid power grid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119538467A true CN119538467A (en) | 2025-02-28 |
Family
ID=94699742
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411691516.5A Pending CN119538467A (en) | 2024-11-25 | 2024-11-25 | Cloud simulation experiment system based on AC/DC hybrid power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119538467A (en) |
-
2024
- 2024-11-25 CN CN202411691516.5A patent/CN119538467A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Multiagent-based distributed state of charge balancing control for distributed energy storage units in AC microgrids | |
| RU2601957C2 (en) | Method and apparatus for controlling energy services based on market data | |
| Alavi et al. | Forecast-based consensus control for DC microgrids using distributed long short-term memory deep learning models | |
| Cai et al. | A hierarchical multi-agent control scheme for a black start-capable microgrid | |
| US20150261892A1 (en) | Integrated optimal placement, sizing, and operation of energy storage devices in electric distribution networks | |
| CN110249348A (en) | The configuration and parametrization of energy management system | |
| CN108830451B (en) | Aggregation potential evaluation method and system for user-side distributed energy storage | |
| CN108875103A (en) | A kind of powernet analogue system and method | |
| CN113517702B (en) | Emergency control method and system for source-storage-load adjustment-switching linkage | |
| Haileselassie et al. | Secondary control in multi-terminal VSC-HVDC transmission system | |
| CN119538467A (en) | Cloud simulation experiment system based on AC/DC hybrid power grid | |
| CN112039120A (en) | Reactive power adjusting method and system for grid-connected wind power plant | |
| CN111342462A (en) | A microgrid optimal scheduling system, method, storage medium, and computer program | |
| CN108551164B (en) | Voltage stability control method and device for direct-current micro-grid | |
| CN111835014B (en) | A multi-node power grid voltage regulation control system | |
| JP2020048299A (en) | Power generation control device, power generation control method, and program | |
| Starke et al. | A low voltage DC power electronic hub to support buildings | |
| Zehir et al. | Development of a field data-based virtual test bed for microgrid integration of building automation technologies | |
| Huang et al. | Distributed Hybrid Secondary Control of Virtual Synchronous Generators for Isolated AC Microgrids with Low Bandwidth Communication | |
| Huang et al. | A comparison of implicit and explicit methods for contingency constrained unit commitment | |
| CN119010054B (en) | A park energy optimization method, device, electronic equipment and storage medium | |
| CN110580659A (en) | Intelligent structure of energy system of nano network based on multi-dimensional heterogeneous data flow driving and control method thereof | |
| CN115190155B (en) | A cyber-physical control system and method based on power distribution internet of things | |
| CN117937625B (en) | Dispatching method, device, storage medium and electronic device for active distribution network | |
| Chakraborty et al. | Voltage unbalance and inertia based frequency control framework considering intelligent load management for stand-alone operation of microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |