Digital twinning-based power grid dynamic frequency adaptive regulation and control method, system, equipment and medium

Abstract

The invention discloses a digital twinning-based power grid dynamic frequency adaptive regulation and control method, system, equipment and medium, and belongs to the technical field of power grid dynamic frequency regulation and control, and the method comprises the steps: collecting power grid operation data through a data collection device disposed at a physical power grid key node; transmitting power grid operation data to a digital twinning construction and operation platform, and synchronously updating a virtual power grid state in real time; a digital twinning construction and operation platform is used for obtaining the operation state and dynamic parameters of the power grid; predicting a frequency dynamic response track of the power grid in a disturbance or expected scene based on the power grid operation state and the dynamic parameters; adjusting control parameters of resources participating in frequency regulation and control according to the frequency dynamic response track; and issuing the control parameters to a corresponding device of the physical power grid for execution through the control instruction execution interface. The frequency dynamic quality of the power grid is remarkably improved, and the frequency stability margin and the overall operation safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid dynamic frequency regulation, and particularly relates to a power grid dynamic frequency adaptive regulation method, system, device and medium based on digital twinning. BACKGROUND

[0002] Power grid frequency stability is the core of ensuring the safe operation of the power system. Existing frequency regulation mainly relies on a hierarchical coordination system: primary frequency regulation uses the droop characteristics of generator sets to respond quickly and independently; secondary frequency regulation (such as AGC) eliminates steady-state errors and restores tie-line power; and tertiary frequency regulation performs economic dispatching and backup optimization. Traditionally, this system highly depends on the rotational inertia and speed regulation system response provided by synchronous generators. However, with the large-scale access of renewable energy sources such as wind power and photovoltaic power to the power grid through power electronic converters, their inherent low or zero inertia characteristics have significantly changed the dynamic behavior of the power grid, making the traditional frequency regulation method based on fixed parameters and simplified models face serious challenges and difficult to effectively respond to increasingly complex frequency fluctuations.

[0003] The existing power grid frequency regulation system has exposed several inherent defects when dealing with the challenges brought by high proportion of renewable energy access. Its control strategy is often based on fixed parameter settings, such as droop coefficients and AGC gains, which are usually set according to offline analysis or typical operating conditions, making it difficult to adapt to the dynamic changes of power grid operating conditions in real time, resulting in suboptimal regulation effect under different operating conditions or even causing oscillation. In addition, traditional control methods rely on simplified grid models and cannot accurately capture the complex nonlinear dynamics introduced by power electronic devices and the rapid fluctuation characteristics of new energy output, which limits the accuracy of control decisions. The existing regulation system is essentially a passive response, mainly relying on lagging measurements of frequency deviation for adjustment, lacking effective prediction ability for future frequency evolution trends, and difficult to implement proactive intervention, especially when responding to fast frequency events. At the same time, the coordination and optimization scheduling capabilities of various regulation resources such as hydropower, thermal power, energy storage, and demand side response are insufficient, making it difficult to fully consider the dynamic response characteristics, regulation cost, and network transmission constraints of various resources, resulting in low resource utilization efficiency and inability to achieve globally optimal frequency support. These shortcomings are particularly prominent in the context of high proportion of new energy penetration and continuous decline of system inertia, reducing the power grid frequency stability margin and weakening the ability to respond to large disturbances.

[0004] In view of the problems existing in the prior art, the present application is proposed. SUMMARY

[0005] In view of the above-mentioned problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is: how to solve the problems existing in the prior art of power grid frequency regulation technology, such as fixed parameters, simplified models, lack of predictability, and insufficient resource collaborative optimization, especially in the context of high proportion of renewable energy grid-connected leading to system inertia decline and increasingly complex dynamic characteristics, traditional regulation strategies are difficult to ensure the real-time stability and economic operation of power grid frequency.

[0007] To solve the above technical problems, the present application provides the following technical scheme: a power grid dynamic frequency adaptive regulation method based on digital twinning, which comprises: collecting power grid operation data through a data acquisition system deployed at key nodes of the physical power grid; transmitting the power grid operation data to a digital twinning construction and operation platform to update the virtual power grid state in real time; obtaining the power grid operation state and dynamic parameters using the digital twinning construction and operation platform; predicting the frequency dynamic response trajectory of the power grid under disturbance or expected scenarios based on the power grid operation state and dynamic parameters; adjusting the control parameters of the frequency regulation resources according to the frequency dynamic response trajectory; and issuing the control parameters to the corresponding devices of the physical power grid through a control instruction execution interface for execution.

[0008] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the collected power grid operation data comprises unified scheduling and data aggregation of measurement devices deployed at key positions of the physical power grid, so as to obtain operation information meeting the accuracy requirement of regulation.

[0009] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the real-time synchronous updating of the virtual power grid state comprises continuous dynamic correction of the virtual power grid state quantity based on a bidirectional information channel.

[0010] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the acquisition of the power grid operation state and dynamic parameters comprises combining the collected power grid operation data with the power grid structure information in the digital twinning construction and operation platform, extracting the frequency state based on the synchronous state information, and updating the frequency state, system inertia value and damping level according to real-time data changes.

[0011] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the prediction of the frequency dynamic response trajectory of the power grid under disturbance or a predetermined scenario comprises taking the currently identified frequency state, system inertia value and damping level as input conditions, constructing the operation trend of the power grid under disturbance or a predetermined scenario, calculating the frequency response value at different time points, and forming a complete frequency change trajectory including a starting point, a change rate, a minimum point and a recovery interval.

[0012] The preferred scheme can accurately restore the frequency dynamic evolution process of the power grid under disturbance or a predetermined operation condition by calculating the frequency response value at different time points, identify the minimum frequency point, change rate and recovery interval, so as to realize quantitative evaluation of the frequency stability margin and improve the foresight and accuracy of regulation prediction.

[0013] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the adjustment of the control parameters of the frequency regulation resources comprises setting the frequency control parameters of the synchronous generator, the energy storage system and the new energy inverter according to the position of the minimum frequency point in the frequency change trajectory, the frequency drop duration, the recovery speed and the current system inertia level, so that different types of resources have hierarchical response capability and coordinated control characteristics.

[0014] The preferred scheme realizes differentiated and hierarchical configuration of control response by analyzing key feature points in the frequency trajectory and setting control parameters of multiple types of resources in combination with the system inertia level, so that the frequency regulation capability can be dynamically adapted to the power grid operation state, and the coordination and control robustness of different resources are effectively enhanced.

[0015] As a preferred scheme of the power grid dynamic frequency adaptive regulation method based on digital twinning, the control parameters are issued to the corresponding devices of the physical power grid through the control instruction execution interface, including setting the parameter values and response priority information of each type of control object into standardized control instructions, and sending them to the generator speed regulation device, the excitation control unit, the energy storage converter controller, the new energy station power control system and the load side control platform, to ensure that each resource accurately performs the frequency regulation task according to the response order.

[0016] The preferred scheme can ensure that the control intention is quickly and accurately executed in each control node by constructing the standardized control instructions containing parameter setting values and response priorities and accurately issuing them to each type of execution device, so as to rationalize the response timing between multiple types of frequency regulation resources and improve the landing effect and execution consistency of the frequency regulation task.

[0017] The application provides a power grid dynamic frequency adaptive regulation system based on digital twinning.

[0018] To solve the above technical problems, the application provides the following technical scheme: a power grid dynamic frequency adaptive regulation system based on digital twinning, comprising a data acquisition module, a digital twinning construction and operation platform module, a state estimation and parameter identification module, a frequency dynamic prediction module, an adaptive control parameter setting module and a control instruction execution module; the data acquisition module is used to acquire power grid operation data through a data acquisition system deployed at key nodes of a physical power grid; the digital twinning construction and operation platform module is used to transmit the power grid operation data to a digital twinning construction and operation platform to update the virtual power grid state in real time; the state estimation and parameter identification module is used to obtain the power grid operation state and dynamic parameters by using the digital twinning construction and operation platform; the frequency dynamic prediction module is used to predict the frequency dynamic response trajectory of the power grid under disturbance or an expected scenario based on the power grid operation state and dynamic parameters; the adaptive control parameter setting module is used to adjust the control parameters of the frequency regulation resources according to the frequency dynamic response trajectory; and the control instruction execution module is used to issue the control parameters to the corresponding devices of the physical power grid through a control instruction execution interface for execution.

[0019] The application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the power grid dynamic frequency adaptive regulation method based on digital twinning when executing the computer program.

[0020] The application provides a computer readable storage medium, which stores a computer program, wherein the computer program implements the steps of the power grid dynamic frequency adaptive regulation method based on digital twinning when executed by a processor.

[0021] The application has the beneficial effects that: the application uses a high-precision digital twinning platform to realize accurate real-time perception of power grid dynamics and online identification of key parameters, and perform forward-looking frequency risk prediction. Based on this, the system can adaptively set the control parameters (such as virtual inertia and droop coefficient) of each frequency regulation resource, ensure that the control strategy always closely matches the actual working condition, greatly improve the effectiveness and pertinence of frequency response, and significantly enhance the operation resilience of the power grid in complex scenarios such as low inertia and high disturbance. The multi-resource collaborative optimization mechanism comprehensively considers the characteristics, cost and power grid safety constraints of various resources, performs global optimal scheduling, not only ensures the rapid reliability of frequency regulation, but also realizes the minimization of regulation cost and the maximization of resource utilization efficiency. The application can significantly improve the power grid frequency dynamic quality (suppress frequency deviation, reduce RoCoF, and accelerate recovery), improve the frequency stability margin and overall operation safety. This provides key technical support for the safe interconnection and efficient consumption of larger-scale fluctuating renewable energy, and effectively promotes the optimization and transformation of energy structure. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 A general flowchart of the power grid dynamic frequency adaptive regulation method based on digital twinning provided by an embodiment of the application.

[0024] Figure 2 A digital twinning core analysis and decision flowchart of the power grid dynamic frequency adaptive regulation method based on digital twinning provided by an embodiment of the application.

[0025] Figure 3 An adaptive parameter setting flowchart of the power grid dynamic frequency adaptive regulation method based on digital twinning provided by an embodiment of the application.

[0026] Figure 4 A scheme module diagram of a power grid dynamic frequency adaptive regulation system based on digital twinning is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0028] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a power grid dynamic frequency adaptive regulation method based on digital twinning, comprising:

[0029] S1, collecting power grid operation data through a data acquisition system deployed at a key node of a physical power grid.

[0030] S2, transmitting the power grid operation data to a digital twinning construction and operation platform to update the virtual power grid state in real time.

[0031] S3, obtaining the power grid operation state and dynamic parameters by using the digital twinning construction and operation platform.

[0032] S4, predicting the frequency dynamic response trajectory of the power grid under disturbance or expected scenarios based on the power grid operation state and dynamic parameters.

[0033] S5, adjusting the control parameters of the frequency regulation resources according to the frequency dynamic response trajectory.

[0034] S6, issuing the control parameters to the corresponding devices of the physical power grid through a control instruction execution interface for execution.

[0035] The present application realizes the full-process automatic management from operation data acquisition, state and parameter identification, frequency response prediction, to control parameter setting and instruction issuing by constructing a complete digital twinning driven frequency regulation process. The method can effectively improve the real-time performance, predictability and adaptive ability of power grid frequency regulation, and is suitable for complex power operation scenarios with high proportion of new energy access and dynamic change of system inertia level.

[0036] Embodiment 2, refer to Figure 2 and Figure 3 For an embodiment of the present application, a power grid dynamic frequency adaptive regulation method based on digital twinning is provided based on the previous embodiment, comprising:

[0037] In the embodiment of the present application, the power grid operation data is collected by the data collection system deployed at the key nodes of the physical power grid in step S1.

[0038] The measurement devices deployed at the key positions of the physical power grid are uniformly scheduled and data is aggregated to obtain operation information meeting the accuracy requirements of regulation and control.

[0039] The data collection system is deployed at the key nodes of the physical power grid, and the data collection system includes a synchronized phasor measurement unit (PMU), a SCADA system interface, and other sensor interfaces, for real-time collection of high-precision, high-sampling-rate operation data of the power grid, such as frequency, voltage, phase angle, power flow, switch state, etc.; the data collection system transmits the collected data to the digital twin construction and operation platform through a communication network.

[0040] The communication network adopts a communication technology (such as 5G, dedicated optical fiber network) with high bandwidth, low delay, and high reliability to ensure the real-time performance and accuracy of data collection, model synchronization, and instruction issuance.

[0041] Specifically, the data collection system is a bridge connecting the physical power grid and the digital twin system. The synchronized phasor measurement unit (PMU) and intelligent electronic device (IED) deployed at key positions of the power grid (such as large power plants, substations, new energy stations, and large user sides) and through the SCADA system interface, real-time collection of high-precision, time-stamped power grid operation data, including but not limited to: frequency, voltage amplitude and phase angle of each node, line active / reactive power, generator speed / output, switch state, new energy predicted output, load level, etc. These data are transmitted in real time to the digital twin construction and operation platform through a high-speed, reliable communication network (for example, based on 5G or dedicated optical fiber).

[0042] In the embodiment of the present application, the power grid operation data is transmitted to the digital twin construction and operation platform in step S2 to update the virtual power grid state in real time.

[0043] Real-time synchronous updating of the virtual power grid state includes continuous dynamic correction of the virtual power grid state based on a bidirectional information channel.

[0044] The digital twin construction and operation platform is the core of the system, and the platform has a high-fidelity power grid topology model, a device parameter model (including generators, loads, new energy stations, and energy storage), and a dynamic simulation engine built-in; the platform receives real-time data from the data collection system for driving real-time state updating and synchronization of the digital twin model, ensuring high consistency between the virtual power grid and the physical power grid state.

[0045] In particular, the digital twin construction and operation platform serves as the digital core of the system. The platform first constructs a high-fidelity power grid digital twin model based on the actual topology of the power grid, device account parameters (generator model parameters, line impedance, transformer parameters, load model, new energy model, energy storage model, etc.). The platform receives real-time measurement data from the data acquisition system, and continuously corrects and updates the state of the digital twin model through a state synchronization algorithm (such as data assimilation technology), so that it is highly consistent with the actual operating state of the physical power grid. The platform has a powerful dynamic simulation engine that can perform simulation calculations faster than real time. The digital twin core analysis and decision-making flowchart is shown in Figure 2 The input data is evaluated for real-time measurement and prediction. Real-time measurement includes model synchronization and state estimation, and online identification of key parameters (such as inertia). Prediction evaluation includes frequency evolution trend prediction (ultra-real-time simulation) and frequency stability evaluation (margin, RoCoF). After real-time measurement and prediction evaluation, adaptive control strategy optimization and collaborative scheduling (parameter setting, resource allocation) are performed, and the optimized control parameters and collaborative instructions are output.

[0046] In the embodiments of the present application, the power grid operating state and dynamic parameters are obtained in step S3 using the digital twin construction and operation platform.

[0047] The frequency state is extracted based on the synchronized state information, and the frequency state, system inertia value, and damping level are updated according to the real-time data changes, in combination with the collected power grid operating data and power grid structure information in the digital twin construction and operation platform.

[0048] There are advanced state estimation algorithms and online parameter identification algorithms (such as extended Kalman filter, unscented Kalman filter, machine learning method, etc.). Real-time measurement data and the digital twin construction and operation platform are used to accurately estimate the dynamic operating state of the power grid and online identify key dynamic parameters, especially the system equivalent inertia, damping coefficient, and frequency response characteristics that change with operating conditions.

[0049] In particular, real-time synchronized data and high-fidelity models provided by the digital twin construction and operation platform are used to accurately estimate the complete dynamic operating state of the current power grid using advanced estimation algorithms (such as wide-area dynamic state estimation based on PMU data, extended Kalman filter, etc.). Online parameter identification function (such as methods based on disturbance response or machine learning) can identify key parameters that reflect the current dynamic characteristics of the power grid in real time, especially the system equivalent inertia (Hsys) and frequency response characteristics (such as equivalent damping coefficient) that change with operating conditions (such as grid-connected unit combination and new energy penetration).

[0050] In the embodiments of the present application, the power grid frequency dynamic response trajectory under disturbance or expected scenarios is predicted based on the power grid operating state and dynamic parameters in step S4.

[0051] With the current identified frequency state, system inertia value and damping level as input conditions, the operation trend of the power grid under disturbance or preset scenario is constructed, the frequency response value at different time points is calculated respectively, and a complete frequency change trajectory including the starting point, the change rate, the lowest point and the recovery interval is formed.

[0052] The frequency dynamic response trajectory and stability margin (such as the lowest frequency point, the frequency change rate RoCoF, and the recovery time) of the power grid under different disturbances (such as N-1 fault, load mutation, and new energy output fluctuation) or expected operation scenarios in a future period of time (such as seconds to minutes) are predicted.

[0053] Specifically, the latest dynamic parameters identified by the real-time state and state estimation and parameter identification module provided by the digital twin construction and operation platform are used for super-real-time simulation prediction. Simulate the preset disturbance event (such as N-1 fault, large load fluctuation, and new energy output sudden drop) or evaluate the future short period (seconds to minutes) operation scenario, predict the dynamic evolution trajectory of the power grid frequency, including the frequency nadir (fnadir), the frequency change rate peak (RoCoFmax) and the frequency recovery time, and evaluate the frequency stability margin.

[0054] In an optional embodiment, the prediction of the frequency dynamic response trajectory can also include setting a sudden drop in new energy output as a disturbance condition, predicting the dynamic change process of the frequency within the next 30 seconds based on the current system operation state and inverter output characteristics, and generating a time series including the frequency fluctuation amplitude, response time and fluctuation period for evaluating the frequency control demand.

[0055] In another optional embodiment, the prediction of the frequency dynamic response trajectory can also include constructing a frequency change trajectory sample set for a plurality of typical disturbance scenarios, performing parallel prediction in the digital twin platform, and outputting the frequency response interval range under different disturbances to support robust setting of control parameters under multiple scenarios.

[0056] The present application realizes fine-grained modeling of the frequency fluctuation process under typical load mutation, providing a quantitative basis for subsequent control parameter setting.

[0057] In the embodiments of the present application, the control parameters of the frequency regulation resources are adjusted according to the frequency dynamic response trajectory in step S5.

[0058] According to the position of the lowest frequency point in the frequency change trajectory, the duration of the frequency drop, the recovery speed, and the current system inertia level, the frequency control parameters of the synchronous generator, the energy storage system and the new energy inverter are set respectively, so that different types of resources have layered response ability and coordinated control characteristics.

[0059] According to the acquisition of power grid operation state and dynamic parameters and the predicted frequency dynamic response trajectory of the power grid under disturbance or expected scenario, including adaptive control law design and optimization algorithm (such as fuzzy logic control, model predictive control, reinforcement learning, etc.); dynamically adjust the key control parameters of various resources participating in frequency regulation (such as synchronous generators, energy storage converters, wind / light inverters, flexible loads), such as droop coefficient, virtual inertia time constant, primary frequency regulation dead zone, AGC participation factor and response rate, etc., to achieve the optimal frequency response effect and stability.

[0060] Specifically, adaptive control theory (such as model predictive control MPC, fuzzy logic, reinforcement learning, etc.) is used to dynamically optimize and set the control parameters of various resources participating in frequency regulation (synchronous generators, energy storage systems, new energy inverters with frequency support capability, interruptible loads, etc.). For example, dynamically adjust the droop coefficient (Ri) of the generator and the primary frequency regulation dead zone; optimize the virtual inertia control parameters (Kvi) of the energy storage and new energy inverter and the fast frequency response strategy; adjust the gain and integral time constant of the AGC system. The goal is to make the frequency response optimal and the stability strongest under the current and expected power grid state. The adaptive parameter setting flowchart is shown in Figure 3 The input is received: real-time state, identification parameters (Hsys, etc.), frequency prediction (Fnadir, RoCoF), performance target; state evaluation and demand analysis, system characteristics evaluation, frequency risk evaluation; determine whether the parameters need to be adjusted, if not, directly generate parameter update instructions, package the optimized parameters, if the parameters need to be adjusted, select optimization target and strategy (based on rules, MPC, fuzzy, ML, etc.), perform parameter optimization calculation, calculate new parameter values (R, Kvi, etc.), verify whether the parameters meet the standards, if not, rejudge whether the parameters need to be adjusted, if yes, perform simulation verification and safety check, simulate and check constraints in DT, verify whether it passes, if not, reselect optimization target and strategy (based on rules, MPC, fuzzy, ML, etc.), if yes, generate parameter update instructions, package the optimized parameters, output instructions to the execution interface.

[0061] The core adaptive optimization law formula is:

[0062]

[0063] Constraint conditions:

[0064] SystemLimits DT (t)

[0065] Where, U * (t) represents the optimal control strategy, which represents an optimization process for the control strategy U(t), f(t+Δt) DT is the future frequency predicted by the digital twin, RoCoF(t+Δt) DT is the future rate of change of frequency predicted by the digital twin, Cost is the control cost, x DT (t) is the real-time state and parameters provided by the digital twin, SystemLimits DT (t) is the system constraints evaluated by the digital twin.

[0066] Using the real-time state (X DT ) and the prediction of the future (f DT , RoCoF DT ) obtained by the digital twin, the control strategy (U) is dynamically optimized to balance the future frequency performance and operating cost while meeting the system constraints.

[0067] In an optional embodiment, adjusting the control parameters can also include determining the degree of inertia response lag of the power grid according to the duration of the frequency drop phase and the time position of the lowest point, and preferentially adjusting the fast regulation parameters of the energy storage system and the new energy station to improve the initial support capability of the system; the droop coefficient of the synchronous generator is then corrected for frequency stability control in the recovery phase.

[0068] In another optional embodiment, adjusting the control parameters can also include constructing a response level sequence, sorting various control resources according to the degree of influence on frequency fluctuation, and setting the control parameter adjustment amplitude and adjustment priority in combination with the maximum change rate of the frequency trajectory, to form a hierarchical batch control strategy through grouping setting, and realize on-demand linkage response.

[0069] The present application realizes dynamic matching of frequency control parameters with changes in system operating state, improving the accuracy of frequency response and system stability.

[0070] In the embodiments of the present application, the control parameters in step S6 are issued to the corresponding devices of the physical power grid through the control instruction execution interface for execution.

[0071] The parameter setting values and response priority information of each type of control object are organized into standardized control instructions, which are sent to the generator speed regulation device, the excitation control unit, the energy storage converter controller, the new energy station power control system, and the load side control platform, to ensure that each resource accurately performs the frequency regulation task according to the response order.

[0072] The generated parameter setting values and scheduling instructions are safely and reliably transmitted to the corresponding generator speed regulator, excitation system controller, energy storage controller, AGC system, new energy station controller, and load aggregator control system execution units in the physical power grid through the communication network.

[0073] The application realizes real-time accurate perception and online identification of power grid dynamic characteristics through digital twinning technology, overcomes the limitations of traditional methods relying on offline setting and simplified models, and can accurately reflect the current real operating state and response capability of the power grid. Using the simulation prediction capability of digital twinning, the frequency evolution trend and potential risks can be predicted, realizing the transition from passive response to active prevention, gaining valuable time for taking preventive control measures, and improving the ability to respond to rapid frequency events (such as high RoCoF). The control parameters and strategies can be dynamically adjusted according to the real-time operating state of the power grid (such as system inertia level, network topology, load composition), so that the control strategy always adapts to the current operating condition, ensuring optimal control effect and robustness in various complex operating scenarios. Unified modeling, evaluation and optimal scheduling of various heterogeneous frequency modulation resources (including traditional power sources, new energy, energy storage, demand side) are realized, fully considering the dynamic characteristics, cost and network constraints of various resources, significantly improving resource utilization efficiency and overall economic operation of the system. Through more accurate, fast and coordinated frequency regulation, the frequency stability margin and disturbance resistance of the power grid under the background of high proportion of new energy access and system inertia reduction are effectively improved, and the overall safety and resilience of the power system are enhanced.

[0074] Embodiment 3, refer to Figure 4 As an embodiment of the application, the embodiment provides a power grid dynamic frequency adaptive regulation system based on digital twinning, comprising a data acquisition module, a digital twinning construction and operation platform module, a state estimation and parameter identification module, a frequency dynamic prediction module, an adaptive control parameter setting module and a control instruction execution module.

[0075] The data acquisition module is used to acquire power grid operating data through a data acquisition system deployed at key nodes of the physical power grid.

[0076] The digital twinning construction and operation platform module is used to transmit the power grid operating data to the digital twinning construction and operation platform to update the virtual power grid state in real time.

[0077] The state estimation and parameter identification module is used to obtain the power grid operating state and dynamic parameters using the digital twinning construction and operation platform.

[0078] The frequency dynamic prediction module is used to predict the frequency dynamic response trajectory of the power grid under disturbance or expected scenarios based on the power grid operating state and dynamic parameters.

[0079] The adaptive control parameter setting module is used to adjust the control parameters of the frequency regulation resources according to the frequency dynamic response trajectory.

[0080] The control instruction execution module is used to issue the control parameters to the corresponding devices of the physical power grid through the control instruction execution interface for execution.

[0081] The digital twin construction and operation platform module is the core of the system, and the platform is built-in with high-fidelity power grid topology model, equipment parameter model (including generator, load, new energy station, energy storage, etc.) and dynamic simulation engine; the platform receives real-time data from the data acquisition module for driving real-time state update and synchronization of the digital twin model, ensuring high consistency of the virtual power grid and the physical power grid state.

[0082] The state estimation and parameter identification module is closely integrated with the digital twin construction and operation platform module, and the state estimation and parameter identification module is built-in with advanced state estimation algorithm and online parameter identification algorithm (such as extended Kalman filter, unscented Kalman filter, machine learning method, etc.); the state estimation and parameter identification module uses real-time measurement data and digital twin model to accurately estimate the dynamic operating state of the power grid, and online identifies key dynamic parameters, especially the system equivalent inertia, damping coefficient and frequency response characteristics which change with operating conditions.

[0083] The frequency dynamic prediction module is based on the real-time state and identified parameters provided by the digital twin construction and operation platform module, and the frequency dynamic prediction module is built-in with ultra-real-time simulation or model-based prediction algorithm (such as time series prediction, differential equation solver); the frequency dynamic prediction module is used to predict the frequency dynamic response trajectory and stability margin (such as minimum frequency point, frequency change rate RoCoF, recovery time) of the power grid under different disturbances (such as N-1 fault, load mutation, new energy output fluctuation) or expected operating scenarios in a period of time (such as seconds to minutes).

[0084] The adaptive control parameter setting module is based on the real-time system characteristics provided by the state estimation and parameter identification module and the prediction results of the frequency dynamic prediction module, and the adaptive control parameter setting module is built-in with adaptive control law design and optimization algorithm (such as fuzzy logic control, model predictive control, reinforcement learning, etc.); the adaptive control parameter setting module dynamically adjusts the key control parameters of various resources (such as synchronous generator, energy storage converter, wind / light inverter, flexible load) participating in frequency regulation, such as droop coefficient, virtual inertia time constant, primary frequency regulation dead zone, AGC participation factor and response rate, to achieve optimal frequency response effect and stability.

[0085] The control instruction execution module is responsible for safely and reliably transmitting the parameter setting values and scheduling instructions generated by the adaptive control parameter setting module to the corresponding generator governor, excitation system controller, energy storage controller, AGC system, new energy station controller, load aggregator control system and other execution units in the physical power grid through the communication network.

[0086] The embodiment also provides an electronic device suitable for the case of the power grid dynamic frequency adaptive regulation method based on digital twinning, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the power grid dynamic frequency adaptive regulation method based on digital twinning.

[0087] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to realize the power grid dynamic frequency adaptive regulation method based on digital twinning.

[0088] The storage medium proposed in the embodiment and the power grid dynamic frequency adaptive regulation method based on digital twinning proposed in the above embodiment belong to the same inventive concept, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0089] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk or an optical disk, etc., including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for adaptive dynamic frequency control of a power grid based on digital twins, characterized in that: include, Data acquisition systems are deployed at key nodes of the physical power grid to collect power grid operation data. Transmit power grid operation data to the digital twin construction and operation platform to update the virtual power grid status in real time. Utilize digital twins to build and operate a platform to obtain power grid operating status and dynamic parameters; Based on the power grid's operating status and dynamic parameters, predict the power grid's frequency dynamic response trajectory under disturbances or anticipated scenarios; Adjust the control parameters of the resources participating in frequency regulation based on the frequency dynamic response trajectory; The control parameters are sent to the corresponding devices in the physical power grid for execution through the control command execution interface.

2. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 1, characterized in that: The collection of power grid operation data includes unified scheduling and data aggregation of measuring devices deployed at key locations in the physical power grid to obtain operation information that meets the accuracy requirements for regulation.

3. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 2, characterized in that: The real-time synchronous update of the virtual power grid status includes continuous dynamic correction of the virtual power grid status quantities based on a two-way information channel.

4. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 3, characterized in that: The acquisition of power grid operating status and dynamic parameters includes combining the collected power grid operating data with the power grid structure information in the digital twin construction and operation platform, extracting the frequency status based on the synchronization status information, and updating the frequency status, system inertia value and damping level according to real-time data changes.

5. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 4, characterized in that: The predicted frequency dynamic response trajectory of the power grid under disturbance or anticipated scenarios includes, Using the currently identified frequency state, system inertia value, and damping level as input conditions, the operating trend of the power grid under disturbance or preset scenario is constructed, the frequency response value at different time points is calculated, and a complete frequency change trajectory including the starting point, rate of change, minimum point, and recovery interval is formed.

6. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 5, characterized in that: The adjustment of control parameters for resources participating in frequency regulation includes, Based on the location of the lowest frequency point in the frequency change trajectory, the duration of frequency drop, the recovery speed, and the current system inertia level, the frequency control parameters of the synchronous generator, energy storage system, and new energy inverter are set respectively, so that different types of resources have hierarchical response capabilities and coordinated control characteristics.

7. The power grid dynamic frequency adaptive control method based on digital twin as described in claim 6, characterized in that: The step of sending control parameters to the corresponding device in the physical power grid for execution via the control command execution interface includes: The parameter settings and response priority information of each type of controlled object are organized into standardized control commands, which are then sent to the generator speed control device, excitation control unit, energy storage converter controller, new energy power control system, and load-side control platform to ensure that each resource accurately executes frequency regulation tasks in accordance with the response sequence.

8. A power grid dynamic frequency adaptive control system based on digital twins, employing the power grid dynamic frequency adaptive control method based on digital twins as described in any one of claims 1 to 7, characterized in that, include: The module includes a data acquisition module, a digital twin construction and operation platform module, a state estimation and parameter identification module, a frequency dynamic prediction module, an adaptive control parameter tuning module, and a control command execution module. The data acquisition module is used to collect power grid operation data through a data acquisition system deployed at key nodes of the physical power grid. The digital twin construction and operation platform module is used to transmit power grid operation data to the digital twin construction and operation platform and update the virtual power grid status in real time. The state estimation and parameter identification module is used to acquire the power grid operating status and dynamic parameters by utilizing a digital twin construction and operation platform. The frequency dynamic prediction module is used to predict the frequency dynamic response trajectory of the power grid under disturbance or expected scenarios based on the power grid operating status and dynamic parameters. The adaptive control parameter tuning module is used to adjust the control parameters of the resources participating in frequency regulation according to the frequency dynamic response trajectory. The control command execution module is used to send control parameters to the corresponding devices in the physical power grid for execution through the control command execution interface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power grid dynamic frequency adaptive control method based on digital twin as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power grid dynamic frequency adaptive control method based on digital twin as described in any one of claims 1 to 7.

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