CN111722540A - Energy Internet digital twin simulation system and method - Google Patents
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Abstract
The invention discloses an energy internet digital twin simulation system and method. The system comprises: the physical layer is used for acquiring basic data information of a physical object; the perception communication layer is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object; the platform layer is used for supporting the real-time subsystem and the simulation subsystem in a digital twin mode; the real-time subsystem is used for carrying out real-time monitoring and determining an operation control strategy according to the target data information; and the simulation subsystem is used for matching the source load of the energy Internet by prediction and correcting the control strategy of the real-time subsystem according to the matching result. By executing the technical scheme, the optimized operation of the energy Internet can be realized, the system risk is reduced, and the utilization efficiency of renewable energy is improved.
Description
Technical Field
The invention relates to the technical field of energy Internet, in particular to an energy Internet digital twin simulation system and method.
Background
With the continuous development of scientific technology, the high-proportion penetration of renewable energy, the flexible interaction of emerging loads and the wide access of multiple elements, the uncertainty and complexity of the energy internet are higher and higher. For the complex energy Internet, modeling and simulation are always important research tools. Therefore, modeling and simulation of the energy Internet always provide important technical support for fusion, innovation and development of the energy Internet multi-field technology and landing application of business.
The current energy Internet modeling and simulation mostly adopt the mode of traditional physical mechanism. The traditional physical mechanism modeling has certain certainty, and is difficult to adapt to the complex and changeable energy Internet operation mode brought by multi-energy flow coupling in the energy Internet. Meanwhile, the physical mechanism model adopts a large amount of approximation and simplification, and the accuracy and the reliability of the energy Internet under the condition of high proportion of renewable energy infiltration are directly influenced.
Disclosure of Invention
The invention provides an energy internet digital twin simulation system and method, which can enable a simulation module and an energy internet actual operation module to work in parallel based on a large amount of data collected by energy internet measurement monitoring equipment and the like, compare states with each other, perform data interaction and iteration, finally realize the optimized operation of the energy internet, reduce the system risk and improve the utilization efficiency of renewable energy.
The invention provides an energy internet digital twin simulation system, which comprises:
the physical layer is used for acquiring basic data information of a physical object;
the perception communication layer is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
the platform execution layer is used for carrying out real-time monitoring and determining an operation control strategy according to the target data information in a digital twin mode; and matching the source load of the energy Internet by prediction, and correcting the control strategy according to the matching result.
Preferably, the platform execution layer is specifically configured to:
constructing a simulation model based on a data-driven principle; determining a multi-energy flow optimization scheme of the energy Internet by using the constructed simulation model through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm; and matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement of the energy Internet, and correcting the control strategy of the real-time subsystem according to the matching result.
Further, the platform execution layer is specifically configured to construct: the system comprises an illumination primary energy model, a wind power primary energy model, a combined cooling heating and power system model, a wind-solar energy storage complementary system model and a user comprehensive load model.
Preferably, the platform execution layer is specifically configured to:
performing model operation on the target data information by operating a control model, a network topology model and an equipment model;
and monitoring and controlling the energy Internet in real time according to the model operation results of the operation control model, the network topology model and the equipment model.
Further, the platform execution layer is specifically configured to:
and performing event and alarm processing on the energy Internet according to the model operation results of the operation control model, the network topology model and the equipment model.
Preferably, the sensing communication layer is specifically configured to:
acquiring at least one of state data, magnitude transmission data, environmental monitoring data and behavior tracking data of a physical object; and the number of the first and second groups,
and transmitting at least one of the state data, the magnitude transmission data, the environment monitoring data and the behavior tracking data in a preset transmission mode.
Further, the sensing communication layer, which is specifically used for a preset transmission mode, includes: the satellite-ground 5G fusion optical fiber communication system comprises at least one of satellite-ground 5G fusion optical fiber communication, high-precision time-frequency synchronization networking communication, a broadband ad hoc network and a local micropower wireless ad hoc network.
Preferably, the physical layer, specifically, the physical object for acquiring includes: the source-end renewable energy power generation object specifically comprises at least one of a power grid, an air grid and a heat supply network.
An energy internet digital twin simulation method comprises the following steps:
acquiring basic data information of a physical object;
determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
performing real-time monitoring and determining an operation control strategy according to the target data information in a digital twinning mode; and matching the source load of the energy Internet through prediction, and correcting the control strategy according to the matching result.
Preferably, a digital twin mode is adopted, and real-time monitoring and operation control strategies are determined according to the target data information; matching the source load through prediction, and correcting the control strategy according to the matching result; the method specifically comprises the following steps:
constructing a simulation model based on a data-driven principle;
determining a multi-energy flow optimization scheme by using a simulation model constructed by the model layer through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm;
and matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement, and correcting the control strategy according to the matching result.
According to the technical scheme provided by the invention, the physical layer is used for acquiring basic data information of a physical object; the perception communication layer is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object; the real-time subsystem is used for carrying out real-time monitoring and determining an operation control strategy according to the target data information; and the simulation subsystem is used for matching the source load of the energy Internet by prediction and correcting the control strategy of the real-time subsystem according to the matching result.
By adopting the technical scheme provided by the invention, the simulation module and the energy internet actual operation module can work in parallel based on a large amount of data collected by energy internet measurement monitoring equipment and the like, data interaction and iteration can be carried out between the simulation module and the actual operation module because the basic data are synchronous in real time, and in the process of actual operation control, learning and prediction can be carried out through the simulation module, and testing can be carried out according to the prediction result and subsequently collected data, so that the accuracy of the prediction result is determined. And the parameters of the actual operation module can be adjusted based on the prediction result, so that the optimal operation of the energy Internet is finally realized, the system risk is reduced, and the utilization efficiency of renewable energy is improved. By executing the preferable scheme, the system architecture of the energy Internet digital twin simulation system can be optimized, and the purpose of optimally matching the source load in the energy Internet by the energy Internet digital twin simulation system can be improved by providing different levels and supporting different functional modules or algorithm modules in the different levels to control the source load.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of an energy Internet digital twin simulation system provided by an embodiment of the invention;
fig. 2 is a schematic flow diagram of an energy internet digital twin simulation method provided by an embodiment of the invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Fig. 1 is a schematic diagram of an energy internet digital twin simulation system provided by an embodiment of the present invention, which is applicable to the case of controlling the operation of the energy internet, and the system may be implemented by software and/or hardware.
As shown in fig. 1, the energy internet digital twin simulation system includes:
a physical layer 110 for acquiring basic data information of a physical object;
the perception communication layer 120 is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
a platform execution layer comprising a platform layer 130, a real-time subsystem 140, and a simulation subsystem 150;
the platform layer 130 is used for supporting the real-time subsystem 140 and the simulation subsystem 150 in a digital twin mode;
the real-time subsystem 140 is configured to perform real-time monitoring and determine an operation control strategy according to the target data information;
and the simulation subsystem 150 is used for matching the source load of the energy Internet by prediction and correcting the control strategy of the real-time subsystem according to the matching result.
The physical layer 110 is configured to obtain basic data information of a physical object, where the physical object may be an energy obtaining object at a source end in an energy internet, such as a photovoltaic power station, a wind turbine generator, and the like, and may also be an energy using object at a terminal in the energy internet, such as a scientific research center, a living area, an experiment center, and the like.
In a possible embodiment, optionally, the physical object includes a source renewable energy power generation object, specifically at least one of a power grid, an air grid, and a heat grid. Data information in the power grid, the gas grid and the heat supply network can be extracted to be used as data information of the physical object. Each network may include a renewable energy power generation object at a source end, for example, a power grid may perform photovoltaic power generation, wind power generation, and the like, a gas grid may perform energy conversion and use through natural gas, and a heat supply network may generate heat through water, and the like.
In this embodiment, the physical layer is a physical basis for energy internet digital twin modeling and simulation, and is a mapping object of the digital twin. The energy Internet physical object mainly comprises renewable energy power generation at a source end; multi-energy networks, such as power, gas, heat networks, and the like; as a dummy load for an adjustable load, and as an energy storage device.
The perception communication layer 120 is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
in this embodiment, optionally, the sensing communication layer is specifically configured to:
acquiring at least one of state data, magnitude transmission data, environmental monitoring data and behavior tracking data of a physical object; and the number of the first and second groups,
and transmitting at least one of the state data, the magnitude transmission data, the environment monitoring data and the behavior tracking data in a preset transmission mode.
On the basis of the technical scheme, at least one of satellite-ground 5G fusion optical fiber communication, high-precision time-frequency synchronization networking communication, broadband ad hoc network and local micropower wireless ad hoc network can be selected.
Specifically, the work done by the sensing communication layer includes two aspects of sensing and communication:
in the perception aspect: and acquiring data information of the physical layer object, such as state, quantity value transmission, environment monitoring, behavior tracking and the like.
In the aspect of communication: the high-efficiency transmission of sensing data is realized through satellite-ground 5G fusion optical fiber communication, high-precision time-frequency synchronization networking and monitoring, broadband multi-hop ad hoc networks and local micropower wireless ad hoc networks.
The platform layer 130 is used for supporting the real-time subsystem 140 and the simulation subsystem 150 in a digital twin mode;
the platform layer can transmit data to the real-time subsystem and the simulation subsystem at the same time, so that the real-time subsystem and the simulation subsystem have the same data source, and a data twin effect is achieved. It can be said that the two are data twins with each other.
Specifically, the platform layer defines management and access of massive heterogeneous terminals through software, provides high-performance storage and calculation analysis services for data information transmitted from the sensing communication layer, and provides data management and support for subsequent levels.
The real-time subsystem 140 is configured to perform real-time monitoring and determine an operation control strategy according to the target data information;
the target data information may be valuable data information obtained through screening, and specifically, may be data information such as energy supply, energy utilization rate, and load. The real-time subsystem can regulate and control the balance of energy consumption and use in real time according to the relation between the source load and the load, so that real-time monitoring and operation control strategy determination are achieved.
In this embodiment, optionally, the real-time subsystem includes:
the real-time subsystem model layer is used for carrying out model operation on the target data information by operating a control model, a network topology model and an equipment model;
and the real-time subsystem application layer is used for monitoring and controlling the energy Internet in real time according to the model operation result of the real-time subsystem model layer.
In this scheme, the real-time subsystem model layer mainly includes an operation control model, a network topology model, and an equipment model. The real-time subsystem application layer realizes real-time operation and real-time control of the energy Internet by using a model layer of a real-time system, and has the functions of events, alarms and the like.
And the simulation subsystem 150 is used for matching the source load of the energy Internet by prediction and correcting the control strategy of the real-time subsystem according to the matching result.
The simulation subsystem can comprise a data algorithm of the real-time subsystem, and can also comprise a prediction algorithm and other algorithms, so that the simulation subsystem can realize the operation of data, has certain prediction capability, and can correct the prediction result according to actual data, thereby obtaining a relatively accurate prediction result. In the scheme, the simulation subsystem and the real-time subsystem can realize the guidance and optimization of the energy Internet simulation subsystem to the real-time subsystem through data fusion and communication.
In this embodiment, optionally, the simulation subsystem includes:
the simulation subsystem model layer is used for constructing a simulation model based on a data-driven principle;
the simulation subsystem algorithm layer determines a multi-energy flow optimization scheme of the energy Internet by using a simulation model constructed by the model layer and through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm;
and the simulation subsystem application layer is used for matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement of the energy Internet, and correcting the control strategy of the real-time subsystem according to the matching result.
The simulation subsystem model layer is based on a data-driven principle, utilizes data stored and managed by the platform layer to construct a relevant model of the energy Internet modeling and simulation system, and mainly comprises primary energy models such as illumination and wind power, a combined cooling heating and power system, a wind-solar energy storage complementary system and a user comprehensive load. The model layer of the simulation system provides basic model support for the algorithm layer.
And the simulation subsystem algorithm layer is used for constructing a simulation model constructed by the model layer, and finally realizing the multi-energy flow optimization operation of the energy internet by sequentially constructing a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm based on integrated clustering, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm module.
And the simulation subsystem application layer utilizes the algorithm layer module, combines the service requirements of the energy Internet, realizes the matching of the energy Internet source network and the storage through state estimation, prediction and the like, and supports the full-service application of energy Internet planning, operation, new technology verification and the like.
On the basis of the above technical solution, optionally, the building of the simulation model includes building: the system comprises an illumination primary energy model, a wind power primary energy model, a combined cooling heating and power system model, a wind-solar energy storage complementary system model and a user comprehensive load model. The system comprises a primary illumination energy model, a primary wind energy model, a combined cooling heating and power system model, a wind-solar energy storage complementary system model and a user comprehensive load model, wherein the primary illumination energy model and the primary wind energy model are primary energy models, the combined cooling-heating and power system model and the wind-solar energy storage complementary system model are multiple energy models, and the user comprehensive load model is a load model. Each model can realize data operation on a corresponding target to obtain a model object of the energy Internet.
In the technical scheme provided by the embodiment of the invention, the physical layer is used for acquiring basic data information of a physical object; the perception communication layer is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object; the platform layer is used for supporting the real-time subsystem and the simulation subsystem in a digital twin mode; the real-time subsystem is used for carrying out real-time monitoring and determining an operation control strategy according to the target data information; and the simulation subsystem is used for matching the source load of the energy Internet by prediction and correcting the control strategy of the real-time subsystem according to the matching result. Through adopting this scheme, can be based on a large amount of data that energy internet measuration supervisory equipment etc. gathered, make emulation module and energy internet actual operation module work side by side to contrast state each other, carry out data interaction and iteration, finally realize energy internet's optimized operation, reduce the system risk, improve renewable energy utilization efficiency.
On the basis of the above technical solutions, optionally, the real-time subsystem application layer is further configured to:
and performing event and alarm processing on the energy Internet according to the model operation result of the real-time subsystem model layer.
The energy Internet can be monitored and controlled, and meanwhile, event and alarm processing can be carried out on the energy Internet. For example, an event which affects source load matching balance and occurs at a source end or a terminal of the energy internet is reported, and an alarm is given when a certain threshold value is exceeded, so that a worker can process the corresponding event, and the effect of ensuring stable operation of the energy internet can be achieved.
Fig. 2 is a schematic flow diagram of an energy internet digital twin simulation method provided by an embodiment of the present invention, and as shown in fig. 2, the method includes:
s210, acquiring basic data information of the physical object.
S220, determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
s230, performing real-time monitoring and determining an operation control strategy according to the target data information in a digital twinning mode; and matching the source load through prediction, and correcting the control strategy according to the matching result.
According to the technical scheme provided by the invention, basic data information of a physical object is obtained; determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object; a digital twinning mode is adopted to support the real-time subsystem and the simulation subsystem; according to the target data information, real-time monitoring is carried out and an operation control strategy is determined; and matching the source load of the energy Internet by prediction, and correcting the control strategy of the real-time subsystem according to the matching result. Through adopting this scheme, can be based on a large amount of data that energy internet measuration supervisory equipment etc. gathered, make emulation module and energy internet actual operation module work side by side to contrast state each other, carry out data interaction and iteration, finally realize energy internet's optimized operation, reduce the system risk, improve renewable energy utilization efficiency.
Optionally on the basis of the technical scheme, a digital twin mode is adopted, and real-time monitoring and operation control strategies are determined according to the target data information; and matching the source load through prediction, and correcting the control strategy according to the matching result, wherein the method comprises the following steps:
constructing a simulation model based on a data-driven principle;
determining a multi-energy flow optimization scheme by using a simulation model constructed by the model layer through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm;
and matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement, and correcting the control strategy according to the matching result. The energy internet digital twin simulation method provided by the embodiment can be realized by the energy internet digital twin simulation system provided by the embodiment, and has corresponding beneficial effects, which are not described herein again.
It can be understood that the energy internet digital twin simulation method may further perform other steps of the energy internet digital twin simulation system, which may be specifically referred to as an energy internet digital twin simulation system, and details are not described herein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or system. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An energy internet digital twin simulation system, characterized in that the system comprises:
the physical layer is used for acquiring basic data information of a physical object;
the perception communication layer is used for determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
the platform execution layer is used for carrying out real-time monitoring and determining an operation control strategy according to the target data information in a digital twin mode; and matching the source load of the energy Internet by prediction, and correcting the control strategy according to the matching result.
2. The system of claim 1, wherein the platform execution layer is specifically configured to:
constructing a simulation model based on a data-driven principle; determining a multi-energy flow optimization scheme of the energy Internet by using the constructed simulation model through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm; and matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement of the energy Internet, and correcting the control strategy of the real-time subsystem according to the matching result.
3. The system of claim 2, wherein the platform execution layer is specifically configured to construct: the system comprises an illumination primary energy model, a wind power primary energy model, a combined cooling heating and power system model, a wind-solar energy storage complementary system model and a user comprehensive load model.
4. The system of claim 1, wherein the platform execution layer is specifically configured to:
performing model operation on the target data information by operating a control model, a network topology model and an equipment model;
and monitoring and controlling the energy Internet in real time according to the model operation results of the operation control model, the network topology model and the equipment model.
5. The system of claim 4, wherein the platform execution layer is specifically configured to:
and performing event and alarm processing on the energy Internet according to the model operation results of the operation control model, the network topology model and the equipment model.
6. The system of claim 1, wherein the perceptual communication layer is specifically configured to:
acquiring at least one of state data, magnitude transmission data, environmental monitoring data and behavior tracking data of a physical object; and the number of the first and second groups,
and transmitting at least one of the state data, the magnitude transmission data, the environment monitoring data and the behavior tracking data in a preset transmission mode.
7. The system of claim 6, wherein the perceptual communication layer, specifically for a preset transmission mode, comprises: the satellite-ground 5G fusion optical fiber communication system comprises at least one of satellite-ground 5G fusion optical fiber communication, high-precision time-frequency synchronization networking communication, a broadband ad hoc network and a local micropower wireless ad hoc network.
8. The system of claim 1, wherein the physical layer, in particular the physical objects used for acquisition, comprises: the source-end renewable energy power generation object specifically comprises at least one of a power grid, an air grid and a heat supply network.
9. A digital twin simulation method for an energy internet is characterized by comprising the following steps:
acquiring basic data information of a physical object;
determining target data information of the physical object according to the basic data information; wherein the target data information includes at least one of magnitude information, environment information, and behavior information of the physical object;
performing real-time monitoring and determining an operation control strategy according to the target data information in a digital twinning mode; and matching the source load of the energy Internet through prediction, and correcting the control strategy according to the matching result.
10. The simulation method of claim 9, wherein a digital twin mode is adopted to perform real-time monitoring and determine an operation control strategy according to the target data information; matching the source load through prediction, and correcting the control strategy according to the matching result; the method specifically comprises the following steps:
constructing a simulation model based on a data-driven principle;
determining a multi-energy flow optimization scheme by using a simulation model constructed by the model layer through a state perception algorithm, a scene generation algorithm, a user behavior analysis algorithm, a renewable energy-oriented probabilistic neural network algorithm and a deep reinforcement learning algorithm;
and matching the source load of the energy Internet according to the multi-energy flow optimization scheme and the service requirement, and correcting the control strategy according to the matching result.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN112631144A (en) * | 2020-11-20 | 2021-04-09 | 国网山东省电力公司电力科学研究院 | Comprehensive energy real-time digital physical hybrid simulation system |
| CN112711631A (en) * | 2020-11-27 | 2021-04-27 | 国网山东省电力公司电力科学研究院 | Digital twin information synchronization method, system, readable storage medium and device |
| CN112835337A (en) * | 2021-01-04 | 2021-05-25 | 山东省计算中心（国家超级计算济南中心） | Industrial control safety target range platform and method |
| CN113219926A (en) * | 2021-05-13 | 2021-08-06 | 中国计量大学 | Human-machine co-fusion manufacturing unit safety risk assessment method based on digital twin system |
| CN113591173A (en) * | 2021-07-30 | 2021-11-02 | 贵州电网有限责任公司 | Data visualization interaction method for multi-energy system digital twin |
| CN113884899A (en) * | 2021-09-28 | 2022-01-04 | 中汽创智科技有限公司 | Fuel cell simulation calibration system and method based on digital twinning |
| CN113890831A (en) * | 2021-10-20 | 2022-01-04 | 中国联合网络通信集团有限公司 | Method, device and storage medium for simulating network equipment |
| CN114374233A (en) * | 2022-03-22 | 2022-04-19 | 长沙电机厂集团长瑞有限公司 | Method and system for adjusting micro-grid power output based on virtual generator |
| CN116683542A (en) * | 2023-07-26 | 2023-09-01 | 国网浙江省电力有限公司宁波供电公司 | Source network charge storage control method and device, computer equipment and storage medium |
| CN116700049A (en) * | 2023-07-12 | 2023-09-05 | 山东大学 | Multi-energy network digital twin real-time simulation system and method based on data driving |
| CN117369307A (en) * | 2023-11-17 | 2024-01-09 | 南京千智电气科技有限公司 | Transient modeling simulation system of photovoltaic power station |
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| CN112380676B (en) * | 2020-10-29 | 2023-07-14 | 贵州电网有限责任公司 | Digital twin data stream modeling and compression method for multi-energy system |
| CN112380676A (en) * | 2020-10-29 | 2021-02-19 | 贵州电网有限责任公司 | Multi-energy system digital twin data stream modeling and compressing method |
| CN112631144A (en) * | 2020-11-20 | 2021-04-09 | 国网山东省电力公司电力科学研究院 | Comprehensive energy real-time digital physical hybrid simulation system |
| CN112631144B (en) * | 2020-11-20 | 2023-09-05 | 国网山东省电力公司电力科学研究院 | Comprehensive energy real-time digital physical hybrid simulation system |
| CN112711631A (en) * | 2020-11-27 | 2021-04-27 | 国网山东省电力公司电力科学研究院 | Digital twin information synchronization method, system, readable storage medium and device |
| CN112711631B (en) * | 2020-11-27 | 2022-07-08 | 国网山东省电力公司营销服务中心(计量中心) | Digital twin information synchronization method, system, readable storage medium and device |
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| CN113219926A (en) * | 2021-05-13 | 2021-08-06 | 中国计量大学 | Human-machine co-fusion manufacturing unit safety risk assessment method based on digital twin system |
| CN113591173A (en) * | 2021-07-30 | 2021-11-02 | 贵州电网有限责任公司 | Data visualization interaction method for multi-energy system digital twin |
| CN113884899A (en) * | 2021-09-28 | 2022-01-04 | 中汽创智科技有限公司 | Fuel cell simulation calibration system and method based on digital twinning |
| CN113890831A (en) * | 2021-10-20 | 2022-01-04 | 中国联合网络通信集团有限公司 | Method, device and storage medium for simulating network equipment |
| CN113890831B (en) * | 2021-10-20 | 2024-02-13 | 中国联合网络通信集团有限公司 | Method, device and storage medium for simulating network equipment |
| CN114374233A (en) * | 2022-03-22 | 2022-04-19 | 长沙电机厂集团长瑞有限公司 | Method and system for adjusting micro-grid power output based on virtual generator |
| CN116700049A (en) * | 2023-07-12 | 2023-09-05 | 山东大学 | Multi-energy network digital twin real-time simulation system and method based on data driving |
| CN116700049B (en) * | 2023-07-12 | 2024-05-28 | 山东大学 | Multi-energy network digital twin real-time simulation system and method based on data driving |
| CN116683542A (en) * | 2023-07-26 | 2023-09-01 | 国网浙江省电力有限公司宁波供电公司 | Source network charge storage control method and device, computer equipment and storage medium |
| CN117369307A (en) * | 2023-11-17 | 2024-01-09 | 南京千智电气科技有限公司 | Transient modeling simulation system of photovoltaic power station |
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